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Comparison of spatial interpolation methods to create high-resolution poverty maps for low- and middle-income countries.

ABSTRACT: High-resolution poverty maps are important tools for promoting equitable and sustainable development. In settings without data at every location, we can use spatial interpolation (SI) to create such maps using sample-based surveys and additional covariates. In the model-based geostatistics (MBG) framework for SI, it is typically assumed that the similarity of two areas is inversely related to their distance between one another. Applications of spline interpolation take a contrasting approach that an area's absolute location and its characteristics are more important for prediction than distance to/characteristics of other locations. This study compares prediction accuracy of the MBG approach with spline interpolation as part of a generalized additive model (GAM) for four low- and middle-income countries. We also identify any potentially generalizable data characteristics influencing comparative accuracy. We found spatially scattered pockets of wealth in Malawi and Tanzania (corresponding to the major cities), and overarching spatial gradients in Kenya and Nigeria. Spline interpolation/GAM performed better than MBG for Malawi, Nigeria and Tanzania, but marginally worse in Kenya. We conclude that the spatial patterns of wealth and other covariates should be carefully accounted for when choosing the best SI approach. This is particularly pertinent as different methods capture geographical variation differently.

SUBMITTER: Wong KLM

PROVIDER: S-EPMC6228471 | biostudies-literature | 2018 Oct

REPOSITORIES: biostudies-literature

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Comparison of spatial interpolation methods to create high-resolution poverty maps for low- and middle-income countries.

Wong Kerry L M KLM Brady Oliver J OJ Campbell Oona M R OMR Benova Lenka L

Journal of the Royal Society, Interface 20181017 147

High-resolution poverty maps are important tools for promoting equitable and sustainable development. In settings without data at every location, we can use spatial interpolation (SI) to create such maps using sample-based surveys and additional covariates. In the model-based geostatistics (MBG) framework for SI, it is typically assumed that the similarity of two areas is inversely related to their distance between one another. Applications of spline interpolation take a contrasting approach tha ...[more]

PMID: 30333244

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Project description:INTRODUCTIONDisability and poverty are believed to operate in a cycle, with each reinforcing the other. While agreement on the existence of a link is strong, robust empirical evidence substantiating and describing this potential association is lacking. Consequently, a systematic review was undertaken to explore the relationship between disability and economic poverty, with a focus on the situation in low and middle income countries (LMICs).METHODSTen electronic databases were searched to retrieve studies of any epidemiological design, published between 1990-March 2016 with data comparing the level of poverty between people with and without disabilities in LMICs (World Bank classifications). Poverty was defined using economic measures (e.g. assets, income), while disability included both broad assessments (e.g. self-reported functional or activity limitations) and specific impairments/disorders. Data extracted included: measures of association between disability and poverty, population characteristics and study characteristics. Proportions of studies finding positive, negative, null or mixed associations between poverty and disability were then disaggregated by population and study characteristics.RESULTSFrom the 15,500 records retrieved and screened, 150 studies were included in the final sample. Almost half of included studies were conducted in China, India or Brazil (n = 70, 47%). Most studies were cross-sectional in design (n = 124, 83%), focussed on specific impairment types (n = 115, 77%) and used income as the measure for economic poverty (n = 82, 55%). 122 studies (81%) found evidence of a positive association between disability and a poverty marker. This relationship persisted when results were disaggregated by gender, measure of poverty used and impairment types. By country income group at the time of data collection, the proportion of country-level analyses with a positive association increased with the rising income level, with 59% of low-income, 67% of lower-middle and 72% of upper-middle income countries finding a positive relationship. By age group, the proportion of studies reporting a positive association between disability and poverty was lowest for older adults and highest for working-age adults (69% vs. 86%).CONCLUSIONSThere is strong evidence for a link between disability and poverty in LMICs and an urgent need for further research and programmatic/policy action to break the cycle.

Project description:BACKGROUND:The aim of this study was to assess the relationship between early childhood caries (ECC) in 3-5-year-old children, seven indicators of poverty and the indicator of monetary poverty in low- and middle-income countries (LICs, MICs). METHODS:This ecologic study utilized 2007 to 2017 country-level data for LICs and MICs. Explanatory variables were seven indicators of poverty namely food, water, sanitation, health, shelter, access to information, education; and monetary poverty. The outcome variable was the percentage of 3-5-year-old children with ECC. A series of univariate general linear regression models were used to assess the relationship between the percentage of 3-5?year-old children with ECC and each of the seven indicators of poverty, and monetary poverty. This was followed by multivariable regression models to determined the combined effect of the seven indicators of poverty, as well as the combined effect of the seven indicators of poverty and monetary poverty. Adjusted R2 measured models' ability to explain the variation among LICs and MICs in the percentage of 3-5-year-old children with ECC. RESULTS:Significantly more people had food, sanitation, shelter, access to information, education and monetary poverty in LICs than in MICs. There was no difference in the prevalence of ECC in 3-5-year-old children between LICs and MICs. The combination of the seven indicators of poverty explained 15% of the variation in the percentage of 3-5-year-old children with ECC compared to 1% explained by monetary poverty. When the seven indicators of poverty and the indicator for monetary poverty were combined, the amount of variation explained by them was 10%. Only two of the poverty indicators had a direct relationship with the percentage of children with ECC; there was a higher percentage of ECC in countries with higher percentage of population living in slums (B?=?0.35) and in those countries with higher percentage of the population living below poverty lines (B?=?0.19). The other indicators had an inverse relationship. CONCLUSION:The use of multiple indicators to measures of poverty explained greater amount of variation in the percentage of 3-5-year-olds with ECC in LICs and MICs than using only the indicator for monetary poverty.

Project description:ObjectivesLow-income and lower middle-income countries (LLMICs) bear a disproportionate burden of non-communicable diseases (NCDs). WHO has repeatedly called for more research on poverty and NCDs in these settings, but the current situation remains unquantified. We aimed to assess research output on poverty and NCD risk factors from these countries in relation to upper middle-income and high-income countries.DesignBibliometric analysis of primary research published between 1 January 1990 and 4 May 2017. We searched 13 databases, combining terms for poverty and NCD behavioural risk factors (tobacco, alcohol, diet and physical activity). Independent dual review was used to screen titles, abstracts and full papers. Two-tailed t-testing and multiple linear regression analyses were used to compare differences in means.Outcomes(1) Proportion of lead authors affiliated with institutions based in high and upper middle-income countries vs LLMICs. (2) Mean number of citations for publications from each region. (3) Mean journal impact factor for studies from each region.ResultsNinety-one (67%) of the 136 included studies were led by scientists affiliated with LLMIC-based institutions. These authors represented 17/83 LLMICs (20%), and their studies garnered 4.8 fewer citations per paper than studies led by high-income and upper middle-income-affiliated authors; however, this finding was non-significant (P=0.67). Papers led by authors based in high-income and upper middle-income countries were published in journals with a mean impact factor 3.1 points higher than those from LLMICs (4.9 vs 1.7) adjusting for year of publication and number of citations (P<0.001).ConclusionsMost poverty and NCD risk factor research is led by authors from a small number of LLMICs. These studies are being published in relatively low-impact journals, and the vast majority of LLMICs are not producing any research in this area that is vital to their social and economic development. The paucity of domestic evidence must be addressed to inform global policy.

Project description:ObjectivesStudies indicate that many types of surgical care are cost-effective compared with other health interventions in low-income and middle-income countries (LMICs). However, global health investments to support these interventions remain limited. This study undertakes a scoping review of research on the economic impact of surgical interventions in LMICs to determine the methodologies used in measuring economic benefits.DesignThe Arksey and O'Malley methodological framework for scoping reviews and the Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews checklist were used to review the data systematically. Online databases were used to identify papers published between 2005 and 2020, from which we selected 19 publications that quantitatively examined the economic benefits of surgical interventions in LMICs.ResultsMajority of publications (79%) reported the use of disability-adjusted life-years (DALYs) to assess economic impact. In comparison, 21% used other measures, such as the value of statistical life or cost-effectiveness ratios, or no measure at all. 31% were systematic or retrospective reviews of the literature on surgical procedures in LMICs, while 69% either directly assessed economic impact in a specific area or evaluated the need for surgical procedures in LMICs. All studies reviewed related to the economic impact of surgical procedures in LMICs, with most about paediatric surgical procedures or a specific surgical specialty.ConclusionTo make informed policy decisions regarding global health investments, the economic impact must be accurately measured. Researchers employ a range of techniques to quantify the economic benefit of surgeries in LMICs, which limits understanding of overall economic value. We conclude that the literature would benefit from a careful selection of methods, incorporating age and disability weights based on the Global Burden of Disease weights, and converting DALYs to dollars using the value of statistical life approach and the human capital approach, reporting both estimates.

Project description:BackgroundThe expansion of childhood vaccination programs in low and middle income countries has been a substantial public health success story. Indicators of the performance of intervention programmes such as coverage levels and numbers covered are typically measured through national statistics or at the scale of large regions due to survey design, administrative convenience or operational limitations. These mask heterogeneities and 'coldspots' of low coverage that may allow diseases to persist, even if overall coverage is high. Hence, to decrease inequities and accelerate progress towards disease elimination goals, fine-scale variation in coverage should be better characterized.MethodsUsing measles as an example, cluster-level Demographic and Health Surveys (DHS) data were used to map vaccination coverage at 1?km spatial resolution in Cambodia, Mozambique and Nigeria for varying age-group categories of children under five years, using Bayesian geostatistical techniques built on a suite of publicly available geospatial covariates and implemented via Markov Chain Monte Carlo (MCMC) methods.ResultsMeasles vaccination coverage was found to be strongly predicted by just 4-5 covariates in geostatistical models, with remoteness consistently selected as a key variable. The output 1?×?1?km maps revealed significant heterogeneities within the three countries that were not captured using province-level summaries. Integration with population data showed that at the time of the surveys, few districts attained the 80% coverage, that is one component of the WHO Global Vaccine Action Plan 2020 targets.ConclusionThe elimination of vaccine-preventable diseases requires a strong evidence base to guide strategies and inform efficient use of limited resources. The approaches outlined here provide a route to moving beyond large area summaries of vaccination coverage that mask epidemiologically-important heterogeneities to detailed maps that capture subnational vulnerabilities. The output datasets are built on open data and methods, and in flexible format that can be aggregated to more operationally-relevant administrative unit levels.

Project description:Injury is a leading cause of the global disease burden, accounting for 10 percent of all deaths worldwide. Despite 90 percent of these deaths occurring in low and middle-income countries (LMICs), the majority of trauma research and infrastructure development has taken place in high-income settings. Furthermore, although accessible services are of central importance to a mature trauma system, there remains a paucity of literature describing the spatial accessibility of emergency services in LMICs. Using data from the Service Provision Assessment component of the Demographic and Health Surveys of Namibia and Haiti we defined the capabilities of healthcare facilities in each country in terms of their preparedness to provide emergency services. A Geographic Information System-based network analysis method was used to define 5- 10- and 50-kilometer catchment areas for all facilities capable of providing 24-hour care, higher-level resuscitative services or tertiary care. The proportion of a country's population with access to each level of service was obtained by amalgamating the catchment areas with a population layer. A significant proportion of the population of both countries had poor spatial access to lower level services with 25% of the population of Haiti and 51% of the population of Namibia living further than 50 kilometers from a facility capable of providing 24-hour care. Spatial access to tertiary care was considerably lower with 51% of Haitians and 72% of Namibians having no access to these higher-level services within 50 kilometers. These results demonstrate a significant disparity in potential spatial access to emergency services in two LMICs compared to analogous estimates from high-income settings, and suggest that strengthening the capabilities of existing facilities may improve the equity of emergency services in these countries. Routine collection of georeferenced patient and facility data in LMICs will be important to understanding how spatial access to services influences outcomes.

Dataset Information

Comparison of spatial interpolation methods to create high-resolution poverty maps for low- and middle-income countries.

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Comparison of spatial interpolation methods to create high-resolution poverty maps for low- and middle-income countries.

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