scholarly journals District level child nutrition status in Bangladesh: An application of small area estimation method

2019 ◽  
Vol 53 (1) ◽  
pp. 45-61
Author(s):  
Mossamet Kamrun Nesa
Keyword(s):  
Small Area ◽  
Child Nutrition ◽  
Small Area Estimation ◽  
Estimation Method ◽  
Nutrition Status ◽  
Estimation Methods ◽  
Small Scale ◽  
Demographic Health Survey ◽  
Area Estimation ◽  
National Surveys

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.

scholarly journals SMALL AREA ESTIMATION METHOD WITH EMPIRICAL BAYES BASED ON BETA BINOMIAL MODEL IN GENERATED DATA

2020 ◽  
Vol 14 (1) ◽  
pp. 1-9
Author(s):  
Ferra Yanuar ◽  
Rahmatika Fajriyah ◽  
Dodi Devianto
Keyword(s):  
Binary Data ◽  
Small Area ◽  
Empirical Bayes ◽  
Small Area Estimation ◽  
Estimation Method ◽  
Estimation Methods ◽  
Auxiliary Variables ◽  
Indirect Estimation ◽  
Area Estimation ◽  
Direct Estimation

Small Area Estimation is one of the methods that can be used to estimate parameters in an area that has a small population. This study aims to estimate the value of the binary data parameter using the direct estimation method and an indirect estimation method by using the Empirical Bayes approach. To illustrate the method, we consider three conditions: direct estimator, empirical Bayes (EB) with auxiliary variables, and empirical Bayes without auxiliary variables. The smaller value of Mean Square Error is used to determine the better method. The results showed that the indirect estimation methods (EB method) gave the parameter value that was not much different from the direct estimation value. Then, the MSE values of indirect estimation with an auxiliary variable are smaller than the direct estimation method.

scholarly journals Simple House Needs in Jember with Robust Small Area Estimation

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.

scholarly journals Binomial Regression in Small Area Estimation Method for Estimate Proportion of Cultural Indicator

2018 ◽  
Author(s):  
Yudistira Yudistira ◽  
Anang Kurnia ◽  
Agus Mohamad Soleh
Keyword(s):  
Regression Model ◽  
Sample Size ◽  
Small Area ◽  
Small Area Estimation ◽  
Estimation Method ◽  
Alternative Methods ◽  
Area Estimation ◽  
Binomial Regression ◽  
Cultural Indicator ◽  
Sampling Survey

In sampling survey, it was necessary to have sufficient sample size in order to get accurate direct estimator about parameter, but there are many difficulties to fulfill them in practice. Small Area Estimation (SAE) is one of alternative methods to estimate parameter when sample size is not adequate. This method has been widely applied in such variation of model and many fields of research. Our research mainly focused on study how SAE method with binomial regression model is applied to obtained estimate proportion of cultural indicator, especially to estimate proportion of people who appreciate heritages and museums in each regency/city level in West Java Province. Data analysis approach used in our research with resurrected data and variables in order to be compared with previous research. The result later showed that binomial regression model could be used to estimate proportion of cultural indicator in Regency/City in Indonesia with better result than direct estimation method.

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

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.

scholarly journals A COMPARISON OF SMALL AREA ESTIMATION METHODS FOR POVERTY MAPPING

2016 ◽  
Vol 17 (1) ◽  
pp. 41-66 ◽  
Author(s):  
María Guadarrama ◽  
Isabel Molina ◽  
J. N. K. Rao
Keyword(s):  
Small Area ◽  
Small Area Estimation ◽  
Estimation Methods ◽  
Area Estimation ◽  
Poverty Mapping

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

BMJ Open ◽  
2017 ◽  
Vol 7 (8) ◽  
pp. e016936 ◽  
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.

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

2019 ◽  
Vol 65 (4) ◽  
pp. 449-472
Author(s):  
Tomasz Klimanek ◽  
Marcin Szymkowiak ◽  
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.

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

PLoS ONE ◽  
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.

scholarly journals Who are and where are the Rwanda’s poorest? A Small Area Estimation Method

2019 ◽  
pp. 245-264
Author(s):  
Mongongo Dosa Pacifique ◽  
Rutagarama Ephrem
Keyword(s):  
Rural Areas ◽  
Small Area ◽  
Small Area Estimation ◽  
Estimation Method ◽  
Middle Income ◽  
Policy Makers ◽  
The Sustainable Development ◽  
Area Estimation ◽  
Development Goals ◽  
Household Heads

As Rwanda is achieving its vision of moving from a low to a middle–income country during the period 2000–2020, its capability of ending poverty along the Sustainable Development Goals’ era (2015–2030) mostly depends on how well the increasing prosperity will be shared among Rwandans along the way up to the 2030 horizon. Knowing those who have not yet benefited enough from the ongoing progress should help Rwanda’s policy makers and other development agencies to serve that purpose. With this perspective, this work has the two major objectives of estimating poverty by sector and studying the relationship between poverty and related variables in Rwanda. We tackle the first objective with the Small Area Estimation method (SAE) and covers the second with the Poisson regression. We find that (1) most of the very poor are located within rural areas, (2) live in larger households and, (3) have female household heads.

scholarly journals A method for small-area estimation of population mortality in settings affected by crises

2022 ◽  
Vol 20 (1) ◽  
Author(s):  
Francesco Checchi ◽  
Adrienne Testa ◽  
Amy Gimma ◽  
Emilie Koum-Besson ◽  
Abdihamid Warsame
Keyword(s):  
Death Rate ◽  
Small Area ◽  
Small Area Estimation ◽  
Descriptive Analysis ◽  
Estimation Method ◽  
Attributable Mortality ◽  
Area Estimation ◽  
Ground Survey ◽  
Population Mortality ◽  
Generic Set

Abstract Background Populations affected by crises (armed conflict, food insecurity, natural disasters) are poorly covered by demographic surveillance. As such, crisis-wide estimation of population mortality is extremely challenging, resulting in a lack of evidence to inform humanitarian response and conflict resolution. Methods We describe here a ‘small-area estimation’ method to circumvent these data gaps and quantify both total and excess (i.e. crisis-attributable) death rates and tolls, both overall and for granular geographic (e.g. district) and time (e.g. month) strata. The method is based on analysis of data previously collected by national and humanitarian actors, including ground survey observations of mortality, displacement-adjusted population denominators and datasets of variables that may predict the death rate. We describe the six sequential steps required for the method’s implementation and illustrate its recent application in Somalia, South Sudan and northeast Nigeria, based on a generic set of analysis scripts. Results Descriptive analysis of ground survey data reveals informative patterns, e.g. concerning the contribution of injuries to overall mortality, or household net migration. Despite some data sparsity, for each crisis that we have applied the method to thus far, available predictor data allow the specification of reasonably predictive mixed effects models of crude and under 5 years death rate, validated using cross-validation. Assumptions about values of the predictors in the absence of a crisis provide counterfactual and excess mortality estimates. Conclusions The method enables retrospective estimation of crisis-attributable mortality with considerable geographic and period stratification, and can therefore contribute to better understanding and historical memorialisation of the public health effects of crises. We discuss key limitations and areas for further development.

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