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.

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.

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.

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.

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 PM_2.5 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 PM_2.5 concentrations (mean PM_2.5) in high-income regions. Over Mexico, none have been developed to predict subdaily peak levels, such as the maximum daily 1-h concentration (max PM_2.5).

OBJECTIVE

Our goal was to develop a machine-learning model to predict mean PM_2.5 and max PM_2.5 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 PM_2.5 predictions that can aid local authorities in air-quality management and public-health surveillance, such as the co-occurrence of high PM_2.5 and heat, compliance with local air-quality standards, and the relationship of PM_2.5 exposure with social marginalization.

RESULTS

Our models for mean and max PM_2.5 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 PM_2.5. Hotter days had greater PM_2.5 concentrations. Finally, we found similar exposure to PM_2.5 across levels of social marginalization.

SIGNIFICANCE

Machine learning algorithms can be used to predict highly spatiotemporally resolved PM_2.5 concentrations even in regions with sparse monitoring.

IMPACT

Our PM_2.5 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.

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.

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.].

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.

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.

A cluster randomised trial of cookstove interventions to improve infant health in Ghana

Introduction

Household air pollution from solid fuel combustion for cooking and heating is a leading cause of childhood morbidity and mortality worldwide. We hypothesised that clean cooking interventions delivered during pregnancy would improve child health.

Methods

We conducted a cluster randomised trial in rural Ghana to test whether providing pregnant women liquefied petroleum gas (LPG) cookstoves or improved biomass cookstoves would reduce personal carbon monoxide and fine particulate pollution exposure, increase birth weight and reduce physician-assessed severe pneumonia in the first 12 months of life, compared with control participants who continued to cook with traditional stoves. Primary analyses were intention-to-treat. The trial was registered with ClinicalTrials.gov and follow-up is complete.

Results

Enrolment began on 14 April 2014, and ended on 20 August 2015. We enrolled 1414 pregnant women; 361 in the LPG arm, 527 in the improved biomass cookstove arm and 526 controls. We saw no improvement in birth weight (the difference in mean birth weight for LPG arm births was 29 g lighter (95% CI −113 to 56, p=0.51) and for improved biomass arm births was 9 g heavier (95% CI −64 to 82, p=0.81), compared with control newborns) nor severe child pneumonia (the rate ratio for pneumonia in the LPG arm was 0.98 (95% CI 0.58 to 1.70; p=0.95) and for the improved biomass arm was 1.21 (95% CI 0.78 to 1.90; p=0.52), compared with the control arm). Air pollution exposures in the LPG arm remained above WHO health-based targets (LPG median particulate matter less than 2.5 microns in diameter (PM_2.5) 45 µg/m³; IQR 32–65 vs control median PM_2.5 67 µg/m³, IQR 46–97).

Conclusions

Neither prenatally-introduced LPG nor improved biomass cookstoves improved birth weight or reduced severe pneumonia risk in the first 12 months of life. We hypothesise that this is due to lower-than-expected exposure reductions in the intervention arms.

Trial registration number

NCT01335490.

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.

Using longitudinal survey and sensor data to understand the social and ecological determinants of clean fuels use and discontinuance in rural Ghana

Efforts to reduce the health and ecological burdens of household biomass combustion are underway in Ghana, principally by promoting clean cookstoves and fuels. Recent studies have focused on the sustained use of clean cookstoves, but sometimes household adopt a new cookstove and then end use of that stove. In this study, we introduce a novel framework for understanding and encouraging household transitions to cleaner cooking: clean fuel discontinuance. We leveraged data from the Ghana Randomized Air Pollution and Health Study (GRAPHS) (N = 1412) where pregnant women received either improved biomass (BioLite) or dual burner LPG stoves for free. LPG users were given free LPG refills during GRAPHS. Weekly questionnaires were administered. Stove use monitors tracked a sub-cohort (n = 220) 6 months before and after the fuel subsidy. We examined social and ecological determinants of stove use and discontinuance. Overall intervention stove use adherence was high throughout GRAPHS, with self-reported use at 69% and 86% of participant-weeks for BioLite and LPG arms respectively. Participants used intervention stoves less for meals requiring vigorous stirring. Burns from intervention stoves decreased use among BioLite (RR: 0.96, p = 0.009), but not LPG users. Device breakage was mentioned as an impediment in 18% of free-text responses for LPG users and 1% for BioLite. Tree canopy within a spatial buffer-a plausible proxy for biomass fuels access-was the only variable explaining LPG discontinued stove use in adjusted Cox time-to-event analyses (HR = -0.56, p < 0.001). Future studies should consider the stove use discontinuance framework.

Assessing capacity to social distance and neighborhood-level health disparities during the COVID-19 pandemic

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. Here, we investigate the role of neighborhood social disadvantage on the ability to socially distance, infections, and mortality. 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 relative weights for social factors facilitating infection risk. We find a positive association between neighborhood social disadvantage and infections, adjusting for the number of tests administered. Neighborhood infection risk is also associated with capacity to socially isolate, as measured by NYC subway data. Finally, infection risk is associated with COVID-19-related mortality. These analyses support that differences in capacity to socially isolate is a credible pathway between disadvantage and COVID-19 disparities.

Examining the relationship between household air pollution and infant microbial nasal carriage in a Ghanaian cohort

BACKGROUND

Pneumonia, a leading cause of childhood mortality, is associated with household air pollution (HAP) exposure. Mechanisms between HAP and pneumonia are poorly understood, but studies suggest that HAP may increase the likelihood of bacterial, instead of viral, pneumonia. We assessed the relationship between HAP and infant microbial nasal carriage among 260 infants participating in the Ghana Randomized Air Pollution and Health Study (GRAPHS).

METHODS

Data are from GRAPHS, a cluster-randomized controlled trial of cookstove interventions (improved biomass or LPG) versus the 3-stone (baseline) cookstove. Infants were surveyed for pneumonia during the first year of life and had routine personal exposure assessments. Nasopharyngeal swabs collected from pneumonia cases (n = 130) and healthy controls (n = 130) were analyzed for presence of 22 common respiratory microbes by MassTag polymerase chain reaction. Data analyses included intention-to-treat (ITT) comparisons of microbial species presence by study arm, and exposure-response relationships.

RESULTS

In ITT analyses, 3-stone arm participants had a higher mean number of microbial species than the LPG (LPG: 2.71, 3-stone: 3.34, p < 0.0001, n = 260). This difference was driven by increased bacterial (p < 0.0001) rather than viral species presence (non-significant). Results were pronounced in pneumonia cases and attenuated in healthy controls. Higher prevalence bacterial species were Haemophilus influenzae, Streptococcus pneumoniae, and Moraxella catarrhalis. Exposure-response relationships did not yield significant associations between measured CO and nasal microbial carriage.

CONCLUSIONS

Our intention-to-treat findings are consistent with a link between HAP and bacterial nasal carriage. No relationships were found for viral carriage. Given the null results in exposure-response analysis, it is likely that a pollutant besides CO is driving these differences.

Climate Change and Physical Activity: Estimated Impacts of Ambient Temperatures on Bikeshare Usage in New York City

BACKGROUND

Physical activity is one of the best disease prevention strategies, and it is influenced by environmental factors such as temperature.

OBJECTIVES

We aimed to illuminate the relation between ambient temperature and bikeshare usage and to project how climate change-induced increasing ambient temperatures may influence active transportation in New York City.

METHODS

The analysis leverages Citi Bike® bikeshare data to estimate participation in outdoor bicycling in New York City. Exposure-response functions are estimated for the relation between daily temperature and bike usage from 2013 to 2017. The estimated exposure-response relation is combined with temperature outputs from 21 climate models (run with emissions scenarios RCP4.5 and RCP8.5) to explore how climate change may influence future bike utilization.

RESULTS

Estimated daily hours and distance ridden significantly increased as temperatures increased, but then declined at temperatures above 26-28°C. Bike usage may increase by up to 3.1% by 2070 due to climate change. Future ridership increases during the winter, spring, and fall may more than offset future declines in summer ridership.

DISCUSSION

Evidence suggesting nonlinear impacts of rising temperatures on health-promoting bicycle ridership demonstrates how challenging it is to anticipate the health consequences of climate change. We project increases in bicycling by mid-century in NYC, but this trend may reverse as temperatures continue to rise further into the future. https://doi.org/10.1289/EHP4039.

Ghana's Rural Liquefied Petroleum Gas Program Scale Up: A case study

BACKGROUND

The Government of Ghana launched the Rural LPG (RLP) promotion program in 2013 as part of its efforts to reduce fuelwood consumption. The aim of the RLP is to contribute to Ghana's overarching goal to provide LPG access to 50% of Ghana's population by 2020. The RLP has not announced long-term program objectives. However, in the interim the RLP targeted a cumulative total of 170,000 LPG cookstoves to rural households by the end of 2017. As of November 2017, 149,500 rural households had received the LPG cook stoves. Our case study documents Ghana's experiences to date with LPG scale up.

METHODS

We carried out a desktop review/document analysis of literature on the RLP. Each document was reviewed for information related to the elements of the Reach, Effectiveness, Adoption, Implementation and Maintenance (RE-AIM) framework as it pertained to LPG promotion and adoption. In-depth interviews were held among key stakeholders in Ghana. Previously collected data from a field evaluation of the RLP was also assessed.

FINDINGS

Generally, our evaluation suggests that the current form of the RLP is not achieving its stated goal. Our evaluation of the RLP in five rural communities showed that about 58% of households had never refilled their LPG cylinders nine months after the initial delivery of a filled cylinder. Only 8% still used their LPG at 18 months post distribution. Cost and distance to LPG filling stations were the main reasons for low LPG use. Beneficiaries did not exclusively use their LPG even at the initial stages when all of them had LPG in their cylinders. Ghana is currently undergoing transitions in the LPG sector including a change from the current private cylinder ownership model to a cylinder recirculation model for the distribution of LPG. There was no evidence of a well-documented implementation framework for the RLP.

CONCLUSION

Fuel cost, poor LPG access, and an inadequate implementation framework hinder the RLP implementation.

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).

Electronic supplementary material

The online version of this article (10.1186/s12889-018-5622-3) contains supplementary material, which is available to authorized users.

Residual Inequity: Assessing the Unintended Consequences of New York City's Clean Heat Transition

Energy policies and public health are intimately intertwined. In New York City, a series of policies, known as the Clean Heat Program (CHP), were designed to reduce air pollution by banning residual diesel fuel oils, #6 in 2015 and #4 by 2030. This measure is expected to yield environmental and public health benefits over time. While there is near-universal compliance with the #6 ban, a substantial number of buildings still use #4. In this paper, geographic analysis and qualitative interviews with stakeholders were used to interrogate the CHP's policy implementation in Northern Manhattan and the Bronx. A total of 1724 (53%) of all residential residual fuel burning buildings are located in this region. Stakeholders reflected mostly on the need for the program, and overall reactions to its execution. Major findings include that government partnerships with non-governmental organizations were effectively employed. However, weaknesses with the policy were also identified, including missed opportunities for more rapid transitions away from residual fuels, unsuccessful outreach efforts, cost-prohibitive conversion opportunities, and (the perception of) a volatile energy market for clean fuels. Ultimately, this analysis serves as a case study of a unique and innovative urban policy initiative to improve air quality and, consequently, public health.

Housing hardship and energy insecurity among native-born and immigrant low-income families with children in the United States

The costs for rent and utilities account for the largest share of living expenses, yet these two critical dimensions of material hardship have seldom been examined concurrently in population-based studies. This paper employs multivariate statistical analysis using American Community Survey data to demonstrate the relative risk ratio of low-income renter-occupied households with children experiencing "rent burden," "energy insecurity," or a "double burden" as opposed to no burden. Findings suggest that low-income households are more likely to experience these economic hardships in general but that specific groups are disproportionately burdened in different ways. For instance, whereas immigrants are more likely to experience rental burden, they are less likely to experience energy insecurity and are also spared from the double burden. In contrast, native-born African Americans are more likely than all other groups to experience the double burden. These results may be driven by the housing stock available to certain groups due to racial residential segregation, decisions regarding the quality of housing low-income householders are able to afford, as well as home-country values, such as modest living and energy conservation practices, among immigrant families. This paper also points to important policy gaps in safety net benefits related to housing and energy targeting low-income households.

Quality pork genes and meat production

Functional genomics, including analysis of the transcriptome and proteome, provides new opportunities for understanding the molecular processes in muscle and how these influence its conversion to meat. The Quality Pork Genes project was established to identify genes associated with variation in different aspects of raw material (muscle) quality and to then develop genetic tools that could be utilized to improve this quality. DNA polymorphisms identified in the porcine PRKAG3 and CAST genes illustrate the impact that such tools can have in improving meat quality. The resources developed in Quality Pork Genes provide the basis for identifying more of these tools.

Effects of extra feeding in mid-pregnancy for three successive parities on lean sows’ productive performance and longevity

The aim of this study was to investigate the long-term effects of increasing feeding allowance during mid-pregnancy in sows. A total of 103 PIC pregnant sows (mixed parity) were allocated to two treatments: control (C, n = 49) were fed 2.5-3.0 kg d-1 (12.1 MJ ME kg-1) and extra-fed (E, n = 54) received +2.0 kg d-1 of the same feed from day 45 to 85 of gestation over three consecutive cycles. Body weight, backfat thickness (BF) and loin depth were measured on days 45 and 85 of gestation, farrowing and weaning. Litter and sows performance were recorded during lactation and post-weaning. Overall culling rates were 61 and 67% for C and E groups, respectively. After three cycles, E sows showed a positive BF balance in contrast to C sows (E = +1.46 mm and C= -1.81 mm, P < 0.05). In cycle 3, E sows presented greater piglet birth weights than C sows, being mainly evident in sows that were nulliparous at the onset of the experiment (P < 0.05). Extra-fed sows showed a greater incidence of mastitis-metritis-agalactia syndrome than C sows (P = 0.003). Thus, increasing feeding allowance during mid-pregnancy positively affected BF balance and birth weight in nulliparous, but impaired the sows’ ability to produce milk in the long-term.

Increased sow nutrition during midgestation affects muscle fiber development and meat quality, with no consequences on growth performance

Pregnant sow nutrition has potential effects on the muscle fiber development of progeny in utero. A total of 199 Landrace x Large White sows from parities 0 to 6 and their offspring were used to evaluate the effects of increasing the feeding amount during midpregnancy on the muscle tissue, growth performance, and meat quality of the progeny. The experiment was divided into 2 study replicates, and in each replicate, sows were assigned to 1 of the 2 treatments: 1) sows in the control group (C sows) were fed 2.5 to 3.0 kg/d (feed: 12.1 MJ of ME/kg and 0.62% lysine) throughout gestation; and 2) sows in the high group (H sows) received an extra feed allowance of 1.5 kg/d for gilts and 2.0 kg/d for multiparous sows above the C amount from d 45 to 85 of gestation (period of secondary muscle fiber formation). Sow backfat was recorded on d 40 and 85 of gestation. Sow performance (litter size and piglet BW) at farrowing and on d 18 of lactation was measured. At weaning, pigs were divided into 5 BW groups/treatment, and progeny growth performance was measured during the nursery (n = 958) and the growing-finishing (n = 636) periods. At slaughter, carcass and meat quality traits (lean content, main cut weight, pH, Minolta color, and drip loss) were recorded from the second lightest group at weaning (BW group 4; n = 90), and samples from the longissimus thoracis muscle were taken to study muscle fiber characteristics (n = 70). The extra nutrition from d 45 to 85 of gestation did not lead to differences in litter size or piglet BW at farrowing and on d 18 of lactation. Pigs born to H mothers had fewer muscle fibers and fewer estimated primary and secondary fibers than did pigs born to C mothers (P < 0.05). However, postnatal growth performance was not consistently affected by the maternal treatment. The smaller number of muscle fibers found in the H group of pigs was associated with fewer type IIB fibers (P < 0.05) with greater cross-sectional areas (P < 0.10), which might be related to the significantly greater meat pH at 24 h postmortem and the smaller L* (lightness) values recorded in the H group of pigs. Results from the present study confirm the existence of effects of maternal nutrition on fetal development, at least in terms of muscle tissue development and meat quality, although with no beneficial effects were found for the postnatal growth performance of the progeny.

Age-related body weight constraints on prenatal and milk provisioning in Iberian red deer (Cervus elaphus hispanicus) affect allocation of maternal resources

Maternal phenotypic characteristics can influence key life history variables of their offspring through maternal effects. In this study, we examined how body size constraints on maternal weight in yearling and subadult compared to adult hinds (age class effects) affected prenatal (calf birth weight, calf to hind weight ratio) and postnatal (milk) provisioning of Iberian red deer calves. Age correlated with all prenatal and postnatal investment traits except calf gains, although correlations were weaker than those with maternal weight. Once the effect of linear increase in weight with age was removed from models, yearlings showed additional reductions in calf birth weight, calf gains, and milk provisioning. The low-calf birth weight might increase the risk of calf mortality during lactation, as this occurs primarily during the first day of life and is strongly related to birth weight. Yearlings showed a greater prenatal allocation of resources in terms of greater calf to hind weight ratio probably as an extra effort by yearling mothers to balance calf neonatal mortality. It might compensate young mothers to produce low-quality calves while still growing rather than waiting for the uncertain possibility of surviving to the next reproductive season.

Relationships between carcass quality parameters and genetic types

It is important for the meat industry, including pig farmers and breeding companies, to know the composition of the carcass including the proportions of the different commercial cuts when making decisions on the type of pigs to be produced and marketed. Carcass composition is influenced significantly by the genotype of the animals. The aim of this work was to characterise carcasses from five different pig genotypes, by means of the quantification and comparison of their physical composition. Carcasses from 500 gilts from five different genetic types were studied. These lines were based on the following breeds: Large White, Landrace, Duroc, Piétrain and a Meishan synthetic population developed from a cross with a Large White based line. Measurements were taken in the carcasses directly with a ruler and with the Fat-O-Meat'er. The carcasses were cut following the European reference method and the four main joints were dissected. Carcasses from the Piétrain based line, which was halothane negative, presented the highest killing-out (83.34%) and were the shortest (81.81cm). The Piétrain based line was also the leanest and the Meishan based line the fattest. The highest proportion of ham (270.9g/kg) and the lowest proportion of belly (97.97g/kg) were found in the Piétrain line. The proportion of lean in all of the dissected cuts was higher in carcasses from this line while the Meishan line presented the highest proportions of intermuscular fat in all of the pieces. Carcasses came from the Piétrain line received the highest conformation scores and they were leaner and with better ham yield.