Simulating Energy Use, Indoor Temperatures, and Utility Cost Impacts Amidst a Warming Climate in a Multi-family Housing Model.

Rising ambient temperatures due to climate change will impact both indoor temperatures and heating and cooling utility costs. In traditionally colder climates, there are potential tradeoffs in how to meet the reduced heating and increased cooling demands, and issues related to lack of air conditioning (AC) access in older homes and among lower-income populations to prevent extreme heat exposure. We modeled a typical multi-family home in Boston (MA) in the building simulation program EnergyPlus to assess indoor temperature and energy consumption in current (2020) and projected future (2050) weather conditions. Selected households were those without AC (no AC), those who ran AC sometimes (some AC), and those with sufficient resources to run AC always (full AC). We considered stylized cooling subsidy policies that allowed households to move between groups, both independently and in conjunction with energy efficiency retrofits. Results showed that future weather conditions without policy changes yielded an increase in indoor summer temperatures of 2.1 °C (no AC), increased cooling demand (range: 34-50%), but led to a decrease in net yearly total utility costs per apartment (range: - $21 to - $38). Policies that allowed households to move to greater AC utilization yielded average indoor summer temperature decreases (- 3.5 °C to - 6.2 °C) and net yearly total utility increases (range: + $2 to + $94) per apartment unit, with greater savings for retrofitted homes primarily due to large decreases in heating use. Our model results reinforce the importance of coordinated public policies addressing climate change that have an equity lens for both health and climate goals.

Characterizing temporal trends in populations exposed to aircraft noise around U.S. airports: 1995-2015.

BACKGROUND: Aircraft noise is a key concern for communities surrounding airports, with increasing evidence for health effects and inequitable distributions of exposure. However, there have been limited national-scale assessments of aircraft noise exposure over time and across noise metrics, limiting evaluation of population exposure patterns. OBJECTIVE: We evaluated national-scale temporal trends in aviation noise exposure by airport characteristics and across racial/ethnic populations in the U.S. METHODS: Noise contours were modeled for 90 U.S. airports in 5-year intervals between 1995 and 2015 using the Federal Aviation Administration's Aviation Environmental Design Tool. We utilized linear fixed effects models to estimate changes in noise exposure areas for day-night average sound levels (DNL) of 45, 65, and a nighttime equivalent sound level (Lnight) of 45 A-weighted decibels (dB[A]). We used group-based trajectory modeling to identify distinct groups of airports sharing underlying characteristics. We overlaid noise contours and Census tract data from the U.S. Census Bureau and American Community Surveys for 2000 to 2015 to estimate exposure changes overall and by race/ethnicity. RESULTS: National-scale analyses showed non-monotonic trends in mean exposed areas that peaked in 2000, followed by a 37% decrease from 2005 to 2010 and a subsequent increase in 2015. We identified four distinct trajectory groups of airports sharing latent characteristics related to size and activity patterns. Those populations identifying as minority (e.g., Hispanic/Latino, Black/African American, Asian) experienced higher proportions of exposure relative to their subgroup populations compared to non-Hispanic or White populations across all years, indicating ethnic and racial disparities in airport noise exposure that persist over time. SIGNIFICANCE: Overall, these data identified differential exposure trends across airports and subpopulations, helping to identify vulnerable communities for aviation noise in the U.S. IMPACT STATEMENT: We conducted a descriptive analysis of temporal trends in aviation noise exposure in the U.S. at a national level. Using data from 90 U.S. airports over a span of two decades, we characterized the noise exposure trends overall and by airport characteristics, while estimating the numbers of exposed by population demographics to help identify the impact on vulnerable communities who may bear the burden of aircraft noise exposure.

2p25.3 microduplications involving MYT1L: further phenotypic characterization through an assessment of 16 new cases and a literature review.

Microduplications involving the MYT1L gene have mostly been described in series of patients with isolated schizophrenia. However, few reports have been published, and the phenotype has still not been well characterized. We sought to further characterize the phenotypic spectrum of this condition by describing the clinical features of patients with a pure 2p25.3 microduplication that includes all or part of MYT1L. We assessed 16 new patients with pure 2p25.3 microduplications recruited through a French national collaboration (n = 15) and the DECIPHER database (n = 1). We also reviewed 27 patients reported in the literature. For each case, we recorded clinical data, the microduplication size, and the inheritance pattern. The clinical features were variable and included developmental and speech delays (33%), autism spectrum disorder (ASD, 23%), mild-to-moderate intellectual disability (ID, 21%), schizophrenia (23%), or behavioral disorders (16%). Eleven patients did not have an obvious neuropsychiatric disorder. The microduplications ranged from 62.4 kb to 3.8 Mb in size and led to duplication of all or part of MYT1L; seven of these duplications were intragenic. The inheritance pattern was available for 18 patients: the microduplication was inherited in 13 cases, and all parents but one had normal phenotype. Our comprehensive review and expansion of the phenotypic spectrum associated with 2p25.3 microduplications involving MYT1L should help clinicians to better assess, counsel and manage affected individuals. MYT1L microduplications are characterized by a spectrum of neuropsychiatric phenotypes with incomplete penetrance and variable expressivity, which are probably due to as-yet unknown genetic and nongenetic modifiers.

A science-based agenda for health-protective chemical assessments and decisions: overview and consensus statement.

The manufacture and production of industrial chemicals continues to increase, with hundreds of thousands of chemicals and chemical mixtures used worldwide, leading to widespread population exposures and resultant health impacts. Low-wealth communities and communities of color often bear disproportionate burdens of exposure and impact; all compounded by regulatory delays to the detriment of public health. Multiple authoritative bodies and scientific consensus groups have called for actions to prevent harmful exposures via improved policy approaches. We worked across multiple disciplines to develop consensus recommendations for health-protective, scientific approaches to reduce harmful chemical exposures, which can be applied to current US policies governing industrial chemicals and environmental pollutants. This consensus identifies five principles and scientific recommendations for improving how agencies like the US Environmental Protection Agency (EPA) approach and conduct hazard and risk assessment and risk management analyses: (1) the financial burden of data generation for any given chemical on (or to be introduced to) the market should be on the chemical producers that benefit from their production and use; (2) lack of data does not equate to lack of hazard, exposure, or risk; (3) populations at greater risk, including those that are more susceptible or more highly exposed, must be better identified and protected to account for their real-world risks; (4) hazard and risk assessments should not assume existence of a "safe" or "no-risk" level of chemical exposure in the diverse general population; and (5) hazard and risk assessments must evaluate and account for financial conflicts of interest in the body of evidence. While many of these recommendations focus specifically on the EPA, they are general principles for environmental health that could be adopted by any agency or entity engaged in exposure, hazard, and risk assessment. We also detail recommendations for four priority areas in companion papers (exposure assessment methods, human variability assessment, methods for quantifying non-cancer health outcomes, and a framework for defining chemical classes). These recommendations constitute key steps for improved evidence-based environmental health decision-making and public health protection.

Iron deficiency anemia is associated with increased medical and implant-related complications and length of stay for patients undergoing total shoulder arthroplasty.

BACKGROUND: Iron deficiency anemia (IDA) is associated with decreased bone mineral density and osteoporosis; however, studies investigating the effects of IDA in patients undergoing primary total shoulder arthroplasty (TSA) have not been well studied. The purpose of this study is to utilize a nationwide administrative claims database to investigate whether patients with diagnosed IDA undergoing primary TSA have higher rates of 1) in-hospital length of stay (LOS); 2) medical complications; and 3) implant-related complications. METHODS: A retrospective review from 2005 to 2014 was conducted using the Medicare Standard Analytical Files. Patients with IDA undergoing primary TSA were identified and matched to controls without IDA, in a 1:5 ratio by age, sex, and medical comorbidities. Outcomes analyzed included in-hospital LOS and 90-day medical and implant-related complications. Mann-Whitney U tests compared in-hospital LOS, and multivariate logistic regression was used to calculate odds ratios (ORs) on the effects of IDA on postoperative complications after adjusting for age, sex, and Elixhauser Comorbidity Index. RESULTS: A total of 17,689 patients with IDA and 88,445 without IDA participated in the matched-cohort analysis, with no differences in age, gender, and comorbidities (P = .99). IDA patients were found to have significantly longer in-hospital LOS (3-days vs. 2-days, P < .0001). IDA patients were also found to have significantly higher odds of 90-day implant-related complications (OR: 1.65, P < .0001), such as periprosthetic joint infections (OR: 1.80, P < .0001) and 90-day medical complications (OR: 2.87, P < .0001), including blood transfusions (OR: 10.37, P < .0001). CONCLUSION: Patients with IDA undergoing primary TSA have significantly longer in-hospital LOS, and medical and implant-related complications. Patients were 10 times more likely to undergo a blood transfusion and 2 times more likely to have a periprosthetic fracture.

Mixed methods assessment of personal heat exposure, sleep, physical activity, and heat adaptation strategies among urban residents in the Boston area, MA.

The growing frequency, intensity, and duration of extreme heat events necessitates interventions to reduce heat exposures. Local opportunities for heat adaptation may be optimally identified through collection of both quantitative exposure metrics and qualitative data on perceptions of heat. In this study, we used mixed methods to characterize heat exposure among urban residents in the area of Boston, Massachusetts, US, in summer 2020. Repeated interviews of N = 24 study participants ascertained heat vulnerability and adaptation strategies. Participants also used low-cost sensors to collect temperature, location, sleep, and physical activity data. We saw significant differences across temperature metrics: median personal temperature exposures were 3.9 °C higher than median ambient weather station temperatures. Existing air conditioning (AC) units did not adequately control indoor temperatures to desired thermostat levels: even with AC use, indoor maximum temperatures increased by 0.24 °C per °C of maximum outdoor temperature. Sleep duration was not associated with indoor or outdoor temperature. On warmer days, we observed a range of changes in time-at-home, expected given our small study size. Interview results further indicated opportunities for heat adaptation interventions including AC upgrades, hydration education campaigns, and amelioration of energy costs during high heat periods. Our mixed methods design informs heat adaptation interventions tailored to the challenges faced by residents in the study area. The strength of our community-academic partnership was a large part of the success of the mixed methods approach.

Assessment of the health benefits to children of a transportation climate policy in New York City.

BACKGROUND: Assessments of health and environmental effects of clean air and climate policies have revealed substantial health benefits due to reductions in air pollution, but have included few pediatric outcomes or assessed benefits at the neighborhood level. OBJECTIVES: We estimated benefits across a suite of child health outcomes in 42 New York City (NYC) neighborhoods under the proposed regional Transportation and Climate Initiative. We also estimated their distribution across racial/ethnic and socioeconomic groups. METHODS: We estimated changes in ambient fine particulate matter (PM2.5) and nitrogen dioxide (NO2) concentrations associated with on-road emissions under nine different predefined cap-and-invest scenarios. Health outcomes, including selected adverse birth, respiratory, and neurodevelopmental outcomes, were estimated using a program similar to the U.S. EPA BenMAP program. We stratified the associated monetized benefits across racial/ethnic and socioeconomic groups. RESULTS: The benefits varied widely over the different cap-and-investment scenarios. For a 25% reduction in carbon emissions from 2022 to 2032 and a strategy prioritizing public transit investments, NYC would have an estimated 48 fewer medical visits for childhood asthma, 13,000 avoided asthma exacerbations not requiring medical visits, 640 fewer respiratory illnesses unrelated to asthma, and 9 avoided adverse birth outcomes (infant mortality, preterm birth, and term low birth weight) annually, starting in 2032. The total estimated annual avoided costs are $22 million. City-wide, Black and Hispanic children would experience 1.7 times the health benefits per capita than White and Non-Hispanic White children, respectively. Under the same scenario, neighborhoods experiencing the highest poverty rates in NYC would experience about 2.5 times the health benefits per capita than the lowest poverty neighborhoods. CONCLUSION: A cap-and-invest strategy to reduce carbon emissions from the transportation sector could provide substantial health and monetized benefits to children in NYC through reductions in criteria pollutant concentrations, with greater benefits among Black and Hispanic children.

The immigrant birthweight paradox in an urban cohort: Role of immigrant enclaves and ambient air pollution.

BACKGROUND: Foreign-born Black and Latina women on average have higher birthweight infants than their US-born counterparts, despite generally worse socioeconomic indicators and prenatal care access, i.e., "immigrant birthweight paradox" (IBP). Residence in immigrant enclaves and associated social-cultural and economic benefits may be drivers of IBP. Yet, enclaves have been found to have higher air pollution, a risk factor for lower birthweight. OBJECTIVE: We investigated the association of immigrant enclaves and children's birthweight accounting for prenatal ambient air pollution exposure. METHODS: In the Boston-based Children's HealthWatch cohort of mother-child dyads, we obtained birthweight-for-gestational-age z-scores (BWGAZ) for US-born births, 2006-2015. We developed an immigrant enclave score based on census-tract percentages of foreign-born, non-citizen, and linguistically-isolated households statewide. We estimated trimester-specific PM2.5 concentrations and proximity to major roads based residential address at birth. We fit multivariable linear regressions of BWGAZ and examined effect modification by maternal nativity. Analyses were restricted to nonsmoking women and term births. RESULTS: Foreign-born women had children with 0.176 (95% CI: 0.092, 0.261) higher BWGAZ than US-born women, demonstrating the IBP in our cohort. Immigrant enclave score was not associated with BWGAZ, even after adjusting for air pollution exposures. However, this association was significantly modified by maternal nativity (pinteraction = 0.014), in which immigrant enclave score was positively associated with BWGAZ for only foreign-born women (0.090, 95% CI: 0.007, 0.172). Proximity to major roads was negatively associated with BWGAZ (-0.018 per 10 m, 95% CI: -0.032, -0.003) and positively correlated with immigrant enclave scores. Trimester-specific PM2.5 concentrations were not associated with BWGAZ. SIGNIFICANCE: Residence in immigrant enclaves was associated with higher birthweight children for foreign-born women, supporting the role of immigrant enclaves in the IBP. Future research of the IBP should account for immigrant enclaves and assess their spatial correlation with potential environmental risk factors and protective resources.

Community predictors of COVID-19 cases and deaths in Massachusetts: Evaluating changes over time using geospatially refined data.

BACKGROUND: The COVID-19 pandemic has highlighted the need for targeted local interventions given substantial heterogeneity within cities and counties. Publicly available case data are typically aggregated to the city or county level to protect patient privacy, but more granular data are necessary to identify and act upon community-level risk factors that can change over time. METHODS: Individual COVID-19 case and mortality data from Massachusetts were geocoded to residential addresses and aggregated into two time periods: "Phase 1" (March-June 2020) and "Phase 2" (September 2020 to February 2021). Institutional cases associated with long-term care facilities, prisons, or homeless shelters were identified using address data and modeled separately. Census tract sociodemographic and occupational predictors were drawn from the 2015-2019 American Community Survey. We used mixed-effects negative binomial regression to estimate incidence rate ratios (IRRs), accounting for town-level spatial autocorrelation. RESULTS: Case incidence was elevated in census tracts with higher proportions of Black and Latinx residents, with larger associations in Phase 1 than Phase 2. Case incidence associated with proportion of essential workers was similarly elevated in both Phases. Mortality IRRs had differing patterns from case IRRs, decreasing less substantially between Phases for Black and Latinx populations and increasing between Phases for proportion of essential workers. Mortality models excluding institutional cases yielded stronger associations for age, race/ethnicity, and essential worker status. CONCLUSIONS: Geocoded home address data can allow for nuanced analyses of community disease patterns, identification of high-risk subgroups, and exclusion of institutional cases to comprehensively reflect community risk.

Time-varying associations between COVID-19 case incidence and community-level sociodemographic, occupational, environmental, and mobility risk factors in Massachusetts.

BACKGROUND: Associations between community-level risk factors and COVID-19 incidence have been used to identify vulnerable subpopulations and target interventions, but the variability of these associations over time remains largely unknown. We evaluated variability in the associations between community-level predictors and COVID-19 case incidence in 351 cities and towns in Massachusetts from March to October 2020. METHODS: Using publicly available sociodemographic, occupational, environmental, and mobility datasets, we developed mixed-effect, adjusted Poisson regression models to depict associations between these variables and town-level COVID-19 case incidence data across five distinct time periods from March to October 2020. We examined town-level demographic variables, including population proportions by race, ethnicity, and age, as well as factors related to occupation, housing density, economic vulnerability, air pollution (PM2.5), and institutional facilities. We calculated incidence rate ratios (IRR) associated with these predictors and compared these values across the multiple time periods to assess variability in the observed associations over time. RESULTS: Associations between key predictor variables and town-level incidence varied across the five time periods. We observed reductions over time in the association with percentage of Black residents (IRR = 1.12 [95%CI: 1.12-1.13]) in early spring, IRR = 1.01 [95%CI: 1.00-1.01] in early fall) and COVID-19 incidence. The association with number of long-term care facility beds per capita also decreased over time (IRR = 1.28 [95%CI: 1.26-1.31] in spring, IRR = 1.07 [95%CI: 1.05-1.09] in fall). Controlling for other factors, towns with higher percentages of essential workers experienced elevated incidences of COVID-19 throughout the pandemic (e.g., IRR = 1.30 [95%CI: 1.27-1.33] in spring, IRR = 1.20 [95%CI: 1.17-1.22] in fall). Towns with higher proportions of Latinx residents also had sustained elevated incidence over time (IRR = 1.19 [95%CI: 1.18-1.21] in spring, IRR = 1.14 [95%CI: 1.13-1.15] in fall). CONCLUSIONS: Town-level COVID-19 risk factors varied with time in this study. In Massachusetts, racial (but not ethnic) disparities in COVID-19 incidence may have decreased across the first 8 months of the pandemic, perhaps indicating greater success in risk mitigation in selected communities. Our approach can be used to evaluate effectiveness of public health interventions and target specific mitigation efforts on the community level.

Value Analysis of Anatomic and Reverse Shoulder Arthroplasty for Glenohumeral Osteoarthritis with an Intact Rotator Cuff.

BACKGROUND: While anatomic total shoulder arthroplasty (TSA) has historically been considered the ideal treatment for end-stage glenohumeral osteoarthritis, reverse shoulder arthroplasty (RSA) has recently gained popularity. With substantial differences in implant design and cost between TSA and RSA, further investigation of outcomes and value is needed to support recent trends. The purpose of this study was to use the average and incremental cost-effectiveness ratio (ACER and ICER) and the procedure value index (PVI) to examine differences in outcomes and value between TSA and RSA for treatment of glenohumeral osteoarthritis with an intact rotator cuff. METHODS: We performed a retrospective matched-cohort study of patients treated with primary shoulder arthroplasty for osteoarthritis with an intact rotator cuff who had a minimum 2-year follow-up. Outcome measures analyzed included the Simple Shoulder Test (SST), American Shoulder and Elbow Surgeons (ASES) questionnaire, visual analog scale (VAS) for pain, Single Assessment Numeric Evaluation (SANE), and overall satisfaction. Patients treated with TSA were matched 4:1 to those treated with RSA based on sex, age, and preoperative SST score. Value differences between TSA and RSA were calculated. Radiographs were analyzed for preoperative glenoid classification and postoperative radiolucent lines and gross loosening. RESULTS: Two hundred and fifty-two TSA-treated patients were matched to 63 RSA-treated patients with no significant differences in sex, age, or preoperative SST score. Total hospitalization costs, charges, and reimbursements along with outcome improvements in units of minimal clinically important differences (MCIDs) and patient satisfaction did not differ between the groups. For RSA, the implant cost was significantly higher than that for TSA, but the operating room, anesthesia, and cement costs were lower. The TSA group had a 3.2% rate of gross glenoid loosening and a 2.4% revision rate. There was no loosening or revision in the RSA group. None of the value analytics differed between groups even after inclusion of the outcomes and costs of early TSA revisions. CONCLUSIONS: TSA and RSA demonstrated similar outcomes and value when used to manage glenohumeral osteoarthritis with an intact rotator cuff. LEVEL OF EVIDENCE: Therapeutic Level III. See Instructions for Authors for a complete description of levels of evidence.

Modeling the impact of exposure reductions using multi-stressor epidemiology, exposure models, and synthetic microdata: an application to birthweight in two environmental justice communities.

BACKGROUND: Many vulnerable populations experience elevated exposures to environmental and social stressors, with deleterious effects on health. Multi-stressor epidemiological models can be used to assess benefits of exposure reductions. However, requisite individual-level risk factor data are often unavailable at adequate spatial resolution. OBJECTIVE: To leverage public data and novel simulation methods to estimate birthweight changes following simulated environmental interventions in two environmental justice communities in Massachusetts, USA. METHODS: We gathered risk factor data from public sources (US Census, Behavioral Risk Factor Surveillance System, and Massachusetts Department of Health). We then created synthetic individual-level data sets using combinatorial optimization, and probabilistic and logistic modeling. Finally, we used coefficients from a multi-stressor epidemiological model to estimate birthweight and birthweight improvement associated with simulated environmental interventions. RESULTS: We created geographically resolved synthetic microdata. Mothers with the lowest predicted birthweight were those identifying as Black or Hispanic, with parity > 1, utilization of government prenatal support, and lower educational attainment. Birthweight improvements following greenness and temperature improvements were similar for all high-risk groups and were larger than benefits from smoking cessation. SIGNIFICANCE: Absent private health data, this methodology allows for assessment of cumulative risk and health inequities, and comparison of individual-level impacts of localized health interventions.

Time-varying associations between COVID-19 case incidence and community-level sociodemographic, occupational, environmental, and mobility risk factors in Massachusetts.

BACKGROUND: Associations between community-level risk factors and COVID-19 incidence are used to identify vulnerable subpopulations and target interventions, but the variability of these associations over time remains largely unknown. We evaluated variability in the associations between community-level predictors and COVID-19 case incidence in 351 cities and towns in Massachusetts from March to October 2020. METHODS: Using publicly available sociodemographic, occupational, environmental, and mobility datasets, we developed mixed-effect, adjusted Poisson regression models to depict associations between these variables and town-level COVID-19 case incidence data across five distinct time periods. We examined town-level demographic variables, including z-scores of percent Black, Latinx, over 80 years and undergraduate students, as well as factors related to occupation, housing density, economic vulnerability, air pollution (PM 2.5 ), and institutional facilities. RESULTS: Associations between key predictor variables and town-level incidence varied across the five time periods. We observed reductions over time in the association with percentage Black residents (IRR=1.12 CI=(1.12-1.13) in spring, IRR=1.01 CI=(1.00-1.01) in fall). The association with number of long-term care facility beds per capita also decreased over time (IRR=1.28 CI=(1.26-1.31) in spring, IRR=1.07 CI=(1.05-1.09)in fall). Controlling for other factors, towns with higher percentages of essential workers experienced elevated incidence of COVID-19 throughout the pandemic (e.g., IRR=1.30 CI=(1.27-1.33) in spring, IRR=1.20, CI=(1.17-1.22) in fall). Towns with higher percentages of Latinx residents also had sustained elevated incidence over time (e.g., IRR=1.19 CI=(1.18-1.21) in spring, IRR=1.14 CI=(1.13-1.15) in fall). CONCLUSIONS: Town-level COVID-19 risk factors vary with time. In Massachusetts, racial (but not ethnic) disparities in COVID-19 incidence have decreased over time, perhaps indicating greater success in risk mitigation in selected communities. Our approach can be used to evaluate effectiveness of public health interventions and target specific mitigation efforts on the community level.

Accounting for Health Risk Inequality in Regulatory Impact Analysis: Barriers and Opportunities.

There has been increasing interest in accounting for inequality in health risks and benefits within regulatory impact analyses, both given more general interest in the distributions of benefits and growing concerns about inequity (defined as those inequalities deemed unjust or unfair) and environmental injustice (in this context, those health risk inequalities that are correlated with race/ethnicity and certain other sociodemographic factors). Although there has been growing literature on this topic, there has been limited progress in practice, and the lack of quantification limits consideration of inequality in the policy process. Controversy remains regarding the best approaches to formally incorporate inequality, when these approaches should be used, and even whether it makes sense to quantify inequality in this context. The objective of this article is to review the literature on approaches for incorporating estimates of, and concerns for, inequality into regulatory impact analyses, especially those where environmental justice considerations are relevant, and consider the interpretation of these approaches and the implications for decision making. Using the case example of the Transportation and Climate Initiative, a collaboration among Northeast and Mid-Atlantic states to reduce carbon emissions from the transportation sector, multiple strategies are described that could be used to shed light on health risk inequality and inequity, consider them in pending policy decisions, and evaluate their implications for the policy or instrument choice. Given appropriate contextualization and acknowledgment of the multidimensionality of equity, quantitative inequality indicators can provide meaningful insight about both inequality and inequity in health risks.

Real-time indoor PM2.5 monitoring in an urban cohort: Implications for exposure disparities and source control.

Fine particulate matter (PM2.5) concentrations are highly variable indoors, with evidence for exposure disparities. Real-time monitoring coupled with novel statistical approaches can better characterize drivers of elevated PM2.5 indoors. We collected real-time PM2.5 data in 71 homes in an urban community of Greater Boston, Massachusetts using Alphasense OPC-N2 monitors. We estimated indoor PM2.5 concentrations of non-ambient origin using mass balance principles, and investigated their associations with indoor source activities at the 0.50 to 0.95 exposure quantiles using mixed effects quantile regressions, overall and by homeownership. On average, the majority of indoor PM2.5 concentrations were of non-ambient origin (≥77%), with a higher proportion at increasing quantiles of the exposure distribution. Major source predictors of non-ambient PM2.5 concentrations at the upper quantile (0.95) were cooking (1.4-23 µg/m3) and smoking (15 µg/m3, only among renters), with concentrations also increasing with range hood use (3.6 µg/m3) and during the heating season (5.6 µg/m3). Across quantiles, renters in multifamily housing experienced a higher proportion of PM2.5 concentrations from non-ambient sources than homeowners in single- and multifamily housing. Renters also more frequently reported cooking, smoking, spray air freshener use, and second-hand smoke exposure, and lived in units with higher air exchange rate and building density. Accounting for these factors explained observed PM2.5 exposure disparities by homeownership, particularly in the upper exposure quantiles. Our results suggest that renters in multifamily housing may experience higher PM2.5 exposures due to a combination of behavioral and building factors that are amenable to intervention.

Determining the validity of the Outpatient Arthroplasty Risk Assessment (OARA) tool for identifying patients for safe same-day discharge after primary shoulder arthroplasty.

BACKGROUND: Early discharge has been a target of cost-control efforts given the growing demand for joint replacement surgery. The Outpatient Arthroplasty Risk Assessment (OARA) score, a medically based risk-assessment score, has shown high predictive ability in achieving safe early discharge following outpatient lower-extremity arthroplasty using a score threshold initially set at ≤59 points but more recently adapted to ≤79 points. However, no study has been performed using the OARA tool for shoulder replacement, which has been shown to have lower associated medical risks than lower-extremity arthroplasty. The purpose of this study was to determine the OARA score threshold for same-day discharge (SDD) following shoulder arthroplasty and evaluate its effectiveness in selecting patients for SDD. We hypothesized that the OARA score threshold for shoulder arthroplasty would be higher than that for lower-extremity arthroplasty. METHODS: We performed a retrospective review of 422 patients who underwent primary anatomic or reverse shoulder arthroplasty between April 2018 and October 2019 performed by a single surgeon. As standard practice, all patients were counseled preoperatively regarding SDD and given the choice to stay overnight. Medical history, length of stay, and 90-day readmissions were obtained from medical records. Analysis of variance testing and screening test characteristics compared the performance of the OARA score vs. the American Society of Anesthesiologists Physical Status (ASA-PS) class and a previously published OARA score threshold used to define a low risk of outpatient lower-extremity arthroplasty. RESULTS: A preoperative OARA score cutoff of ≤110 points demonstrated a sensitivity of 98.0% for identifying patients with SDD after shoulder arthroplasty, compared with 66.7% using the hip and knee OARA score threshold of ≤59 points (P < .0001) and 80.4% using ASA-PS class ≤ 2 (P = .008). OARA scores ≤ 110 points also showed a negative predictive value of 98.9% and a false-negative rate of 2.0% but remained incomprehensive with a specificity of 24.0% (P < .0001). Analysis of variance demonstrated that mean OARA scores increased significantly with length of stay (P = .001) compared with ASA-PS classes (P = .82). Patients with OARA scores ≤ 110 points were also 2.5 times less likely to have 90-day emergency department visits (P = .04) than those with OARA scores > 110 points. There was no difference in 30- and 90-day readmission rates for patients with OARA scores ≤ 59 points, OARA scores ≤ 110 points, and ASA-PS classes ≤ 2. CONCLUSION: Our study suggests that a preoperative OARA score threshold of ≤110 points is effective and conservative in screening patients for SDD following shoulder arthroplasty, with low rates of 90-day emergency department visits and readmissions. This threshold is a useful screening tool to identify patients who are not good candidates for SDD.