scholarly journals Identification of Disaggregated Hotspots of Child Morbidity in Bangladesh: An Application of Small Area Estimation Method

2019 ◽  
Author(s):  
Sumonkanti Das ◽  
Bappi Kumar ◽  
Luthful Alahi Kawsar
Keyword(s):  
Small Area ◽  
Census Data ◽  
Small Area Estimation ◽  
Random Effect ◽  
National Level ◽  
Estimation Method ◽  
Response Variable ◽  
Area Estimation ◽  
Child Morbidity ◽  
Administrative Units

AbstractAcute respiratory infection (ARI) and diarrhoea are two major causes of child morbidity and mortality in Bangladesh. National and regional level prevalence of ARI and diarrhoea are calculated from nationwide surveys; however, prevalence at micro-level administrative units (say, district and sub-district) is not possible due to lack of sufficient data. In such case, small area estimation (SAE) methods can be applied by combining a survey data with a census data. Using a SAE method for dichotomous response variable, this study aims to estimate the proportions of under-5 children experienced with ARI and diarrhoea separately as well as either ARI or diarrhoea within a period of two-week preceding the survey. The ARI and diarrhoea information extracted from Bangladesh Demographic and Health Survey 2011 are used to develop a random effect logistic model for each of the indicators, and then the prevalence is estimated adapting the World Bank SAE approach for the dichotomous response variable using the 5% data of the Census 2011. The estimated prevalence of each indicator significantly varied by district and sub-district (1.4-11.3% for diarrhoea, 2.2-11.8% for ARI and 4.3-16.5% for ARI/diarrhoea at sub-district level). In a number of districts and sub-district, the proportions are found double the national level. District and sub-district levels spatial distributions of the indicators might help the policy makers to identify the vulnerable disaggregated and remote hotspots. Particularly, aid industries can provide effective interventions at the highly vulnerable spots to overcome the gaps between micro and macro level administrative units.

scholarly journals SPATIAL ANALYSIS OF POVERTY AND HAPPINESS PROFILES IN SPECIAL REGION OF YOGYAKARTA USING SMALL AREA ESTIMATION METHOD

2019 ◽  
Vol XLII-4/W16 ◽  
pp. 189-196
Author(s):  
S. R. El-Yasha ◽  
M. Rizky ◽  
T. W. Wibowo ◽  
Keyword(s):  
Small Area ◽  
Gini Index ◽  
Census Data ◽  
Small Area Estimation ◽  
Estimation Method ◽  
Poverty Line ◽  
Poor People ◽  
Area Estimation ◽  
High Level ◽  
Special Region

Abstract. In March 2017, the Province of Special Region of Yogyakarta (DIY Province) has poverty line of IDR 374,009, percentage of poor people (13.03%) and Gini index (0.432) above the national average (IDR 374,478; 10.64%; 0.393). The result of happiness index in 2017 shows the position of DIY Province (72.93%) is above average of national happiness index (70.69%). Scatterplot between happiness index and percentage of poor people in Indonesia in 2017 shows that DIY Province is on first quadrant. This marks the high level of happiness along with high percentage of poor people. Small area estimation method developed by Elbers et al (known as ELL method) is used to determine spatial characteristics of poverty and happiness profiles in DIY Province. This study used village census data (Podes) 2018; Susenas March 2017 and SPTK 2017 as survey data. There are twenty three household variables and another five variables that are significant to poverty and happiness models at urban and rural provincial level. Rural regency areas dominates high poverty profile (FGT0 0.0491 – 0.1076), low happiness profile (FTG0 0.0087 – 0.0124), and inequality of happiness profile (Gini index 0.0847 – 0.0923). Urban regency areas dominates low poverty profile (FTG0 0.0082 – 0.0491), high happiness profile (FTG0 0 – 0.0087), and perfect equality of both income (Gini index 0.3048 – 0.3604) and happiness profiles (Gini index 0.0624 – 0.0847). Yogyakarta City has happiest and wealthies profiles, whereas Gunung Kidul regency urban area has perfect equality of both income and happiness profiles.

scholarly journals Disaggregated level child morbidity in Bangladesh: An application of small area estimation method

PLoS ONE ◽  
2020 ◽  
Vol 15 (5) ◽  
pp. e0220164
Author(s):  
Sumonkanti Das ◽  
Bappi Kumar ◽  
Luthful Alahi Kawsar
Keyword(s):  
Small Area ◽  
Small Area Estimation ◽  
Estimation Method ◽  
Area Estimation ◽  
Child Morbidity

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 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 Applying Census Data for Small Area Estimation in Community and Social Service Planning

2009 ◽  
Vol 8 (1) ◽  
pp. 299-305
Author(s):  
Michael Wolf-Branigin ◽  
Hyon-Sook Suh ◽  
Star Muir ◽  
Emily S. Ihara
Keyword(s):  
Social Service ◽  
Small Area ◽  
Census Data ◽  
Small Area Estimation ◽  
Service Planning ◽  
Area Estimation

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.

Expenditure Per Capita Model with Spatial Small Area Estimation

2021 ◽  
Vol 1 (2) ◽  
pp. 38-47
Author(s):  
Ahmad Risal
Keyword(s):  
Small Area ◽  
Spatial Information ◽  
Small Area Estimation ◽  
Random Effect ◽  
Development Program ◽  
Data Availability ◽  
Estimation Model ◽  
Policy Makers ◽  
Area Estimation ◽  
Direct Estimation

Indonesia is one of many countries around the world that attempt to suffer from high poverty rates. Since, poverty information in a certain area is a point of interest to researchers and policy makers. One problem faced is for the development program to be carried out more effectively and efficiently, it is necessary to have data availability up to the micro-scale. The technique used to reach the goal is Small Area Estimation (SAE). Fay-herriot (FH) model is one method on Small Area Estimation. Since, the SAE techniques require “borrow strength” across neighbor areas so thus Fay-Herriot model approach was developed by integrating spatial information into the model. This method known as Spatial Fay-Herriot Model (SFH) or Spatial Empirical Best Linear Unbiased Prediction (SEBLUP). This study aims to compare MSE of direct estimation, FH, and SFH Model to see which method gives the best result in estimating expenditure. The MSE value of the estimated SFH is smaller than direct estimation and FH, but it does not significant. It means adding spatial information in the small area estimation model does not give a better prediction than the simple small area estimation which is takes account the area as a specific random effect.

scholarly journals PENERAPAN MODEL SPATIAL LOGIT-NORMAL PADA SMALL AREA ESTIMATION DENGAN METODE HIERARCHICAL BAYES

2020 ◽  
Vol 2019 (1) ◽  
pp. 59-66
Author(s):  
Taly Purwa
Keyword(s):  
Monte Carlo ◽  
Markov Chain ◽  
Small Area ◽  
Small Area Estimation ◽  
Random Effect ◽  
Deviance Information Criterion ◽  
Information Criterion ◽  
Hierarchical Bayes ◽  
Area Estimation ◽  
Spatial Random Effect

Penelitian ini menerapkan model Spatial Logit-normal pada Small Area Estimation (SAE) untuk estimasi proporsi penduduk dengan asupan kalori minimum di bawah 1.400 kkal/kapita/hari pada level kecamatan di Provinsi Bali Tahun 2014 yang merupakan indikator 2.1.2(A) pada tujuan ke-2 SDGs dalam rangka mengukur capaian dan mendukung tercapainya target SDGs pada level lebih tinggi. Terdapat tiga model SAE yang digunakan dengan spesifikasi random effect yang berbeda, yaitu model dengan random effect yang bersifat saling bebas (independen), spatial random effect (iCAR) serta model dengan kedua jenis random effect sekaligus (BYM). Penggunaan unsur spatial random effect diharapkan dapat meningkatkan efisiensi hasil estimasi. Metode estimasi menggunakan pendekatan Hierarchical Bayes (HB) dengan metode Markov Chain Monte Carlo (MCMC) algoritma Gibbs Sampling. Estimasi parameter pada ketiga model menunjukkan hasil yang relatif tidak berbeda dimana hanya ada satu variabel prediktor yang memiliki pengaruh signifikan, yaitu proporsi keluarga pertanian, pada model dengan random effect independen dan model BYM. Sedangkan pada model iCAR tidak ada satu pun variabel prediktor yang berpengaruh signifikan. Berdasarkan nilai Deviance Information Criterion (DIC), model terbaik adalah model BYM. Akan tetapi penambahan unsur spatial random effect bersamaan dengan random effect independen tidak secara signifikan dapat meningkatkan efisiensi hasil estimasi akibat dari minimnya nilai dependensi spasial Moran’s I. Secara visual, pemetaan hasil estimasi dengan model terbaik tidak menunjukkan adanya pola persebaran atau pengelompokan tertentu pada level kecamatan.

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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