Improving estimates of insecticide-treated mosquito net coverage from household surveys: using geographic coordinates to account for endemicity.

BACKGROUND: Coverage estimates of insecticide-treated nets (ITNs) are often calculated at the national level, but are intended to be a proxy for coverage among the population at risk of malaria. The analysis uses data for surveyed households, linking survey enumeration areas (clusters) with levels of malaria endemicity and adjusting coverage estimates based on the population at risk. This analysis proposes an approach that is not dependent on being able to identify malaria risk in a location during the survey design (since survey samples are typically selected on the basis of census sampling frames that do not include information on malaria zones), but rather being able to assign risk zones after a survey has already been completed. METHODS: The analysis uses data from 20 recent nationally representative Demographic and Health Survey (DHS), Malaria Indicator Surveys (MIS), an AIDS Indicator Survey (AIS), and an Anemia and Malaria Prevalence Survey (AMP). The malaria endemicity classification was assigned from the Malaria Atlas Project (MAP) 2010 interpolated data layers, using the Geographic Positioning System (GPS) location of the survey clusters. National ITN coverage estimates were compared with coverage estimates in intermediate/high endemicity zones (i.e., the population at risk of malaria) to determine whether the difference between estimates was statistically different from zero (p-value <0.5). RESULTS: Endemicity varies substantially in eight of the 20 studied countries. In these countries with heterogeneous transmission of malaria, stratification of households by endemicity zones shows that ITN coverage in intermediate/high endemicity zones is significantly higher than ITN coverage at the national level (Burundi, Kenya, Namibia, Rwanda, Tanzania, Senegal, Zambia, and Zimbabwe.). For example in Zimbabwe, the national ownership of ITNs is 28%, but ownership in the intermediate/high endemicity zone is 46%. CONCLUSION: Incorporating this study's basic and easily reproducible approach into estimates of ITN coverage is applicable and even preferable in countries with areas at no/low risk of malaria and will help ensure that the highest-quality data are available to inform programmatic decisions in countries affected by malaria. The extension of this type of analysis to other malaria interventions can provide further valuable information to support evidence-based decision-making.

Providing open access data online to advance malaria research and control.

BACKGROUND: To advance research on malaria, the outputs from existing studies and the data that fed into them need to be made freely available. This will ensure new studies can build on the work that has gone before. These data and results also need to be made available to groups who are developing public health policies based on up-to-date evidence. The Malaria Atlas Project (MAP) has collated and geopositioned over 50,000 parasite prevalence and vector occurrence survey records contributed by over 3,000 sources including research groups, government agencies and non-governmental organizations worldwide. This paper describes the results of a project set up to release data gathered, used and generated by MAP. METHODS: Requests for permission to release data online were sent to 236 groups who had contributed unpublished prevalence (parasite rate) surveys. An online explorer tool was developed so that users can visualize the spatial distribution of the vector and parasite survey data before downloading it. In addition, a consultation group was convened to provide advice on the mode and format of release for data generated by MAP's modelling work. New software was developed to produce a suite of publication-quality map images for download from the internet for use in external publications. CONCLUSION: More than 40,000 survey records can now be visualized on a set of dynamic maps and downloaded from the MAP website on a free and unrestricted basis. As new data are added and new permissions to release existing data come in, the volume of data available for download will increase. The modelled data output from MAP's own analyses are also available online in a range of formats, including image files and GIS surface data, for use in advocacy, education, further research and to help parameterize or validate other mathematical models.

Geographically linking population and facility surveys: methodological considerations.

BACKGROUND: The relationship between health services and population outcomes is an important area of public health research that requires bringing together data on outcomes and the relevant service environment. Linking independent, existing datasets geographically is potentially an efficient approach; however, it raises a number of methodological issues which have not been extensively explored. This sensitivity analysis explores the potential misclassification error introduced when a sample rather than a census of health facilities is used and when household survey clusters are geographically displaced for confidentiality. METHODS: Using the 2007 Rwanda Service Provision Assessment (RSPA) of all public health facilities and the 2007-2008 Rwanda Interim Demographic and Health Survey (RIDHS), five health facility samples and five household cluster displacements were created to simulate typical SPA samples and household cluster datasets. Facility datasets were matched with cluster datasets to create 36 paired datasets. Four geographic techniques were employed to link clusters with facilities in each paired dataset. The links between clusters and facilities were operationalized by creating health service variables from the RSPA and attaching them to linked RIDHS clusters. Comparisons between the original facility census and undisplaced clusters dataset with the multiple samples and displaced clusters datasets enabled measurement of error due to sampling and displacement. RESULTS: Facility sampling produced larger misclassification errors than cluster displacement, underestimating access to services. Distance to the nearest facility was misclassified for over 50% of the clusters when directly linked, while linking to all facilities within an administrative boundary produced the lowest misclassification error. Measuring relative service environment produced equally poor results with over half of the clusters assigned to the incorrect quintile when linked with a sample of facilities and more than one-third misclassified due to displacement. CONCLUSIONS: At low levels of geographic disaggregation, linking independent facility samples and household clusters is not recommended. Linking facility census data with population data at the cluster level is possible, but misclassification errors associated with geographic displacement of clusters will bias estimates of relationships between service environment and health outcomes. The potential need to link facility and population-based data requires consideration when designing a facility survey.

Mapping populations at risk: improving spatial demographic data for infectious disease modeling and metric derivation.

The use of Global Positioning Systems (GPS) and Geographical Information Systems (GIS) in disease surveys and reporting is becoming increasingly routine, enabling a better understanding of spatial epidemiology and the improvement of surveillance and control strategies. In turn, the greater availability of spatially referenced epidemiological data is driving the rapid expansion of disease mapping and spatial modeling methods, which are becoming increasingly detailed and sophisticated, with rigorous handling of uncertainties. This expansion has, however, not been matched by advancements in the development of spatial datasets of human population distribution that accompany disease maps or spatial models.Where risks are heterogeneous across population groups or space or dependent on transmission between individuals, spatial data on human population distributions and demographic structures are required to estimate infectious disease risks, burdens, and dynamics. The disease impact in terms of morbidity, mortality, and speed of spread varies substantially with demographic profiles, so that identifying the most exposed or affected populations becomes a key aspect of planning and targeting interventions. Subnational breakdowns of population counts by age and sex are routinely collected during national censuses and maintained in finer detail within microcensus data. Moreover, demographic and health surveys continue to collect representative and contemporary samples from clusters of communities in low-income countries where census data may be less detailed and not collected regularly. Together, these freely available datasets form a rich resource for quantifying and understanding the spatial variations in the sizes and distributions of those most at risk of disease in low income regions, yet at present, they remain unconnected data scattered across national statistical offices and websites.In this paper we discuss the deficiencies of existing spatial population datasets and their limitations on epidemiological analyses. We review sources of detailed, contemporary, freely available and relevant spatial demographic data focusing on low income regions where such data are often sparse and highlight the value of incorporating these through a set of examples of their application in disease studies. Moreover, the importance of acknowledging, measuring, and accounting for uncertainty in spatial demographic datasets is outlined. Finally, a strategy for building an open-access database of spatial demographic data that is tailored to epidemiological applications is put forward.

Impact of implementation of free high-quality health care on health facility attendance by sick children in rural western Kenya.

OBJECTIVES: To explore whether implementation of free high-quality care as part of research programmes resulted in greater health facility attendance by sick children. METHODS: As part of the Intermittent Preventive Treatment for Malaria in Infants (IPTi), begun in 2004, and population-based infectious disease surveillance (PBIDS), begun in 2005 in Asembo, rural western Kenya, free high-quality care was offered to infants and persons of all ages, respectively, at one Asembo facility, Lwak Hospital. We compared rates of sick-child visits by children <10 years to all seven Asembo clinics before and after implementation of free high-quality care in 10 intervention villages closest to Lwak Hospital and 8 nearby comparison villages not participating in the studies. Incidence rates and rate ratios for sick-child visits were compared between intervention and comparison villages by time period using Poisson regression. RESULTS: After IPTi began, the rate of sick-child visits for infants, the study's target group, in intervention villages increased by 191% (95% CI 75-384) more than in comparison villages, but did not increase significantly more in older children. After PBIDS began, the rate of sick-child visits in intervention villages increased by 267% (95% CI 76-661) more than that in comparison villages for all children <10 years. The greatest increases in visit rates in intervention villages occurred 3-6 months after the intervention started. Visits for cough showed greater increases than visits for fever or diarrhoea. CONCLUSIONS: Implementation of free high-quality care increased healthcare use by sick children. Cost and quality of care are potentially modifiable barriers to improving access to care in rural Africa.

Effects of mutual health organizations on use of priority health-care services in urban and rural Mali: a case-control study.

OBJECTIVE: To examine the effects of a community-based mutual health organization (MHO) on utilization of priority health services, financial protection of its members and inclusion of the poor and other target groups. METHODS: Four MHOs were established in two districts in Mali. A case-control study was carried out in which household survey data were collected from 817 MHO member households, 787 non-member households in MHO catchment areas, and 676 control households in areas without MHOs. We compiled MHO register data by household for a 22-month period. Outcome measures included utilization of priority services, health expenditures and out-of-pocket payments. Independent variables included individual, household and community demographic, socioeconomic and access characteristics, as determined through a household survey in 2004. FINDINGS: MHO members who were up to date on premium payments (controlling for education, distance to the nearest health facility and other factors) were 1.7 times more likely to get treated for fevers in modern facilities; three times more likely to take children with diarrhoea to a health facility and/or treat them with oral rehydration salts at home; twice as likely to make four or more prenatal visits; and twice as likely, if pregnant or younger than 5 years, to sleep under an insecticide-treated net (P < 0.10 or better in all cases). However, distance was also a significant negative predictor for the utilization of many services, particularly assisted deliveries. Household and individual enrolment in an MHO were not significantly associated with socioeconomic status (with the exception of the highest quintile), and MHOs seemed to provide some financial protection for their members. CONCLUSIONS: MHOs are one mechanism that countries strengthening the supply of primary care can use to increase financial access to - and equity in - priority health services.

Influence of Demographic and Health Survey Point Displacements on Point-in-Polygon Analyses.

We use Demographic and Health Survey (DHS) data to evaluate the impact of random spatial displacements on analyses that involve assigning covariate values from ancillary areal and point feature data. We introduce a method to determine the maximum probability covariate (MPC), and compare this to the naive covariate (NC) selection method with respect to obtaining the true covariate of interest. The MPC selection method outperforms the NC selection method by increasing the probability that the correct covariate is chosen. Proposed guidelines also address how characteristics of ancillary areal and point features contribute to uncertainty in covariate assignment.

Influence of Demographic and Health Survey Point Displacements on Distance-Based Analyses.

We evaluate the impacts of random spatial displacements on analyses that involve distance measures from displaced Demographic and Health Survey (DHS) clusters to nearest ancillary point or line features, such as health resources or roads. We use simulation and case studies to address the effects of this introduced error, and propose use of regression calibration (RC) to reduce its impact. Results suggest that RC outperforms analyses involving naive distance-based covariate assignments by reducing the bias and MSE of the main estimator in most settings. Proposed guidelines also address the effect of the spatial density of destination features on observed bias.

Influence of Demographic and Health Survey Point Displacements on Raster-Based Analyses.

With this paper we explore the sensitivity of study results to spatial displacements associated with Demographic and Health Survey (DHS) data in research that integrates ancillary raster data. Through simulation studies, we found that the impact of DHS point displacements on raster-based analyses can be moderated through the generation of covariates representing average values from neighborhood buffers. Additionally, raster surface characteristics (i.e., spatial smoothness) were found to affect the extent of bias introduced through point displacements. Although simple point extraction produced unbiased estimates in analyses involving smooth continuous surfaces, it is not recommended in analyses that involve categorical raster surfaces.

Correction: Equality in Maternal and Newborn Health: Modelling Geographic Disparities in Utilisation of Care in Five East African Countries.

[This corrects the article DOI: 10.1371/journal.pone.0162006.].

Equality in Maternal and Newborn Health: Modelling Geographic Disparities in Utilisation of Care in Five East African Countries.

BACKGROUND: Geographic accessibility to health facilities represents a fundamental barrier to utilisation of maternal and newborn health (MNH) services, driving historically hidden spatial pockets of localized inequalities. Here, we examine utilisation of MNH care as an emergent property of accessibility, highlighting high-resolution spatial heterogeneity and sub-national inequalities in receiving care before, during, and after delivery throughout five East African countries. METHODS: We calculated a geographic inaccessibility score to the nearest health facility at 300 x 300 m using a dataset of 9,314 facilities throughout Burundi, Kenya, Rwanda, Tanzania and Uganda. Using Demographic and Health Surveys data, we utilised hierarchical mixed effects logistic regression to examine the odds of: 1) skilled birth attendance, 2) receiving 4+ antenatal care visits at time of delivery, and 3) receiving a postnatal health check-up within 48 hours of delivery. We applied model results onto the accessibility surface to visualise the probabilities of obtaining MNH care at both high-resolution and sub-national levels after adjusting for live births in 2015. RESULTS: Across all outcomes, decreasing wealth and education levels were associated with lower odds of obtaining MNH care. Increasing geographic inaccessibility scores were associated with the strongest effect in lowering odds of obtaining care observed across outcomes, with the widest disparities observed among skilled birth attendance. Specifically, for each increase in the inaccessibility score to the nearest health facility, the odds of having skilled birth attendance at delivery was reduced by over 75% (0.24; CI: 0.19-0.3), while the odds of receiving antenatal care decreased by nearly 25% (0.74; CI: 0.61-0.89) and 40% for obtaining postnatal care (0.58; CI: 0.45-0.75). CONCLUSIONS: Overall, these results suggest decreasing accessibility to the nearest health facility significantly deterred utilisation of all maternal health care services. These results demonstrate how spatial approaches can inform policy efforts and promote evidence-based decision-making, and are particularly pertinent as the world shifts into the Sustainable Goals Development era, where sub-national applications will become increasingly useful in identifying and reducing persistent inequalities.