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Small area statistics on health outcomes like daily smoking prevalence are vital for measuring community progress towards healthy lifestyles. Due to sparse data and remoteness in Australia, the National Health Survey is insufficient to estimate at the disaggregated statistical areas (SA) in Australia, even sometimes at the sub-national level. This study estimates daily smoking prevalence for Australian adults at various sub-national levels comprising 8 States and Territories, 88 Statistical Areas Level 4 (SA4s), and 334 Statistical Areas Level 3 (SA3s) by extending widely used area-level models developed at the detailed SA3 level as a form of hierarchical Bayesian model and fitted by Markov Chain Monte Carlo simulation. Detailed level direct estimates with their smoothed standard errors from the 2017-18 National Health Survey are used as model inputs. Area-level models accounting for the detailed level structured (spatial) and unstructured (non-spatial) random effects provide slightly higher bias and lower coverage at the disaggregated SA4 level. An extension of area-level models by including intermediate (SA4) level non-spatial random effects is proposed to improve the unbiasedness and coverage of the model-based estimators at higher aggregation levels and is found to be suitable for the Australian smoking prevalence data through examination of model performance statistics.

Bangladesh has experienced a rapid national decline in fertility in recent decades, however, fertility rates vary considerably at the sub-national level (i.e., division). These variations are expected to be more pronounced at lower levels of geography (e.g., district level). However, routinely conducted demographic health surveys are designed for national estimates and do not have adequate samples to produce reliable estimate of fertility rates at lower levels of administrative units, particular when considering district level age-specific fertility rates. Data extracted from the Bangladesh Demographic Health Survey 2014 are used to derive direct estimates of age-specific fertility rates and associated smoothed standard errors. These are used as inputs for developing a small area model, which is expressed in a hierarchical Bayesian framework and fitted by Markov Chain Monte Carlo simulation. The model accounts for variation at different levels—women age-group, division, and district. The modeling results show large reductions in the estimated standards errors and provide consistent estimates of fertility at the detailed district age-specific level. There are significant differences in the fertility levels within and between districts and at the division level. Fertility rates are observed to be higher for Sylhet division and for women aged 20–24 years. We use geo-spatial maps of the fertility rates to visualize the variations over districts, and identify hot and cold-spots to have better targeted local level planning and policy decision making for further reductions in fertility rates in Bangladesh.

The purpose of the Dutch Travel Survey is to produce reliable estimates on mobility of the Dutch population. In this paper mobility trends are estimated at several aggregation levels, using multilevel time series models. The models account for discontinuities induced by two survey redesigns and outliers due to less reliable outcomes in one particular year. The input for the model is direct annual estimates with their standard errors for the period 1999–2017 for a detailed cross-classification in 504 domains. Appropriate transformations for the direct estimates and generalized variance functions to smooth the standard errors of the direct estimates are proposed. The models are fitted in an hierarchical Bayesian framework using MCMC simulations. From the model outputs smooth trend estimates are computed at the most detailed domain level. Predictions at higher aggregation levels obtained by aggregation of the most detailed domain predictions result in a numerically consistent set of trend estimates for all target variables.

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.

Micro-level statistics on child undernutrition are highly prioritized by stakeholders for measuring and monitoring progress on the sustainable development goals. In this regard district-representative data were collected in the Bangladesh Multiple Indicator Cluster Survey 2019 for identifying localised disparities. However, district-level estimates of undernutrition indicators - stunting, wasting and underweight - remain largely unexplored. This study aims to estimate district-level prevalence of these indicators as well as to explore their disparities at sub-national (division) and district level spatio-demographic domains cross-classified by children sex, age-groups, and place of residence. Bayesian multilevel models are developed at the sex-age-residence-district level, accounting for cross-sectional, spatial and spatio-demographic variations. The detailed domain-level predictions are aggregated to higher aggregation levels, which results in numerically consistent and reasonable estimates when compared to the design-based direct estimates. Spatio-demographic distributions of undernutrition indicators indicate south-western districts have lower vulnerability to undernutrition than north-eastern districts, and indicate significant inequalities within and between administrative hierarchies, attributable to child age and place of residence. These disparities in undernutrition at both aggregated and disaggregated spatio-demographic domains can aid policymakers in the social inclusion of the most vulnerable to meet the sustainable development goals by 2030.

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.

This study explores the impact of financial inclusions on financial resilience. In doing so, we utilize the World Bank’s data on global financial inclusions. Our study confirms that the respondents with financial accounts are more resilient than those without accounts. The chances of being financially resilient are around 1.4 times higher for the account holders than their counterparts. We find a significant relationship between gender and financial resilience; males are 1.4 times more resilient than females when other covariates are considered in the regression model.

This study aims to measure the robustness of multi-level models designed for three anthropometric indices – height-for-age (HAZ), weight-for-age (WAZ) and weight-for-height (WHZ) Z-scores for estimating the childhood malnutrition indicators stunting, underweight and wasting in Bangladesh. The 2011 BDHS child malnutrition data have been used in developing multi-level models with and without incorporating specific contextual variables relating to lower administrative units extracted from the 2011 Bangladesh Population and Housing Census. The robustness of the models is examined through (i) testing significance of random effects corresponding to lower administrative units through selection criteria including conditional AIC, R-squared, and LRT; (ii) comparing multi-level model-based estimators to design-based estimators of child malnutrition indicators with their precision at division, district and sub-district levels; and (iii) assessing the impact of contextual variables in capturing higher-order administrative level variations. Findings reveal that the inclusion of important contextual variables helps capture variations at higher-level administrative units, and consequently assists in the selection of robust multi-level models which ultimately provide improved accuracy of estimated parameters. The findings support the application of lower administrative census information in developing a simpler multi-level model by minimizing higher-order variation.

Small area estimation is a widely used indirect estimation technique for micro‐level geographic profiling. Three unit level small area estimation techniques—the ELL or World Bank method, empirical best prediction (EBP) and M‐quantile (MQ) — can estimate micro‐level Foster, Greer, & Thorbecke (FGT) indicators: poverty incidence, gap and severity using both unit level survey and census data. However, they use different assumptions. The effects of using model‐based unit level census data reconstructed from cross‐tabulations and having no cluster level contextual variables for models are discussed, as are effects of small area and cluster level heterogeneity. A simulation‐based comparison of ELL, EBP and MQ uses a model‐based reconstruction of 2000/2001 data from Bangladesh and compares bias and mean square error. A three‐level ELL method is applied for comparison with the standard two‐level ELL that lacks a small area level component. An important finding is that the larger number of small areas for which ELL has been able to produce sufficiently accurate estimates in comparison with EBP and MQ has been driven more by the type of census data available or utilised than by the model per se.

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, policy-makers and development partners for efficient resource allocation.

Summary The method of Elbers, Lanjouw and Lanjouw (ELL) is the small area estimation method developed by the World Bank for poverty mapping and is widely used in developing countries. However, it has been criticized because of its assumption of negligible between-area variability when used to calculate small area poverty estimates. In particular, the mean-squared errors (MSEs) of these estimates are significantly underestimated when this between-area variability cannot be adequately explained by the model covariates. A method of MSE estimation for ELL-type estimates is proposed which is robust to significant unexplained between-area variability. Simulation results show that the method proposed performs better than standard ELL MSE estimators when the area homogeneity assumption is violated. An application to a Bangladesh poverty mapping study provides some empirical evidence for this robustness.

Children of Sunamganj district located in the north-eastern part of Bangladesh are highly vulnerable to undernutrition and chronic food insecurity due to its geographic location, long-time waterlog, frequent flash floods, and underdeveloped infrastructure. In this study, child undernutrition indicators stunting and underweight are estimated at district, sub-district (Upzila) and union level administrative tiers of Sunamganj district employing the World Bank small area estimation (SAE) method to a Sunamganj household level survey data collected in 2018 and the census 2011 data of Sunamganj. District level prevalence of stunting and underweight are estimated as about 48.5% (95% CI: 45.3–51.7%) and 37.0% (95% CI: 34.6–39.8%) based on the SAE method. At upzila level, stunting varied from 41.0% to 54.9% and underweight varied from 24.0% to 53.4%; while the indicators varied over 19.5–59.7% and 20.2–56.8% respectively at union level. A significant number of unions are found as hotspots of higher underweight and stunting over the north, north-eastern and north-western parts of Sunamganj. Though the southern part of Sunamganj was homogeneous in the upzila level maps of stunting and underweight; significant number of heterogeneous unions are found in the union-level maps. The upzilas belong to the northern part particularly closer to the Indian border and haor areas are mostly vulnerable to stunting and underweight. The study findings on disaggregate level prevalence of stunting and underweight might help the concerned government and non-government organizations to prepare and implement aid-related programs on public health and nutrition.

The existence of excess zeros in the distribution of antenatal care (ANC) visits in Bangladesh raises the research question of whether there are two separate generating processes in taking ANC and the frequency of ANC. Thus the main objective of this study is to identify a proper count regression model for the number of ANC visits by pregnant women in Bangladesh covering the issues of overdispersion, zero-inflation, and intra-cluster correlation with an additional objective of determining risk factors for ANC use and its frequency. The data have been extracted from the nationally representative 2014 Bangladesh Demographic and Health Survey, where 22% of the total 4493 women did not take any ANC during pregnancy. Since these zero ANC visits can be either structural or sampling zeros, two-part zero-inflated and hurdle regression models are investigated along with the standard one-part count regression models. Correlation among response values has been accounted for by incorporating cluster-specific random effects in the models. The hurdle negative binomial regression model with cluster-specific random intercepts in both the zero and the count part is found to be the best model according to various diagnostic tools including likelihood ratio and uniformity tests. The results show that women who have poor education, live in poor households, have less access to mass media, or belong to the Sylhet and Chittagong regions are less likely to use ANC and also have fewer ANC visits. Additionally, women who live in rural areas, depend on family members' decisions to take health care, and have unintended pregnancies had fewer ANC visits. The findings recommend taking both cluster-specific random effects and overdispersion and zero-inflation into account in modelling the ANC data of Bangladesh. Moreover, safe motherhood programmes still need to pay particular attention to disadvantaged and vulnerable subgroups of women.

Acute 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.

Public universities in Bangladesh arrange admission test to judge the students’ merit before the enrollment. Academic results of previous examinations (SSC and HSC) are also considered in the admission procedure. There are some disputes regarding the importance of admission test besides the previous academic records. The universities emphasize on the admission test while the government authorities ponder it as a burden for the students. This study has made an attempt to examine the importance of admission test in selection procedure utilizing a particular year admission test database of Shahjalal University of Science and Technology (SUST). Univariate and bivariate analyses along with regression models were used to analyze the data. The results indicate that students with higher score in both SSC and HSC examinations had higher possibility to be eligible for enrollment. However, a vital proportion of applicants with maximum GPA 5.00 in both examinations did not qualify in merit and waiting position. The results also show association and moderate positive correlation of admission test score with SSC and HSC results. Finally, regression analysis indicates that though the contributions of the SSC and HSC results on admission test scores are significant, the variation in admission test scores is not much explained by the previous records. Such findings recommend arranging admission test, besides academic qualification, to select the eligible applicants for enrollment in public universities.

Stunting is the core measure of child health inequalities as it reveals multiple dimensions of child health and development status. The main focus of this study is to show the procedure of selecting the most appropriate logistic regression model for stunting by developing and comparing several plausible models, which ultimately helps to identify the predictors of childhood stunting in bangladesh. This study utilizes child anthropometric data collected in the 2014 bangladesh demographic and health survey. Valid height-for-age anthropometric indices were available for a total of 6,931 children aged 0–59 months, of which about 36% were stunted. Ordinary logistic, survey logistic, marginal logistic, and random intercept logistic regression models were developed assuming independence, sampling design, cluster effect, and hierarchy of the data. Based on a number of model selection criteria, random intercept logistic model is found the most appropriate for the studied children. A number of child, mother, household, regional, and community-level variables were included in the model specification. The factors that increased the odds of stunting are children older than 11 months, short birth interval, recent morbidity of children, lower maternal education, young maternity, lower maternal body mass index, poor household wealth, urban residential place, and living in sylhet division. Findings of this study recommend to utilize an appropriate logistic model considering the issues relevant to the data, particularly sampling design and clustering for determining the risk factors of childhood stunting in bangladesh.

Background Logistic regression analysis is widely used to explore the determinants of child malnutrition status mainly for nominal response variable and non-linear relationship of interval-scale anthropometric measure with nominal-scale predictors. Multiple classification analysis relaxes the linearity assumption and additionally prioritizes the predictors. Main objective of the study is to show how does multiple classification analysis perform like linear and logistic regression analyses for exploring and ranking the determinants of child malnutrition. Methods Anthropometric data of under-5 children are extracted from the 2011 Bangladesh Demographic and Health Survey. The analysis is carried out considering several socio-economic, demographic and environmental explanatory variables. The Height-for-age Z-score is used as the anthropometric measure from which malnutrition status (stunting: below −2.0 Z-score) is identified. Results The fitted multiple classification analysis models show similar results as linear and logistic models. Children age, birth weight and birth interval; mother’s education and nutrition status; household economic status and family size; residential place and regional settings are observed as the significant predictors of both Height-for-age Z-score and stunting. Child, household, and mother level variables have been ranked as the first three significant groups of predictors by multiple classification analysis. Conclusions Detecting and ranking the determinants of child malnutrition through Multiple classification analysis might help the policy makers in priority-based decision-making. Trial registration “Retrospectively registered” Electronic supplementary material The online version of this article (10.1186/s40795-017-0194-7) contains supplementary material, which is available to authorized users.

Description of data: Distribution of mothers by household (HH) wealth status, motherâ s highest level education status and residential place, BDHS 2011. (PDF 185 kb)

Statistical probability density functions are widely used to model tree diameter distributions and to describe stand structure. The objective of this study was to compare the performance of normal, logarithmic-normal and threeparameter Weibull distributions for fitting diameter data from Akashmoni ( Acacia auriculiformis A. Cunn. ex Benth.) plantations grown in the north-eastern region of Bangladesh. Data from 96 plots, established in 24 plantations in north-eastern Bangladesh and ranging in age from 1 to 6 years, were used for this study. The parameters of the Weibull distribution were calculated using maximum likelihood estimation (MLE) and moment estimation (ME) methods. The goodness of fit of normal, lognormal, Weibull MLE and Weibull ME were tested using one-sample Kolmogorov-Smirnov (KS) tests. The KS test results showed that both lognormal and Weibull distributions were equally effective for describing the diameter distributions of these Akashmoni plantations grown in the northeastern region of Bangladesh. Keywords : Acacia auriculiformis , Akashmoni, Kolmogorov-Smirnov test, lognormal distribution, north-eastern Bangladesh, Weibull parameters Southern Forests 2012, 74(3): 175–181

Malnutrition is a persistent problem for both children and mother throughout the world. In developing countries malnutrition is an important root of infant and young child mortality, morbidity and reduced life span. It is considered that if malnutrition cannot be reduced and prevented, it will be impossible to achieve many of the Millennium Development Goals (MDGs) including the goals on extreme poverty and hungry, primary education, child mortality, and incidence of infectious diseases. The World Summit for Children in 1990 recognized malnutrition as a contributing factor in half of all deaths occurring among young children. The nutrition goals for the decade 1990-2000 include reduction of both moderate and severe protein-energy malnutrition among children under five years of age by one half of the 1990 levels (UNICEF, 1990). However, the reduction of child malnutrition by half in a decade was one of the most ambitious goals set by the various summits convened during the 1990s. As a result, all of the nutrition goals were not successfully achieved during the period 1990-2000. As a step towards building a strong foundation for attaining the internally agreed development goals, including the MDGs, a consistent set of intermediate targets and benchmarks during the course of the decade (2000-2010) were set to help the unmet goals (UNICEF, 2003; United Nations, 2001). One of the most important goals regarding nutrition during the period 2000-2010 was the one on reducing child malnutrition among children aged under five by at least one third of the 2000 levels, with special attention paid to children under two years of age—especially reduction of stunted and underweighted children by at least one third during the period 2000-2010 (UNICEF, 2002).

In this article, industrial growth of some manufacturing industries in Bangladesh has been studied using Census of Manufacturing Industries (CMI) Data from the years 1981–82 to 1999–2000. Higher increasing growth in value added for some industries in the post-liberalization period (1991–92 to 1999–2000) while for the most important industries, satisfactory growth level in the pre-liberalization period (1981–82 to 1990–91) has been experienced. Highest growth in fixed capital and improved concentration level in employment have been examined. Comparatively, capital goods industries showed highest significant annual growth rate (13.95 per cent). All manufacturing industries registered positive and negative employment elasticities with respect to value added and fixed capital whereas only positive output elasticities with respect to fixed capital and employment for the same.

This paper investigates the technical efficiency of selected manufacturing industries of Bangladesh using a stochastic frontier production function approach suggested by Battese and Coelli (1992) applied to panel data. A feasible Cobb-Douglas stochastic frontier production function, which has time-varying technical inefficiency effects, was estimated. Two alternative distributions were used to model the random inefficiency term: a truncated normal distribution and a half-normal distribution. The estimated average technical efficiency for four groups of industries of Bangladesh over the reference period was 40.22% of potential output for the truncated normal distribution, whereas it was 55.57% of potential output for the half-normal distribution.

Background The study attempts to develop an ordinal logistic regression (OLR) model to identify the determinants of child malnutrition instead of developing traditional binary logistic regression (BLR) model using the data of Bangladesh Demographic and Health Survey 2004. Methods Based on weight-for-age anthropometric index (Z-score) child nutrition status is categorized into three groups-severely undernourished (< -3.0), moderately undernourished (-3.0 to -2.01) and nourished (≥-2.0). Since nutrition status is ordinal, an OLR model-proportional odds model (POM) can be developed instead of two separate BLR models to find predictors of both malnutrition and severe malnutrition if the proportional odds assumption satisfies. The assumption is satisfied with low p-value (0.144) due to violation of the assumption for one co-variate. So partial proportional odds model (PPOM) and two BLR models have also been developed to check the applicability of the OLR model. Graphical test has also been adopted for checking the proportional odds assumption. Results All the models determine that age of child, birth interval, mothers' education, maternal nutrition, household wealth status, child feeding index, and incidence of fever, ARI & diarrhoea were the significant predictors of child malnutrition; however, results of PPOM were more precise than those of other models. Conclusion These findings clearly justify that OLR models (POM and PPOM) are appropriate to find predictors of malnutrition instead of BLR models.

This study has attempted to develop and apply the analytical hierarchy process model to identify the productive investment sectors in the Sylhet region of Bangladesh, considering nine economic resources available in the region as criteria for selecting the productive sectors from nine potential alternatives. Accordingly, this study has identified accessibility of fund, possibility of high profit and availability of raw materials as the most important criteria for investment and finally, tea industry, fisheries and livestock-based industry and agro-based industry (processing) as the most productive investment sectors in the region.

This report describes the time series models developed for the mobility trend estimation project carried out by Statistics Netherlands in collaboration with KiM/Rijkswaterstaat. First, direct estimates along with standard error estimates are obtained for each year in the period 1999-2017 from the microdata of the Dutch Travel Survey for a detailed cross-classification by person characteristics sex and age class and trip leg characteristics mode and purpose. Consequently, these direct estimates are smoothed by modeling them using multi-level time series models that account for influential outliers as well as for the redesigns of the survey within the Ɵme span considered. Two target variables are modeled in this way: the number of trip legs per person per day and the distance traveled per trip leg. The models are specified in a hierarchical Bayesian framework and esƟmated using a Markov Chain Monte Carlo simulation method. From the model outputs smooth trend esƟmates can be computed at various aggregation levels for the mean number of trip legs per person per day and the mean distance traveled per trip leg, as well as for derived quantities such as the mean distance per person per day. We discuss the model building and evaluation processes as well as the results based on the fitted models.