Examination of gaps in malaria data between the populations captured in household surveys and routine systems?

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Examination of gaps in malaria data between the populations captured in household surveys and routine systems
As countries move from high malaria transmission to pre-elimination there is less reliance on household surveys as the main source of data and more dependence on data from routine systems. However, understanding the gaps in coverage between the populations captured in household surveys and routine systems is essential. The following papers will explore how data collected from household surveys can complement routine data to fill knowledge gaps and guide programmatic decision making. Papers 1 and 2 provide insight into the improvement of denominators of routine data while papers 3 and 4 provide a methodological examination into the comparison of data sources.
1. Pregnant women attending ANC as part of malaria surveillance: who is missing?
Previous studies have shown a strong correlation between the malaria prevalence in pregnant women and children age 0-59 months. With the shifting burden of malaria, pregnant women attending antenatal care (ANC) have been proposed as a pragmatic sentinel population for malaria surveillance. Using Demographic and Health Survey (DHS) and Malaria Indicator Survey (MIS) data, this analysis will explore the household and individual malaria characteristics of pregnant women who did not attend ANC using household survey data. Covariates would include socio-demographic variables, malaria endemicity, and distance to health facility. Analysis would include a country-level descriptive analysis weighted for complex survey design and a multi-country weighted pooled analysis with approximately 40 countries in sub-Saharan Africa. The paper will provide insight into future denominators for pregnant women captured in routine malaria surveillance indicators.
2. Examination of children with severe illness among malaria positive children: an analysis of household survey data from 21 malaria-endemic countries in sub-Saharan Africa
Diagnosing a child with severe malaria or another illness is difficult since symptoms of severe malaria can be clinically indistinguishable from other illnesses. In areas of high malaria transmission, where asymptomatic parasitemia is common, malaria may be incidental to other severe illnesses. An accurate diagnosis of severe illness among malaria positive children is critical to the treatment of sick children. This analysis will examine data from 42 DHS and MIS surveys across 21 countries in sub-Saharan Africa (SSA) between 2011 and 2020. The outcome of interest is the presence of severe illness. The study includes a weighted descriptive country-level analysis and a multilevel mixed-effects logistic regression model to assess the determinants of severe illness. To date, the only robust estimates of severe illness cases include children who present to the formal healthcare system or data from epidemiological studies. It has been a challenge to use these data to model the fraction of severe illness cases across SSA because of varying age ranges of reporting, different diagnosis techniques, surveillance methods, and healthcare utilization. Household surveys test all children aged 6–59 months for malaria, and whereas some of the children who were showing signs of severe illness would have eventually accessed the formal healthcare system, some of these children would have died, recovered at home, or received care outside of the formal healthcare system. This paper will provide insight into the prevalence of severe illness which can help provide external consistency to routine data.
3. Exploration of MIS sampling strategies for programmatic decision making
Malaria Indicator Surveys (MIS) are nationally representative, population-based household surveys designed to produce a wide range of data for in-depth assessment of the malaria situation in countries. MIS surveys typically follow a two-stage stratified sampling design, in which census enumeration areas (EAs) are selected in the first stage as primary sampling units (PSUs) with probability proportional to size. Although reducing malaria prevalence indicates important progress toward the end goal of malaria elimination, progress in many countries over the past decade has made designing MIS surveys more complex, especially in countries with a mix of low and moderate-to-high malaria prevalence areas. To overcome these challenges, this methodological paper will explore four additional sampling strategies that may be appropriate for countries with varying malaria endemicity. The paper will outline the target population, survey domains, sample stratification, sample size and sample allocation. As well as outline the strengths, weaknesses, and the impact each sampling strategy will have on trends. The paper will also discuss how the sampling domains could be comparable to routine and programmatic data to provide insights into data quality, trends, and associations.
4. Using routine data to inform sample size determination of DHS and MIS surveys
For a DHS or MIS survey an appropriate sample size for a survey domain is the minimum number of persons (e.g., women age 15-49, currently married women 15-49, children under age five) that achieves the desired survey precision for core indicators at the domain level. Generally, this is the indicator for which the base population is the smallest “sub-target population,” in terms of its proportion of the total population. The desired precision must of course be taken into account. By basing the sample size on such an estimate, each of the other key estimates is therefore measured with the same or better reliability. Historically DHS and MIS surveys have used past survey estimates as the estimated proportion needed for sample size determination. However, with the increase in robust health facility data, this data hasn’t been thoroughly explored for use in sample size determination. The proposed methodological report would outline various sample size determination scenarios taking into account 1) past DHS or MIS estimates only 2) routine data only and 3) a mix of DHS/MIS estimates and routine data to determine sample size.
Effective start/end date19/04/23 → …

IWETO expertise domain

  • B680-public-health