Mobile applications are promising tools for strengthening service quality and have been an area of considerable mHealth innovation. Despite growing demand for data to guide policymakers, donors, and program managers in making sound investments, there is a paucity of evidence on the cost-effectiveness of mHealth technologies. To address this gap, the HFG Project analyzed a mobile decision support tool with the following objectives: First, it aimed to provide a transparent and detailed methodology for categorizing the costs of building, deploying, and scaling-up mobile decision support tools in Malawi. Second, it evaluated the incremental cost-effectiveness of a mobile tool’s use in improving clinical care. Finally, the evaluation addressed challenges faced in conducting cost-effectiveness analyses of mHealth interventions when they are scaled up and become multifunctional.
2013-14 HIV and AIDS Public Expenditure Review: Tanzania MainlandHFG Project
HFG Tanzania conducted a HIV and AIDS Public Expenditure Review (PER) in collaboration with the Tanzania Commission for AIDS (TACAIDS). The PER analyzes spending by development partners and the government of Tanzania between 2011/12 and 2013/14, and projections until 2017/18. For the first time, this PER combines Health Accounts and PER data to analyze spending by detailed HIV and AIDS program areas. This will provide Tanzania’s new AIDS Trust Fund with much-needed evidence to decide how best to finance the response to the epidemic, assess whether spending aligns with priorities from the National Multi-sectoral Strategic Framework, and determine how HIV and AIDS resources should be allocated in the future.
A Review of Health Financing in NamibiaHFG Project
This document reviews health financing in Namibia. It finds that while Namibia's GDP growth is expected to slow in the short term, limiting additional resources for health, GDP growth is projected to improve after 2017. Currently, Namibia relies heavily on indirect taxes and SACU revenues, though it aims to broaden its tax base. High unemployment and a large informal sector pose challenges. The government provides most health services, while the private sector is financed through medical aid funds. Overall, Namibia's fiscal capacity indicates potential to increase health spending in the medium term by expanding its domestic revenue and improving government efficiencies.
Integrating the HIV Response at the Systems LevelHFG Project
The global response to combat the acquired immunodeficiency syndrome (AIDS) epidemic scaled up considerably in the early 2000s with the establishment of key institutions, notably the Global Fund to Fight AIDS, Tuberculosis and Malaria (Global Fund) and the United States President’s Emergency Plan for AIDS Relief (PEPFAR) (AIDS.gov 2018). In response to high global rates of AIDS-related morbidity and mortality, the internationally supported rapid scale-up of human immunodeficiency virus (HIV) prevention, testing, treatment, and drug development is widely credited with curtailing a global epidemic, thereby limiting the human and financial costs of the virus (Bekker et al. 2018). Still the Joint United Nations Programme on HIV/AIDS (UNAIDS) estimates that 1.8 million people were infected with HIV in 2017, and there are nearly 37 million people living with HIV (PLWHIV) worldwide (UNAIDS 2018a). In many countries, financing and governance of HIV services is transitioning from international donors to national governments.
This funding transition has major implications for the governance, management, and implementation of the HIV response. Governments undergoing funding transitions for the HIV response are integrating aspects of the response into systems and processes for governing, managing, financing, and delivering other essential
health services. But this phenomenon has not been systematically studied, and documentation on how governments achieve this is limited. Understanding how some governments are navigating an HIV funding transition may help other countries and the global health community to better design and plan future or ongoing efforts to transition national HIV responses to domestic resources for health. USAID’s HFG project is helping to fill this gap. In particular, this study helps build an evidence base by exploring whether and how four countries in the process of transitioning to greater domestic financing of their HIV response are integrating HIV programming with local systems and processes for other essential health services.
This study applies the concept of system integration to examine the alignment of rules, policies, and support systems to address HIV and other essential health services in four low and middle-income countries (LMICs). Specifically, the study explores the current extent of integration, the decisions faced by policymakers, and potential barriers/facilitators to integration in four countries. The analysis allows HFG to share lessons learned by each of these countries attempting to optimize rules, policy, and support systems for HIV and other essential health services.
Analyzing the Technical Efficiency of Public Hospitals in NamibiaHFG Project
This document summarizes a study analyzing the technical efficiency of public hospitals in Namibia. The study collected cost and output data from 29 district hospitals and 5 referral hospitals for the 2014/15 financial year. It used Data Envelopment Analysis to calculate efficiency scores for each hospital and identify which were technically inefficient. The study found that 52% of hospitals were technically inefficient, meaning they could reduce inputs by an average of 19% without affecting outputs. Most hospitals also had scale inefficiencies, with the majority experiencing increasing returns to scale. The study estimates potential savings from addressing technical and scale inefficiencies, particularly through reallocating clinical staff and non-salary recurrent budgets. It concludes with recommendations to improve efficiency, such as realloc
South Africa HIV and TB Expenditure Review 2014/15 - 2016/17 Full ReportHFG Project
The South African Government (SAG) and its development partners have mounted a formidable response to the world’s largest HIV epidemic and a persistent burden of tuberculosis (TB), the country’s leading killer. Nearly 4 million South Africans initiated antiretroviral therapy (ART) by the end of financial year 2016/17, helping to curtail new infections and reduce the number of annual HIV-related deaths. Mortality from TB has also declined thanks, in part, to improved treatment success.
Despite progress, challenges remain. Roughly 3 million people living with HIV (PLHIV) lack treatment, and each year more than a quarter million are newly infected. Moreover, nearly a half million South Africans contract TB every year, with an increasing share affected by drug-resistant strains.
To effectively plan and steward the health system, the SAG routinely monitors programmatic and financial performance of the response to HIV and TB, including by tracking expenditure. Analysis of spending, including trends in sources, levels, geographic and programmatic distribution and cost drivers can help policymakers to assess whether resources are reaching priority populations, interventions, and hotspot geographies; to identify potential opportunities to improve allocative and technical efficiency; and to stimulate more productive dialogue at multiple levels of the system.
This review of HIV and TB expenditure in South Africa is an input to policy, planning and management processes within and amongst spheres of government and between government and development partners. The data have been especially useful to national and provincial programme managers as they perform their oversight functions, leading to improved spending of available resources. With 52 annexes, it also serves as an authoritative reference document detailing levels and trends in HIV and TB spending by the three main funders of the disease responses: the SAG, the United States Government (USG), primarily via the President’s Emergency Plan for AIDS Relief (PEPFAR), and the Global Fund to Fight AIDS, Tuberculosis, and Malaria (the Global Fund). The findings have informed South Africa’s report to the UNAIDS Global AIDS Monitor and the country’s forthcoming funding request to the Global Fund.
Unit Cost and Quality of Health Services in NamibiaHFG Project
This document analyzes the unit costs and quality of health services in Namibia. It finds that on average, outpatient unit costs are lowest in health centers and highest in private facilities. Inpatient unit costs are lowest in district hospitals and highest in intermediate hospitals. Quality of services is generally higher in private facilities compared to public facilities. The main drivers of costs include staffing levels, average length of stay for inpatients, and overall facility quality. The study provides recommendations to improve efficiency and quality of health services in Namibia.
Technical Brief: Strategic Purchasing Approaches for the Tuberculosis Hospita...HFG Project
This technical brief discusses strategic purchasing approaches for tuberculosis (TB) hospitals in Ukraine. Ukraine has one of the highest rates of multi-drug resistant TB in the world. Currently, most TB cases are treated as inpatients in TB hospitals, despite recommendations that most cases can be treated as outpatients. The Health Finance and Governance Project worked with partners in Ukraine to develop strategic purchasing systems for TB hospitals, including a cost accounting system, discharged patient system, hospital performance monitoring system, and simulation module. These systems provide data to support evidence-based decisions about optimizing the TB hospital system to improve outcomes and make more efficient use of resources. The systems have been implemented in pilot regions and will inform national rollout of new payment systems for TB
The Efficiency of the El Salvador HIV Program Mission SupportHFG Project
This document summarizes the findings of a survey conducted to identify inefficiencies in El Salvador's national HIV/AIDS program. The survey activities included interviews with health ministry technicians and officials. Key findings included:
- Opportunities for improved coordination between government agencies and civil society organizations involved in HIV prevention and treatment.
- A need to strengthen programs targeting at-risk populations like sex workers, transgender individuals, and men who have sex with men.
- Identifying ways to improve the efficient management and sustainability of program budgets given decreasing donor funding over time.
- Recommendations to update the national HIV law to help ensure long-term public funding for HIV programs.
2013-14 HIV and AIDS Public Expenditure Review: Tanzania MainlandHFG Project
HFG Tanzania conducted a HIV and AIDS Public Expenditure Review (PER) in collaboration with the Tanzania Commission for AIDS (TACAIDS). The PER analyzes spending by development partners and the government of Tanzania between 2011/12 and 2013/14, and projections until 2017/18. For the first time, this PER combines Health Accounts and PER data to analyze spending by detailed HIV and AIDS program areas. This will provide Tanzania’s new AIDS Trust Fund with much-needed evidence to decide how best to finance the response to the epidemic, assess whether spending aligns with priorities from the National Multi-sectoral Strategic Framework, and determine how HIV and AIDS resources should be allocated in the future.
A Review of Health Financing in NamibiaHFG Project
This document reviews health financing in Namibia. It finds that while Namibia's GDP growth is expected to slow in the short term, limiting additional resources for health, GDP growth is projected to improve after 2017. Currently, Namibia relies heavily on indirect taxes and SACU revenues, though it aims to broaden its tax base. High unemployment and a large informal sector pose challenges. The government provides most health services, while the private sector is financed through medical aid funds. Overall, Namibia's fiscal capacity indicates potential to increase health spending in the medium term by expanding its domestic revenue and improving government efficiencies.
Integrating the HIV Response at the Systems LevelHFG Project
The global response to combat the acquired immunodeficiency syndrome (AIDS) epidemic scaled up considerably in the early 2000s with the establishment of key institutions, notably the Global Fund to Fight AIDS, Tuberculosis and Malaria (Global Fund) and the United States President’s Emergency Plan for AIDS Relief (PEPFAR) (AIDS.gov 2018). In response to high global rates of AIDS-related morbidity and mortality, the internationally supported rapid scale-up of human immunodeficiency virus (HIV) prevention, testing, treatment, and drug development is widely credited with curtailing a global epidemic, thereby limiting the human and financial costs of the virus (Bekker et al. 2018). Still the Joint United Nations Programme on HIV/AIDS (UNAIDS) estimates that 1.8 million people were infected with HIV in 2017, and there are nearly 37 million people living with HIV (PLWHIV) worldwide (UNAIDS 2018a). In many countries, financing and governance of HIV services is transitioning from international donors to national governments.
This funding transition has major implications for the governance, management, and implementation of the HIV response. Governments undergoing funding transitions for the HIV response are integrating aspects of the response into systems and processes for governing, managing, financing, and delivering other essential
health services. But this phenomenon has not been systematically studied, and documentation on how governments achieve this is limited. Understanding how some governments are navigating an HIV funding transition may help other countries and the global health community to better design and plan future or ongoing efforts to transition national HIV responses to domestic resources for health. USAID’s HFG project is helping to fill this gap. In particular, this study helps build an evidence base by exploring whether and how four countries in the process of transitioning to greater domestic financing of their HIV response are integrating HIV programming with local systems and processes for other essential health services.
This study applies the concept of system integration to examine the alignment of rules, policies, and support systems to address HIV and other essential health services in four low and middle-income countries (LMICs). Specifically, the study explores the current extent of integration, the decisions faced by policymakers, and potential barriers/facilitators to integration in four countries. The analysis allows HFG to share lessons learned by each of these countries attempting to optimize rules, policy, and support systems for HIV and other essential health services.
Analyzing the Technical Efficiency of Public Hospitals in NamibiaHFG Project
This document summarizes a study analyzing the technical efficiency of public hospitals in Namibia. The study collected cost and output data from 29 district hospitals and 5 referral hospitals for the 2014/15 financial year. It used Data Envelopment Analysis to calculate efficiency scores for each hospital and identify which were technically inefficient. The study found that 52% of hospitals were technically inefficient, meaning they could reduce inputs by an average of 19% without affecting outputs. Most hospitals also had scale inefficiencies, with the majority experiencing increasing returns to scale. The study estimates potential savings from addressing technical and scale inefficiencies, particularly through reallocating clinical staff and non-salary recurrent budgets. It concludes with recommendations to improve efficiency, such as realloc
South Africa HIV and TB Expenditure Review 2014/15 - 2016/17 Full ReportHFG Project
The South African Government (SAG) and its development partners have mounted a formidable response to the world’s largest HIV epidemic and a persistent burden of tuberculosis (TB), the country’s leading killer. Nearly 4 million South Africans initiated antiretroviral therapy (ART) by the end of financial year 2016/17, helping to curtail new infections and reduce the number of annual HIV-related deaths. Mortality from TB has also declined thanks, in part, to improved treatment success.
Despite progress, challenges remain. Roughly 3 million people living with HIV (PLHIV) lack treatment, and each year more than a quarter million are newly infected. Moreover, nearly a half million South Africans contract TB every year, with an increasing share affected by drug-resistant strains.
To effectively plan and steward the health system, the SAG routinely monitors programmatic and financial performance of the response to HIV and TB, including by tracking expenditure. Analysis of spending, including trends in sources, levels, geographic and programmatic distribution and cost drivers can help policymakers to assess whether resources are reaching priority populations, interventions, and hotspot geographies; to identify potential opportunities to improve allocative and technical efficiency; and to stimulate more productive dialogue at multiple levels of the system.
This review of HIV and TB expenditure in South Africa is an input to policy, planning and management processes within and amongst spheres of government and between government and development partners. The data have been especially useful to national and provincial programme managers as they perform their oversight functions, leading to improved spending of available resources. With 52 annexes, it also serves as an authoritative reference document detailing levels and trends in HIV and TB spending by the three main funders of the disease responses: the SAG, the United States Government (USG), primarily via the President’s Emergency Plan for AIDS Relief (PEPFAR), and the Global Fund to Fight AIDS, Tuberculosis, and Malaria (the Global Fund). The findings have informed South Africa’s report to the UNAIDS Global AIDS Monitor and the country’s forthcoming funding request to the Global Fund.
Unit Cost and Quality of Health Services in NamibiaHFG Project
This document analyzes the unit costs and quality of health services in Namibia. It finds that on average, outpatient unit costs are lowest in health centers and highest in private facilities. Inpatient unit costs are lowest in district hospitals and highest in intermediate hospitals. Quality of services is generally higher in private facilities compared to public facilities. The main drivers of costs include staffing levels, average length of stay for inpatients, and overall facility quality. The study provides recommendations to improve efficiency and quality of health services in Namibia.
Technical Brief: Strategic Purchasing Approaches for the Tuberculosis Hospita...HFG Project
This technical brief discusses strategic purchasing approaches for tuberculosis (TB) hospitals in Ukraine. Ukraine has one of the highest rates of multi-drug resistant TB in the world. Currently, most TB cases are treated as inpatients in TB hospitals, despite recommendations that most cases can be treated as outpatients. The Health Finance and Governance Project worked with partners in Ukraine to develop strategic purchasing systems for TB hospitals, including a cost accounting system, discharged patient system, hospital performance monitoring system, and simulation module. These systems provide data to support evidence-based decisions about optimizing the TB hospital system to improve outcomes and make more efficient use of resources. The systems have been implemented in pilot regions and will inform national rollout of new payment systems for TB
The Efficiency of the El Salvador HIV Program Mission SupportHFG Project
This document summarizes the findings of a survey conducted to identify inefficiencies in El Salvador's national HIV/AIDS program. The survey activities included interviews with health ministry technicians and officials. Key findings included:
- Opportunities for improved coordination between government agencies and civil society organizations involved in HIV prevention and treatment.
- A need to strengthen programs targeting at-risk populations like sex workers, transgender individuals, and men who have sex with men.
- Identifying ways to improve the efficient management and sustainability of program budgets given decreasing donor funding over time.
- Recommendations to update the national HIV law to help ensure long-term public funding for HIV programs.
Sustaining the HIV and AIDS Response in Grenada: Investment Case BriefHFG Project
The HIV/AIDS program in Grenada is at a turning point, facing both opportunities to expand and target its efforts and threats of decreasing funding. As its National HIV/AIDS Strategic Plan awaits ratification, the country must consider whether and how to implement strategic priorities related to controlling and mitigating the effects of the epidemic. Critical decisions must be made about programming and budgeting for the HIV response in the coming years.
This brief provides analytic inputs to help Grenada develop an “investment case” for its HIV/AIDS program. The Joint United Nations Program on HIV/AIDS (UNAIDS) and the U.S. President’s Emergency Plan for AIDS Relief (PEPFAR) have encouraged the small-island countries of the eastern Caribbean to develop HIV investment cases, which are reports that aim to help program leaders target investments on the interventions and populations where they will have maximum impact, given limited resources (UNAIDS 2012). The priorities and analysis outlined in this brief will also inform a multi-country regional application to the Global Fund for HIV/AIDS, TB and Malaria.
Guide for the Monitoring and Evaluation of the Transition of Health ProgramsHFG Project
This guide looks at three different transition experiences (funding, technical assistance, and services) to demonstrate variations in the type of transition undertaken, and the corresponding need for M&E. The authors draw upon experience of monitoring and evaluating transition to clarify key elements and dimensions of transition and how they relate to the longer-term goal of program sustainability and to present possible indicators, relevant to different health programs and transition arrangements that can help track transition and offer suggestions on how to select appropriate indicators. This document provides a conceptual framework to guide thinking around the M&E of transitions and will be amended as experience grows.
Trinidad and Tobago 2015 Health Accounts - Main ReportHFG Project
This document summarizes the key findings of the 2015 health accounts report for Trinidad and Tobago. It finds that total health expenditure was 4.5 billion TT dollars in 2015, equivalent to 4.1% of GDP. The government financed 41% of health spending, while households financed 35% through direct out-of-pocket payments. Noncommunicable diseases accounted for the largest share of recurrent health spending at 42%. Out-of-pocket payments remain high, comprising over a third of total health expenditure. The report recommends strengthening government commitment to health financing, increasing risk pooling to reduce out-of-pocket spending, improving access to services, and institutionalizing ongoing health accounts estimations.
Resource Type: Report
Authors: Ministry of Health and Social Services, Republic of Namibia
Published: June 30, 2015
Resource Description:
This report presents the findings and policy implications of Namibia’s Health Accounts estimation for the fiscal year April 2012 through March 2013 (2012/13). Earlier, Namibia completed three rounds of National Health Accounts covering the 11 years of spending between 1998/99 and 2008/09. The 2012/13 Health Accounts estimation in Namibia is the first round conducted using the SHA 2011 methodology. Health Accounts capture spending from all sources: the government, non-governmental organizations, external donors, private employers, private medical aid schemes, and households. The analysis breaks down spending into the standard classifications defined by the System of Health Accounts 2011 framework, namely sources of financing, financing schemes, type of provider, type of activity, and disease/ health condition.
The results of the Health Accounts 2012/13 exercise highlight the strong commitment of the government to financing the general health care of the population and the national HIV response. The commitment is commendable and should be maintained as it will be a key strength in Namibia’s efforts toward achieving universal health coverage.
Benchmarking Costs for Non-Clinical Services in Botswana’s Public HospitalsHFG Project
Authors: Peter Stegman, Elizabeth Ohadi, Heather Cogswell, Carlos Avila and Mompati Buzwani
Published: April 30, 2015
Botswana’s health sector has embarked on a broad program of reforms and, to this end, the Ministry of Health (MOH) has developed the Health Services Outsourcing Strategy and Programme 2011-2016. This planning document emerges from major strategic thrusts outlined in the National Development Plan 10 and the revised National Health Policy. Decision makers at the MOH, as well as hospital managers and others involved in implementing the outsourcing strategy at the facility level, need to know, among other things, how much the provision of non-clinical services is already costing the government under the existing arrangements. The study described here intended to support the implementation of the outsourcing plan by generating actual costs for the delivery of four non-clinical services that are, or will be, the focus of future outsourcing efforts: cleaning, laundry, catering, and grounds maintenance. The study looked at costs in five public sector hospitals: Athlone District Hospital, Deborah Retief Memorial Hospital, Gumare Primary Hospital, Goodhope Primary Hospital, and Mahalapye District Hospital.
An analysis of the costs and cost drivers of delivering non-clinical services in hospitals that are not currently outsourcing service delivery provides a cost benchmark. This will enable MOH decision makers and implementers to better understand the costs and cost drivers of non-clinical services and to compare current costs with estimated private sector costs, effectively negotiate contracts, and move toward greater efficiency and cost-savings. Further, cost benchmarks will provide hospitals with the critical data needed to understand not only the cost foundation of outsourced services but also more about what they can expect to receive for that cost, such as the type, quantity, and quality of service or product they are purchasing.
Landscape of Urban Health Financing and Governance in BangladeshHFG Project
The document provides an overview of urban health care delivery in Bangladesh. It finds that while urban local bodies are legally responsible for primary health care, they lack the infrastructure to provide these services. As a result, the urban population relies on a variety of alternative providers, including private clinics and hospitals, government secondary/tertiary hospitals, donor-funded projects like the Urban Primary Health Care Services Delivery Project and NGO Health Service Delivery Project, international NGOs, and local NGOs/CBOs. These institutions are financed through different mechanisms like user fees, government budgets, and donor funding. Governance also varies depending on the type of organization. The analysis concludes there are significant gaps in knowledge around urban health financing, delivery
Sustaining the HIV and AIDS Response in St. Kitts and Nevis: Investment Case ...HFG Project
The HIV/AIDS program in St. Kitts and Nevis is at a turning point, facing both opportunities to expand and target its efforts and threats of decreasing funding. As its National HIV/AIDS Strategic Plan expires in 2014, the country must consider whether and how to revise strategic priorities related to controlling and mitigating the effects of the epidemic. Critical decisions must be made about programming and budgeting for the HIV response in the coming years.
This brief provides analytic inputs to help St. Kitts and Nevis develop an “investment case” for its HIV/AIDS program. UNAIDS and the U.S. President’s Emergency Plan for AIDS Relief (PEPFAR) have encouraged the small-island countries of the eastern Caribbean to develop HIV investment cases – reports that aim to help program leaders target investments on the interventions and populations where they will have maximum impact, given limited resources (UNAIDS 2012).
Sustaining the HIV/AIDS Response in St. Lucia: Investment Case BriefHFG Project
The HIV prevalence in St. Lucia is estimated at 0.58%1 based on reports from public and private laboratories on clients tested. This estimate is very likely an understatement given that some who engage in risky behavior do not go for testing and others also choose to be tested outside the country for fear of breaches of confidentiality2. The epidemic is concentrated among the most at risk groups including men who have sex with men (MSM), commercial sex workers (CSW), and other groups including prisoners and drug users.
The HIV and AIDS response consists of prevention activities that have been mostly provided through non-governmental organizations and care and treatment provided mostly by the Ministry of Health (MoH). Prevention among most-at-risk populations (MARPS) was mainly provided by the President’s Emergency Plan for AIDS Relief (PEPFAR)-funded Eastern Caribbean Community Action Program (EC CAP II), implemented by the Caribbean HIV/AIDS Alliance (CHAA) whose program ended in September 2014.
Guyana 2016 Health Accounts - Main ReportHFG Project
The document summarizes the key findings of Guyana's first Health Accounts exercise for fiscal year 2016. It found that total health expenditure was G$ 28.6 billion, with the government contributing 81% of funding. Household out-of-pocket spending accounted for 9% of total spending. Non-communicable diseases received the largest share of spending at 34%. The analysis aims to inform strategic health financing decisions and assess domestic resource mobilization as external donor funding declines. Recommendations include increasing prevention spending and strengthening financial commitment to HIV programs.
Botswana 2013-2014 Health Accounts: Main ReportHFG Project
This report describes the key findings and policy implications of the Botswana health accounts (HA) estimation for fiscal year April 2013 through March 2014. Previously Botswana completed two rounds of HA; the first round, in 2006, covered fiscal years 2000/2001, 2001/2002, and 2002/2003; the second round, in 2012, was for fiscal years 2007/2008, 2008/2009, and 2009/2010. Botswana’s 2013/2014 HA is the first round produced using the System of Health Accounts (SHA) 2011 framework. HA provide details on health care spending flows and distribution from government, external donors, private employers, non-government organisations (NGOs), medical aid schemes and households. The analysis breaks down health care spending into the standard classifications defined by the SHA 2011 framework, namely source of financing, financing scheme, financing agent, type of provider, type of activity, and disease/health condition.
Year 2 Annual Performance Monitoring ReportHFG Project
The annual performance monitoring report summarizes the activities of the Health Finance and Governance Project from October 1, 2013 to September 30, 2014. The report outlines cross-bureau activities conducted at a global level, as well as health financing and governance work in key areas such as HIV/AIDS, malaria, and maternal and child health. It also provides details of technical assistance provided by HFG at the country level in Africa, Asia, Eastern Europe/Eurasia, and Latin America/Caribbean. The report is intended to monitor and report on progress of HFG activities over the past year.
Sustaining the HIV/AIDS Response in Antigua and Barbuda: Investment Case BriefHFG Project
Antigua and Barbuda has made great strides in organizing its response to HIV and AIDS in recent years, and has managed to control the growth of the epidemic. The National AIDS Program (NAP) is now at a critical juncture as the country plans to adapt to the changing donor funding landscape, new clinical guidelines, strategic objectives, and changes in policy including greater program integration into primary care, which are designed to increase access and reduce the cost of service delivery.
This document provides analytic inputs that support a case for investment in the Antigua and Barbuda HIV and AIDS response. This report provides a quantitative analysis of trends in the HIV epidemic and the impact of various prevention and treatment efforts to date, along with a projection of possible future programming scenarios, their costs, and their implications for the epidemic. The report describes estimated funding available and gaps in funding that The Goals and Resource Needs models – part of the Spectrum/OneHealth modeling system that estimates the impact and costs of future prevention and treatment interventions – were used for this analysis.
Inventaire Du Secteur Privé De La Santé Du Mali & Proposition D’un Nouveau Fo...HFG Project
This document provides background information on Mali's private health sector and summarizes the findings of a private sector assessment conducted in 2017. Some key points:
- Mali has a population of over 17 million, with 75% living in rural areas. Demographic and health indicators point to a high burden of disease.
- The private health sector has grown slowly since 1985 due to a lack of support. Laws and policies outline its role but implementation has been weak.
- A 2017 assessment aimed to understand the private sector's potential contribution to health goals. It found opportunities for improved collaboration between public and private sectors in areas like service delivery, medicines, and financing.
- Key recommendations include establishing mechanisms for collaboration
Year 3 Annual Performance Monitoring ReportHFG Project
This annual performance monitoring report summarizes the activities of the Health Finance and Governance Project from October 1, 2014 to September 30, 2015. The $209 million, 5-year project works with partner countries to expand access to health care by increasing domestic health resources, improving resource management, and making wise purchasing decisions. Key activities included supporting the development and implementation of national health financing strategies, strengthening health information systems, improving governance and oversight, and providing technical assistance to USAID country missions in over 20 countries worldwide.
Year 4 Annual Performance Monitoring ReportHFG Project
The document provides an annual performance monitoring report for the Health Finance and Governance project from October 1, 2015 to September 30, 2016. It highlights the project's work expanding access to health care in developing countries by increasing domestic health resources, improving resource management, and strengthening health systems. It summarizes the project's activities in areas like global health security, HIV/AIDS, malaria, maternal and child health, and tuberculosis. It also outlines the project's field support activities in multiple countries and regions, including Africa, Asia, Eastern Europe and Eurasia, and Latin America and the Caribbean.
Hôpital Sacré-Coeur de Milot Health Care Production Costing StudyHFG Project
y request of the Hôpital Sacré-Coeur de Milot (HSCM), the United States Agency for International Development (USAID) Mission in Haiti asked the Health Finance and Governance (HFG) Project to conduct a costing study of HSCM. The goal of this study is to supply the data and the information necessary for developing a financial viability plan for HSCM.
The primary goal of this analysis is to analyze the cost structure of HSCM to:
Enable preparation of informed budgets
Provide data for sound planning
Strengthen management systems
Devise a business plan that aligns with HSCM’s financing strategy vision and ensures the sustainability of the model of confessional private hospitals
Moreover, as part of its resources mobilization strategy the hospital wants to offer certain health services to a private clientele of patients. For that, HSCM wanted the detailed treatment cost of 10 diseases whose treatment it could offer private clients concurrently with current care offerings.
This document provides an overview of health insurance options and considerations for Ohio public entities. It discusses fully-insured plans, self-funded plans, and individually purchased plans vs. plans purchased through a health benefit consortium. For each option, it provides a brief description and lists potential advantages and disadvantages. It also notes that the Affordable Care Act has introduced regulatory changes affecting insurance providers and employers. Finally, it reviews the current health insurance purchasing arrangements of Ohio public entities based on data from the research conducted. The document aims to help public entities better understand their health insurance options and factors to consider when making purchasing decisions.
Integrating Financing of Vertical Health Programs: Lessons from Kyrgyzstan an...HFG Project
This document summarizes case studies on transitions in financing vertical health programs in Kyrgyzstan and the Philippines. In Kyrgyzstan, tuberculosis services transitioned from being financed vertically to being integrated under mandatory health insurance. In the Philippines, family planning services transitioned from vertical financing to being covered by national health insurance. Key reasons for the transitions included aligning financing with universal health coverage goals and reducing fragmentation. Operationalizing the transitions involved defining benefit packages, adjusting provider payment methods, and coordinating stakeholders. The case studies provide lessons on successfully transitioning from vertical to integrated financing models.
Expanding Coverage to Informal Workers: A Study of EPCMD Countries’ Efforts t...HFG Project
For many low- and middle-income countries (LMICs), expanding health coverage to informal workers is one of the most common, yet complex challenges requiring action. Informal workers are, by definition, not provided with legal or social protections through their employment, and are vulnerable to health and economic shocks. They also account for a large percentage of the population in LMICs. Expanding or deepening health coverage to informal workers is thus an area of interest for stakeholders pursuing universal health coverage (UHC): the goal that the entire population can access needed good-quality care without risk of impoverishment. Pro-poor coverage schemes that rely on prepayment – payment delinked from the time of care seeking – are a key financing strategy for UHC (WHO 2010). However, including informal workers in such schemes is challenging given that informal workers are not typically registered in taxation systems and social protection systems, nor covered by labor laws and regulations, making them less visible to the government and other stakeholders (Rockefeller Foundation 2013).
This report complements existing literature on how health reforms can improve the welfare of informal workers, focusing on the 25 countries prioritized for development assistance by the United States Agency for International Development (USAID) as part of its Ending Preventable Child and Maternal Deaths (EPCMD) initiative. Given the strong interest in these questions among EPCMD countries, USAID commissioned the Health Finance and Governance project (HFG) to conduct this research and provide recommendations relevant to UHC policy discussions in these countries.
The document provides guidance for testers working in agile teams. It discusses agile testing approaches compared to traditional waterfall methods. Testing occurs iteratively within each sprint rather than at the end. The document outlines an Agile Testing Quadrants framework to guide comprehensive testing, including unit, integration, system, and acceptance testing. It also addresses techniques like test-driven development, exploratory testing, and usability testing to ensure quality.
The document discusses measuring return on investment (ROI) from customer experience (CX) transformation programs. It outlines a 4-step process for CX program planning and ROI simulation: 1) scoping CX projects and mapping their impact on touchpoints, 2) benchmarking touchpoint improvements to overall customer satisfaction, 3) simulating program impacts using an interactive model, and 4) projecting ROI at the project and program level. The simulator allows estimating key metrics like revenue, costs, and satisfaction over time for decision making and portfolio optimization. It facilitates monitoring actual results to recalibrate projections and optimize the CX program.
Sustaining the HIV and AIDS Response in Grenada: Investment Case BriefHFG Project
The HIV/AIDS program in Grenada is at a turning point, facing both opportunities to expand and target its efforts and threats of decreasing funding. As its National HIV/AIDS Strategic Plan awaits ratification, the country must consider whether and how to implement strategic priorities related to controlling and mitigating the effects of the epidemic. Critical decisions must be made about programming and budgeting for the HIV response in the coming years.
This brief provides analytic inputs to help Grenada develop an “investment case” for its HIV/AIDS program. The Joint United Nations Program on HIV/AIDS (UNAIDS) and the U.S. President’s Emergency Plan for AIDS Relief (PEPFAR) have encouraged the small-island countries of the eastern Caribbean to develop HIV investment cases, which are reports that aim to help program leaders target investments on the interventions and populations where they will have maximum impact, given limited resources (UNAIDS 2012). The priorities and analysis outlined in this brief will also inform a multi-country regional application to the Global Fund for HIV/AIDS, TB and Malaria.
Guide for the Monitoring and Evaluation of the Transition of Health ProgramsHFG Project
This guide looks at three different transition experiences (funding, technical assistance, and services) to demonstrate variations in the type of transition undertaken, and the corresponding need for M&E. The authors draw upon experience of monitoring and evaluating transition to clarify key elements and dimensions of transition and how they relate to the longer-term goal of program sustainability and to present possible indicators, relevant to different health programs and transition arrangements that can help track transition and offer suggestions on how to select appropriate indicators. This document provides a conceptual framework to guide thinking around the M&E of transitions and will be amended as experience grows.
Trinidad and Tobago 2015 Health Accounts - Main ReportHFG Project
This document summarizes the key findings of the 2015 health accounts report for Trinidad and Tobago. It finds that total health expenditure was 4.5 billion TT dollars in 2015, equivalent to 4.1% of GDP. The government financed 41% of health spending, while households financed 35% through direct out-of-pocket payments. Noncommunicable diseases accounted for the largest share of recurrent health spending at 42%. Out-of-pocket payments remain high, comprising over a third of total health expenditure. The report recommends strengthening government commitment to health financing, increasing risk pooling to reduce out-of-pocket spending, improving access to services, and institutionalizing ongoing health accounts estimations.
Resource Type: Report
Authors: Ministry of Health and Social Services, Republic of Namibia
Published: June 30, 2015
Resource Description:
This report presents the findings and policy implications of Namibia’s Health Accounts estimation for the fiscal year April 2012 through March 2013 (2012/13). Earlier, Namibia completed three rounds of National Health Accounts covering the 11 years of spending between 1998/99 and 2008/09. The 2012/13 Health Accounts estimation in Namibia is the first round conducted using the SHA 2011 methodology. Health Accounts capture spending from all sources: the government, non-governmental organizations, external donors, private employers, private medical aid schemes, and households. The analysis breaks down spending into the standard classifications defined by the System of Health Accounts 2011 framework, namely sources of financing, financing schemes, type of provider, type of activity, and disease/ health condition.
The results of the Health Accounts 2012/13 exercise highlight the strong commitment of the government to financing the general health care of the population and the national HIV response. The commitment is commendable and should be maintained as it will be a key strength in Namibia’s efforts toward achieving universal health coverage.
Benchmarking Costs for Non-Clinical Services in Botswana’s Public HospitalsHFG Project
Authors: Peter Stegman, Elizabeth Ohadi, Heather Cogswell, Carlos Avila and Mompati Buzwani
Published: April 30, 2015
Botswana’s health sector has embarked on a broad program of reforms and, to this end, the Ministry of Health (MOH) has developed the Health Services Outsourcing Strategy and Programme 2011-2016. This planning document emerges from major strategic thrusts outlined in the National Development Plan 10 and the revised National Health Policy. Decision makers at the MOH, as well as hospital managers and others involved in implementing the outsourcing strategy at the facility level, need to know, among other things, how much the provision of non-clinical services is already costing the government under the existing arrangements. The study described here intended to support the implementation of the outsourcing plan by generating actual costs for the delivery of four non-clinical services that are, or will be, the focus of future outsourcing efforts: cleaning, laundry, catering, and grounds maintenance. The study looked at costs in five public sector hospitals: Athlone District Hospital, Deborah Retief Memorial Hospital, Gumare Primary Hospital, Goodhope Primary Hospital, and Mahalapye District Hospital.
An analysis of the costs and cost drivers of delivering non-clinical services in hospitals that are not currently outsourcing service delivery provides a cost benchmark. This will enable MOH decision makers and implementers to better understand the costs and cost drivers of non-clinical services and to compare current costs with estimated private sector costs, effectively negotiate contracts, and move toward greater efficiency and cost-savings. Further, cost benchmarks will provide hospitals with the critical data needed to understand not only the cost foundation of outsourced services but also more about what they can expect to receive for that cost, such as the type, quantity, and quality of service or product they are purchasing.
Landscape of Urban Health Financing and Governance in BangladeshHFG Project
The document provides an overview of urban health care delivery in Bangladesh. It finds that while urban local bodies are legally responsible for primary health care, they lack the infrastructure to provide these services. As a result, the urban population relies on a variety of alternative providers, including private clinics and hospitals, government secondary/tertiary hospitals, donor-funded projects like the Urban Primary Health Care Services Delivery Project and NGO Health Service Delivery Project, international NGOs, and local NGOs/CBOs. These institutions are financed through different mechanisms like user fees, government budgets, and donor funding. Governance also varies depending on the type of organization. The analysis concludes there are significant gaps in knowledge around urban health financing, delivery
Sustaining the HIV and AIDS Response in St. Kitts and Nevis: Investment Case ...HFG Project
The HIV/AIDS program in St. Kitts and Nevis is at a turning point, facing both opportunities to expand and target its efforts and threats of decreasing funding. As its National HIV/AIDS Strategic Plan expires in 2014, the country must consider whether and how to revise strategic priorities related to controlling and mitigating the effects of the epidemic. Critical decisions must be made about programming and budgeting for the HIV response in the coming years.
This brief provides analytic inputs to help St. Kitts and Nevis develop an “investment case” for its HIV/AIDS program. UNAIDS and the U.S. President’s Emergency Plan for AIDS Relief (PEPFAR) have encouraged the small-island countries of the eastern Caribbean to develop HIV investment cases – reports that aim to help program leaders target investments on the interventions and populations where they will have maximum impact, given limited resources (UNAIDS 2012).
Sustaining the HIV/AIDS Response in St. Lucia: Investment Case BriefHFG Project
The HIV prevalence in St. Lucia is estimated at 0.58%1 based on reports from public and private laboratories on clients tested. This estimate is very likely an understatement given that some who engage in risky behavior do not go for testing and others also choose to be tested outside the country for fear of breaches of confidentiality2. The epidemic is concentrated among the most at risk groups including men who have sex with men (MSM), commercial sex workers (CSW), and other groups including prisoners and drug users.
The HIV and AIDS response consists of prevention activities that have been mostly provided through non-governmental organizations and care and treatment provided mostly by the Ministry of Health (MoH). Prevention among most-at-risk populations (MARPS) was mainly provided by the President’s Emergency Plan for AIDS Relief (PEPFAR)-funded Eastern Caribbean Community Action Program (EC CAP II), implemented by the Caribbean HIV/AIDS Alliance (CHAA) whose program ended in September 2014.
Guyana 2016 Health Accounts - Main ReportHFG Project
The document summarizes the key findings of Guyana's first Health Accounts exercise for fiscal year 2016. It found that total health expenditure was G$ 28.6 billion, with the government contributing 81% of funding. Household out-of-pocket spending accounted for 9% of total spending. Non-communicable diseases received the largest share of spending at 34%. The analysis aims to inform strategic health financing decisions and assess domestic resource mobilization as external donor funding declines. Recommendations include increasing prevention spending and strengthening financial commitment to HIV programs.
Botswana 2013-2014 Health Accounts: Main ReportHFG Project
This report describes the key findings and policy implications of the Botswana health accounts (HA) estimation for fiscal year April 2013 through March 2014. Previously Botswana completed two rounds of HA; the first round, in 2006, covered fiscal years 2000/2001, 2001/2002, and 2002/2003; the second round, in 2012, was for fiscal years 2007/2008, 2008/2009, and 2009/2010. Botswana’s 2013/2014 HA is the first round produced using the System of Health Accounts (SHA) 2011 framework. HA provide details on health care spending flows and distribution from government, external donors, private employers, non-government organisations (NGOs), medical aid schemes and households. The analysis breaks down health care spending into the standard classifications defined by the SHA 2011 framework, namely source of financing, financing scheme, financing agent, type of provider, type of activity, and disease/health condition.
Year 2 Annual Performance Monitoring ReportHFG Project
The annual performance monitoring report summarizes the activities of the Health Finance and Governance Project from October 1, 2013 to September 30, 2014. The report outlines cross-bureau activities conducted at a global level, as well as health financing and governance work in key areas such as HIV/AIDS, malaria, and maternal and child health. It also provides details of technical assistance provided by HFG at the country level in Africa, Asia, Eastern Europe/Eurasia, and Latin America/Caribbean. The report is intended to monitor and report on progress of HFG activities over the past year.
Sustaining the HIV/AIDS Response in Antigua and Barbuda: Investment Case BriefHFG Project
Antigua and Barbuda has made great strides in organizing its response to HIV and AIDS in recent years, and has managed to control the growth of the epidemic. The National AIDS Program (NAP) is now at a critical juncture as the country plans to adapt to the changing donor funding landscape, new clinical guidelines, strategic objectives, and changes in policy including greater program integration into primary care, which are designed to increase access and reduce the cost of service delivery.
This document provides analytic inputs that support a case for investment in the Antigua and Barbuda HIV and AIDS response. This report provides a quantitative analysis of trends in the HIV epidemic and the impact of various prevention and treatment efforts to date, along with a projection of possible future programming scenarios, their costs, and their implications for the epidemic. The report describes estimated funding available and gaps in funding that The Goals and Resource Needs models – part of the Spectrum/OneHealth modeling system that estimates the impact and costs of future prevention and treatment interventions – were used for this analysis.
Inventaire Du Secteur Privé De La Santé Du Mali & Proposition D’un Nouveau Fo...HFG Project
This document provides background information on Mali's private health sector and summarizes the findings of a private sector assessment conducted in 2017. Some key points:
- Mali has a population of over 17 million, with 75% living in rural areas. Demographic and health indicators point to a high burden of disease.
- The private health sector has grown slowly since 1985 due to a lack of support. Laws and policies outline its role but implementation has been weak.
- A 2017 assessment aimed to understand the private sector's potential contribution to health goals. It found opportunities for improved collaboration between public and private sectors in areas like service delivery, medicines, and financing.
- Key recommendations include establishing mechanisms for collaboration
Year 3 Annual Performance Monitoring ReportHFG Project
This annual performance monitoring report summarizes the activities of the Health Finance and Governance Project from October 1, 2014 to September 30, 2015. The $209 million, 5-year project works with partner countries to expand access to health care by increasing domestic health resources, improving resource management, and making wise purchasing decisions. Key activities included supporting the development and implementation of national health financing strategies, strengthening health information systems, improving governance and oversight, and providing technical assistance to USAID country missions in over 20 countries worldwide.
Year 4 Annual Performance Monitoring ReportHFG Project
The document provides an annual performance monitoring report for the Health Finance and Governance project from October 1, 2015 to September 30, 2016. It highlights the project's work expanding access to health care in developing countries by increasing domestic health resources, improving resource management, and strengthening health systems. It summarizes the project's activities in areas like global health security, HIV/AIDS, malaria, maternal and child health, and tuberculosis. It also outlines the project's field support activities in multiple countries and regions, including Africa, Asia, Eastern Europe and Eurasia, and Latin America and the Caribbean.
Hôpital Sacré-Coeur de Milot Health Care Production Costing StudyHFG Project
y request of the Hôpital Sacré-Coeur de Milot (HSCM), the United States Agency for International Development (USAID) Mission in Haiti asked the Health Finance and Governance (HFG) Project to conduct a costing study of HSCM. The goal of this study is to supply the data and the information necessary for developing a financial viability plan for HSCM.
The primary goal of this analysis is to analyze the cost structure of HSCM to:
Enable preparation of informed budgets
Provide data for sound planning
Strengthen management systems
Devise a business plan that aligns with HSCM’s financing strategy vision and ensures the sustainability of the model of confessional private hospitals
Moreover, as part of its resources mobilization strategy the hospital wants to offer certain health services to a private clientele of patients. For that, HSCM wanted the detailed treatment cost of 10 diseases whose treatment it could offer private clients concurrently with current care offerings.
This document provides an overview of health insurance options and considerations for Ohio public entities. It discusses fully-insured plans, self-funded plans, and individually purchased plans vs. plans purchased through a health benefit consortium. For each option, it provides a brief description and lists potential advantages and disadvantages. It also notes that the Affordable Care Act has introduced regulatory changes affecting insurance providers and employers. Finally, it reviews the current health insurance purchasing arrangements of Ohio public entities based on data from the research conducted. The document aims to help public entities better understand their health insurance options and factors to consider when making purchasing decisions.
Integrating Financing of Vertical Health Programs: Lessons from Kyrgyzstan an...HFG Project
This document summarizes case studies on transitions in financing vertical health programs in Kyrgyzstan and the Philippines. In Kyrgyzstan, tuberculosis services transitioned from being financed vertically to being integrated under mandatory health insurance. In the Philippines, family planning services transitioned from vertical financing to being covered by national health insurance. Key reasons for the transitions included aligning financing with universal health coverage goals and reducing fragmentation. Operationalizing the transitions involved defining benefit packages, adjusting provider payment methods, and coordinating stakeholders. The case studies provide lessons on successfully transitioning from vertical to integrated financing models.
Expanding Coverage to Informal Workers: A Study of EPCMD Countries’ Efforts t...HFG Project
For many low- and middle-income countries (LMICs), expanding health coverage to informal workers is one of the most common, yet complex challenges requiring action. Informal workers are, by definition, not provided with legal or social protections through their employment, and are vulnerable to health and economic shocks. They also account for a large percentage of the population in LMICs. Expanding or deepening health coverage to informal workers is thus an area of interest for stakeholders pursuing universal health coverage (UHC): the goal that the entire population can access needed good-quality care without risk of impoverishment. Pro-poor coverage schemes that rely on prepayment – payment delinked from the time of care seeking – are a key financing strategy for UHC (WHO 2010). However, including informal workers in such schemes is challenging given that informal workers are not typically registered in taxation systems and social protection systems, nor covered by labor laws and regulations, making them less visible to the government and other stakeholders (Rockefeller Foundation 2013).
This report complements existing literature on how health reforms can improve the welfare of informal workers, focusing on the 25 countries prioritized for development assistance by the United States Agency for International Development (USAID) as part of its Ending Preventable Child and Maternal Deaths (EPCMD) initiative. Given the strong interest in these questions among EPCMD countries, USAID commissioned the Health Finance and Governance project (HFG) to conduct this research and provide recommendations relevant to UHC policy discussions in these countries.
The document provides guidance for testers working in agile teams. It discusses agile testing approaches compared to traditional waterfall methods. Testing occurs iteratively within each sprint rather than at the end. The document outlines an Agile Testing Quadrants framework to guide comprehensive testing, including unit, integration, system, and acceptance testing. It also addresses techniques like test-driven development, exploratory testing, and usability testing to ensure quality.
The document discusses measuring return on investment (ROI) from customer experience (CX) transformation programs. It outlines a 4-step process for CX program planning and ROI simulation: 1) scoping CX projects and mapping their impact on touchpoints, 2) benchmarking touchpoint improvements to overall customer satisfaction, 3) simulating program impacts using an interactive model, and 4) projecting ROI at the project and program level. The simulator allows estimating key metrics like revenue, costs, and satisfaction over time for decision making and portfolio optimization. It facilitates monitoring actual results to recalibrate projections and optimize the CX program.
Este edicto notifica a dos vecinos, Pedro Guillermo Macías González y Luz Marina Macías González, que se ha recibido una solicitud de licencia de construcción para un predio en el municipio de Santa Fe de Antioquia. Se les informa que tienen cinco días hábiles para acercarse a la Secretaría de Planeación e Infraestructura si tienen alguna objeción o solicitud relacionada con el trámite. El edicto está firmado por José Roso Muñoz Londoño, Secretario de Planeación e Infraestructura.
Este documento presenta la información sobre el XXIV Torneo de Balonmano Escolar Memorial "Nano" que se celebrará los días 27 y 28 de mayo de 2011. Incluye detalles sobre las instalaciones, categorías, inscripciones, sistema de competición, entrega de premios, y agradece la colaboración de las organizaciones que apoyan el evento. Además, promueve una iniciativa de recaudación de alimentos para personas necesitadas.
This document lists 72 movies and TV shows that Kevin Bacon appeared in between 1978 and 2015. Some of his notable roles included Animal House, Footloose, A Few Good Men, Apollo 13, Mystic River, and Black Mass. He has maintained a prolific acting career over several decades across many different genres of film and television.
Jonathan Armstrong has over 20 years experience providing art direction and design services for clients. He has worked on integrated marketing campaigns for global brands across various industries. Some of his past projects include creating advertising, branding, and retail assets for Speedo's "Get Speedo Fit" swimming campaign and Ellese's "Circolo Ellese" sports club campaign. He also led rebranding efforts for startups and worked on product launches, exhibitions, and anniversary campaigns.
Manual de serviço nx150 (1989) mskw8891 p interrupThiago Huari
Este documento fornece instruções de serviço para o sistema de iluminação e buzina de uma motocicleta Honda NX 150, incluindo diagnóstico de defeitos, substituição de lâmpadas e testes de continuidade para interruptores. Fornece especificações técnicas para as lâmpadas e ferramentas necessárias, além de instruções detalhadas para reparos e manutenção.
The document discusses responsive web design for SharePoint sites, noting that responsive design uses fluid grids and CSS media queries to adjust layout as the screen width decreases, allowing all content to remain visible and adapt to different devices. It provides an overview of common responsive design breakpoints and the differences between responsive and adaptive designs, with responsive focusing on content and adaptive on presentation that can hide or not present elements based on screen width.
Pete Rim - Cisco's agile journey, continuous delivery and scaling scrumScrum Australia Pty Ltd
The document discusses Cisco IT's transformation to continuous delivery (CD) through automation, adoption of Agile methodologies, and cultural change. Some key results of the transformation included a significant increase in the number of Agile project releases, a reduction in the average time to release from 49 to 28 days, and growth in CD adoption across the organization from 36% to 64%. The presentation identifies several areas still needing improvement and provides recommendations for a successful CD transformation.
This document provides information on a Spanish language course titled "Spanish for Hospitality & Tourism Level 3" offered at the University of Technology, Jamaica. The 75-hour course is aimed at students in their third year of the Hospitality & Tourism Management program. The course seeks to strengthen students' Spanish language skills while exploring socio-cultural aspects of the hospitality and tourism industries in Spanish-speaking countries. The course content is divided into 4 units covering topics like event planning, travel agencies, ecotourism, and job interviews. Assessment is based on oral exams, reading/writing assignments, projects, web-based activities, and class participation.
Small Business Employment Index - December 2016CBIZ, Inc.
In December, 30 percent of companies in the SBEI increased hiring, 50 percent made no change to staff totals, and 20 percent cut payroll totals. Industries that showed gains in employment include Real Estate, Manufacturing, Health Care, Retail and Non Profits, both of which are seasonal. Regionally, all four sectors across the country saw employment improve.
Kids of all ages: Developing Children’s Apps for Authors and PublishersDean Johnson
At Brandwidth, we take the approach that kids always want to be older than they really are. This is fortunate as we're all big kids at heart.
Our Journey to the Exoplanets and Doctor Who Encyclopedia apps are featured, as is 'Demi' – our Multi-Touch book for Hollywood Records. All this, plus a tantalising glimpse of some stunning future products...
Amazing mobile content should be fun for kids of all ages.
This presentation was part of the Digital Book World (DBW) Webcast 'Developing Children’s Apps for Authors and Publishers' with Louise Rice of Touch Press and Eric Huang of Made In Me:
http://www.digitalbookworld.com/2014/webcast-developing-childrens-apps-for-authors-and-publishers/
Bob Gottfried, a therapist who specializes in learning disorders and attention-based issues, shares helpful habits that can transform your ability to produce results in the school, in the workplace, and beyond.
Evaluating the Costs and Efficiency of Integrating Family Planning Services i...HFG Project
Integrating the delivery of health services is viewed as a priority in the fight for an AIDS-free generation, because this integration has the potential to improve access to HIV, family planning (FP), and other services and provide continuity of care for those living with HIV. At the request of USAID’s Office of HIV/AIDS and the USAID Zambia mission, the Health Finance and Governance (HFG) project conducted a study examining the costs and efficiencies involved in integrating family planning and antiretroviral therapy (ART) services.
Entomological Monitoring, Environmental Compliance, and Vector Control Capaci...HFG Project
The first case of local, vector-borne transmission of the Zika virus in the Americas was identified in May 2015 in Brazil. By July 2016, the virus had spread to nearly all Zika-suitable transmission zones in the Americas, including the majority of countries and territories in the Latin America and the Caribbean region. Governments in the region face a formidable challenge to minimize Zika transmission and limit the impact of Zika on their populations.
The United States Agency for International Development (USAID) supports efforts to strengthen the region’s Zika response through targeted technical assistance, stakeholder coordination, and implementation of key interventions. In Haiti, the USAID-funded Health Finance and Governance project assessed country capacity to conduct vector control and entomological monitoring of Aedes mosquitoes, the primary vector of the virus. The assessment was conducted from June 8 to 17, 2016, and sought to appraise current capacities, identify strengths and weaknesses in these capacities, and recommend countermeasures, i.e., specific strategies to minimize the impact of Zika virus transmission.
The assessment identified several challenges that must be confronted in order to mount an adequately robust response to the threat of Zika in Haiti:
Currently there is no national body to coordinate, plan, and finance a widespread and sustained vector control effort to suppress Zika transmission.
Haiti’s existing vector surveillance and control workforce is inadequately staffed with only 12 brigades of five personnel each for the entire country.
There is no sizeable program for surveillance or control of Zika vectors in the country, nor is there a central database for reporting surveillance and vector control efforts.
Weak infrastructure for waste management and water supply make households susceptible to mosquito breeding via shallow containers and uncovered water storage vessels. This suggests an imperative for environment-centered treatment strategies.
Women of reproductive age and pregnant women in particular are a high-risk population. Reaching them with behavior change communication (BCC) and information, education and communication (IEC) activities is challenged by low levels of antenatal care coverage.
Performance Based Incentives to Strengthen Primary Health Care in Haryana Sta...HFG Project
Authors: Susan Gigli, Jenna Wright, Francis Raj and Mudeit Agarwa
Published: February 28, 2015
The Government of Haryana is interested in adopting a performance-based incentive (PBI) scheme aimed at strengthening primary health care results. In December 2014, the HFG project conducted a qualitative investigation among 10 public health facilities in two Blocks in Haryana in order to understand the existing incentive and operating environments and to inform the design of a PBI scheme. This report presents the findings of the formative investigation and relevant contextual information on the health system in the selected districts with a view toward supporting an effective PBI scheme in Haryana. The findings and considerations fed into a stakeholder PBI design workshop in early 2015.
The study suggested strongly that a PBI scheme—communicated clearly and perceived as fair—could lead to a change in the overall work culture from one that inadvertently encourages passivity to one that promotes teamwork, engagement, initiative, transparency and accountability.
PBI-to-Strengthen-Primary-Health-Care-in-Haryana_Findings-from-a-Formative-In...Dr. M. K. Agarwal
This formative investigation sought to understand the existing incentive environment and operating conditions in public health facilities in Haryana State, India in order to inform the design of a potential performance-based incentive (PBI) scheme. The investigation involved focus groups and interviews at 10 public health facilities across two districts. Key findings included an overall positive yet cautious reaction to PBI among participants. Participants recognized room for improved performance but had concerns about targets and external barriers. Existing government systems like the District Health Information System could potentially support PBI implementation if strengthened. Challenges in the current environment like staffing shortages, pay disparities, and weak performance management would also need to be addressed for PBI to be effective. The investigation provides considerations
Strategic Health Purchasing Progress: A Framework for Policymakers and Practi...HFG Project
This document presents a framework for assessing the progression of strategic health purchasing (SHP) functions in countries. The framework identifies core SHP functions and organizes them into stages that represent increasing maturity and integration. Case studies of Canada, Germany, and Tanzania are used to illustrate how the framework can visualize a country's SHP progression over time. The framework is intended to help policymakers and practitioners understand their country's SHP strengths and weaknesses to guide reforms.
Engaging Civil Society in Health Finance and Governance: A Guide for Practiti...HFG Project
Governments and international donor organizations increasingly acknowledge the role of civil society organizations (CSOs) in strengthening health systems. By facilitating dialogue between government and citizens on issues of health sector priorities, performance, and accountability, CSOs can help to improve health service delivery and contribute to evidence-based policy. Often, however, CSOs lack the skills and tools needed to engage other stakeholders in issues of health finance and governance.
HFG’s guide provides governments and donors practical advice on engaging civil society in health finance and governance in order to meet health sector objectives and to improve health outcomes. Our guide describes the potential and limitations of civil society engagement entry points and presents an array of tools that may be used to do so.
Focusing specifically on the health sector, the HFG Guide offers practitioners a range of tools from which to choose based on the environment they work in and the objectives they seek to achieve. The guide emphasizes approaches that foster collaboration between public health officials and civil society that can improve access to and the quality of health services, ultimately contributing to improved health outcomes. This guide also seeks to provide practical mechanisms for how civil society engagement may be achieved, at the national, subnational, and community levels.
Experiences in Outsourcing Nonclinical Services Among Public Hospitals in Bot...HFG Project
This report summarizes Botswana's experience outsourcing non-clinical services at public hospitals to private vendors. It provides context on Botswana's national privatization policy and timelines. It also describes capacity building workshops held for Ministry of Health and hospital staff on outsourcing best practices. Initial findings show that outsourcing represented a large portion of hospital budgets on average. Perceptions of service quality improved for cleaning, laundry, and security according to a staff survey. The use of service level agreements and improved contract management techniques led to better communication and oversight between hospitals and vendors. However, opportunities remain to strengthen governance and further empower women through outsourcing initiatives.
Using Evidence to Design Health Benefit Plans for Stronger Health Systems: Le...HFG Project
Low- and middle-income country governments face competing health priorities as they try to increase their populations’ access to affordable healthcare with limited resources. Faced with difficult choices, how can governments align their spending with health system objectives? One common policy instrument governments are using is the health benefit plan (HBP), defined here as a pre-determined, publicly managed list of guaranteed health services. Based on country experiences, the authors of this report argue that using evidence improves the potential for HBPs to achieve and balance countries’ objectives for equity, efficiency, financial protection, and sustainability in the health sector.
Governments using—or considering—HBPs as part of their pathway to UHC are faced with complex questions as they prepare to design new HBPs or update existing ones to address technological, epidemiological, economic, or other changes. This report is intended to serve as a resource for these governments. Through a review of 25 countries examining the types of evidence used to design and update HBPs, this report identifies actionable lessons for designing HBPs that advance health systems objectives in a sustainable way. More: www.hfgproject.org and https://www.hfgproject.org/using-evidence-health-benefit-plans/
This document provides guidance on developing and using key performance indicators (KPIs) in the health sector. It discusses how KPIs can help health sector decision-makers track progress toward strategic goals, set performance standards and targets, measure improvements over time, and demonstrate results to stakeholders like the Ministry of Finance. The document emphasizes that KPIs should be linked to a sector's strategic framework and developed through strategic planning processes. It introduces the concept of a logic model to illustrate the logical linkages between problems, policies/measures, and goals. Developing the right KPIs involves aligning them with a sector's overarching strategic goals and objectives.
The Health Finance and Governance Briefing KitHFG Project
Resource Type: Brief
Authors: Megan Meline, Lisa Tarantino, Jeremy Kanthor, and Sharon Nakhimovsky
Published: September 2015
Resource Description: Getting access to affordable, quality health care is a universal story that touches virtually every family in the world. At the same time, providing quality health services and access to trained health professionals is a challenge for governments. The World Health Organization (WHO) estimates that 150 million people worldwide face “catastrophic expenditure” because of high costs of health care. In other words, they may have to forgo paying for basic needs, such as food, housing, or education to pay for medical treatment instead. These costs include transportation, doctors’ fees, medicine, hospitalization bills, and days lost from work.
Behind these sobering statistics lies a wealth of news and feature stories waiting for the media to investigate and share with national leaders and policymakers as well as civil society groups who can advocate for changes to health budgets and policies. At the heart of these stories are important questions about the financing of health care and the quality of governance that ensures responsive and effective management of those resources and services.
But writing health finance and governance stories can be challenging. Health finance is riddled with complex language, technical economic terms, and numbers – not necessarily a journalist’s comfort zone. The right sources for these stories can be difficult to identify and unwilling to talk. Data may be difficult to locate or to understand. And while corruption makes for splashy headlines, the broader systemic challenges of health governance are not widely understood — and yet they are important.
The Health Finance and Governance Briefing Kit is designed to help journalists and their editors uncover and tell these important health stories that affect people all around the world.
The Health Finance and Governance Briefing KitHFG Project
The Health Finance and Governance Briefing Kit is designed to help journalists and their editors uncover and tell these important health stories that affect people all around the world.
Impact of Health Systems Strengthening on HealthHFG Project
Leaders in low- and middle-income countries (LMICs) require timely and compelling evidence about how to strengthen their health systems to improve the health and well-being of their citizens. Yet, evidence on how to strengthen health system performance to achieve sustainable health improvements at scale, particularly toward Ending Preventable Child and Maternal Deaths (EPCMD), fostering an AIDS-Free Generation (AFG), and Protecting Communities against Infectious Diseases (PCID) is limited. The evidence that does exist is scattered, insufficiently analyzed, and not widely disseminated. Without evidence, decision-makers lack a sound basis for investing scarce health funds in health systems strengthening (HSS) in an environment of competing investment options.
USAID is committed to advancing the evidence base on HSS and this commissioned report clearly demonstrates that HSS can improve health in LMICs.
This report, based on a review of systematic reviews of the effects on health of HSS, presents a significant body of evidence linking HSS interventions to measureable impact on health for vulnerable people in LMICs. Making decisions on who delivers health services and where and how these services are organized is important to achieve priority health goals such as EPCMD, AFG, and PCID. The findings of this report document the value of investing in HSS.
Understanding Client Preferences to Guide the Prioritization of Interventions...Md. Tarek Hossain
To summarize, the main findings were:
1. The availability of brand drugs is an important factor in determining which facilities are utilized in this population – more so than any other attribute explored in the study for child health services.
2. Provider attitude is also a key determinant of health facility choice and facilities would benefit from further exploration to define specifically how they can improve this client population's perception of their providers’ attitude.
3. This population, though generally poor, does not have a strong preference for free services (over moderately priced services).
4. Although this population expressed (as expected) strong preferences for a continuum of care that includes effective referral services, higher preference scores for provider attitudes and the availability of brand drugs were observed, suggesting that these should be considered for prioritization.
Progress in Institutionalizing Health Accounts in Indonesia: Where Next?HFG Project
1) The Health Finance and Governance Project provided technical assistance to help institutionalize Health Accounts production in Indonesia led by the Ministry of Health's Center for Health Financing and Health Insurance (PPJK) and the University of Indonesia (UI).
2) With this support, PPJK and UI produced the 2015 and 2016 Health Accounts and PPJK has increased its leadership and capacity to produce future accounts.
3) Challenges remain around maintaining expertise, deepening stakeholder relationships, disseminating data quickly, refining data sources, and expanding work at the sub-national level, but progress has been made in establishing regular production and use of Health Accounts data for policymaking in Indonesia.
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Evaluating the Cost-effectiveness of a Mobile Decision Support Tool in Malawi
1. pg. 1
November 2015
This publication was produced for review by the United States Agency for International Development.
It was prepared by Matt Kukla, Pamela Riley, and Sarah Dominis for the Health Finance and Governance Project.
EVALUATING THE COST-EFFECTIVENESS
OF A MOBILE DECISION SUPPORT TOOL
IN MALAWI
2. 2
The Health Finance and Governance Project
USAID’s Health Finance and Governance (HFG) project will help to improve health in developing countries by
expanding people’s access to health care. Led by Abt Associates, the project team will work with partner countries
to increase their domestic resources for health, manage those precious resources more effectively, and make wise
purchasing decisions. As a result, this five-year, $209 million global project will increase the use of both primary
and priority health services, including HIV/AIDS, tuberculosis, malaria, and reproductive health services. Designed
to fundamentally strengthen health systems, HFG will support countries as they navigate the economic transitions
needed to achieve universal health care.
November 2015
Cooperative Agreement No: AID-OAA-A-12-00080
Submitted to: Scott Stewart, AOR
Office of Health Systems
Bureau for Global Health
Recommended Citation: Kukla, Matt, Pamela Riley, and Sarah Dominis 2015. Evaluating the Cost-Effectiveness of
a Mobile Decision Support Tool in Malawi. Bethesda, MD: Health Finance and Governance Project, Abt Associates Inc.
Abt Associates Inc. | 4550 Montgomery Avenue, Suite 800 North | Bethesda, Maryland 20814
T: 301.347.5000 | F: 301.652.3916 | www.abtassociates.com
Avenir Health | Broad Branch Associates | Development Alternatives Inc. (DAI)
| Johns Hopkins Bloomberg School of Public Health (JHSPH) | Results for Development Institute (R4D)
| RTI International | Training Resources Group, Inc. (TRG)
3. EVALUATING THE
COST-EFFECTIVENESS OF
A MOBILE DECISION SUPPORT TOOL
IN MALAWI
DISCLAIMER
The author’s views expressed in this publication do not necessarily reflect the views of the United States Agency
for International Development (USAID) or the United States Government.
4.
5. i
CONTENTS
Acronyms................................................................................................................. iii
Acknowledgments................................................................................................... v
Executive Summary .............................................................................................. vii
1. Introduction ......................................................................................................... 1
1.1 Background............................................................................................................................1
1.2 iCCM Mobile Tool ..............................................................................................................2
1.3 Study Context and Objectives.........................................................................................2
2. Analytical Framework ........................................................................................ 5
2.1 Analytical Framework.........................................................................................................5
2.2 Cost Categories...................................................................................................................6
3. Data and Methodology........................................................................................ 9
3.1 Data.........................................................................................................................................9
3.2 Evaluation Methods...........................................................................................................12
3.3 Incremental Cost-effectiveness Model.........................................................................13
3.4 Incremental Cost-effectiveness Assumptions............................................................14
4. Results.................................................................................................................15
4.1 Costs.....................................................................................................................................15
4.2 Effects....................................................................................................................................17
4.3 Cost-effectiveness..............................................................................................................21
5. Discussion...........................................................................................................27
5.1 Design, Pilot, Roll-out, and Scale-up Costs................................................................27
5.2 Modelled Annual Costs and Scale-up Costs ..............................................................27
5.3 Diagnosis Follow-up..........................................................................................................28
5.4 Treatment and Referral Decisions................................................................................28
5.5 Cost-effectiveness..............................................................................................................28
5.6 Health System Performance...........................................................................................29
5.7 Limitations ...........................................................................................................................29
5.8 Conclusions.........................................................................................................................30
Annex A: D-tree iCCM Costs (Unadjusted) ...................................................... 33
Annex B: CRS iCCM Costs (Unadjusted)...........................................................34
Annex C: Dedza Health Commission iCCM Costs (Unadjusted)................... 35
Annex D: Sensitivity Analysis of Dosage Accuracy........................................... 36
Annex E: References .............................................................................................37
6. ii
List of Tables
Table 1: Cost Categories and Activities..................................................................................6
Table 2: Scale-up Experiences................................................................................................. 10
Table 3: Cost of Programs to Reach 50 HSAs (2013 US$)............................................ 16
Table 4: Follow-up Questions by Illness Type.................................................................... 17
Table 5: Treatment Patterns by Illness Type...................................................................... 18
Table 6: Dosage by Treatment and Illness Type................................................................ 19
Table 7: Referral Patterns by Condition.............................................................................. 20
Table 8: Reasons for Not Giving Treatment...................................................................... 21
Table 9: Total Annual Costs of Observed Program (2013 US$).................................. 22
Table 10: Cost per HSA per % Change in Cases Correctly Diagnosed and
Treated ............................................................................................................................... 24
Table 11: Incremental Costs and Cost-Effectiveness Ratios of Different Scale-up
Scenarios (2013 US$) ..................................................................................................... 24
List of Figures
Figure 1: Costing Framework ....................................................................................................5
Figure 2: Multivariate Equations ............................................................................................. 12
7. iii
ACRONYMS
CHW Community Health Worker
CRS Catholic Relief Services
DHC Dedza Health Commission
DHO District Health Office
FTE Full-time Equivalent
HSA Health Surveillance Assistant
iCCM Integrated Community Case Management
ICER Incremental Cost-effectiveness Ratio
IMCI Integrated Management of Childhood Illness
LA Lumefantrine Artemether
NGO Non-governmental Organization
ORS Oral Rehydration Solution
TCO Total Cost of Ownership
USAID United States Agency for International Development
WHO World Health Organization
8.
9. v
ACKNOWLEDGMENTS
The authors would like to acknowledge the substantial contributions of D-tree International in the
conception, stakeholder support, IRB approval, data collection, and review of this report. D-tree
provided all of the outcomes data collected from 50 service delivery sites, assisted in data organization
and usability, provided data and estimations for all costs they incurred as developers of the mobile
application, facilitated access to other key partners in Malawi, and shared their expertise and insights for
the development of the cost-effectiveness model. Their collaboration was an essential element of this
research, with particular thanks to Alison Clark, Erica Layer, Marije Geldof, Chris Kulanga, and Marc
Mitchell. Additional thanks go to current and past staff of Catholic Relief Services in Malawi including
Kate Greenaway and Antonia Powell. Significant guidance and contributions were also made by Benjamin
Johns, Elaine Baruwa, and Sofie Faye at Abt Associates. Finally, the authors would like to thank Ishrat
Hussein from USAID’s Africa Bureau.
10.
11. vii
EXECUTIVE SUMMARY
Background and Objectives
Mobile applications are promising tools for strengthening service quality and have been an area of
considerable mHealth innovation. Despite growing demand for data to guide policymakers, donors, and
program managers in making sound investments, there is a paucity of evidence on the cost-effectiveness
of mHealth technologies (Braun et al. 2013). To address this gap, USAID’s Health Finance and
Governance Project analyzed a mobile decision support tool with the following objectives: First, it aimed
to provide a transparent and detailed methodology for categorizing the costs of building, deploying, and
scaling-up mobile decision support tools in Malawi. Second, it evaluated the incremental cost-
effectiveness of a mobile tool’s use in improving clinical care. Finally, the evaluation addressed challenges
faced in conducting cost-effectiveness analyses of mHealth interventions when they are scaled up and
become multifunctional.
Mobile Application
D-tree International (D-tree), a health NGO, in partnership with the PEPFAR-funded IMPACT project
and the Malawi Ministry of Health, developed a mobile-based decision support tool for health
surveillance assistants (HSAs). The tool incorporates the World Health Organization’s Integrated
Community Case Management (iCCM) approach to child health through the application of checklists and
decision information. The mobile iCCM application is designed to improve adherence to recommended
steps in the assessment, diagnosis, and treatment of children presenting with symptoms commonly
linked to childhood morbidity and mortality, such as fever, diarrhea, cough, and rapid breathing.
Methodology
Cost data on the iCCM mobile tool were collected for the period October 2010 through March 2013
from three program partners. Overall costs included project management, software development, a
pilot, and the roll-out to 50 HSAs. The mobile iCCM version evaluated in this study was not a
replacement for existing health worker training, record-keeping, or reporting requirements. Thus, cost
data were collected and analyzed only for the mobile tool given that it supplements the existing system
of paper job aids. Based on D-tree’s experience through 2015, costs were presented based on the tool’s
scale-up to 5,000 HSAs.
Effect data were collected from a study conducted between March 2011 and March 2013. A
convenience sample of 25 HSAs used the mobile tool to treat children in village health posts in three
districts of Malawi, while another 25 HSAs delivered care to children at different village health posts (in
the same districts) using only the existing paper-based registries. Treatment records from a random
sample of 625 patients from the HSA mobile group and 625 from the HSA paper group were collected;
data were recorded on patient medical conditions (fever, diarrhea, rapid breathing, and red eye), patient
clinical severity, treatments received, whether patients with more severe illness were referred, reasons
why providers failed to administer appropriate treatments, as well as other patient and village health
post characteristics. The study evaluated whether the presence of the mobile decision support tool was
associated with a greater probability of HSAs (a) asking follow-up diagnostic questions, (b) providing the
12. viii
correct treatment for a given medical condition, and (c) providing the correct medication dosage for a
given treatment. It applied logit multivariate regressions to correct for potential biases.
The study assessed the mobile tool’s incremental cost-effectiveness, relative to the standard paper-
based system, by measuring the cost of a 1 percent change in the proportion of children correctly
diagnosed and treated per HSA. Incremental cost-effectiveness ratios were developed for 50, 500, 1,000,
and 5,000 HSAs to project how the tool’s cost-effectiveness would change if scaled up nationally. These
ratios were adjusted for inflation, annualized, and built on key assumptions, such as the maintenance of
D-tree’s existing cost structure in designing, piloting, and scaling up the tool to 1,000 HSAs in Malawi.
Results
Costs
The total cost of designing and implementing the mobile decision support tool for 50 HSAs amounted to
$175,678, or $3,514 per HSA ($172,483 without adjusting for inflation). These costs, both capital and
recurrent, were incurred by the three partners over the life of the pilot: D-tree ($149,469), Catholic
Relief Services ($20,717), and Dedza Health Commission ($5,492). Labor costs accounted for roughly 73
percent of total costs, followed by other direct costs (13 percent), with mobile phones accounting for 2
percent of total costs.
The costs of the program for 50 HSAs under the scale-up assumptions is $24,035 per year (compared
to $47,857 per year as observed); some of these lower costs reflect genuine savings from more efficient
implementation and some reflect missing costs for program management. Under the various scenarios
presented, the cost of the mHealth program would be about $66,000, $115,000, and $490,000 per year
for 500, 1000, and 5,000 HSAs implementing the program.
Effects
Under the assumption that providers in both groups correctly diagnosed patients into general illness
categories, providers in the mobile group and in the paper group asked the correct follow-up questions
99 percent of the time. While not statistically significant (p=0.28), the mobile tool was associated with
0.57 lower odds of prescribing the correct treatment for a given medical symptom. This finding is likely
attributable to greater stock-out of drugs in the mobile group and not due to the tools used.
The sample size was too small to demonstrate whether providers using the mobile tool had significantly
higher referral rates for patients experiencing danger signs for a given medical condition. However,
providers were statistically more likely to prescribe the correct dosage of medication for a given
treatment when they used the mobile tool (p<.001). Excluding oral rehydration solution (ORS) and
tetracycline eye ointment, HSAs delivered the clinically appropriate medication dosage to patients 5
percent of the time when only using the existing paper-based registries, compared with 95 percent, on
average, for those using the mobile tool. However, under assumptions about how HSAs using the
existing paper-based registries may have misreported the data, these HSAs may have given the correct
dosage to up to 91 percent of patients.
Unlike providers using the paper registries, from whom no data were recorded or collected explaining
decisions, those using the mobile tool provided documentation about why they did not prescribe a
particular medication. The latter group indicated that stock-outs were the most common reason for not
prescribing recommended treatments such as ORS, zinc, lumefantrine arthmether, antibiotics, or
paracetamol.
13. ix
Cost-effectiveness
Compared with the existing paper-based system, the mobile tool added to the total cost of service
during the introductory deployment to 50 HSAs, but also had a positive effect. Specifically, providers
using the mobile tool (a) asked all necessary follow-up questions for a given condition; (b) delivered the
appropriate treatment/medication for that condition; and (c) administered the correct dosage for that
treatment/medication to almost 92 percent more patients than HSAs using the existing paper-based
system. The cost-effectiveness ratio was interpreted as follows: compared with the existing paper-based
system, the mobile tool costs an additional $10.43 per annum for an HSA to improve his/her diagnostic
and treatment accuracy by 1 percent.
Assuming that the relative effect difference does not change over time or over the number of HSAs
implementing the program, the tool’s cost-effectiveness improves as more HSAs enter the program. The
annual cost per HSA falls by 80 percent when scaled up from 50 HSAs to 5,000 HSAs. The resulting
improvement in the tool’s incremental cost-effectiveness ($1.07 for 5,000 HSAs) suggests that
policymakers are more likely to witness greater returns on investment if the tool is scaled up nationally
and maintained over time.
Limitations
This study was conceived and designed when evaluation funding became available, after the initial
implementing partners had closed the project. Cost data were derived based on staff estimates post
facto. Effect data used in this study lacked quality standards, because data collection relied only on health
worker records rather than direct observation of treatment and client outcomes. In turn, these
methodological limitations may have inflated the benefits of the mobile tool. In particular, research
shows a significant gap between what health workers say they do, and what they actually do. The
generalizability of results will vary depending upon local context for health system, mobile environment,
and implementing partners.
Discussion
What does mHealth cost?
The total cost of developing and deploying an mHealth application is often underrepresented in mHealth
budgets, which focus on software licensing, phones, and data plans. The quarter-by-quarter breakdown
of costs, categorized as planning, stakeholder relations, training, monitoring, and program management,
highlights the high upfront labor costs incurred by mHealth pioneers to design, refine, and deploy
interventions. As demonstrated by the scale-up scenarios, these nonrecurring development costs can
provide “sticker shock” at pilot level, but decline dramatically per HSA at scale.
Do mobile decision support tools work?
Data from this study suggest there is little room for improvement in whether HSAs ask follow-up
questions, and prescribing the correct drug given diagnosis may be more a function of drug availability
than the tool used. Mobile tools are likely associated with greater accuracy in treatment dosage
decisions. Mobile tools also create the conditions for more systematic and complete documentation of
case management.
14. x
What is the threshold for cost-effectiveness?
To compare the mobile decision support tool’s cost-effectiveness with other health investments, it is
critical to find interventions that also contribute toward improvements in process measures of quality
(e.g., correct diagnosis and treatment). While commonly found in middle- and upper-income countries,
such interventions have not been systematically introduced and measured in low-income countries.
Translating process measures of quality to outcome measures of quality (e.g., health outcomes) can
allow for comparison with a broader range of interventions that improve child health. However, that
analysis was not undertaken in this study due to the data limitations linking reported diagnoses and
treatments with correct diagnosis, and the inherent uncertainties in modelling the pathway from clinical
processes to health outcomes. Given the lack of mHealth cost-effectiveness data, there is no benchmark
for assessing whether the iCCM tool offers adequate value for money, nor is there consensus on what
threshold is acceptable for a given intervention. Further study is needed to establish benchmarks for
assessing value for money.
The challenge of expanding functionality of mHealth tools
The mobile application evaluated in this study was designed and deployed five years ago and has long
since been superseded by enhanced versions, which built upon the initial functions at modest marginal
cost. Each additional function of the tool – improved data capture, new content modules, improved data
analysis and data visualization, supervisory checklists, automated client contacts, and automated referral
follow-up – provides potential clinical effects as well as net program cost savings. The cost-effectiveness
of today’s decision support software is likely to be greater than results generated by the version used in
the present study. However, methodologies are needed that integrate the interactive effects of mHealth
applications producing disparate effects. Cost-benefit analyses offer one solution for future research, as
they derive a monetary value for each effect.
Conclusion
The decision-support mobile application evaluated in this study demonstrated that the estimated $1.07
cost per health worker at scale is effective in improving accuracy of treatment dose, but paper based
methods were equally effective in assessing clinical severity and referring cases presenting danger signs.
The primary contribution of this study is the documentation of a systematic methodology and study
design for evaluating the cost effectiveness of a mHealth application. To determine whether the
investment in this case reaches a threshold of cost-effectiveness, policymakers would need to consider
overall budget expenditures and baseline quality indicators for iCCM deployment, and data on cost-
effectiveness of alternative interventions to improve adherence to iCCM protocols.
Additional research is needed to continue building the evidence base for mHealth cost effectiveness.
One key topic not addressed by the present study is the link between improved process inputs (such as
correct dose) with health outcomes such as QALYs, DALYs, or lives saved. Adjustments to the
coverage assumptions for iCCM based on improved probability of correct adherence to protocol may
not produce detectable effects on health in environments where many health interventions are
underway. The measurable value of mHealth is likely to be demonstrated primarily in documenting
lower costs per unit of service delivery.
15. 1
1. INTRODUCTION
1.1 Background
Mobile phone penetration has increased rapidly in the developing world, with 89 percent of all
individuals owning a mobile phone in 2013 (International Telecommunications Union 2013 Facts and
Figures). As mobile technology access has increased, mHealth, or the use of mobile technology to
improve health outcomes, has grown rapidly in sub-Saharan Africa. mHealth has potential to address
many of the challenges health systems in Africa have faced, including the severe shortages in health
workers. The use of Community Health Workers (CHWs) to deliver health interventions has greatly
increased in the last decade. CHWs receive limited medical training and are often deployed to the
frontline of health care, identifying cases in the community and providing basic treatment. Mobile
applications are promising tools for strengthening service quality provided by this dispersed workforce,
and have been an area of considerable mHealth innovation. In a systematic review of studies on the use
of mobile technology by CHWs, one third of the studies reported using mHealth to increase CHW’s
adherence to health service delivery standards and guidelines (Braun et al. 2013).
Rigorous evaluation of mHealth interventions is limited, and there have been repeated calls for more
evidence of mHealth’s impact on outcomes of interest (Tomlinson et al. 2013). Several individual studies
have found that the quality of care provided by health workers increases when using mHealth decision
support tools. A study in Tanzania found that using mobile compared to the existing paper-based
systems for Integrated Management of Childhood Illnesses (IMCI) improved the completeness of patient
assessments (Mitchell et al. 2013). Using short message service (SMS) has been shown to increase family
planning, reproductive health, and HIV communication between CHWs and supervisors, enabling more
responsive care for patients, and improved outpatient malaria care in Malawi and Kenya (Lemay et al.
2012, Zurovac et al. 2011).
A recent review argued that there is “little or no evidence around calculating costs, including both initial
investments and maintenance over time” related to mHealth interventions (Braun et al. 2013). A pilot at
St. Gabriel’s Hospital in Malawi found that giving 75 CHWs cell phones for patient adherence reporting,
appointment reminders, and physician queries saved the hospital $2,750 in fuel costs and doubled the
capacity of the hospital’s TB program (Mahmud et al. 2010). Tools and frameworks for assessing costs
are under development, and are just starting to be applied in real-world settings and shared in the
literature (Dimagi 2014, Futures Group 2014, Johns Hopkins University 2015, Nethope 2014)). To guide
decision-makers, studies aimed at assessing mHealth technologies must consider the total cost of
ownership for designing, piloting, and scaling up such technologies.
Despite the numerous pilots, wide scale adoption of mobile decision support tools remains low. The
World Health Organization (WHO) found that broader adoption of mHealth initiatives in high, upper-
middle and lower-middle income countries is hampered by competing priorities as well as lack of
knowledge of these tools’ value for money (WHO 2011). The absence of evidence on value and cost-
effectiveness limits investments in mHealth solutions (Mechael et al. 2010, Olale et al. 2014, Futures
Group 2014, Betjeman et al. 2013). To make decisions about whether to invest in particular applications,
ministries of health and other program officers need to know the anticipated effects on their programs,
what it costs to add a mobile component to a health program, whether the addition of a mobile
component raises or lowers overall program costs, whether mobile solutions are more cost-effective
16. 2
than alternative solutions for achieving program objectives, as well as the settings in which mobile tools
offer the greatest value-for-money.
1.2 iCCM Mobile Tool
D-tree International (D-tree), a health NGO, uses mobile technology to provide decision support tools
for health workers. The PEPFAR-funded IMPACT project, led by Catholic Relief Services (CRS),
partnered with D-tree International in Malawi to support its development of a phone-based application
incorporating WHO’s Integrated Community Case Management (iCCM) approach to child health. CRS
partnered with the Malawi Ministry of Health to improve the quality of care provided by CHWs, known
in Malawi as Health Surveillance Assistants (HSAs). D-tree’s electronic iCCM application uses checklists
and decision information designed to promote thorough assessment of each child, reduce diagnostic
errors, improve adherence to recommended treatment, and facilitate patient follow-up for acutely ill
children to guide workers. It is designed for use by HSAs previously trained in the assessment of
children presenting with common symptoms linked to childhood morbidity and mortality, including
fever, diarrhea, cough, and rapid breathing.
The iCCM application evaluated in this study captures all the iCCM elements in the paper registers used
by the HSAs. The iCCM form includes space to record personal information about the child (e.g., name,
address, age), symptoms present (e.g., diarrhea, fever), danger signs (e.g., blood in the stool, fever more
than seven days), decision to refer to a health facility or not, and treatment and dosage prescribed. D-
tree’s original mobile application served only as a decision support tool to help providers diagnose and
treat childhood illnesses and was not designed to replace the existing paper-based system in Malawi.
Rather, the tool offers a supplemental functionality to the health worker. Paper registries are still used
to manage patient records and submit data, and all health workers receive identical iCCM face-to-face
training. The tool has since been expanded to include additional functionality such as record
management capabilities, but these functions were not in place during the time period covered by the
data included in our study.
1.3 Study Context and Objectives
The present study is designed to contribute to mHealth evidence in several ways. Given the growing
interest by policymakers and donors in designing, evaluating, and scaling up mHealth tools to improve
health system performance, a primary objective of this study is to provide a transparent and detailed
methodology for categorizing the total costs of building, deploying, and scaling mHealth interventions,
particularly for mHealth decision support tools. It also provides an observational evaluation of the
mobile application’s effectiveness at improving clinical quality of care. In doing so, this study aims to
contribute to evidence-based decisions about whether and under what conditions mobile decision
support tools should be implemented.
A secondary objective of this study is to contribute evidence on the cost-effectiveness of D-tree’s
mobile iCCM decision support tool and its ability to improve the quality of health worker management
of childhood illness. Specifically, this study aims to provide an analytical approach for quantifying the cost
per child correctly diagnosed and treated for the mobile application relative to the existing paper-based
system. This analysis will also inform policymakers, donors, and program managers on how the cost-
effectiveness of such tools change as they are scaled up, in addition to how value for money changes as
mHealth tools expand from single function to multifunctional devices.
17. 3
In conducting cost-effectiveness analyses, this study deviates from some commonly used methodologies
(Drummond et al. 2005; Briggs 2001). First, this study collected secondary data on process measures
such as adherence to protocol, diagnoses, and treatment decisions, but not outcome measures
(Donabedian 1988). Summary health outcome indicators that measure child mortality and/or morbidity
are commonly used in cost-effectiveness evaluations and provide a common metric for policymakers to
compare the cost-effectiveness of disparate interventions. Because this study includes only process
measures, it would need to take an additional step of translating the process measures into health
outcomes such as lives saved or DALYs. Existing tools, such as LiST, provide modeling software that
enable programs to estimate the impact of scaling up community-based interventions on health
outcomes. However, this study does not attempt to model health impact, because it does not have the
data to quantify the relationship between process quality and health impact with strong statistical validity
and confidence. There is nonetheless growing evidence linking the positive impact of improved clinical
process measures, such as diagnoses and treatment decisions, on health outcomes (Rubin et al. 2001;
Donabedian 1988).
Second, the nature of mobile applications such as the D-tree decision support tool is that they facilitate
many independent micro decisions, behaviors, and processes. This study focuses on understanding and
measuring the tool’s overall effectiveness at facilitating adherence to multiple steps within the iCCM
protocol and for multiple clinical conditions. Thus, understanding the changes in the proportion of
children correctly diagnosed and treated provides a means of illuminating the overall value for money of
investing in the tool directly linked to behavior changes directly targeted by the tool. However, it does
not attempt to assess the relative cost-effectiveness for each clinical condition or behavioral outcome.
Finally, the tool evaluated in this study is an early-stage prototype developed in 2011 that has since been
completely redesigned and enhanced. As such, the dollar amounts per improvement are not intended to
inform investment in this particular tool, which has been superseded by a later edition. D-tree’s updated
application has since evolved to integrate extensive additional functionality including case management,
data collection and reporting, referral management, and client follow-up messages. Mobile tools with
more functionality offer savings and efficiencies across more service elements, providing more bang for
each investment buck. Future studies will face the challenge of monetizing and aggregating the cumulative
benefits of timely accurate data, reduced health worker administrative burdens, and other health system
savings to determine the cost-effectiveness of the current iterations of multifunctional mHealth tools.
18.
19. 5
2. ANALYTICAL FRAMEWORK
2.1 Analytical Framework
This study used the WHO guidelines contained in “Cost Analysis in Primary Health Care” to develop
the costing model and categorize costs (Creese and Parker 1994). The WHO model classifies input
costs into two categories: capital and recurrent. Capital costs are purchased a single time, and used for
longer than one year. The length of time such a resource can be used is considered the expected useful
life, and the cost of the resource is typically annualized across the expected life. Recurrent costs are
used up within the course of a year, and are usually purchased regularly.
In identifying the costs to be included, the study used the Total Cost of Ownership (TCO) Life Cycle
Analysis, commonly used for decision making in business and for IT infrastructure acquisition. The TCO
Life Cycle Analysis considers the costs at each stage of ownership or implementation, from purchase
through retirement. For the purposes of this study, the costs of evolving and maintaining the tool, and
retiring the technology were not included, as the study was a pilot and did not extend to these stages.
Rather, the study assessed the first four stages: development/purchasing, set-up and installment,
deployment, and management support of the tool.
Using the WHO and TCO models to guide the development of the analytical framework, costs were
categorized by life cycle stage and type of input: capital or recurrent. The life cycle stages were defined
as: project management, including planning, administration, and obtaining stakeholder buy-in; developing
the software; piloting the tool with a small group of health workers; rolling out the tool to additional
health workers; and finally scale-up (see Figure 1).
FIGURE 1: COSTING FRAMEWORK
The stages in this study’s costing framework are not necessarily sequential. The costs of project
management, software development, licensing fees, and training health workers to use the tool continue
throughout the life of the tool’s implementation. While most stages include both capital and recurrent
costs, some include only capital costs, others only recurrent costs.
Project
Management;
Planning,
Administration,
Stakeholder
Buy-in
(Recurrent Costs)
Software
Development
(Capital and
Recurrent Costs)
Pilot
(Capital Costs)
Roll-out to
Health
Workers
(Capital and
Recurrent Costs)
Scale-up
(Capital and
Recurrent Costs)
20. 6
2.2 Cost Categories
Based on the costing framework presented in Figure 1, this section discusses specific costs that were
included in each life cycle stage of D-tree’s mobile decision support tool (see Table 1). Expense items
are classified as either start-up costs or recurrent costs. Start-up costs are defined as the costs
associated with activities needed at the start of the program and are not expected to be incurred again
over the life of the program. Recurrent costs are defined as costs associated with activities that are
needed on an annual basis in order to operate the program. Additionally, costs are divided into national-
level costs and HSA-level costs. National costs are activities that need to occur no matter how many
HSAs are using the mobile decision support tool, whereas HSA-level costs depend directly on the
number of HSAs engaged in the program. Note that potential costs at the district level, such as for
supportive supervision, are not included in these analyses because district-level staff already engage in
these activities for the paper-based system.
TABLE 1: COST CATEGORIES AND ACTIVITIES
Expense item Activities
National-level costs
Start-up costs
Labor
General Stakeholder Engagement
and Project Management
Contracting, budgeting, work planning, reporting, administration, stakeholder
outreach, buy-in meetings, communication, travel
Software Development CommCare agreement negotiation, software coding, software testing, software
development meetings and communication, content translation and approval
Mobile Services Landscaping, device and data plan negotiations, phone specifications
Training Pilot (training protocol development, health worker training, feedback
collection and analysis)
Other Direct Network charges, server hosting, registration (for period before start of
program)
Indirect Rent, office supplies, vehicle, gas, utilities, and other overhead
Recurrent costs
Labor
General Stakeholder and
Management
Contracting, budgeting, work planning, reporting, administration, stakeholder
outreach, buy-in meetings, communication, travel
Software Support Device and data plan negotiations, software coding, redesign, and debugging
Monitoring HSA troubleshooting, tech support
Other Direct Network charges, server hosting, registration (after start of program)
Indirect Rent, office supplies, vehicle, gas, utilities, and other overhead
21. 7
Expense item Activities
HSA-level costs
Labor
Training (International Support) Labor for support to training
Training (Local Support) Per diem, accommodation, transportation, training materials (staff costs for
training not included)
Other Direct
Phone purchase Mobile device procurement
2.2.1 General Stakeholder Engagement and Project Management
General stakeholder engagement and project management includes the start-up cost of planning,
administration, communication, and stakeholder meetings and buy-in. The project set-up and pilot stages
required intensive meetings among D-tree staff, partners and stakeholders. Much of this early project
management included gaining buy-in on the mobile iCCM tool from national and local stakeholders.
However, stakeholder meetings, administrative support, and general project management are necessary
over the project’s entire life cycle. Some resources for continued planning, administration, and
stakeholder engagement are thus considered to be recurrent costs.
2.2.2 Software Development and Support
Software development is an intensive, initial development phase of the mobile decision support tool
followed by ongoing testing and refinement. Tool development included the translation of iCCM content
into a digital format. The D-tree mobile decision-making tool was built upon an existing “CommCare”
platform, which required significant modification to support the mobile decision-making tool. Early
software development and testing included D-tree staff and clinicians. Throughout the tool’s pilot, roll-
out, and eventual scale-up, costs are incurred to modify the tool and, eventually, add new functions. For
this reason, software development includes both capital and recurrent costs, reflecting the initial
development of the software and continued technical support to update and troubleshoot the software.
2.2.3 Mobile Services
Mobile services include the process of landscaping phone companies and networks, as well as
determining phone specifications. It also includes mobile device and data plan negotiations with
providers. Even though these contracts are impacted by the number of phones procured, and thus the
number of HSAs, these costs are largely national costs negotiated and incurred during start-up.
2.2.4 Training
In preparation for the pilot, D-tree negotiated device prices and data plans with vendors and developed
a health worker training protocol. The pilot included six HSAs and meetings with users every two
weeks to learn about their experiences and problems with the application. Finally, these six HSAs tested
mobile decision support tool on patients and provided feedback to D-tree on its issues, challenges, and
benefits. Costs incurred to develop the training protocols and pilot the tool are considered start-up
costs, because they are incurred only once.
22. 8
2.2.5 Other Direct Costs
Network charges, server hosting, and registration occurred both in preparation for the program (for
period before start of program, considered to be start-up costs) and on a routine basis throughout the
life of the program, and are considered recurrent costs. However, these costs are fixed costs, in that
they are incurred no matter how many HSAs are using the mHealth iCCM application.
2.2.6 Indirect Costs
Indirect costs include office rent, utilities, transportation, and other overhead incurred to support staff
and activities associated with other activities. Indirect costs are calculated as a percentage of the cost of
labor, based on the implementing partners’ specific accounting rules. Indirect costs incurred before the
training of the initial HSAs on the mHealth application are considered start-up costs.
2.2.7 Monitoring
Unlike training costs, monitoring costs are considered to be national, recurrent costs, because HSAs are
in regular need of troubleshooting and technical support. A specified number of staff must always be on
call to address technical or nontechnical issues, irrespective of the exact number of HSAs trained and
utilizing the mobile devices. This is particularly the case as the functionality of a mobile device expands.
2.2.8 HSA Costs
HSA costs are costs associated with each HSA when the tool is disseminated and utilized by a large
number of HSAs across the country. These costs cover the costs of training HSAs, including the
associated support, per diems, accommodation, transportation, and training materials. Staff salaries paid
to the trainers or training recipients during training not included. They also include the cost of procuring
phones for the HSAs to use, and their maintenance over time.
23. 9
3. DATA AND METHODOLOGY
3.1 Data
3.1.1 Costs
Cost data on the iCCM mobile tool were collected for the period October 2010 through March 2013
from three secondary sources: D-tree, the developer of the mobile decision support tool; CRS, the
implementer of the iCCM program; and Dedza Health Commission (DHC), which trained HSAs on the
mobile tool.
Because the mobile tool supplements but does not replace the existing paper-based system, the only
cost data collected relate to the design, pilot, and roll-out of the mobile tool. Cost data were not
collected or used for the existing paper-based system. For instance, HSAs using the mobile application
received the same iCCM face-to-face training as those who only use and fill out the paper-based system.
As such the cost of this training was not considered in the study.
Moreover, due to the fact that cost data could not be collected from all local NGO partners, data were
collected only from DHC and used as a proxy for all other eight partners. To calculate total NGO
partner costs, DHC costs were scaled up for 50 HSAs. Cost data also likely contain some measurement
error, because certain costs (e.g., labor) were estimated retrospectively by D-tree, CRS, and DHC. The
effect of such error is discussed in Section 3.3.
Costs are calculated in two ways. First, costs for both roll-out to 50 HSAs and the scale-up to 1,000
HSAs are presented as incurred during the actual program. These costs are both unadjusted (raw) and
adjusted for inflation, and presented in 2013 U.S. dollars. For the latter group, costs incurred by D-tree
and CRS are adjusted using the inflation rate in the United States, since costs for these partners are
budgeted and incurred directly in U.S. dollars. Costs for DHC were adjusted for inflation using the
applicable inflation rates from Malawi. International Monetary Fund GDP deflators were used to
calculate inflation (IMF 2015).
Second, for the cost-effectiveness analysis, we calculate the annual equivalent costs of the program. For
this calculation, only costs incurred by D-tree and Malawi’s government are considered, even during the
initial 50 HSA roll-out phase. Moreover, the costs of start-up activities and durable goods (including
mobile phones and training of HSAs) are amortized over their assumed useful life. We use a discount
rate of 3 percent to calculate the annual equivalent costs, per WHO guidelines (Baltussen et al. 2003).
3.1.2 Scale-up Costs
The main analyses of this study present the actual and inflation-adjusted costs, based on D-tree’s
experience through 2013. Since that time, D-tree has scaled up the program to reach 1,000 HSAS.
These scale-up experiences are presented in Table 2 and are organized into two groups: cost sources
and cost categories. While the three primary cost sources in this study were D-tree, CRS, and DHC,
these sources and their respective roles changed considerably during scale-up.
After establishing offices in country, it was decided that either D-tree or its partner (in this case CRS)
would stay on in country to scale up the tool while the other would stop contributing. In the case of the
mobile decision support tool, CRS’s role in project management, training, and monitoring was no longer
24. 10
needed, and thus no scale-up costs were incurred by CRS. D-tree has handed over most of the roles
and responsibilities for scale-up to Malawi’s government. As such, the government has been responsible
for training new HSAS, purchasing phones, and paying existing staff. D-tree has taken on the role of
advisor to the government, whereby it assists in supervision and training HSAS when needed. For this
reason, DHC and other local NGOs, whose original role was to train HSAS on the mobile decision
support tool, have also no longer been needed during scale-up.
TABLE 2: SCALE-UP EXPERIENCES
Overview
of Roles
Government assumes responsibility of scale-up; Only one implementing partner needed to
assist government (D-tree), while all others (CRS, DHC) drop off
Source of
Costs
1. D-tree: Labor costs for content/programming/mobile services fall to zero during scale-up;
labor costs for monitoring, stakeholder engagement, management, and training become
minimal
2. CRS: Cost of labor, travel, supplies, phones, and rent fall to zero during scale-up
3. DHC: Cost of labor, travel, and supplies fall to zero during scale-up
4. Government: No role during design phase, but takes over labor costs (salaries), training
supplies, and phones
Scale-up
Cost
Categories
1. Government
a. Labor: Existing salaries, and thus no additional costs incurred
b. Training:
i. Per diem/accommodation: 1.5 days per training session, 2 trainers per session, 20
HSAs per session;
$50 per HSA and $5 per HSA (for trainers) per training session.
ii. Transportation: Costs include trainers and HSAs; $17 per HSA per training session
iii. Supplies: $2 per HSA for training materials
c. Phones: No economies of scale; $76 per phone per HSA; $8 per airtime per HAS
2. D-tree
a. Labor:
i. Stakeholder relations/management/supervision: Costs by district rather than number
of HSAs; D-tree is last point of contact and only addresses software issues; $11 per
HAS
ii. Training: One staff member per five HSA training sessions over two days; $2 per HSA
Cost categories during scale-up have been broken down by the two sources of funding active during the
scale-up: D-tree and Malawi’s government. The only costs incurred by D-tree after the initial 50 HSA
roll-out are those for labor – specifically, general stakeholder relations, management, technical support,
and training. For stakeholder outreach and management, D-tree has incurred costs according to the
number of districts rather than number of HSAs.
25. 11
D-tree staff is working across these districts on program development, staff supervision, training, and
financial management. For supervision, the existing model in Malawi is such that HSAs must first rely on
government employees to troubleshoot issues with the mobile tool, followed by district health IMCI
supervisors. Only problems requiring software changes are addressed by D-tree staff, who act as the
third backstop for supervision and oversight. Management, outreach, and supervision have required
roughly one D-tree staff member working at half time for every 1,000 HSAs, the equivalent of $11 per
HSA. D-tree staff are also expected to oversee five training sessions at a time, whereby each training
session includes 20 HSAs and lasts for two days. Thus, the cost for D-tree staff of training has been
roughly $2 per HSA.
CRS and DHC costs have fallen to zero during scale-up, as neither organization incurred costs after the
initial design, pilot, and roll-out to 50 HSAs. Malawi’s government has incurred costs for salaries,
training, and phones. Labor costs attributed to mobile tool have been zero, because training and
monitoring costs are already covered by the government’s salaried responsibilities. Data are not
available on the time cost or productivity loss associated with these changing roles.
D-tree has not witnessed economies of scale in the bulk purchasing of phones, because D-tree’s
experience has been that these are offset by increased functionality and costs as successive phones are
purchased. The cost of training has been broken into three components: per diems/accommodation,
transportation, and materials. The cost per HSA for per diems and accommodation of a typical 1.5 day
training session has been $50, while the per diem and accommodation costs for a trainer to train one
HSA (two trainers are required for a 20 HSA training session) has been $5. This totals $55 per has for
training logistics. Transportation per HSA, accounting for both trainers and the HSAs, has been $17.
Training materials equate to $2 per HSA. Across both D-tree and Malawi’s government, the total cost
per HSA during scale-up to date has been $172. As discussed in Section 3.1.1, costs for training and
phone procurement are annualized for the cost-effectiveness analysis.
3.1.3 Effects
Effect data were collected from a D-tree study conducted between March 2011 and March 2013. Based
on a convenience sample of HSAs, 50 HSAs went through training on IMCI and the iCCM mobile
decision support tool. These 50 HSAs were non-randomly selected from different village health posts
across three districts (Lilongwe, Zomba, and Ntcheu) in Malawi, where 25 HSAs treated children using
the mobile tool, while the other 25 HSAs treated children using only paper registries. Over the two-
year period, a random sample of 625 patient records for children under five years of age was taken from
each group (i.e., 1,250 child cases in total).
As such, the total sample size in this study was 1,250 patients, 625 of whom were diagnosed and treated
with the mobile tool and 625 of whom received care from HSAs using only the paper-based registries.
For each patient-level observation, HSAs recorded data on patient symptoms and diagnoses (fever,
diarrhea, fast breathing, and red eye), patient clinical severity, treatments provided by health providers
(per iCCM protocol) for those conditions, whether patients with more severe illness were referred,
reasons why providers failed to administer appropriate treatments, as well as patient and village health
post characteristics. With regards to referrals, all HSAs have the option to refer patients to health
facilities for more complex and serious clinical conditions.
For the mobile group, HSAs were prompted and required to ask diagnostic questions to patients based
on the symptoms and diagnosis; they were also required to confirm this behavior and record the
patients’ response to these questions in the mobile tool. Subsequent questions were presented to HSAs
(which are then asked to patients), which eventually led to a recommended treatment. This was done as
an app running on the phone. The data from each encounter were electronically uploaded to a central
server for further use as a medical record.
26. 12
For the group using the paper-based system, HSAs were provided an identical list of instructions and
questions based on a patient’s condition. They were asked to independently follow the instructions and
fill out the paper registries during each patient visit. In addition to the fact that this information is
recorded on a paper registry instead of a mobile phone, two other differences exist. First, unlike users
of the mobile tool, HSAs are not obligated to record data or information into the registries. Second,
unlike the mobile tool, the paper registries do not provide clinical treatment guidelines based on a given
diagnosis. D-tree staff collected the paper registries from the HSAs’ records, cleaned the data, and
provided both datasets (mobile and paper) to HFG for analysis.
3.2 Evaluation Methods
The purpose of the evaluation was to assess whether the presence of the mobile decision support tool
was associated with (a) a greater probability of HSAs asking follow-up diagnostic questions, (b) a greater
probability of HSAs providing the correct treatment for a given medical condition, and (c) a greater
probability of HSAs providing the correct medication dosage for a given treatment. To evaluate these
three objectives, the study applied a range of statistical models including Fisher’s exact test, bi-variate
regressions, and multivariate regressions. Multi-variate analyses were conducted using logit models,
because the dependent variables were all binary. Co-variates included case-mix (clinical severity) and
other patient-level characteristics, as shown in Figure 2. These equations are as follows:
FIGURE 2: MULTIVARIATE EQUATIONS
Symptom is measured as the presence of diarrhea, fever, fast breathing, red eye, and cough individually
(as recorded by the HSAs). Clinical severity is measured as whether there was one symptom present or
multiple symptoms, and treatment is measured as whether only one symptom was given a treatment or
multiple symptoms were treated.
The absence of data on other co-variates, such as provider-level characteristics, availability of medical
supplies, and drugs, was expected to artificially inflate the relationship between the mobile tool’s use and
the study’s three variables of interest. Several factors were likely to create biases. First, the absence of
randomization at the HSA level and potential impact of unobserved/unrecorded variables may have led
to selection bias. For example, HSAs may have been selected to implement the mHealth tool based on
their perceived performance (good or bad), and thus their performance may differ from the comparison
HSAs (for better or worse) without the mHealth intervention. Second, the lack of oversight for HSAs
using the mobile tool and paper registries likely led to measurement error. The effects and implications
were threefold.
1. An assumption had to be made that HSAs correctly diagnosed their patients for broad clinical
conditions, such as diarrhea, red eye, fever, or fast breathing. Without observers in place to
monitor those HSAs, it was not possible to know if these decisions were accurate. However,
the outcome variables used in these analyses are conditional upon the broadly defined clinical
condition. For example, HSAs should ask the relevant follow-up questions, provide treatment,
and provide the correct dosage of treatment for diarrhea regardless of whether the child was
1. Logit (Diagnostic Questions = 1) = Mobile Tool + Gender + Age + Symptom
2. Logit (Correct Treatment = 1) = Mobile Tool + Gender + Age + Symptom + Clinical Severity
3. Logit (Correct Dosage = 1) = Mobile Tool + Gender + Age + Symptom + Clinical Severity + Treatment
27. 13
correctly diagnosed for diarrhea. On the other hand, if HSAs tended to not diagnose broad
categories that are ‘harder’ to follow-up and treat (e.g., involve more questions or assessment)
this assumption may bias results if HSAs in one treatment arm were more likely to misdiagnose
than HSAs in the other treatment arm.
2. Data were missing for some observations. We observe that the paper-based group was more
likely to have missing data, although missing data for the outcome variables was always less than
5 percent. While unlikely, this could bias the findings.
3. It is feasible that HSAs using the paper-based registries may have incorrectly recorded data by
misinterpreting instructions. Under such a scenario, HSAs using the paper tool could have
diagnosed or treated patients correctly, but the results would suggest otherwise (or vice versa).
While the mobile tool and the paper registries have identical instructions, the mobile interface
helps HSAs record data correctly. Thus, recording error may potentially bias findings.
To assess the potential impact of erroneously recorded data, we conducted a sensitivity analysis on the
data from the HSAs using the paper-based system. This sensitivity analysis makes arbitrary, but
potentially valid, adjustments to the data to measure the degree to which erroneously recorded data
may have influenced the results. Specifically, the assumption is made that HSAs were recording (i) the
type of blister pack given for lumafenitrine artemether (LA) instead of the number of pills (i.e., a record
of “1” indicates the HSA gave a 1x6 blister pack, with 6 pills instead of 1, and a record of “2” indicates
the HSA gave a 2x6 blister pack, with 12 pills instead of 2), and (ii) for cotrimoxazole, zinc, and
paracetamol, it was assumed that paper data recorded number of pills per day instead of the total
number of pills).
3.3 Incremental Cost-effectiveness Model
This study examined the mobile tool’s incremental cost-effectiveness, relative to the standard paper-
based system, by measuring the cost per HSA per percent change in children correctly diagnosed and
treated. For “correct diagnosis and treatment” to be achieved, this study required that the following
conditions be met: (a) all appropriate follow-up questions for a given condition were asked; (b) the
appropriate treatment/medication for that condition was delivered; and (c) the correct dosage for that
treatment/medication was administered. In other words, if the mobile tool improves diagnostic and
treatment accuracy, the above incremental cost-effectiveness ratio would be interpreted as “the cost of
an HSA improving their diagnosis and treatment accuracy by 1 percent, above and beyond the existing
paper-based system.”
The percent of correctly diagnosed and treated patients was recorded for the 625 sample cases being
treated by mobile users as well as the 625 sample cases being treated by paper users. This approach was
taken after risk adjusting for patient case mix (clinical conditions included diarrhea, fever, fast breathing,
and red eye). The cost used in this model was the cost per HSA of designing and implementing D-tree’s
mobile decision support tool in Malawi.
The incremental cost-effectiveness ratio above was derived using the following logic model.
1.
𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶 𝑝𝑝𝑝𝑝𝑝𝑝 𝐻𝐻𝐻𝐻𝐻𝐻 𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀 𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇
% 𝑜𝑜𝑜𝑜 𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀 𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶 𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶 𝐶𝐶 𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷 & 𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇 − % 𝑜𝑜𝑜𝑜 𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃 𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶 𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶 𝐶𝐶 𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷 & 𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇
28. 14
For the numerator, the change in cost per HSA is simply the total cost per HSA of the mobile tool,
since the costs of the paper-based were incurred by both groups. For the denominator, the percent of
cases (out of 625) correctly diagnosed and treated for HSAs using the paper-based system was
subtracted from the percent of cases (out of 625) for HSAs using the mobile tool.
This leads to the change in cost per HSA (e.g., the cost per HSA of the mobile tool) over the percent
change in the number of cases correctly diagnosed and treated.
This ratio can be broken down further, thus giving the cost per HSA per percent change in cases
correctly diagnosed and treated when the mobile tool is used in lieu of the existing paper-based system.
Because the mobile tool supplements the existing paper-based system, one would expect the cost
(numerator) to be positive. However, depending on whether the mobile or existing paper-based
systems lead to a greater percent of cases correctly diagnosed and treated (denominator), the
incremental cost-effectiveness ratio may be positive or negative.
3.4 Incremental Cost-effectiveness Assumptions
The following assumptions were made for the study’s cost-effectiveness analyses, which include (a) the
cost-effectiveness of designing, piloting, and rolling it out to 50 HSAs and (b) the incremental cost-
effectiveness of scaling up the mobile tool to 5,000 HSAs:
1. A commonly applied practice in cost-effectiveness analyses is to choose an analysis perspective,
such as that of society, the government, health care providers, donors, or implementers. This
study takes the perspective of both implementers and governments. For implementers, this
study provides the cost to them of designing, piloting, and rolling out a mobile decision support
tool to a small number of HSAs. During scale-up, the costs in this study include the government
perspective, because D-tree handed off most of the responsibility for the mobile decision
support tool’s scale-up to the Malawian government, as well as international technical support
from D-tree.
2. While actual (raw) data are separately presented for those costs incurred by D-tree, CRS, and
DHC, the cost-effectiveness section of the study uses inflation adjusted, annualized costs.
3. Incremental cost-effectiveness ratios are presented at different levels of program scale, up to
5,000 HSAs. As of 2009, there were 10,055 HSAs in Malawi, though it is unlikely that every one
of them would receive a mobile tool (because not all HSAs implement iCCM). This figure was
chosen to show how the cost-effectiveness of the mobile decision support tool would change as
it is gradually scaled up to half of all HSAs in Malawi. To derive the cost-effectiveness of scaling
up the mobile decision support tool, it is assumed that the cost structure presented in Section
3.1, which represents costs incurred during D-tree’s scale-up to 1,000 HSAs, would remain the
same when scaling up to 5,000 HSAs. Moreover, it is assumed that mobile phones will need to
be replaced every two years and HSAs given refresher training courses every five years.
2.
𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶 𝑝𝑝𝑝𝑝𝑝𝑝 𝐻𝐻𝐻𝐻𝐻𝐻
1 % 𝑜𝑜𝑜𝑜 𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃 𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶 𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶 𝐶𝐶 𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷 & 𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇
1
𝐶𝐶ℎ𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎 𝑖𝑖𝑖𝑖 𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶 𝑝𝑝𝑝𝑝𝑝𝑝 𝐻𝐻𝐻𝐻𝐻𝐻
𝐶𝐶ℎ𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎 𝑖𝑖𝑖𝑖 % 𝑜𝑜𝑜𝑜 𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶 𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶𝐶 𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷 & 𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇
29. 15
4. RESULTS
We present first the costs as incurred by the program and its three implementing partners. These costs
include those for start-up, design of the mobile tool, a pilot, and roll-out to 50 HSAs, as well as the
existing scale-up to 1,000 HSAs. These results are intended to inform other programs considering the
implementation of similar programs as to the amounts of financial resources needed. Evaluation results
are then presented showing where and to what extent differences in diagnoses and treatments exist
between mobile and paper groups. The costs and effects are combined to report the incremental cost-
effectiveness of the program as observed. Finally, findings from the study’s cost and incremental cost-
effectiveness analysis are modelled and presented for the mobile tool’s national scale-up to 5,000 HSAs.
4.1 Costs
4.1.1 Design, Pilot, and Roll-Out
In Sections 4.1.1.1 through 4.1.1.3, costs are presented for each of the primary stakeholders involved in
the design, implementation, and roll-out of the iCCM tool to 50 HSAs in Malawi, by expense category,
and finally by time period. Costs were collected from the period October 2010 to March 2013. The
former marks the study inception when the mobile tool was being designed in Malawi; the end date
marks the final month of “effect” data collected by the HSAs. Total inflation-adjusted costs over this
period were $175,678 ($172,483 without adjusting for inflation). Unadjusted costs were broken down
by quarter for each implementing partner (Annexes A-C). This time interval was chosen because it
offered the best combination of data granularity, availability, and quality.
4.1.1.1 D-tree Costs
D-tree incurred $149,649 (inflation-adjusted) over four years to start the program, representing 85
percent of all the costs incurred (Annex A and Table 3). About 77 percent of D-tree costs were
incurred for labor. About 46 percent of a full-time equivalent (FTE) person working on the program in
2011 and 2012; smaller proportions of an FTE were incurred in 2010 and 2013 because the program
was not implemented for the full year. Labor costs include D-tree staff, fringe benefits, and consultants.
Labor is broken down by general stakeholder and management, iCCM content and programming, mobile
services, training, and monitoring. iCCM content and programming represented 39 percent of total
labor costs for the iCCM project, monitoring costs represented 23 percent of costs, and training
represented 20 percent of costs; the other labor categories together constituted under 20 percent of
costs. Other direct costs and indirect costs constituted the remainder of D-tree costs.
30. 16
TABLE 3: COST OF PROGRAMS TO REACH 50 HSAS (2013 US$)
Category
2010 2011 2012 2013 Percentage
of Total
FTE Salary FTE Salary FTE Salary FTE Salary
D-tree
Labor
General Stakeholder and
Management
1% $730 7% $7,435 5% $4,237 1% $579 7%
ICCM Content and
Programming
4% $2,433 23% $35,675 5% $6,653 1% $629 26%
Mobile Services 1% $487 5% $4,958 2% $2,118 1% $290 4%
Training 1% $730 7% $7,435 23% $13,615 6% $869 13%
Monitoring 1% $487 5% $4,958 11% $18,159 3% $3,280 15%
Sub-total, labor 8% $4,867 46% $60,460 46% $44,782 13% $5,647 66%
Other Direct NA $2,639 NA $13,254 NA $4,948 NA $2,618 13%
Indirect 0% $0 3% $2,203 14% $6,740 4% $1,311 6%
Total D-tree Costs $7,506 $75,917 $56,471 $9,576 85%
CRS
Labor 10% $3,157 29% $9,301 7%
Travel NA $468 NA $1,378 1%
Office Supplies NA $363 NA $1,068 1%
Phones NA $938 NA $2,762 2%
Rent NA $325 NA $957 1%
Other NA $0 NA $0 0%
Total CRS Costs $5,250 $15,466 12%
DHC
Training Costs
Labor NA $96 NA $196 0.2%
Travel NA $25 NA $245 0.2%
Training Supplies NA $449 NA $4,481 3%
Total DHC Costs $570 $4,922 3%
Total Costs of iCCM
Program
$7,506 $81,737 $76,859 $9,576
NA: Not applicable; FTE: Full-time equivalent
31. 17
4.1.1.2 Catholic Relief Services Costs
After D-tree, CRS incurred the second most costs for the program at $20,717 (inflation-adjusted),
which represented 12 percent of the cost of the entire program (Annex B and Table 3). About 60
percent of CRS costs were for labor, with 18 percent of costs for phone procurement. Travel, supplies,
and rent constitute the remainder of CRS costs.
4.1.1.3 Dedza Health Commission Costs
The DHC supported the training of HSAs on the mHealth tool (Annex C and Table 3). The DHC
incurred $5,492 (inflation-adjusted) in 2011-2012 in support of the program (3 percent of all costs), with
90 percent of costs incurred relating to training supplies.
4.2 Effects
In the following sections, findings are presented from calculations identified in Section 3.3. It is again
critical to note that the absence of HSA observers led to two limitations/assumptions: (a) the study
assumed that health providers were able to correctly diagnose patients into the following conditions:
diarrhea, fever, rapid breathing, or red eye; (b) HSAs using the paper registries may not have recorded
data or instead recorded data incorrectly. As such, the magnitude of these effects may be inflated.
4.2.1 Diagnosis Questions to Assess Clinical Severity
This section presents results on whether providers were more likely to follow government-approved
and evidence-based protocols and asked the required follow-up questions for each illness once a patient
was diagnosed with that particular condition. As shown in Table 4, patients/caretakers were asked the
appropriate follow-up question 99 percent of the time for patients diagnosed with diarrhea, fever,
cough, fast breathing, and red eye among health providers using both the mobile phone and the existing
paper-based system. Multivariate findings indicated that use of the mobile tool was associated with 2.19
times greater odds of asking follow-up questions, but the association is not statistically significant
(p = 0.28).
TABLE 4: FOLLOW-UP QUESTIONS BY ILLNESS TYPE
Illness Diagnosis Mobile Paper
Unadjusted
P-Value
Diarrhea Asked # of days of diarrhea 100% 100% n/a
Asked if blood in stool 100% 100% n/a
Fever Asked # of days of fever 100% 100% n/a
Cough Counted breaths per minute 97% 98% 0.56
Fast Breathing Asked # of days of cough 100% 99% 0.13
Looked for chest in-drawing 100% 100% n/a
Red Eye Asked # of days of red eye 100% 100% n/a
Asked # of days difficulty 100% 100% n/a
Total 99% 99% 0.67
Adjusted Odds Ratio 2.19 0.28
Diagnostic questions with * and ** represent those significant at the p<.10 and p<0.05 levels, respectively
32. 18
4.2.2 Treatment
For each medical condition, guidelines stipulate certain treatments: oral rehydration solutions (ORS) and
zinc for diarrhea, LA and paracetamol for fever, oral antibiotics (cotrimoxazole) for respiratory
infections, and tetracycline eye ointment (an antibiotic) for red eye. In adjusted terms, HSAs employing
the existing paper-based system were associated with more accurate treatment patterns for illness types
(Table 5). They were more likely than mobile users to prescribe zinc for diarrhea (p = 0.002), and were
more likely to prescribe paracetamol for fever (p = 0.01). Mobile users, on the other hand, were more
likely to prescribe LA for fever (p<0.001). Overall, mobile users prescribed the appropriate medication
for the diagnoses of patients 65 percent of the time, compared with 77 percent of the time for the users
of the existing paper-based system (p = 0.02). In multivariate analysis, the mobile tool was associated
with 0.57 times lower odds of prescribing the correct treatment, but the association is not statistically
significant (p = 0.28).
Mobile users recorded the lowest prescription accuracy for paracetamol among patients with fever and
antibiotics for red eye among patients that were referred. As discussed later, mobile users reported high
stock-out rates for paracetamol (see Table 8). Users of the existing paper-based also had the lowest
percent of correct prescriptions for paracetamol among patients with fever.
TABLE 5: TREATMENT PATTERNS BY ILLNESS TYPE
Illness Treatment Mobile Paper
Unadjusted
P-Value
Diarrhea Prescribed ORS (Village posts) 100% 99% 0.50
Prescribed zinc (Village posts) 89% 99% 0.002**
Prescribed ORS (Refer) 94% 100% 0.99
Fever Prescribed LA (Village posts) 100% 96% <0.001**
Prescribed paracetamol (Village posts) 57% 66% 0.01**
Prescribed LA (Refer) 90% 75% 0.27
Respiratory
infection
Prescribed oral antibiotics (Village posts) 99% 97% 0.06
Prescribed oral antibiotics (Refer) 95% 100% 0.99
Red Eye Prescribed antibiotic treatment (Village posts) 100% 86% 0.23
Prescribed antibiotic treatment (Refer) 13% n/a n/a
Total 65% 77% 0.02**
Adjusted Odds Ratio 0.57 0.28
Treatments with * and ** represent those significant at the p<.10 and p<0.05 levels, respectively
33. 19
The mobile tool was, on average, associated with significantly greater odds of prescribing the clinically
recommended dosage given for those treatments (p<0.001) in bivariate and multivariate analyses (Table
6). While mobile users gave the correct dosage 95 percent of the time, based on the data as recorded in
the registers, users of the existing paper-based system gave the correct dosage 5 percent of the time.
The only category of patients given the correct dosage of treatment among users of the existing paper-
based system were for LA among children aged up to five months presenting with fever, for which the
correct dosage is none (because these children are too young to take the pills). On the other hand,
mobile users prescribed the correct dosage over 90 percent of the time across all diagnoses and age
groups. Note that Table 6 does not present dosage for ointments (red eye) or ORS (diarrhea), as there
were no clinically defined dosage for these treatments (patients are rather given packets of ORS or
tubes of ointment).
Given the absence of HSA oversight, it is conceivable that HSAs using the paper-based system
incorrectly recorded their prescribed dosage for a given treatment, because they misinterpreted the
instructions. For instance, in the above example, 268 of the 470 paper users who prescribed LA
recorded a “1,” which might represent a single LA tablet. But, as noted in Section 3.2 above, “1” might
also mean one pack of six tablets, and thus HSAs actually provided the correct dosage. However, it
should once again be noted that HSAs received identical instructions for the mobile tool and paper
registries. Moreover, rather than speculate how HSAs interpreted the paper-based forms, this study
merely presents findings based on the data and acknowledges this potential limitation. To assess the
potential impact of erroneously recorded data, the study applied a sensitivity analysis on the data from
the HSAs using the paper-based system, as outlined in Section 3.2. Findings from this analysis, which
suggest less significant differences in dosage accuracy, are presented in Annex D. These results suggest
that HSAs employing the existing paper-based system gave the correct dosage 91 percent of the time
(which is statistically significantly lower than mobile tool users in an unadjusted comparison, but not in
the adjusted comparison).
TABLE 6: DOSAGE BY TREATMENT AND ILLNESS TYPE
Illness Treatment
Mobile Paper
Unadjusted
P-Value
#
Given
Any
Dose
# Given
Correct
Dose
Percent
Given
Correct
Dose
#
Given
Any
Dose
# Given
Correct
Dose
Percent
Given
Correct
Dose
Diarrhea Zinc (2-5 months) 1 1 100% 7 0 0% 0.13
Zinc (6-59 months) 91 88 97% 118 0 0% <0.001**
Fever LA (up to 5 months) 103 103 100% 143 141 99% 0.51
LA (5-35 months) 255 250 98% 248 0 0% <0.001**
LA (36- 59 months) 132 122 92% 168 0 0% <0.001**
Paracetamol (2-35 months) 161 159 99% 147 0 0% <0.001**
Paracetamol (36-59 months) 69 63 91% 85 0 0% <0.001**
Fast
Breathing
Oral antibiotic (2-11 months) 41 39 95% 45 0 0% <0.001**
Oral antibiotic (12-59 months) 134 132 99% 181 0 0% <0.001**
Total 498 473 95% 567 28 5% <0.001**
Adjusted Odds Ratio 10,808 <0.001**
Treatments with * and ** represent those significant at the p<.10 and p<0.05 levels, respectively
34. 20
4.2.3 Clinical Decision Making
Table 7 provides data on the number of patients experiencing “danger signs” for given medical
conditions as well as the percent of those patients who were referred, broken down by mobile users
and paper users. For most medical conditions, there appeared to be no difference in the referral rate
among providers who used the mobile tool and those who used the existing paper-based system;
however, sample size was not large enough to demonstrate statistical significance.
TABLE 7: REFERRAL PATTERNS BY CONDITION
Mobile Paper
#
Identified
#
Referred
Percent
Referred
#
Identified
#
Referred
Percent
Referred
Cough for 21 days or
more
2 1 50% 0 0 n/a
Diarrhea 14 days or more 0 0 n/a 0 0 n/a
Blood in stool 6 6 100% 3 3 100%
Fever for last 7 days 1 1 100% 1 1 100%
Convulsions 4 4 100% 2 2 100%
Not able to drink or feed
anything
5 5 100% 1 1 100%
Vomits everyday 8 8 100% 1 1 100%
Red eye for 4 days or
more
3 3 100% 1 1 100%
Red eye with visual
problem
4 4 100% 0 0 n/a
Chest in-drawing 13 12 92% 0 0 n/a
Very sleepy or
unconscious
2 2 100% 1 1 100%
Palmar pallor 3 3 100% 0 0 n/a
Red on MUAC tape 0 0 n/a 2 0 0%
Swelling of both feet 4 3 75% 0 0 n/a
**Diagnoses in bold represent those significant at the p<0.05 level
35. 21
4.2.4 Quantity and Quality of Data Collected
Beyond improving clinical quality of care (i.e., diagnoses and treatments), future versions of the mobile
tool aim to increase the quantity and improve the quality of data collected by health providers. While
not a primary function of this iCCM platform, additional analyses were done describing, for each medical
condition, reasons why providers did not prescribe a particular treatment (see Table 8).
Across all clinical conditions and treatments, there was an overall absence of data from providers that
used the existing paper-based system. Specifically, nearly 100 percent of paper users failed to explain
why their patients were not treated with a particular medication. It is impossible to assess why
providers did not treat patients with a particular medication and therefore difficult for policymakers to
develop solutions for improving the quality of and access to key health services. Among providers who
used the mobile tool, however, most indicated that they could not prescribe ORS, zinc, LA,
paracetamol, or oral antibiotics, because they were stocked out of medications. For instance, when
dealing with cases of fever, 198 of the 201 providers who did not prescribe paracetamol indicated that
they could not prescribe the drug because it was out of stock.
TABLE 8: REASONS FOR NOT GIVING TREATMENT
Tool Treatment Sample
Reason for not giving treatment
No Reason Given Out of Medication Other Reason
Diagnosis: diarrhea
Mobile ORS 7 2 4 1
Zinc 18 0 18 0
Paper ORS 2 2 0 0
Zinc 2 2 0 0
Diagnosis: Fever
Mobile LA 25 8 14 3
Paracetamol 201 1 198 2
Paper LA 19 19 0 0
Paracetamol 122 121 0 1
Diagnosis: Fast breathing
Mobile Oral antibiotic 6 0 6 0
Paper Oral antibiotic 0 0 0 0
4.3 Cost-effectiveness
The first part of this section explores the incremental cost-effectiveness ratio (ICER) of the program as
observed for the first 50 HSAs trained. The annual inflation-adjusted costs for the program are
presented, and then the overall effectiveness. These data are combined to calculate the ICER. Next, we
present the costs of the program if it were implemented at scales of 50, 500, 1,000, and 5,000 HSAs
(based on the methodology defined in Section 3.1) as well as the ICERs for each scale of
implementation. Note that it is assumed that treatment effectiveness does not change with the scale of
the intervention. That is, we assume the same effectiveness in the change in the percentage of children
correctly treated found amongst 50 HSAs will also apply to 500, 1,000, and 5,000 HSAs.
36. 22
4.3.1 Annualized Cost of the Mobile Intervention
The total, annualized cost of the mobile application is estimated to be $47,857, or $957 per HSA (see
Table 9). The majority (65 percent) of costs are incurred as national-level (or program management
level) recurrent costs, 19 percent of costs are considered start-up costs, and 16 percent of costs are
incurred at the HSA level (for training, phone purchase, and airtime). These costs are less than the costs
reported in Table 3 due to amortization of start-up and capital costs.
TABLE 9: TOTAL ANNUAL COSTS OF OBSERVED PROGRAM (2013 US$)
Expense Item 50 HSAs (observed)
National-level costs
Start-up costs, amortized
Labor
General Stakeholder Engagement and Project Management $2,352
Software Development $4,372
Mobile Services $824
Training $86
Sub-total, labor $7,634
Other Direct $534
Indirect $698
Sub-total, start-up costs $8,867
Recurrent costs, per year
Labor
General Stakeholder Engagement and Project Management $2,316
Software Support $2,516
Monitoring $13,120
Sub-total, labor $17,952
Other Direct $10,472
Indirect $2,872
Sub-total, recurrent costs $31,296
37. 23
Expense Item 50 HSAs (observed)
HSA-level costs
Technical Support (D-tree) $0
Training (international, annualized) $3,378
Training (local, annualized) $1,221
Mobile Services (international support and airtime) $1,160
Phone purchase (annualized) $1,934
Sub-total, annual HSA-level cost $7,693
Total annual equivalent costs $47,857
Cost per HSA $957
4.3.2 Percentage of Children Correctly Treated and Incremental Cost-
effectiveness Ratio
To calculate the percentage of children correctly treated, we combined the data for the number asked
the appropriate follow-up questions, the number given the correct prescription, and the number given
the correct dosage. Failure to meet any one of these criteria indicates that a child was not correctly
treated. For the HSAs employing the paper-based system, 548 children had complete data across the
three categories of data, and 14 were correctly treated (2.6 percent). Complete data were available for
493 children treated by HSAs using the mobile tool, of whom 304 (61.7 percent) were correctly
treated. The difference between the two groups is statistically significant (p<0.001) for both the
unadjusted and adjusted comparisons, with an unadjusted difference of 59.1 percent between HSAs
using the mobile tool and HSAs using the paper-based system only. After adjusting for case mix, an
estimated 3.2 percent of children treated by HSAs using only the paper-based system would have been
correctly treated (had they seen the same cases as was seen, on average, across the two groups of
HSAs), while HSAs using the mobile tool would have correctly treated 94.9 percent of cases.
Thus, the cost per HSA for the mHealth intervention was $957 per HSA per year, and the mHealth
intervention was associated with a 91.2 percent increase in the proportion of children correctly treated.
These results suggest that the intervention incurred $10.43 per 1 percent increase in the proportion of
children treated correctly (Table 10). The “Direction of Change,” column in Table 10 indicated that,
compared with the existing paper-based system, the mobile tool cost more but also had a positive
effect. As such, the cost-effectiveness ratio in Table 10 can be interpreted as, “Compared with the
existing paper-based system, the mobile tool costs an additional $10.43 for an HSA to improve his/her
diagnostic and treatment accuracy by 1 percent.”
38. 24
TABLE 10: COST PER HSA PER % CHANGE IN CASES CORRECTLY DIAGNOSED AND
TREATED
Mobile Paper
Mobile -
Paper
ICER
Direction of
Change
Cost per HSA $ 957 $ - $ 957 $10.43 per % Positive
Effect 94.9% 3.2% 91.2%
In the base sensitivity analysis, the mobile tool was not associated with an increase in the percentage of
children correctly treated, and thus would both cost more and be less effective than the existing paper-
based tool. This finding is the result of the existing paper-based tool HSAs doing better at prescribing
the correct drug given diagnosis. However, as noted above, the poorer performance of mobile users on
this metric is likely due to drug stock-outs, and not to the mobile tool. Excluding the ‘correct
prescription given the diagnosis’ metric from the sensitivity analysis suggests that mobile HSAs correctly
treat 1.6 percent more children than the existing paper-based HSAs (p = 0.24). Excluding this step, but
including more generous assumptions about correct dosage, results in an ICER of $583 per 1 percent
increase in the proportion of children treated correctly (although the results would not be considered
statistically significant).
Under the scale-up scenarios, national-level start-up costs remain the same as observed in the program
(Table 11). However, most national-level recurrent costs are assumed by the government, and were not
available for our analysis. These costs, which were the majority of costs in the observed program, are
not included in our scale-up scenarios. This results in an underestimation of the level of effort needed to
run the program. Further, costs of training under the government program were less, per HSA trained,
than they were under the observed CRS/DHC model for 50 HSAs. Thus, the costs of the program for
50 HSAs under the scale-up assumptions is $24,035 (compared to $47,857 as observed); some of these
lower costs reflect genuine savings from more efficient implementation and some reflect missing costs
for program management (although from a government perspective many of these costs would reflect
shifting resources to the mHealth intervention, and not additional indemnities incurred). Under the
various scenarios presented, the cost of the mHealth program would be about $66,000, $115,000, and
$490,000 per year for 500, 1,000, and 5,000 HSA implementing the program.
TABLE 11: INCREMENTAL COSTS AND COST-EFFECTIVENESS RATIOS OF DIFFERENT
SCALE-UP SCENARIOS (2013 US$)
Expense Item
Cost
Assumptions
for Scale-up
Scale-up Scenarios
50 HSAs
(modelled)
500
HSAs
1,000
HSAs
5,000
HSAs
National-level Costs
Start-up costs, amortized Total
per year
Labor
General Stakeholder Engagement and Project
Management
$2,352 $2,352 $2,352 $2,352 $2,352
Software development $4,372 $4,372 $4,372 $4,372 $4,372
Mobile Services $824 $824 $824 $824 $824
39. 25
Expense Item
Cost
Assumptions
for Scale-up
Scale-up Scenarios
50 HSAs
(modelled)
500
HSAs
1,000
HSAs
5,000
HSAs
Training $86 $86 $86 $86 $86
Sub-total, labor $7,634 $7,634 $7,634 $7,634 $7,634
Other Direct $534 $534 $534 $534 $534
Indirect $698 $698 $698 $698 $698
Sub-total, start-up costs $8,867 $8,867 $8,867 $8,867 $8,867
Recurrent costs, per year Total
per year
Labor
General Stakeholder Engagement and Project
Management
$0 $0 $0 $0 $0
Software support $0 $0 $0 $0 $0
Monitoring $0 $0 $0 $0 $0
Sub-total, labor $0 $0 $0 $0 $0
Other Direct $10,472 $10,472 $10,472 $10,472 $10,472
Indirect $0 $0 $0 $0 $0
Sub-total, recurrent costs $10,472 $10,472 $10,472 $10,472 $10,472
HSA-level costs
Per HSA
per year
Technical support (D-tree) $11 $550 $5,500 $11,000 $55,000
Training (international, annualized) $0.44 $22 $218 $437 $2,184
Training (local, annualized) $14 $690 $6,898 $13,796 $68,980
Mobile Services (international support and
airtime)
$31 $1,560 $15,600 $31,200 $156,000
Phone purchase (annualized) $37 $1,874 $18,741 $37,481 $187,406
Sub-total, annual HSA-level cost $94 $4,696 $46,957 $93,914 $469,569
Total annual equivalent costs $24,035 $66,296 $113,253 $488,908
Cost per HSA $481 $133 $113 $98
Incremental cost-effectiveness ratio $5.24 $1.45 $1.23 $1.07
40. 26
As a consequence of spreading fixed costs (program management and start-up costs) over a greater
number of HSAs, the cost per HSA is lower for scenarios with more HSAs. At 5,000 HSAs, this
represents an incremental cost of $98 per HSA. Thus, the cost per 1 percent increase in the proportion
of children treated correctly also is lower for scenarios with more HSAs (see final row of Table 11).
With 50 HSAs enrolled in the program, it costs an estimated $5.24 per 1 percent increase in the
proportion of children treated correctly, while in the scenario with 5,000 HSAs, it costs an estimated
$1.07 per 1 percent increase in the proportion of children treated correctly (this latter figure is
estimated at $59.52 in sensitivity analysis).
41. 27
5. DISCUSSION
The primary purpose of this study was to develop an approach for estimating and scaling up costs of an
iCCM mobile tool in Malawi, as well as evaluating the effectiveness of this tool. A secondary objective
was to contribute evidence on the cost-effectiveness of D-tree’s mobile decision support tool. It was
expected that findings could inform policymakers in Malawi and other developing countries on whether
and under what conditions mHealth applications, such as D-tree’s mobile decision support tool, could
be implemented and scaled up. Results from this study were also intended to guide policymakers and
donors toward investing in mHealth applications that offer the greatest value for money. The following
section summarizes key findings from this study and addresses these issues.
5.1 Design, Pilot, Roll-out, and Scale-up Costs
During the initial design, pilot, and roll-out phase to 50 HSAs, data from D-tree, CRS, and DHC suggest
that labor costs accounted for the greatest share of costs (73 percent) in designing and implementing
the iCCM mobile decision support tool. Of these, about 90 percent came from D-tree, the tool’s
developer. When broken down by category, 40 percent of D-tree’s labor costs stemmed from the
development iCCM content and programming, while another 43 percent went to monitoring and
training. Other costs components included other direct costs (13 percent of total costs), indirect costs
(7 percent of costs), training costs not including labor (4 percent of costs), and mobile phones (2
percent of costs).
For policymakers looking to design and implement similar mHealth applications, cost data, particularly
the scale-up from 50 to 1,000 HSAs, provides two key lessons. First, while labor costs represent a
significant portion of total costs during the design, piloting, and roll-out to 50 HSAs, these costs are a
function of the wages needed to hire staff, the quality of that staff, and the total quantity of staff needed.
For developing countries aiming to develop mHealth tools domestically, wages will be much lower than
those incurred by D-tree, though finding qualified staff to design and oversee the roll-out of such a tool
would be challenging in some settings. Second, additional cost savings could be realized during the design
stage if the mobile tool were produced domestically, because significant travel and start-up costs, which
were incurred by D-tree in the early stages of the tools’ development, would be greatly reduced. These
costs would decline substantially (a) if the organization (e.g., D-tree) already has a presence in country
during the design phase and (b) the government or a local private organization were to spearhead
development and roll-out of the mobile tool.
5.2 Modelled Annual Costs and Scale-up Costs
During the modelled scale-up from 50 to 5,000 HSAs, total annual costs rose in absolute terms from
about $24,000 to about $490,000. While the total costs, as expected, increase as more HSAs are
enrolled in the program, the cost per HSA is expected to decline by roughly 80 percent, from $481 to
$98 per HSA. Results from the mobile tool’s scale-up highlight several key takeaways for policymakers.
During scale-up, mobile phone costs will increase as a proportion of total costs (from 8 percent to 40
percent). These findings assume that when governments take over responsibility for rolling out mobile
decision support tools to the health workforce, the training and monitoring functions are inherently
covered by salaried supervisors.
42. 28
5.3 Diagnosis Follow-up
Findings indicated that HSAs are recording answers to follow-up questions to patients at a very high
level (99 percent of the time) for both HSAs using the mobile tool and using the existing paper-based
system. Given the high levels of follow-up questions in the HSAs using the existing paper-based system,
it is unlikely that the mobile tool would increase this metric in Malawi.
An assumption was made that health providers correctly diagnosed their patients for broad clinical
conditions, such as diarrhea, red eye, fever, or fast breathing. Without individuals in place to monitor
those providers, it was not possible to know if these decisions were accurate. For example, previous
research in Malawi suggested that HSAs correctly classify simple illnesses in children correctly about 68
percent of the time (Gilroy et al. 2013).
5.4 Treatment and Referral Decisions
Findings indicated that the mobile tool had a mixed impact on general treatment patterns; specifically,
HSAs prescribed the correct medication for a given illness 71 percent of the time regardless whether
the mobile was used. This is higher than previous studies done at the beginning of the iCCM program,
which found 62 percent of HSAs gave the correct medication (Gilroy et al. 2013). While HSAs using the
existing paper-based system gave the correct treatment more often than HSAs using the mobile tool,
this may be due to drug stock-outs and not the tool used.
There were, however, dramatic differences in dosage patterns for those treatments. Excluding patients
given ointment and ORS, for which dosage was not measured, on average, 95 percent of cases treated
by HSAs using the mobile tool received the correct medication dosage compared with 5 percent of the
cases treated by the paper group. Given the absence of HSA oversight, it is conceivable that HSAs using
the paper-based system incorrectly recorded their prescribed dosage for a given treatment. Findings
from a sensitivity analysis outline the impact if this were true. However, even if differences were less
significant than those presented in this report, results suggest that the mobile tool still results in greater
dosage accuracy and dramatically improve the quality of data recorded.
It should be noted that this analyses treats all decisions and actions as equal. Thus, for example, the
decision to treat fever with paracetamol (an analgesic drug) is given the same weight as the decision to
treat fever with LA (an antimalarial drug), and giving the correct dosage of paracetamol is treated the
same as giving the correct dosage of LA. However, it is likely that the medical implications of these
decisions and actions are not equal. Further refinement of the measure of effectiveness giving due
attention to the medical implications of the decisions and actions would help to give more accurate
information on the health implications of the mobile tool.
5.5 Cost-effectiveness
For the HSAs employing the paper-based system, 548 children had complete data across the three
categories of data, and 14 were correctly treated (2.6 percent). Complete data were available for 493
children treated by HSAs using the mobile tool, of which 304 (61.7 percent) were correctly treated. In
other words, the mobile tool was associated with significantly better quality of care than the existing
paper-based system, as defined by greater clinical accuracy and adherence to protocols.
When only the cost of designing, piloting, and roll-out of the tool to 50 HSAs is considered, results from
this study’s cost-effectiveness analyses suggest that, compared with the existing paper-based system, the
mobile tool costs $10.43 per annum for an HSA to improve his/her diagnostic and treatment accuracy
by 1 percent. This means that the mobile tool is associated with an improvement in diagnoses and
treatment accuracy relative to paper registries, but this improvement comes at a cost. Previous studies
43. 29
have estimated the cost per case (among children under five years of age) to be about $2.15 or about
$575 per HSA per year (Collins et al. 2014). This indicates that a small-scale program would increase
cost per HSA by about 166 percent, whereas a large-scale program would increase the cost per HSA
per year by 17 percent.
Assuming that the relative effect difference does not change over time or the number of HSAs in the
program, the tool’s cost-effectiveness improves substantially as it is scaled up nationally. This is due to
the significant drop in the tool’s annual cost per HSA, which falls by roughly 80 percent when scaled up
from 50 HSAs to 5,000 HSAs. The improvement in the tool’s incremental cost-effectiveness ($5.24 vs.
$1.07) suggests that policymakers are more likely to witness greater returns on investment if the tool is
scaled up and maintained over time.
5.6 Health System Performance
While data collection was not yet a primary function of the mobile tool during this study period, it
proved a useful mechanism for collecting high-quality data on patient health conditions and clinical
severity, provider diagnostic and treatment patterns, as well as reasons for clinical decision making. This
data are often not available with the current paper-based system.
Among these benefits, the mobile tool requires that health providers indicate reasons for not
administering medical treatments when patients present their respective clinical conditions. In this study,
providers using the mobile tool nearly always cited drug availability as the reason for not treating
patients. The absence of data for this issue among paper users presents two challenges for policymakers
looking to improve access to and quality of medical care in Malawi.
First, the lack of information makes it impossible to discern reasons why providers failed to administer
correct medical treatments. Is this decision due to poor medical knowledge and quality of care by health
providers, a lack of drugs, patient decisions not to receive medications, or other factors? Second, by
knowing that incorrect treatment decisions were, for mobile users, due to drug stock-outs,
policymakers can learn which facilities are out of medications, examine why they are out of stock, and
ensure that the supply of medications is replenished.
5.7 Limitations
While this study offers an approach for estimating and projecting costs of mHealth interventions, as well
as evaluating the effectiveness and cost-effectiveness of a mobile decision support tool in Malawi, several
limitations should be noted. This study was conceived and designed when evaluation funding became
available, after the initial implementing partners had closed the project. Cost data were derived based on
staff estimates post facto, including estimates for NGO partners who were not contacted. Moreover,
costs in this study did not include some costs, such as additional time spent and possible loss in
productivity by HSAs who use the mobile tool. This led to findings that likely underestimated costs and
inflated the incremental cost-effectiveness of the mobile tool. Limited effect data and oversight of health
providers also made it impossible to assess the accuracy of diagnosing patient medical conditions, such
as referral rates for severe health conditions. It also raises the possibility of error in dosage accuracy
data.