A 1-km hourly air-temperature model for 13 northeastern U.S. states using remotely sensed and ground-based measurements.

BACKGROUND: Accurate and precise estimates of ambient air temperatures that can capture fine-scale within-day variability are necessary for studies of air temperature and health. METHOD: We developed statistical models to predict temperature at each hour in each cell of a 927-m square grid across the Northeast and Mid-Atlantic United States from 2003 to 2019, across ~4000 meteorological stations from the Integrated Mesonet, using inputs such as elevation, an inverse-distance-weighted interpolation of temperature, and satellite-based vegetation and land surface temperature. We used a rigorous spatial cross-validation scheme and spatially weighted the errors to estimate how well model predictions would generalize to new cell-days. We assess the within-county association of temperature and social vulnerability in a heat wave as an example application. RESULTS: We found that a model based on the XGBoost machine-learning algorithm was fast and accurate, obtaining weighted root mean square errors (RMSEs) around 1.6 K, compared to standard deviations around 11.0 K. We found similar accuracy when validating our model on an external dataset from Weather Underground. Assessing predictions from the North American Land Data Assimilation System-2 (NLDAS-2), another hourly model, in the same way, we found it was much less accurate, with RMSEs around 2.5 K. This is likely due to the NLDAS-2 model's coarser spatial resolution, and the dynamic variability of temperature within its grid cells. Finally, we demonstrated the health relevance of our model by showing that our temperature estimates were associated with social vulnerability across the region during a heat wave, whereas the NLDAS-2 showed a much weaker association. CONCLUSION: Our high spatiotemporal resolution air temperature model provides a strong contribution for future health studies in this region.

Enhancing LPG adoption in Ghana (ELAG): a factorial cluster-randomized controlled trial to Enhance LPG Adoption & Sustained use.

BACKGROUND: Three billion individuals worldwide rely on biomass fuel [dung, wood, crops] for cooking and heating. Further, health conditions resulting from household air pollution (HAP) are responsible for approximately 3.9 million premature deaths each year. Though transition away from traditional biomass stoves is projected curb the health effects of HAP by mitigating exposure, the benefits of newer clean cookstove technologies can only be fully realized if use of these new stoves is exclusive and sustained. However, the conditions under which individuals adopt and sustain use of clean cookstoves is not well understood. METHODS: The Enhancing LPG Adoption in Ghana (ELAG) study is a cluster-randomized controlled trial employing a factorial intervention design. The first component is a behavior change intervention based on the Risks, Attitudes, Norms, Abilities, and Self-regulation (RANAS) model. This intervention seeks to align these five behavioral factors with clean cookstove adoption and sustained use. A second intervention is access-related and will improve LPG availability by offering a direct-delivery refueling service. These two interventions will be integrated via a factorial design whereby 27 communities are assigned to one of the following: the control arm, the educational intervention, the delivery, or a combined intervention. Intervention allocation is determined by a covariate-constrained randomization approach. After intervention, approximately 900 households' individual fuel use is tracked for 12 months via iButton stove use monitors. Analysis will include hierarchical linear models used to compare intervention households' fuel use to control households. DISCUSSION: Literature to-date demonstrates that recipients of improved cookstoves rarely completely adopt the new technology. Instead, they often practice partial adoption (fuel stacking). Consequently, interventions are needed to influence adoption patterns and simultaneously to understand drivers of fuel adoption. Ensuring uptake, adoption, and sustained use of improved cookstove technologies can then lead to HAP-reductions and consequent improvements in public health. TRIAL REGISTRATION: NCT03352830 (November 24, 2017).

Heatwaves and Hardship: Shortcomings and Solutions for Enhancing the Low Income Home Energy Assistance Program to Mitigate Extreme Heat and Energy Insecurity.

The Low Income Home Energy Assistance Program (LIHEAP) must adapt and evolve to keep pace with the challenges posed by climate change and increased economic strain. Urgent action is needed to improve LIHEAP to effectively address extreme heat and energy insecurity faced by low-income households and protect the health and well-being of disadvantaged groups spurred by climate change. In evaluating LIHEAP's shortcomings, we demonstrate that there is a substantial gap between program eligibility and enrollment, such that many households are not receiving this vital benefit or do so mainly when facing a crisis. We also show that LIHEAP funds overwhelmingly support cold-weather states even as record-breaking heat is a critical stressor. The spatial mismatch we unveil shows that southern states receive less LIHEAP funds despite higher cooling degree days and higher rates of energy insecurity. The importance of swift action based on sound data and up-to-date research can enhance the efficacy of LIHEAP, expand its reach, and ultimately improve the living conditions of millions of energy insecure households. We offer several recommendations to improve LIHEAP to ensure that this critical lifeline program remains an effective tool to mitigate energy insecurity and safeguard livelihoods in the face of extreme heat.

Residential segregation and summertime air temperature across 13 northeastern U.S. states: Potential implications for energy burden.

High ambient summertime temperatures are an increasing health concern with climate change. This is a particular concern for minoritized households in the United States, for which differential energy burden may compromise adaptive capacity to high temperatures. Our research question was: Do minoritized groups experience hotter summers than the area average, and do non-Hispanic white people experience cooler summers? Using a fine-scaled spatiotemporal air temperature model and U.S. census data, we examined local (within-county) differences in warm season cooling degree days (CDDs) by ethnoracial group as a proxy for local energy demand for space cooling across states of the northeast and mid-Atlantic U.S. in 2003-2019. Using state-specific regression models adjusted for year and county, we found that Black and Latino people consistently experienced more CDDs, non-Hispanic white people experienced fewer CDDs, and Asian populations showed mixed results. We also explored a concentration-based measure of residential segregation for each ethnoracial group as one possible pathway towards temperature disparities. We included the segregation measure as a smooth term in a regression model adjusted for county and year. The results were nonlinear, but higher concentrations of white people were associated with lower annual CDDs and higher concentrations of Latino people were associated with higher annual CDDs than the county average. Concentrations for Black and Asian people were nonmonotonic, sometimes with bowed associations. These findings suggest that present-day residential segregation, as modeled by spatially smoothed ethnoracial subgroup concentrations, may contribute to summertime air temperature disparities and influence adaptive capacity. We hope these findings can support place-based interventions, including targeting of energy insecurity relief programs.

Simulating desegregation through affordable housing development: an environmental health impact assessment of Connecticut zoning law.

Residential segregation shapes access to health-promoting resources and drives health inequities in the United States. Connecticut's Section 8-30g incentivizes municipalities to develop a housing stock that is at least 10% affordable housing. We used this implicit target to project the impact of increasing affordable housing across all 169 Connecticut municipalities on all-cause mortality among low-income residents. We modeled six ambient environmental exposures: fine particulate matter (PM2.5), ozone (O3), nitrogen dioxide (NO2), summertime daily maximum heat index, greenness, and road traffic noise. We allocated new affordable housing to reach the 10% target in each town and simulated random movement of low-income households into new units using an inverse distance weighting penalty. We then quantified exposure changes and used established exposure-response functions to estimate deaths averted stratified by four ethnoracial groups: Asian, Hispanic or Latino, non-Hispanic Black, and non-Hispanic White. We quantified racialized segregation by computing a multi-group index of dissimilarity at baseline and post-simulation. Across 1,000 simulations, in one year (2019) we found on average 169 (95% CI: 84, 255) deaths averted from changes in greenness, 71 (95% CI: 49, 94) deaths averted from NO2, 9 (95% CI: 4, 14) deaths averted from noise, and marginal impacts from other exposures, with the highest rates of deaths averted observed among non-Hispanic Black and non-Hispanic White residents. Multi-group index of dissimilarity declined on average in all eight Connecticut counties post-simulation. Sensitivity analyses simulating a different population movement strategy and modeling a different year (2018) yielded consistent results. Strengthening desegregation policy may reduce deaths from environmental exposures among low-income residents. Further research should explore non-mortality impacts and additional mechanisms by which desegregation may advance health equity.

Simulating desegregation through affordable housing development: An environmental health impact assessment of Connecticut zoning law.

Residential segregation drives exposure and health inequities. We projected the mortality impacts among low-income residents of leveraging an existing 10% affordable housing target as a case study of desegregation policy. We simulated movement into newly allocated housing, quantified changes in six ambient environmental exposures, and used exposure-response functions to estimate deaths averted. Across 1000 simulations, in one year, we found on average 169 (95% CI: 84, 255) deaths averted from changes in greenness, 71 (49, 94) deaths averted from NO2, 9 (4, 14) deaths averted from noise, 1 (1, 2) excess death from O3, and 2 (1, 2) excess deaths from PM2.5, with rates of deaths averted highest among non-Hispanic Black and non-Hispanic White residents. Strengthening desegregation policy may advance environmental health equity.

Geospatial Assessment of Racial/Ethnic Composition, Social Vulnerability, and Lead Water Service Lines in New York City.

BACKGROUND: The state of New York expects to receive $115 million in 2022 alone from the U.S. Infrastructure Investment and Jobs Act to support the replacement of lead water service lines. OBJECTIVES: Our objective was to determine the number and proportion of potential lead water service lines across New York City (NYC) and to evaluate the association between census tract-level racial/ethnic composition, housing vulnerability, and child lead exposure vulnerability with service line type (Potential Lead, Unknown) for n=2,083 NYC tracts. METHODS: We conducted a descriptive analysis assessing water service line material recorded in the NYC Department of Environmental Protection's Lead Service Line Location Coordinates database. We used conditional autoregressive Bayesian Poisson models to assess the relative risk [RR; median posterior estimates, and 95% credible interval (CrI)] of service line type per 20% higher proportion of residents in a given racial/ethnic group and per higher housing vulnerability and child lead exposure vulnerability index scores corresponding to the interquartile range. We also evaluated the associations in flexible natural cubic spline models. RESULTS: Out of 854,672 residential service line records, 136,891 (16.0%) were Potential Lead, and 227,443 (26.6%) were Unknown. In fully adjusted models, higher proportions of Hispanic/Latino residents and higher child lead exposure vulnerability were associated with Potential Lead service lines in flexible spline models and linear models [RR=1.15 (95% CrI: 1.11, 1.21) and RR=1.11 (95% CrI: 1.02, 1.20), respectively]. Associations were modified by borough; Potential Lead service lines were associated with higher proportions of non-Hispanic White and non-Hispanic Asian residents in the Bronx and Manhattan, and with higher proportions of non-Hispanic Black residents in Queens. DISCUSSION: NYC has a high number of Potential Lead and Unknown water service lines. Communities with a high proportion of Hispanic/Latino residents and those with children who are already highly vulnerable to lead exposures from numerous sources are disproportionately impacted by Potential Lead service lines. These findings can inform equitable service line replacement across New York state and NYC. https://doi.org/10.1289/EHP12276.

Impaired spatial working memory and reduced hippocampal neuronal density in a rat model of neurocysticercosis.

Neurocysticercosis (NCC) is the most common parasitic disease affecting the nervous system and is a leading cause of acquired epilepsy worldwide, as well as cognitive impairment, especially affecting memory. The aim of this study was to evaluate the effect of NCC on spatial working memory and its correlation with hippocampal neuronal density, in a rat model of NCC. This experimental study was conducted on female (n = 60) and male (n = 73) Holtzman rats. NCC was induced by intracranial inoculation of T. solium oncospheres in 14 day-old-rats. Spatial working memory was assessed using the T-maze test at 3, 6, 9, and 12 months post-inoculation, and sensorimotor evaluation was performed at 12 months post-inoculation. Hippocampal neuronal density was evaluated by immunostaining of NeuN-positive cells of the CA1 region. Of the rats inoculated with T. solium oncospheres, 87.2% (82/94) developed NCC. The study showed a significant decline in spatial working memory over a 1-year follow-up period in rats experimentally infected with NCC. Males showed an early decline that started at 3 months, while females demonstrated it at 9 months. Additionally, a decrease in neuronal density was observed in the hippocampus of NCC-infected rats, with a more significant reduction in rats with cysts in the hippocampus than in rats with cysts in other brain areas and control rats. This rat model of NCC provides valuable support for the relationship between neurocysticercosis and spatial working memory deficits. Further investigations are required to determine the mechanisms involved in cognitive impairment and establish the basis for future treatments.

Using time-resolved monitor wearing data to study the effect of clean cooking interventions on personal air pollution exposures.

BACKGROUND: Personal monitoring can estimate individuals' exposures to environmental pollutants; however, accuracy depends on consistent monitor wearing, which is under evaluated. OBJECTIVE: To study the association between device wearing and personal air pollution exposure. METHODS: Using personal device accelerometry data collected in the context of a randomized cooking intervention in Ghana with three study arms (control, improved biomass, and liquified petroleum gas (LPG) arms; N = 1414), we account for device wearing to infer parameters of PM2.5 and CO exposure. RESULTS: Device wearing was positively associated with exposure in the control and improved biomass arms, but weakly in the LPG arm. Inferred community-level air pollution was similar across study arms (~45 µg/m3). The estimated direct contribution of individuals' cooking to PM2.5 exposure was 64 µg/m3 for the control arm, 74 µg/m3 for improved biomass, and 6 µg/m3 for LPG. Arm-specific average PM2.5 exposure at near-maximum wearing was significantly lower in the LPG arm as compared to the improved biomass and control arms. Analysis of personal CO exposure mirrored PM2.5 results. CONCLUSIONS: Personal monitor wearing was positively associated with average air pollution exposure, emphasizing the importance of high device wearing during monitoring periods and directly assessing device wearing for each deployment. SIGNIFICANCE: We demonstrate that personal monitor wearing data can be used to refine exposure estimates and infer unobserved parameters related to the timing and source of environmental exposures. IMPACT STATEMENTS: In a cookstove trial among pregnant women, time-resolved personal air pollution device wearing data were used to refine exposure estimates and infer unobserved exposure parameters, including community-level air pollution, the direct contribution of cooking to personal exposure, and the effect of clean cooking interventions on personal exposure. For example, in the control arm, while average 48 h personal PM2.5 exposure was 77 µg/m3, average predicted exposure at near-maximum daytime device wearing was 108 µg/m3 and 48 µg/m3 at zero daytime device wearing. Wearing-corrected average 48 h personal PM2.5 exposures were 50% lower in the LPG arm than the control and improved biomass and inferred direct cooking contributions to personal PM2.5 from LPG were 90% lower than the other arms. Our recommendation is that studies assessing personal exposures should examine the direct association between device wearing and estimated mean personal exposure.

Residential and Race/Ethnicity Disparities in Heat Vulnerability in the United States.

Adverse health outcomes caused by extreme heat represent the most direct human health threat associated with the warming of the Earth's climate. Socioeconomic, demographic, health, land cover, and temperature determinants contribute to heat vulnerability; however, nationwide patterns of residential and race/ethnicity disparities in heat vulnerability in the United States are poorly understood. This study aimed to develop a Heat Vulnerability Index (HVI) for the United States; to assess differences in heat vulnerability across geographies that have experienced historical and/or contemporary forms of marginalization; and to quantify HVI by race/ethnicity. Principal component analysis was used to calculate census tract level HVI scores based on the 2019 population characteristics of the United States. Differences in HVI scores were analyzed across the Home Owners' Loan Corporation (HOLC) "redlining" grades, the Climate and Economic Justice Screening Tool (CEJST) disadvantaged versus non-disadvantaged communities, and race/ethnicity groups. HVI scores were calculated for 55,267 U.S. census tracts. Mean HVI scores were 17.56, 18.61, 19.45, and 19.93 for HOLC grades "A"-"D," respectively. CEJST-defined disadvantaged census tracts had a significantly higher mean HVI score (19.13) than non-disadvantaged tracts (16.68). The non-Hispanic African American or Black race/ethnicity group had the highest HVI score (18.51), followed by Hispanic or Latino (18.19). Historically redlined and contemporary CEJST disadvantaged census tracts and communities of color were found to be associated with increased vulnerability to heat. These findings can help promote equitable climate change adaptation policies by informing policymakers about the national distribution of place- and race/ethnicity-based disparities in heat vulnerability.

Adding and subtracting by hand: Metaphorical representations of arithmetic in spontaneous co-speech gestures.

This study investigated the spontaneous co-speech gestures produced by speakers who were talking about the concepts of addition and subtraction in a television news setting. We performed a linguistic and co-speech gesture analysis of expressions related to the concepts of addition (N plus N, addition, add) and subtraction (N minus N, subtraction, subtract). First, we compared the linguistic frequency of these structures across several corpora. Second, we performed a multimodal gesture analysis, drawing data from a television news repository. We analyzed 423 co-speech gestures (169 for subtraction and 254 for addition) in terms of their axis (e.g., lateral, sagittal) and their direction (e.g., leftwards, away from their body). Third, we examined the semantic properties of the direct object that was added or subtracted. There were two main findings. First, low-frequency linguistic expressions were more likely to be accompanied by co-speech gestures. Second, most gestures about addition and subtraction were produced along the lateral or sagittal axes. When people spoke about addition, they tended to produce lateral, rightwards movements or movements away from the body. When people spoke about subtraction, they tended to produce lateral, leftwards movements or movements towards the body. This co-speech gesture data provides evidence that people activate two different metaphors for arithmetic in spontaneous behavior: arithmetic is motion along a path and arithmetic is collecting objects.

Prediction of daily mean and one-hour maximum PM2.5 concentrations and applications in Central Mexico using satellite-based machine-learning models.

BACKGROUND: Machine-learning algorithms are becoming popular techniques to predict ambient air PM2.5 concentrations at high spatial resolutions (1 × 1 km) using satellite-based aerosol optical depth (AOD). Most machine-learning models have aimed to predict 24 h-averaged PM2.5 concentrations (mean PM2.5) in high-income regions. Over Mexico, none have been developed to predict subdaily peak levels, such as the maximum daily 1-h concentration (max PM2.5). OBJECTIVE: Our goal was to develop a machine-learning model to predict mean PM2.5 and max PM2.5 concentrations in the Mexico City Metropolitan Area from 2004 through 2019. METHODS: We present a new modeling approach based on extreme gradient boosting (XGBoost) and inverse-distance weighting that uses AOD, meteorology, and land-use variables. We also investigated applications of our mean PM2.5 predictions that can aid local authorities in air-quality management and public-health surveillance, such as the co-occurrence of high PM2.5 and heat, compliance with local air-quality standards, and the relationship of PM2.5 exposure with social marginalization. RESULTS: Our models for mean and max PM2.5 exhibited good performance, with overall cross-validated mean absolute errors (MAE) of 3.68 and 9.20 µg/m3, respectively, compared to mean absolute deviations from the median (MAD) of 8.55 and 15.64 µg/m3. In 2010, everybody in the study region was exposed to unhealthy levels of PM2.5. Hotter days had greater PM2.5 concentrations. Finally, we found similar exposure to PM2.5 across levels of social marginalization. SIGNIFICANCE: Machine learning algorithms can be used to predict highly spatiotemporally resolved PM2.5 concentrations even in regions with sparse monitoring. IMPACT: Our PM2.5 predictions can aid local authorities in air-quality management and public-health surveillance, and they can advance epidemiological research in Central Mexico with state-of-the-art exposure assessment methods.

Corrigendum to "Prenatal household air pollutant exposure is associated with reduced size and gestational age at birth among a cohort of Ghanaian infants" [Environ. Int. 155 (2021) 106659].

[This corrects the article PMC8628363.].

The influence of structural racism, pandemic stress, and SARS-CoV-2 infection during pregnancy with adverse birth outcomes.

BACKGROUND: Structural racism and pandemic-related stress from the COVID-19 pandemic may increase the risk of adverse birth outcomes. OBJECTIVE: Our objective was to examine associations between neighborhood measures of structural racism and pandemic stress with 3 outcomes: SARS-CoV-2 infection, preterm birth, and delivering small-for-gestational-age newborns. Our secondary objective was to investigate the joint association of SARS-CoV-2 infection during pregnancy and neighborhood measures with preterm birth and delivering small-for-gestational-age newborns. STUDY DESIGN: We analyzed data of 967 patients from a prospective cohort of pregnant persons in New York City, comprising 367 White (38%), 169 Black (17%), 293 Latina (30%), and 87 Asian persons (9%), 41 persons of other race or ethnicity (4%), and 10 of unknown race or ethnicity (1%). We evaluated structural racism (social/built structural disadvantage, racial-economic segregation) and pandemic-related stress (community COVID-19 mortality, community unemployment rate increase) in quartiles by zone improvement plan code. SARS-CoV-2 serologic enzyme-linked immunosorbent assay was performed on blood samples from pregnant persons. We obtained data on preterm birth and small-for-gestational-age newborns from an electronic medical record database. We used log-binomial regression with robust standard error for clustering by zone improvement plan code to estimate associations of each neighborhood measure separately with 3 outcomes: SARS-CoV-2 infection, preterm birth, and small-for-gestational-age newborns. Covariates included maternal age, parity, insurance status, and body mass index. Models with preterm birth and small-for-gestational-age newborns as the dependent variables additionally adjusted for SARS-CoV-2 infection. RESULTS: A total of 193 (20%) persons were SARS-CoV-2-seropositive, and the overall risks of preterm birth and small-for-gestational-age newborns were 8.4% and 9.8%, respectively. Among birthing persons in neighborhoods in the highest quartile of structural disadvantage (n=190), 94% were non-White, 50% had public insurance, 41% were obese, 32% were seropositive, 11% delivered preterm, and 12% delivered a small-for-gestational-age infant. Among birthing persons in neighborhoods in the lowest quartile of structural disadvantage (n=360), 39% were non-White, 17% had public insurance, 15% were obese, 9% were seropositive, 6% delivered preterm, and 10% delivered a small-for-gestational-age infant. In adjusted analyses, structural racism measures and community unemployment were associated with both SARS-CoV-2 infection and preterm birth, but not small-for-gestational-age infants. High vs low structural disadvantage was associated with an adjusted relative risk of 2.6 for infection (95% confidence interval, 1.7-3.9) and 1.7 for preterm birth (95% confidence interval, 1.0-2.9); high vs low racial-economic segregation was associated with adjusted relative risk of 1.9 (95% confidence interval, 1.3-2.8) for infection and 2.0 (95% confidence interval, 1.3-3.2) for preterm birth; high vs low community unemployment increase was associated with adjusted relative risk of 1.7 (95% confidence interval, 1.2-1.5) for infection and 1.6 (95% confidence interval, 1.0-2.8) for preterm birth. COVID-19 mortality rate was associated with SARS-CoV-2 infection but not preterm birth or small-for-gestational-age infants. SARS-CoV-2 infection was not independently associated with birth outcomes. We found no interaction between SARS-CoV-2 infection and neighborhood measures on preterm birth or small-for-gestational-age infants. CONCLUSION: Neighborhood measures of structural racism were associated with both SARS-CoV-2 infection and preterm birth, but these associations were independent and did not have a synergistic effect. Community unemployment rate increases were also associated with an increased risk of preterm birth independently of SARS-CoV-2 infection. Mitigating these factors might reduce the impact of the pandemic on pregnant people.

Neighborhood-level disparities and subway utilization during the COVID-19 pandemic in New York City.

The COVID-19 pandemic has yielded disproportionate impacts on communities of color in New York City (NYC). Researchers have noted that social disadvantage may result in limited capacity to socially distance, and consequent disparities. We investigate the association between neighborhood social disadvantage and the ability to socially distance, infections, and mortality in Spring 2020. We combine Census Bureau and NYC open data with SARS-CoV-2 testing data using supervised dimensionality-reduction with Bayesian Weighted Quantile Sums regression. The result is a ZIP code-level index with weighted social factors associated with infection risk. We find a positive association between neighborhood social disadvantage and infections, adjusting for the number of tests administered. Neighborhood disadvantage is also associated with a proxy of the capacity to socially isolate, NYC subway usage data. Finally, our index is associated with COVID-19-related mortality.

Enhancing LPG Adoption in Ghana (ELAG): A Trial Testing Policy-Relevant Interventions to Increase Sustained Use of Clean Fuels.

Rural Ghanaians rely on solid biomass fuels for their cooking. National efforts to promote the Sustainable Development Goals include the Rural Liquefied Petroleum Gas Promotion Program (RLP), which freely distributes LPG stoves, but evaluations have demonstrated low sustained use among recipients. Our study objective was to assess if cheap and scalable add-on interventions could increase sustained use of LPG stoves under the RLP scheme. We replicated RLP conditions among participants in 27 communities in Kintampo, Ghana, but cluster-randomized them to four add-on interventions: a behavioral intervention, fuel delivery service, combined intervention, or control. We reported on the final 6 months of a 12-month follow-up for participants (n = 778). Results demonstrated increased use for each intervention, but magnitudes were small. The direct delivery intervention induced the largest increase: 280 min over 6 months (p < 0.001), ∼1.5 min per day. Self-reported refills (a secondary outcome), support increased use for the dual intervention arm (IRR = 2.2, p = 0.026). Past literature demonstrates that recipients of clean cookstoves rarely achieve sustained use of the technologies. While these results are statistically significant, we interpret them as null given the implied persistent reliance on solid fuels. Future research should investigate if fuel subsidies would increase sustained use since current LPG promotion activities do not.

Infant Nasopharyngeal Microbiota Subphenotypes and Early Childhood Lung Function: Evidence from a Rural Ghanaian Pregnancy Cohort.

Early life respiratory microbiota may increase risk for future pulmonary disease. Associations between respiratory microbiota and lung health in children from low- and middle-income countries are not well-described. Leveraging the Ghana Randomized Air Pollution and Health Study (GRAPHS) prospective pregnancy cohort in Kintampo, Ghana, we collected nasopharyngeal swabs in 112 asymptomatic children aged median 4.3 months (interquartile range (IQR) 2.9, 7.1) and analyzed 22 common bacterial and viral pathogens with MassTag polymerase chain reaction (PCR). We prospectively followed the cohort and measured lung function at age four years by impulse oscillometry. First, we employed latent class analysis (LCA) to identify nasopharyngeal microbiota (NPM) subphenotypes. Then, we used linear regression to analyze associations between subphenotype assignment and lung function. LCA suggest that a two-class model best described the infant NPM. We identified a higher diversity subphenotype (N = 38, 34%) with more pathogens (median 4; IQR 3.25, 4.75) and a lower diversity subphenotype (N = 74, 66%) with fewer pathogens (median 1; IQR 1, 2). In multivariable linear regression models, the less diverse NPM subphenotype had higher small airway resistance (R5-R20 ß = 17.9%, 95% CI 35.6, 0.23; p = 0.047) compared with the more diverse subphenotype. Further studies are required to understand the role of the microbiota in future lung health.

Prenatal household air pollutant exposure is associated with reduced size and gestational age at birth among a cohort of Ghanaian infants.

BACKGROUND: Low birth weight and prematurity are important risk factors for death and disability, and may be affected by prenatal exposure to household air pollution (HAP). METHODS: We investigate associations between maternal exposure to carbon monoxide (CO) during pregnancy and birth outcomes (birth weight, birth length, head circumference, gestational age, low birth weight, small for gestational age, and preterm birth) among 1288 live-born infants in the Ghana Randomized Air Pollution and Health Study (GRAPHS). We evaluate whether evidence of malaria during pregnancy, as determined by placental histopathology, modifies these associations. RESULTS: We observed effects of CO on birth weight, birth length, and gestational age that were modified by placental malarial status. Among infants from pregnancies without evidence of placental malaria, each 1 ppm increase in CO was associated with reduced birth weight (-53.4 g [95% CI: -84.8, -21.9 g]), birth length (-0.3 cm [-0.6, -0.1 cm]), gestational age (-1.0 days [-1.8, -0.2 days]), and weight-for-age Z score (-0.08 standard deviations [-0.16, -0.01 standard deviations]). These associations were not observed in pregnancies with evidence of placental malaria. Each 1 ppm increase in maternal exposure to CO was associated with elevated odds of low birth weight (LBW, OR 1.14 [0.97, 1.33]) and small for gestational age (SGA, OR 1.14 [0.98, 1.32]) among all infants. CONCLUSIONS: Even modest reductions in exposure to HAP among pregnant women could yield substantial public health benefits, underscoring a need for interventions to effectively reduce exposure. Adverse associations with HAP were discernible only among those without evidence of placental malaria, a key driver of impaired fetal growth in this malaria-endemic area.