In the context of global warming, individuals may, in their lifetimes, experience the gradual increase in average warming and drying conditions, repeated extreme weather events, or small but significant shifts in seasonal precipitation and temperature. Individual-...

Although more than half of the world’s population lives in urban areas, there is a dire lack of data on trade, cost, and origin of the food that the urban dwellers rely on. Understanding the impact of escalating water-food systems variability on urban quality of life is critical for designing data systems needed to implement appropriate policies and state-supported interventions in urban areas.

Seasonal shifts in community-level agricultural production and their impact on the severity of the annual hunger season and household coping behaviors are important themes in climate change–health ...

Soil moisture reflects the amount of water available to crops in the top layer of soil. As such, considering soil moisture provides important insight into water availability and ultimately crop yields in agricultural settings. In studies of climate change, food security, and health, however, soil moisture is rarely empirically considered despite its connection to crop health and yields. In this project, we aim to advance understanding of climate impacts on food security by incorporating soil moisture into quantitative models of child health. Combining spatially referenced health survey data from the Demographic and Health Surveys for 2005 and 2010 in Senegal and 2007, 2011, and 2014 in Bangladesh, with soil moisture data from the Famine Early Warning System Network Land Data Assimilation System, we explore the linkages between sub-annual and sub-seasonal climate conditions and child malnutrition in two rainfed agriculture dependent countries—Bangladesh and Senegal. Results suggest that soil moisture, measured on very short time scales, may be associated with reductions in anthropometric weight-for-height z-scores, but the relationship is highly dependent upon geographic context.

Acute malnutrition affects a sizeable number of young children around the world, with serious repercussions for mortality and morbidity. Among the top priorities in addressing this problem are to anticipate which children tend to be susceptible and where and when crises of high prevalence rates would be likely to arise. In this article, we highlight the potential role of conflict and climate conditions as risk factors for acute malnutrition, while also assessing other vulnerabilities at the individual- and household-levels. Existing research reflects these features selectively, whereas we incorporate all the features into the same study. The empirical analysis relies on integration of health, conflict, and environmental data at multiple scales of observation to focuses on how local conflict and climate factors relate to an individual child’s health. The centerpiece of the analysis is data from the Demographic and Health Surveys conducted in several different cross-sectional waves covering 2003–2016 in Kenya, Nigeria, and Uganda. The results obtained from multi-level statistical models indicate that in Kenya and Nigeria, conflict is associated with lower weight-for-height scores among children, even after accounting for individual-level and climate factors. In Nigeria and Kenya, conflict lagged 1–3 months and occurring within the growing season tends to reduce WHZ scores. In Uganda, however, weight-for-height scores are primarily associated with individual-level and household-level conditions and demonstrate little association with conflict or climate factors. The findings are valuable to guide humanitarian policymakers and practitioners in effective and efficient targeting of attention, interventions, and resources that lessen burdens of acute malnutrition in countries prone to conflict and climate shocks.

The COVID-19 pandemic ushered in a bereavement crisis unparalleled in a generation, with devastating consequences for the mental health of those who lost a loved one to the virus. Using national su...

Acute malnutrition affects a sizeable number of young children around the world, with serious repercussions for mortality and morbidity. Among the top priorities in addressing this problem are to anticipate which children tend to be susceptible and where and when crises of high prevalence rates would be likely to arise. In this article, we highlight the potential role of conflict and climate conditions as risk factors for acute malnutrition, while also assessing other vulnerabilities at the individual- and household-levels. Existing research reflects these features selectively, whereas we incorporate all the features into the same study. The empirical analysis relies on integration of health, conflict, and environmental data at multiple scales of observation to focuses on how local conflict and climate factors relate to an individual child’s health. The centerpiece of the analysis is data from the Demographic and Health Surveys conducted in several different cross-sectional waves covering 2003–2016 in Kenya, Nigeria, and Uganda. The results obtained from multi-level statistical models indicate that in Kenya and Nigeria, conflict is associated with lower weight-for-height scores among children, even after accounting for individual-level and climate factors. In Nigeria and Kenya, conflict lagged 1–3 months and occurring within the growing season tends to reduce WHZ scores. In Uganda, however, weight-for-height scores are primarily associated with individual-level and household-level conditions and demonstrate little association with conflict or climate factors. The findings are valuable to guide humanitarian policymakers and practitioners in effective and efficient targeting of attention, interventions, and resources that lessen burdens of acute malnutrition in countries prone to conflict and climate shocks.

Abstract: Prior evidence suggests an association among food insecurity, poor health, and increased health care spending. In this study, we are using a natural experiment to confirm if longer participation in the Supplemental Nutrition Assistance Program (SNAP) is associated with reduced Medicaid among a highly impoverished group of adults. In 2013, the mandatory work requirements associated with SNAP benefits were lifted for able-bodied adults without dependents (ABAWDs). Using 2013 to 2015 Medicaid and SNAP data of 24,181 Minnesotans aged 18-49, we examined if changes in SNAP enrollment duration affect health care expenditures. In fully adjusted within-participant regression models, for each additional month of SNAP, average annual health care spending was $98.8 lower (95% CI: -131.7, -66.0; p<.001) per person. Our data suggests that allowing ABAWDs to receive SNAP even in months they are not working may be critical to their health as well as cost-effective.

This study investigates how two aspects of agricultural production diversity – farm production diversity and composition of production – relate to child height-for-age and weight-for-height in Ethiopia. We use longitudinal data on child anthropometric measurements, household farm production diversity and farm production composition from the Ethiopia Socioeconomic Survey for 2011, 2013, and 2015 available through the World Bank. Using longitudinal fixed effects models, we show that an increase in farm production diversity reduces the risk of chronic food insecurity (child height-for-age) but has no impact on acute measures of food insecurity (child weight-for-height). Results also suggest that, in a context of poor rainfall, more diversity in farm production can adversely impact child height-for-age, although livestock sales might mitigate that detrimental effect. These findings highlight the importance of considering the relationship between farm-level food production and child nutrition in a context of climate change.

Background. The quality and quantity of food available to children affect their nutritional status, with implications for long-term health and development. In Burkina Faso, households rely on rainfed agriculture, but climate change is making crop production unreliable. We explore spatial patterns of growing season quality on dimensions of nutritional status and complementary feeding practices in children 6-23 months. Methods. The 2017 Performance Monitoring and Accountability 2020 (PMA2020) nutritional survey was spatially integrated with a contemporaneous remotely sensed drought indicator, the Water Requirement Satisfaction Index (WRSI), which captures local anomalous growing season conditions. Multi-level mixed-effects logistic regression models were estimated to explore the effects of WRSI on child mid-upper arm circumference (MUAC) score (indicating malnutrition), and two components of complementary feeding practices, adjusting for demographic and household characteristics. Results. The data set included 1,721 children. Higher WRSI values (better agricultural conditions and crop performance) were associated with 3% lower odds of malnutrition (Odds Ratio (OR) = 0.971; 95% confidence interval (CI): [0.942, 1.00]) and 7% higher odds of a child attaining minimum dietary diversity (OR=1.07; 95% CI: [1.01, 1.14]). Undernourished mothers were significantly (p < 0.001) more likely to have an undernourished child. Minimum dietary diversity met for the child was protective against malnutrition; the association between WRSI and malnutrition persisted after adjustment. Conclusions. WRSI was associated with the child's dietary diversity and malnutrition, highlighting the importance of seasonally and spatially varying local agricultural production and the relationship between growing season conditions and child nutritional status, with dietary diversity providing a potential mechanism for intervention.

Individuals living in every region of the world are increasingly vulnerable to negative health outcomes due to extreme heat exposure. Children, in particular, may face long-term consequences associated with heat stress that affect their educational attainment and later life health and well-being. Retrospective individual-level analyses are useful for determining the effects of extreme heat exposure on health outcomes. Typically, future risk is inferred by extrapolating these effects using future warming scenarios that are applied uniformly over space and time without consideration of topographical or climatological gradients. We propose an alternative approach using a stochastic weather generator. This approach employs a 1 °C warming scenario to produce an ensemble of plausible future weather scenarios, and subsequently a distribution of future health risks. We focus on the effect of global warming on fetal development as measured by birth weight in Ethiopia. We demonstrate that predicted changes in birth weight are sensitive to the evolution of temperatures not quantified in a uniform warming scenario. Distributions of predicted changes in birth weight vary in magnitude and variability depending on geographic and socioeconomic region. We present these distributions alongside results from the uniform warming scenario and discuss the spatiotemporal variability of these predicted changes.

Individuals living in every region of the world are increasingly vulnerable to negative health outcomes due to extreme heat exposure. Children, in particular, may face long-term consequences associated with heat stress that affect their educational attainment and later life health and well-being. Retrospective individual-level analyses are useful for determining the effects of extreme heat exposure on health outcomes. Typically, future risk is inferred by extrapolating these effects using future warming scenarios that are applied uniformly over space and time without consideration of topographical or climatological gradients. We propose an alternative approach using a stochastic weather generator. This approach employs a 1 °C warming scenario to produce an ensemble of plausible future weather scenarios, and subsequently a distribution of future health risks. We focus on the effect of global warming on fetal development as measured by birth weight in Ethiopia. We demonstrate that predicted changes in birth weight are sensitive to the evolution of temperatures not quantified in a uniform warming scenario. Distributions of predicted changes in birth weight vary in magnitude and variability depending on geographic and socioeconomic region. We present these distributions alongside results from the uniform warming scenario and discuss the spatiotemporal variability of these predicted changes.

We examine the relationships between climatic conditions, breastfeeding behavior, and maternal time use in Ethiopia. Infant feeding practices are important predictors of child nutrition that may be affected by a number of factors including mother’s time engaging in agricultural labor, food security, cultural beliefs, and antenatal care. We use panel data from the Living Standards Measurement Study to investigate linkages between climatic conditions during a child’s first year of life and year prior to birth and duration of exclusive breastfeeding. We then explore one potential mechanism: women’s agricultural labor. Results indicate that rainfall during the primary agricultural season—kiremt—in a child’s first year of life plays an important role in duration of exclusive breastfeeding. Experiencing 25 cm of average monthly kiremt rainfall, versus 5 cm, is associated with a 20-percentage-point decrease in the likelihood of being exclusively breastfed for the recommended 6 months. More kiremt rainfall is associated with a greater number of days that women spend planting and harvesting, and at high levels of rainfall women with infants do not engage in significantly fewer days of agricultural labor than those without infants. Lastly, we find that during the year before birth, greater rainfall during kiremt as well as the dry season is associated with a lower likelihood of 6 months of exclusive breastfeeding, potentially due to the early introduction of complementary foods. Our findings indicate that agricultural labor demands may in part drive breastfeeding behaviors, leading to “sub-optimal” feeding practices in the short-term, but resulting in improved household food security in the longer-term.

In this project we consider the ways that different livelihood strategies impact the climate-health linkage. Specifically, we build on knowledge of livestock mobility in the Sahel and use remotely sensed-based measures of waterholes with health survey data to investigate the linkages between child health outcomes related to food security. We focus on the landscape characteristics relevant to limitedly studied, but highly climate-vulnerable populations, pastoralists and agro-pastoralists in the Sahel. We combine remotely sensed-based data on surface waterholes and spatially referenced health survey data and use flexible regression modeling techniques to uncover the quantitative relationship between waterhole depth and a child’s height-for-age z-score (HAZ). The results suggest that the water depth level of nearby waterholes does indeed impact a child’s HAZ, even after accounting for other environmental factors. This relationship is impacted, however, by the livelihood practices of the area as well as by the source of household drinking water.

<p> We present a high-resolution daily temperature data set, CHIRTS-daily, which is derived by merging the monthly Climate Hazards center InfraRed Temperature with Stations climate record with daily temperatures from version 5 of the European Centre for Medium-Range Weather Forecasts Re-Analysis. We demonstrate that remotely sensed temperature estimates may more closely represent true conditions than those that rely on interpolation, especially in regions with sparse <italic>in situ</italic> data. By leveraging remotely sensed infrared temperature observations, CHIRTS-daily provides estimates of 2-meter air temperature for 1983–2016 with a footprint covering 60°S-70°N. We describe this data set and perform a series of validations using station observations from two prominent climate data sources. The validations indicate high levels of accuracy, with CHIRTS-daily correlations with observations ranging from 0.7 to 0.9, and very good representation of heat wave trends. </p>

Poor pregnancy outcomes include miscarriages, stillbirths, and low birth weights. Stress from heat and lack of resources play a potentially important role in producing these poor outcomes. Women and couples who experience these poor outcomes rather than a healthy birth suffer psychological, physical, social, and financial costs as well. We use detailed reproductive data in combination with fine-scale climate data to examine pregnancy outcomes among women in sub-Saharan Africa, a region that shelters some of the poorest families in the world. Fine-scale precipitation and temperature data allow each pregnancy to be matched to the relevant climate exposures. We investigate the linkages between climate and pregnancy outcomes using linear probability models with fixed effects to minimize confounding due to factors that vary by location, season, and year. We analyze retrospective pregnancy data from more than 65,000 pregnancies recorded in 23 surveys across 15 African countries. Our results indicate that pregnancy outcomes are indeed impacted by exposure to hot days even after considering other individual-level characteristics. This research provides insight into the linkages between climate and a major adverse health outcome faced by women. In doing so, this research expands scientific understanding of the impact of environmental factors on fertility outcomes.

We analyze fertility preferences among women at risk of pregnancy with children ages five or younger as a function of two food security metrics: perceptions of household hunger and child stunting (height for age z scores ≤ −2.0) in order to convey a robust picture of food insecurity. We use data from the 2016 Tanzania Demographic and Health Surveys to analyze this research question. Multinomial generalized logit models with cluster-adjusted standard errors are used to determine the association between different dimensions of food insecurity and individual-level fertility preferences. On average, women who experience household hunger are 19% less likely to want more children compared to women who do not experience household hunger (AOR: 0.81, p = 0.02) when controlling for education, residence, maternal age, number of living children, and survey month. Adjusting for the same covariates, having at least one child ≤ 5 years old who is stunted is associated with 13% reduced odds of wanting more children compared to having no children stunted (AOR: 0.87, p = 0.06). In the context of a divided literature base, this research aligns with the previous work identifying a preference among women to delay or avoid pregnancy during times of food insecurity. The similarity in magnitude and direction of the association between food insecurity and fertility preferences across the two measures of food insecurity suggest a potential association between lived or perceived resource insecurity and fertility aspirations. Further research is needed in order to establish a mechanism through which food insecurity affects fertility preferences. Individual fertility preferences are sensitive to dynamic multi-level factors in a woman’s life. While qualitative research has explored the effect that food insecurity and associated resource constraints have on fertility preferences, results are conflicting. Here, we quantitatively examine how individual woman’s fertility preferences associate with two measures of food insecurity and qualitatively compare the associations across food insecurity measures. We establish that two food insecurity measures- household hunger and child stunting- capture similar populations and have similar associations with fertility preferences. This is a critical step forward in understanding the dynamic relationship between resource availability, child well-being, and fertility preferences.

Great gains have been made in providing researchers geo-spatial data that can be combined with population health data. This development is crucial given concerns over the human health outcomes associated with a changing climate. Merging population and environmental data remains both conceptually and technically challenging because of a large range of temporal and spatial scales. Here we propose a framework that addresses and advances both conceptual and technical aspects of population-environment research. This framework can be useful for considering how any time or space-based environmental occurrence influences population health outcomes and can be used to guide different data aggregation strategies. The primary consideration discussed here is how to properly model the space and time effects of environmental context on individual-level health outcomes, specifically maternal, child and reproductive health outcomes. The influx of geospatial health data and highly detailed environmental data, often at daily scales, provide an opportunity for population-environment researchers to move towards a more theoretically and analytically sound approach for studying environment and health linkages.