Creating local estimates from a population health survey: practical application of small area estimation methods

National level indicators of child undernutrition often hide the real scenario across a country. In order to construct a child nutrition map, accurate estimates of undernutrition are required at very small spatial scales, typically the administrative units of a country or a region within a country. Although comprehensive data on child nutrition are collected in national surveys, the small scale estimates cannot be calculated using the standard estimation methods employed in national surveys, since such methods are designed to produce national or regional level estimates, and assume large samples. Small area estimation method has been widely used to find such micro-level estimates. Due to lack of unit level data, area level small area estimation methods (e.g., Fay-Herriot method) are widely used to calculate small-scale estimates. In Bangladesh, a few works have been done to estimate district level child nutrition status. The Bangladesh Demographic Health Survey covers all districts but district wise sample sizes are very small to get consistent estimates. In this paper, Fay-Herriot Model has been developed to calculate district wise estimates with efficient mean squared error. The Bangladesh Demographic Health Survey 2011 and Population Census 2011 are utilized for this study.

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Simple House Needs in Jember with Robust Small Area Estimation

Jurnal ILMU DASAR ◽

10.19184/jid.v18i1.3159 ◽

2017 ◽

Vol 18 (1) ◽

pp. 1

Author(s):

Frida Murtinasari ◽

Alfian Futuhul Hadi ◽

Dian Anggraeni

Keyword(s):

Small Area ◽

Robust Regression ◽

Small Sample Size ◽

Small Area Estimation ◽

Small Sample ◽

Estimation Methods ◽

Linear Unbiased Prediction ◽

Area Estimation ◽

Best Linear Unbiased ◽

M Estimation

SAE (Small Area Estimation) is often used by researchers, especially statisticians to estimate parameters of a subpopulation which has a small sample size. Empirical Best Linear Unbiased Prediction (EBLUP) is one of the indirect estimation methods in Small Area Estimation. The presence of outliers in the data can not guarantee that these methods yield precise predictions . Robust regression is one approach that is used in the model Small Area Estimation. Robust approach in estimating such a small area known as the Robust Small Area Estimation. Robust Small Area Estimation divided into several approaches. It calls Maximum Likelihood and M- Estimation. From the result, Robust Small Area Estimation with M-Estimation has the smallest RMSE than others. The value is 1473.7 (with outliers) and 1279.6 (without outlier). In addition the research also indicated that REBLUP with M-Estimation more robust to outliers. It causes the RMSE value with EBLUP has five times to be large with only one outlier are included in the data analysis. As for the REBLUP method is relatively more stable RMSE results.

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Adult diet in England: where is more support needed to achieve dietary recommendations?

10.21203/rs.3.rs-67773/v1 ◽

2020 ◽

Author(s):

Dianna Smith ◽

Christina Vogel ◽

Monique Campbell ◽

Nisreen Alwan ◽

Graham Moon

Keyword(s):

Urban Areas ◽

Small Area ◽

Vegetable Consumption ◽

Small Area Estimation ◽

Estimation Methods ◽

Dietary Recommendations ◽

Nutrition Survey ◽

Targeted Interventions ◽

Area Estimation ◽

Adult Diet

Abstract Background: Small-area estimation models are regularly commissioned by public health bodies to identify areas of greater inequality and target areas for intervention in a range of behaviours and outcomes. Such local modelling has not been completed for diet consumption in England despite diet being an important predictor of health status. The study sets out whether aspects of adult diet can be modelled from previously collected data to define and evaluate area-level interventions to address obesity and ill-health.Methods: Adults aged 16 years and over living in England. Consumption of fruit, vegetables, and sugar-sweetened beverages (SSB) are modelled using small-area estimation methods in English neighbourhoods (Middle Super Output Areas [MSOA]) to identify areas where reported portions are significantly different from recommended levels of consumption. The selected aspects of diet are modelled from respondents in the National Diet and Nutrition Survey using pooled data from 2008-2016.Results: Estimates indicate that the average prevalence of adults consuming less than one portion of fruit, vegetables or 100% juice each day by MSOA is 6.9% (range of 4.3 to 14.7%, SE 0.06) and the average prevalence of drinking more than 330ml/day of SSB is 11.5% (range of 5.7 to 30.5%, SE 0.03). Credible intervals around the estimates are wider for SSB consumption. The results identify areas including regions in London and urban areas in the North of England which may be prioritised for targeted interventions to support reduced consumption of SSB and/or an increase in portions of fruit and vegetables.Conclusion: These estimates provide valuable information at a finer spatial scale than is presently feasible, allowing for within-country and locality prioritisation of resources to improve diet. Local, targeted interventions to improve fruit and vegetable consumption such as subsidies or voucher schemes should be considered where consumption of these foods is predicted to be low.

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A COMPARISON OF SMALL AREA ESTIMATION METHODS FOR POVERTY MAPPING

Statistics in Transition New Series ◽

10.21307/stattrans-2016-005 ◽

2016 ◽

Vol 17 (1) ◽

pp. 41-66 ◽

Cited By ~ 6

Author(s):

María Guadarrama ◽

Isabel Molina ◽

J. N. K. Rao

Keyword(s):

Small Area ◽

Small Area Estimation ◽

Estimation Methods ◽

Area Estimation ◽

Poverty Mapping

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Integrating national surveys to estimate small area variations in poor health and limiting long-term illness in Great Britain

BMJ Open ◽

10.1136/bmjopen-2017-016936 ◽

2017 ◽

Vol 7 (8) ◽

pp. e016936 ◽

Cited By ~ 2

Author(s):

Graham Moon ◽

Grant Aitken ◽

Joanna Taylor ◽

Liz Twigg

Keyword(s):

Health Status ◽

Health Survey ◽

Small Area ◽

Census Data ◽

Small Area Estimation ◽

Secondary Outcome ◽

Area Estimation ◽

National Surveys ◽

Self Rated Health

ObjectivesThis study aims to address, for the first time, the challenges of constructing small area estimates of health status using linked national surveys. The study also seeks to assess the concordance of these small area estimates with data from national censuses.SettingPopulation level health status in England, Scotland and Wales.ParticipantsA linked integrated dataset of 23 374 survey respondents (16+ years) from the 2011 waves of the Health Survey for England (n=8603), the Scottish Health Survey (n=7537) and the Welsh Health Survey (n=7234).Primary and secondary outcome measuresPopulation prevalence of poorer self-rated health and limiting long-term illness. A multilevel small area estimation modelling approach was used to estimate prevalence of these outcomes for middle super output areas in England and Wales and intermediate zones in Scotland. The estimates were then compared with matched measures from the contemporaneous 2011 UK Census.ResultsThere was a strong positive association between the small area estimates and matched census measures for all three countries for both poorer self-rated health (r=0.828, 95% CI 0.821 to 0.834) and limiting long-term illness (r=0.831, 95% CI 0.824 to 0.837), although systematic differences were evident, and small area estimation tended to indicate higher prevalences than census data.ConclusionsDespite strong concordance, variations in the small area prevalences of poorer self-rated health and limiting long-term illness evident in census data cannot be replicated perfectly using small area estimation with linked national surveys. This reflects a lack of harmonisation between surveys over question wording and design. The nature of small area estimates as ‘expected values’ also needs to be better understood.

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A Study of Disability Across Districts of the Province of Wielkopolska Using Small Area Estimation Methods

Przegląd Statystyczny ◽

10.5604/01.3001.0014.0604 ◽

2019 ◽

Vol 65 (4) ◽

pp. 449-472

Author(s):

Tomasz Klimanek ◽

Marcin Szymkowiak ◽

Tomasz Józefowski

Keyword(s):

Small Area ◽

Small Area Estimation ◽

Estimation Methods ◽

Sources Of Information ◽

Estimation Errors ◽

Area Estimation ◽

Central Statistical ◽

Central Statistical Office ◽

Level Of Aggregation ◽

High Level

Surveys and censuses conducted by the Central Statistical Office in Poland are the main sources of information about disability for official statistics. Because sample sizes for relevant cross-classification domains are too small to employ classical estimation methods, results are usually published at a relatively high level of aggregation (at country or province level) or for very broadly defined domains. To meet the growing demand for detailed information about disability, the authors present an attempt of applying the methodology of small area estimation to estimate the percentage of disabled people, in the legal and biological sense, across districts (NUTS 4/LAU 1 units) of the province of Wielkopolska crossclassified by the level of education. This methodological exercise is based on data from the 2011 census and employs selected techniques of indirect estimation. Estimates obtained in the study provide an indication of the spatial variation of disability in the target domains with greater precision. It is worth noting that this level of aggregation has not been considered for purposes of official statistical outputs because of unacceptably high estimation errors of the direct estimator.

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Calculation of smoking rates by dong/eup/myeon unit using small-area estimation in the Community Health Survey

Epidemiology and Health ◽

10.4178/epih/e2015013 ◽

2015 ◽

Vol 37 ◽

pp. e2015013

Author(s):

Kay O Lee ◽

Jong Seok Byun ◽

Yang Wha Kang ◽

Yun Sil Ko ◽

Hyo Jin Kim

Keyword(s):

Community Health ◽

Health Survey ◽

Small Area ◽

Small Area Estimation ◽

Area Estimation ◽

Community Health Survey

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Adult diet in England: Where is more support needed to achieve dietary recommendations?

PLoS ONE ◽

10.1371/journal.pone.0252877 ◽

2021 ◽

Vol 16 (6) ◽

pp. e0252877

Author(s):

Dianna M. Smith ◽

Christina Vogel ◽

Monique Campbell ◽

Nisreen Alwan ◽

Graham Moon

Keyword(s):

Urban Areas ◽

Small Area ◽

Vegetable Consumption ◽

Small Area Estimation ◽

Estimation Methods ◽

Dietary Recommendations ◽

Nutrition Survey ◽

Targeted Interventions ◽

Area Estimation ◽

Adult Diet

Background Small-area estimation models are regularly commissioned by public health bodies to identify areas of greater inequality and target areas for intervention in a range of behaviours and outcomes. Such local modelling has not been completed for diet consumption in England despite diet being an important predictor of health status. The study sets out whether aspects of adult diet can be modelled from previously collected data to define and evaluate area-level interventions to address obesity and ill-health. Methods Adults aged 16 years and over living in England. Consumption of fruit, vegetables, and sugar-sweetened beverages (SSB) are modelled using small-area estimation methods in English neighbourhoods (Middle Super Output Areas [MSOA]) to identify areas where reported portions are significantly different from recommended levels of consumption. The selected aspects of diet are modelled from respondents in the National Diet and Nutrition Survey using pooled data from 2008–2016. Results Estimates indicate that the average prevalence of adults consuming less than one portion of fruit, vegetables or 100% juice each day by MSOA is 6.9% (range of 4.3 to 14.7%, SE 0.06) and the average prevalence of drinking more than 330ml/day of SSB is 11.5% (range of 5.7 to 30.5%, SE 0.03). Credible intervals around the estimates are wider for SSB consumption. The results identify areas including regions in London, urban areas in the North of England and the South coast which may be prioritised for targeted interventions to support reduced consumption of SSB and/or an increase in portions of fruit and vegetables. Conclusion These estimates provide valuable information at a finer spatial scale than is presently feasible, allowing for within-country and locality prioritisation of resources to improve diet. Local, targeted interventions to improve fruit and vegetable consumption such as subsidies or voucher schemes should be considered where consumption of these foods is predicted to be low.

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Rescaling and Small Area Estimation of Health Survey Data as applied to Smoking Rates in Allegheny County, Pennsylvania

10.1101/2021.03.21.21254074 ◽

2021 ◽

Author(s):

Shaina L Stacy ◽

Hukum Chandra ◽

Raanan Gurewitsch ◽

LuAnn L. Brink ◽

Linda B. Robertson ◽

...

Keyword(s):

Survey Data ◽

Health Survey ◽

Small Area ◽

Small Area Estimation ◽

Local Health ◽

Smoking Rate ◽

Allegheny County ◽

Census Tracts ◽

Area Estimation ◽

Health Survey Data

We propose a novel, two-step method for rescaling health survey data and creating small area estimates of smoking rates using a Behavioral Risk Factor Surveillance System (BRFSS) survey administered in 2015 to participants living in Allegheny County, in the state of Pennsylvania, USA. 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 and select from available census tract specific ancillary data on social vulnerability for small area estimation (SAE) 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. The ever-smoking rate was slightly 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. These small area estimates 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 other health-related behaviors and outcomes.

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A Map of the Poor or a Poor Map?

Mathematics ◽

10.3390/math9212780 ◽

2021 ◽

Vol 9 (21) ◽

pp. 2780

Author(s):

Paul Corral ◽

Kristen Himelein ◽

Kevin McGee ◽

Isabel Molina

Keyword(s):

Small Area ◽

Census Data ◽

Mean Squared Error ◽

Small Area Estimation ◽

Selection Procedure ◽

Data Transformation ◽

Estimation Methods ◽

Simulation Experiments ◽

Area Estimation ◽

Level Information

This paper evaluates the performance of different small area estimation methods using model and design-based simulation experiments. Design-based simulation experiments are carried out using the Mexican Intra Censal survey as a census of roughly 3.9 million households from which 500 samples are drawn using a two-stage selection procedure similar to that of Living Standards Measurement Study (LSMS) surveys. The estimation methods considered are that of Elbers, Lanjouw and Lanjouw (2003), the empirical best predictor of Molina and Rao (2010), the twofold nested error extension presented by Marhuenda et al. (2017), and finally an adaptation, presented by Nguyen (2012), that combines unit and area level information, and which has been proposed as an alternative when the available census data is outdated. The findings show the importance of selecting a proper model and data transformation so that model assumptions hold. A proper data transformation can lead to a considerable improvement in mean squared error (MSE). Results from design-based validation show that all small area estimation methods represent an improvement, in terms of MSE, over direct estimates. However, methods that model unit level welfare using only area level information suffer from considerable bias. Because the magnitude and direction of the bias is unknown ex ante, methods relying only on aggregated covariates should be used with caution, but may be an alternative to traditional area level models when these are not applicable.

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