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---Surveillance Considerations for Malaria Elimination

Sunday, 7th of October 2012

• SURVEILLANCE CONSIDERATIONS FOR MALARIA ELIMINATION

 

Malaria Journal 2012, 11:304 doi:10.1186/1475-2875-11-304

Victoria C Barclay (vcb11@psu.edu)

Rachel A Smith (ras57@psu.edu)

Jill L Findeis (findeisj@missouri.edu)

 

Article type Commentary

Submission date 9 July 2012

Acceptance date 28 August 2012

Publication date 31 August 2012

 

Article URL http://www.malariajournal.com/content/11/1/304

 

This peer-reviewed article can be downloaded, printed and distributed freely for any purposes (see copyright notice below).

 

Surveillance considerations for malaria elimination

Victoria C Barclay1*

* Corresponding author

Email: vcb11@psu.edu

Rachel A Smith2

Email: ras57@psu.edu

Jill L Findeis3

Email: findeisj@missouri

1 Department of Biology and Center for Infectious Disease Dynamics, The

Pennsylvania State University, University Park, PA, USA

2 Department of Communication Arts & Sciences, Human Development &

Family Studies, and Center for Infectious Disease Dynamics, The Pennsylvania

State University, University Park, PA, USA

3 Division of Applied Social Sciences (DASS), Agricultural & Applied

Economics, CAFNR, University of Missouri, Columbia, MO, USA

 

Abstract

 

Constant malaria monitoring and surveillance systems have been highlighted as critical for

malaria elimination. The absence of robust monitoring and surveillance systems able to

respond to outbreaks in a timely manner undeniably contributed to the failure of the last

global attempt to eradicate malaria. Today, technological advances could allow for rapid

detection of focal outbreaks and improved deployment of diagnostic and treatment supplies to

areas needing support. However, optimizing diffusion activities (e.g., distributing vector

controls and medicines, as well as deploying behaviour change campaigns) requires networks

of diverse scholars to monitor, learn, and evaluate data and multiple organizations to

coordinate their intervention activities. Surveillance systems that can gather, store and

process information, from communities to national levels, in a centralized, widely accessible

system will allow tailoring of surveillance and intervention efforts. Different systems and,

thus reactions, will be effective in different endemic, geographical or socio-cultural contexts.

Investing in carefully designed monitoring technologies, built for a multiple-acter, dynamic

system, will help to improve malaria elimination efforts by improving the coordination,

timing, coverage, and deployment of malaria technologies.

 

Background

 

For those countries close to malaria elimination, real-time, on-going monitoring systems are

important for at least four reasons. They allow for (1) rapid detection of existing, new or reintroduced (e g, across country and regional borders) infections [1,2]; (2) identification of

periods of low transmission (e g, from symptomatic and asymptomatic infections) when the

parasite population could be most amenable to elimination [3,4]; (3) understanding trends in

malaria incidence and prevalence (shifts in age groups, increasing parasite heterogeneity,

changes in seasonality) and, (4) detection of resistance. As malaria becomes less prevalent in

a country, intervention efforts may weaken, which in turn may create more resistant parasite

and mosquito populations [5-7]. In addition, surveillance itself can be an intervention that

reduces transmission by identifying and rapidly treating infections from the infectious

reservoir [8]. Monitoring systems that can rapidly detect and help excise low-level malaria

transmission, identify optimal intervention windows and flag emerging resistance are all

essential for disease elimination.

 

Discussion

 

A real-time, on-going, integrated data reservoir capturing multiple scales of disease dynamics

- from cells to society - is a plausible, achievable enterprise for malaria elimination in the

next decade. The finest, comprehensive, malaria-monitoring systems include portable and

sensitive diagnostic tests, real-time data about patients showing drug resistant parasites and

vectors showing insecticide resistance, transmission intensity markers (e g, sampling, surveys

and biomarkers), climatic data (e g, rainfall as early warning systems), geo-spatial and

demographic information for villages known to be particularly at risk, and continuous,

frequent (e g, at least monthly) local-level malaria incidence counts [8-13]. Together, these

data represent biological, clinical, social scientific and logistical information that inform

different aspects of disease detection and intervention planning aiming to sustainably impact

malaria-burdened populations.

 

However, monitoring is not enough: learning and evaluation are also needed, which is

complicated in a multiple-organization (National Malaria Control Programmes (NMCPs),

government ministries, international health agencies, private industry, non-governmental

organizations, funding agencies, and local health organizations, etc.), multiple-acter

(scientists, politicians, and interventionists) effort [14]. To optimize malaria intervention

efforts, both organizations and acters need (a) timely, robust information about disease

epidemiology and dissemination activities (e g, bed nets, spraying, testing supplies, drugs,

and vaccine trials); (b) an ability to access data and each other to coordinate activities for

integrated vector-control and public health responses; and, (c) conditions (both online and

offline) that facilitate data sharing and optimal, group-based decision-making [15]. Research

into designing data reservoir platforms that facilitate monitoring, learning and evaluation

(MLE) among multiple acters is needed to optimize coordinated, integrated disease detection

and intervention efforts.

 

However, the use and maintenance of MLE systems requires a level of infrastructure

(communication networks) absent in some malaria-burdened countries. For example, in some

of the most remote parts of Africa, there are few landline telephones, computers with fast

internet-access, or roads in good condition, for the rapid transfer of disease information

[16,17]. Mobile phones, diffusing widely in African countries, may offer great possibilities

for MLE efforts. Mobile phones can allow researchers to collect and share data faster and

easier in countries lacking other infrastructures. Data can be collected unobtrusively:

monitoring people’s movement back and forth, from low-transmission Zanzibar to malaria burdened Tanzania [2]. Short message service (SMS) technology has also been utilized to

strengthen the routine reporting of anti-malaria drug supplies at health facilities [18,19].

Notably, real-time, comprehensive monitoring does not inherently lead to access or to

collaboration and coordination among different organizations and acters. For enriched MLE

technologies, such as mobile phones, to improve coordination, optimization, and deployment

of intervention resources [2,20], there should be investment in real-time, updating data

reservoir platforms that are compatible across hardware systems and national boundaries,

readily accessible for scientists and interventionists, and built to facilitate transdisciplinary

learning and evaluation. If each entity interested in monitoring gathers, stores, and shares

their data differently, by the time integration takes place, the data’s utility may have expired.

Likewise, multiple organizations attempting to deploy interventions may duplicate services in

some areas while missing others needing assistance, particularly when NCMPs are weakened

due to other factors such as post-emergency or during war-time. Further, compatible data

management tools should be developed regardless of the specific factor being surveyed (e g,

drug and vector resistance, number of infected cases, climate data, and so forth) because

future research efforts may benefit from integrating these different factors to best manage

malaria efforts. Last, designing platforms and management tools informed by social science,

computer science, biology and epidemiology are needed to help facilitate trans-disciplinary

learning, and system-wide collaboration and coordination.

 

Platforms are already being developed by those who recognize the benefits of integrating

global positioning systems (GPS), geographic information systems (GIS) and mobile

computing technology into modern reporting applications [11]. Platforms that can collect

information on the spatial distribution of malaria and other variables such as weather and

climate [21,22], land use and demography [23] and vector breeding sites [24,25] will help to

identify malaria risks and distribution across a variety of scales (i.e. globally, nationally and

locally). The development of those platforms for mapping malaria risk is encouraging, and

could be expanded to include the collection of other disease indicators.

 

First, MLE systems should be designed with local communities in mind, such as allowing for

data entry interfaces and reports to be personalized by the user. Different systems may be

more effective in one setting over another. Existing models suggest that in communities

where disease incidence is low, a simple ‘eyes and ears’ approach for early treatment -

seeking could be equally effective as more technical methods of disease detection [14,26].

Community vigilance is not sufficient to achieve elimination [3], however, because

community vigilance at the local level may not inform national-level policies or activities and

because the time delay between the recognition of disease symptoms (e.g. fever) and the

reporting to a health clinic, extends the window for onward transmission. As technology

continues to diffuse across African countries, MLE systems could complement community

approaches by increasing the speed by which regional and national surveillance teams are

alerted to local events and prepare intervention services for local demands. Analogous to

improvements in the marketing of agricultural products in African countries due to rapid

access to information on market prices [27], MLE systems for disease surveillance should

work to complement, not replace, community vigilance.

 

As in the development of agricultural innovations and systems [28], end-users and

stakeholders must also be involved in the design process. Different countries have

implemented their own malaria surveillance systems with varying degrees of success

[9,11,29-31]. For new MLE-collaboration platforms to succeed, targeting and tailoring of

MLE technologies, standards, and communication to users is needed to improve the

comprehension, diffusion and adherence of new surveillance tools [32].

 

In essence, ensuring access to data within and across different regions and countries is as

important as developing and diffusing technologies to monitor malaria dynamics and

intervention activities. Data collected and stored in an accessible manner can quicken the

speed at which scientists can evaluate an intervention’s impact, forecast possible changes to

improve future efforts, and adjust to incoming results. Speed plays an important role in

malaria management, where outbreaks occur across numerous ecosystems and throughout the

seasonal variation [33]. With current, dynamic, accessible information and trained personnel

who can to learn from it and act on it, resources can be allocated more efficiently and adjust

programmes and policies more appropriately.

 

Conclusions

 

In summary, malaria MLE systems from community to national levels, and informed

reactions (interventions), can provide valuable insights needed to understand, forecast, and

evaluate complex, multiple-organization-and-acter efforts, such as eliminating malaria. In

order to meet malaria elimination objectives, monitoring systems must be able to respond

rapidly to the heterogeneity in malaria epidemiology. Many malaria-burdened countries are

experiencing advances in technology and research, which may be harnessed to optimize the

feasibility, efficiency and cost-effectiveness of readily accessible, shared data collection,

evaluation, and collaboration systems. This research should strive to improve MLE systems

in even the most remote locations. Investment in integrated MLE systems alone will not

eradicate malaria, but it will bring us closer.

 

Abbreviations

 

MLE, Monitoring learning and evaluation; NMCPs, National malaria control programmes;

GPS, Global positioning systems; GIS, Geographic information systems; SMS, Short

message service

 

Authors’ contributions

 

VB reviewed the literature and drafted the paper. RAS reviewed the literature and edited the

manuscript. JFL edited the manuscript. All three authors conceived the idea equally. All

authors read and approved the final manuscript.

 

Competing interests

The authors declare that they have no competing interests.

 

Acknowledgements

Pilot funding supported this project provided by the Clinical and Translational Science

Institute (CTSI) and the Social Science Research Institute (SSRI) at The Pennsylvania State

University.

 

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