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Food production is a major driver of greenhouse gas (GHG) emissions, water and land use, and dietary risk factors are contributors to non-communicable diseases. Shifts in dietary patterns can therefore potentially provide benefits for both the environment and health. However, there is uncertainty about the magnitude of these impacts, and the dietary changes necessary to achieve them. We systematically review the evidence on changes in GHG emissions, land use, and water use, from shifting current dietary intakes to environmentally sustainable dietary patterns. We find 14 common sustainable dietary patterns across reviewed studies, with reductions as high as 70-80% of GHG emissions and land use, and 50% of water use (with medians of about 20-30% for these indicators across all studies) possible by adopting sustainable dietary patterns. Reductions in environmental footprints were generally proportional to the magnitude of animal-based food restriction. Dietary shifts also yielded modest benefits in all-cause mortality risk. Our review reveals that environmental and health benefits are possible by shifting current Western diets to a variety of more sustainable dietary patterns.

India's 2011 census revealed a growing imbalance between the numbers of girls and boys aged 0–6 years, which we postulate is due to increased prenatal sex determination with subsequent selective abortion of female fetuses. We aimed to establish the trends in sex ratio by birth order from 1990 to 2005 with three nationally representative surveys and to quantify the totals of selective abortions of girls with census cohort data. We assessed sex ratios by birth order in 0·25 million births in three rounds of the nationally representative National Family Health Survey covering the period from 1990 to 2005. We estimated totals of selective abortion of girls by assessing the birth cohorts of children aged 0–6 years in the 1991, 2001, and 2011 censuses. Our main statistic was the conditional sex ratio of second-order births after a firstborn girl and we used 3-year rolling weighted averages to test for trends, with differences between trends compared by linear regression. The conditional sex ratio for second-order births when the firstborn was a girl fell from 906 per 1000 boys (99% CI 798–1013) in 1990 to 836 (733–939) in 2005; an annual decline of 0·52% (p for trend=0·002). Declines were much greater in mothers with 10 or more years of education than in mothers with no education, and in wealthier households compared with poorer households. By contrast, we did not detect any significant declines in the sex ratio for second-order births if the firstborn was a boy, or for firstborns. Between the 2001 and 2011 censuses, more than twice the number of Indian districts (local administrative areas) showed declines in the child sex ratio as districts with no change or increases. After adjusting for excess mortality rates in girls, our estimates of number of selective abortions of girls rose from 0–2·0 million in the 1980s, to 1·2–4·1 million in the 1990s, and to 3·1–6·0 million in the 2000s. Each 1% decline in child sex ratio at ages 0–6 years implied 1·2–3·6 million more selective abortions of girls. Selective abortions of girls totalled about 4·2–12·1 million from 1980–2010, with a greater rate of increase in the 1990s than in the 2000s. Selective abortion of girls, especially for pregnancies after a firstborn girl, has increased substantially in India. Most of India's population now live in states where selective abortion of girls is common. US National Institutes of Health, Canadian Institute of Health Research, International Development Research Centre, and Li Ka Shing Knowledge Institute.

The age-specific mortality rates and total deaths from specific cancers have not been documented for the various regions and subpopulations of India. We therefore assessed the cause of death in 2001–03 in homes in small areas that were chosen to be representative of all the parts of India. At least 130 trained physicians independently assigned causes to 122 429 deaths, which occurred in 1·1 million homes in 6671 small areas that were randomly selected to be representative of all of India, based on a structured non-medical surveyor's field report. 7137 of 122 429 study deaths were due to cancer, corresponding to 556 400 national cancer deaths in India in 2010. 395 400 (71%) cancer deaths occurred in people aged 30–69 years (200 100 men and 195 300 women). At 30–69 years, the three most common fatal cancers were oral (including lip and pharynx, 45 800 [22·9%]), stomach (25 200 [12·6%]), and lung (including trachea and larynx, 22 900 [11·4%]) in men, and cervical (33 400 [17·1%]), stomach (27 500 [14·1%]), and breast (19 900 [10·2%]) in women. Tobacco-related cancers represented 42·0% (84 000) of male and 18·3% (35 700) of female cancer deaths and there were twice as many deaths from oral cancers as lung cancers. Age-standardised cancer mortality rates per 100 000 were similar in rural (men 95·6 [99% CI 89·6–101·7] and women 96·6 [90·7–102·6]) and urban areas (men 102·4 [92·7–112·1] and women 91·2 [81·9–100·5]), but varied greatly between the states, and were two times higher in the least educated than in the most educated adults (men, illiterate 106·6 [97·4–115·7] vs most educated 45·7 [37·8–53·6]; women, illiterate 106·7 [99·9–113·6] vs most educated 43·4 [30·7–56·1]). Cervical cancer was far less common in Muslim than in Hindu women (study deaths 24, age-standardised mortality ratio 0·68 [0·64–0·71] vs 340, 1·06 [1·05–1·08]). Prevention of tobacco-related and cervical cancers and earlier detection of treatable cancers would reduce cancer deaths in India, particularly in the rural areas that are underserved by cancer services. The substantial variation in cancer rates in India suggests other risk factors or causative agents that remain to be discovered. Bill & Melinda Gates Foundation and US National Institutes of Health.

Undernutrition and non-communicable disease (NCD) are important public health issues in India, yet their relationship with dietary patterns is poorly understood. The current study identified distinct dietary patterns and their association with micronutrient undernutrition (Ca, Fe, Zn) and NCD risk factors (underweight, obesity, waist:hip ratio, hypertension, total:HDL cholesterol, diabetes). Data were from the cross-sectional Indian Migration Study, including semi-quantitative FFQ. Distinct dietary patterns were identified using finite mixture modelling; associations with NCD risk factors were assessed using mixed-effects logistic regression models. India. Migrant factory workers, their rural-dwelling siblings and urban non-migrants. Participants (7067 adults) resided mainly in Karnataka, Andhra Pradesh, Maharashtra and Uttar Pradesh. Five distinct, regionally distributed, dietary patterns were identified, with rice-based patterns in the south and wheat-based patterns in the north-west. A rice-based pattern characterised by low energy consumption and dietary diversity ('Rice & low diversity') was consumed predominantly by adults with little formal education in rural settings, while a rice-based pattern with high fruit consumption ('Rice & fruit') was consumed by more educated adults in urban settings. Dietary patterns met WHO macronutrient recommendations, but some had low micronutrient contents. Dietary pattern membership was associated with several NCD risk factors. Five distinct dietary patterns were identified, supporting sub-national assessments of the implications of dietary patterns for various health, food system or environment outcomes.

Background Verbal autopsies (VA) are increasingly used in low- and middle-income countries where most causes of death (COD) occur at home without medical attention, and home deaths differ substantially from hospital deaths. Hence, there is no plausible “standard” against which VAs for home deaths may be validated. Previous studies have shown contradictory performance of automated methods compared to physician-based classification of CODs. We sought to compare the performance of the classic naive Bayes classifier (NBC) versus existing automated classifiers, using physician-based classification as the reference. Methods We compared the performance of NBC, an open-source Tariff Method (OTM), and InterVA-4 on three datasets covering about 21,000 child and adult deaths: the ongoing Million Death Study in India, and health and demographic surveillance sites in Agincourt, South Africa and Matlab, Bangladesh. We applied several training and testing splits of the data to quantify the sensitivity and specificity compared to physician coding for individual CODs and to test the cause-specific mortality fractions at the population level. Results The NBC achieved comparable sensitivity (median 0.51, range 0.48-0.58) to OTM (median 0.50, range 0.41-0.51), with InterVA-4 having lower sensitivity (median 0.43, range 0.36-0.47) in all three datasets, across all CODs. Consistency of CODs was comparable for NBC and InterVA-4 but lower for OTM. NBC and OTM achieved better performance when using a local rather than a non-local training dataset. At the population level, NBC scored the highest cause-specific mortality fraction accuracy across the datasets (median 0.88, range 0.87-0.93), followed by InterVA-4 (median 0.66, range 0.62-0.73) and OTM (median 0.57, range 0.42-0.58). Conclusions NBC outperforms current similar COD classifiers at the population level. Nevertheless, no current automated classifier adequately replicates physician classification for individual CODs. There is a need for further research on automated classifiers using local training and test data in diverse settings prior to recommending any replacement of physician-based classification of verbal autopsies.

Background Verbal autopsies with physician assignment of cause of death (COD) are commonly used in settings where medical certification of deaths is uncommon. It remains unanswered if automated algorithms can replace physician assignment. Methods We randomized verbal autopsy interviews for deaths in 117 villages in rural India to either physician or automated COD assignment. Twenty-four trained lay (non-medical) surveyors applied the allocated method using a laptop-based electronic system. Two of 25 physicians were allocated randomly to independently code the deaths in the physician assignment arm. Six algorithms (Naïve Bayes Classifier (NBC), King-Lu, InSilicoVA, InSilicoVA-NT, InterVA-4, and SmartVA) coded each death in the automated arm. The primary outcome was concordance with the COD distribution in the standard physician-assigned arm. Four thousand six hundred fifty-one (4651) deaths were allocated to physician (standard), and 4723 to automated arms. Results The two arms were nearly identical in demographics and key symptom patterns. The average concordances of automated algorithms with the standard were 62%, 56%, and 59% for adult, child, and neonatal deaths, respectively. Automated algorithms showed inconsistent results, even for causes that are relatively easy to identify such as road traffic injuries. Automated algorithms underestimated the number of cancer and suicide deaths in adults and overestimated other injuries in adults and children. Across all ages, average weighted concordance with the standard was 62% (range 79–45%) with the best to worst ranking automated algorithms being InterVA-4, InSilicoVA-NT, InSilicoVA, SmartVA, NBC, and King-Lu. Individual-level sensitivity for causes of adult deaths in the automated arm was low between the algorithms but high between two independent physicians in the physician arm. Conclusions While desirable, automated algorithms require further development and rigorous evaluation. Lay reporting of deaths paired with physician COD assignment of verbal autopsies, despite some limitations, remains a practicable method to document the patterns of mortality reliably for unattended deaths. Trial registration ClinicalTrials.gov , NCT02810366. Submitted on 11 April 2016.

Food production is a major driver of environmental change, while dietary risks are the leading cause of global disease burden. Dietary shifts in high-income countries can provide benefits for both health and the environment. However, little is known about such options in low-income and middle-income countries, which often face high burdens of both undernutrition and diet-related chronic disease. As an example, we assessed the changes in greenhouse gas emissions, water footprints, and land use, from shifting current nationally representative patterns of Indian food consumption to healthy diets. Dietary data were derived from a national 2011–12 household expenditure survey. We modelled the changes in consumption of 36 food groups necessary to meet Indian dietary guidelines. These changes were combined with food-specific data for greenhouse gases emissions, calculated using the Cool Farm Tool, water footprints, from the Water Footprint Network, and land use adapted from the UN Food and Agriculture Organisation. Shifts to healthy diets nationally required a minor increase in calories (3%), with larger increases in fruit (12%) and vegetables (20%). Percentage of calories from fat and protein were adequate. Meeting healthy guidelines marginally increased environmental footprints, between 1–4% for greenhouses gas emissions, water footprint, and land use. However, these national averages masked substantial variation within subpopulations. For example, shifting to healthy diets among those at risk of undernutrition would require increases of 11% in greenhouse gas emissions, 28% in water footprint, and 39% in land use, whereas decreasing environmental effects from those who currently consume above recommended calories. Environmental effects also varied substantially between six major Indian subregions. Providing healthy diets in India, a country of 1·3 billion, might only necessitate slight increases in environmental footprints. However, major efforts could be required to prevent widespread business-as-usual shifts to caloric-intensive and environmentally intensive affluent diets. Leverhulme Centre for Integrative Research on Agriculture and Health; Wellcome Trust (Our Planet, Our Health programme).

Food production is a major driver of greenhouse gas (GHG) emission and other environmental footprints, and dietary risk factors are contributors to non-communicable diseases. A growing body of evidence has shown that changes in what and how much we eat can offer benefits for both the environment and health. However, several data gaps and complexities remain in this research area. A better understanding and increased uptake of sustainable diets will require further research, investment, and interdisciplinary collaboration.

Accurate data on dietary intake are important for public health, nutrition and agricultural policy. The National Sample Survey is widely used by policymakers in India to estimate nutritional outcomes in the country, but has not been compared with other dietary data sources. To assess relative differences across available Indian dietary data sources, we compare intake of food groups across six national and sub-national surveys between 2004 and 2012, representing various dietary intake estimation methodologies, including Household Consumption Expenditure Surveys (HCES), FFQ, food balance sheets (FBS), and 24-h recall (24HR) surveys. We matched data for relevant years, regions and economic groups, for ages 16–59. One set of national HCES and the 24HR showed a decline in food intake in India between 2004–2005 and 2011–2012, whereas another HCES and FBS showed an increase. Differences in intake were smallest between the two HCES (1 % relative difference). Relative to these, FFQ and FBS had higher intake (13 and 35 %), and the 24HR lower intake (−9 %). Cereal consumption had high agreement across comparisons (average 5 % difference), whereas fruit and nuts, eggs, meat and fish and sugar had the least (120, 119, 56 and 50 % average differences, respectively). Spearman’s coefficients showed high correlation of ranked food group intake across surveys. The underlying methods of the compared data highlight possible sources of under- or over-estimation, and influence their relevance for addressing various research questions and programmatic needs.

Background: Modeling suggests that climate change mitigation actions can have substantial human health benefits that accrue quickly and locally. Documenting the benefits can help drive more ambitious and health-protective climate change mitigation actions; however, documenting the adverse health effects can help to avoid them. Estimating the health effects of mitigation (HEM) actions can help policy makers prioritize investments based not only on mitigation potential but also on expected health benefits. To date, however, the wide range of incompatible approaches taken to developing and reporting HEM estimates has limited their comparability and usefulness to policymakers. Objective: The objective of this effort was to generate guidance for modeling studies on scoping, estimating, and reporting population health effects from climate change mitigation actions. Methods: An expert panel of HEM researchers was recruited to participate in developing guidance for conducting HEM studies. The primary literature and a synthesis of HEM studies were provided to the panel. Panel members then participated in a modified Delphi exercise to identify areas of consensus regarding HEM estimation. Finally, the panel met to review and discuss consensus findings, resolve remaining differences, and generate guidance regarding conducting HEM studies. Results: The panel generated a checklist of recommendations regarding stakeholder engagement: HEM modeling, including model structure, scope and scale, demographics, time horizons, counterfactuals, health response functions, and metrics; parameterization and reporting; approaches to uncertainty and sensitivity analysis; accounting for policy uptake; and discounting. Discussion: This checklist provides guidance for conducting and reporting HEM estimates to make them more comparable and useful for policymakers. Harmonization of HEM estimates has the potential to lead to advances in and improved synthesis of policy-relevant research that can inform evidence-based decision making and practice. https://doi.org/10.1289/EHP6745