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.
There are emerging opportunities to assess health indicators at truly small areas with increasing availability of data geocoded to micro geographic units and advanced modeling techniques. The utility of such fine-grained data can be fully leveraged if linked to local governance units that are accountable for implementation of programs and interventions. We used data from the 2011 Indian Census for village-level demographic and amenities features and the 2016 Indian Demographic and Health Survey in a bias-corrected semisupervised regression framework to predict child anthropometric failures for all villages in India. Of the total geographic variation in predicted child anthropometric failure estimates, 54.2 to 72.3% were attributed to the village level followed by 20.6 to 39.5% to the state level. The mean predicted stunting was 37.9% (SD: 10.1%; IQR: 31.2 to 44.7%), and substantial variation was found across villages ranging from less than 5% for 691 villages to over 70% in 453 villages. Estimates at the village level can potentially shift the paradigm of policy discussion in India by enabling more informed prioritization and precise targeting. The proposed methodology can be adapted and applied to diverse population health indicators, and in other contexts, to reveal spatial heterogeneity at a finer geographic scale and identify local areas with the greatest needs and with direct implications for actions to take place.
In India, districts serve as central policy unit for program development, administration and implementation. The one-size-fits-all approach based on average prevalence estimates at the district level fails to capture the substantial small area variation. In addition to district average, heterogeneity within districts should be considered in policy design. The objective of this study was to quantify the extent of small area variation in child stunting, underweight and wasting across 36 states/Union Territories (UTs), 640 districts (and 543 PCs), and villages/blocks in India. We utilized the 4th round of Indian National Family Health Survey (NFHS-4) conducted in 2015–2016. The study population included 225,002 children aged 0–59 months whose height and weight information were available. Stunting was defined as height-for-age z-score below 2 SD from the World Health Organization child growth reference standards. Similarly, underweight and wasting were each defined as weight-for-ageþinspace}< -2 SD and weight-for-heightþinspace}< -2 SD from the age- and sex-specific medians. We adopted a four-level logistic regression model to partition the total variation in stunting, underweight and wasting. We computed precision-weighted prevalence of child anthropometric failures across districts and PCs as well as within-district/PC variation using standard deviation (SD) measures. For stunting, 56.4% (var: 0.237; SE: 0.008) of the total variation was attributed to villages/blocks, followed by 25.8% (var: 0.109; SE: 0.030) to states/UTs, and 17.7% (Var: 0.074; SE: 0.006) to districts. For underweight and wasting, villages/blocks accounted for 38.4% (var: 0.224; SE: 0.007) and 50% (var: 0.285; SE: 0.009), respectively, of the total contextual variance in India. Similar findings were shown in multilevel models incorporating PC as a geographical unit instead of districts. We found high positive correlations between mean prevalence and SD for stunting (rþinspace}=þinspace}0.780, pþinspace}<þinspace}0.001), underweight (rþinspace}=þinspace}0.860, pþinspace}<þinspace}0.001), and wasting (rþinspace}=þinspace}0.857, pþinspace}<þinspace}0.001) across all districts in India. A similar pattern of correlation was found for PCs. Within-district and within-PC variation are the primary source of variation for child malnutrition in India. Our results suggest the importance of considering heterogeneity within districts and PCs when planning and administering child nutrition policies.
In India and worldwide, there has been increased strategic focus on multisectoral convergence of nutrition-specific and nutrition-sensitive interventions to attain rapid reductions in child undernutrition. For instance, a Convergence Action Plan in India has been formed to synchronize and converge various nutrition-related interventions across ministries of union and state governments under a single umbrella. Given the large variation in number, nature and impact of these interventions, this paper aims to quantify the contribution of each intervention (proxied by relevant covariates) toward reducing child stunting and underweight in India. The interventions are classified under six sectors: (a) health, (b) women and child development, (c)education, (d) water, sanitation, and hygiene, (e) clean energy, and (f) growth sector. We estimate the potential reduction in child stunting and underweight in a counterfactual scenario of “convergence” where all the interventions across all the sectors are simultaneously and successfully implemented. The findings from our econometric analysis suggests that under this counterfactual scenario, a reduction of 18.37% points (95% CI: 16.77; 19.95) in stunting and 20.26% points (95% CI: 19.13; 21.39) in underweight can be potentially achieved. Across all the sectors, women and child development and clean energy were identified as the biggest contributors to the potential reductions in stunting and underweight, underscoring the importance of improving sanitation-related practices and clean cooking fuel. The overall impact of this convergent action was relatively stronger for less developed districts. These findings reiterate a clear role and scope of convergent action in achieving India’s national nutritional goals. This warrants a complete outreach of all the interventions from different sectors.
The congressional district (CD) geography in the United States (US) represents a policy-relevant and a politically important scale at which to monitor the coronavirus disease 2019 (COVID-19) crisis. In this study, we present the first estimates of COVID-19 cumulative cases and deaths (per 1,000 people) and the case fatality rate as of July 13, 2020, as well as for the recent period between June 13 and July 13, 2020 using a population-weighting methodology for the 436 CDs in the US. Access to CD-level information about the impact of COVID-19 can enhance the ability of elected officials and the constituents they represent, to monitor and develop testing strategies and other measures to allow the US to open safely.
Minimum Dietary Diversity (MDD) is a widely used indicator of adequate dietary micronutrient density for children 6–23 mo old. MDD food-group data remain underutilized, despite their potential for further informing nutrition programs and policies. We aimed to describe the diets of children meeting MDD and not meeting MDD in India using food group data, nationally and subnationally. Food group data for children 6–23 mo old (n = 73,036) from the 2015–16 National Family Health Survey in India were analyzed. Per WHO standards, children consuming ≥5 of the following food groups in the past day or night met MDD: breast milk; grains, roots, or tubers; legumes or nuts; dairy; flesh foods; eggs; vitamin A–rich fruits and vegetables; and other fruits and vegetables. Children not meeting MDD consumed <5 food groups. We analyzed the number and types of foods consumed by children meeting MDD and not meeting MDD at the national and subnational geographic levels. Nationally, children not meeting MDD most often consumed breast milk (84.5%), grains, roots, and tubers (62.0%), and/or dairy (42.9%). Children meeting MDD most often consumed grains, roots, and tubers (97.6%), vitamin A–rich fruits and vegetables (93.8%), breast milk (84.1%), dairy (82.1%), other fruits and vegetables (79.5%), and/or eggs (56.5%). For children not meeting MDD, district-level dairy consumption varied the most (6.4%–79.9%), whereas flesh foods consumption varied the least (0.0%–43.8%). For children meeting MDD, district-level egg consumption varied the most (0.0%–100.0%), whereas grains, roots, and tubers consumption varied the least (66.8%–100.0%). Children not meeting MDD had low fruit, vegetable, and protein-rich food consumption. Many children meeting MDD also had low protein-rich food consumption. Examining the number and types of foods consumed highlights priorities for children experiencing the greatest dietary deprivation, providing valuable complementary information to MDD.
Ratio-based prevalence and absolute headcounts are the two most commonly accepted metrics to measure the burden of various socioeconomic phenomenon. However, ratio-based prevalence, calculated as the number of cases with certain conditions relative to the total population, is by far the most widely used to rank burden and consequently for targeting, across different populations, often defined in terms of geographical areas. In this regard, targeting areas exclusively based on prevalence-based metric poses certain fundamental difficulties with some serious policy implications. Drawing the data from the National Family Health Survey 2015–2016, and Census 2011, this paper takes four indicators of child undernutrition in India as an example to examine two contextual questions: first, does the choice of metric matter for targeting areas for reducing child undernutrition in India? and second; which metric should be used to facilitate comparisons and targeting across variable populations? Our findings suggest a moderate correlation between prevalence estimates and absolute headcounts implying that choice of metric does matter when targeting child undernutrition. Huge variations were observed between prevalence-based and absolute countbased ranking of the districts. In fact, in various cases, districts with the highest absolute number of undernourished children were ranked as relatively lower-burden districts based on prevalence. A simple comparison between the two approaches—when applied to targeting undernourished children in India—indicates that prevalence-based prioritization may miss high-burden areas where substantially higher number of undernourished children are concentrated. For developing populous countries like India, which is already grappling with high levels of maternal and child malnutrition and poor health infrastructure along with intrinsic socioeconomic inequalities, it is critical to adopt an appropriate metric for effective targeting and prioritization.
The National Family Health Survey is analysed to develop critical insights on child anthropometric failure in India. The analysis finds non-response of economic growth on nutritional well-being and greater burden among the poor as two fundamental concerns. This calls for strengthening developmental finance for socio-economic upliftment as well as enhanced programmatic support for nutritional interventions. The gaps in analytical inputs for programmatic purposes also deserves attention to unravel intricacies that otherwise remain obscured through customary enquiries. On the one hand, this may serve well to improve policy targeting, and on the other, this can help comprehend the nature and reasons of heterogeneities and inequities in nutritional outcomes across subgroups. Strengthening the analytical capacities of programme managers and health functionaries is recommended.Against this backdrop, this paper outlines key programmatic concerns that require substantial local-level insights for strategic feedback and course corrections to achieve accelerated reductions in child undernutrition. The issues discussed are based on the analysis of household survey data from NFHS 2015–16.
Researchers from the Geographic Insights Lab at the Harvard Center for Population and Development Studies and the Institute of Economic Growth geo-visualised diet, anthropometric and clinical indicators for children across districts in India and provide a clear snapshot of high priority districts for targeting nutritional interventions among children in India.
We assessed district-level geospatial trends in precision weighted prevalence and absolute wealth disparity in stunting, underweight, wasting, low birthweight, and anemia among children under five in India. The largest wealth disparities were found for anthropometric failures and substantial variation existed across states. We identified statistically significant (p < 0.001) geospatial patterns in district-wide wealth disparities for all outcomes, which differed from geospatial patterns for the overall prevalence. We characterized each district as either a “Disparity”, “Pitfall”, “Intensity”, or “Prosperity” area based on its overall burden and wealth disparity, as well as discuss the importance of considering both measures for geographically-targeted public health interventions to improve health equity.
While some improvement has been made by the country, efforts to ensure nutritional security are more important than ever
It’s been three years since the Government of India launched the Prime Minister’s Overarching Scheme for Holistic Nutrition, or Poshan Abhiyaan . The goal was to improve the nutritional status of children and adolescent girls, as well as pregnant and lactating mothers. The urgency was evident as a clear time-frame of three years was set, and five indicators—prevalence of low birthweight, stunting, underweight and anaemia among children and women—were identified for substantial improvement. The government showed its commitment by putting money where its mouth was; it allocated ₹9,046.17 crore for the mission.
The trigger to go on an all-India mission was the disconcerting statistics revealed by the fourth National Family Health Survey (NFHS-4, 2015-16) on the above indicators. The fact sheets for 342 districts from 17 states and five Union territories (UTs) from the NFHS-5 , (bit.ly/2WQA51y) conducted in 2019-20, are now out. With the exception of prevalence of low birthweight, the just released district fact sheets provide data on four of the five Poshan Abhiyaan indicators. So, how have the 342 districts done with regard to meeting the targets set?
Comparing performance across districts: Of the four indicators, stunting across districts improved the most, with 69/342 districts experiencing a decline of more than 6 percentage points (the Poshan Abhiyaan target) between the two surveys. Child underweight followed a pattern similar to stunting with 42/342 districts meeting the target. Anaemia, however, is a different story; the Poshan Abhiyaan target of a 9-percentage-point decline in three years was observed for only 18/342 (women and adolescent girls) and 20/342 (children) districts.
Some degree of decline in the prevalence of stunting and underweight was observed in nearly half of the districts. At the same time, a little over half of them also disconcertingly experienced a reversal. In 74/342 (stunting) and 66/342 (underweight) districts, the reversal was of the same magnitude as the target (i.e., 6 percentage point), but in the detrimental direction.
Anaemia not only showed minimal improvement across districts, it reversed in 81.3% (278/342 for children) and 74.3% (254/342 for adolescent girls/women) of the districts. Disturbingly, the reversal was greater than 9 percentage points in 186/342 (children) and 139/342 (adolescent girls and women) districts.
The way forward: The following two insights emerging from the data patterns would be crucial to consider if India chooses to upgrade and implement Poshan Abhiyaan 2.0.
First, within the same state, there are districts that experienced improvement as well as reversal, especially with regards to stunting and underweight findings. It is important that an immediate effort is made to learn from both success and failure in these districts. Such learning can focus on both the distinct components of the Poshan Abhiyaan programme, as well as how synergistically they were implemented.
Second, the fact that a notable number of districts experienced improvement and reversal suggests that inequality between districts (even within the same state) has increased. This might be an unintended consequence of a concentrated focus on certain districts over others in recent years.
Prioritizing certain districts over others is inevitable in any policy formulation and implementation. An examination of the districts that experienced substantial improvement or reversal, and whether they were priority districts for Poshan Abhiyaan or not would be necessary. Learning from this should then be used to modify or tweak using other methods for prioritization, both for efficiency as well as for promoting geographic equity. Further indicator-specific prioritization is also necessary as the data reveals different patterns for anthropometric-based nutritional measures of stunting/underweight from the more direct measures of dietary deficiency.
Recent evidence also makes it clear that it would be prudent to equally focus on within-district variation, in particular between villages. Villages are not only a setting for social engagement, but also are the unit where public policies and programmes come to fruition for the target population.
The three-year time-frame of Poshan Abhiyaan, incidentally, ended this month. It is unclear what the future of Poshan Abhiyaan will be. Regardless, the NFHS-5 data makes it obvious that reducing the burden of undernutrition, especially among children and women, will need to remain a greater priority than ever before. It is, therefore, imperative that the government undertakes a rigorous assessment of Poshan Abhiyaan, including an exploration of any changes and course corrections that may be necessary.
It is critical to remember that these statistics reflect a scenario prior to the covid pandemic and the 2020 lockdown. From all accounts, these two events hurt health and nutrition services, which are vital to any child’s first 1,000 days and a core feature of Poshan Abhiyaan. Whatever form the next phase of India’s mission to eliminate undernutrition takes, reversals experienced by a majority of districts on nutrition indicators suggest that India needs to make food security a centrepiece of its overall development agenda.
Laxmi Kant Dwivedi of International Institute for Population Sciences and Weiyu Wang and Weixing Zhang of Harvard Center for Population and Development Studies assisted with this article.
S.V. Subramanian is professor of population health and geography, Harvard Center for Population and Development Studies.
The public discourse around the COVID-19 pandemic has been strikingly quantitative. Worldwide, the mainstream media has regularly informed the public of confirmed COVID-19 cases and deaths, including projections of worst-case scenarios drawn from esoteric epidemiological models. The prominence and visibility of data, regardless of its completeness or quality, underscored the threat of COVID-19 to policy makers and lay individuals alike. It also prompted governments to swiftly lock down their societies, despite the socioeconomic disruptions and human suffering associated with such lockdowns. The widespread media coverage of COVID-19 data and swift response from governments highlight the potency of real-time data, and contain important lessons for public health policy, which when applied, could raise the profile of other health issues and spur action among key stakeholders.
The National Family Health Survey (NFHS) 2015–16, finds that every second child in India suffers from at least one form of nutrition failure. Dichotomised indicators of underweight and wasting based on z-score cut-off does not provide any information regarding those children who are clustered around the threshold and are at an elevated risk of undernutrition through any minor weight-loss. This paper aims to estimate the effect of bodyweight shocks on net increments in the prevalence of child underweight and wasting among the poorest households in India. We used cross-sectional information from NFHS 2015–16 to estimate possible increase in the prevalence of child underweight and wasting as a result of reduction in their bodyweight. The shocks are presumed to range from a minimum of 0.5% to a maximum 5% reduction in the bodyweight for every child from the poorest 20% households. Various raw weight measures scenarios were developed and transformed into age- specific z-scores using World Health Organization child growth standards. Nutritional status of children is sensitive to smallest of the shocks to bodyweight. In fact, a reduction of 0.5 and 1 percent in weight can lead to substantial increase in underweight and wasting prevalence. Under a scenario of bodyweight shock of 0.5 percent, the prevalence of underweight and wasting will increase by 1.42 and 1.36 percentage points, respectively. These estimates get translated into 410,413 and 392,886 additional cases of underweight and wasting, respectively. With such high concentration of children around the undernutrition threshold, any minor shock to nutritional health of the children can have major implications. In the current scenario of national lockdown and restrictions due to coronavirus disease 2019 pandemic, it is critical to ensure an uninterrupted supply of nutritious meals and food supplements to the poor children while arresting the infection spread.
Reducing the burden of child undernutrition has been central to the developmental goals of the Government of India. The flagship programme, POSHAN Abhiyaan, launched in 2018, provided a much-needed fillip to the nutrition agenda. Dedicated budgetary allocation, and administrative and community efforts are expected to accelerate nutritional improvements in the years to come. Until now, the latest available estimates on various measures of children’s nutritional status came from the fourth round of the National Family Health Survey, or the NFHS, conducted in 2015-16. The much-awaited statistics on the prevalence of nutritional status among children from the fifth round of the NFHS conducted in 2019 in 17 states and five Union Territories (UTs) is out.
What can we learn with regards to the progress the country is making in terms of reducing the burden of undernutrition among Indian children?
Considering the commonly used measure of anthropometric failure as an indication of undernutrition, it is clear that there cannot be any let up to the efforts that have been put in place recently. On all three measures of anthropometric failure (stunting, underweight and wasting) the mean prevalence across these 22 states/UTs has increased (see table).
Stunting prevalence decreased in nine states/UTs and increased in 13 states/UTs. Greatest reduction occurred in Sikkim, Manipur and Bihar. However, stunting increased in Kerala, Goa and Himachal Pradesh, i.e., states typically seen as ‘better performing’. It also increased in Maharashtra, Gujarat, and West Bengal. The prevalence of underweight, meanwhile, shows reduction in six states/UTs whereas wasting reduced in only eight states/UTs.
The POSHAN Abhiyaan had set a target of achieving two percentage points per annum reduction in stunting and underweight prevalence. None of the 22 states/UTs were able to achieve these aspirational targets in stunting or underweight, with Tripura and Nagaland showing an increase of more than two percentage points per annum. It is concerning that in seven states/UTs, all the three indicators have worsened since NFHS 2015-16. These include Himachal Pradesh, Kerala, Lakshadweep, Mizoram, Nagaland, Telangana and Tripura. Only three states i.e., Andhra Pradesh, Karnataka and Sikkim showed improvements in these three anthropometric indicators.
The decline in stunting by 5.4 per cent for Bihar is a bright spot in what appears to be a disconcerting trend. We hope there are opportunities to learn from this decline that can then be useful for other states, and to ensure that Bihar sustains this trajectory of decline. Notwithstanding this welcome trend, Bihar remains the state with the highest prevalence for stunting and underweight.
With the economy shrinking in the first two quarters of FY21, the Union and the state governments will also find themselves constrained in enhancing developmental spending directly related to nutritional health and well-being. But it is important that flagship schemes of the government of India display the same commitment as before, pandemic or not. In fact, these areas need an ever-greater intent and purpose in supporting states/UTs in their mission to reduce the burden of undernutrition among children.
What needs to be done?
It should be noted that ‘averages’ in India – even at the state level – mask more than they reveal, whether it relates to the geographic differences within a state (e.g., districts) or the differences in the prevalence across socio-economic groups. It is well known that the most crucial determinant of a child’s anthropometric status is the household socio-economic well-being. It is crucial that the POSHAN Abhiyaan should find means to enhance and optimise resource allocations with a targeted focus on the poor and deprived sections. In addition to the programmes that are directly related to nutrition (e.g., food), the time is ripe to link sector- specific initiatives with the broader goal of poverty alleviation and improving the overall standard of living through employment generation and asset creation. Since the burden of anthropometric failure is known to be a consequence of multiple factors, the response to reducing it needs to embrace this perspective by convergence across sectors.
It should be noted that the sobering statistics delivered by the latest NFHS predates the Covid- 19 pandemic and the subsequent 2020 lockdown that followed as a response. Thus, the data that got released for 22 states/UTs, especially for those that appeared to have experienced a decline, needs to be interpreted cautiously as situation may have worsened. Similarly, states that experienced an increase, the magnitude could be even greater.
The NFHS 2019-20 is currently underway in the remaining 14 states/UTs, and we may be able to use this creatively to assess the impact of the 2020 lockdown that has relegated the economy and brought widespread disruptions, impacting the lower socio-economic groups disproportionately. For the first time since NFHS started measuring nutritional status systematically, India finds itself at an elevated risk of experiencing a trend-reversal in the prevalence of stunting and underweight. It may seem that child undernutrition — at least as measured through anthropometry — is turning out to be the Achilles’ heel of India.
Background The conventional indicators of infant and under-five mortality are aggregate deaths occurring in the first year and the first five years, respectively. Monitoring deaths by <1 month (neonatal), 1-11 months (post-neonatal), and 12- 59 months (child) can be more informative given various etiological causes that may require different interventions across these three mutually exclusive periods. For optimal resource allocation, it is also necessary to track progress in robust estimates of child survival at a smaller geographic and administrative level.
Methods Data on 259627 children came from the 2015-2016 Indian National Family Health Survey. We used a random effects model to account for the complex survey design and sampling variability, and predicted district-specific probabilities of neonatal, post-neonatal, and child mortality. The resulting precision-weighted estimates are more reliable as they pool information and borrow strength from other districts that share the same state membership. The Pearson correlation and Spearman’s rank correlation were assessed for the three mortality estimates, and the Moran’s I measure was used to detect spatial clustering of high burden districts for each outcome.
Results The majority of under-five deaths was disproportionately concentrated in the neonatal period. Across all districts, the predicted probability of neonatal, post-neonatal, and child mortality varied from 6.0 to 63.9 deaths, 3.8 to 47.6 deaths, and 1.7 to 11.8 deaths per 1000 live births, respectively. The overall correlation between district-wide probabilities of mortality for the three mutually exclusive periods was moderate (Pearson correlation=0.47-0.58, Spearman’s rank correlation=0.58-0.64). For each outcome, a relatively strong spatial clustering was detected across districts that transcended state boundaries (Moran’s I=0.61-0.76).
Conclusions Sufficiently breaking down the under-five mortality to distinct age groups and using the precision-weighted estimations to monitor performances at smaller geographic and administrative units can inform more targeted interventions and foster accountability to improve child survival