Re-scaling and small area estimation of behavioral risk survey guided by social vulnerability data.

BACKGROUND: Local governments and other public health entities often need population health measures at the county or subcounty level for activities such as resource allocation and targeting public health interventions, among others. Information collected via national surveys alone cannot fill these needs. We propose a novel, two-step method for rescaling health survey data and creating small area estimates (SAEs) of smoking rates using a Behavioral Risk Factor Surveillance System survey administered in 2015 to participants living in Allegheny County, Pennsylvania, USA. METHODS: The first step consisted of a spatial microsimulation to rescale location of survey respondents from zip codes to tracts based on census population distributions by age, sex, race, and education. The rescaling allowed us, in the second step, to utilize available census tract-specific ancillary data on social vulnerability for small area estimation of local health risk using an area-level version of a logistic linear mixed model. To demonstrate this new two-step algorithm, we estimated the ever-smoking rate for the census tracts of Allegheny County. RESULTS: The ever-smoking rate was above 70% for two census tracts to the southeast of the city of Pittsburgh. Several tracts in the southern and eastern sections of Pittsburgh also had relatively high (> 65%) ever-smoking rates. CONCLUSIONS: These SAEs may be used in local public health efforts to target interventions and educational resources aimed at reducing cigarette smoking. Further, our new two-step methodology may be extended to small area estimation for other locations and health outcomes.

Small area estimation of proportions with different levels of auxiliary data.

Binary data are often of interest in many small areas of applications. The use of standard small area estimation methods based on linear mixed models becomes problematic for such data. An empirical plug-in predictor (EPP) under a unit-level generalized linear mixed model with logit link function is often used for the estimation of a small area proportion. However, this EPP requires the availability of unit-level population information for auxiliary data that may not be always accessible. As a consequence, in many practical situations, this EPP approach cannot be applied. Based on the level of auxiliary information available, different small area predictors for estimation of proportions are proposed. Analytic and bootstrap approaches to estimating the mean squared error of the proposed small area predictors are also developed. Monte Carlo simulations based on both simulated and real data show that the proposed small area predictors work well for generating the small area estimates of proportions and represent a practical alternative to the above approach. The developed predictor is applied to generate estimates of the proportions of indebted farm households at district-level using debt investment survey data from India.

Small area estimation for semicontinuous data.

Survey data often contain measurements for variables that are semicontinuous in nature, i.e. they either take a single fixed value (we assume this is zero) or they have a continuous, often skewed, distribution on the positive real line. Standard methods for small area estimation (SAE) based on the use of linear mixed models can be inefficient for such variables. We discuss SAE techniques for semicontinuous variables under a two part random effects model that allows for the presence of excess zeros as well as the skewed nature of the nonzero values of the response variable. In particular, we first model the excess zeros via a generalized linear mixed model fitted to the probability of a nonzero, i.e. strictly positive, value being observed, and then model the response, given that it is strictly positive, using a linear mixed model fitted on the logarithmic scale. Empirical results suggest that the proposed method leads to efficient small area estimates for semicontinuous data of this type. We also propose a parametric bootstrap method to estimate the MSE of the proposed small area estimator. These bootstrap estimates of the MSE are compared to the true MSE in a simulation study.

Multivariate small area modelling of undernutrition prevalence among under-five children in Bangladesh.

District-representative data are rarely collected in the surveys for identifying localised disparities in Bangladesh, and so district-level estimates of undernutrition indicators - stunting, wasting and underweight - have remained largely unexplored. This study aims to estimate district-level prevalence of these indicators by employing a multivariate Fay-Herriot (MFH) model which accounts for the underlying correlation among the undernutrition indicators. Direct estimates (DIR) of the target indicators and their variance-covariance matrices calculated from the 2019 Bangladesh Multiple Indicator Cluster Survey microdata have been used as input for developing univariate Fay-Herriot (UFH), bivariate Fay-Herriot (BFH) and MFH models. The comparison of the various model-based estimates and their relative standard errors with the corresponding direct estimates reveals that the MFH estimator provides unbiased estimates with more accuracy than the DIR, UFH and BFH estimators. The MFH model-based district level estimates of stunting, wasting and underweight range between 16 and 43%, 15 and 36%, and 6 and 13% respectively. District level bivariate maps of undernutrition indicators show that districts in north-eastern and south-eastern parts are highly exposed to either form of undernutrition, than the districts in south-western and central parts of the country. In terms of the number of undernourished children, millions of children affected by either form of undernutrition are living in densely populated districts like the capital district Dhaka, though undernutrition indicators (as a proportion) are comparatively lower. These findings can be used to target districts with a concurrence of multiple forms of undernutrition, and in the design of urgent intervention programs to reduce the inequality in child undernutrition at the localised district level.

Food insecurity in the Eastern Indo-Gangetic plain: Taking a closer look.

OBJECTIVE: Food security is an important policy issue in India. As India recently ranked 107th out of 121 countries in the 2022 Global Hunger Index, there is an urgent need to dissect, and gain insights into, such a major decline at the national level. However, the existing surveys, due to small sample sizes, cannot be used directly to produce reliable estimates at local administrative levels such as districts. DESIGN: The latest round of available data from the Household Consumer Expenditure Survey (HCES 2011-12) done by the National Sample Survey Office of India used stratified multi-stage random sampling with districts as strata, villages as first stage and households as second stage units. SETTING: Our Small Area Estimation approach estimated food insecurity prevalence, gap, and severity of each rural district of the Eastern Indo-Gangetic Plain (EIGP) region by modeling the HCES data, guided by local covariates from the 2011 Indian Population Census. PARTICIPANTS: In HCES, 5915 (34429), 3310 (17534) and 3566 (15223) households (persons) were surveyed from the 71, 38 and 18 districts of the EIGP states of Uttar Pradesh, Bihar and West Bengal respectively. RESULTS: We estimated the district-specific food insecurity indicators, and mapped their local disparities over the EIGP region. By comparing food insecurity with indicators of climate vulnerability, poverty and crop diversity, we shortlisted the vulnerable districts in EIGP. CONCLUSIONS: Our district-level estimates and maps can be effective for informed policy-making to build local resiliency and address systemic vulnerabilities where they matter most in the post-pandemic era. ADVANCES: Our study computed, for the Indian states in the EIGP region, the first area-level small area estimates of food insecurity as well as poverty over the past decade, and generated a ranked list of districts upon combining these data with measures of crop diversity and climatic vulnerability.

Estimating unmet need for contraception by district within Ghana: an application of small-area estimation techniques.

The importance of meeting the unmet need for contraception is nowhere more urgent than in the countries of sub-Saharan Africa, where the fertility decline is stalling and total unmet need exceeds 30 per cent among married women. In Ghana, where fertility levels vary considerably, demographic information at sub-national level is essential for building effective family planning programmes. We used small-area estimation techniques, linking data from the 2003 Ghana Demographic and Health Survey to the 2000 Ghana Population and Housing Census, to derive district-level estimates of contraceptive use and unmet need for contraception. The results show considerable variation between districts in contraceptive use and unmet need. The prevalence of contraceptive use varies from 4.1 to 41.7 per cent, while that of the use of modern methods varies from 4.0 to 34.8 per cent. The findings identify districts where family planning programmes need to be strengthened.

Multivariate Small Area Modelling for Measuring Micro Level Earning Inequality: Evidence from Periodic Labour Force Survey of India.

The economy of India is growing continuously with its gross domestic product increasing rapidly than most of the developing countries. Nonetheless an increase in national gross domestic product is not revealing the earning parity at micro level in the country. The earning inequality in a country like India has adversely obstructed under privileged in accessing basic needs such as health and education. The Periodic labour force survey (PLFS) conducted by the National Statistical Office of India generates estimates on earning status at state and national level for both rural and urban sectors separately. However, due to a small sample size problem that leads to high sampling variability, these surveys cannot be used directly to produce reliable estimates at micro level such as district or further disaggregate levels. As earnings are often unevenly distributed among the subgroups of comparatively small areas, disaggregate level statistics are inevitably needed in the country for target specific policy planning and monitoring to reduce the earning disparity. Nonetheless, owing to unavailability of estimates at district level, the analysis and spatial mapping related to earning inequality are limited to the national and state level. As a result, the existing variability in disaggregate level earning distribution are often unavailable. This article describes multivariate small area estimation (SAE) to generate precise and representative district-wise model-based estimates of inequality in earning distribution in rural and urban areas of Uttar Pradesh state in India by linking the latest round of PLFS 2018-2019 data and the 2011 Indian Population Census data. The diagnostic measures demonstrate that the district-wise estimates of earning generated by multivariate SAE method are reliable and representative. The spatial maps produced in this analysis reveal district level inequality in earning distribution in the state of Uttar Pradesh. These disaggregate level estimates and spatial mapping of earning distribution are directly pertinent to measuring and monitoring the sustainable development goal 10 of inequality reduction within countries. These expected to offer evidence to executive policy-makers and experts for recognizing the areas demanding additional consideration. This study will definitely provide added advantage to the newly launched schemes of Government of India for fund distribution along with the better monitoring of these schemes.

Mapping changes in district level prevalence of childhood stunting in India 1998-2016: An application of small area estimation techniques.

The four rounds of National Family Health Survey (NFHS) conducted during 1992-93, 1998-99, 2005-06 and 2015-16 is main source to track the health and development related indicators including nutritional status of children at national and state level in India. Except NFHS-4, first three rounds of NFHS were unable to provides district-level estimates of childhood stunting due to the insufficient sample sizes. The small area estimation (SAE) techniques offer a viable solution to overcome the problem of small sample size. Therefore, this study uses SAE techniques to derive district level prevalence of childhood stunting corresponding to NFHS-2 (1998-99). Study further estimated GIS maps, univariate Local indicator of spatial autocorrelation (LISA) and Moran's I to understand the trend in district level childhood stunting between NFHS-2 and NFHS-4. Estimates obtained by SAE techniques suggest that prevalence of childhood stunting ranges from 20.7% (95% CI: 18.8-22.7) in South Goa district of Goa to 64.4% (95%CI: 63.1-65.7) in Dhaulpur district of Rajasthan during 1998-99. The diagnostic measures used to validate the reliability of estimates obtained by SAE techniques indicate that the model-based estimates are reliable and representative at district level. Results of geospatial analysis indicates substantial reduction in childhood stunting between 1998 and 2016. Out of 640 district,about 81 district experience reduction of more than 50%. At the same time 60 district experience less than 10% of reduction between 1998 and 2016. Spatial clustering of childhood stunting remains same over the study period except few additional cluster in Maharashtra, Andhra and Meghalaya in 2016. The district level estimates obtained from this study might be helpful in framing decentralized policies and implementation of vertical programs to enhance the efficacy of various nutrition interventions in priority districts of the country.

Disaggregate level estimates and spatial mapping of food insecurity in Bangladesh by linking survey and census data.

Food insecurity is an important and persistent social issue in Bangladesh. Existing data based on socio-economic surveys produce divisional and nationally representative food insecurity estimates but these surveys cannot be used directly to generate reliable district level estimates. We deliberate small area estimation (SAE) approach for estimating the food insecurity status at district level in Bangladesh by combining Household Income and Expenditure Survey 2010 with the Bangladesh Population and Housing Census 2011. The food insecurity prevalence, gap and severity status have been determined based on per capita calorie intake with a threshold of 2122 kcal per day, as specified by the Bangladesh Bureau of Statistics.The results show that the food insecurity estimates generated from SAE are precise and representative of the spatial heterogeneity in the socioeconomic conditions than do the direct estimates. The maps showing the food insecurity indicators by district indicate that a number of districts in northern and southern parts are more vulnerable in terms of all indicators. These maps will guide the government, international organizations, policymakers and development partners for efficient resource allocation.

District level estimates and mapping of prevalence of diarrhoea among under-five children in Bangladesh by combining survey and census data.

The demand for district level statistics has increased tremendously in Bangladesh due to existence of decentralised approach to governance and service provision. The Bangladesh Demographic Health Surveys (BDHS) provide a wide range of invaluable data at the national and divisional level but they cannot be used directly to produce reliable district-level estimates due to insufficient sample sizes. The small area estimation (SAE) technique overcomes the sample size challenges and can produce reliable estimates at the district level. This paper uses SAE approach to generate model-based district-level estimates of diarrhoea prevalence among under-5 children in Bangladesh by linking data from the 2014 BDHS and the 2011 Population Census. The diagnostics measures show that the model-based estimates are precise and representative when compared to the direct survey estimates. Spatial distribution of the precise estimates of diarrhoea prevalence reveals significant inequality at district-level (ranged 1.1-13.4%) with particular emphasis in the coastal and north-eastern districts. Findings of the study might be useful for designing effective policies, interventions and strengthening local-level governance.

Localised estimates and spatial mapping of poverty incidence in the state of Bihar in India-An application of small area estimation techniques.

Poverty affects many people, but the ramifications and impacts affect all aspects of society. Information about the incidence of poverty is therefore an important parameter of the population for policy analysis and decision making. In order to provide specific, targeted solutions when addressing poverty disadvantage small area statistics are needed. Surveys are typically designed and planned to produce reliable estimates of population characteristics of interest mainly at higher geographic area such as national and state level. Sample sizes are usually not large enough to provide reliable estimates for disaggregated analysis. In many instances estimates are required for areas of the population for which the survey providing the data was unplanned. Then, for areas with small sample sizes, direct survey estimation of population characteristics based only on the data available from the particular area tends to be unreliable. This paper describes an application of small area estimation (SAE) approach to improve the precision of estimates of poverty incidence at district level in the State of Bihar in India by linking data from the Household Consumer Expenditure Survey 2011-12 of NSSO and the Population Census 2011. The results show that the district level estimates generated by SAE method are more precise and representative. In contrast, the direct survey estimates based on survey data alone are less stable.