Suchergebnisse

In 2017, the Joint Monitoring Programme estimated that 520 million people in India were defecating in the open every day. This is despite efforts made by the government, Non-Governmental Organizations (NGOs), and multilaterals to improve latrine coverage throughout India. We hypothesize that this might be because current interventions focus mostly on individual-level determinants, such as attitudes and beliefs, instead of considering all possible social determinants of latrine ownership. Given this, we ask two questions: what is the association between the amount of dwelling space owned by households in rural India and their likelihood of toilet ownership and what proportion of the variation in household latrine ownership is attributable to villages and states? We used multilevel modeling and found significant associations between the amount of household dwelling space and the likelihood of latrine ownership. Furthermore, considerable variation in latrine ownership is attributable to villages and states, suggesting that additional research is required to elucidate the contextual effects of villages and states on household latrine ownership. Thus, sanitation interventions should consider household dwelling space and village and state context as important social determinants of latrine ownership in rural India. Doing so could bolster progress towards Sustainable Development Goal (SDG) 6.

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 ...

Despite the substantial burden of child undernutrition in South Asia, little is known on the relative importance and contribution of individual and micro/macro environments in shaping variation in child undernutrition. Using measures of anthropometry, we decompose the variation in child undernutrition in India to the levels of child, communities and states, quantifying the extent to which variation at each of these levels can be explained by known proximal and distal risk factors, measured at the individual (child/household) level. Data are from under-five singleton children participating in the 2005–2006 National Family Health Survey (NFHS-3). The outcome variables were: height-for-age z-score (HAZ), weight-for-age z-score (WAZ) and weight-for-height z-score (WHZ), as well as their associated measures of anthropometric failure: stunting, underweight and wasting, defined as more than two standard deviations below the median of the referred z-scores, respectively. We also considered the composite index of anthropometric failure (CIAF), defined by combinations of child anthropometric failure. After accounting for risk factors, of the total variation in HAZ, 93.2 per cent, 4.9 per cent and 1.9 per cent were attributable to the individual, community and state levels, respectively. The observed risk factors explained 6.3 per cent and 46.9 per cent of the variation at the individual and community level, respectively; however, between-state variation was not explained by these risk factors. Variability in other measures of anthropometry and anthropometric failure largely followed this pattern. Additionally, there were also considerable differences in the amount of variation at the individual and community levels among different states. Hence, there is a substantial variability at the community level compared to the state level, suggesting the presence of micro-geographies of undernutrition. Additionally, while a substantial majority of the variation in child undernutrition is at the individual level, our ability to explain variability in undernutrition at the individual-level risk factors is extremely limited. Further research is needed to explore community level or environmental factors affecting child undernutrition, generating evidence for policies to target these determinants.

Data were analysed from the Organization for Economic Cooperation and Development (OECD) Program for International Student Assessment (PISA) to examine whether the relationship between parental unemployment status and child reading literacy is modified by the level of unemployment protection provided by the nation. The sample consisted of 61,946 children, nested in 3,918 schools among 17 market economies. The results of multi‐level analyses indicated that, after controlling for a range of individual, family and school covariates, children with unemployed fathers in all countries had significantly lower reading literacy scores than those of employed fathers (β = −8.84, SE = 2.01). The contextual effect of unemployment protection was not significant after accounting for fathers' employment status (β = −18.63, SE = 16.26). However, there was a significant negative interaction between unemployment protection and fathers' unemployment, yielding the unexpected suggestion that, in countries with higher levels of unemployment protection, children with unemployed fathers fare worse, both in relation to children with unemployed fathers in lower protection countries, and in comparison with children with employed fathers (β = −26.96, SE = 8.08). Possible explanations are advanced for this result, including the potential for a 'discouraged child effect' arising from the potential association between unemployment protection and higher local unemployment rates (though unemployment rates at the national level were not significant).

Female disadvantage in child mortality, intra-household allocation of food and coverage of health interventions has been shown to exist in India. At the same time, there has been limited examination of female disadvantage in nutritional status. Using measures of anthropometry and anthropometric failure, we study female disadvantage in child nutritional status from the data collected from the Indian National Family Health Survey (NFHS) undertaken in 1992–1993 and 2005–2006. Height and/or weight measurements were available on 70,148 children aged 0–47 months in both survey periods. Child anthropometry (height/length-for-age [HAZ], weight-for-age [WAZ], weight-for-height/length [WHZ]) and anthropometric failure (defined according to the 2006 WHO growth standards as stunting, wasting and underweight) were analysed using linear and logistic regression models. In pooled regression models, boys were more likely to have lower anthropometric scores and higher rates of anthropometric failure. Across survey periods, the change in anthropometric status was greater for boys compared to girls for WAZ/underweight and WHZ/wasting, but was similar for HAZ/stunting. Boy–girl differences in anthropometry (with boys doing worse) were greater at less than 24 months of age and narrowed over time particularly in the 0–5 and 6–11 month age groups, resulting in no gender differences in anthropometric status. Declines in anthropometric status in HAZ/stunting and WAZ/underweight were found among third or higher birth order boys and girls, especially within families with two preceding children of the same sex but also in households with preceding children of mixed genders, suggesting a birth order effect as opposed to a birth order and gender effect. In 1992–1993 and 2005–2006, levels of anthropometric failure were higher among boys compared to girls in a majority of states. Although girls had lower levels of anthropometric failure, the magnitude of the between survey period decline was higher in girls in fewer states compared to boys (5/20, 7/25 and 10/20 states for stunting, underweight and wasting, respectively).  In summary, in the most recently available data, using measures of anthropometric status, we did not find consistent evidence for female disadvantage in nutritional status among girls in India.

We analysed socio-economic inequalities in stunting in South Asia and investigated disparities associated with factors at the individual, caregiver, and household levels (poor dietary diversity, low maternal education, and household poverty). We used time-series analysis of data from 55,459 children ages 6–23 months from Demographic and Health Surveys in Bangladesh, India, Nepal, and Pakistan (1991–2014). Logistic regression models, adjusted for age, sex, birth order, and place of residency, examined associations between stunting and multiple types of socio-economic disadvantage. All countries had high stunting rates. Bangladesh and Nepal recorded the largest reductions—2.9 and 4.1 percentage points per year, respectively—compared to 1.3 and 0.6 percentage points in India and Pakistan, respectively. Socio-economic adversity was associated with increased risk of stunting, regardless of disadvantage type. Poor children with inadequate diets and with poorly educated mothers experienced greater risk of stunting. Although stunting rates declined in the most deprived groups, socio-economic differences were largely preserved over time and in some cases worsened, namely, between wealth quintiles. The disproportionate burden of stunting experienced by the most disadvantaged children and the worsening inequalities between socio-economic groups are of concern in countries with substantial stunting burdens. Closing the gap between best and worst performing countries, and between most and least disadvantaged groups within countries, would yield substantial improvements in stunting rates in South Asia. To do so, greater attention needs to be paid to addressing the social, economic, and political drivers of stunting with targeted efforts towards the populations experiencing the greatest disadvantage and child growth faltering.

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.

PURPOSE: The purpose was to use Twitter to conduct online surveillance of negative sentiment towards Mexicans and Hispanics during the 2016 United States presidential election, and to examine its relationship with mental well-being in this targeted group at the population level. METHODS: Tweets containing the terms Mexican(s) and Hispanic(s) were collected within a 20-week period of the 2016 United States presidential election (November 9th 2016). Sentiment analysis was used to capture percent negative tweets. A time series lag regression model was used to examine the association between percent count of negative tweets mentioning Mexicans and Hispanics and percent count of worry among Hispanic Gallup poll respondents. RESULTS: Of 2,809,641 tweets containing terms Mexican(s) and Hispanic(s), 687,291 tweets were negative. Among 8,314 Hispanic Gallup respondents, a mean of 33.5% responded to be worried on a daily basis. A significant lead time of 1 week was observed, showing that negative tweets mentioning Mexicans and Hispanics appeared to forecast daily worry among Hispanics by 1 week. CONCLUSION: Surveillance of online negative sentiment towards racially vulnerable population groups can be captured using social media. This has potential to identify early warning signals for symptoms of mental well-being among targeted groups at the population level.

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.