Gender,HIV/AIDs Transmission: Socio Economic And Socio Cultural Impact in Tan...Sandeep Singh
The research was done in Tanzania and presented at Banaras Hindu University International Conference who also published Complete article in "EDUCATION FOR THE NEW MILLENNIUM" by Nutan Publication Chapter 6 ISBN: 978 81 927002 1 2
Presentation for the Grand European Symposium: Training, Research and Innovation in the Europe of Health”, on September 30th 2021, The Sorbonne Grand Amphitheater
210923 middletonj sorbonne vr2
Gender,HIV/AIDs Transmission: Socio Economic And Socio Cultural Impact in Tan...Sandeep Singh
The research was done in Tanzania and presented at Banaras Hindu University International Conference who also published Complete article in "EDUCATION FOR THE NEW MILLENNIUM" by Nutan Publication Chapter 6 ISBN: 978 81 927002 1 2
Presentation for the Grand European Symposium: Training, Research and Innovation in the Europe of Health”, on September 30th 2021, The Sorbonne Grand Amphitheater
210923 middletonj sorbonne vr2
AIDSTAR-One Issue Paper: The Debilitating Cycle of HIV, Food Insecurity, and ...AIDSTAROne
This document aims to facilitate an understanding of the bi-directional relationship between HIV and food and nutrition security. It illuminates the causes of HIV-related food and nutrition insecurity, and points to a list of programmatic interventions and resources to consider for addressing each cause in detail. http://j.mp/U1L0iV
Earning Their Way to Healthier Lives: Women First in MozambiqueAIDSTAROne
A complex matrix of factors, such as low literacy, early sexual initiation, and limited economic opportunities, increases the vulnerability of women to HIV infection in Mozambique. The Women First program addresses the role that poverty and lack of access to health information play in the spread of HIV through legal rights and income-generating activities.
This case study covers one of the 31 programs from the Africa Gender Compendium, an AIDSTAR-One gender and HIV integration resource. A series of five Africa Gender Compendium case studies is accompanied by a findings report, which describes lessons learned, gaps, and common experiences across the programs.
Download this and other gender & HIV resources: http://j.mp/zyjmG7
AIDS - Status & challenges of the epidemicGreenFacts
In just 25 years, HIV has spread relentlessly from a few widely scattered “hot spots” to virtually every country in the world, infecting 65 million people and killing 25 million.
What has been done since 2001 and what can be done in the future to halt the spread of AIDS?
Note: Figures have been updated in 2008.
AIDSTAR-One Findings Report: Analysis of Services to Address Gender-based Vio...AIDSTAROne
Because gender-based violence (GBV) is widely recognized as both a cause and a consequence of HIV infection, the President's Emergency Plan for AIDS Relief (PEPFAR) has identified reducing GBV as one of its five high-priority gender strategies. As part of this effort, AIDSTAR-One conducted case studies in three countries where GBV services were available: Swaziland, Vietnam, and Ecuador. The case studies and this accompanying findings report aim to identify and share promising programmatic approaches and disseminate key elements of success for replication and scale-up.
www.aidstar-one.com/focus_areas/gender/resources/reports/gbv_series_findings_report
Assessment of Social Determinants of Health in Selected Slum Areas in Jordan ...Musa Ajlouni
This presentation summaries the main findings of a study which was performed to asses the Social Determinants of Health (SDH) in selected slum Areas in Jordan and suggest some policy directions to deal with the challenges related to these SDH.
Challenge yourself to reduce aboriginal incarceration v2Dayna Veraguth
a resource to help increase awareness of culturally safe and effective, evidence-based wellbeing programs specifically for Aboriginal and Torres Strait Islander people in custodial and forensic systems.
A self-paced, evidence-based learning kit, called ‘Challenge Yourself’.
AIDSTAR-One Issue Paper: The Debilitating Cycle of HIV, Food Insecurity, and ...AIDSTAROne
This document aims to facilitate an understanding of the bi-directional relationship between HIV and food and nutrition security. It illuminates the causes of HIV-related food and nutrition insecurity, and points to a list of programmatic interventions and resources to consider for addressing each cause in detail. http://j.mp/U1L0iV
Earning Their Way to Healthier Lives: Women First in MozambiqueAIDSTAROne
A complex matrix of factors, such as low literacy, early sexual initiation, and limited economic opportunities, increases the vulnerability of women to HIV infection in Mozambique. The Women First program addresses the role that poverty and lack of access to health information play in the spread of HIV through legal rights and income-generating activities.
This case study covers one of the 31 programs from the Africa Gender Compendium, an AIDSTAR-One gender and HIV integration resource. A series of five Africa Gender Compendium case studies is accompanied by a findings report, which describes lessons learned, gaps, and common experiences across the programs.
Download this and other gender & HIV resources: http://j.mp/zyjmG7
AIDS - Status & challenges of the epidemicGreenFacts
In just 25 years, HIV has spread relentlessly from a few widely scattered “hot spots” to virtually every country in the world, infecting 65 million people and killing 25 million.
What has been done since 2001 and what can be done in the future to halt the spread of AIDS?
Note: Figures have been updated in 2008.
AIDSTAR-One Findings Report: Analysis of Services to Address Gender-based Vio...AIDSTAROne
Because gender-based violence (GBV) is widely recognized as both a cause and a consequence of HIV infection, the President's Emergency Plan for AIDS Relief (PEPFAR) has identified reducing GBV as one of its five high-priority gender strategies. As part of this effort, AIDSTAR-One conducted case studies in three countries where GBV services were available: Swaziland, Vietnam, and Ecuador. The case studies and this accompanying findings report aim to identify and share promising programmatic approaches and disseminate key elements of success for replication and scale-up.
www.aidstar-one.com/focus_areas/gender/resources/reports/gbv_series_findings_report
Assessment of Social Determinants of Health in Selected Slum Areas in Jordan ...Musa Ajlouni
This presentation summaries the main findings of a study which was performed to asses the Social Determinants of Health (SDH) in selected slum Areas in Jordan and suggest some policy directions to deal with the challenges related to these SDH.
Challenge yourself to reduce aboriginal incarceration v2Dayna Veraguth
a resource to help increase awareness of culturally safe and effective, evidence-based wellbeing programs specifically for Aboriginal and Torres Strait Islander people in custodial and forensic systems.
A self-paced, evidence-based learning kit, called ‘Challenge Yourself’.
Effectiveness of Community based Interventions in Reducing Maternal Mortality...ObinnaOrjingene1
Background & Aim: Maternal mortality ratio for sub-Saharan Africa in 2010 was estimated to be about 600 per 100,000 live births, which is approximately higher than what is obtainable in advanced countries. To this end, several community-based interventions have been put in place by governments and developmental partners in the region to address the situation. This review aimed to seek evidence from existing literature on the level of effectiveness of these interventions in improving maternal health outcomes in the region. The literature search process resulted in retrieval of six full text studies that were written in English, published between 2000 and 2019 and were focused on intervention based at the community level which resulted in the reduction of maternal deaths in some sub-Saharan African countries. The Critical Appraisal Skills Programme (CASP) tool was used to critically review retrieved literature.
Findings: Findings from the articles reviewed show that community-based interventions with a direct reduction in maternal mortality were implemented in Ethiopia and Nigeria and were effective since maternal mortality declined by 64% and 43.5% respectively. Other community based interventions did not directly address the reduction in maternal mortality but rather addressed leading causes of maternal mortality such as home and unskilled birth attendance, low Ante-Natal Care (ANC) & Post-Natal Care (PNC) services utilization, Eclampsia, delay in accessing care and Postpartum Hemorrhage (PPH). Such interventions were implemented in Nigeria, Zambia, Tanzania, and the Democratic Republic of Congo and were proved to be effective in reducing maternal mortality.
Conclusions and Recommendations: Based on the literatures reviewed, it was concluded that community based interventions were effective in reducing maternal mortality in Sub-Saharan Africa. The following recommendations were made based on gaps observed in the implementation of some interventions. Introduction of emergency transport scheme in countries where they do not exist as despite the existence of maternity waiting homes and dedicated maternity ambulances in Zambia, many expectant mothers still had difficulty reaching the health facilities in time to deliver, Engagement and training of more health workers so as to avoid human resources challenges that may be associated with increased demand for health facility deliveries.
Socio-Economic Effect of HIV/AIDS on Orphans and Vulnerable Children in Nyami...paperpublications3
Since the first case of HIV/AIDS was reported in Kenya in 1984 the numbers of those infected have risen and many people have since died or are living with the HIV/AIDS since the epidemic started in 1980s in the drug injecting people and the homosexuals. These deaths have resulted in Orphans and Vulnerable Children (OVC). This was a descriptive cross-sectional study, with one of the objective of finding out the socio-economic effect of HIV/AIDS on Orphans and Vulnerable Children in Nyamira district. A sample of 384 people participated in the study. The people were through simple random selected from Bonyegwe sublocation of Nyamusi division. Semi-structured interview schedules were used in data collection from the households. To remove ambiguity, the research tools were pre-tested to both HIV/AIDS organizations and householders not in the sample population but with similar characteristics. The research tools were refined and used on the actual sample population. Text, graphs, figures and tables were used in data presentation. The study indicates that those people who had not attained any level of formal education were (17%). The study revealed that householders (40%) had higher proportion of secondary education as compared with members of HIV/AIDS organizations (37%). Most of the members of HIV/AIDS organizations (89%) indicated that farming is their main source of income and a cushion for food security as compared with householders (63%). The ministry of health should strengthen provision of PMTCT services at the ANC clinic so that we prevent more cases of orphans and vulnerable children. The study suggests that in future all mothers who test positive for HIV virus should be put on treatment in order to reduce defaulters at the same time reach sustainable coverage in the provision of HIV/AIDS services to the orphans and vulnerable children in the society.
An Appraisal of the Impact of the Dearth of Pre-Hospital Emergency Medical Se...inventionjournals
The significant role of pre-hospital emergency medical services (EMS) cannot be over emphasized as it encompasses minimizing the consequences of accidents and provides rapid response and relieve materials to victims of road traffic accidents at the scene of the crash. The paper therefore tries to analytically x-ray the relationship between income GDP per capital and the rate of road traffic death to determine the effect of absences of pre-hospital emergency medical services to road traffic victims in Nigeria. The paper makes use of regression as a tool of analysis, with the aid of variables such as record of road traffic accident death and indices on income GDP per capita of the country in focus to draw conclusion or the relationship or otherwise of the argument above. the correlation between the calculated data on death rate from road traffic accident per 10000 population and GDP per capita resulted in a negative strong significant relationship as, r (19) = –0.79,P = < 0.0001, ß= 0.79. The coefficient of the predictor GDP per capita is Significant. (P=0.0001<p>< 0.05). Hence rejecting the null hypotheses and accepting the alternative hypotheses. There is a negative significant correlation between income GNI and Road Traffic rate. The paper concludes that there is a significant correlation between the country’s income GDP per capita and the rate of death in road traffic accident due to the absences of pre-emergency medical services at the accident scene. It there recommends amongst others that; government must take pro-active measures to abate the occurrences of road crashes and equip the agency responsible for meting out pre-hospital emergency medical services with the requisite tools to function.
Integrating severe acute malnutrition into the management of childhood diseas...Malaria Consortium
Since December 2010, Malaria Consortium has been implementing an innovative approach to community management of severe acute malnutrition, together with an existing integrated community case management (ICCM) programme in South Sudan. This learning paper considers Malaria Consortium’s experience of this combined approach in a highly complex context and shows whether the management of severe acute malnutrition is an effective, acceptable and feasible component of ICCM programming.
Zika Virus: analysis, discussions and impacts in BrazilAJHSSR Journal
The ZikaDesease is increasing in Brazil since 2014. The causative vector is Aedesaegypti, which
through its bite can transmit the virus, causing microcephaly, it can causes consequences thought life. Beyond
the number of microcephaly cases growth, the Zika virus generates major problems involving the whole society
and economy of the country, such as the cost of medical treatment of the microcephalic child and his family that
will stop working to support and follow the treatment, and also to the cost of basic sanitation, as a prime factor
for mosquito control and the elaboration of public policies. Thus, the present work analyzed economic and
environmental aspects for the understanding the virus’ factors that provided the vector growth. A bibliographic
research carried out to understand the effects of the Zika virus and its economic, social and environmental
impacts. In this study, the costs related to microcephaly, the loss of income of microcephalic child’s relatives
and the country’s investment in basic sanitation were estimated
Infectious minds canadian institutes of health research, international infect...Gordon Otieno Odundo
Canadian Institutes of Health Research, International Infectious Disease and Global Health Training Programme (CIHR, IID & GHTP).This is a scholarship program run across four countries: Canada, Colombia, Kenya and India where advanced level students (PhD, Post Doctoral and Clinical fellows) undertake additional training on Infectious Diseases all geared towards being experts in matters pertaining to Global Health. Every month an 'Infectious Minds' sessionis held for two hours via a videoconference link across the four sites. On 15th May 2014 Gordon Otieno Odundo was the Guest Speaker presenting on infectious diseases in children the venue was at the University of Nairobi Institute of Tropical and Infectious Diseases, College of Health Sciences, Kenyatta National Hospital. The audience was primarily Doctoral (PhD) and Post-Doctoral students across the four sites; from Basic Science and Social Science disciplines.
website: http://www.iidandghtp.com/
Economic analysis of malaria burden in kenyaNanyingi Mark
This framework uses a cost of illness approach to evaluate the burden of malaria. The evaluation is based on private direct costs (PDC) and private indirect cost (PIC) of malaria attack per episode.
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NVBDCP.pptx Nation vector borne disease control programSapna Thakur
NVBDCP was launched in 2003-2004 . Vector-Borne Disease: Disease that results from an infection transmitted to humans and other animals by blood-feeding arthropods, such as mosquitoes, ticks, and fleas. Examples of vector-borne diseases include Dengue fever, West Nile Virus, Lyme disease, and malaria.
263778731218 Abortion Clinic /Pills In Harare ,sisternakatoto
263778731218 Abortion Clinic /Pills In Harare ,ABORTION WOMEN’S CLINIC +27730423979 IN women clinic we believe that every woman should be able to make choices in her pregnancy. Our job is to provide compassionate care, safety,affordable and confidential services. That’s why we have won the trust from all generations of women all over the world. we use non surgical method(Abortion pills) to terminate…Dr.LISA +27730423979women Clinic is committed to providing the highest quality of obstetrical and gynecological care to women of all ages. Our dedicated staff aim to treat each patient and her health concerns with compassion and respect.Our dedicated group ABORTION WOMEN’S CLINIC +27730423979 IN women clinic we believe that every woman should be able to make choices in her pregnancy. Our job is to provide compassionate care, safety,affordable and confidential services. That’s why we have won the trust from all generations of women all over the world. we use non surgical method(Abortion pills) to terminate…Dr.LISA +27730423979women Clinic is committed to providing the highest quality of obstetrical and gynecological care to women of all ages. Our dedicated staff aim to treat each patient and her health concerns with compassion and respect.Our dedicated group of receptionists, nurses, and physicians have worked together as a teamof receptionists, nurses, and physicians have worked together as a team wwww.lisywomensclinic.co.za/
New Drug Discovery and Development .....NEHA GUPTA
The "New Drug Discovery and Development" process involves the identification, design, testing, and manufacturing of novel pharmaceutical compounds with the aim of introducing new and improved treatments for various medical conditions. This comprehensive endeavor encompasses various stages, including target identification, preclinical studies, clinical trials, regulatory approval, and post-market surveillance. It involves multidisciplinary collaboration among scientists, researchers, clinicians, regulatory experts, and pharmaceutical companies to bring innovative therapies to market and address unmet medical needs.
Flu Vaccine Alert in Bangalore Karnatakaaddon Scans
As flu season approaches, health officials in Bangalore, Karnataka, are urging residents to get their flu vaccinations. The seasonal flu, while common, can lead to severe health complications, particularly for vulnerable populations such as young children, the elderly, and those with underlying health conditions.
Dr. Vidisha Kumari, a leading epidemiologist in Bangalore, emphasizes the importance of getting vaccinated. "The flu vaccine is our best defense against the influenza virus. It not only protects individuals but also helps prevent the spread of the virus in our communities," he says.
This year, the flu season is expected to coincide with a potential increase in other respiratory illnesses. The Karnataka Health Department has launched an awareness campaign highlighting the significance of flu vaccinations. They have set up multiple vaccination centers across Bangalore, making it convenient for residents to receive their shots.
To encourage widespread vaccination, the government is also collaborating with local schools, workplaces, and community centers to facilitate vaccination drives. Special attention is being given to ensuring that the vaccine is accessible to all, including marginalized communities who may have limited access to healthcare.
Residents are reminded that the flu vaccine is safe and effective. Common side effects are mild and may include soreness at the injection site, mild fever, or muscle aches. These side effects are generally short-lived and far less severe than the flu itself.
Healthcare providers are also stressing the importance of continuing COVID-19 precautions. Wearing masks, practicing good hand hygiene, and maintaining social distancing are still crucial, especially in crowded places.
Protect yourself and your loved ones by getting vaccinated. Together, we can help keep Bangalore healthy and safe this flu season. For more information on vaccination centers and schedules, residents can visit the Karnataka Health Department’s official website or follow their social media pages.
Stay informed, stay safe, and get your flu shot today!
The prostate is an exocrine gland of the male mammalian reproductive system
It is a walnut-sized gland that forms part of the male reproductive system and is located in front of the rectum and just below the urinary bladder
Function is to store and secrete a clear, slightly alkaline fluid that constitutes 10-30% of the volume of the seminal fluid that along with the spermatozoa, constitutes semen
A healthy human prostate measures (4cm-vertical, by 3cm-horizontal, 2cm ant-post ).
It surrounds the urethra just below the urinary bladder. It has anterior, median, posterior and two lateral lobes
It’s work is regulated by androgens which are responsible for male sex characteristics
Generalised disease of the prostate due to hormonal derangement which leads to non malignant enlargement of the gland (increase in the number of epithelial cells and stromal tissue)to cause compression of the urethra leading to symptoms (LUTS
Lung Cancer: Artificial Intelligence, Synergetics, Complex System Analysis, S...Oleg Kshivets
RESULTS: Overall life span (LS) was 2252.1±1742.5 days and cumulative 5-year survival (5YS) reached 73.2%, 10 years – 64.8%, 20 years – 42.5%. 513 LCP lived more than 5 years (LS=3124.6±1525.6 days), 148 LCP – more than 10 years (LS=5054.4±1504.1 days).199 LCP died because of LC (LS=562.7±374.5 days). 5YS of LCP after bi/lobectomies was significantly superior in comparison with LCP after pneumonectomies (78.1% vs.63.7%, P=0.00001 by log-rank test). AT significantly improved 5YS (66.3% vs. 34.8%) (P=0.00000 by log-rank test) only for LCP with N1-2. Cox modeling displayed that 5YS of LCP significantly depended on: phase transition (PT) early-invasive LC in terms of synergetics, PT N0—N12, cell ratio factors (ratio between cancer cells- CC and blood cells subpopulations), G1-3, histology, glucose, AT, blood cell circuit, prothrombin index, heparin tolerance, recalcification time (P=0.000-0.038). Neural networks, genetic algorithm selection and bootstrap simulation revealed relationships between 5YS and PT early-invasive LC (rank=1), PT N0—N12 (rank=2), thrombocytes/CC (3), erythrocytes/CC (4), eosinophils/CC (5), healthy cells/CC (6), lymphocytes/CC (7), segmented neutrophils/CC (8), stick neutrophils/CC (9), monocytes/CC (10); leucocytes/CC (11). Correct prediction of 5YS was 100% by neural networks computing (area under ROC curve=1.0; error=0.0).
CONCLUSIONS: 5YS of LCP after radical procedures significantly depended on: 1) PT early-invasive cancer; 2) PT N0--N12; 3) cell ratio factors; 4) blood cell circuit; 5) biochemical factors; 6) hemostasis system; 7) AT; 8) LC characteristics; 9) LC cell dynamics; 10) surgery type: lobectomy/pneumonectomy; 11) anthropometric data. Optimal diagnosis and treatment strategies for LC are: 1) screening and early detection of LC; 2) availability of experienced thoracic surgeons because of complexity of radical procedures; 3) aggressive en block surgery and adequate lymph node dissection for completeness; 4) precise prediction; 5) adjuvant chemoimmunoradiotherapy for LCP with unfavorable prognosis.
ARTIFICIAL INTELLIGENCE IN HEALTHCARE.pdfAnujkumaranit
Artificial intelligence (AI) refers to the simulation of human intelligence processes by machines, especially computer systems. It encompasses tasks such as learning, reasoning, problem-solving, perception, and language understanding. AI technologies are revolutionizing various fields, from healthcare to finance, by enabling machines to perform tasks that typically require human intelligence.
TEST BANK for Operations Management, 14th Edition by William J. Stevenson, Ve...kevinkariuki227
TEST BANK for Operations Management, 14th Edition by William J. Stevenson, Verified Chapters 1 - 19, Complete Newest Version.pdf
TEST BANK for Operations Management, 14th Edition by William J. Stevenson, Verified Chapters 1 - 19, Complete Newest Version.pdf
Ethanol (CH3CH2OH), or beverage alcohol, is a two-carbon alcohol
that is rapidly distributed in the body and brain. Ethanol alters many
neurochemical systems and has rewarding and addictive properties. It
is the oldest recreational drug and likely contributes to more morbidity,
mortality, and public health costs than all illicit drugs combined. The
5th edition of the Diagnostic and Statistical Manual of Mental Disorders
(DSM-5) integrates alcohol abuse and alcohol dependence into a single
disorder called alcohol use disorder (AUD), with mild, moderate,
and severe subclassifications (American Psychiatric Association, 2013).
In the DSM-5, all types of substance abuse and dependence have been
combined into a single substance use disorder (SUD) on a continuum
from mild to severe. A diagnosis of AUD requires that at least two of
the 11 DSM-5 behaviors be present within a 12-month period (mild
AUD: 2–3 criteria; moderate AUD: 4–5 criteria; severe AUD: 6–11 criteria).
The four main behavioral effects of AUD are impaired control over
drinking, negative social consequences, risky use, and altered physiological
effects (tolerance, withdrawal). This chapter presents an overview
of the prevalence and harmful consequences of AUD in the U.S.,
the systemic nature of the disease, neurocircuitry and stages of AUD,
comorbidities, fetal alcohol spectrum disorders, genetic risk factors, and
pharmacotherapies for AUD.
Maxilla, Mandible & Hyoid Bone & Clinical Correlations by Dr. RIG.pptx
Cost of malaria morbidity in uganda
1. Journal of Economics and Sustainable Development www.iiste.org
ISSN 2222-1700 (Paper) ISSN 2222-2855 (Online)
Vol.3, No.2, 2012
Cost of malaria morbidity in Uganda
Juliet Nabyonga Orem
World Health Organization, Country Office, Kampala, Uganda
E-mail: nabyongaj@ug.afro.who.int
Joses Muthuri Kirigia
World Health Organization,
Regional Office for Africa,
Brazzaville, Congo
E-mail: kirigiaj@afro.who.int
Robert Azairwe
World Health Organization, Country Office, Kampala, Uganda
E-mail: azairwer@ug.afro.who.int
Charlotte Muheki Zikusooka
Healthnet Consult - Uganda
Email: charlotte@healthnetconsult.com
Juliet Bataringaya
World Health Organization, Country Office, Kampala, Uganda
E-mail: bataringayaj@ug.afro.who.int
Peter Ogwal Ogwang
Danish International development agency - Uganda
Email::petogwa@um.dk
Abstract
Background: The high burden of malaria, among others, is a key challenge to both human and economic
development in malaria endemic countries. The impact of malaria can be categorized from three dimensions, namely:
health, social and economic. The economic dimension focuses on three types of effects, namely: direct, indirect and
intangible effects which are felt at both macro and micro levels. The objective of this study was to estimate the costs
of malaria morbidity in Uganda using the cost-of-illness approach.
Methods: The study covered 4 districts, which were selected randomly after stratification by malaria endemicity into
Hyper endemic (Kamuli and Mubende districts); Meso endemic (Mubende) and Hypo endemic (Kabale). A survey
was undertaken to collect data on cost of illness at the household level while data on institutional costs was collected
from the Ministry of Health and Development Partners.
Results: Our study revealed that: (i) in 2003, the Ugandan economy lost a total of about US$658,200,599 (US$24.8
per capita) due to 12,343,411 cases malaria; (ii) the total consisted of US$49,122,349 (7%) direct costs and US$
609,078,209 (92%) indirect costs or productivity losses; (iv) the total malaria treatment-related spending was
US$46,134,999; out of which 90% was incurred by households or individual; (v) only US$2,987,351 was spent on
malaria prevention; out of which 81% was borne by MOH and development partners..
Conclusion: Malaria poses a heavy economic burden on households, which may expose them to financial
catastrophe and impoverishment. This calls for the upholding of the no-user fees policy as well as increased
investments in improving access to quality of health services and to proven community preventive interventions in
order to further reduce the cost of illness borne by patients and their families.
58
2. Journal of Economics and Sustainable Development www.iiste.org
ISSN 2222-1700 (Paper) ISSN 2222-2855 (Online)
Vol.3, No.2, 2012
Introduction
The burden of malaria, among others, poses a challenge to economic development in malaria endemic countries.
Sub-Saharan Africa alone accounts for 90% of the 500 million annual malaria cases and a substantive proportion of
malaria deaths [1].
In 2004 Uganda registered a total of 405,736.875 deaths from all causes. About 70.8% of those deaths were caused by
communicable, maternal, perinatal and nutritional conditions; 19.9% were caused by communicable diseases; and
9.3% from unintentional and intentional injuries. Malaria alone was responsible for 9.5 of all deaths in the country;
and 13.5% of deaths from communicable diseases [2].
The abovementioned deaths and morbidity from all causes lost Uganda a total of 14,145,832.5 disability adjusted life
years (DALYs). Approximately 72.2% of DALYs lost resulted from communicable, maternal, perinatal and
nutritional conditions; 17.5% from noncommunicable diseases; and 10.4% from injuries. Malaria only accounted for
10.7% of the grand total DALYs; and 14.8% of DALYs lost from communicable, maternal, perinatal and nutritional
conditions [2].
The impact of malaria has been categorized from three dimensions, namely: health, social and economic. Broadly,
the economic dimension of disease burden focuses on 3 main types of effects, namely: direct, indirect and intangible
effects. These effects are felt at both macro (national and community) and micro (household and individual) levels.
A number of studies in Africa have attempted to estimate the cost of malaria, e.g. Chuma et al [3] in Kenya;
Onwujekwe et al [4] in Nigeria; Ayieko et al [5] in Kenya; Castillo-Riquelme, McIntyre and Barnes [6] in South
Africa; Deressa and Hailemariam [7] in Ethiopia; Mustafa and Babiker [8] Sudan; Somi et al [9] in Tanzania; Akazili,
Aikins and Binka [10] in Ghana; Onwujekwe et al [11] in Nigeria; Onwujekwe et al [12] in Nigeria; Kirigia et al [13]
in Kenya; Asenso-Okyere and Dzator [14] in Ghana; Guiguemde et al [15] in Burkina Faso; Sauerborn et al [16] in
Burkina Faso; and Shepard et al [17] in Burkina Faso, Chad, Congo, and Rwanda.
To the best of our knowledge, prior to the study reported in this paper, no study had attempted to estimate the cost of
malaria in Uganda. Therefore, our study was meant to contribute to bridging that knowledge gap in Uganda. The
specific objective of this study was to estimate the costs of malaria morbidity (illness) in Uganda using the
cost-of-illness approach.
Methods
Conceptual framework
Definition of costs estimated
The economic burden of malaria consists of three components: direct costs, indirect costs and intangible costs.
Firstly, the direct costs, on the part of government and development partners, typically would include all expenditures
on health system inputs used in the prevention and treatment (management) of malaria, and research. It also includes
out-of-pocket expenditure by households (patients, family members and friends) on prevention and treatment of the
illness as well as transportation costs for both the patient and accompanying family members. Even in the poor
countries of Sub-Saharan Africa, households have been found to spend between US$2 and US$25 on malaria
treatment, and between US$0.20 and US$15 on prevention each month [18].
Secondly, the indirect costs relate to productivity losses, at individual, household and national levels, usually
resulting from the indirect effects of treatment seeking, malaria morbidity, mortality and debility. Malaria-related
absenteeism, debility and mortality diminish the quantity and quality of working days with resultant adverse effect on
economic output. Time lost for caring for sick children, who are more frequently and seriously affected by malaria,
exacerbate this economic loss.
Thirdly, the intangible costs include the psychic costs due to anxiety and pain resulting from the malaria illness to the
patients, family members and friends. The cost-of-illness approach does not quantify and value this component.
Analytical model
The total cost (TC) incurred by society due to malaria can be expressed as follows:
TC = TDC + TIC + ITC ...................(1)
59
3. Journal of Economics and Sustainable Development www.iiste.org
ISSN 2222-1700 (Paper) ISSN 2222-2855 (Online)
Vol.3, No.2, 2012
Where: TDC is total direct cost, TIC is total indirect cost or productivity loss, and ITC is intangible cost (capturing
physical and psychological pain).
The TDC was estimated using equations 2 to 6:
TDC = ISC + HDC .......................................................................(2)
Where: ISC are institutional expenditures incurred by the government, development partners, and other health care
providers to treat or prevent malaria; and HDC are expenditures borne by households (including patients, family
members and friends) in prevention and treatment of malaria.
ISC = MOH ME + NMS ME + DPME ................................................(3)
where: MOH ME is expenditure on the malaria control program at the central level; EMRI is expenditure on malaria
research for research institutions; NMS ME is expenditure on antimalarials from the National Medical Stores (given
that currently purchases are centralised); and DPME refers to all expenditures on malaria control activities by
involved development partners. The data on MOH ME , NMS ME and DPME components were obtained
through a review of Ministry of Health records and interviews of the health development partners (e.g. WHO,
Malaria Consortium and USAID) involved in the prevention and management of malaria at the time.
HDC = HEP + HET ...............................................(4)
Where: HEP is household expenditure on malaria prevention measures such as mosquito sprays, mosquito coils, and
ITNs; and HET is household expenditure on treatment per episode including out-of-pocket expenditures for transport
to and from clinic, registration fees, consultation fees, laboratory fees, treatment fees, medicines cost, and the cost of
subsistence at a health facility.
HEP = HPM × TNH × ATEP ...................................(5)
Where: HPM is percentage of households using prevention measures that require money; TNH is the total
number of households in Uganda; and ATEP is the average total annual household expenditure on protective
measures.
To obtain an average cost of treatment for a patient per episode, we have to take into consideration the different
choices of treatment (self-medication vs. clinic/hospital) & whether one was treated as an outpatient or admitted at
the clinic/hospital. The total annual direct cost of treatment by household is a product of average cost per episode and
the total annual number of malaria episodes in the country:
ADCT = [(SM × AC SM ) + ( ADM × C ADM ) + (OPD × C OPD )] × AME ...........(6)
where: ADCT is the annual direct cost of treatment by household; SM is the percentage of cases that
self-medicated; AC SM is the overall annual expenditure on transport, medication and other items for those who
self-medicated; ADM is the percentage of malaria cases admitted; C ADM is the overall annual expenditure on
transport, registration, consultation, laboratory, medicines and other inputs for malaria cases admitted; C OPD is the
overall annual expenditure on transport, registration, consultation, laboratory, medicines and other inputs for malaria
cases treated at clinic/hospital outpatient departments; and AME is the total number of episodes. This data was
obtained from primary household surveys undertaken for this purpose.
The total indirect costs (TIC), i.e. labour productivity losses, were estimated using equations 7 to 11:
TIC = LHH + LCG ...............................(7)
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Where: LHH are the productivity losses due to work days lost by patients; and LCG are the productivity losses due
to the work-time lost by relatives accompanying and visiting patients;
LHH = AYLTW + APLS .................................(8)
where: AYLTW is the household annual loss of income due to travel and waiting time and APLS is the household
annual loss of income due to malaria-related absence from work;
AYLTW = (TT + WT )× YH × AME .............................(9)
where: TT is return travel time to a clinic/hospital; WT is time spent waiting at the health facility, e.g. obtaining
registration card, consultation, diagnosis (laboratory test), pharmacy for prescribed medicines; YH is household
income per hour; and AME is the number of annual malaria episodes;
APLS = Y AL × SAW × AME ....................................(10)
where: APLS is household annual productivity loss due to malaria sickness; Y AL is average annual income loss
per household; SAW is percent of people who stay away from work due to malaria episode.
LCG = Y AYLC + ( ACA × AME ) ...........................(11)
where: Y AYLC is average annual income lost per caregiver or accompanying person; ACA is average percentage
of total number of consultations accompanied by a caregiver. This data was obtained from primary household
surveys undertaken for this purpose. The parameter values used in estimating the aforementioned equations are
contained in Table 1.
Sampling methods and data
Sample size estimation
According to Bennett et al [19], a sample size of at least 200 households per district is adequate to provide results at
95% confidence level. The formula takes into consideration a design effect of 1.7 to correct for the bias created when
using cluster sampling in place of simple random sampling technique. For the four districts, a sample size of 800
households would have been sufficient. However, this survey covered a bigger sample size of 973 households. The
sample sizes allow for interpretation of results at the level of a district.
Sampling procedure
All districts in the country were stratified by malaria endemicity into Hyper/Holo endemic; Mesoendemic and Hypo
endemic. Four districts (Kabale (Hypo), Kamuli (Hyper), Mubende (Meso) and Tororo(Hyper)) were then selected
randomly from these strata and included in the survey . Districts from the North were not included in the study due to
insecurity in the region at the time.
Fifty percent of the sub-counties were then selected randomly from each of the study districts. From the selected
sub-counties, 50% of parishes were selected randomly giving a total of 25 parishes for the 4 districts. In each district,
30 villages (LC1) were then selected from the parishes using the probability proportionate to size technique from a
sampling frame of villages obtained from the 2002 Census. The technique involved a number of steps. In the first
step, a list of villages and their population sizes was drawn. At step two, cumulative totals of the village populations
were calculated and entered in a column. At step three, the sampling interval (SI) was determined by dividing the
total population in the selected parishes by 30 (the number of villages to be studied). At step four, a number was
randomly chosen between 1 and the SI and marked the first selected village. At step five, S1 was serially added to
first number and the villages with the corresponding cumulative totals chosen, until 30 villages were selected.
Human capital approach was use to estimate loss in income in case of unemployed individuals.
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Selection of Households
The process of selecting households began at a central location (either at a bar, shop or cross-road) within each
village. For this study a village was taken to correspond to a local council (LC1). The direction was determined by
spinning a pen and the first household selected; thereafter the survey team moved to the front-door neighbouring
household until a minimum of 7 households were studied in each village. If no appropriate respondent was found in
a selected household, the next neighbouring household replaced it.
Study population
The study population comprised of all members in the sampled households. A household was defined as a group of
people living together (having lived together for at least one month) and sharing meals. The questionnaires were
administered to adults/heads of households.
Data collection
This survey employed structured interviews and collected data on expenditures for malaria for the past one month. A
structured questionnaire was used to collect data from households on their expenditure on treatment and prevention
of malaria and; working hours lost due to illness in the one month prior to the survey. This involved the estimation of
time lost by the malaria sufferers and carers. This was then monetised to estimate the economic opportunity cost. For
preventive measures, data on the rate of use of a given intervention in the past two months was collected.
In order to ensure that respondents have a common understanding of malaria, the following symptoms were taken as
indicative of malaria:
• For children: Fever and/or a hot body with or without any of the following; weakness; sleepiness; loss
of appetite; vomiting; and diarrhoea.
• For adults: Headaches, weakness, fever and joint pains with or without any of the following;
temperature; bitterness of the mouth and vomiting.
For institutional costs, a separate structured questionnaire was used for data collection from Ministry of Health
Malaria Control Program, National Medical Stores, expenditures on Malaria at the district level (Public and Donors),
and public and donor expenditure on malaria research.
Results
Characteristics of household members
Out of the 973 households included in the survey, 23.9% were from Kabale, 27.6% from Kamuli, 22.2% from
Mubende and 24.7% from Tororo districts. The total number of household members in the survey was 5597 with
49.5% being male and 50.5% being female. The average household size was 5.8 persons. About 79% of the
household members were above 5 years, 20% were between 1–5 years, and only 1% was less than 1 year. Figure 1
portrays that 4% of household members had more than 11 years of education, 39% had 1–4 years of education, and
14% had no education. Overall, only 47% had had more than 4 years of education (Figure 1).
Figure 2 shows that 40% of household members were students and 26% were peasant farmers. Only 8% of the
household members sampled were earning a salary from their primary occupation.
Morbidity and health seeking behaviour
Table 2 presents frequency of malaria episodes by district and age. Tororo district had the highest one-month malaria
prevalence (36 cases per 100 population) while Kabale district had the lowest prevalence (22 cases per 100
population). The prevalence did not vary much across the districts. About 24.6% of the 5621 household members
reported having experienced an episode of malaria during the last one month. Of those that had had malaria, 87.1%
had only one episode, 10.0% had two episodes, and 2.9% had more than two episodes. About 0.7% of persons with a
malaria episode were under one year old, 34.8% were 1-5 years old, and 64.5% were above five years of age.
Action taken by patients for malaria treatment
Table 3 presents the actions taken to treat malaria by 1383 persons who reported to had malaria a month prior to the
survey. About 2% did nothing, 39% self-medicated, 1% consulted herbalist, 56% went to clinic/hospital and 1%
another source.
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Table 4 shows the patients average expenditure by action taken to treat malaria. The overall expenditure per case for
those who self-medicated was US$1.00 and for those who went to the clinic/hospital (OPD) was US$4.8. The
average overall expenditure per case for those who self-medicated as second action was higher than those who
self-medicated as first action and the cost of medication was the main determinant. Similarly, for those who went to a
clinic/hospital as a second action, the average overall expenditure per case was higher than for those who went as a
first action; drug and treatment costs were again the main determinant.
Households/individuals preventive costs
Table 5 depicts the percent distribution of households by mode of protection against mosquito bites. Overall,
mosquito nets, mosquito repellents and other modes of protection were used in almost the same proportions in the
sampled households that protected themselves against mosquitoes. Overall, 16.4% of households did not use any
protective measure against mosquitoes; this was more pronounced in Kabale district.
Table 6 presents the average annual household expenditure on protective measures by district. The total annual
average household expenditure on protection against mosquito for the 387 households that protected themselves
against mosquitoes was US$125 giving an average expenditure of US$0.32 per household. The greatest average
expenditure was on sprays US$61.49 and the least on mosquito nets US$5.96.
Figure 3 presents reasons for using the different modes of protection against malaria infection. Majority of
households using bednets and aerosol sprays said they preferred them because their perceived effectiveness.
Mosquito coils and other modes of protection were preferred because of they are cheap.
Some of the factors considered in estimating indirect costs included company to consultation, distance to
clinic/hospital, travel time, waiting time, sick days and lost income, and lost income of caregivers.
Company to consultation: The majority (59.4%), of the household members who consulted a clinic/hospital were
accompanied by a parent/guardian with a smaller proportion (14%) accompanied by their spouses or relatives. In
23.6% of the consultations, the patients were unaccompanied.
Distance to clinic/hospital: The distance to a clinic/hospital for most of the household members who consulted a
clinic/hospital was less than 5 kilometers (KM) overall and in the individual districts. Figure 4 depicts that Kabale
district had the highest proportion (43%) of its household members traveling for more than 5 KM to get to a
clinic/hospital.
Travel time: Figure 5 shows that other than Kabale, majority of household members in the rest of the districts took
not more than one hour to get to a clinic/hospital. In Kabale, majority of the household members (48.5%) took 1-2
hours to get to a clinic/hospital for treatment.
The monetary value of travel time can be estimated on the basis of average income and the average amount of time
spent traveling.
Waiting time: As shown in Table 7, the average waiting times before obtaining services at the clinic/hospital was
longest for obtaining cards and consultation; between 12-29 min. Overall, Mubende district household members
experienced the shortest waiting times (less than 60 min for all services). Household members in Kamuli experienced
the longest waiting times, up to 106 min (1hr 45 min) for all services, just over 30 min on consultations and just over
20 min on laboratory services.
On average travel to a clinic/hospital takes 1 hour, hence 2 hours for a return journey, and waiting at the health facility
takes 1.5 hours. In total, about 3.5 to 4hours are spent on these two activities per episode of malaria. Average income
per working day (8hours) of the sampled group is US$2.25. Hence, income per hour is US$0.28. Four hours lost in
travel and waiting amounts to about US$1.12 per malaria episode.
Sick days and lost income: Figure 6 portrays the occupation of household members who suffered from malaria by
district. Of the household members who got malaria in the one month prior to the survey, 75.2% reported to have
been cured within 7 days and 24.8% after 7 days. Most household members who suffered from malaria were
preschool children (37.8%), students (30.8%) and peasants (20.8%). Unlike other districts, peasants formed the
majority in Kabale district. In all districts the employees and self-employed formed less than 10% of household
members who suffered from malaria.
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Overall, 52.4% of household members with malaria stopped work/normal activities. The proportions of members
who stopped work/normal activities in the different districts were: Kabale 50.9%, Kamuli 27.1%, Mubende 52.4%,
and Tororo 79.2%. For household members with malaria who did not stop work, overall 15.5% reported to have cut
down work/normal activities while the rest continued to work normally. The proportions of members who cut down
work/normal activities in the different districts were: Kabale 11.3%, Kamuli 5.6%, Mubende 36.7%, and Tororo
39.3%.
For those household members who stopped work/normal activities, those with jobs/duties lost on average 8.4 days
and those going to school lost on average 6.2 days. On average work/normal activities was cut down by an average of
5.5 hours/day.
Overall, the average household loss in earnings due to absence from work by malaria patients was US$4.12 per
month with Mubende and Tororo districts having the highest average household loss of US$5.91 and US$5.64
respectively. As shown in Table 8 average annual household loss in earnings was US$49.47.
Lost income of caregivers: Figure 7 shows that of the caregivers who suspended normal duties to care for the malaria
patients, the majority were adults (95%) and female (90.6%). Most of the caregivers were peasants (70.1%) or
housewives (18.9%). Table 9 presents average monthly and annual loss in earning of caregivers by occupation. The
overall average monthly loss in earnings by the caregivers when taking care of malaria patients was US$2.50, while
the annual loss was US$30.0. Self-employed caregivers incurred the greatest average loss in earnings of about
US$18.58 while housewives incurred the least average loss of about US$2.53.
Summary of direct and indirect costs
Table 10 provides a summary of the direct and indirect costs of malaria morbidity. The annual total direct cost
(TDC) was US$ 49,122,349 – 94% for treatment and 6% for prevention. Out of which 14.1% was annual institutional
expenditures on malaria control (i.e. ministry of health, national medical stores and development partners) (ISC),
1.1% was annual total household expenditure on malaria (HEP), and the 84.8% was annual total household direct cost
of treatment (ADCT). Approximately 73% of the ISC was borne by development partners. About 78% of HEP was
borne by malaria patients who sought care at the clinic/hospital outpatient department. Clearly, the household bore
the majority of direct costs of malaria morbidity in Uganda.
The annual total indirect cost was US$609,078,209. Fifty-two percent of the total productivity losses were attributed
to patients’ absence from work due to malaria sickness ( APLS ) . Forty-six percent of the of the total productivity
losses consisted of work time lost by relatives and friends accompanying and visiting patients (LCG ) . Two percent
of the total productivity losses were due to patients’ travel and waiting time ( AYLTW ) .
The grand total economic loss attributable to the 12,343,411 malaria cases in Uganda was US$658,200,558, i.e.
92.5% indirect costs and 7.5% direct cost. The average grand total economic loss per malaria case was US$ 53.32;
which consists of direct cost of US$4 per case and indirect cost of US$49.3 per case.
Discussion
Due to the high morbidity of malaria, Uganda incurred a substantial cost of about US$658,200,558 in the year 2003.
Remarkably, a very significant proportion (92%) of this burden was related to loss of productivity as a result of
morbidity. Moreover, this amount excludes costs related to premature death due to malaria. The biggest economic
burden (98.9%) is borne by households/communities.
Out of the total direct cost of US$49.1 million, about US$42.2 million (86%) came from household’s out-of-pocket
payments. Dividing the latter by the total number of cases yields average direct cost borne by households of US$3.4
per case. This Uganda estimate is lower than US$6.50 per case in Mozambique [6], US$6.3 per case in Sudan [8] and
US$8 per case in Burkina Faso [15] but higher than US$2.50 per case in South Africa [6], US$2.71 per case in Ghana
[10], US$0.102 per case during rainy season and US$0.153 per case during dry season in Tanzania [9], US$2.76 per
case in private clinics and US$1.44 per case at public facilities in Ethiopia [7], US$1.683 per case in Nigeria [11],
US$1.84 per case in Nigeria [12], US$1.81 per case in Ghana [14], US$1.83 in Burkina Faso, Chad, Congo [17], and
US$2.58 in Rwanda [20]. The high cost of treatment burden shouldered by households may expose them to
catastrophe and impoverishment. This calls for the upholding of the no-user fees policy as well as more investments
in improving access to quality of health services and community preventive measures in order to further reduce the
cost of illness borne by patients and their families [21].
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In this study, the majority of malaria patients (56%) went to a clinic or hospital for their treatment, 39%
self-medicated and only 3% did nothing. This strongly justifies efforts to improve coverage of services. It is
important to understand the barriers faced by the 3% of malaria patients that did nothing who are likely to be among
the poorest in the community. Not seeking care at all may cause negligible direct costs but they may incur enormous
indirect costs as a result of not seeking care.
For those who self-medicated, the average costs were estimated at about US$1.00 per person per episode out of
which 62% was contributed by the costs of drugs. This finding is comparable to findings of studies undertaken
elsewhere. For example, a study on the economic impact of malaria in Africa estimated that out of pocket expenses
for a mild malaria episode was about US$0.82 of which 87% was the cost of drugs and the rest was the travel costs
[17]. Another study done in Nigeria estimated the household expenditure on per episode of a malaria case at US$1.84
[12]. Self-medication may contribute to fuelling the growing problem of parasite resistance to malaria medicines in
Africa; partially due to the fact that patients may not purchase the full dosage of medicines.
At the household level, the annual indirect costs of seeking treatment included those relating to travel time and
waiting time (US$13,824,620), sick days (US$317,526,842) and time of caregivers (US$277,726,747). The annual
average total indirect cost was US$ 49.3 per case of malaria. This consists of US$1.12 per case due to annual losses in
patient travel and waiting time; US$25.72 per case due to patients annual total loss absence from work due to malaria
sickness; and US$22.5 per case due to annual total productivity losses incurred by relatives accompanying and
visiting patients.
In Uganda the average monthly income loss from: travel and waiting time was US$1.12 per case of malaria; absence
from work due to sickness was US$4.12 per case; and care givers loss of working time was US$2.50 per case.
Therefore, the average total monthly productivity loss was of US$7.74 was lower than the US$8.01 per case in
Burkina Faso, Chad, Congo, and Rwanda [17]. However, the monthly productivity loss in Uganda was higher than
US$4.08 per case in Ethiopia [7], US$3.2 per case in Sudan [8], US$0.597 during rainy season and US$0.889 during
dry season in Tanzania [9]; US$4.52 indirect cost per case in Ghana [10]; US$5.998 per case in Nigeria [11];
US$1.28 per case in Nigeria [12]; US$6.87 per case in Ghana [14]; and US$3.7 per case in Burkina Faso [15].
Conclusion
In a nutshell, the costs of malaria are quite high both at the individual household and institutional levels. Since the
disease affects the young people, it leads to decreased long-term economic growth and thus presents a big economic
burden for the country.
Household survey information has been very instrumental in the calculation of both direct and indirect costs incurred
on malaria treatment and prevention efforts. As Sauerborn et al [16], the estimation of the burden to the households is
essential given the substantive costs related with productivity losses. Unfortunately, due to insufficient data and
methodological challenges, these costs are usually not estimated when assessing the malaria burden. Our results
show that productivity losses constitute about 93% of the total cost of illness.
The study has shown that labour loss due to malaria (US$609,078,210) far outweighs both direct cost of operating
and organizing health services (US$49,122,349), which works against poverty eradication efforts and socioeconomic
development of the country.
There is need for intensified sensitization about malaria prevention to increase uptake of preventive measures such as
treated insecticide-treated nets (ITNs) to offer more effective protection against mosquito bites.
Availability, affordability and perceived effectiveness are the main determinants in choosing a protection measure
against malaria. Efforts should be made to increase availability and minimize costs of the recommended preventive
measures e.g. ITNs if coverage of these interventions is to increased. There is need to target the poor in the
distribution of ITNs because they suffer more serious economic consequences and higher cost burdens.
Acknowledgement
We acknowledge with thanks contributions from the Malaria Control Programme, (Ministry of Health); Health
Planning Department (Ministry of Health); Ministry of Finance, Planning and Economic Development and Uganda
Martyrs University, Nkozi.
The multifaceted contributions of Dr. Nathan Bakyaita, Dr. Patrobas Mufubenga (MoH); Ms. Samantha Smith
(MoFPED); Dr. Everd Maniple and Dr. John Odaga (UMU, Nkozi); Mr. Timothy Wakabi (IPH); Dr Tuoyo
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Okorosobo, participating districts and research assistants towards the study were greatly appreciated. We owe
profound gratitude to Jehovah Jireh for meeting our needs in the life-course of this study.
This article contains the views of the authors only and does not represent the decisions or the stated policies of the
organizations they work for.
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13. Kirigia JM, Snow RW, Fox-Rushby J, Mills A: The cost of treating paediatric malaria admissions and the
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14. Asenso-Okyere WK, Dzator JA: Household cost of seeking malaria care. A retrospective study of two
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15. Guiguemde TR, Coulibaly N, Coulibaly SO, Ouedraogo JB, Gbary AR: An outline of a method for
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Table 1: Parameter values used in the calculations of cost of malaria in Uganda
Variable Value
MOH US$247,222
ME
NMS ME US$1,592,288
DPME US$5,074,059.26
HPM 35%
TNH 4,938,400
ATEP US$0.323
SM 39%
AC SM US$1
ADM 10%
C ADM US$5.73
OPD 90%
C OPD US$4.8
AME 12,343,411
TT 2 hours
WT 2 hours
YH US$0.28
Y AL US$49.47
SAW 52%
Y AYLC US$30
ACA 76.4%
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Table 2: Malaria episodes by district and age
Number of Malaria episodes
Total
Characteristic household One Two More than Two
members No % No % No % No %
District
Kabale 1341 240 20.0 20 14.5 5 12.5 265 19.2
Kamuli 1615 376 31.3 21 15.2 10 25.0 407 29.5
Mubende 1177 225 18.7 23 16.7 15 37.5 263 19.1
Tororo 1488 361 30.0 74 53.6 10 25.0 445 32.2
5621 1202 100.0 138 100.0 40 100.0 1380 100.0
Age
< 1 year 6 0.5 1 0.7 2 5.0 9 0.7
1 - 5 years 407 34.0 57 42.2 14 35.0 478 34.8
> 5 years 785 65.5 77 57.0 24 60.0 886 64.5
1198 100.0 135 100.0 40 100.0 1373 100.0
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Table 3: Action taken to treat malaria by district
Action taken to treat malaria
Self-medicat Consulted Went to clinic/
Nothing ed herbalist hospital Other Total
Characteristic No. % No. % No. % No. % No. % No. %
District
Kabale 6 17.6 45 8.3 0 0.0 218 27.9 2 28.6 271 19.6
Kamuli 2 5.9 261 48.2 8 40.0 128 16.4 3 42.9 402 29.1
Mubende 15 44.1 109 20.1 11 55.0 127 16.3 2 28.6 264 19.1
Tororo 11 32.4 126 23.3 1 5.0 308 39.4 0 0.0 446 32.2
Total 34 2.5* 541 39.1* 20 1.4* 781 56.5* 7 0.58* 1383 100*
*Indicates percentage of the total malaria episodes
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Table 4: Households average treatment expenditure by action and action number
Number of action
Overall
First Second Third
US$
Action taken US$ US$ US$
Self medication
Transport 0.05 0.17 0.03 0.06
Medication 0.55 1.44 0.70 0.62
Other costs 0.25 1.02 0.33 0.32
Average overall expenditure per
case* 0.81 2.56 1.05 1.00
Clinic / hospital
Transport to and from clinic/hospital 0.74 0.63 1.48 0.73
Registration fee 0.09 0.15 0.51 0.11
Consultation fee 0.17 0.24 0.02 0.18
Laboratory cost 0.18 0.16 0.07 0.18
Total drugs cost at clinic 1.07 1.32 0.38 1.10
Treatment cost 2.14 2.05 0.53 2.10
Total drugs cost at drug store 0.39 0.18 0.40 0.36
Transport cost to and from purchasing
drugs at a drug store 0.01 0.04 0 0.03
Average overall expenditure per
case* 4.05 4.30 3.17 4.8
*Overall average expenditures were based on total cases within each action number.
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Table 5: Percent distribution of households by mode of protection against mosquito bites
DISTRICT
Protection against mosquitoes Kabale Kamuli Mubende Tororo Total
Nothing 37.9 21.6 1.9 1.7 16.4
Sleep under bed nets 10.9 13.8 5.6 26.3 14.3
Sleep under treated bed nets 2.4 2.2 0.9 7.1 3.2
Have door/window nets 0.4 0.4 0 0.4 0.3
Indoor residual spraying 1.6 0.4 0 1.7 0.9
Use of mosquito repellents 8.4 16.7 6.5 31.6 16
Other modes of protection* 48 4.5 3.7 10 16.8
Number of households** 248 269 216 240 973
Note: Other methods include clearing bush and stagnant water around the home, closing windows and door early
and burning of leaves. Percentages were computed basing on number households within each district
Table 6: Average annual household expenditure on protective measures by district
District
Protection Total
measure Kabale Kamuli Mubende Tororo
n* US$ n* US$ n* US$ N* US$ n* US$
Bed nets 35 6.50 61 5.33 22 6.93 67 5.94 185 5.96
Sprays 15 60.15 3 120.37 7 37.30 17 62.24 42 61.49
Repellants 1 33.33 0 0 1 16.67 4 11.67 6 16.11
Mosquito
coils 1 2.89 64 33.59 12 28.62 69 22.90 146 27.92
Other
protection
methods 3 29.55 0 0 3 5.55 2 1.67 8 13.58
Totals 55 132.43 128 159.30 45 95.07 159 104.42 387 125.07
Note: n is number of households that spent on a given protection measure
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Table 7: Average waiting time (minutes) for obtaining various services
District Service
Obtaining Lab
card Consultation services Injection Dispensary Total
Kabale 21.8 24.1 12.1 5.7 16.4 80
Kamuli 26.7 31.1 12 15.2 21.2 106
Mubende 11.7 18.9 4.7 8.7 13.4 57
Tororo 28.5 17.8 8 16.3 13.1 84
Average 22.175 22.975 9.2 11.475 16.025
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Table 8: Average monthly and annual household loss in earnings due to absence from work by district
Average monthly loss per Average annual loss per
Total loss
District No. of household household
US$
Households* US$ US$
Kabale 96 245.97 2.56 30.75
Kamuli 81 206.96 2.55 30.66
Mubende 68 402.30 5.91 80
Tororo 102 575.26 5.64 67.68
Total 347 1,430.50 4.12 49.47
Note: Only households whose members were sick and reported their earnings were included
Table 9: Average monthly and annual loss in earnings of caregivers by occupation (US$)
total No. of Overall Overall
House monthly care monthly annual
Unempl Self-emp Employe wife loss givers average average
oyed Peasant loyed e loss loss
Amount 2.21 2.22 3.26 2.50 2.00 12.19 6 2.10 25.25
caregiver
paid
someone
Loss in 1.95 1.90 15.32 8.11 0.53 27.82 10 2.67 32.09
earnings
due to
absence
from work
Total 4.17 4.12 18.58 10.61 2.53 40 16 2.50 30.0
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Table 10: Direct and indirect costs of malaria morbidity in Uganda
Cost components Cost (US$) Percentage of
total
Direct costs:
Annual institutional expenditures on Ministry of health 247,222 0.0%
malaria control (ISC)
National medical stores 1,592,288 0.2%
Development partners 5,074,059 0.8%
Annual total household expenditure on malaria prevention (HEP) 553,101 0.1%
Annual total household cost of Self-medication 4,813,930 0.7%
treatment (ADCT)
Admission 4,314,392 0.7%
Outpatient department care 32,527,357 4.9%
Subtotal direct costs 49,122,349
Indirect costs:
Annual patients total loss of income due to travel and waiting time 13,824,620 2.1%
( AYLTW )
317,526,842 48.2%
Annual patients total loss of income due to malaria sickness
( APLS ) 277,726,747 42.2%
Annual total productivity losses incurred by relatives accompanying and
visiting patients ( L CG )
Subtotal indirect costs 609,078,209
TOTAL COST 658,200,558 100
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Figure 1: Years of education for household members
4%
14%
11%
32%
39%
No education 1-4 years 5-7 years 8-11 years more than 11 years
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Figure 2: Primary occupation of household members
Preschool
21%
Student
40%
Housewife
3%
Employee
(Govt/NGO)
3%
Self-employed
5%
Unemployed
2%
Peasant
26%
Figure 3: Percent distribution of households by reasons for using different modes of protection
100%
90%
Percent distrib ution
80%
70%
60%
50%
40%
30%
20%
10%
0%
Sleep under Sleep under Indoor Use mosquito Burn Aerosol Other modes
bed nets treated bed residual repellants mosquito sprays of protection
nets spraying coils
Mode of protection
Availability Cheaper Very effective Convinient to use
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Figure 4: Distance to clinic/hospital by district
100%
12
90% 20.3 22.1 23.9
80%
Percentage numbers
42.7
70%
60%
50% > 5 km
40% 1 - 5 km
30% < 1 km
20%
10%
0% 1.7 1.7 3.6 1 1.7
Kabale Kamuli Mubende Tororo Total
Districts
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Figure 5: Time taken to reach facility (one-way)
100 1.5
11.6 8.1 6.3 6.3
90 24.3
80
36.6 35.8
70 39.5
Percentages
48.5
60 32.7
50
23.3 30.3 28.2
40
30 24.1
20 41.4
29.1 26.8 29.7
10 15.8
0
Kabale Kamuli Mubende Tororo Total
< 30 mins 30 mins - 1 hr 1 - 2 hrs > 2 hrs
Figure 6: Malaria patient’s occupation by district
100
90
80 pre school
Percent distribution
70 Housewife
60 Employee
50 Self-employed
40 Peasant
30 Unemployed
20
Student
10
0
Kabale Kamuli Mubende Tororo Total
Figure 7: Occupation of caregivers by district
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100%
90%
80%
70%
Percent distribution
60%
50%
40%
30%
20%
10%
0%
Kabale Kamuli Mubende Tororo Total
Districts
Student Unemployed Peasant Self-employed Employee Housewife
80