Suchergebnisse

Background: This study examines the geographic variation and the magnitude of wealth inequities in birth registration in India between 2005 and 2015. Methods: Data came from India's 2005 (n = 51,940) and 2015 (n = 250,194) Demographic Health Surveys. We estimated absolute wealth inequities at the national and state-level and specified three-level logistic regression models (children, communities, and states) to calculate the variance partitioning coefficient attributable to each level to examine the variation in birth registration at each time point. Results: National birth registration coverage was 41.2% in 2005 and improved to 79.6% in 2015. Between 2005 and 2015, coverage among children in the poorest quintile (Q1) improved from 23.9% to 63.8% while coverage among the wealthiest children (Q5) improved from 72.4% to 92.8%. Although the absolute wealth inequity decreased from 48.6%-points to 29.1%-points, children in Q1 still had levels of coverage in 2015 that were lower than children in Q5 in 2005. Between 2005 and 2015, birth registration improved in every state and coverage was higher than 90% in 13 states. Wealth inequities decreased in 21 states and increased in 8 states. In adjusted multi-level models the proportion of total variation in birth registration attributable to states (35.7% 2005 and 29% in 2015) was larger than the variation attributable to communities (15% in 2005 and 13.7% in 2015). Conclusion: Birth registration is essential for ensuring inclusive population counts of birth and mortality rates. Efforts to reach universal birth registration in India will require a commitment to reducing wealth inequities within states.

BACKGROUND: This study examines the geographic variation and the magnitude of wealth inequities in birth registration in India between 2005 and 2015. METHODS: Data came from India's 2005 (n = 51,940) and 2015 (n = 250,194) Demographic Health Surveys. We estimated absolute wealth inequities at the national and state-level and specified three-level logistic regression models (children, communities, and states) to calculate the variance partitioning coefficient attributable to each level to examine the variation in birth registration at each time point. RESULTS: National birth registration coverage was 41.2% in 2005 and improved to 79.6% in 2015. Between 2005 and 2015, coverage among children in the poorest quintile (Q1) improved from 23.9% to 63.8% while coverage among the wealthiest children (Q5) improved from 72.4% to 92.8%. Although the absolute wealth inequity decreased from 48.6%-points to 29.1%-points, children in Q1 still had levels of coverage in 2015 that were lower than children in Q5 in 2005. Between 2005 and 2015, birth registration improved in every state and coverage was higher than 90% in 13 states. Wealth inequities decreased in 21 states and increased in 8 states. In adjusted multi-level models the proportion of total variation in birth registration attributable to states (35.7% 2005 and 29% in 2015) was larger than the variation attributable to communities (15% in 2005 and 13.7% in 2015). CONCLUSION: Birth registration is essential for ensuring inclusive population counts of birth and mortality rates. Efforts to reach universal birth registration in India will require a commitment to reducing wealth inequities within states.

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.

POLICY POINTS: Per‐capita household health spending was higher in economically developed states and was associated with ability to pay, but catastrophic health spending (CHS) was equally high in both poorer and more developed states in India. Based on multilevel modeling, we found that the largest geographic variation in health spending and CHS was at the state and village levels, reflecting wide inequality in the accessibility to and cost of health care at these levels. Contextual factors at macro and micro political units are important to reduce health spending and CHS in India. CONTEXT: In India, health care is a local good, and households are the major source of financing it. Earlier studies have examined diverse determinants of health care spending, but no attempt has been made to understand the geographical variation in household and catastrophic health spending. We used multilevel modeling to assess the relative importance of villages, districts, and states to health spending in India. METHODS: We used data on the health expenditures of 101,576 households collected in the consumption expenditure schedule (68th round) carried out by the National Sample Survey in 2011‐2012. We examined 4 dependent variables: per‐capita health spending (PHS), per‐capita institutional health spending (PIHS), per‐capita noninstitutional health spending (PNHS), and catastrophic health spending (CHS). CHS was defined as household health spending exceeding 40% of its capacity to pay. We used multilevel linear regression and logistic models to decompose the variation in each outcome by state, region, district, village, and household levels. FINDINGS: The average PHS was 1,331 Indian rupees (INR), which varied by state‐level economic development. About one‐fourth of Indian households incurred CHS, which was equally high in both the economically developed and poorer states. After controlling for household level factors, 77.1% of the total variation in PHS was attributable to households, 10.1% to states, 9.5% to villages, 2.6% to ...

Though the Census of India and large-scale demographic surveys have bridged the data gap on key demographic and health parameters, estimates on poverty and inequality remain deficient for the districts of India. The estimates on poverty and inequality indices across the states of India conceal large variations among districts. We use an innovative approach to provide consumption-based estimates of poverty and inequality indices in the districts of India by pooling the 66th and 68th rounds of consumption expenditure carried out by the National Sample Survey. The new official poverty line of 2009–2010 and 2011–2012 as recommended by the Rangarajan Committee and adopted by the Government of India is used in the estimation of poverty. A set of poverty and inequality indices, the poverty head count ratio, poverty gap square, the Gini index, Theil index and mean log deviation (MLD) are used to estimate poverty and inequality indices for 623 of the 640 districts in India. Estimates of poverty are obtained by pooling the estimates of 2009-10 and 2011-12. Results suggest wide variations in the level, depth and incidence of poverty among the districts of India irrespective of size, stage and governance in the states. The pattern of inequality is different from that of poverty; it is higher in districts with a higher level of development. Estimates of poverty are consistently correlated with wealth index, agricultural labour and female literacy. Among various factors, the fertility level, wealth index and the proportion of agricultural worker are significant predictors of poverty. Based on the findings, we suggest to increase the sample size to estimate consumption poverty in every alternate quinquennial survey and undertake a special round of survey in multidimensional poverty. Districts ranked low in poverty head count ratio should be accorded high priority in planning and program implementation.

OBJECTIVE: National averages obscure geographic variation in program performance. We determined Parliamentary Constituency (PC)‐wise estimates of TB notification to guide political engagement. METHODS: We extracted district‐level TB notification data from the 2018 annual TB report. We derived PC‐level estimates by building a 'cross‐walk' between districts and PCs using boundary shapefiles. We described the spatial distribution of the PC‐wise estimates of Total Notification Rate and percentage of Private Sector Notification. RESULTS: The median PC‐wise Total Notification Rate was 126.24/100 000 (IQR: 94.86/100 000, 162.22/100 000). The median PC‐wise Percentage Private Sector Notification was 18.03% (IQR: 9.56%, 26.84%). Only 16 (2.94%) PCs met the target of 50% private sector notification. Most of high notification rates in PCs were driven by high notification in public sector. There was geographic – both interstate and within state inter‐PC – variation in the estimates of these indicators. The study identified some geographic patterns of notification – high positive outlier PCs with adjoining PCs in lower deciles of notification rates, intra‐state differences in PC performance, and similarities in notification rates of adjoining PCs in different states. CONCLUSION: In addition to regional inequality, the study identified geospatial patterns that can aid in the formulation of suitable interventions. These include decongestion of overburdened facilities by strengthening poorly performing units. The PCs with a high percentage Private Sector Notification can act as role models for neighbouring PCs to improve private sector engagement. MPs can play a crucial role in mobilising additional resources, creating awareness, and establishing inter‐PC and inter‐state collaboration to improve TB program performance.

Studies on geographic inequalities in life expectancy in the United States have exclusively focused on single-level analyses of aggregated data at state or county level. This study develops a multilevel perspective to understanding variation in life expectancy by simultaneously modeling the geographic variation at the levels of census tracts (CTs), counties, and states. We analyzed data from 65,662 CTs, nested within 3,020 counties and 48 states (plus District of Columbia). The dependent variable was age-specific life expectancy observed in each of the CTs. We also considered the following CT-level socioeconomic and demographic characteristics as independent variables: population density; proportions of population who are black, who are single parents, who are below the federal poverty line, and who are aged 25 or older who have a bachelor's degree or higher; and median household income. Of the total geographic variation in life expectancy at birth, 70.4% of the variation was attributed to CTs, followed by 19.0% for states and 10.7% for counties. The relative importance of CTs was greater for life expectancy at older ages (70.4 to 96.8%). The CT-level independent variables explained 5 to 76.6% of between-state variation, 11.1 to 58.6% of between-county variation, and 0.7 to 44.9% of between-CT variation in life expectancy across different age groups. Our findings indicate that population inequalities in longevity in the United States are primarily a local phenomenon. There is a need for greater precision and targeting of local geographies in public policy discourse aimed at reducing health inequalities in the United States.

In India, data on key developmental indicators used to formulate policies and interventions are routinely available for the administrative unit of districts but not for the political unit of parliamentary constituencies (PC). Recently, Swaminathan et al. proposed two methodologies to generate PC estimates using randomly displaced GPS locations of the sampling clusters ('direct') and by building a crosswalk between districts and PCs using boundary shapefiles ('indirect'). We advance these methodologies by using precision-weighted estimations based on hierarchical logistic regression modeling to account for the complex survey design and sampling variability. We exemplify this application using the latest National Family Health Survey (NFHS, 2016) to generate PC-level estimates for two important indicators of child malnutrition – stunting and low birth weight – that are being monitored by the Government of India for the National Nutrition Mission targets. Overall, we found a substantial variation in child malnutrition across 543 PCs. The different methodologies yielded highly consistent estimates with correlation ranging r = 0.92-0.99 for stunting and r = 0.81-0.98 for low birth weight. For analyses involving data with comparable nature to the NFHS (i.e., complex data structure and possibility to identify a potential PC membership), modeling for precision-weighted estimates and direct methodology are preferable. Further field work and data collection at the PC level are necessary to accurately validate our estimates. An ideal solution to overcome this gap in data for PCs would be to make PC identifiers available in routinely collected surveys and the Census.