Background: Access to health facilities remains limited in many resource-poor settings, and women and their children often have to travel far to seek care. However, data on distance are scarce, and it is unclear whether distance is associated with worse child health outcomes. We estimate the relationships between distance to facility, service utilization and child mortality in low- and middle-income countries. Methods: Population-representative data are pooled from 29 demographic and health surveys across 21 low- and middle-income countries. Multivariable logistic models and meta-analysis regressions are used to estimate associations between facility distance, child mortality, and health care utilization in the pooled sample as well as for each survey. Results: Compared with children who live within 1 km of a facility, children living within 2 km, 3 km, and 5 km of a facility have a 7.7% [95% confidence interval (CI): 0.927 – 1.251], 16.3% (95% CI: 1.020 – 1.327) and 25% (95% CI: 1.087 – 1.439) higher odds of neonatal mortality, respectively; children living farther than 10 km have a 26.6% (95% CI: 1.108 – 1.445) higher odds of neonatal mortality. Women living farther than 10 km from a facility have a 55.3% lower odds of in-facility delivery compared with women who live within 1 km [odds ratio (OR): 0.447; 95% CI: 0.394 – 0.508]. Conclusions: Even relatively small distances from health facilities are associated with substantial mortality penalties for children. Policies that reduce travel distances and travel times are likely to increase utilization of health services and reduce neonatal mortality.

The pathway from evidence generation to consumption contains many steps which can lead to overstatement or misinformation. The proliferation of internet-based health news may encourage selection of media and academic research articles that overstate strength of causal inference. We investigated the state of causal inference in health research as it appears at the end of the pathway, at the point of social media consumption. We screened the NewsWhip Insights database for the most shared media articles on Facebook and Twitter reporting about peer-reviewed academic studies associating an exposure with a health outcome in 2015, extracting the 50 most-shared academic articles and media articles covering them. We designed and utilized a review tool to systematically assess and summarize studies’ strength of causal inference, including generalizability, potential confounders, and methods used. These were then compared with the strength of causal language used to describe results in both academic and media articles. Two randomly assigned independent reviewers and one arbitrating reviewer from a pool of 21 reviewers assessed each article. We accepted the most shared 64 media articles pertaining to 50 academic articles for review, representing 68% of Facebook and 45% of Twitter shares in 2015. Thirty-four percent of academic studies and 48% of media articles used language that reviewers considered too strong for their strength of causal inference. Seventy percent of academic studies were considered low or very low strength of inference, with only 6% considered high or very high strength of causal inference. The most severe issues with academic studies’ causal inference were reported to be omitted confounding variables and generalizability. Fifty-eight percent of media articles were found to have inaccurately reported the question, results, intervention, or population of the academic study. We find a large disparity between the strength of language as presented to the research consumer and the underlying strength of causal inference among the studies most widely shared on social media. However, because this sample was designed to be representative of the articles selected and shared on social media, it is unlikely to be representative of all academic and media work. More research is needed to determine how academic institutions, media organizations, and social network sharing patterns impact causal inference and language as received by the research consumer.

Background: Many low- and middle-income countries are experiencing high and increasing exposure to ambient fine particulate air pollution (PM2.5). The effect of PM2.5 on infant and child mortality is usually modelled using concentration response curves extrapolated from studies conducted in settings with low ambient air pollution, which may not capture its full effect. Methods: We pool data on more than half a million births from 69 nationally representative Demographic and Health Surveys that were conducted in 43 low- and middle-income countries between 1998 and 2014, and we calculate early-life exposure (exposure in utero and post partum) to ambient PM2.5 using high-resolution calibrated satellite data matched to the child’s place of residence. We estimate the association between the log of early-life PM2.5 exposure, both overall and separated by type, and the odds of neonatal and infant mortality, adjusting for child-level, parent-level and household-level characteristics. Results: We find little evidence that early-life exposure to overall PM2.5 is associated with higher odds of mortality relative to low exposure to PM2.5. However, about half of PM2.5 is naturally occurring dust and sea-salt whereas half is from other sources, comprising mainly carbon-based compounds, which are mostly due to human activity. We find a very strong association between exposure to carbonaceous PM2.5 and infant mortality, particularly neonatal mortality, i.e. mortality in the first 28 days after birth. We estimate that, at the mean level of exposure in the sample to carbonaceous PM2.5—10.9 µg/m3—the odds of neonatal mortality are over 50% higher than in the absence of pollution. Conclusion: Our results suggest that the current World Health Organization guideline of limiting the overall ambient PM2.5 level to less than 10 µg/m³ should be augmented with a lower limit for harmful carbonaceous PM2.5.

Background: Public health authorities recommend that people practice social distancing, especially if they have symptoms of coronavirus disease (COVID-19), or are older and more at risk of serious illness if they become infected. We test the hypothesis that these groups are following these recommendations and are more likely to undertake social distancing. Methods: We conducted an open online survey of 4,676 U.S. adults aged 18 and older between April 4 and April 7, 2020. We model the effects of age and common COVID-19 symptoms in the last two weeks on going out of the home for non-healthcare reasons the day before taking the survey, using a logistic model and the number of close contacts (within 6 feet) that respondents had with non-household members, using a Poisson count model. Our models control for several covariates, including other flu-like symptoms, sex, education, income, whether the respondent worked in February, household size, population density in the respondent’s ZIP code, state fixed effects, and the day of completion of the survey. We also weight our analyses to make the sample representative of the U.S. adult population. Findings: About 52 percent of the adult United States population went out of their home the previous day. On average, adults had close contact with 1.9 non-household members. We find that having at least one COVID-19 symptom (fever, dry cough, or shortness of breath) increased the likelihood of going out the previous day and having additional close contacts with non-household members; however, the estimates were not statistically significant. When disaggregating our analysis by COVID-19 symptoms, we find no strong evidence of greater social distancing by people with a fever or cough in the last two weeks, but we do find that those who experienced shortness of breath have fewer close contacts, with an incidence rate ratio (IRR) of 0.49 (95% CI: 0.30–0.78). Having other flu-like symptoms reduces the odds of going out by 0.32 (95% CI: 0.18–0.60) and the incidence rate of having close contacts by 42 percent (IRR = 0.58; 95% CI: 0.38–0.88). We find that older people are just as likely to leave their homes as younger people, but people over the age of 50 had less than half the predicted number of close contacts than those who were younger than 30. Our approach has the limitation that the survey sample is self-selected. Our findings may therefore be subject to selection bias that is not adequately controlled for by weighting. In addition, the possibility exists of confounding of the results due to omitted variable bias. Conclusions: We provide evidence that older people are having significantly fewer close contacts than younger people, which is in line with the public health authorities’ recommendations. We also find that people experiencing shortness of breath are practicing more intense social distancing. However, we find that those with two other common COVID-19 symptoms, fever and dry cough, are not engaging in greater social distancing, suggesting that increased targeting on relevant symptoms, and messaging, may be required.