Burgess COMMENTARY

Peter Burgess

Information Systems to Support Surveillance for Malaria Elimination
Colin Ohrt*, Kathryn W. Roberts, Hugh J. W. Sturrock, Jennifer Wegbreit, Bruce Y. Lee and Roly D. Gosling
+ Author Affiliations

Malaria Elimination Initiative, Global Health Group, University of California, San Francisco, California; Public Health Computational and Operational Research (PHICOR), Johns Hopkins School of Public Health, Baltimore, Maryland + Author Notes

Authors' addresses: Colin Ohrt, Malaria Elimination Initiative, Global Health Group, University of California, Hanoi, Vietnam, E-mails: colin.ohrt@ucsf.edu or colin8994@gmail.com. Kathryn W. Roberts, Hugh J. W. Sturrock, Jennifer Wegbreit, and Roly D. Gosling, Global Health Group, University of California, San Francisco, CA, E-mails: kathryn.roberts@ucsf.edu, hugh.sturrock@ucsf.edu, jennifer.wegbreit@ucsf.edu, and roly.gosling@ucsf.edu. Bruce Y. Lee, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, E-mail: brucelee@jhu.edu.

↵* Address correspondence to Colin Ohrt, Malaria Elimination Initiative, Global Health Group, University of California, National Institute for Malariology, Parasitology and Entomology, 35 Trung Van Road, Tu Liem, Hanoi, Vietnam. E-mails: colin.ohrt@ucsf.edu or colin8994@gmail.com

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Abstract.

Robust and responsive surveillance systems are critical for malaria elimination. The ideal information system that supports malaria elimination includes: rapid and complete case reporting, incorporation of related data, such as census or health survey information, central data storage and management, automated and expert data analysis, and customized outputs and feedback that lead to timely and targeted responses. Spatial information enhances such a system, ensuring cases are tracked and mapped over time. Data sharing and coordination across borders are vital and new technologies can improve data speed, accuracy, and quality. Parts of this ideal information system exist and are in use, but have yet to be linked together coherently. Malaria elimination programs should support the implementation and refinement of information systems to support surveillance and response and ensure political and financial commitment to maintain the systems and the human resources needed to run them. National malaria programs should strive to improve the access and utility of these information systems and establish cross-border data sharing mechanisms through the use of standard indicators for malaria surveillance. Ultimately, investment in the information technologies that support a timely and targeted surveillance and response system is essential for malaria elimination.

Disclaimer: The following individuals reviewed the report and provided important assistance and feedback: Chris Cotter (Global Health Group, University of California San Francisco, San Francisco, CA), Gerard Kelly (University of Queensland, Brisbane, Australia), Richard Reithinger (RTI International, Washington, DC), Arantxa Roca (Malaria Consortium, Phnom Penh, Cambodia), Chris Schwabe (Medical Care Development International, Silver Spring, MD), David Sintasath (FHI 360, Washington, DC) and Rick Steketee (PATH: MACEPA, Seattle, WA). Previous Section Next Section

Introduction

Robust and responsive information systems are critical for successful malaria control and elimination.1–5 In elimination settings, surveillance must be an intervention where data collection, analysis, output, and response occur quickly to identify symptomatic and asymptomatic cases, prevent onward transmission, and reduce vectorial capacity. The best way to ensure that this occurs rapidly and efficiently is to work with information systems designed to support malaria surveillance and response. Specifically, in an elimination setting, case reporting needs to shift from being periodic and aggregated at the district or provincial level to real-time reporting of individual geo-located cases (Figure 1).

Figure 1. View larger version: In this page In a new window Figure 1.

Changes in the spatial and temporal scale of malaria surveillance and response in the shift to elimination (Modified from Cao and others6).

Technologies that support elimination surveillance can facilitate many essential elements such as real-time or rapid reporting and case and intervention mapping. Here we describe the characteristics of an ideal malaria elimination information system that has the capacity to identify individual cases, analyze and share information, and stimulate real-time action to prevent onward transmission.

This article is one in a series of four that is intended to guide malaria elimination program decision making. These articles draw on both published and unpublished literature and qualitative data gathered from key informant interviews. This article offers specific recommendations to guide the choice of information systems in elimination settings.

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Methods

These findings were informed by published and grey literature. In addition to a review of publications specific to malaria elimination and other disease eradication, the literature search included combinations of the following topics and search terms: chagas, cross-border data sharing, data management, dengue, geospatial, guidelines, influenza, information systems for health, polio, imported malaria, integrated disease surveillance, inter-country collaboration, malaria elimination, monitoring and evaluation, outbreak alert, rapid reporting, sentinel surveillance, surveillance, World health Organization (WHO), yellow fever, zero reporting, and specific country programs mentioned by key informants. A total of 157 documents were identified and reviewed. The authors conducted 21 key informant interviews with malaria field experts, surveillance specialists, geographic information systems (GIS) experts, information technology experts, and members of malaria control and elimination programs, as well as experts in the control and eradication of diseases other than malaria.

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Characteristics of an Ideal Elimination Information System

Surveillance for malaria control aims to estimate the burden of malaria and inform population-level programs, whereas surveillance for malaria elimination strives to capture and respond to every malaria case.7 An ideal malaria elimination information system to support surveillance and response activities collects and transmits data about cases and program activities swiftly, incorporates data from other existing surveillance systems in real time and analyzes data to inform rapid response strategies (Figure 2).8

Figure 2. View larger version: In this page In a new window Figure 2.

Malaria surveillance systems. (A) Traditional malaria surveillance. In a traditional malaria surveillance system, data movement is unidirectional, and outputs do not inform community-level response. Additional data are not incorporated into a central database. (B) Ideal malaria surveillance. In an ideal malaria surveillance system, all levels contribute data to a central database, the central database provides data analysis and guidelines to all levels, and communication is bi-directional.

To achieve these aims, the system requires several key features as follows:

Rapid and complete reporting.
Data should be collected from the lowest level and in the most direct manner possible.9 This includes data collected passively at health facilities and in communities from community health workers as well as through active case detection during case investigations or screening activities and intervention data from district-level malaria programs. Consensus on key indicators, or minimum essential data, that a malaria elimination information system needs to capture is fundamental. Complete, timely reporting is an essential element of a malaria elimination surveillance system. Methods for capturing data must be rapid, locally appropriate, feasible, and sustainable by the malaria program. Malaria should be made a notifiable disease once incidence is low enough that malaria surveillance teams can investigate and report every individual case. Instituting a “zero reporting” policy that requires all reporting sites to communicate the number of cases tested and detected regularly, regardless of whether a new case has been detected. This policy, as outlined in the WHO's surveillance guidelines for poliomyelitis and Japanese encephalitis, will further improve data quality.10 Zero reporting reduces missing data and helps identify locations where reporting is irregular or incomplete.11

Incorporation of additional data.
Incorporating data sources external to the malaria program, and even the health system, into the malaria elimination information system will improve decision making. For example, the use of census data can provide population denominators, climate and land use data warn of potential areas at risk of outbreaks, and population movement information may indicate the need for a rapid shift in resource targeting. The malaria elimination information system must be flexible enough to receive information from and export to external databases to ensure data can be used by and shared among multiple stakeholders.

Accessible data storage and management.
Data should be accessible to key members of the health system from the central level down to the implementation units in the communities.9,12,13 The database should be appropriate for local conditions, taking into account existing community-level assets and skills, as well as access to power and equipment repair.14,15 In some locations, a cloud-based database will make the most sense because anyone with Internet access and administrative clearance can submit and obtain data in real time. However, in locations where reliable Internet access is unavailable, local databases that feed into a central server may help ensure that work can continue during Internet outages. Regardless of the type of data storage used, guidelines for timeliness of reporting must be established.6,16 Data storage and management systems must be computer based and should include a plan for ongoing maintenance.9

Automated and expert analysis. A malaria elimination information system should include automated data analysis to ensure timely outputs and expert analysis for policy and programming decisions.9,17 Automation is vital because a malaria elimination information system must receive and analyze data and output results quickly to identify threats such as outbreaks, inform responses, and monitor the functioning of the whole elimination program. It is essential to incorporate technical assistance and capacity building for malaria program staff at all levels to ensure the database is used effectively. Malaria experts working where the surveillance system is implemented, including in the field, should participate in analysis and interpretation of outputs to ensure that recommended interventions are feasible and reflect local conditions.

Expert analysis can be used to model the expected impact of different combinations of interventions and has been used in other contexts to improve complex processes and systems and decision making.18 The models can use data from the surveillance system to help determine which interventions should be used and in what manner to ensure the most impactful, efficient, and cost-effective response.19,20 The impact of the response can be captured by the surveillance system and can inform further iterative changes to the interventions. Geospatial modeling was conducted in Haiti to produce malaria risk maps as part of an assessment of the feasibility of malaria elimination.21 Because of the unreliability of passive surveillance data, parasite prevalence data were used to better understand the temporal and spatial distribution of malaria. From this work, optimal interventions and treatment strategies for various populations and locations were suggested.

Customized output and feedback. An ideal malaria information system should automatically generate outputs tailored to the level of the health system that receives them, including visualizations of analyzed data, work task lists, and reports for internal use, external organizations, and donors. Once data are analyzed, visualizing results is essential to effectively share the information. Outputs to the lowest levels should be understandable and directly useful for operational responses, for example, including information that directs the surveillance officer to the household or health facility of the case. Monitoring and evaluation of the outputs are necessary to measure the value added of the malaria elimination information system itself and understand how the system can be improved.

Targeted response. Response to malaria elimination information system outputs needs to be timely, effective, and targeted.9,13,22 At the local level, once health staff receive outputs, including a work task list, they must take immediate action.23,24 This list may include households to be screened for infection, receive preventive interventions such as indoor residual spraying with insecticide, and receive health education. As the workers are implementing their task list, they can also collect data that should be uploaded into the information system that will further inform the intervention strategy, such as coverage and use of interventions, and the occupational risk factors of the people within the target area. The uploading of data from the response activity acts to inform the information system that the activity has taken place operating as a tool for monitoring and evaluation. Findings from the review and key informant interviews highlighted that connecting outputs from the information system to action is the weakest element of existing systems.

Previous Section Next Section Description of Existing Information Systems

Currently, few malaria information systems exist that can collect, store, analyze, and provide feedback to implementers based on real-time information. Many existing systems are limited in geographic coverage, do not collect sufficient data to inform rapid response, or are not connected to decision making. While no existing malaria information system contains all the elements listed above, below are examples of existing systems that contain elements of what an ideal system might look like and offer valuable lessons on how to conduct surveillance that can lead to effective responses. Comparisons of these and other systems are highlighted in Table 1.

View this table: In this window In a new window Table 1

Existing surveillance systems for malaria elimination

China. The strength of the Chinese Information System for Disease Control and Prevention is its timeliness, ease of reporting, and intuitive 1-3-7 monitoring framework. The 1-3-7 framework dictates that malaria cases be reported within one day, case investigation must occur within three days, and foci investigation and increased prevention measures implemented within 7 days.6 The recommended responses vary by the levels of endemicity and risk, with “active and passive surveillance, with particular attention to mobile populations,” in areas with higher incidence, “passive surveillance in the transmission season and active surveillance targeting transmission foci” in zones with seasonal malaria, and “intensified surveillance and response” in border areas.34

Solomon Islands and Vanuatu. Automated analyses and customized outputs, as well as the potential to guide targeted, rapid response, are the strengths of the Spatial Decision Support System (SDSS), implemented in Vanuatu and the Solomon Islands.35 This GIS uses the time and place of malaria cases and intervention coverage to automatically classify areas according to risk and then generate specific response recommendations. The information system creates automated maps of households, including coverage, incidence, and additional geographic and entomologic data. Work task lists are generated for intervention and case management teams for each geo-located house they should visit.

Swaziland. The strengths of Swaziland's malaria information system include rapid case reporting through the Immediate Disease Notification System (IDNS), a surveillance system integrated with the reportable disease system, and surveillance outputs that are rapidly relayed to a team that can initiate a response.36 The health facility staff members call a toll-free number to report cases to the IDNS, which then sends multiple short message service (SMS) messages with case details to the local malaria program manager and the surveillance team, who investigate within 48 hours. Weekly goals and feedback are provided to surveillance officers to improve coverage and speed of follow-up and screening.

Zanzibar. The strengths of Zanzibar's Malaria Case Notification (MCN) system are its rapid reporting and outputs detailing geo-location of cases.37 Through MCN, cases are reported in real time and then a tablet-based platform alerts district malaria officers to follow-up, guiding which households are visited to conduct reactive case detection. In this system, surveillance is an intervention, where mobile reporting allows the collection of data in real time that are used to guide a local response.15,38

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Linkage Between Regional and Global Information Systems

Ideally, national malaria control and elimination information systems would link seamlessly with related regional and global structures, prioritizing cross-border intelligence sharing information regarding transmission hotspots, outbreaks, and human movement. This would lead to appropriate allocation of national and regional resources and timelier targeted action. However, database linkage between countries and within regions is difficult due to the sensitivity of sharing and nonstandardized collection of data. As more countries move toward malaria elimination and cross-border and regional malaria elimination initiatives are implemented, sharing of data should become a priority. In an effort to facilitate data sharing for malaria control, WHO now coordinates a “situation room” that is focused on the 10 African countries with the highest malaria burden, bringing country representatives together virtually every 2 weeks to discuss stock control, funding issues, and to track current and potential outbreaks. Similar regional situation rooms such as the data sharing hub being developed by the Emergency Response to Artemisinin Resistance in the Greater Mekong Subregion could facilitate data sharing and coordination among malaria-eliminating countries.

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Recommendations

To build a robust and action-oriented malaria elimination information system, a number of key issues require consideration.

Reporting. In countries pursuing malaria elimination, when incidence is low enough, rapid reporting of cases should be implemented. Once in the malaria elimination phase reporting must be required by law, preferably within a defined period, and appropriately incentivized in all sectors caring for malaria patients, including private sector health providers and militaries. Reporting should include negative diagnostic test results and zero case reporting.

Database management. The malaria elimination database must be manageable by the National Malaria Control Program (NMCP). A malaria elimination surveillance system must provide a framework to guide strategic decision making and support the effective management, coordination, and implementation of interventions. All levels of the malaria control program, from the community to the national level, should receive information from the system. Expert epidemiological and information technology oversight of the system is crucial requiring human resources to support data analysis, including surveillance database managers and epidemiologists who can program database queries, analyze, and interpret data.

Information and results feedback. An effective information system must feed analyzed data back to those executing the malaria program, particularly at the community level. In this review, we found few examples of systems that rapidly shared analyzed surveillance information, which could contribute to more rapid and complete responses. For surveillance to function as an intervention, real-time feedback and effective responses are essential. Global stakeholders must take note of this weakness and target investments to improve appropriate rapid feedback from malaria information systems that lead to effective responses.

Technology. Locally appropriate technologies, such as mobile phones and web-based systems, can help support data quality improvements and reporting timeliness. Most importantly, comprehensive spatial decision support systems that incorporate GIS are invaluable, as they enable mapping of cases and interventions, automated foci identification, and targeted responses.35,39

Data sharing and commitment. Real-time sharing of standardized malaria data across borders has the potential to contribute to malaria elimination. A key element of malaria elimination programs is rapid and appropriate response to malaria cases. Standardized and streamlined methods and indicators will improve reporting and decision making. Interventions will need to be adapted to the location and population; however, there is an urgent need for generic and adaptable standard operating procedures on which NMCPs can base their surveillance and response strategies. The effective implementation of regional collaborations within malarious regions looking to eliminate may be crucial for the success of national and regional malaria elimination. Currently there are few functioning cross-border malaria elimination collaborations. Ideally, surveillance systems would be unified across countries and would incorporate information from militaries who liaise with government and civilian authorities. Harmonizing existing surveillance systems will require both political and financial commitments in short term and long term. In short-term, commitment is needed to bring stakeholders together to develop political and financial capital for malaria elimination surveillance and information systems. Malaria elimination is a long-term strategy, therefore, commitment is needed to maintain a cadre of workers who can work with the software and adapt it to fit changing circumstances.

Many new technology developments to improve surveillance for malaria elimination appear attractive for investment. However, an investment in technologies is not a panacea; a surveillance system is only as good as its implementation. An excellent information system should be at the core of malaria elimination programs to ensure that all cases are detected and responded to in an effective and timely manner. Investment in robust, response-focused systems is essential to achieve malaria elimination.

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ACKNOWLEDGMENTS

We acknowledge with thanks the contributions of the people who have participated in the collection and exploration of information contained in this report: Ahmed Mohammad Abdalla (National Malaria Control Program, Abu Dhabi, United Arab Emirates), Patrick Blair (Naval Medical Research Center-Asia, Singapore), Bradley Ringeisen (Defense Threat Reduction Agency, Portsmouth, VA), Daniel Bridges and Anna Winters (Akros Global Health, Lusaka, Zambia), Keith Carter (Pan American Health Organization, Washington, DC), Richard Cibulskis and Raman Velayudhan (World Health Organization [WHO], Geneva, Switzerland), Rajeev Colaco, Richard Reithinger and Gordon Cressman (RTI International, Washington, DC), Michael Coleman (Liverpool School of Tropical Medicine, Liverpool, UK), Peggy D'Adamo (USAID, Washington, DC), Arantxa Roca (Malaria Consortium, Phnom Penh, Cambodia), Mitiku Aychiluhim Damtew, Kaddu Ssentamu Simon and Ella Watson-Stryker (WHO, Freetown, Sierra Leone), Eric Hale (Solomon Islands Ministry of Health [MoH], Honiara, Solomon Islands), Herdie Hizon (Philippines MoH, Manila, Philippines), Eduardo Jezierski, Dennis Israelski and Channe Suy (Innovative Support To Emergencies Diseases and Disasters [InSTEDD], Sunnyvale, CA), Jun Cao (Jiangsu Institute of Parasitic Diseases, Wuxi, China), Jaranit Kaewkungwal and Amnat Khamsiriwatchara (Mahidol University, Bangkok, Thailand), Gerard Kelly (University of Queensland, Brisbane, Australia), Jirapat Ketkaew, Apinya Niramitramtipong, Duangdeun Puangmanee and Wittaya Saipromsut (Thailand MoH, Bangkok, Thailand), Dysoley Lek and Siv Sovannaroth (Cambodia MoH Phnom Penh, Cambodia), Adam Liederman (Fio Corporation, Toronto, Canada), John Miller and Rick Steketee (Program for Appropriate Technology in Health [PATH]: The Malaria Control and Elimination Partnership in Africa [MACEPA], Seattle, WA), Mwiyini I. Msellem (Zanzibar Malaria Elimination Program, Zanzibar City, Unguja, Zanzibar), Rose Nani Mudin (Malaysia MoH, Kuala Lumpur, Malaysia), Thang Duc Ngo (National Institute of Malariology Parasitology and Entomology, Hanoi, Vietnam), Nyasatu Ntshalintshali (Clinton Health Access Initiative [CHAI], Mbabane, Swaziland), Elvieda Saraiwati, Budi Pramono and Mienerva Theodora (Indonesia MoH, Jakarta, Indonesia), Chris Schwabe (Medical Care Development International, Silver Spring, MD), David Sintasath (Family Health International [FHI] 360, Washington, DC), Mark Smolinski and Jennifer Olsen (Skoll Global Threats Fund, Palo Alto, CA), Jeff Tackle and Kathleen Poer (Abt Associates, Bethesda, MD), Marcel Tanner (Swiss Tropical and Public Health Institute, Basel, Switzerland), and Allison Tatarsky (CHAI, Gaborone, Botswana). We acknowledge the participants of the Asia Pacific Malaria Elimination Network Surveillance and Response Thematic Group for their insightful discussions on the program requirements for malaria elimination surveillance systems.

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Footnotes Financial support: This background article is a rapid synthesis of current evidence commissioned and funded by the Bill and Melinda Gates Foundation. Received April 25, 2014. Accepted April 13, 2015. © The American Society of Tropical Medicine and Hygiene This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Previous Section

References

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The American Journal of Tropical Medicine and Hygiene Information Systems to Support Surveillance for Malaria Elimination Table 1 Existing surveillance systems for malaria elimination

Country System description Data capture Outputs Strengths Challenges

Cambodia MIS is a stand-alone system developed to assess malaria transmission and intervention coverage.25 Two additional pilot systems Passive Case Detection case notification MIS: Automatically generated report including tabular summaries, graphics and mapping to village level26 MIS: Covers all endemic areas Uncaptured private, migrant, military sectors D0AS Pf cases MIS: District level data reported monthly, including species, severe malaria, cases, deaths D0AS: Real-time SMS alert to Provincial Health Department and National Malaria Center. Day-28 follow-up reminder is sent to the same plus health center management Tracks severe malaria, deaths Most data aggregated monthly, challenge to get real-time data D3AS Day 3 positive malaria smears to identify resistance D0AS: Health staff send SMS for Pf cases from pilot areas D3AS: Real-time SMS when parasites remain by Day 3 Malaria incidence and intervention coverage to village level Inconsistent decision making and response based on available data Population covered: > 3M D3AS: Only includes Pf cases parasitemic after three days of treatment in areas of artemisinin-resistance Automatically generated monthly bulletin Does not capture time-to-case reporting, or intervention quality Pilot D0AS and D3AS Case follow-up challenges SMS and Internet-based notification systems No mapping to household or where case acquired Integrated with MIS

China Two integrated web-based systems: febrile illness reporting and focus investigation and intervention tracking. Data stored at the National Centers for Disease Control and Prevention.6,27 PCD case notification: Data entered within 24 hours. Data include date, facility, reporting person, patient info and diagnostic result with method and treatment SMS alerts Web-based system integrated with reportable diseases system Mobile technology not integrated Population covered: > 1.3B Monthly MoH report, tabular summary results, graphics and mapping Data fed into HMIS Limited baseline data “1-3-7 strategy” time tracking to case notification (one day), case investigation (three days), completed interventions (seven days) Very little missing data Does not capture new interventions or intervention quality Rapid case reporting No mapping to household or where case acquired Diagnosis is confirmed by microscopy and PCR “1-3-7 strategy” is easy to use and understand

Solomon Islands/Vanuatu SDSS.17,35 Data are stored in a relational database, using local, provincial and nationally based servers (three levels for backup). PCD case notification: Health facility calls provincial center within 48 hours. Real-time case reporting SDSS includes extensive baseline data28 Mobile technology not integrated Population covered: > 90 k, implemented in four island provinces Frontline and active case detection planning by household, follow-up list of households that did not receive intervention Rapid case reporting Inconsistent decision making and response Tabular output, spatial analysis, graphics, and mapping, including foci classification Automated GIS-based queries with high-resolution mapping Does not capture time-to-case reporting, or intervention quality Generates lists to support targeted action at the household level Human resource constraints Readily adaptable to other locations or systems No mapping to where case was acquired Swaziland HMIS, IDNS for 15 reportable diseases, and MSDS for case investigation and interventions.16,29,36 PCD case notification: RDT or microscopy-confirmed malaria cases dictated through a toll-free hotline. Data entered on a central server, surveillance agent receives an SMS with date, facility, reporting person, patient info and case number to conduct case investigation and intervention. IDNS: Toll-free hotline resulting in SMS to surveillance agent Integrated with notifiable disease reporting system Relatively low reporting completeness to IDNS Population covered: 1.2M MSDS: Monthly tabular and graphic summary, mapping to household. Maps of cases investigated, locations of positive cases, IRS, ITNs, breeding sites, risk maps, households screened, or remaining. Web-based system using mobile technology Low case reporting from private sector facilities Free mobile reporting Does not capture time-to-case reporting or intervention quality Entire country covered No mapping to where case was acquired Simple, rapid case notification Temporal–spatial analysis of case distribution

Thailand Stand-alone, web-based system. Data storage is in a database at Mahidol University. GPS-enabled tablets for patient follow-up, data captured in same server. PCD case notification: Case data entered at malaria clinic level within 24 hours. Data include date, facility, reporting person, patient info, diagnostic result with method and foci classification.30 Pf case alerts to malaria clinic staffs' tablets within 24 hours Web-based system with mobile technology being integrated Hospital-based cases in a separate system Population covered: > 21M Tablet-based follow-up form for directly observed therapy and resistance monitoring Implemented in large regions, covering all areas of multi-drug resistance Challenges with migrant and cross-border follow-up Monthly MoH report, tabular summary, graphics, maps, with mapping to Pf case household and likely case location.31 Rapid case reporting More baseline data needed, such as intervention coverage and forest sleeping locations Captures DOT No time to case reporting or intervention quality Captures Pf resistance

Zambia DHIS2 is a web-based health information system. Data storage and mobile phones linked to the same database.32,33 PCD case notification: urban and rural health staff report weekly by mobile phone. Data include clinic visits, clinical cases, RDT-tested and positive cases, microscopy-tested and positive cases, ACT and RDT stock tracking. CHWs report cases monthly by mobile phone.33 Regular reports, with online access to data in real-time Open source free web-based system fully integrated with HMIS Case data not reported to DHIS2 in real-time Population covered: > 6M Graphs created and provided in real time to mobile phones or computers, summarizing case reporting and stock data, with summary data from all areas, reporting to the facility Tables, charts and maps shared with all users with online dashboard Does not capture time to case reporting or intervention quality Maps, graphs display village, clinic-level malaria incidence Mobile technology fully integrated Remains to be determined if DHIS2 can support full malaria elimination surveillance system to household level Timeliness and completeness of data reporting tracked

Zanzibar, Tanzania Integrated system combining Coconut Surveillance and MCN. MCN includes rapid reporting and analysis, outputs with geo-location of cases, through Coconut Surveillance. Cases reported to health staff via SMS. Coconut uses data to guide household oriented index case follow up. PCD case notification: MCN: Real-time case reporting via Coconut Surveillance, monthly MoH reports. Tabular summary results, graphics and mapping to the village level. MCN and Coconut are an integrated SMS-based system and tablet web-based system Cases from extensive private sector not captured Population covered: ∼1.3M Public health unit staff send an SMS for each positive case. Data include all-cause visits, malaria tested/positive cases and age. Coconut: Real-time tabular summary results, graphics, and detailed mapping to the household level. Real-time tracking of case follow-up and new interventions. Mobile technology fully integrated Limited capture of baseline data Coconut Surveillance notifies malaria officers of cases immediately via SMS. Patient and household follow-up with GPS enabled tablet. Rapid case reporting Does not currently capture intervention quality Real-time tabular output of key variables makes it easy for management to track progress real time No mapping to where case was acquired MEEDS data are used to calculate supply orders Denominator (population) data not captured with Coconut D0AS = Day 0 Alert System; D3AS = Day 3 Alert System; DOT = directly observed therapy; HMIS = Health Management Information System; GIS = geographic information systems: MCN = malaria case notification; IDNS = Immediate Disease Notification System; MIS = Malaria Information System; MoH = Ministry of Health; MSDS = Malaria Surveillance Database System; Pf = Plasmodium falciparum; PCR = polymerase chain reaction; SDSS = Integrated Spatial Decision Support System; SMS = short message service. Add to CiteULikeCiteULike Add to DeliciousDelicious Add to DiggDigg Add to FacebookFacebook Google+ Add to LinkedInLinkedIn Add to MendeleyMendeley Add to RedditReddit Add to StumbleUponStumbleUpon Add to TwitterTwitter

What's this? This Article Published online 2015, doi: 10.4269/ajtmh.14-0257 Am J Trop Med Hyg 2015 vol. 93 no. 1 145-152 Free via Open Access AbstractFree Full TextFree Full Text (PDF) This Article Published online 2015, doi: 10.4269/ajtmh.14-0257 Am J Trop Med Hyg 2015 vol. 93 no. 1 145-152 Free via Open Access AbstractFree Full TextFree Full Text (PDF) Current Issue 2016, 95 (1) Current Issue Alert me to new issues of Am J Trop Med Hyg FOR AUTHORS ABOUT THE JOURNAL EDITORIAL BOARD SUBJECT COLLECTIONS ADVERTISING REPRINTS AND PERMISSIONS RECOMMEND TO LIBRARY ABOUT ASTMH JOB LISTINGS RSS FEEDS RSS Feeds FEEDBACK HELP Society Logo HighWire Press Logo MostViewedCited FOUR MALARIA SUCCESS STORIES: HOW MALARIA BURDEN WAS SUCCESSFULLY REDUCED IN BRAZIL, ERITREA, INDIA, AND VIETNAM DIFFERENCES IN DENGUE SEVERITY IN INFANTS, CHILDREN, AND ADULTS IN A 3-YEAR HOSPITAL-BASED STUDY IN NICARAGUA Anticomplementary Activity of Horse IgG and F(ab')2 Antivenoms Glucose-6-Phosphate Dehydrogenase Deficiency and Antimalarial Drug Development A Review of Malaria Diagnostic Tools: Microscopy and Rapid Diagnostic Test (RDT) » View all Most Viewed articles Copyright © 2016 by the American Society of Tropical Medicine and Hygiene Print ISSN: 0002-9637

Table 1 Existing surveillance systems for malaria elimination Country System description Data capture Outputs Strengths Challenges

Cambodia MIS is a stand-alone system developed to assess malaria transmission and intervention coverage.25 Two additional pilot systems Passive Case Detection case notification MIS: Automatically generated report including tabular summaries, graphics and mapping to village level26 MIS: Covers all endemic areas Uncaptured private, migrant, military sectors D0AS Pf cases MIS: District level data reported monthly, including species, severe malaria, cases, deaths D0AS: Real-time SMS alert to Provincial Health Department and National Malaria Center. Day-28 follow-up reminder is sent to the same plus health center management Tracks severe malaria, deaths Most data aggregated monthly, challenge to get real-time data D3AS Day 3 positive malaria smears to identify resistance D0AS: Health staff send SMS for Pf cases from pilot areas D3AS: Real-time SMS when parasites remain by Day 3 Malaria incidence and intervention coverage to village level Inconsistent decision making and response based on available data Population covered: > 3M D3AS: Only includes Pf cases parasitemic after three days of treatment in areas of artemisinin-resistance Automatically generated monthly bulletin Does not capture time-to-case reporting, or intervention quality Pilot D0AS and D3AS Case follow-up challenges SMS and Internet-based notification systems No mapping to household or where case acquired Integrated with MIS

China Two integrated web-based systems: febrile illness reporting and focus investigation and intervention tracking. Data stored at the National Centers for Disease Control and Prevention.6,27 PCD case notification: Data entered within 24 hours. Data include date, facility, reporting person, patient info and diagnostic result with method and treatment SMS alerts Web-based system integrated with reportable diseases system Mobile technology not integrated Population covered: > 1.3B Monthly MoH report, tabular summary results, graphics and mapping Data fed into HMIS Limited baseline data “1-3-7 strategy” time tracking to case notification (one day), case investigation (three days), completed interventions (seven days) Very little missing data Does not capture new interventions or intervention quality Rapid case reporting No mapping to household or where case acquired Diagnosis is confirmed by microscopy and PCR “1-3-7 strategy” is easy to use and understand

Solomon Islands/Vanuatu SDSS.17,35 Data are stored in a relational database, using local, provincial and nationally based servers (three levels for backup). PCD case notification: Health facility calls provincial center within 48 hours. Real-time case reporting SDSS includes extensive baseline data28 Mobile technology not integrated Population covered: > 90 k, implemented in four island provinces Frontline and active case detection planning by household, follow-up list of households that did not receive intervention Rapid case reporting Inconsistent decision making and response Tabular output, spatial analysis, graphics, and mapping, including foci classification Automated GIS-based queries with high-resolution mapping Does not capture time-to-case reporting, or intervention quality Generates lists to support targeted action at the household level Human resource constraints Readily adaptable to other locations or systems No mapping to where case was acquired

Swaziland HMIS, IDNS for 15 reportable diseases, and MSDS for case investigation and interventions.16,29,36 PCD case notification: RDT or microscopy-confirmed malaria cases dictated through a toll-free hotline. Data entered on a central server, surveillance agent receives an SMS with date, facility, reporting person, patient info and case number to conduct case investigation and intervention. IDNS: Toll-free hotline resulting in SMS to surveillance agent Integrated with notifiable disease reporting system Relatively low reporting completeness to IDNS Population covered: 1.2M MSDS: Monthly tabular and graphic summary, mapping to household. Maps of cases investigated, locations of positive cases, IRS, ITNs, breeding sites, risk maps, households screened, or remaining. Web-based system using mobile technology Low case reporting from private sector facilities Free mobile reporting Does not capture time-to-case reporting or intervention quality Entire country covered No mapping to where case was acquired Simple, rapid case notification Temporal–spatial analysis of case distribution

Thailand Stand-alone, web-based system. Data storage is in a database at Mahidol University. GPS-enabled tablets for patient follow-up, data captured in same server. PCD case notification: Case data entered at malaria clinic level within 24 hours. Data include date, facility, reporting person, patient info, diagnostic result with method and foci classification.30 Pf case alerts to malaria clinic staffs' tablets within 24 hours Web-based system with mobile technology being integrated Hospital-based cases in a separate system Population covered: > 21M Tablet-based follow-up form for directly observed therapy and resistance monitoring Implemented in large regions, covering all areas of multi-drug resistance Challenges with migrant and cross-border follow-up Monthly MoH report, tabular summary, graphics, maps, with mapping to Pf case household and likely case location.31 Rapid case reporting More baseline data needed, such as intervention coverage and forest sleeping locations Captures DOT No time to case reporting or intervention quality Captures Pf resistance

Zambia DHIS2 is a web-based health information system. Data storage and mobile phones linked to the same database.32,33 PCD case notification: urban and rural health staff report weekly by mobile phone. Data include clinic visits, clinical cases, RDT-tested and positive cases, microscopy-tested and positive cases, ACT and RDT stock tracking. CHWs report cases monthly by mobile phone.33 Regular reports, with online access to data in real-time Open source free web-based system fully integrated with HMIS Case data not reported to DHIS2 in real-time Population covered: > 6M Graphs created and provided in real time to mobile phones or computers, summarizing case reporting and stock data, with summary data from all areas, reporting to the facility Tables, charts and maps shared with all users with online dashboard Does not capture time to case reporting or intervention quality Maps, graphs display village, clinic-level malaria incidence Mobile technology fully integrated Remains to be determined if DHIS2 can support full malaria elimination surveillance system to household level Timeliness and completeness of data reporting tracked

Zanzibar, Tanzania Integrated system combining Coconut Surveillance and MCN. MCN includes rapid reporting and analysis, outputs with geo-location of cases, through Coconut Surveillance. Cases reported to health staff via SMS. Coconut uses data to guide household oriented index case follow up. PCD case notification: MCN: Real-time case reporting via Coconut Surveillance, monthly MoH reports. Tabular summary results, graphics and mapping to the village level. MCN and Coconut are an integrated SMS-based system and tablet web-based system Cases from extensive private sector not captured Population covered: ∼1.3M Public health unit staff send an SMS for each positive case. Data include all-cause visits, malaria tested/positive cases and age. Coconut: Real-time tabular summary results, graphics, and detailed mapping to the household level. Real-time tracking of case follow-up and new interventions. Mobile technology fully integrated Limited capture of baseline data Coconut Surveillance notifies malaria officers of cases immediately via SMS. Patient and household follow-up with GPS enabled tablet. Rapid case reporting Does not currently capture intervention quality Real-time tabular output of key variables makes it easy for management to track progress real time No mapping to where case was acquired MEEDS data are used to calculate supply orders Denominator (population) data not captured with Coconut D0AS = Day 0 Alert System; D3AS = Day 3 Alert System; DOT = directly observed therapy; HMIS = Health Management Information System; GIS = geographic information systems: MCN = malaria case notification; IDNS = Immediate Disease Notification System; MIS = Malaria Information System; MoH = Ministry of Health; MSDS = Malaria Surveillance Database System; Pf = Plasmodium falciparum; PCR = polymerase chain reaction; SDSS = Integrated Spatial Decision Support System; SMS = short message service.