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Background Guyana is one of four countries in the Latin American Region where lymphatic filariasis (LF) remains endemic. In preparation for the introduction of a new triple drug therapy regimen (ivermectin, diethylcarbamazine, and albendazole (IDA)) in 2019, an acceptability study was embedded within sentinel site mapping in four regions to assess mass drug administration (MDA) coverage and compliance, acceptability, and perceptions about treatment and disease. The results from this survey would inform the rollout of IDA in Guyana in 2019. Methods Data collection for the study occurred in August 2019, using a validated questionnaire administered by trained enumerators. Across all regions, a total of 1,248 participants were sampled by the Filarial Mapping team. Four-hundred and fifty-one participants aged over 18 years were randomly selected for participation in an expanded acceptability questionnaire. All data were captured in Secure Data Kit (SDK). Results Acceptability was measured using a mean acceptability score. Unadjusted mean scores ranged from 24.6 to 29.3, with 22.5 as the threshold of acceptability. Regional variation occurred across many indicators of interest: self-rated understanding about LF, mechanisms of LF transmission, LF drug safety and history of treatment during MDA. Region IV (Georgetown) recorded higher knowledge about LF, but lower compliance and acceptability. Number of pills was not perceived as a concern. Conclusion Acceptability of MDA was good across all four regions under study. Results from this study set a baseline level for key indicators and acceptability, from which the acceptability of IDA can be measured. Regional variations across indicators suggest that localized approaches should be considered for social mobilization and MDA delivery to capture these contextual differences., Author summary Guyana is one of four countries in the Latin American Region where lymphatic filariasis (LF) remains endemic in the community. In preparation for a new treatment regimen using three drugs instead of two, the research team assessed how community members in four regions of Guyana accepted the current two-drug treatment offered for LF during mass drug administration. Results from 390 participants showed that people generally accepted the treatment; however regional variation was high. Recommendations were made to the government program to inform the use of the new three-drug treatment regimen, including the need for localized approaches.

As trachoma programs move towards eliminating trachoma as a public health problem, the number of surveys necessary to evaluate the status of trachomatous trichiasis (TT) increases. Currently, the World Health Organization endorses a district-level population-based prevalence survey for trachoma that involves a two-stage cluster design. We explored the validity of implementing this survey design in larger geographic areas to gain cost efficiencies. We evaluated the change in precision due to combining geographically contiguous and homogenous districts into single evaluation units (EUs) and modulating the sample size by running simulations on existing datasets. Preliminary findings from two opportunities in Tanzania show variability in the appropriateness in conducting this survey across larger geographies. These preliminary findings stress the importance of determining what is meant by homogeneity in terms of TT before combining multiple districts into a single EU.

Background Malawi has halved the neonatal mortality rate between 1990–2018, however, is not on track to achieve the Sustainable Development Goal 12 per 1,000 live births. Despite a high facility birth rate (91%), mother-newborn dyads may not remain in facilities long enough to receive recommended care and quality of care improvements are needed to reach global targets. Physical access and distance to health facilities remain barriers to quality postnatal care. Methods Using data We used individual data from the 2015–16 Malawi Demographic and Health Survey and facility data from the 2013–14 Malawi Service Provision Assessment, linking households to all health facilities within specified distances and travel times. We calculated service readiness scores for facilities to measure their capacity to provide birth/newborn care services. We fitted multi-level regression models to evaluate the association between the service readiness and appropriate newborn care (receiving at least five of six interventions). Results Households with recent births (n = 6010) linked to a median of two birth facilities within 5–10 km and one facility within a two-hour walk. The maximum service environment scores for linked facilities median was 77.5 for facilities within 5–10 km and 75.5 for facilities within a two-hour walk. While linking to one or more facilities within 5-10km or a two-hour walk was not associated with appropriate newborn care, higher levels of service readiness in nearby facilities was associated with an increased risk of appropriate newborn care. Conclusions Women’s choice of nearby facilities and quality facilities is limited. High quality newborn care is sub-optimal despite high coverage of facility birth and some newborn care interventions. While we did not find proximity to more facilities was associated with increased risk of appropriate care, high levels of service readiness was, showing facility birth and improved access to well-prepared facilities are important for improving newborn care.

Understanding the ways that people live given certain environmental conditions is of central concern to researchers in health, development, population, climate change, and other related fields (see Grace et al. 2014; Balk et al. 2005; de Sherbinin 2011). One major source of data on health and development is the USAID‐funded Demographic and Health Surveys (DHS) program. DHS is a major source of population and health data for the poorest countries in the world and provides high‐quality and detailed data on individual health outcomes—particularly outcomes related to maternal and child health. The primary sampling unit in the DHS are villages or village “clusters.” Cluster size can vary but contains a number of households within a geographic area who participated in the survey. Since many of the data included in DHS are personal and potentially sensitive, the DHS maintains confidentiality of the respondents by shifting the spatial coordinates of the cluster in the published data (Burgert et al. 2013). The spatial coordinates for rural locations are displaced by 0–5 km in any direction. Additionally, a small fraction of coordinates, 1 percent, are randomly shifted up to 10 km. For urban locations, the displacement is up to 2 km only. DHS recommends that researchers average any environmental data over a 5–10 km buffer around each DHS rural cluster with the specific community falling somewhere within the disc around each point (Perez‐Heydrich et al. 2016). This approach to maintaining confidentiality while collecting survey information has been adopted by other international organizations as well (e.g., World Bank's Living Standards Measurement Study). Building on the rapid growth of literature around activity space, the geographic theory of close things being more alike (Tobler's First Law), as well as the understanding that people interact disproportionately with the landscape immediately surrounding a settlement, we propose an alternative method for evaluating environmental and contextual variables (Tobler 1970; Miller, 2004). Instead of calculating a 5–10 km buffer around each published point, we propose that the user selects a settlement near the DHS’ published cluster location and measures the environmental conditions around the settlement using a buffer much smaller than 10 km. We assume that the “true” context is a small, precise buffer around the correct settlement. We hypothesize that a small, precise buffer around an incorrect settlement is a better measure of truth than is an overly large buffer around the published point. Settlements can be identified through interpreting remotely sensed imagery. Corresponding features—for example, types of land‐use strategies or adjacency to reservoirs for irrigation—can be more easily identified and evaluated when using a much more precise buffer. While the settlement that is being used to provide this contextual information is likely not the original DHS cluster, it is a neighbor of the cluster and we assume that neighboring settlements are more similar to each other than to the broader environment in which they are situated. We theorize that this approach will introduce less measurement error than the larger 10 km buffer. To test this theory, we select three countries that are topographically diverse and that represent unique regions of the world—Burkina Faso, Kenya, and Tajikistan. As with most of the poorest countries in the world, these countries are heavily dependent on the landscape to produce food and earn money. However, each of these countries is quite distinct from the others in terms of environmental characteristics (rainfall and topography) and cultural characteristics (the types of crops produced as well as the farming strategies used to produce the crops). We select these countries to develop a thorough understanding of how our methodology will function under different settings. We evaluate a remotely‐sensed estimate of cultivated area and vegetation features of the DHS clusters using the 5–10 km buffer approach and our proposed neighboring settlement approach. We compare these to the true values through the use of the actual, confidential, locations of settlements in the DHS sample.

Achieving elimination of lymphatic filariasis (LF) as a public health problem requires a minimum of five effective rounds of mass drug administration (MDA) and demonstrating low prevalence in subsequent assessments. The first assessments recommended by the World Health Organization (WHO) are sentinel and spot-check sites—referred to as pre-transmission assessment surveys (pre-TAS)—in each implementation unit after MDA. If pre-TAS shows that prevalence in each site has been lowered to less than 1% microfilaremia or less than 2% antigenemia, the implementation unit conducts a TAS to determine whether MDA can be stopped. Failure to pass pre-TAS means that further rounds of MDA are required. This study aims to understand factors influencing pre-TAS results using existing programmatic data from 554 implementation units, of which 74 (13%) failed, in 13 countries. Secondary data analysis was completed using existing data from Bangladesh, Benin, Burkina Faso, Cameroon, Ghana, Haiti, Indonesia, Mali, Nepal, Niger, Sierra Leone, Tanzania, and Uganda. Additional covariate data were obtained from spatial raster data sets. Bivariate analysis and multilinear regression were performed to establish potential relationships between variables and the pre-TAS result. Higher baseline prevalence and lower elevation were significant in the regression model. Variables statistically significantly associated with failure (p-value ≤0.05) in the bivariate analyses included baseline prevalence at or above 5% or 10%, use of Filariasis Test Strips (FTS), primary vector of Culex, treatment with diethylcarbamazine-albendazole, higher elevation, higher population density, higher enhanced vegetation index (EVI), higher annual rainfall, and 6 or more rounds of MDA. This paper reports for the first time factors associated with pre-TAS results from a multi-country analysis. This information can help countries more effectively forecast program activities, such as the potential need for more rounds of MDA, and prioritize resources to ensure adequate coverage of all persons in areas at highest risk of failing pre-TAS., Author summary Achieving elimination of lymphatic filariasis (LF) as a public health problem requires a minimum of five rounds of mass drug administration (MDA) and being able to demonstrate low prevalence in several subsequent assessments. LF elimination programs implement sentinel and spot-check site assessments, called pre-TAS, to determine whether districts are eligible to implement more rigorous population-based surveys to determine whether MDA can be stopped or if further rounds are required. Reasons for failing pre-TAS are not well understood and have not previously been examined with data compiled from multiple countries. For this analysis, we analyzed data from routine USAID and WHO reports from Bangladesh, Benin, Burkina Faso, Cameroon, Ghana, Haiti, Indonesia, Mali, Nepal, Niger, Sierra Leone, Tanzania, and Uganda. In a model that included multiple variables, high baseline prevalence and lower elevation were significant. In models comparing only one variable to the outcome, the following were statistically significantly associated with failure: higher baseline prevalence at or above 5% or 10%, use of the FTS, primary vector of Culex, treatment with diethylcarbamazine-albendazole, lower elevation, higher population density, higher Enhanced Vegetation Index, higher annual rainfall, and six or more rounds of mass drug administration. These results can help national programs plan MDA more effectively, e.g., by focusing resources on areas with higher baseline prevalence and/or lower elevation.

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.

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

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. Conclusion 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.

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.

Das von uns angewandte Verfahren der Gaumennaht-Erweiterungsplatte, d.h. die Befestigung mit der Klebetechnik, hat sich klinisch bewahrt. Im Vergleich zu anderen therapeutischen Moglichkeiten zur Durchfuhrung einer forcierten Oberkiefer-Dehnung ist der technische Aufwand gering, die Verankerung der Platte eine gute, der Zeitaufwand am Patienten gering und das Vorgehen vom parodontologischen Aspekt gewebeschonend.

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., Journal Article, SCOPUS: re.j, info:eu-repo/semantics/published