Using Mobile Technology to Facilitate Reactive Case Detection of Malaria
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This presentation will share findings from more than three years of using mobile technology for reactive case detection (RACD) to help eliminate malaria in a well-defined geographic area. It will review the concepts of RACD, the application of mobile technology, lessons learned from more than three years of application, and considerations in applying this technology in other malaria elimination contexts.
![Malaria Elimination
Cases
214 million
global malaria cases
estimated in 2015
Incidence
37%
global decrease in malaria
incidence between 2000
and 2015
Mortality
60%
global decrease in malaria
mortality rates between
2000 and 2015
-
0.10
0.20
0.30
0.40
0.50
0.60
0.70
0.80
0.90
-
50.00
100.00
150.00
200.00
250.00
300.00
2000 2005 2010 2015
EstimatedNumberofDeaths(Millions)
EstimatedNumberofCases(Millions)
Year
Estimated Numbers of Malaria Cases and Deaths
Estimated Number of Malaria Deaths
Estimated Number of Malaria Cases
President’s Malaria Initiative
Global Fund to Fight AIDS, TB, and Malaria
2
Sources: Global Health Observatory data repository: World Health Organization; [11/16/2016]. Available from:
http://apps.who.int/gho/data. World Malaria Report 2015. World Health Organization, 2015.](data:image/gif;base64,R0lGODlhAQABAIAAAAAAAP///yH5BAEAAAAALAAAAAABAAEAAAIBRAA7)
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Using Mobile Technology to Facilitate Reactive Case Detection of Malaria
- 1. www.rti.orgRTI International is a registered trademark and a trade name of Research Triangle Institute. Using Mobile Technology to Facilitate Reactive Case Detection of Malaria Gordon M. Cressman,1 Michael V. McKay,1 Abdul-wahid Al-Mafazy,2 Mahdi M. Ramsan,3 Abdullah S. Ali,2 Issa A. Garimo,2 Humphrey Mkali,4 Jeremiah J. M. Ngondi3 1. RTI International, Research Triangle Park, USA 2. Zanzibar Malaria Elimination Programme, Zanzibar 3. RTI International, Dar es Salaam, Tanzania 4. Palladium
- 2. Malaria Elimination Cases 214 million global malaria cases estimated in 2015 Incidence 37% global decrease in malaria incidence between 2000 and 2015 Mortality 60% global decrease in malaria mortality rates between 2000 and 2015 - 0.10 0.20 0.30 0.40 0.50 0.60 0.70 0.80 0.90 - 50.00 100.00 150.00 200.00 250.00 300.00 2000 2005 2010 2015 EstimatedNumberofDeaths(Millions) EstimatedNumberofCases(Millions) Year Estimated Numbers of Malaria Cases and Deaths Estimated Number of Malaria Deaths Estimated Number of Malaria Cases President’s Malaria Initiative Global Fund to Fight AIDS, TB, and Malaria 2 Sources: Global Health Observatory data repository: World Health Organization; [11/16/2016]. Available from: http://apps.who.int/gho/data. World Malaria Report 2015. World Health Organization, 2015.
- 3. WHO Country Classification 3 Malaria program phases and milestones on the path to malaria elimination Source: Malaria Elimination: A field manual for low and moderate endemic countries. World Health Organization, 2007. control pre-elimination elimination prevention of reintroduction SPR <5% In fever cases <1 case/1000 population at risk/year 0 locally acquired cases 1st program reorientation 2nd program reorientation SPR: slide or rapid diagnostic test positivity rate. 3 years WHO certification
- 4. Country Malaria Classification 4 © 2016 Global Malaria Mapper, World Health Organisation Eliminating 35 countries have embarked on malaria elimination 0 Indigenous cases 13 countries reported zero indigenous cases in 2014 <20 Indigenous cases 4 countries reported less than 20 indigenous cases in 2014 Prevention of reintroduction Elimination Pre-elimination Control
- 5. Malaria Control Phases in Zanzibar 5 Health facility trends: Malaria confirmed cases and test positivity rate (1999–2014) 0 500 1000 1500 2000 2500 0.0 5.0 10.0 15.0 20.0 25.0 30.0 35.0 40.0 45.0 Jan-99 Jul-99 Jan-00 Jul-00 Jan-01 Jul-01 Jan-02 Jul-02 Jan-03 Jul-03 Jan-04 Jul-04 Jan-05 Jul-05 Jan-06 Jul-06 Jan-07 Jul-07 Jan-08 Jul-08 Jan-09 Jul-09 Jan-10 Jul-10 Jan-11 Jul-11 Jan-12 Jul-12 Jan-13 Jul-13 Jan-14 Jul-14 #ofpositives Positivityrate(%) Positive slides Positivity rate Gain after ACT introduction and before scale-up of mosquito control Gain before ACT introduction Source: Zanzibar Malaria Elimination Programme
- 6. Reactive Case Detection 6 Patient tested positive Health care facility Patient seeks treatment Case notification Surveillance officer Household screening and treatment Facility follow-up Focal area screening and testing Tested positive using rapid diagnostic test or microscopy
- 7. Malaria Surveillance as a Core Intervention 7 Countries with active malaria transmission 95 Case-based surveillance systems 4 Reusable open source 2
- 8. Scale of Deployment 8 Public Health Care Facilities 157 (100%) Surveillance Officers 20 (100%) Private Health Care Facilities 77 (100%) Districts 10 (100%)
- 11. Automating Alert and Alarm Thresholds 11
- 13. Reactive Case Detection 13 Case Notifications 11,756 Follow-up data complete 91% 9,892 complete Aug 2012 – Oct 2016 Household members tested 43,494 Household follow-up 92% 10,855 visits Members positive 5% 2,303 positive 0 100 200 300 400 500 600 700 800 900 1000 Aug Sep Oct Nov Dec Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Jan Feb Mar Apr May Jun Jul Aug Sep Oct 2012 2013 2014 2015 2016 NumberofCases Case Notifications Positive Household Members
- 14. Timeliness of Case Follow-up after Facility Notification Number of Cases Proportion of Cases (%) 14 0 250 500 750 1000 Sep Oct Nov Dec Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Jan Feb Mar Apr May Jun Jul Aug 2012 2013 2014 2015 Numberofmalariacases Within 48 hours 3 to7 days More than1 week Not followed up 0% 20% 40% 60% 80% 100% Sep Oct Nov Dec Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Jan Feb Mar Apr May Jun Jul Aug 2012 2013 2014 2015 Proportionofcases(%) Within 48 hours 3 to7 days More than 1 week Not followed up
- 15. Challenges 15 Issue Possible Causes Data records at health facilities missing contact and location information • Inadequate training or motivation of clinicians • Patient load • Inability or reluctance of patients to provide information Inadequate transport for surveillance officers (maintenance and fuel for motorbikes) • Inadequate funding • Procurement problems • Poor management of transport Resistance by household members to testing • Inadequate or ineffective public information systems Unable to sync data from facility or household location • Mobile network coverage; Reliance on a single network operator
- 16. Questions and Discussion 16 RTI International and the Zanzibar Malaria Elimination Programme gratefully acknowledge the collaboration and support of the U.S. President’s Malaria Initiative. Gordon M. Cressman Senior Program Director RTI International +1 (919) 541-6363 gmc@rti.org Jeremiah Ngondi Senior Epidemiologist jngondi@rti.org Part of the work represented in this presentation was supported by Cooperative Agreement 621-A-00-10-00015-00, funded by the U.S. Agency for International Development (USAID). Its contents are solely the responsibility of the authors and do not necessarily represent the official views of USAID or the U.S. Government.
Editor's Notes
- This graph shows the numbers of estimated malaria cases and deaths worldwide, using data from the World Health Organization. The blue line (left vertical axis) shows the estimated numbers of malaria cases. The pink line (right vertical axis) shows the estimated numbers of malaria deaths The vertical yellow lines shows the launch of the Global Fund to Fight AIDS, TB, and Malaria, and the President’s Malaria Initiative. Investments in these and other large malaria control programs have contributed to a significant reduction in malaria cases and deaths worldwide. By 2015 the estimated number of malaria cases worldwide had fallen to 214 million. This is a 37% global decrease in malaria incidence since 2000. The WHO estimates that global mortality due to malaria has decreased by 60% over this same period.
- In 2007, the WHO Global Malaria Program began classifying endemic countries by the phase of their malaria program. This diagram shows the phases and milestones on the path to malaria elimination. On this diagram, SPR means the slide or rapid diagnostic test positivity rate. These milestones are indicative only. The transitions will depend on the malaria burden that a program can realistically handle (including case notification, case investigation, etc.). The situation is dynamic along this entire progression from one phase to another and within each phase. The term “reorientation” on this diagram refers to a major change in program approach and interventions. The most significant change between pre-elimination and elimination is the necessary shift to individual case investigations.
- 35 countries have formally declared and embarked on a malaria elimination strategy. 5 countries have successfully reduced their malaria incidence to zero, but need to sustain this for 3 consecutive years before applying for malaria-free certification. In 2014, 4 countries reported less than 20 confirmed indigenous cases of malaria And 13 countries reported 0 indigenous cases of malaria
- This graph shows the dramatic gains against malaria in Zanzibar from January 1999 through July 2014. The vertical blue bars are the number of positive slides, as indicated on the right axis. The orange line is the slide positivity rate, as indicated by the left axis. The shaded block in the upper left shows the gain before the introduction of artemisinin-based combination therapy (ACT). The green shaded block shows the gain after the introduction of ACT and before the scale-up of mosquito control. The horizontal red line is the milestone between control phase and pre-elimination phase. The milestone for the transition from control phase to pre-elimination phase was achieved in 2007. In 2008 we worked with the Zanzibar Malaria Control Program to develop a simple mobile phone system to enable heath care facilities to report aggregate data weekly concerning malaria cases and related supply stocks. By 2012 the program was implementing aggressive malaria elimination interventions, including reactive case detection, or RACD)
- Reactive case detection (RACD) is being implemented in many malaria elimination countries. RACD is being used in Zanzibar as an intervention. The objective is to quickly interrupt transmission and reduce the reservoir of the parasite. There are currently multiple definitions of this term. No standard definition has been provided by the World Health Organization (WHO). This diagram depicts perhaps the most widely understood implementation of RACD. A febrile patient seeks treatment at a health care facility. The patient tests positive for malaria via a rapid diagnostic test (RDT). In Zanzibar this case is referred to as an index case. The facility issues a new case notification. In the case of Zanzibar, this notification is sent to the relevant district malaria surveillance officers. A surveillance officer may follow up with the health care facility to get additional case information from the patient register. A surveillance officer then visits the household to gather information about the household, and to test and treat all other household members. Depending on the response protocol, the surveillance officer may then test and treat residents in households within a specified distance of the index case household. The time for case notification, follow-up, and focus investigation may be monitored against targets. This version of RACD is sometimes called reactive screen-and-treat. Regardless of the RACD approach, an information system is needed to facilitate and monitor the process, and to enable programs to target limited intervention resources as efficiently and effectively as possible. Since contexts vary and response protocols change, such a system must be flexible.
- Zanzibar’s use of open source mobile technology to facilitate RACD is advanced. This diagram shows how the technology is used in the RACD workflow. Public and private health care facilities use simple mobile phones to issue new case notifications. Surveillance officers receive new case alerts electronically on mobile phones and tablet computers running open source software called Coconut Surveillance, that has been designed specifically for malaria surveillance and response. Surveillance officers collect additional case information at the facility, and at the index case household, including geo-locating the household. Information is also gathered from each household resident tested and treated. Depending on the latest transmission risk estimate, the software advises the surveillance officer whether to treat and test residents of surrounding households. The case data are synchronized over the mobile phone network with a database. This case-level synchronization is important. It makes it possible for field workers to collaborate on completing cases and to transfer cases to other field workers. It also maintains versions of cases, making it possible to track and audit case history. It also makes it possible to get updated risk stratification data onto the mobile device. Program supervisors monitor the response process and analyze the data via a web application. With this system, surveillance is an intervention.
- This photo shows Coconut Surveillance in use at an index case household. The person to the left is a district surveillance officer, who travelled to the household by motorbike. The household resident on the right is holding a coupon for an insecticide-treated bed net. Coupons are issued if additional bed nets are needed. They can be redeemed for bed nets at the nearest health care facility. All public and private healthcare facilities are able to submit case notifications, and are also required to submit aggregate case data weekly, even if there are no cases (zero-based reporting). Coconut surveillance is used by all malaria surveillance officers in all of Zanzibar’s 10 districts.
- An analytics application enables program managers to monitor the data in real-time. The analytics application has evolved based on feedback from the Zanzibar Malaria Elimination Programme. It includes reports, graphs, and maps that have been designed specifically to support malaria elimination efforts. We are continuing to refine these.
- The status of follow-up can be monitored for each case. Half icons are used to indicate incomplete data. There are no examples on this particular display. Highlighted icons indicate index case households with no travel history, positive household residents, and positive household residents in households with no travel history. Highlighting is also used to indicate cases that have not been followed up within the target period, and cases in areas that have been designated as high risk of transmission.
- Alert and Alarm thresholds have been set at 4 different levels: facility, shehia (ward), village, and district. Alerts and Alarms can be monitored using this report, but they also generate SMS and email messages to program managers.
- The geo-located household data help to identify areas of persistent high transmission or focal areas. These also make it possible to animate the data over time to look for transmission patterns.
- This graph shows the number of case notifications and positive household members through October. The vertical axis on the graph is the number of cases. The horizontal axis is graduated in months. The blue series is the number of case notifications. The red series is the number of household members who tested positive. From August 2012 through October 2016, health care facilities in Zanzibar issued more than 11,700 malaria case notifications. Malaria surveillance officers responded to 92% of these notifications and completed all follow-up data for 91% of these index cases. 5% of nearly 4,500 household members tested during follow-up visits tested positive and were treated.
- These graphs show the timeliness of case follow-up after a new case notification has been issued by a health care facility. The top graph shows the numbers of cases by follow-up time. The bottom graph shows the percentages of cases by follow-up time. The data have been aggregated by month. In Zanzibar, the target time for completing follow-up screening and testing at an index case household is within 48 hours of the index case notification. This is shown in green. The orange sections are within 3 to 7 days. The red sections are more than 1 week. The grey sections are cases that were not followed up. The seasonal variation in the numbers of cases is clear in the top graph. The bottom graph shows a significant decrease in follow-up during the peak transmission season in 2013. Several adjustments, including increasing the numbers of surveillance officers, resulted in significant improvements in 2014 and 2015.
- The malaria surveillance system in Zanzibar has been the subject of two recent assessments. Many aspects of the performance of the system can be assessed using data from the system itself. Other aspects can be assessed by examining cost records, and gathering qualitative data through surveys and focus groups. In addition to these two assessments, the Zanzibar Malaria Elimination Programme and RTI International have more than 4 years of experience implementing and operating this system. Many issues need to be considered in introducing case-based surveillance for malaria elimination. The appropriateness of the technology and the capacity to use, manage, and sustain the technology, are essential. Other factors include sustainable costs, training for clinicians and surveillance officers, transport, and public communications. The issues listed here are were identified through two separate assessments, one in 2015, and the other in 2016. They highlight the importance of factors that have nothing to do with the information technology. Godbout S. Assessing Surveillance Elements of the Zanzibar Malaria Elimination Program to Support Malaria Elimination in Zanzibar. Durham, NC: Duke University; 2016. Khandekar E. Performance Evaluation of Zanzibar's Malaria Case Notification (MCN) Surveillance System: The Assessment of Timeliness and Stakeholder Interaction. Durham, NC: Duke University; 2015