Subnational Estimates Research Articles

Issues Associated With the Formulation of a Small Area Model for Estimation of State-Level Crime Victimization Rates

Subnational estimation is an important and challenging problem in the context of the National Crime Victimization Survey (NCVS). Direct estimates for subnational domains are often unreliable due to small sample sizes. Model-based procedures have potential to improve upon direct estimators. However, model-based estimation presents several new challenges. One must identify suitable covariates, and an appropriate model form must be specified. This article discusses issues associated with the formulation of a small area model for production of state-level estimates of crime victimization rates. An analysis of direct estimates and covariates motivates the development of a Bayesian multivariate model. A model in the original scale is compared to a model in the log scale. Efficiency gains from the model relative to the direct estimators are examined. One challenge is that direct estimates can equal zero. Two ways of handling zero direct estimates are discussed.

Poverty mapping in the age of machine learning

Recent years have witnessed considerable methodological advances in poverty mapping, much of which has focused on the application of modern machine-learning approaches to remotely-sensed data. Poverty maps produced with these methods generally share a common validation procedure, which assesses model performance by comparing sub-national poverty estimates with survey-based, direct estimates. While unbiased, direct estimates can be imprecise measures of true poverty rates, meaning that it is unclear whether these validation procedures are informative of actual model performance. In this paper, we use a rich dataset from Mexico to provide a more rigorous assessment of the modern approach to poverty mapping by evaluating its performance against a credible ground truth. We find that the modern method under-performs relative to benchmark traditional methods, largely because of the limited predictive capacity of remotely-sensed covariates. For a given covariate set, we also find that machine learning produces more biased poverty estimates than the traditional procedures, particularly for the poorest geographic areas.

Examining geographical inequalities for malaria outcomes and spending on malaria in 40 malaria-endemic countries, 2010–2020

BackgroundWhile substantial gains have been made in the fight against malaria over the past 20 years, malaria morbidity and mortality are marked by inequality. The equitable elimination of malaria within countries will be determined in part by greater spending on malaria interventions, and how those investments are allocated. This study aims to identify potential drivers of malaria outcome inequality and to demonstrate how spending through different mechanisms might lead to greater health equity.MethodsUsing the Gini index, subnational estimates of malaria incidence and mortality rates from 2010 to 2020 were used to quantify the degree of inequality in malaria burden within countries with incidence rates above 5000 cases per 100,000 people in 2020. Estimates of Gini indices represent within-country distributions of disease burden, with high values corresponding to inequitable distributions of malaria burden within a country. Time series analyses were used to quantify associations of malaria inequality with malaria spending, controlling for country socioeconomic and population characteristics.ResultsBetween 2010 and 2020, varying levels of inequality in malaria burden within malaria-endemic countries was found. In 2020, values of the Gini index ranged from 0.06 to 0.73 for incidence, 0.07 to 0.73 for mortality, and 0.00 to 0.36 for case fatality. Greater total malaria spending, spending on health systems strengthening for malaria, healthcare access and quality, and national malaria incidence were associated with reductions in malaria outcomes inequality within countries. In addition, government expenditure on malaria, aggregated government and donor spending on treatment, and maternal educational attainment were also associated with changes in malaria outcome inequality among countries with the greatest malaria burden.ConclusionsThe findings from this study suggest that prioritizing health systems strengthening in malaria spending and malaria spending in general especially from governments will help to reduce inequality of the malaria burden within countries. Given heterogeneity in outcomes in countries currently fighting to control malaria, and the challenges in increasing both domestic and international funding allocated to control and eliminate malaria, the efficient targeting of limited resources is critical to attain global malaria eradication goals.

Programmatic implications of a sub-national TB prevalence survey in India.

<sec><title>BACKGROUND</title>Subnational TB estimates are crucial for making informed decisions to tailor TB control activities to local TB epidemiology.</sec><sec><title>METHODS</title>A cross-sectional survey was conducted among 143,005 individuals in Tamil Nadu, India. Participants were screened for symptoms and underwent chest X-ray (CXR). Participants with symptoms of TB and/or abnormal CXR were tested for TB using Xpert, smear, and liquid culture.</sec><sec><title>RESULTS</title>The prevalence of microbiologically confirmed pulmonary TB (MCPTB) was 212 (95% CI 184-239) per 100,000 population. The prevalence-to-notification ratio (P:N) in the state was 2.05 (95% CI 1.8-2.29). Low body mass index and diabetes together had a population attributable fraction of 54.15 (95% CI 45.68-61.97). Approximately 39% of the TB cases were asymptomatic and were identified only by CXR screening. In the general population, only 26.9% sought care at a health facility among those with symptoms suggestive of TB.</sec><sec><title>CONCLUSION</title>The programme needs to prioritise screening with CXR to potentially detect cases earlier and curtail the transmission and upscale molecular tests in the selected population to increase the yield of case finding. Innovative health education strategies must be devised to address health-seeking behaviour.</sec>.

The Subnational Corruption Database: Grand and petty corruption in 1,473 regions of 178 countries, 1995–2022

This data descriptor presents the Subnational Corruption Database (SCD), which provides data on corruption in 1,473 subnational areas of 178 countries. The SCD includes a comprehensive overall corruption index, the Subnational Corruption Index (SCI), and its two components: the Subnational Grand Corruption Index (SGCI) and Subnational Petty Corruption Index (SPCI). The SCD is constructed by combining data of 807 surveys held in the period 1995–2022 and includes the corruption experiences and perceptions of 1,326,656 respondents along 19 separate dimensions. The data are available for multiple years, allowing longitudinal analyses. At the national level, the SCI correlates strongly with established corruption indices, like the Transparency International Corruption Perceptions Index (CPI) and the World Bank Control of Corruption Index (CCI). We create subnational estimates of the CPI and CCI by superimposing the subnational variation of the SCI around the national averages of these indices. The presentation of subnational data in the SCD and the separation between grand and petty corruption significantly broaden the global knowledge base in the field of corruption.

Assessing the Progress toward a Water-Efficient Economy in the United States from 1985 to 2015.

United Nations Sustainable Development Goal 6 tackles the long-neglected economic dimension of water utilization by monitoring nations' water use efficiency (WUE). However, it is imperative to emphasize the need for consistent spatial-temporal subnational WUE estimates, rather than relying solely on recent national trends, which can obscure crucial water use concerns and improvement opportunities. Here, a time series analysis of national, state, and sectoral (e.g., industrial, service, and agriculture) WUE from 1980 to 2015 was developed by compiling the most comprehensive and disaggregated water and economic data from 3243 US counties and 50 US states. The US total WUE increased by 181% from 16.2 (1985) to 45.6 USD/m3 (2015), driven by service sector WUE enhancements. The increased industry and service WUEs in most states were more strongly correlated with decreased per capita water withdrawal than with economic growth. Simultaneously, reductions in agriculture WUE were observed in 18 states potentially because of the complicated interaction of diverse factors specific to local communities. Expanding WUE gaps between affluent and less affluent states, and persisting WUE gaps between water-abundant andwater-scarce states highlight the need to advance policies to support under-resourced communities in effective water planning and water pricing for advancing equitable development.

Mapping TB incidence across districts in Uganda to inform health program activities.

Identifying spatial variation in TB burden can help national TB programs effectively allocate resources to reach and treat all people with TB. However, data limitations pose challenges for subnational TB burden estimation. We developed a small-area modeling approach using geo-positioned prevalence survey data, case notifications, and geospatial covariates to simultaneously estimate spatial variation in TB incidence and case notification completeness across districts in Uganda from 2016-2019. TB incidence was estimated using 1) cluster-level data from the national 2014-2015 TB prevalence survey transformed to incidence, and 2) case notifications adjusted for geospatial covariates of health system access. The case notification completeness surface was fit jointly using observed case notifications and estimated incidence. Estimated pulmonary TB incidence among adults varied >10-fold across Ugandan districts in 2019. Case detection increased nationwide from 2016 to 2019, and the number of districts with case detection rates >70% quadrupled. District-level estimates of TB incidence were five times more precise than a model using TB prevalence survey data alone. A joint spatial modeling approach provides useful insights for TB program operation, outlining areas where TB incidence estimates are highest and health programs should concentrate their efforts. This approach can be applied in many countries with high TB burden.

Subnational estimates of life expectancy at birth in India: evidence from NFHS and SRS data

BackgroundMortality estimates at the subnational level are of urgent need in India for the formulation of policies and programmes at the district level. This is the first-ever study which used survey data for the estimation of life expectancy at birth (\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$ {\ ext{e}}_{0}$$\\end{document}) for the 640 districts from NFHS-4 (2015-16) and 707 districts from NFHS-5 (2019-21) for the total, male and female population in India.MethodsThis study calculated annual age-specific mortality rates from NFHS-4 and NFHS-5 for India and all 36 states for the total, male and female population. This paper constructed the abridged life tables and estimated life expectancy at birth \\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$({e_0})$$\\end{document} and further estimated the model parameters for all 36 states. This study linked state-specific parameters to the respective districts for the estimation of life expectancy at birth \\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$({e_0})$$\\end{document}for 640 districts from NFHS-4 and 707 districts from NFHS-5 for the total, male and female population in India.ResultsFindings at the state level showed that there were similarities between the estimated and calculated \\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${e_0}$$\\end{document} in most of the states. The results of this article observed that the highest \\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${e_0}$$\\end{document} varies in the ranges of 70 to 90 years among the districts of the southern region. \\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${e_0}$$\\end{document} falls below 70 years among most of the central and eastern region districts. In the northern region districts \\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${e_0}$$\\end{document} lies in the range of 70 years to 75 years. The estimates of life expectancy at birth \\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$({e_0})$$\\end{document} shows the noticeable variations at the state and district levels for the person, male, and female populations from the NFHS (2015-16) and NFHS (2019-21). In the absence of age-specific mortality data at the district level in India, this study used the indirect estimation method of relating state-specific model parameters with the IMR of their respective districts and estimated \\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${e_0}$$\\end{document} across the 640 districts from NFHS-4 (2015-16) and 707 districts from NFHS-5 (2019-21). The findings of this study have similarities with the state-level estimations of \\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${e_0}$$\\end{document} from both data sources of SRS and NFHS and found the highest \\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${e_0}$$\\end{document} in the southern region and the lowest \\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${e_0}$$\\end{document} in the eastern and central region districts.ConclusionsIn the lack of \\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${e_0}$$\\end{document} estimates at the district level in India, this study could be beneficial in providing timely life expectancy estimates from the survey data. The findings clearly shows variations in the district level \\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${e_0}$$\\end{document}. The districts from the southern region show the highest \\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${e_0}$$\\end{document} and districts from the central and eastern region has lower \\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${e_0}$$\\end{document}. Females have higher \\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${e_0}$$\\end{document} as compared to the male population in most of the districts in India.

Leveraging Routinely Collected Program Data to Inform Extrapolated Size Estimates for Key Populations in Namibia: Small Area Estimation Study.

Estimating the size of key populations, including female sex workers (FSW) and men who have sex with men (MSM), can inform planning and resource allocation for HIV programs at local and national levels. In geographic areas where direct population size estimates (PSEs) for key populations have not been collected, small area estimation (SAE) can help fill in gaps using supplemental data sources known as auxiliary data. However, routinely collected program data have not historically been used as auxiliary data to generate subnational estimates for key populations, including in Namibia. To systematically generate regional size estimates for FSW and MSM in Namibia, we used a consensus-informed estimation approach with local stakeholders that included the integration of routinely collected HIV program data provided by key populations' HIV service providers. We used quarterly program data reported by key population implementing partners, including counts of the number of individuals accessing HIV services over time, to weight existing PSEs collected through bio-behavioral surveys using a Bayesian triangulation approach. SAEs were generated through simple imputation, stratified imputation, and multivariable Poisson regression models. We selected final estimates using an iterative qualitative ranking process with local key population implementing partners. Extrapolated national estimates for FSW ranged from 4777 to 13,148 across Namibia, comprising 1.5% to 3.6% of female individuals aged between 15 and 49 years. For MSM, estimates ranged from 4611 to 10,171, comprising 0.7% to 1.5% of male individuals aged between 15 and 49 years. After the inclusion of program data as priors, the estimated proportion of FSW derived from simple imputation increased from 1.9% to 2.8%, and the proportion of MSM decreased from 1.5% to 0.75%. When stratified imputation was implemented using HIV prevalence to inform strata, the inclusion of program data increased the proportion of FSW from 2.6% to 4.0% in regions with high prevalence and decreased the proportion from 1.4% to 1.2% in regions with low prevalence. When population density was used to inform strata, the inclusion of program data also increased the proportion of FSW in high-density regions (from 1.1% to 3.4%) and decreased the proportion of MSM in all regions. Using SAE approaches, we combined epidemiologic and program data to generate subnational size estimates for key populations in Namibia. Overall, estimates were highly sensitive to the inclusion of program data. Program data represent a supplemental source of information that can be used to align PSEs with real-world HIV programs, particularly in regions where population-based data collection methods are challenging to implement. Future work is needed to determine how best to include and validate program data in target settings and in key population size estimation studies, ultimately bridging research with practice to support a more comprehensive HIV response.

Modelling urban/rural fractions in low- and middle-income countries

Abstract In low- and middle-income countries, household surveys are the most reliable data source to examine health and demographic indicators at the subnational level, an exercise in small area estimation. Model-based unit-level models are favoured for producing the subnational estimates at fine scale, such as the admin-2 level. Typically, the surveys employ stratified 2-stage cluster sampling with strata consisting of an urban/rural designation crossed with administrative regions. To avoid bias and increase predictive precision, the stratification should be acknowledged in the analysis. To move from the cluster to the area requires an aggregation step in which the prevalence surface is averaged with respect to population density. This requires estimating a partition of the study area into its urban and rural components, and to do this we experiment with a variety of classification algorithms, including logistic regression, Bayesian additive regression trees, and gradient boosted trees. Pixel-level covariate surfaces are used to improve prediction. We estimate spatial HIV prevalence in women of age 15–49 in Malawi using the stratification/aggregation method we propose.

Systematic review of seroprevalence of SARS-CoV-2 antibodies and appraisal of evidence, prior to the widespread introduction of vaccine programmes in the WHO European Region, January–December 2020

ObjectivesSystematic review of SARS-CoV-2 seroprevalence studies undertaken in the WHO European Region to measure pre-existing and cumulative seropositivity prior to the roll out of vaccination programmes.DesignA systematic review of the...

Subnational estimates of maternal mortality in Nigeria: Secondary Data Analysis of female siblings' survivorship histories.

High Maternal Mortality (MM) in Nigeria is complicated by the absence of reliable estimates at subnational levels. Obtaining accurate data at the state and geopolitical region levels is crucial for effective policy-making and targeted interventions. This study employs novel small area estimation techniques to derive plausible estimates of Maternal Mortality rates and ratios for Nigerian states and geopolitical regions. Data from 293,769 female siblings, provided by 114,154 women in the Nigeria Demographic and Health Surveys of 2008, 2013, and 2018, are used. Empirical Bayesian technique and the James-Stein estimator are applied to estimate MM Rates and Ratios, respectively. Maternal Mortality Ratio is highest in rural areas, Northern Nigeria states, and regions. While the South West exhibits lower MMRatio, the Northern regions, particularly the North-East, show consistently higher ratios. Mortality trends have decreased in the North West and South East regions but increased in the South West from 2008 to 2018. Addressing these disparities is essential for achieving sustainable development goals and improving maternal health in Nigeria.

Seasonality of Acute Malnutrition in African Drylands: Evidence From 15 Years of SMART Surveys.

Reduction of wasting, or low weight-for-height, is a critical target for the Zero Hunger Sustainable Development Goal, yet robust evidence establishing continuous seasonal patterns of wasting is presently lacking. The current consensus of greatest hunger during the preharvest period is based on survey designs and analytical methods, which discretize time frame into preharvest/postharvest, dry/wet, or lean/plenty seasons. We present a spatiotemporally nuanced study of acute malnutrition seasonality in African drylands using a 15-year data set of Standardized Monitoring and Assessment of Relief and Transition surveys (n = 412,370). Climatological similarity was ensured by selecting subnational survey regions with 1 rainy season and by spatially matching each survey to aridity and livelihood zones. Harmonic logit regression models indicate 2 peaks of wasting during the calendar year. Greatest wasting prevalence is estimated in April to May, coincident with the primary peak of temperature. A secondary peak of wasting is observed in August to October, coinciding with the primary peak of rainfall and secondary peak of temperature. This pattern is retained across aridity and livelihood zones and is sensitive to temperature, precipitation, and vegetation. Improved subnational estimation of acute malnutrition seasonality can thus assist decision makers and practitioners in data-sparse settings and facilitate global progress toward Zero Hunger.

Approaches to improve and adapt maternal mortality estimations in low- and middle-income countries: A scoping review.

In the absence of robust vital registration systems, many low- and middle-income countries (LMICs) rely on national surveys or routine surveillance systems to estimate the maternal mortality ratio (MMR). Although the importance of MMR estimates in ending preventable maternal deaths is acknowledged, there is limited research on how different approaches are used and adapted, and how these adaptations function. To assess methods for estimating maternal mortality in LMICs and the rationale for these modifications. A literature search with the terms "maternal death", "surveys" and "low- and middle-income countries" was performed in Medline, Embase, Web of Science, Scopus, CINAHL, APA PsycINFO, ERIC, and IBSS from January 2013 to March 17, 2023. Studies were eligible if their main focus was to compare, adapt, or assess methods to estimate maternal mortality in LMICs. Titles and abstracts were screened using Rayyan. Relevant articles were independently reviewed by two reviewers against inclusion criteria. Data were extracted on mortality measurement methods, their context, and results. Nineteen studies were included, focusing on data completeness, subnational estimates, and community involvement. Routinely generated MMR estimates are more complete when multiple data sources are triangulated, including data from public and private health facilities, the community, and local authorities (e.g. vital registration, police reports). For subnational estimates, existing (e.g. the sisterhood method and reproductive-age mortality surveys [RAMOS]) and adapted methods (e.g. RAMOS 4 + 2 and Pictorial Sisterhood Method) provided reliable confidence intervals. Community engagement in data collection increased community awareness of maternal deaths, provided local ownership, and was expected to reduce implementation costs. However, most studies did not include a cost-effectiveness analysis. Household surveys with community involvement and RAMOS can be used to increase data validity, improve local awareness of maternal mortality estimates, and reduce costs in LMICs.

Smoothed model-assisted small area estimation of proportions.

In countries where population census data are limited, generating accurate subnational estimates of health and demographic indicators is challenging. Existing model-based geostatistical methods leverage covariate information and spatial smoothing to reduce the variability of estimates but often ignore the survey design, while traditional small area estimation approaches may not incorporate both unit-level covariate information and spatial smoothing in a design consistent way. We propose a smoothed model-assisted estimator that accounts for survey design and leverages both unit-level covariates and spatial smoothing. Under certain regularity assumptions, this estimator is both design consistent and model consistent. We compare it with existing design-based and model-based estimators using real and simulated data.

Global air pollution exposure and poverty

Air pollution is one of the leading causes of health complications and mortality worldwide, especially affecting lower-income groups, who tend to be more exposed and vulnerable. This study documents the relationship between ambient air pollution exposure and poverty in 211 countries and territories. Using the World Health Organization’s (WHO) 2021 revised fine particulate matter (PM2.5) thresholds, we show that globally, 7.3 billion people are directly exposed to unsafe average annual PM2.5 concentrations, 80 percent of whom live in low- and middle-income countries. Moreover, 716 million of the world’s lowest income people (living on less than $1.90 per day) live in areas with unsafe levels of air pollution, especially in Sub-Saharan Africa. Air pollution levels are particularly high in lower-middle-income countries, where economies tend to rely more heavily on polluting industries and technologies. These findings are based on high-resolution air pollution and population maps with global coverage, as well as subnational poverty estimates based on harmonized household surveys.

National and Sub-National Estimates of Household Coverage of Iodized Salt and Urinary Iodine Status among Women of Reproductive Age in India: Insights from the India Iodine Survey, 2018–19

BackgroundIodine deficiency is a significant public health problem for many populations worldwide, including India, particularly during the “first 1000 days” of life. Though Universal Salt iodization (USI) is mandatory in India, prior to 2018–19, there was no state-wide survey with estimates of iodine concentrations in salt using iodometric titration. Taking cognizance of this fact, Nutrition International commissioned the first-of-its-kind national-level survey in India, titled the India Iodine Survey 2018–19. ObjectivesThe study was conducted across the country to provide national and subnational estimates of iodine concentrations in household salt using iodometric titration and iodine nutrition status among women of reproductive age (15–49 y). MethodsThe survey adopted a multi-stage random cluster probability proportional to size sampling design, covering 21,406 households in all the states and union territories (UTs) of India. ResultsAt the national level, the household coverage of edible salt with adequate iodine (content ≥15 parts/million) was 76.3%. At the sub-national level, the coverage varied, with 10 states and 3 UTs achieving USI and 11 states and 2 UTs falling below the national average, with the highest among all the states and UTs, being Jammu and Kashmir and the lowest being Tamil Nadu. At the national level, the median urinary iodine concentration for pregnant women was 173.4 μg/L, for lactating women was 172.8 μg/L, and for non-pregnant, non-lactating women, it was 178.0 μg/L, which is within the adequate iodine nutrition range according to the WHO guidelines. ConclusionsThe survey results can be widely used by various stakeholders, including government, academia, and industry, to understand the iodine nutrition status of the population, enable the scale-up of sustained efforts toward consolidating gains and achieving USI, leading to the reduction and elimination of Iodine Deficiency Disorders.

Where does it hurt? Small area estimates and inequality in the prevalence of chronic pain.

Chronic pain affects up to half of UK adults, impacting quality of life and demand on local health services. Whilst local health planning is currently based on subnational prevalence estimates, associations between pain and sociodemographic characteristics suggest that inequalities in the prevalence of chronic and high-impact chronic pain between neighbourhoods within local authorities are likely. We aimed to derive lower super output area (LSOA) estimates of the prevalence of chronic and high-impact chronic pain. Presence of self-reported chronic and high-impact chronic pain were measured in adults aged 35+ in North Staffordshire and modelled using multilevel regression as a function of demographic and geographic predictors. Multilevel model predictions were post-stratified using the North Staffordshire age-sex population structure and LSOA demographic characteristics to estimate the prevalence of chronic and high-impact chronic pain in 298 LSOAs, corrected for ethnic diversity underrepresented in the data. Confidence intervals were generated for high-impact chronic pain using bootstrapping. Data were analysed from 4162 survey respondents (2358 women, 1804 men). The estimated prevalence of chronic and high-impact chronic pain in North Staffordshire LSOAs ranged from 18.6% to 50.1% and 6.18 [1.71, 16.0]% to 33.09 [13.3, 44.7]%, respectively. Prevalence of chronic and high-impact chronic pain in adults aged 35+ varies substantially between neighbourhoods within local authorities. Further insight into small-area level variation will help target resources to improve the management and prevention of chronic and high-impact chronic pain to reduce the impact on individuals, communities, workplaces, services and the economy. Post-stratified multilevel model predictions can produce small-area estimates of pain prevalence and impact. The evidence of substantial variation indicates a need to collect local-level data on pain and its impact to understand health needs and to guide interventions.

A practical illustration of spatial smoothing methods for disconnected regions with INLA: spatial survey on overweight and obesity in Malaysia

BackgroundNational prevalence could mask subnational heterogeneity in disease occurrence, and disease mapping is an important tool to illustrate the spatial pattern of disease. However, there is limited information on techniques for the specification of conditional autoregressive models in disease mapping involving disconnected regions. This study explores available techniques for producing district-level prevalence estimates for disconnected regions, using as an example childhood overweight in Malaysia, which consists of the Peninsular and Borneo regions separated by the South China Sea. We used data from Malaysia National Health and Morbidity Survey conducted in 2015. We adopted Bayesian hierarchical modelling using the integrated nested Laplace approximation (INLA) program in R-software to model the spatial distribution of overweight among 6301 children aged 5–17 years across 144 districts located in two disconnected regions. We illustrate different types of spatial models for prevalence mapping across disconnected regions, taking into account the survey design and adjusting for district-level demographic and socioeconomic covariates.ResultsThe spatial model with split random effects and a common intercept has the lowest Deviance and Watanabe Information Criteria. There was evidence of a spatial pattern in the prevalence of childhood overweight across districts. An increasing trend in smoothed prevalence of overweight was observed when moving from the east to the west of the Peninsular and Borneo regions. The proportion of Bumiputera ethnicity in the district had a significant negative association with childhood overweight: the higher the proportion of Bumiputera ethnicity in the district, the lower the prevalence of childhood overweight.ConclusionThis study illustrates different available techniques for mapping prevalence across districts in disconnected regions using survey data. These techniques can be utilized to produce reliable subnational estimates for any areas that comprise of disconnected regions. Through the example, we learned that the best-fit model was the one that considered the separate variations of the individual regions. We discovered that the occurrence of childhood overweight in Malaysia followed a spatial pattern with an east–west gradient trend, and we identified districts with high prevalence of overweight. This information could help policy makers in making informed decisions for targeted public health interventions in high-risk areas.

Aligning the Epidemiology of Malnutrition with Food Fortification: Grasp Versus Reach

Large-scale food fortification (LSFF) has been recognized as one of the most cost-effective interventions to improve the intake of vitamins and minerals and decrease the burden of micronutrient deficiency. Indeed, the simple addition of micronutrients to staple foods, such as wheat, maize and rice, or condiments, including salt and bouillon, has tremendous potential to impact malnutrition. However, most LSFF programs have been poorly designed and have not taken into consideration critical inputs, including current levels of nutrient inadequacy and per capita consumption of different food vehicles when deciding which nutrients to add and at what concentrations. LSFF programs, like some other nutrition interventions, also tend to have low coverage and reach and lack monitoring to measure this and course correct. These program design flaws have resulted in limited effectiveness and have made it difficult to determine how best to harmonize LSFF with other interventions to reduce micronutrient deficiencies, including efforts to enhance dietary diversity, biofortification and supplementation. Furthermore, LSFF has often been touted as a population-based intervention, but in fact has heterogenous effects among sub-groups, particularly those with limited access to or inability to afford fortified foods, as well as those with higher physiological requirements, such as pregnant and lactating women. This article focuses on these limitations and the concerted efforts underway to improve the collection, analysis, and use of data to better plan LSFF programs, track implementation, and monitor coverage and impact. This includes a more sophisticated secondary analysis of existing data, innovations to increase the frequency of primary data collection and programmatically relevant visualizations of data of sub-national estimates. These improvements will enable better use of data to target resources and programmatic efforts to reach those who stand to benefit most from fortification.