How climate change degrades child health: A systematic review and meta-analysis.

BACKGROUND: Children are more vulnerable than adults to climate-related health threats, but reviews examining how climate change affects human health have been mainly descriptive and lack an assessment of the magnitude of health effects children face. This is the first systematic review and meta-analysis that identifies which climate-health relationships pose the greatest threats to children. OBJECTIVES: We reviewed epidemiologic studies to analyse various child health outcomes due to climate change and identify the relationships with the largest effect size. We identify population-specific risks and provide recommendations for future research. METHODS: We searched four large online databases for observational studies published up to 5 January 2023 following PRISMA (systematic review) guidelines. We evaluated each included study individually and aggregated relevant quantitative data. We used quantitative data in our meta-analysis, where we standardised effect sizes and compared them among different groupings of climate variables and health outcomes. RESULTS: Of 1301 articles we identified, 163 studies were eligible for analysis. We identified many relationships between climate change and child health, the strongest of which was increasing risk (60 % on average) of preterm birth from exposure to temperature extremes. Respiratory disease, mortality, and morbidity, among others, were also influenced by climate changes. The effects of different air pollutants on health outcomes were considerably smaller compared to temperature effects, but with most (16/20 = 80 %) pollutant studies indicating at least a weak effect. Most studies occurred in high-income regions, but we found no geographical clustering according to health outcome, climate variable, or magnitude of risk. The following factors were protective of climate-related child-health threats: (i) economic stability and strength, (ii) access to quality healthcare, (iii) adequate infrastructure, and (iv) food security. Threats to these services vary by local geographical, climate, and socio-economic conditions. Children will have increased prevalence of disease due to anthropogenic climate change, and our quantification of the impact of various aspects of climate change on child health can contribute to the planning of mitigation that will improve the health of current and future generations.

Lower infant mortality, higher household size, and more access to contraception reduce fertility in low- and middle-income nations.

Although average contraceptive use has increased globally in recent decades, an estimated 222 million (26%) of women of child-bearing age worldwide face an unmet need for family planning-defined as a discrepancy between fertility preferences and contraception practice, or failing to translate desires to avoid pregnancy into preventative behaviours and practices. While many studies have reported relationships between availability/quality of contraception and family planning, infant mortality, and fertility, these relationships have not been evaluated quantitatively across a broad range of low- and middle-income countries. Using publicly available data from 64 low- and middle-income countries, we collated test and control variables in six themes: (i) availability of family planning, (ii) quality of family planning, (iii) female education, (iv) religion, (v) mortality, and (vi) socio-economic conditions. We predicted that higher nation-level availability/quality of family-planning services and female education reduce average fertility, whereas higher infant mortality, greater household size (a proxy for population density), and religious adherence increase it. Given the sample size, we first constructed general linear models to test for relationships between fertility and the variables from each theme, from which we retained those with the highest explanatory power within a final general linear model set to determine the partial correlation of dominant test variables. We also applied boosted regression trees, generalised least-squares models, and generalised linear mixed-effects models to account for non-linearity and spatial autocorrelation. On average among all countries, we found the strongest associations between fertility and infant mortality, household size, and access to any form of contraception. Higher infant mortality and household size increased fertility, whereas greater access to any form of contraception decreased fertility. Female education, home visitations by health workers, quality of family planning, and religious adherence all had weak, if any, explanatory power. Our models suggest that decreasing infant mortality, ensuring sufficient housing to reduce household size, and increasing access to contraception will have the greatest effect on decreasing global fertility. We thus provide new evidence that progressing the United Nation's Sustainable Development Goals for reducing infant mortality can be accelerated by increasing access to family planning.

Estimating Childhood Stunting and Overweight Trends in the European Region from Sparse Longitudinal Data.

BACKGROUND: Monitoring countries' progress toward the achievement of their nutrition targets is an important task, but data sparsity makes monitoring trends challenging. Childhood stunting and overweight data in the European region over the last 30 y have had low coverage and frequency, with most data only covering a portion of the complete age interval of 0-59 mo. OBJECTIVES: We implemented a statistical method to extract useful information on child malnutrition trends from sparse longitudinal data for these indicators. METHODS: Heteroscedastic penalized longitudinal mixed models were used to accommodate data sparsity and predict region-wide, country-level trends over time. We leveraged prevalence estimates stratified by sex and partial age intervals (i.e., intervals that do not cover the complete 0-59 mo), which expanded the available data (for stunting: from 84 sources and 428 prevalence estimates to 99 sources and 1786 estimates), improving the robustness of our analysis. RESULTS: Results indicated a generally decreasing trend in stunting and a stable, slightly diminishing rate for overweight, with large differences in trends between low- and middle-income countries compared with high-income countries. No differences were found between age groups and between sexes. Cross-validation results indicated that both stunting and overweight models were robust in estimating the indicators for our data (root mean squared error: 0.061 and 0.056; median absolute deviation: 0.045 and 0.042; for stunting and overweight, respectively). CONCLUSIONS: These statistical methods can provide useful and robust information on child malnutrition trends over time, even when data are sparse.

Spatially explicit analyses of environmental and health data to determine past, emerging and future threats to child health.

BACKGROUND: Dire forecasts predict that an increasingly hostile environment globally will increase the threats to human health. Infants and young children are especially at risk because children are particularly vulnerable to climate-related stressors. The childhood diseases most affected, the breadth and magnitude of future health problems and the time frame over which these problems will manifest remain largely unknown. OBJECTIVES: To review the possibility that spacially explicit analyses can be used to determine how climate change has affected children's health to date and whether these analyses can be used for future projections. METHODS: As an example of whether these objectives can be achieved, all available Australian environmental and health databases were reviewed. RESULTS: Environmental and health data in Australia have been collected for up to 30 years for the same spatial areas at 'Statistical Area level 1' (SA1) scale. SA1s are defined as having a population of between 200 and 800 people and collectively they cover the whole of Australia without gaps or overlap. Although the SA1 environmental and health data have been collected separately, they can be merged to allow detailed statistical analyses that can determine how climate change has affected the health of children. CONCLUSIONS: The availability of environmental and health datasets that share the same precise spatial coordinates provides a pathway whereby past and emerging effects on child health can be measured and predicted into the future. Given that the future health and well-being of children is one of society's greatest concerns, this information is urgently needed.