Ultra-processed Food Intake and Risk of Type 2 Diabetes in Korean Adults.

BACKGROUND: Several studies from the United States and European countries reported a positive association between ultra-processed food intake and diabetes risk. However, little is known about the association in Asian populations. It is also unknown about the individual ultra-processed food items that are most unfavorably associated with diabetes risk. OBJECTIVE: We examined the associations of ultra-processed food intake (combined, as well as individual ultra-processed food items) with the risk of type 2 diabetes. METHODS: This prospective analysis included 7438 participants aged 40-69 y from the Korean Genome and Epidemiology Study Ansan-Ansung cohort. Dietary intake was assessed at baseline using a 103-item semiquantitative food-frequency questionnaire. Ultra-processed foods were classified using the Nova definition. Incident type 2 diabetes cases were identified via follow-up interviews and health examination. Multivariable Cox proportional hazard models were used to estimate hazard ratios (HRs) and 95% confidence intervals (CIs), adjusting for potential confounders. RESULTS: During the follow-up (2001-2019; median: 15 y), a total of 1187 type 2 diabetes cases were identified. Compared with the lowest quartile of ultra-processed food intake, the highest quartile was positively associated with diabetes risk [HR (95% CI) = 1.34 (1.13, 1.59), P-trend = 0.002]. The association did not change after additional adjustment for diet quality or BMI. Among individual ultra-processed food items, a higher consumption of ham/sausage [per 1% increase in the weight ratio: HR (95% CI) = 1.40 (1.05, 1.86)], instant noodles [1.07 (1.02, 1.11)], ice cream [1.08 (1.03, 1.13)], and carbonated beverages [1.02 (1.00, 1.04)] were associated with an increased risk of type 2 diabetes, whereas a higher intake of candy/chocolate was associated with a decreased risk [0.78 (0.62, 0.99)]. CONCLUSIONS: Our data suggest that the high intake of ultra-processed foods, particularly ham/sausage, instant noodles, ice cream, and carbonated beverages, is associated with an increased risk of type 2 diabetes in Korean adults.

Polygenic and socioeconomic contributions to nicotine use and cardiometabolic health in early mid-life.

INTRODUCTION: Early mid-life is marked by accumulating risks for cardiometabolic illness linked to health-risk behaviors like nicotine use. Identifying polygenic indices (PGI) has enriched scientific understanding of the cumulative genetic contributions to behavioral and cardiometabolic health, though few studies have assessed these associations alongside socioeconomic (SES) and lifestyle factors. METHODS: Drawing on data from 2,337 individuals from the United States participating in the National Longitudinal Study of Adolescent to Adult Health, the current study assesses the fraction of variance in five related outcomes - use of conventional and electronic cigarettes, body mass index (BMI), waist circumference, and glycosylated hemoglobin (A1c) - explained by PGI, SES, and lifestyle. RESULTS: Regression models on African ancestry (AA) and European ancestry (EA) subsamples reveal that the fraction of variance explained by PGI ranges across outcomes. While adjusting for sex and age, PGI explained 3.5%, 2.2%, and 0% in the AA subsample of variability in BMI, waist circumference, and A1c, respectively (in the EA subsample these figures were 7.7%, 9.4%, and 1.3%). The proportion of variance explained by PGI in nicotine-use outcomes is also variable. Results further indicate that PGI and SES are generally complementary, accounting for more variance in the outcomes when modeled together versus separately. CONCLUSIONS: PGI are gaining attention in population health surveillance, but polygenic variability might not align clearly with health differences in populations or surpass SES as a fundamental cause of health disparities. We discuss future steps in integrating PGI and SES to refine population health prediction rules. IMPLICATIONS: Study findings point to the complementary relationship of polygenic indices (PGI) and socioeconomic indicators in explaining population variance in nicotine outcomes and cardiometabolic wellness. Population health surveillance and prediction rules would benefit from the combination of information from both polygenic and socioeconomic risks. Additionally, the risk for electronic cigarette use among users of conventional cigarettes may have a genetic component tied to the cumulative genetic propensity for heavy smoking. Further research on PGI for vaping is needed.

Problems accessing health care and under-5 mortality: a pooled analysis of 50 low- and middle-income countries.

BACKGROUND: Access to health care remains suboptimal in low- and middle-income countries (LMICs) and continues to hinder survival in early childhood. We systematically assessed the association between problems accessing health care (PAHC) and under-five mortality (U5M). METHODS: Child mortality data on 724 335 livebirths came from the latest Demographic and Health Surveys of 50 LMICs (2013-2021). Reasons for PAHC were classified into three domains: 'money needed for treatment' (economic), 'distance to health facility' (physical), 'getting permission' or 'not wanting to go alone' (socio-cultural). Multivariable logistic regression was used to estimate the association between PAHC (any and by each type) and U5M. RESULTS: In our pooled sample, 47.3 children per 1000 livebirths died before age of 5, and 57.1% reported having experienced PAHC (ranging from 45.3% in Europe & Central Asia to 72.7% in Latin America & Caribbean). Children with any PAHC had higher odds of U5M (OR: 1.05, 95% CI: 1.02, 1.09), and this association was especially significant in sub-Saharan Africa. Of different domains of PAHC, socio-cultural PAHC was found to be most significant. CONCLUSIONS: Access to health care in LMICs needs to be improved by expanding health care coverage, building health facilities, and focusing more on context-specific socio-cultural barriers.

Temporal change in prevalence of BMI categories in India: patterns across States and Union territories of India, 1999-2021.

BACKGROUND: The problem of overweight/obesity often coexists with the burden of undernutrition in most low- and middle-income countries. BMI change in India incorporating the most recent trends has been under-researched. METHODS: This repeated cross-sectional study of 1,477,885 adults in India analyzed the prevalence of different categories of BMI among adults (age 20-54) in 4 rounds of National Family Health Surveys (1998-1999, 2005-2006, 2015-2016, and 2019-2021) for 36 states/UTs. State differences across time were harmonized for accurate analysis. The categories were Severely/Moderately Thin (BMI < 17.0), Mildly Thin (17.0-18.4), Normal (18.5-24.9), Overweight (25.0-29.9), and Obese (≥ 30.0). We also estimated change in Standardized Absolute Change (SAC), ranking of states, and headcount burden to quantify the trend of BMI distribution across time periods for all-India, urban/rural residence, and by states/UTs. RESULTS: The prevalence of thinness declined from 31.7% in 1999 to 14.2% in 2021 for women, and from 23.4% in 2006 to 10.0% in 2021 for men. Obesity prevalence increased from 2.9% (1999) to 6.3% (2021) for women, and from 2.0% (2006) to 4.2% (2021) for men. In 2021, the states with the highest obesity prevalence were Puducherry, Chandigarh, and Delhi. These states also had a high prevalence of overweight. Dadra and Nagar Haveli and Diu, Gujarat, Jharkhand, and Bihar had the highest prevalence of severe/moderately thin. Prevalence of extreme categories (severely/moderately thin and obese) was larger in the case of women than men. While States/UTs with a higher prevalence of thin populations tend to have a larger absolute burden of severe or moderate thinness, the relationship between headcount burden and prevalence for overweight and obese is unclear. CONCLUSIONS: We found persistent interstate inequalities of undernutrition. Tailored efforts at state levels are required to further strengthen existing policies and develop new interventions to target both forms of malnutrition.

Precision mapping child undernutrition for nearly 600,000 inhabited census villages in India.

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.

Trends in prevalence of unmet need for family planning in India: patterns of change across 36 States and Union Territories, 1993-2021.

BACKGROUND: Eliminating unmet need for family planning by 2030 is a global priority for ensuring healthy lives and promoting well-being for all at all ages. We estimate the sub-national trends in prevalence of unmet need for family planning over 30 years in India and study differences based on socio-economic and demographic factors. METHODS: We used data from five National Family Health Surveys (NFHS) conducted between 1993 to 2021 for the 36 states/Union Territories (UTs) of India. The study population included women of ages 15-49 years who were married or in a union at the time of the survey. The outcome was unmet need for family planning which captures the prevalence of fecund and sexually active women not using contraception, who want to delay or limit childbearing. We calculated the standardized absolute change to estimate the change in prevalence on an annual basis across all states/UTs. We examined the patterning of prevalence of across demographic and socioeconomic characteristics and estimated the headcount of women with unmet need in 2021. RESULTS: The prevalence of unmet need in India decreased from 20·6% (95% CI: 20·1- 21·2%) in 1993, to 9·4% (95% CI: 9·3-9·6%) in 2021. Median unmet need prevalence across states/UTs decreased from 17·80% in 1993 to 8·95% in 2021. The north-eastern states of Meghalaya (26·9%, 95% CI: 25·3-28·6%) and Mizoram (18·9%, 95% CI: 17·2-20·6%), followed by the northern states of Bihar (13·6%, 95% CI: 13·1-14·1%) and Uttar Pradesh (12·9%, 95% CI: 12·5-13·2%), had the highest unmet need prevalence in 2021. As of 2021, the estimated number of women with an unmet need for family planning was 24,194,428. Uttar Pradesh, Bihar, Maharashtra, and West Bengal accounted for half of this headcount. Women of ages 15-19 and those belonging the poorest wealth quintile had a relatively high prevalence of unmet need in 2021. CONCLUSIONS: The existing initiatives under the National Family Planning Programme should be strengthened, and new policies should be developed with a focus on states/UTs with high prevalence, to ensure unmet need for family planning is eliminated by 2030.

This study looked at the trends in unmet need for family planning in India, which is defined as the percentage of women of reproductive age who want to delay or limit childbearing but are not using any contraceptive method. A public dataset was used to analyze national and sub-national trends from 1993 to 2021. It was determined that although the percentage prevalence of unmet need decreased in the last 30 years, there were still a substantial number of women with unmet need in 2021. More than half of these women were in Uttar Pradesh, Bihar, Maharashtra, and West Bengal. Furthermore, it was found that percentage prevalence of unmet need was relatively higher amongst younger women and those belonging to poorer households in 2021. Initiatives and policies aimed at reducing unmet need for family planning should be implemented while considering geographic, socioeconomic, and demographic differences.

Socio-economic distribution of modifiable risk factors for cardiovascular diseases: An analysis of the national longitudinal ageing study in India.

The association of socioeconomic status (SES) with modifiable risk factors for cardiovascular diseases (CVDs) is unclear in developing nations. We studied SES variations in major risk factors and their percentage distribution for adults aged 45 years or above in India. Using individual records of 59,672 individuals aged 45 years or above from the Longitudinal Ageing Study in India Wave 1 (cross-sectional study design), 2017-18, we chart age-and-sex-adjusted prevalence of clinical risk factors such as measured high blood pressure, hypertension, overweight, obesity, central adiposity and self-reported high blood glucose; and lifestyle risk factors such as excessive use of alcohol, current use of smoking and smokeless tobacco and physical inactivity across SES variables of education, quintiles of mean per capita expenditure and social caste. Multivariable analysis was used to explore the SES gradient of risk factors. The sample used in the study is predominantly rural (69.9%), illiterate (50.7%), has more females (54.2%), and belongs to other backward classes (45.6%). Prevalence of high blood pressure, overweight, obesity, central adiposity, high blood glucose, and physical inactivity increased; and excessive alcohol consumption and current use of smoking/smokeless tobacco decreased with income, education, and social caste. However, no significant income gradient was noted for lifestyle risk factors except the use of smokeless tobacco. The income gradient was largest for central adiposity (waist-circumference) with a difference of 23.4 percentage points as it increased from 38.7% among the poorest to 62.1% among the richest. The major burden of CVDs risk factors among older adults aged 45+ years falls among high SES.

Associations between discrimination experiences and symptoms of depression and anxiety among transgender adults: a nationwide cohort study of 269 transgender adults in South Korea.

PURPOSE: Recently, there has been an increase in awareness of social stigma and mental health issues experienced by transgender individuals in South Korea. To provide quantitative evidence, we conducted a nationwide cohort study of transgender adults, first of its kind in Asia. The aim of the study is to assess the prevalence of depressive and anxiety symptoms and examine their associations with discrimination experiences among transgender adults. METHODS: We conducted a two-wave longitudinal survey of 269 Korean transgender adults, where the baseline was collected in October 2020 and the follow-up in October 2021. Experiences of discrimination in the past 12 months at follow-up were categorized accordingly: those who experienced (1) none, (2) only anti-transgender discrimination, (3) only other types of discrimination, and (4) both anti-transgender and other types of discrimination. Depressive symptoms were measured using the Center for Epidemiologic Studies Depression scale at both waves and anxiety symptoms were measured using the Generalized Anxiety Disorder 7 only at follow-up. We used modified Poisson regression to examine the association between experiences of discrimination and mental health outcomes at follow-up and adjusted for sociodemographic characteristics and baseline depressive symptoms. RESULTS: A total of 63.9% had depressive symptoms and 47.2% had anxiety symptoms. Participants who experienced both anti-transgender and other types of discrimination had 1.38-times (95% CI 1.06-1.81) and 1.77-times (95% CI 1.16-2.70) higher prevalence of depressive and anxiety symptoms, respectively, compared to those without any experiences of discrimination. CONCLUSIONS: Interventions to lessen discrimination towards transgender individuals are needed for the promotion of mental health among transgender individuals.

Quantifying and explaining variation in life expectancy at census tract, county, and state levels in the United States.

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.

Patterns in child stunting by age: A cross-sectional study of 94 low- and middle-income countries.

Child stunting prevalence is primarily used as an indicator of impeded physical growth due to undernutrition and infections, which also increases the risk of mortality, morbidity and cognitive problems, particularly when occurring during the 1000 days from conception to age 2 years. This paper estimated the relationship between stunting prevalence and age for children 0-59 months old in 94 low- and middle-income countries. The overall stunting prevalence was 32%. We found higher stunting prevalence among older children until around 28 months of age-presumably from longer exposure times and accumulation of adverse exposures to undernutrition and infections. In most countries, the stunting prevalence was lower for older children after around 28 months-presumably mostly due to further adverse exposures being less detrimental for older children, and catch-up growth. The age for which stunting prevalence was the highest was fairly consistent across countries. Stunting prevalence and gradient of the rise in stunting prevalence by age varied across world regions, countries, living standards and sex. Poorer countries and households had a higher prevalence at all ages and a sharper positive age gradient before age 2. Boys had higher stunting prevalence but had peak stunting prevalence at lower ages than girls. Stunting prevalence was similar for boys and girls after around age 45 months. These results suggest that programmes to prevent undernutrition and infections should focus on younger children to optimise impact in reducing stunting prevalence. Importantly, however, since some catch-up growth may be achieved after age 2, screening around this time can be beneficial.

Geographic variation in caesarean delivery in India.

BACKGROUND: The rate of caesarean delivery has increased markedly both globally and within India. However, there is considerable variation within countries. No previous studies have examined the relative importance of multiple geographic levels in shaping the distribution of caesarean delivery and to what extent they can be explained by individual-level risk factors. OBJECTIVES: To describe geographic variation in caesarean delivery and quantify the contribution of individual-level risk factors to the variation in India. METHODS: We conducted four-level logistic regression analysis to partition total variation in caesarean delivery to three geographic levels (states, districts and communities) and quantify the extent to which variance at each level was explained by a set of 20 sociodemographic, medical and institutional risk factors. Stratified analyses were conducted by the type of delivery facility (public/private). RESULTS: Overall prevalence of caesarean delivery was 19.3% in India in 2016. Most geographic variation was attributable to states (44%), followed by communities (32%), and lastly districts (24%). Adjustment for all risk factors explained 44%, 52% and 46% of variance for states, districts and communities, respectively. The proportion explained by individual risk factors was larger in public facilities than in private facilities at all three levels. A substantial proportion of between-population variation still existed even after clustering of individual risk factors was comprehensively adjusted for. CONCLUSIONS: Diverse contextual factors driving high or low rate of caesarean delivery at each geographic level should be explored in future studies so that tailored intervention can be implemented to reduce the overall variation in caesarean delivery.

Maternal Height-standardized Prevalence of Stunting in 67 Low- and Middle-income Countries.

BACKGROUND: Prevalence of stunting is frequently used as a marker of population-level child undernutrition. Parental height varies widely in low- and middle-income countries (LMIC) and is also a major determinant of stunting. While stunting is a useful measure of child health, with multiple causal components, removing the component attributable to parental height may in some cases be helpful to identify shortcoming in current environments. METHODS: We estimated maternal height-standardized prevalence of stunting (SPS) in 67 LMICs and parental height-SPS in 20 LMICs and compared with crude prevalence of stunting (CPS) using data on 575,767 children under-five from 67 Demographic and Health Surveys (DHS). We supplemented the DHS with population-level measures of other child health outcomes from the World Health Organization's (WHO) Global Health Observatory and the United Nations' Inter-Agency Group for Child Mortality Estimation. Prevalence of stunting was defined as percentage of children with height-for-age falling below -2 z-scores from the median of the 2006 WHO growth standard. RESULTS: The average CPS across countries was 27.8% (95% confidence interval [CI], 27.5-28.1%) and the average SPS was 23.3% (95% CI, 23.0-23.6%). The rank of countries according to SPS differed substantially from the rank according to CPS. Guatemala, Bangladesh, and Nepal had the biggest improvement in ranking according to SPS compared to CPS, while Gambia, Mali, and Senegal had the biggest decline in ranking. Guatemala had the largest difference between CPS and SPS with a CPS of 45.2 (95% CI, 43.7-46.9%) and SPS of 14.1 (95% CI, 12.6-15.8%). Senegal had the largest increase in the prevalence after standardizing maternal height, with a CPS of 28.0% (95% CI, 25.8-30.2%) and SPS of 31.6% (95% CI, 29.5-33.8%). SPS correlated better than CPS with other population-level measures of child health. CONCLUSION: Our study suggests that CPS is sensitive to adjustment for maternal height. Maternal height, while a strong predictor of child stunting, is not amenable to policy interventions. We showed the plausibility of SPS in capturing current exposures to undernutrition and infections in children.

Impact of air pollution on stunting among children in Africa.

BACKGROUND: Undernutrition is a global public health crisis, causing nearly half of deaths for children under age 5 years. Little is known regarding the impact of air pollution in-utero and early childhood on health outcomes related to undernutrition. The aim of our study is to evaluate the association of prenatal and early-life exposure to PM2.5 and child malnutrition as captured by the height-for-age z-score (HAZ), and stunting in 32 countries in Africa. We also evaluated critical windows of susceptibility during pregnancy to each environmental risk. METHODS: We linked nationally representative anthropometric data from 58 Demographic and Health Surveys (DHS) (n = 264,207 children < 5 years of age) with the average in-utero PM2.5 concentrations derived from satellite imagery. We then estimated associations between PM2.5 and stunting and HAZ after controlling for child, mother and household factors, and trends in time and seasonality. RESULTS: We observed lower HAZ and increased stunting with higher in-utero PM2.5 exposure, with statistically significant associations observed for stunting (OR: 1.016 (95% CI: 1.002, 1.030), for a 10 µg/m3 increase). The associations observed were robust to various model specifications. Wald tests revealed that sex, wealth quintile and urban/rural were not significant effect modifiers of these associations. When evaluating associations between trimester-specific PM2.5 levels, we observed that associations between PM2.5 and stunting was the largest. CONCLUSIONS: This is one of the first studies for the African continent to investigate in-utero and early-life exposure to PM2.5 is an important marker of childhood undernutrition. Our results highlight that PM2.5 concentrations need to be urgently mitigated to help address undernutrition in children on the continent.

Small area variations in low birth weight and small size of births in India.

The states and districts are the primary focal points for policy formulation and programme intervention in India. The within-districts variation of key health indicators is not well understood and consequently underemphasised. This study aims to partition geographic variation in low birthweight (LBW) and small birth size (SBS) in India and geovisualize the distribution of small area estimates. Applying a four-level logistic regression model to the latest round of the National Family Health Survey (2015-2016) covering 640 districts within 36 states and union territories of India, the variance partitioning coefficient and precision-weighted prevalence of LBW (<2.5 kg) and SBS (mother's self-report) were estimated. For each outcome, the spatial distribution by districts of mean prevalence and small area variation (as measured by standard deviation) and the correlation between them were computed. Of the total valid sample, 17.6% (out of 193,345 children) had LBW and 12.4% (out of 253,213 children) had SBS. The small areas contributed the highest share of total geographic variance in LBW (52%) and SBS (78%). The variance of LBW attributed to small areas was unevenly distributed across the regions of India. While a strong correlation between district-wide percent and within-district standard deviation was identified in both LBW (r = 0.88) and SBS (r = 0.87), they were not necessarily concentrated in the aspirational districts. We find the necessity of precise policy attention specifically to the small areas in the districts of India with a high prevalence of LBW and SBS in programme formulation and intervention that may be beneficial to improve childbirth outcomes.

Multilevel population and socioeconomic variation in health insurance coverage in India.

OBJECTIVES: This study explores population-level variation in different types of health insurance coverage in India. We aimed to estimate the extent to which contextual factors at community, district, and state levels may contribute to place-based inequalities in coverage after accounting for household-level socioeconomic factors. METHODS: We used data from the 2015-2016 National Family Health Survey in India, which provides the most recent and comprehensive information available on reports of different types of household health insurance coverage. We used multilevel regression models to estimate the relative contribution of different population levels to variation in coverage by national, state, and private health insurance schemes. RESULTS: Among 601,509 households in India, 29% reported having coverage in 2015-2016. Variation in each type of coverage existed between population levels before and after adjusting for differences in the distribution of household socioeconomic and demographic factors. For example, the state level accounted for 36% of variation in national scheme coverage and 41% of variation in state scheme coverage after adjusting for household characteristics. In contrast, the community level was the largest contextual source of variation in private insurance coverage (accounting for 24%). Each type of coverage was associated with higher socioeconomic status and urban location. CONCLUSIONS: Contextual factors at community, district, and state levels contribute to variation in household health insurance coverage even after accounting for socioeconomic and demographic factors. Opportunities exist to reduce disparities in coverage by focusing on drivers of place-based differences at multiple population levels. Future research should assess whether new insurance schemes exacerbate or reduce place-based disparities in coverage.

TB notification rates across parliamentary constituencies in India: a step towards data-driven political engagement.

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.

The relative importance of households as a source of variation in child malnutrition: a multilevel analysis in India.

BACKGROUND: Child malnutrition remains a major public health issue in India. Along with myriad upstream and social determinants of these adverse outcomes, recent studies have highlighted regional differences in mean child malnutrition rates. This research helps policy makers look between urban and rural communities and states to take a population-level approach to addressing the root causes of child malnutrition. However, one gap in this between-population approach has been the omission of households as a unit of analysis. Households could represent important sources of variation in child malnutrition within communities, districts, and states. METHODS: Using the fourth round of India's National Family Health Survey from 2015 to 2016, we analyzed four and five-level multilevel models to estimate the proportion of variation in child malnutrition attributable to states, districts, communities, households, and children. RESULTS: Overall, we found that of the four levels that children were nested in (households, communities, districts, and states), the greatest proportion of variation in child height-for-age Z score, weight-for-age Z score, weight-for-height Z score, hemoglobin, birthweight, stunting, underweight, wasting, anemia, and low birthweight was attributable to households. Furthermore, we found that when the household level is omitted from models, the variance estimates for communities and children are overestimated. CONCLUSIONS: These findings highlight the importance of households as an important source of clustering and variation in child malnutrition outcomes. As such, policies and interventions should address household-level social determinants, such as asset and social deprivations, in order to prevent poor child growth outcomes among the most vulnerable households in India.

Distribution of under-5 deaths in the neonatal, postneonatal, and childhood periods: a multicountry analysis in 64 low- and middle-income countries.

BACKGROUND: As under-5 mortality rates declined all over the world, the relative distribution of under-5 deaths during different periods of life changed. To provide information for policymakers to plan for multi-layer health strategies targeting child health, it is essential to quantify the distribution of under-5 deaths by age groups. METHODS: Using 245 Demographic and Health Surveys from 64 low- and middle-income countries conducted between 1986 and 2018, we compiled a database of 2,437,718 children under-5 years old with 173,493 deaths. We examined the share of deaths that occurred in the neonatal (< 1 month), postneonatal (1 month to 1 year old), and childhood (1 to 5 years old) periods to the total number of under-5 deaths at both aggregate- and country-level. We estimated the annual change in share of deaths to track the changes over time. We also assessed the association between share of deaths and Gross Domestic Product (GDP) per capita. RESULTS: Neonatal deaths accounted for 53.1% (95% confidence interval [CI]: 52.7, 53.4) of the total under-5 deaths. The neonatal share of deaths was lower in low-income countries at 44.0% (43.5, 44.5), and higher in lower-middle-income and upper-middle income countries at 57.2% (56.8, 57.6) and 54.7% (53.8, 55.5) respectively. There was substantial heterogeneity in share of deaths across countries; for example, the share of neonatal to total under-5 deaths ranged from 20.9% (14.1, 27.6) in Eswatini to 82.8% (73.0, 92.6) in Dominican Republic. The shares of deaths in all three periods were significantly associated with GDP per capita, but in different directions-as GDP per capita increased by 10%, the neonatal share of deaths would significantly increase by 0.78 percentage points [PPs] (0.43, 1.13), and the postneonatal and childhood shares of deaths would significantly decrease by 0.29 PPs (0.04, 0.54) and 0.49 PPs (0.24, 0.74) respectively. CONCLUSIONS: Along with the countries' economic development, an increasing proportion of under-5 deaths occurs in the neonatal period, suggesting a need for multi-layer health strategies with potentially heavier investment in newborn health.

Multilevel analysis of geographic variation among correlates of child undernutrition in India.

Prior research has identified a number of risk factors ranging from inadequate household sanitation to maternal characteristics as important determinants of child malnutrition and health in India. What is less known is the extent to which these individual-level risk factors are geographically distributed. Assessing the geographic distribution, especially at multiple levels, matters as it can inform where, and at what level, interventions should be targeted. The three levels of significance in the Indian context are villages, districts, and states. Thus, the purpose of this paper was to (a) examine what proportion of the variation in 21 risk factors is attributable to villages, districts, and states in India and (b) elucidate the specific states where these risk factors are clustered within India. Using the fourth National Family Health Survey dataset, from 2015 to 2016, we found that the proportion of variation attributable to villages ranged from 14% to 63%, 10% to 29% for districts and 17% to 62% for states. Furthermore, we found that Bihar, Jharkhand, Madhya Pradesh, and Uttar Pradesh were in the highest risk quintile for more than 10 of the risk factors included in our study. This is an indication of geographic clustering of risk factors. The risk factors that are clustered in states such as Bihar, Jharkhand, Madhya Pradesh and Uttar Pradesh underscore the need for policies and interventions that address a broader set of child malnutrition determinants beyond those that are nutrition specific.

The Relative Contributions of Socioeconomic and Genetic Factors to Variations in Body Mass Index Among Young Adults.

In light of recent findings on the small proportion of variance in body mass index (BMI) explained by shared environment, and growing interests in the role of genetic susceptibility, we assessed the relative contribution of socioeconomic status (SES) and genome-wide polygenic score for BMI to explaining variation in BMI. Our final analytic sample included 4,918 White and 1,546 Black individuals from the US National Longitudinal Study of Adolescent to Adult Health Wave IV (2007-2008) who had complete measures on BMI, demographics, SES, genetic data, and health behaviors. We used ordinary least-squares regression to assess variation in log(BMI) as a function of the aforementioned predictors, independently and mutually adjusted. All analyses were stratified by race/ethnicity in the main analysis, and further by sex. The age-adjusted variation in log(BMI) was 0.055 among Whites and 0.066 among Blacks. The contribution of SES and polygenic score ranged from less than1% to 6% and from 2% to 8%, respectively, and majority of the variation (87%-96%) in log(BMI) remained unexplained. Differential distribution of socioeconomic resources, stressors, and buffers may interact to produce systematically larger variation in vulnerable populations. More understanding of the contribution of biological, genetic, and environmental factors, as well as stochastic elements, in diverse phenotypic variance is needed in population health sciences.