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The R package emdi enables the estimation of regionally disaggregated indicators using small area estimation methods and includes tools for processing, assessing, and presenting the results. The mean of the target variable, the quantiles of its distribution, the headcount ratio, the poverty gap, the Gini coefficient, the quintile share ratio, and customized indicators are estimated using direct and model-based estimation with the empirical best predictor (Molina and Rao 2010). The user is assisted by automatic estimation of datadriven transformation parameters. Parametric and semi-parametric, wild bootstrap for mean squared error estimation are implemented with the latter offering protection against possible misspecification of the error distribution. Tools for (a) customized parallel computing, (b) model diagnostic analyses, (c) creating high quality maps and (d) exporting the results to Excel and OpenDocument Spreadsheets are included. The functionality of the package is illustrated with example data sets for estimating the Gini coefficient and median income for districts in Austria.

Governments are increasingly interested in estimating the prevalence of substance abuse with social indicators, largely because of the high cost of estimating prevalence with surveys of random samples of the population. With both the individual and county as the unit, we regress measures of the use of alcohol, marijuana, and other drugs on social indicators that fall into three categories: demographics, measures of social disorganization, and measures more directly related to the use of substances. The measures of explained variance are fairly low, but even more troubling is that the effects of several social indicators are in the "wrong" direction. Reliance on social indicator data to supplant survey estimates of the prevalence of substance abuse requires further validation, attention to sources of bias in the indicator data, and replication of the models over time.

Biodiversity indicators summarise extensive, complex ecological data sets and are important in influencing government policy. Component data consist of time-varying indices for each of a number of different species. However, current biodiversity indicators suffer from multiple statistical shortcomings. We describe a state-space formulation for new multispecies biodiversity indicators, based on rates of change in the abundance or occupancy probability of the contributing individual species. The formulation is flexible and applicable to different taxa. It possesses several advantages, including the ability to accommodate the sporadic unavailability of data, incorporate variation in the estimation precision of the individual species' indices when appropriate, and allow the direct incorporation of smoothing over time. Furthermore, model fitting is straightforward in Bayesian and classical implementations, the latter adopting either efficient Hidden Markov modelling or the Kalman filter. Conveniently, the same algorithms can be adopted for cases based on abundance or occupancy data—only the subsequent interpretation differs. The procedure removes the need for bootstrapping which can be prohibitive. We recommend which of two alternatives to use when taxa are fully or partially sampled. The performance of the new approach is demonstrated on simulated data, and through application to three diverse national UK data sets on butterflies, bats and dragonflies. We see that uncritical incorporation of index standard errors should be avoided.

With the launch of the Swachh Bharat Mission (SBM), India accelerated access to improved sanitation in a 'mass movement' emphasising people's participation and political leadership. However, SBM continues to be implemented at the administrative unit of districts, disassociated from the political and electoral units of Parliamentary Constituencies (PC). We provide estimates of India's 543 PCs by their performance on three important Water Sanitation and Hygiene (WASH) indicators: unsafe disposal of child stool, unimproved drinking water supply, and unimproved sanitary facilities. We used multilevel modelling to generate precision-weighted estimates of each indicator at PC-level, based on recently developed methodologies linking cluster GPS data from the National Family Health Survey (NFHS), 2016 to potential PCs. We found very high heterogeneity across PCs ranging from 0.95 per cent–95.85 per cent for unsafe stool disposal, 0.35 per cent–64.17 per cent for unimproved drinking water source, and 0.19 per cent–90.69 per cent for unimproved sanitation facility. Unsafe child stool disposal and unimproved sanitary facility were strongly correlated ( r = 0.85, Pearson and r = 0.83, Spearman). Monitoring of SBM data at the PC level will allow parliamentarians to effectively improve WASH conditions in their constituencies, while accounting for critical between-PC variability that may be obfuscated in an approach focussed on state or district means.

Everything you need to easily get a handle on economic indicators In today's volatile, often troubling economic landscape, there are myriad statistics and reports that paint an economic picture that can sometimes resemble a work by Jackson Pollock. These complex and often-conflicting reports could vex even the savviest investor. Economic Indicators For Dummies explains how to interpret and use key global economic indicators to make solid investments, aid in business planning, and help develop informed decisions. In plain English, it breaks down the complex language and statistics to help you m

AbstractIntroductionHIV planning requires granular estimates for the number of people living with HIV (PLHIV), antiretroviral treatment (ART) coverage and unmet need, and new HIV infections by district, or equivalent subnational administrative level. We developed a Bayesian small‐area estimation model, called Naomi, to estimate these quantities stratified by subnational administrative units, sex, and five‐year age groups.MethodsSmall‐area regressions for HIV prevalence, ART coverage and HIV incidence were jointly calibrated using subnational household survey data on all three indicators, routine antenatal service delivery data on HIV prevalence and ART coverage among pregnant women, and service delivery data on the number of PLHIV receiving ART. Incidence was modelled by district‐level HIV prevalence and ART coverage. Model outputs of counts and rates for each indicator were aggregated to multiple geographic and demographic stratifications of interest. The model was estimated in an empirical Bayes framework, furnishing probabilistic uncertainty ranges for all output indicators. Example results were presented using data from Malawi during 2016–2018.ResultsAdult HIV prevalence in September 2018 ranged from 3.2% to 17.1% across Malawi's districts and was higher in southern districts and in metropolitan areas. ART coverage was more homogenous, ranging from 75% to 82%. The largest number of PLHIV was among ages 35 to 39 for both women and men, while the most untreated PLHIV were among ages 25 to 29 for women and 30 to 34 for men. Relative uncertainty was larger for the untreated PLHIV than the number on ART or total PLHIV. Among clients receiving ART at facilities in Lilongwe city, an estimated 71% (95% CI, 61% to 79%) resided in Lilongwe city, 20% (14% to 27%) in Lilongwe district outside the metropolis, and 9% (6% to 12%) in neighbouring Dowa district. Thirty‐eight percent (26% to 50%) of Lilongwe rural residents and 39% (27% to 50%) of Dowa residents received treatment at facilities in Lilongwe city.ConclusionsThe Naomi model synthesizes multiple subnational data sources to furnish estimates of key indicators for HIV programme planning, resource allocation, and target setting. Further model development to meet evolving HIV policy priorities and programme need should be accompanied by continued strengthening and understanding of routine health system data.