Distance to Health Centers and Effectiveness of Azithromycin Mass Administration for Children in Niger: A Secondary Analysis of the MORDOR Cluster Randomized Trial.

Importance: The MORDOR (Macrolides Oraux pour Réduire les Décès avec un Oeil sur la Résistance) trial demonstrated that mass azithromycin administration reduced mortality by 18% among children aged 1 to 59 months in Niger. The identification of high-risk subgroups to target with this intervention could reduce the risk of antimicrobial resistance. Objective: To evaluate whether distance to the nearest primary health center modifies the effect of azithromycin administration to children aged 1 to 59 months on child mortality. Design, Setting, and Participants: The MORDOR cluster randomized trial was conducted from December 1, 2014, to July 31, 2017; this post hoc secondary analysis was conducted in 2023 among 594 clusters (communities or grappes) in the Boboye and Loga departments in Niger. All children aged 1 to 59 months in eligible communities were evaluated. Interventions: Biannual (twice-yearly) administration of a single dose of oral azithromycin or matching placebo over 2 years. Main Outcomes and Measures: A population-based census was used to monitor mortality and person-time at risk (trial primary outcome). Community distance to a primary health center was calculated as kilometers between the center of each community and the nearest health center. Negative binomial regression was used to evaluate the interaction between distance and the effect of azithromycin on the incidence of all-cause mortality among children aged 1 to 59 months. Results: Between December 1, 2014, and July 31, 2017, a total of 594 communities were enrolled, with 76 092 children (mean [SD] age, 31 [2] months; 39 022 [51.3%] male) included at baseline, for a mean (SD) of 128 (91) children per community. Median (IQR) distance to the nearest primary health center was 5.0 (3.2-7.1) km. Over 2 years, 145 693 person-years at risk were monitored and 3615 deaths were recorded. Overall, mortality rates were 27.5 deaths (95% CI, 26.2-28.7 deaths) per 1000 person-years at risk in the placebo arm and 22.5 deaths (95% CI, 21.4-23.5 deaths) per 1000 person-years at risk in the azithromycin arm. For each kilometer increase in distance in the placebo arm, mortality increased by 5% (adjusted incidence rate ratio, 1.05; 95% CI, 1.03-1.07; P < .001). The effect of azithromycin on mortality varied significantly by distance (interaction P = .02). Mortality reduction with azithromycin compared with placebo was 0% at 0 km from the health center (95% CI, -19% to 17%), 4% at 1 km (95% CI, -12% to 17%), 16% at 5 km (95% CI, 7% to 23%), and 28% at 10 km (95% CI, 17% to 38%). Conclusions and Relevance: In this secondary analysis of a cluster randomized trial of mass azithromycin administration for child mortality, children younger than 5 years who lived farthest from health facilities appeared to benefit the most from azithromycin administration. These findings may help guide the allocation of resources to ensure that those with the least access to existing health resources are prioritized in program implementation. Trial Registration: ClinicalTrials.gov Identifier: NCT02047981.

Gender and zoonotic pathogen exposure pathways in a resource-limited community, Mpumalanga, South Africa: A qualitative analysis.

The Mnisi community is a livestock-dependent community neighboring the Great Limpopo Transfrontier Conservation Area in South Africa. Here, zoonotic pathogens contribute to as many as 77% of cases of acute febrile illness. Previous gender-disaggregated analysis in the community has shown that men and women have different risks of zoonotic illness, suggesting that exposure routes for zoonotic infections should be further explored to inform gender-sensitive risk mitigation strategies. Using a One Health approach and ethnographic methodology, we examined interactions between community residents, domestic animals, and the built and natural environment to investigate potential exposure pathways for zoonotic infections from a gendered perspective. We combined data from direct household observations and focus group discussions on previously identified gendered tasks such as domestic animal care, water collection, and food preparation, and how and by whom these tasks were performed. We noted gender differences for household tasks, animal care duties, and environmental exposure. Both men and women access grazing land but for different tasks (water collection-females, cattle grazing-males), and both men and women experience more time in the bush in recent years due to decreased water availability. From observations, it was noted that men wore covered protective work clothes (such as long trousers and closed-toe shoes) more commonly than women did; women did not often wear these for household duties including water collection in the bush. We recommend that these gender-typed roles serve as critical control points for zoonotic pathogen exposure. For example, tick-bite exposure prevention should be directed at both men and women based on their daily activities, but prevention in men should target exposure from cattle and prevention in women should focus on personal protective measures during water and firewood collection. These findings can contribute to a more detailed understanding of the role of human behavior and critical control points for zoonotic disease-a significant contributor to acute febrile illness in this rural, resource-limited setting.

Spatiotemporal Patterns of Deaths of Despair Across the U.S., 2000-2019.

INTRODUCTION: Deaths of despair (i.e., suicide, drug/alcohol overdose, and chronic liver disease and cirrhosis) have been increasing over the past 2 decades. However, no large-scale studies have examined geographic patterns of deaths of despair in the U.S. This ecologic study identifies geographic and temporal patterns of individual and co-occurring clusters of deaths of despair. METHODS: All individuals aged ≥10 years who died in the U.S. between 2000 and 2019 and resided within the 48 contiguous states and Washington, District of Columbia were included (N=2,171,105). Causes of death were limited to deaths of despair, namely suicide, drug/alcohol overdose, and chronic liver disease and cirrhosis. Univariate and multivariate space-time scan statistics were used to identify individual and co-occurring clusters with excess risk of deaths of despair. County-level RRs account for heterogeneity within each cluster. Analyses were conducted from late 2021 to early 2022. RESULTS: Six suicide clusters, four overdose clusters, nine liver disease clusters, and three co-occurring clusters of all three types of deaths were identified. A large portion of the western U.S., southeastern U.S., and Appalachia/rust belt were contained within the co-occurring clusters. The co-occurring clusters had average county RRs ranging from 1.17 (p<0.001) in the southeastern U.S. to 4.90 (p<0.001) in the western U.S. CONCLUSIONS: Findings support identifying and targeting risk factors common to all types of deaths of despair when planning public health interventions. Resources and policies that address all deaths of despair simultaneously may be beneficial for the areas contained within the co-occurring high-risk clusters.

Projecting COVID-19 cases and hospital burden in Ohio.

As the Coronavirus 2019 disease (COVID-19) started to spread rapidly in the state of Ohio, the Ecology, Epidemiology and Population Health (EEPH) program within the Infectious Diseases Institute (IDI) at The Ohio State University (OSU) took the initiative to offer epidemic modeling and decision analytics support to the Ohio Department of Health (ODH). This paper describes the methodology used by the OSU/IDI response modeling team to predict statewide cases of new infections as well as potential hospital burden in the state. The methodology has two components: (1) A Dynamical Survival Analysis (DSA)-based statistical method to perform parameter inference, statewide prediction and uncertainty quantification. (2) A geographic component that down-projects statewide predicted counts to potential hospital burden across the state. We demonstrate the overall methodology with publicly available data. A Python implementation of the methodology is also made publicly available. This manuscript was submitted as part of a theme issue on "Modelling COVID-19 and Preparedness for Future Pandemics".

Projecting COVID-19 Cases and Subsequent Hospital Burden in Ohio.

As the Coronavirus 2019 (COVID-19) disease started to spread rapidly in the state of Ohio, the Ecology, Epidemiology and Population Health (EEPH) program within the Infectious Diseases Institute (IDI) at the Ohio State University (OSU) took the initiative to offer epidemic modeling and decision analytics support to the Ohio Department of Health (ODH). This paper describes the methodology used by the OSU/IDI response modeling team to predict statewide cases of new infections as well as potential hospital burden in the state. The methodology has two components: 1) A Dynamic Survival Analysis (DSA)-based statistical method to perform parameter inference, statewide prediction and uncertainty quantification. 2) A geographic component that down-projects statewide predicted counts to potential hospital burden across the state. We demonstrate the overall methodology with publicly available data. A Python implementation of the methodology has been made available publicly. Highlights: We present a novel statistical approach called Dynamic Survival Analysis (DSA) to model an epidemic curve with incomplete data. The DSA approach is advantageous over standard statistical methods primarily because it does not require prior knowledge of the size of the susceptible population, the overall prevalence of the disease, and also the shape of the epidemic curve.The principal motivation behind the study was to obtain predictions of case counts of COVID-19 and the resulting hospital burden in the state of Ohio during the early phase of the pandemic.The proposed methodology was applied to the COVID-19 incidence data in the state of Ohio to support the Ohio Department of Health (ODH) and the Ohio Hospital Association (OHA) with predictions of hospital burden in each of the Hospital Catchment Areas (HCAs) of the state.

Quantifying the Population-Level Effect of the COVID-19 Mass Vaccination Campaign in Israel: A Modeling Study.

Background: Estimating real-world vaccine effectiveness is challenging as a variety of population factors can impact vaccine effectiveness. We aimed to assess the population-level reduction in cumulative severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) cases, hospitalizations, and mortality due to the BNT162b2 mRNA coronavirus disease 2019 (COVID-19) vaccination campaign in Israel during January-February 2021. Methods: A susceptible-infected-recovered/removed (SIR) model and a Dynamic Survival Analysis (DSA) statistical approach were used. Daily counts of individuals who tested positive and of vaccine doses administered, obtained from the Israeli Ministry of Health, were used to calibrate the model. The model was parameterized using values derived from a previous phase of the pandemic during which similar lockdown and other preventive measures were implemented in order to take into account the effect of these prevention measures on COVID-19 spread. Results: Our model predicted for the total population a reduction of 648 585 SARS-CoV-2 cases (75% confidence interval [CI], 25 877-1 396 963) during the first 2 months of the vaccination campaign. The number of averted hospitalizations for moderate to severe conditions was 16 101 (75% CI, 2010-33 035), and reduction of death was estimated at 5123 (75% CI, 388-10 815) fatalities. Among children aged 0-19 years, we estimated a reduction of 163 436 (75% CI, 0-433 233) SARS-CoV-2 cases, which we consider to be an indirect effect of the vaccine. Conclusions: Our results suggest that the rapid vaccination campaign prevented hundreds of thousands of new cases as well as thousands of hospitalizations and fatalities and has probably averted a major health care crisis.

Understanding the role of urban social and physical environment in opioid overdose events using found geospatial data.

Opioid use disorder is a serious public health crisis in the United States. Manifestations such as opioid overdose events (OOEs) vary within and across communities and there is growing evidence that this variation is partially rooted in community-level social and economic conditions. The lack of high spatial resolution, timely data has hampered research into the associations between OOEs and social and physical environments. We explore the use of non-traditional, "found" geospatial data collected for other purposes as indicators of urban social-environmental conditions and their relationships with OOEs at the neighborhood level. We evaluate the use of Google Street View images and non-emergency "311" service requests, along with US Census data as indicators of social and physical conditions in community neighborhoods. We estimate negative binomial regression models with OOE data from first responders in Columbus, Ohio, USA between January 1, 2016, and December 31, 2017. Higher numbers of OOEs were positively associated with service request indicators of neighborhood physical and social disorder and street view imagery rated as boring or depressing based on a pre-trained random forest regression model. Perceived safety, wealth, and liveliness measures from the street view imagery were negatively associated with risk of an OOE. Age group 50-64 was positively associated with risk of an OOE but age 35-49 was negative. White population, percentage of individuals living in poverty, and percentage of vacant housing units were also found significantly positive however, median income and percentage of people with a bachelor's degree or higher were found negative. Our result shows neighborhood social and physical environment characteristics are associated with likelihood of OOEs. Our study adds to the scientific evidence that the opioid epidemic crisis is partially rooted in social inequality, distress and underinvestment. It also shows the previously underutilized data sources hold promise for providing insights into this complex problem to help inform the development of population-level interventions and harm reduction policies.

A Bayesian Spatiotemporal Nowcasting Model for Public Health Decision-Making and Surveillance.

As coronavirus disease 2019 (COVID-19) spread through the United States in 2020, states began to set up alert systems to inform policy decisions and serve as risk communication tools for the general public. Many of these systems included indicators based on an assessment of trends in numbers of reported cases. However, when cases are indexed by date of disease onset, reporting delays complicate the interpretation of trends. Despite a foundation of statistical literature with which to address this problem, these methods have not been widely applied in practice. In this paper, we develop a Bayesian spatiotemporal nowcasting model for assessing trends in county-level COVID-19 cases in Ohio. We compare the performance of our model with the approach used in Ohio and the approach included in decision support materials from the Centers for Disease Control and Prevention. We demonstrate gains in performance while still retaining interpretability using our model. In addition, we are able to fully account for uncertainty in both the time series of cases and the reporting process. While we cannot eliminate all of the uncertainty in public health surveillance and subsequent decision-making, we must use approaches that embrace these challenges and deliver more accurate and honest assessments to policy-makers.

Prevalence of current and past COVID-19 in Ohio adults.

Purpose To estimate the prevalence of current and past COVID-19 in Ohio adults. Methods We used stratified, probability-proportionate-to-size cluster sampling. During July 2020, we enrolled 727 randomly-sampled adult English- and Spanish-speaking participants through a household survey. Participants provided nasopharyngeal swabs and blood samples to detect current and past COVID-19. We used Bayesian latent class models with multilevel regression and poststratification to calculate the adjusted prevalence of current and past COVID-19. We accounted for the potential effects of non-ignorable non-response bias. Results The estimated statewide prevalence of current COVID-19 was 0.9% (95% credible interval: 0.1%-2.0%), corresponding to ∼85,000 prevalent infections (95% credible interval: 6,300-177,000) in Ohio adults during the study period. The estimated statewide prevalence of past COVID-19 was 1.3% (95% credible interval: 0.2%-2.7%), corresponding to ∼118,000 Ohio adults (95% credible interval: 22,000-240,000). Estimates did not change meaningfully due to non-response bias. Conclusions Total COVID-19 cases in Ohio in July 2020 were approximately 3.5 times as high as diagnosed cases. The lack of broad COVID-19 screening in the United States early in the pandemic resulted in a paucity of population-representative prevalence data, limiting the ability to measure the effects of statewide control efforts.

The impact of the national stay-at-home order on emergency department visits for suspected opioid overdose during the first wave of the COVID-19 pandemic.

BACKGROUND: Although national syndromic surveillance data reported declines in emergency department (ED) visits after the declaration of the national stay-at-home order for COVID-19, little is known whether these declines were observed for suspected opioid overdose. METHODS: This interrupted time series study used syndromic surveillance data from four states participating in the HEALing Communities Study: Kentucky, Massachusetts, New York, and Ohio. All ED encounters for suspected opioid overdose (n = 48,301) occurring during the first 31 weeks of 2020 were included. We examined the impact of the national public health emergency for COVID-19 (declared on March 14, 2020) on trends in ED encounters for suspected opioid overdose. RESULTS: Three of four states (Massachusetts, New York and Ohio) experienced a statistically significant immediate decline in the rate of ED encounters for suspected opioid overdose (per 100,000) after the nationwide public health emergency declaration (MA: -0.99; 95 % CI: -1.75, -0.24; NY: -0.10; 95 % CI, -0.20, 0.0; OH: -0.33, 95 % CI: -0.58, -0.07). After this date, Ohio and Kentucky experienced a sustained rate of increase for a 13-week period. New York experienced a decrease in the rate of ED encounters for a 10-week period, after which the rate began to increase. In Massachusetts after a significant immediate decline in the rate of ED encounters, there was no significant difference in the rate of change for a 6-week period, followed by an immediate increase in the ED rate to higher than pre-COVID levels. CONCLUSIONS: The heterogeneity in the trends in ED encounters between the four sites show that the national stay-at-home order had a differential impact on opioid overdose ED presentation in each state.

Disparities in Opioid Overdose Death Trends by Race/Ethnicity, 2018-2019, From the HEALing Communities Study.

Objectives. To examine trends in opioid overdose deaths by race/ethnicity from 2018 to 2019 across 67 HEALing Communities Study (HCS) communities in Kentucky, New York, Massachusetts, and Ohio. Methods. We used state death certificate records to calculate opioid overdose death rates per 100 000 adult residents of the 67 HCS communities for 2018 and 2019. We used Poisson regression to calculate the ratio of 2019 to 2018 rates. We compared changes by race/ethnicity by calculating a ratio of rate ratios (RRR) for each racial/ethnic group compared with non-Hispanic White individuals. Results. Opioid overdose death rates were 38.3 and 39.5 per 100 000 for 2018 and 2019, respectively, without a significant change from 2018 to 2019 (rate ratio = 1.03; 95% confidence interval [CI] = 0.98, 1.08). We estimated a 40% increase in opioid overdose death rate for non-Hispanic Black individuals (RRR = 1.40; 95% CI = 1.22, 1.62) relative to non-Hispanic White individuals but no change among other race/ethnicities. Conclusions. Overall opioid overdose death rates have leveled off but have increased among non-Hispanic Black individuals. Public Health Implications. An antiracist public health approach is needed to address the crisis of opioid-related harms. (Am J Public Health. 2021;111(10):1851-1854. https://doi.org/10.2105/AJPH.2021.306431).

Thirty-five years later: Long-term effects of the Matlab maternal and child health/family planning program on older women's well-being.

Family planning programs are believed to have substantial long-term benefits for women's health and well-being, yet few studies have established either extent or direction of long-term effects. The Matlab, Bangladesh, maternal and child health/family planning (MCH/FP) program afforded a 12-y period of well-documented differential access to services. We evaluate its impacts on women's lifetime fertility, adult health, and economic outcomes 35 y after program initiation. We followed 1,820 women who were of reproductive age during the differential access period (born 1938-1973) from 1978 to 2012 using prospectively collected data from the Matlab Health and Demographic Surveillance System and the 1996 and 2012 Matlab Health and Socioeconomic Surveys. We estimated intent-to-treat single-difference models comparing treatment and comparison area women. MCH/FP significantly increased contraceptive use, reduced completed fertility, lengthened birth intervals, and reduced age at last birth, but had no significant positive impacts on health or economic outcomes. Treatment area women had modestly poorer overall health (+0.07 SD) and respiratory health (+0.12 SD), and those born 1950-1961 had significantly higher body mass index (BMI) in 1996 (0.76 kg/m2) and 2012 (0.57 kg/m2); fewer were underweight in 1996, but more were overweight or obese in 2012. Overall, there was a +2.5 kg/m2 secular increase in BMI. We found substantial changes in lifetime contraceptive and fertility behavior but no long-term health or economic benefits of the program. We observed modest negative health impacts that likely result from an accelerated nutritional transition among treated women, a transition that would, in an earlier context, have been beneficial.

Investigating the association of room features with healthcare-facility-onset Clostridioides difficile: An exploratory study.

OBJECTIVE: To investigate hospital room and patient-level risk factors associated with increased risk of healthcare-facility-onset Clostridioides difficile infection (HO-CDI). DESIGN: The study used a retrospective cohort design that included patient data from the institution's electronic health record, existing surveillance data on HO-CDI, and a walk-through survey of hospital rooms to identify potential room-level risk factors. The primary outcome was HO-CDI diagnosis. SETTING: A large academic medical center. PATIENTS AND PARTICIPANTS: All adult patients admitted between January 1, 2015, and December 31, 2016 were eligible for inclusion. Prisoners were excluded. Patients who only stayed in rooms that were not surveyed were excluded. RESULTS: The hospital room survey collected room-level data on 806 rooms. Included in the study were 17,034 patients without HO-CDI and 251 with HO-CDI nested within 535 unique rooms. In this exploratory study, room-level risk factors associated with the outcome in the multivariate model included wear on furniture and flooring and antibiotic use by the prior room occupant. Hand hygiene devices and fixed in-room computers were associated with reduced odds of a HO-CDI. Differences between hospital buildings were also detected. The only individual patient factors that were associated with increased odds of HO-CDI were antibiotic use and comorbidity score. CONCLUSION: Combining a hospital-room walk-through data collection survey, EHR data, and CDI surveillance data, we were able to develop a model to investigate room and patient-level risks for HO-CDI.

Electronic cigarette use and risk of cigarette and smokeless tobacco initiation among adolescent boys: A propensity score matched analysis.

INTRODUCTION: Electronic cigarette (e-cigarette) use among adolescents is associated with increased risk of subsequent cigarette smoking initiation in observational research. However, the existing research was not designed to answer causal questions about whether adolescent e-cigarette users would have initiated cigarette smoking if they had never used e-cigarettes. The current study used a causal inference framework to identify whether male adolescent e-cigarette users were at increased risk of initiating cigarette smoking and smokeless tobacco (SLT) use, compared to similar boys who had never used e-cigarettes. METHODS: Boys from urban and Appalachian Ohio (N = 1220; ages 11-16 years at enrollment) reported use of e-cigarettes, cigarettes, and SLT at baseline and every six months for two years. A propensity score matching design was implemented, matching one e-cigarette user to two similar e-cigarette non-users. This analysis was completed in 25 multiple imputed datasets to account for missing data. Risk ratios (RRs) comparing risk of initiating cigarettes and SLT for e-cigarette users and nonusers were estimated. RESULTS: Compared to non-users, e-cigarette users were more than twice as likely to later initiate both cigarette smoking (RR = 2.71; 95% CI: 1.89, 3.87) and SLT (RR = 2.42; 95% CI: 1.73, 3.38). They were also more likely to become current (i.e., past 30-day) cigarette smokers (RR = 2.20; 95% CI: 1.33, 3.64) and SLT users (RR = 1.64; 95% CI: 1.01, 2.64). CONCLUSIONS: Adolescent boys who used e-cigarettes had increased risk of later initiating traditional tobacco products when compared to similar boys who had never used e-cigarettes.

Operationalizing and selecting outcome measures for the HEALing Communities Study.

BACKGROUND: The Helping to End Addiction Long-termSM (HEALing) Communities Study (HCS) is a multisite, parallel-group, cluster randomized wait-list controlled trial evaluating the impact of the Communities That HEAL intervention to reduce opioid overdose deaths and associated adverse outcomes. This paper presents the approach used to define and align administrative data across the four research sites to measure key study outcomes. METHODS: Priority was given to using administrative data and established data collection infrastructure to ensure reliable, timely, and sustainable measures and to harmonize study outcomes across the HCS sites. RESULTS: The research teams established multiple data use agreements and developed technical specifications for more than 80 study measures. The primary outcome, number of opioid overdose deaths, will be measured from death certificate data. Three secondary outcome measures will support hypothesis testing for specific evidence-based practices known to decrease opioid overdose deaths: (1) number of naloxone units distributed in HCS communities; (2) number of unique HCS residents receiving Food and Drug Administration-approved buprenorphine products for treatment of opioid use disorder; and (3) number of HCS residents with new incidents of high-risk opioid prescribing. CONCLUSIONS: The HCS has already made an impact on existing data capacity in the four states. In addition to providing data needed to measure study outcomes, the HCS will provide methodology and tools to facilitate data-driven responses to the opioid epidemic, and establish a central repository for community-level longitudinal data to help researchers and public health practitioners study and understand different aspects of the Communities That HEAL framework.

Organizations matter in local governance: evidence from health sector decentralization in Honduras.

Health systems strengthening is at the forefront of the global health agenda. Many health systems in low-resource settings face profound challenges, and robust causal evidence on the effects of health systems reforms is lacking. Decentralization has been one of the most prominent reforms, and after more than 50 years of implementation and hundreds of studies, we still know little about whether these policies improve, harm or are inconsequential for the performance of health systems in less-developed countries. A persistent problem in existing studies is the inability to isolate the effect of decentralization on health outcomes, struggling with heterogeneous meanings of decentralization and missing counterfactuals. We address these shortcomings with a quasi-experimental, longitudinal research design that takes advantage of a unique staggered reform process in Honduras. Using three waves of household survey data over 10 years for a matched sample of 65 municipalities in Honduras, we estimated difference-in-difference models comparing changes in outcomes over time between local health systems that were decentralized using one of three types of organizations [municipal governments, associations of mayors or non-governmental organization (NGOs)] and those that remained centrally administered. We find evidence of overall improvements between 2005 and 2016 in several service delivery-related outcomes, and additional improvements in decentralized municipalities governed by NGOs. NGO-led municipalities saw a 15% decrease in home delivery relative to centralized municipalities in 2016, a 12.5% increase in MCH facility delivery and a 7% increase in the use of a skilled birth attendant. There were no detectable positive treatment effects for vaccination, and a slight decline in the weight-for-length z-scores in NGO municipalities, but we find no systematic evidence of decentralization negatively impacting any maternal and child health outcomes. These findings highlight the importance of considering implementation context, namely organization type, when assessing the effects of decentralization reform.

A Community Development Program and Reduction in High-Cost Health Care Use.

Adverse housing and neighborhood conditions influence child health. The Healthy Neighborhoods Healthy Families community development initiative was established in 2008 to address housing, education, employment, and other neighborhood-level, child health-influencing factors on the south side of Columbus, Ohio, with the goal of improving child health and well-being. In this article, we discuss the path from advocacy to outcomes analysis in this initiative and assess changes in high-cost health care use by children in the target area over the first decade of implementation. Change in health care use was measured by using a difference-in-differences approach comparing emergency department visits, inpatient stays, and inpatient length of stay in the intervention neighborhood and a propensity score-matched, pooled comparator neighborhood in the same city. The baseline and follow-up periods were August 2008 to July 2010 and August 2015 to July 2017, respectively. Findings from this analysis reveal that compared to 2 pooled comparison neighborhoods, the intervention neighborhood trended, nonsignificantly, toward greater decreases in inpatient stays and emergency department visits and smaller increases in length of stays. These results suggest that our community development activities may be influencing health care use outcomes, but in the early years of the intervention relative changes are modest and are variable based on the definition of the intervention and comparator neighborhoods. Lessons learned in expanding from advocacy to analysis include the importance of building multidisciplinary teams that can apply novel approaches to analysis, moderating expectations, and retaining focus on the broader social context.

Geovisualization and Spatial Analysis of Infant Mortality and Preterm Birth in Ohio, 2008-2015: Opportunities to Enhance Spatial Thinking.

OBJECTIVES: Geovisualization and spatial analysis are valuable tools for exploring and evaluating the complex social, economic, and environmental interactions that lead to spatial inequalities in health. The objective of this study was to describe spatial patterns of infant mortality and preterm birth in Ohio by using interactive mapping and spatial analysis. METHODS: We conducted a retrospective cohort study using Ohio vital statistics records from 2008-2015. We geocoded live births and infant deaths by using residential address at birth. We used multivariable logistic regression to adjust spatial and space-time cluster analyses that examined the geographic clustering of infant mortality and preterm birth and changes in spatial distribution over time. RESULTS: The overall infant mortality rate in Ohio during the study period was 6.55 per 1000 births; of 1 097 507 births, 10.3% (n = 112 552) were preterm. We found significant geographic clustering of both infant mortality and preterm birth centered on large urban areas. However, when known demographic risk factors were taken into account, urban clusters disappeared and, for preterm birth, new rural clusters appeared. CONCLUSIONS: Although many public health agencies have the capacity to create maps of health outcomes, complex spatial analysis and geovisualization techniques are still challenging for public health practitioners to use and understand. We found that actively engaging policymakers in reviewing results of the cluster analysis improved understanding of the processes driving spatial patterns of birth outcomes in the state.

Relationship between cyanobacterial bloom impacted drinking water sources and hepatocellular carcinoma incidence rates.

Freshwater cyanobacterial blooms have increased in geographic distribution and intensity in recent decades worldwide. Cyanotoxins produced by many of these blooms, such as microcystins, are observed to play a role in tumor promotion and have been associated with increased liver cancer rates at the population level. Exposure occurs primarily via contaminated water (ingestion, inhalation, dermal contact), either from treated drinking water or during recreation in impacted surface waters; additional sources of exposure include consumption of fresh produce grown in cyanotoxin-contaminated environments or through the consumption of seafood caught in bloom-impacted waters. The current ecological study investigates whether populations served by cyanobacterial bloom-impacted surface waters for their drinking water source have higher hepatocellular carcinoma (HCC) incidence rates than those served by non-impacted surface waters and groundwater. Census tract level cancer incidence in the state of Ohio, United States was modeled using a negative binomial generalized linear model, controlling for differences in demographic composition (e.g. age, race, and income) at the census tract level. Presence of cyanobacterial blooms in surface waters was estimated using satellite multi-spectral remote sensing and in situ public water system cyanotoxin monitoring data. Census tracts estimated to be served by bloom-impacted surface waters had 14.2% higher HCC incidence rates than those served by non-bloom-impacted surface waters (incidence rate ratio, IRR: 1.142; 95% CI: 1.037-1.257). Additionally, these bloom-impacted census tracts had a 17.4% higher HCC incidence rate as compared to those estimated to receive drinking water from a groundwater source (IRR: 1.174; 95% CI: 1.101-1.252). No statistical difference was found in HCC incidence rates when comparing areas presumed to be served by non-bloom-impacted surface waters and those presumed to be served by groundwater sources. An important consideration for environmental justice, areas estimated to be served by bloom-impacted surface waters had higher levels of poverty and included a higher percentage of racial and ethnic minority populations than areas served by groundwater. These findings support the need for additional in-depth research into the potential hepatic carcinogenicity and exposures of cyanotoxins in those areas where severe blooms are chronically observed.

Air infiltration in low-income, urban homes and its relationship to lung function.

Previous research has found increased home ventilation, which may affect health by altering the composition of indoor air, is associated with improvement of respiratory health, but evidence linking home ventilation to objectively measured lung function is sparse. The Colorado Home Energy Efficiency and Respiratory health (CHEER) study, a cross-sectional study of low-income, urban, nonsmoking homes across the Northern Front Range of Colorado, USA, focused on elucidating this link. We used a multipoint depressurization blower door test to measure the air tightness of the homes and calculate the annual average infiltration rate (AAIR). Lung function tests were administered to eligible participants. We analyzed data from 253 participants in 187 homes with two or more acceptable spirometry tests. We used generalized estimating equations to model forced expiratory volume in 1 s (FEV1), forced vital capacity (FVC), and FEV1/FVC z-scores as a function of AAIR. AAIRs ranged from 0.10 to 1.98 air changes per hour. Mean z-scores for FEV1, FVC, and FEV1/FVC were -0.57, 0.32, and -0.43, respectively. AAIR was positively associated with increased FEV1/FVC z-scores, such that a 1-unit change in AAIR corresponded to a half of a standard deviation in lung function (ß = 0.51, CI: 0.02-0.99). These associations were strongest for healthy populations and weaker for those with asthma and asthma-like symptoms. AAIR was not associated with FEV1 or FVC. Our study is the first in the United States to link home ventilation by infiltration to objectively measured lung function in low-income, urban households.