Assessing the prevalence of young children living in households prepared for COVID-19 in 56 low- and middle-income countries

BACKGROUND

The COVID-19 pandemic and governments' attempts to contain it are negatively affecting young children's health and development in ways we are only beginning to understand and measure. Responses to the pandemic are driven largely by confining children and families to their homes. This study aims to assess the levels of and associated socioeconomic disparities in household preparedness for protecting young children under the age of five from being exposed to communicable diseases, such as COVID-19, in low- and middle-income countries (LMICs).

METHODS

Using data from nationally representative household surveys in 56 LMICs since 2016, we estimated the percentages of young children under the age of five living in households prepared for communicable diseases (e.g., COVID-19) and associated residential and wealth disparities at the country- and aggregate-level. Preparedness was defined on the basis of space for quarantine, adequacy of toilet facilities and hand hygiene, mass media exposure at least once a week, and phone ownership. Disparities within countries were measured as the absolute gap in two domains-household wealth and residential area - and compared across regions and country income groups.

RESULTS

The final data set included 766,313 children under age five. On average, 19.4% of young children in the 56 countries lived in households prepared for COVID-19, ranging from 0.6% in Ethiopia in 2016 to 70.9% in Tunisia in 2018. In close to 90% of countries (50), fewer than 50% of young children lived in prepared households. Young children in rural areas or in the poorest households were less likely to live in prepared households than their counterparts.

CONCLUSIONS

A large portion of young children under the age of five in LMICs were living in households that did not meet all preparedness guidelines for preventing COVID-19 and caring for patients at home. This study highlights the need to ensure all families in LMICs have the means to prevent the spread of the pandemic or other communicable illnesses to young children during pandemics.

Trends in Diet Quality and Cardiometabolic Risk Factors Among Korean Adults, 2007-2018

IMPORTANCE

Few studies have examined the dietary trends in Korea beyond evaluating selected food groups. To help prevent cardiometabolic disease burdens, a comprehensive investigation of the trends in overall diet quality and identification of possible contributing factors would be useful.

OBJECTIVE

To investigate the trends and independent associations of age, period, and birth cohort with diet quality and cardiometabolic risk factors among Korean adults.

DESIGN, SETTING, AND PARTICIPANTS

Serial cross-sectional and age-period-cohort analyses were conducted of nationally representative 24-hour dietary recall data from the Korea National Health and Nutrition Examination Survey 2007-2018. The study population included 65 416 Korean adults aged 19 to 79 years. Data analyses were conducted from March 1, 2020, through April 30, 2021.

EXPOSURES

Age, calendar year, birth cohort, and population sociodemographic characteristics.

MAIN OUTCOMES AND MEASURES

Korean Healthy Eating Index (KHEI), a validated diet quality score (range, 0-90, with higher scores indicating greater diet quality), and 8 cardiometabolic risk factors (waist circumference and systolic blood pressure, diastolic blood pressure, serum total cholesterol, low-density lipoprotein cholesterol, high-density lipoprotein cholesterol, triglyceride, and fasting blood glucose levels).

RESULTS

Among 65 416 participants, mean age (SD) was 44.5 (0.1) years and 36 631 were women (55.8%). In 2007-2018, the age-standardized mean (SE) KHEI score increased from 51.0 (0.4) to 52.1 (0.5), which was associated with reduced sodium intake and increased whole grain, dairy, and protein-rich food intakes. The mean (SE) KHEI score was lowest at age 39 years (50.1 [0.3]) and increased at older ages (58.0 [0.3] at 79 years). Controlling for age and period effects, the highest KHEI score was observed among the birth cohorts of 1960-1964 (53.6 [0.9]) and decreased in subsequent cohorts (45.5 [1.2] in the 1990-1999 birth cohort). Similar cohort effects in cardiometabolic risk factors were observed, showing the lowest waist circumference, blood pressure, and total cholesterol levels among the birth cohorts of the 1960s and 1970s and higher levels among more recent birth cohorts (1990-1999 vs 1960-1964: waist circumference, 83.8 [0.5] vs 81.4 [0.4] cm; systolic blood pressure, 118.7 [0.7] vs 116.4 [0.4] mm Hg; total cholesterol, 200.2 [0.9] vs 198.9 [0.7] mg/dL). At most ages, periods, and birth cohorts, the mean KHEI score was consistently higher in adults living in urban areas (at age 45 years: 50.5 [1.0] vs 49.7 [0.9] rural) and among high-income (at age 45 years: 50.7 [1.1] vs 49.3 [0.9] low income) and educational levels (at age 45 years: 53.1 [0.9] vs 49.1 [1.0] low educational level).

CONCLUSIONS AND RELEVANCE

The findings of this study suggest that, from 2007 to 2018, the diet quality of Korean adults modestly improved. Despite the improvement, inequalities in diet among age, birth cohort, and socioeconomic subgroups persisted, suggesting that more intense interventions may be needed to target the susceptible groups.

Components of Out-of-Pocket Expenditure and Their Relative Contribution to Economic Burden of Diseases in India

Importance

High out-of-pocket expenditure (OOPE) on health in India may limit achieving universal health coverage. A clear insight on the components of health expenditure may be necessary to make allocative decisions to reduce OOPE, and such details by sociodemographic group and state have not been studied in India.

Objective

To analyze the relative contribution of drugs, diagnostic tests, doctor and surgeon fees, and expenditure on other medical services and nonmedical health-related services, such as transport, lodging, and food, by sociodemographic characteristics of patients, geography, and type of illness.

Design, Setting, and Participants

A population-based cross-sectional health consumption survey conducted by the National Sample Survey Organisation in 2018 was analyzed in this cross-sectional study. Respondents who provided complete information on costs of medicine, doctors, diagnostics tests, other medical costs, and nonmedical costs were selected. Data were analyzed from August through September 2021.

Main Outcomes and Measures

Mean and median share of components (ie, medicine, diagnostic tests, doctor fees, other medical costs, and nonmedical costs) in total health care expenditure and income were calculated. Bivariate survey-weighted mean (with 95% CI) and median (IQR) expenditures were calculated for each component across sociodemographic characteristics. The proportion of total expenditure and income contributed by each cost was calculated for each individual. Mean and median were then used to summarize such proportions at the population level. The association between state net domestic product per capita and component share of each health care service was graphically explored.

Results

Health expenditure details were analyzed for 43 781 individuals for inpatient costs (27 272 [64.3%] women; 26 830 individuals aged 25-64 years [59.9%]) and 8914 individuals for outpatient costs (4176 [48.2%] women; 4901 individuals aged 25-64 years [54.2%]); most individuals were rural residents (24 106 inpatients [67.0]; 4591 outpatients [63.9%]). Medicines accounted for a mean of 29.1% (95% CI, 28.9%-29.2%) of OOPE among inpatients and 60.3% (95% CI, 59.7%-60.9%) of OOPE among outpatients. Doctor consultation charges were a mean of 15.3% (95% CI, 15.1%-15.4%) of OOPE among inpatients and 12.4% (95% CI, 12.1%-12.6%) of OOPE among outpatients. Diagnostic tests accounted for a mean of 12.3% (95% CI, 12.2%-12.4%) of OOPE for inpatient and 9.2% (95% CI, 8.9%-9.5%) of OOPE for outpatient services. Nonmedical costs accounted for a mean of 23.6% (95% CI, 23.3%-23.8%) of OOPE among inpatients and 14.6% (95% CI, 14.1%-15.1%) of OOPE among outpatients. Mean share of OOPE from doctor consultations and diagnostic test charges increased with socioeconomic status. For example, for the lowest vs highest monthly per capita income quintile among inpatients, doctor consultations accounted for 11.5% (95% CI, 11.1%-11.8%) vs 21.2% (95% CI, 20.8%-21.6%), and diagnostic test charges accounted for 10.9% (95% CI, 10.6%-11.1%) vs 14.3% (95% CI, 14.0%-14.5%). The proportion of mean annual health expenditure from mean annual income was $299 of $1918 (15.6%) for inpatient and $391 of $1788 (21.9%) for outpatient services.

Conclusions and Relevance

This study found that nonmedical costs were significant, share of total health care OOPE from doctor consultation and diagnostic test charges increased with socioeconomic status, and annual cost as a proportion of annual income was lower for inpatient than outpatient services.

Age Distribution of All-Cause Mortality Among Children Younger Than 5 Years in Low- and Middle-Income Countries

Importance

Coverage of essential child health and nutrition interventions in low- and middle-income countries remains suboptimal. Adverse exposures, such as undernutrition and infections, are particularly harmful during the 1000 days from conception until 2 years of age.

Objective

To investigate whether deaths in children younger than 5 years-which also reflect adverse exposures faced by children more broadly-are concentrated in the first 2 years after birth.

Design, Setting, and Participants

This cross-sectional study used a synthetic cohort probability method with Demographic and Health Surveys and Multiple Indicator Cluster Surveys from 77 low- and middle-income countries, with the earliest survey starting data collection in March 2010 and the most recent survey ending data collection in December 2019. Participants included 2 827 515 children who were younger than 5 years at any point 10 years before survey. Data were analyzed from March 11 to 21, 2022.

Main Outcomes and Measures

Share of deaths among children younger than 5 years occurring before 1 month, at 1 to 23 months, and at 24 to 59 months of age.

Results

Among the 2 827 515 children included in the analysis, 81.5% (95% CI, 81.0%-82.0%) of deaths occurred in the first 2 years after birth ranging from 63.7% (95% CI, 61.6%-65.7%) in Niger to 97.8% (95% CI, 85.9%-99.7%) in Albania. An estimated 18.5% (95% CI, 18.0%-19.0%) of child deaths occurred at 24 to 59 months of age. Countries with higher mortality rates among children younger than 5 years had a lower share of deaths occurring in the neonatal period.

Conclusions and Relevance

In this sample of 77 low- and middle-income countries, a large majority of deaths among children younger than 5 years occurred before 2 years of age in all countries among boys and girls and in households with the worst and best living standards. Research has highlighted perinatal complications, infections, and undernutrition as primary causes of death among children younger than 5 years. Therefore, coverage of interventions to reduce these adverse exposures should be ensured during pregnancy and the first 2 years after birth, which is also a crucial period for human development.

Air Pollution, Socioeconomic Status, and Age-Specific Mortality Risk in the United States

IMPORTANCE

Prior studies on the association between fine particulate matter with diameters 2.5 μm or smaller (PM2.5) and probability of death have not applied multilevel analysis disaggregating data for US census tract, states, and counties, nor tested its interaction by socioeconomic status (SES). Such an approach could provide a more refined identification and targeting of populations exposed to increased risk from PM2.5.

OBJECTIVE

To assess the association between PM2.5 and age-specific mortality risk (ASMR) using disaggregated data at the census tract level and evaluate such association according to census tract SES.

DESIGN, SETTING, AND PARTICIPANTS

This nationwide cross-sectional study used a linkage of 3 different data sets. ASMR for the period of 2010 to 2015 was obtained from the National Center for Health Statistic, SES data covering a period from 2006 to 2016 came from the American Community Survey, and mean PM2.5 exposure levels from 2010 to 2015 were derived from well-validated atmospheric chemistry and machine learning models. Data were analyzed in April 2021.

EXPOSURES

The main exploratory variable was mean census tract-level long-term exposure to PM2.5 from 2010 to 2015.

MAIN OUTCOMES AND MEASURES

The primary outcome was census tract-level ASMR. Multilevel models were used to quantify the geographic variation in ASMR at levels of census tract, county, and state. Additional analysis explored the interaction of SES in the association of ASMR with PM2.5 exposure.

RESULTS

Data from 67 148 census tracts nested in 3087 counties and 50 states were analyzed. The association between exposure to PM2.5 and ASMR varied substantially across census tracts. The magnitude of such association also varied across age groups, being higher among adults and older adults. Census tracts accounted for most of the total geographic variation in mortality risk (range, 77.0%-94.2%). ASMR was higher in deciles with greater PM2.5 concentration. For example, ASMR for age 75 to 84 years was 54.6 per 1000 population higher in the decile with the second-highest PM2.5 concentration than in the decile with the lowest PM2.5 concentration. The ASMR, PM2.5 concentrations, and magnitude of the association between both were higher in the census tracts with the lowest SES.

CONCLUSIONS AND RELEVANCE

This cross-sectional study found that census tracts with lower SES presented higher PM2.5 concentrations. ASMR and air pollution varied substantially across census tracts. There was an association between air pollution and ASMR across all age groups in the United States. These findings suggest that equitable public policies aimed at improving air quality are needed and important to increase life expectancy.

Assessment of heterogeneous Head Start treatment effects on cognitive and social-emotional outcomes

Head Start is a federally funded, nation-wide program in the U.S. for enhancing school readiness of children aged 3–5 from low-income families. Understanding heterogeneity in treatment effects (HTE) is an important task when evaluating programs, but most attempts to explore HTE in Head Start have been limited to subgroup analyses that rely on average treatment effects by subgroups. This study applies an extension of multilevel modelling, complex variance modelling, to data from a randomized controlled trial of Head Start, Head Start Impact Study (HSIS). The treatment effects on the variance, in addition to the mean, of nine cognitive and social-emotional outcomes were assessed for 4,442 children aged 3–4 years who were followed until their 3rd grade year. Head Start had positive short-term effects on the means of multiple cognitive outcomes while having no effect on the means of social-emotional outcomes. Head Start reduced the variances of multiple cognitive and one social-emotional outcomes, meaning that substantial HTE exists. In particular, the increased mean and decreased variance reflect the ability of Head Start to improve the outcomes and reduce their variability. Exploratory secondary analyses suggested that larger benefits for children with Spanish as a primary language and low parental educational level partly explained the reduced variability, but the HTE remained and the variability was reduced even within these subgroups. Routinely monitoring the treatment effects on the variance, in addition to the mean, would lead to a more comprehensive program evaluation that describes how a program performs on average and on the entire distribution.

Gender and tobacco epidemic in South Korea: implications from age-period-cohort analysis and the DPSEEA framework

OBJECTIVES

To understand a 20-year trend of gender-specific smoking prevalence among adults in South Korea.

DESIGN

Age-period-cohort analysis using the intrinsic estimator method was applied to examine the separate contribution of age, period and cohort effect on smoking prevalence. The Driving Force-Pressure-State-Exposure-Effect-Action (DPSEEA) framework was used to explain the observed smoking trends by mapping potential determinants and to address policy implications.

SETTING

General adult population in South Korea.

PARTICIPANTS

34 828 men and 43 632 women who aged 19-78 years, were not currently pregnant and were without a prior diagnosis of cardiovascular disease or cancer.

OUTCOME MEASURES

Gender-specific current smoking prevalence using the 1998-2017 Korea National Health and Nutrition Examination Survey.

RESULTS

Our results showed gender-specific age and birth cohort effects. More specifically, the smoking prevalence peaked at their mid-20s (prevalence rate ratio (PRR): 1.54, 95% CI: 1.49 to 1.59) and cohort born in 1959-1963 (PRR: 1.63, 95% CI: 1.57 to 1.70) and then decreased in men. On the other hand, in women, the smoking prevalence consistently increased until their mid-40s (PRR: 1.53, 95% CI: 1.27 to 1.84) and in recent birth cohort groups (PRR in 1994-1998 cohort: 1.55, 95% CI: 1.13 to 2.13). The period effects declined from 1998-2002 to 2003-2007, following increasing fluctuations in both genders. The smoking-DPSEEA framework showed the absence of policy actions to target female smokers and emphasised a proactive approach that tackles the upstream causes for smoking in women.

CONCLUSIONS

Men and women are clearly in different phases of the smoking epidemic in Korean population, and gender-tailored policies should be implemented.

Life expectancies across congressional districts in the United States

Examining data on the congressional district level may better align with the interests of Members of Congress who have the power to implement federal health policies. Despite this importance, measurement of health indicators at the congressional district level remains widely understudied. In this study, we estimated overall life expectancy and variation within each congressional district by computing standard deviations of census tract-level age-specific life expectancies for 2010-2015. We found smaller standard deviations in congressional districts with higher overall life expectancy at younger ages, but the pattern was reversed at older ages.

Robust relationship between ambient air pollution and infant mortality in India

BACKGROUND

Ambient exposure to fine particulate matter (PM_2.5) is one of the top global health concerns. We estimate the associations between in-utero and perinatal exposure to PM_2.5 and infant, neonatal and postneonatal mortality in India. We evaluate the sensitivity of this association to two widely-used exposure assessments.

METHOD

We linked nationally representative anthropometric data from India's 2015-2016 Demographic and Health Survey (n = 259,627 children under five across 640 districts of India) with satellite-based PM_2.5 concentrations during the month of birth of each child. We then estimated the associations between PM_2.5 from each dataset and child mortality, after controlling for child, mother and household factors including trends in time and seasonality. We examined if factors: urban/rural, sex, wealth quintile and state modified the associations derived from the two datasets using Wald tests.

RESULTS

We found evidence that PM_2.5 impacts infant mortality primarily through neonatal mortality. The estimated association between neonatal mortality and PM_2.5 in trimester 3 was OR: 1.016 (95% CI: 1.003, 1.030) for every 10 μg/m³ increase in exposure. This association was robust to the exposure assessment used. Child sex was a significant effect modifier, with PM_2.5 impacting mortality in infant girls more than boys.

CONCLUSIONS

Our results revealed a robust association between ambient exposure to PM_2.5 in the latter period of pregnancy and early life with infant and neonatal mortality in India. Urgent air pollution management plans are needed to improve infant mortality in India.

Comparison of Mortality Risk With Different Surgeon and Hospital Operative Volumes Among Individuals Undergoing Pancreatectomy by Emulating Target Trials in US Medicare Beneficiaries

IMPORTANCE

The association of surgeons' and hospitals' operative volumes with postoperative patient outcomes has been studied for decades and holds important policy implications; however, in many volume-outcome analyses, this association is described without the envisioning of a clear intervention, which often introduces unintentional bias. Acting on such results may lead to unintended consequences from policy interventions or patient recommendations.

OBJECTIVE

To specify how (hypothetical) target trials would be designed to estimate the association between postoperative mortality of patients undergoing operations and a range of surgeon and hospital volume conditions and then to emulate these trials by using observational data.

DESIGN, SETTING, AND PARTICIPANTS

This observational data analysis emulated 4 hypothetical target trials of increasing complexity, ranging from a poorly defined trial that would randomly assign participants only to surgeon volume to one that would randomly assign participants to surgeon volume, hospital volume, and specific surgeon and hospital. This population-based cohort study included 9136 Medicare beneficiaries with a first diagnosis of pancreatic malignant neoplasm who did not require neoadjuvant therapy and underwent pancreatectomy between January 1, 2012, and September 30, 2016. Data analysis was performed between September 1, 2019, and October 8, 2021.

EXPOSURES

Number of pancreatectomies performed by surgeon and hospital during the prior year.

MAIN OUTCOMES AND MEASURES

Ninety-day mortality.

RESULTS

The analyses included 9136 Medicare beneficiaries treated by 1358 surgeons at 697 hospitals; median age was 73.3 years (IQR, 69.1-78.1 years), and 4642 were men (51%). When trials with poorly defined interventions on surgeon volume were emulated, the estimated 90-day mortality was 7.9% (95% CI, 6.4%-9.4%) for lower-volume surgeons and 5.2% (95% CI, 2.7%-10.9%) for higher-volume surgeons. When trials with better-defined interventions were emulated, the difference was reduced: 7.8% (95% CI, 6.3%-9.3%) for lower-volume surgeons and 7.2% (95% CI, 6.0%-8.7%) for higher-volume surgeons.

CONCLUSIONS AND RELEVANCE

In this cohort study that emulated 4 different target trials with data from Medicare beneficiaries undergoing pancreatectomy, mortality differences across surgical volume levels were attenuated when the interventions were well defined. The application of the hypothetical target trial framework to this specific volume-outcomes scenario revealed the complexities of this research question and the unintentional biases introduced in prior studies, which emulated poorly defined trials whose results are therefore difficult to interpret. The target trial framework may be of value to outcomes researchers asking questions that correspond to well-defined interventions for the real world.

Comparison of Child Undernutrition Anthropometric Indicators Across 56 Low- and Middle-Income Countries

IMPORTANCE

The United Nations' Sustainable Development Goal Target 2.2 seeks to end all forms of malnutrition by 2030 by meeting targets, including the elimination of stunting and wasting in all children younger than 5 years. Such indicators are used to monitor childhood undernutrition but may not provide a complete picture at a population level.

OBJECTIVE

To compare global estimates of the prevalence of undernutrition using conventional indicators of anthropometric failure (AF; stunting, underweight, and wasting); the Composite Index of Anthropometric Failure (CIAF); and a proposed classification system called Categories of Anthropometric Failure (CAF) as well as to investigate the association of the conventional indicators, CIAF, and CAF with diarrheal disease as an assessment of the validity of each measure.

DESIGN, SETTING, AND PARTICIPANTS

Cross-sectional study of the prevalence of undernutrition among children in 56 low- and middle-income countries using data from the nationally representative Demographic and Health Surveys. The study included 530 906 children younger than 5 years. Data were collected from June 2005 to December 2018 and analyzed from September 27, 2020, to February 4, 2021.

MAIN OUTCOMES AND MEASURES

Undernutrition identified according to conventional indicators (stunting, underweight, and wasting), the CIAF, and the proposed CAF classification system was estimated and compared. Six logistic regression models were used to examine the association between different classifications of anthropometric failure (AF) and morbidity.

RESULTS

A total of 530 906 children (mean [SD] age, 29.0 [17.2] months; 272 355 [51.3%] boys and 258 551 [48.7%] girls) from 56 low- and middle-income countries were included in the analysis. Estimates of undernutrition generated using the conventional indicators of stunting, underweight, and wasting were lower than estimates generated using the CIAF in all countries. The CAF classification system pointed to considerable variation across countries in children with multiple AFs, which does not correspond to the overall prevalence of undernutrition. For example, 7.5% of children in Niger and 7.1% of children in Timor-Leste were stunted, underweight, and wasted, while 56.0% of children in Niger and 71.1% of children in Timor-Leste were undernourished according to the CIAF. In addition, children who had stunting, underweight, and wasting had 1.52 (95% CI, 1.45-1.61) times the odds of diarrhea compared with children who exhibited no AFs.

CONCLUSIONS AND RELEVANCE

The results of this study highlight the importance of using different approaches to aid understanding of the entire spectrum of AF with regard to research and development of policies and programs to address AF. The use of the CIAF and the CAF classification system may be useful for treatment to prevent AFs and could accelerate progress in meeting targets for the Sustainable Development Goal.

Neighborhood socioeconomic inequality based on everyday mobility predicts COVID-19 infection in San Francisco, Seattle, and Wisconsin

Race and class disparities in COVID-19 cases are well documented, but pathways of possible transmission by neighborhood inequality are not. This study uses administrative data on COVID-19 cases for roughly 2000 census tracts in Wisconsin, Seattle/King County, and San Francisco to analyze how neighborhood socioeconomic (dis)advantage predicts cumulative caseloads through February 2021. Unlike past research, we measure a neighborhood's disadvantage level using both its residents' demographics and the demographics of neighborhoods its residents visit and are visited by, leveraging daily mobility data from 45 million mobile devices. In all three jurisdictions, we find sizable disparities in COVID-19 caseloads. Disadvantage in a neighborhood's mobility network has greater impact than its residents' socioeconomic characteristics. We also find disparities by neighborhood racial/ethnic composition, which can be explained, in part, by residential and mobility-based disadvantage. Neighborhood conditions measured before a pandemic offer substantial predictive power for subsequent incidence, with mobility-based disadvantage playing an important role.

Small Area Variations in Dietary Diversity Among Children in India: A Multilevel Analysis of 6–23-Month-Old Children

Dietary diversity is an important indicator of child malnutrition. However, little is known about the geographic variation of diet indicators across India, particularly within districts and across states. As such, the purpose of this paper was to elucidate the small area variations in diet indicators between clusters within districts of India. Overall, we found that clusters were the largest source of variation for children not eating grains, roots, and tubers, legumes and nuts, dairy, vitamin A-rich vegetables and fruits, and other vegetables and fruits. We also found positive correlations between the district percent and cluster standard deviations of children not breastfeeding or eating grains, roots, and tubers, but negative correlations between the district percent and cluster standard deviation for the remaining seven outcomes. These findings underscore the importance of targeting clusters to improve child dietary diversity.

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.

The Associations between Member of Parliament Characteristics and Child Malnutrition and Mortality in India

Child health outcomes vary between Parliamentary Constituencies (PCs) in India. There are a total of 543 PCs in India, each of which is a geographical unit represented by a Member of Parliament (MP). MP characteristics, such as age, gender, education, the number of terms they have served, and whether they belong to a Scheduled Caste or Scheduled Tribe, might be associated with indicators of child malnutrition and child mortality. The purpose of this paper was to examine the associations between MP characteristics and measures of child malnutrition and mortality. We did not find any meaningful associations between MP characteristics and child anthropometry, anemia, and mortality. Future research should consider the size of a constituency served by an MP along with MP party affiliations as these factors might help explain between-PC variations in child health outcomes. Our findings also underscore the need to better support female MPs and MPs from marginalized caste and tribal groups.

Consumption of Vitamin-A-Rich Foods and Vitamin A Supplementation for Children under Two Years Old in 51 Low- and Middle-Income Countries

Vitamin A supplementation for children 6-59 months old is an important intervention that boosts immune function, especially where children do not consume enough vitamin-A-rich foods. However, the low coverage of vitamin A supplementation is a persistent problem in low- and middle-income countries. We first estimated the percentage of children 6-23 months old receiving the minimum dietary diversity, vitamin-A-rich foods, and vitamin A supplementation, and second, the difference in the percentage receiving vitamin A supplementation between children 6-23 months old and children 24-59 months old using nationally representative cross-sectional household surveys, namely, the Demographic and Health Surveys, conducted from 2010 to 2019 in 51 low- and middle-income countries. Overall, 22% (95% CI: 22, 23) of children received the minimum dietary diversity, 55% (95% CI: 54, 55) received vitamin-A-rich foods, 59% (95% CI: 58, 59) received vitamin A supplementation, and 78% (95% CI: 78, 79) received either vitamin-A-rich foods or supplementation. A wide variation across countries was observed; for example, the percentage of children that received either vitamin-A-rich foods or supplementation ranged from 53% (95% CI: 49, 57) in Guinea to 96% (95% CI: 95, 97) in Burundi. The coverage of vitamin A supplementation should be improved, especially for children 6-23 months old, in most countries, particularly where the consumption of vitamin-A-rich foods is inadequate.

Strengthening Health Services Research Using Target Trial Emulation: An Application to Volume-Outcomes Studies

The number of operations that surgeons have previously performed is associated with their patients' outcomes. However, this association may not be causal, because previous studies have often been cross-sectional and their analyses have not considered time-varying confounding or positivity violations. In this paper, using the example of surgeons who perform coronary artery bypass grafting, we describe (hypothetical) target trials for estimation of the causal effect of the surgeons' operative volumes on patient mortality. We then demonstrate how to emulate these target trials using data from US Medicare claims and provide effect estimates. Our target trial emulations suggest that interventions on physicians' volume of coronary artery bypass grafting operations have little effect on patient mortality. The target trial framework highlights key assumptions and draws attention to areas of bias in previous observational analyses that deviated from their implicit target trials. The principles of the presented methodology may be adapted to other scenarios of substantive interest in health services research.

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.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12939-021-01563-7.

Association of maternal history of neonatal death with subsequent neonatal death across 56 low- and middle-income countries

Early identification of high-risk pregnancies can reduce global neonatal mortality rate. Using the most recent Demographic and Health Surveys from 56 low- and middle-income countries, we examined the proportion of mothers with history of neonatal deaths. Logistic regression models were used to assess the association between maternal history of neonatal death and subsequent neonatal mortality. The adjusted models controlled for socioeconomic, child, and pregnancy-related factors. Country-specific analyses were performed to assess heterogeneity in this association across countries. Among the 437,049 live births included in the study, 6910 resulted in neonatal deaths. In general, 22.4% (1549) occurred to mothers with previous history of neonatal death; at the country-level, this proportion ranged from 1.2% (95% confidence interval [CI] 0.0, 2.6) in Dominican Republic to 38.1% (95% CI 26.0, 50.1) in Niger. Maternal history of neonatal death was significantly associated with subsequent neonatal death in both the pooled and the subgroup analyses. In the fully adjusted model, history of neonatal death was associated with 2.1 (95% CI 1.9, 2.4) times higher odds of subsequent neonatal mortality in the pooled analysis. We observed large variation in the associations across countries ranging from fully adjusted odds ratio (FAOR) of 0.4 (95% CI 0.0, 4.0) in Dominican Republic to 16.1 (95% CI 3.6, 42.0) in South Africa. Our study suggests that maternal history of neonatal death could be an effective early identifier of high-risk pregnancies in resource-poor countries. However, country-specific contexts must be considered in national policy discussions.

Assessment of Undernutrition Among Children in 55 Low- and Middle-Income Countries Using Dietary and Anthropometric Measures

IMPORTANCE

Evidence on the suitability of anthropometric failure (ie, stunting, underweight, and wasting) as a stand-alone measure of child undernutrition can inform global and national nutrition and health agendas.

OBJECTIVE

To provide a comprehensive estimate of the prevalence of child undernutrition by evaluating both dietary and anthropometric measures simultaneously across 55 low- and middle-income countries.

DESIGN, SETTING, AND PARTICIPANTS

This was a cross-sectional study that used Demographic and Health Surveys program data from July 2009 to January 2019, to allocate children into dietary and anthropometric failure categories. Nationally representative household surveys were conducted in 55 low- and middle-income countries. Participants included children aged 6 to 23 months who were born singleton and had valid anthropometric measures as well as available 24-hour food intake recollection. Data analysis was conducted from August 23 to October 22, 2020.

EXPOSURES

Two factors were considered to allocate children into the respective categories. Dietary failure was based on the World Health Organization standards for minimum dietary diversity. Anthropometric failure was constructed using the World Health Organization child growth reference standard z score for stunted growth, muscle wasting, and less than average weight for age.

MAIN OUTCOMES AND MEASURES

Dietary and anthropometric failures were cross-tabulated, which yielded 4 potential outcomes: dietary failure only, anthropometric failure only, both failures, and neither failure. Total child populations for each category were extrapolated from United Nations population estimates.

RESULTS

Of the 162 589 children (median age [range], 14 months [6-23 months]; 83 467 boys [51.3%]; 78 894 Asian children [48.5%]) in our sample, 42.9% of children had dietary failure according to the standard World Health Organization definition without being identified as having anthropometric failures. In all, 34.7% had both failures, 42.9% had dietary failure only, 8.3% had anthropometric failure only, and 14.1% had neither failure. Dietary and anthropometric measures were discordant for 51.2% of children; these children had nutritional needs identified by only 1 of the 2 measures. Dietary failure doubled the proportion of children in need of dietary interventions compared with anthropometry alone (43%). A total of 45.3 million additional children who experienced undernutrition in these 55 countries were not captured through the evaluation of anthropometric failures only. These results were consistent across geographic regions.

CONCLUSIONS AND RELEVANCE

The results of this cross-sectional study suggest that the current standard of measuring child undernutrition by estimating the prevalence of anthropometric failure should be complemented with dietary and food-based measures. Anthropometry alone may fail to identify many children who have insufficient dietary intake.