A Dynamic Prognostic Model for Identifying Vulnerable COVID-19 Patients at High Risk of Rapid Deterioration.

PURPOSE: We aimed to validate and, if performance was unsatisfactory, update the previously published prognostic model to predict clinical deterioration in patients hospitalized for COVID-19, using data following vaccine availability. METHODS: Using electronic health records of patients ≥18 years, with laboratory-confirmed COVID-19, from a large care-delivery network in Massachusetts, USA, from March 2020 to November 2021, we tested the performance of the previously developed prediction model and updated the prediction model by incorporating data after availability of COVID-19 vaccines. We randomly divided data into development (70%) and validation (30%) cohorts. We built a model predicting worsening in a published severity scale in 24 h by LASSO regression and evaluated performance by c-statistic and Brier score. RESULTS: Our study cohort consisted of 8185 patients (Development: 5730 patients [mean age: 62; 44% female] and Validation: 2455 patients [mean age: 62; 45% female]). The previously published model had suboptimal performance using data after November 2020 (N = 4973, c-statistic = 0.60. Brier score = 0.11). After retraining with the new data, the updated model included 38 predictors including 18 changing biomarkers. Patients hospitalized after Jun 1st, 2021 (when COVID-19 vaccines became widely available in Massachusetts) were younger and had fewer comorbidities than those hospitalized before. The c-statistic and Brier score were 0.77 and 0.13 in the development cohort, and 0.73 and 0.14 in the validation cohort. CONCLUSION: The characteristics of patients hospitalized for COVID-19 differed substantially over time. We developed a new dynamic model for rapid progression with satisfactory performance in the validation set.

Minding the margins: Evaluating the impact of COVID-19 among Latinx and Black communities with optimal qualitative serological assessment tools.

COVID-19 disproportionately affected minorities, while research barriers to engage underserved communities persist. Serological studies reveal infection and vaccination histories within these communities, however lack of consensus on downstream evaluation methods impede meta-analyses and dampen the broader public health impact. To reveal the impact of COVID-19 and vaccine uptake among diverse communities and to develop rigorous serological downstream evaluation methods, we engaged racial and ethnic minorities in Massachusetts in a cross-sectional study (April-July 2022), screened blood and saliva for SARS-CoV-2 and human endemic coronavirus (hCoV) antibodies by bead-based multiplex assay and point-of-care (POC) test and developed across-plate normalization and classification boundary methods for optimal qualitative serological assessments. Among 290 participants, 91.4% reported receiving at least one dose of a COVID-19 vaccine, while 41.7% reported past SARS-CoV-2 infections, which was confirmed by POC- and multiplex-based saliva and blood IgG seroprevalences. We found significant differences in antigen-specific IgA and IgG antibody outcomes and indication of cross-reactivity with hCoV OC43. Finally, 26.5% of participants reported lingering COVID-19 symptoms, mostly middle-aged Latinas. Hence, prolonged COVID-19 symptoms were common among our underserved population and require public health attention, despite high COVID-19 vaccine uptake. Saliva served as a less-invasive sample-type for IgG-based serosurveys and hCoV cross-reactivity needed to be evaluated for reliable SARS-CoV-2 serosurvey results. The use of the developed rigorous downstream qualitative serological assessment methods will help standardize serosurvey outcomes and meta-analyses for future serosurveys beyond SARS-CoV-2.

Insurance Instability for Patients With Opioid Use Disorder in the Year After Diagnosis.

Importance: Transitions in insurance coverage may be associated with worse health care outcomes. Little is known about insurance stability for individuals with opioid use disorder (OUD). Objective: To examine insurance transitions among adults with newly diagnosed OUD in the 12 months after diagnosis. Design, Setting, and Participants: Longitudinal cohort study using data from the Massachusetts Public Health Data Warehouse. The cohort includes adults aged 18 to 63 years diagnosed with incident OUD between July 1, 2014, and December 31, 2014, who were enrolled in commercial insurance or Medicaid at diagnosis; individuals diagnosed after 2014 were excluded from the main analyses due to changes in the reporting of insurance claims. Data were analyzed from November 10, 2022, to May 6, 2024. Exposure: Insurance type at time of diagnosis (commercial and Medicaid). Main Outcomes and Measures: The primary outcome was the cumulative incidence of insurance transitions in the 12 months after diagnosis. Logistic regression models were used to generate estimated probabilities of insurance transitions by insurance type and diagnosis for several characteristics including age, race and ethnicity, and whether an individual started medication for OUD (MOUD) within 30 days after diagnosis. Results: There were 20 768 individuals with newly diagnosed OUD between July 1, 2014, and December 31, 2014. Most individuals with newly diagnosed OUD were covered by Medicaid (75.4%). Those with newly diagnosed OUD were primarily male (67% in commercial insurance, 61.8% in Medicaid). In the 12 months following OUD diagnosis, 30.4% of individuals experienced an insurance transition, with adjusted models demonstrating higher transition rates among those starting with Medicaid (31.3%; 95% CI, 30.5%-32.0%) compared with commercial insurance (27.9%; 95% CI, 26.6%-29.1%). The probability of insurance transitions was generally higher for younger individuals than older individuals irrespective of insurance type, although there were notable differences by race and ethnicity. Conclusions and Relevance: This study found that nearly 1 in 3 individuals experience insurance transitions in the 12 months after OUD diagnosis. Insurance transitions may represent an important yet underrecognized factor in OUD treatment outcomes.

Risk factors for persistent fatal opioid-involved overdose clusters in Massachusetts 2011-2021: a spatial statistical analysis with socio-economic, accessibility, and prescription factors.

BACKGROUND: Fatal opioid-involved overdose rates increased precipitously from 5.0 per 100,000 population to 33.5 in Massachusetts between 1999 and 2022. METHODS: We used spatial rate smoothing techniques to identify persistent opioid overdose-involved fatality clusters at the ZIP Code Tabulation Area (ZCTA) level. Rate smoothing techniques were employed to identify locations of high fatal opioid overdose rates where population counts were low. In Massachusetts, this included areas with both sparse data and low population density. We used Local Indicators of Spatial Association (LISA) cluster analyses with the raw incidence rates, and the Empirical Bayes smoothed rates to identify clusters from 2011 to 2021. We also estimated Empirical Bayes LISA cluster estimates to identify clusters during the same period. We constructed measures of the socio-built environment and potentially inappropriate prescribing using principal components analysis. The resulting measures were used as covariates in Conditional Autoregressive Bayesian models that acknowledge spatial autocorrelation to predict both, if a ZCTA was part of an opioid-involved cluster for fatal overdose rates, as well as the number of times that it was part of a cluster of high incidence rates. RESULTS: LISA clusters for smoothed data were able to identify whether a ZCTA was part of a opioid involved fatality incidence cluster earlier in the study period, when compared to LISA clusters based on raw rates. PCA helped in identifying unique socio-environmental factors, such as minoritized populations and poverty, potentially inappropriate prescribing, access to amenities, and rurality by combining socioeconomic, built environment and prescription variables that were highly correlated with each other. In all models except for those that used raw rates to estimate whether a ZCTA was part of a high fatality cluster, opioid overdose fatality clusters in Massachusetts had high percentages of Black and Hispanic residents, and households experiencing poverty. The models that were fitted on Empirical Bayes LISA identified this phenomenon earlier in the study period than the raw rate LISA. However, all the models identified minoritized populations and poverty as significant factors in predicting the persistence of a ZCTA being part of a high opioid overdose cluster during this time period. CONCLUSION: Conducting spatially robust analyses may help inform policies to identify community-level risks for opioid-involved overdose deaths sooner than depending on raw incidence rates alone. The results can help inform policy makers and planners about locations of persistent risk.

PREVALENCE OF THE BOPYRID ISOPOD PROBOPYRUS PANDALICOLA IN DAGGERBLADE GRASS SHRIMP FROM SALT MARSH CREEKS AND PANNES OF CAPE COD, MASSACHUSETTS.

In salt marsh ecosystems, daggerblade grass shrimp, Palaemon (Palaemonetes) pugio, play a crucial role in food webs and serve as the definitive host for the bopyrid isopod Probopyrus pandalicola. These ectoparasites infest the branchial chambers of grass shrimp, which can lead to decreased energy availability and sterilization of infected hosts. Although bopyrid isopod infestation of daggerblade grass shrimp has been frequently reported in literature from coastal marshes of the southeastern United States, the prevalence of this parasite has not been recently documented in daggerblade grass shrimp from marshes of the northeastern United States. The goal of this project was to quantify the prevalence of Pr. pandalicola infestations in Pa. pugio across Cape Cod, Massachusetts. We evaluated bopyrid isopod prevalence from shrimp collected from 5 different salt marsh habitats along Cape Cod in August 2021. Bopyrid isopod infestations were found in shrimp at 4 of 5 salt marshes, with prevalence ranging from 0.04 to 14.1%. Seasonal resampling of one of the salt marshes revealed the highest average infestation prevalence in spring (<17.1%) and an isolated high of 30.3% prevalence in a single salt panne. A series of linear and multivariate models showed that panne area, shrimp abundance, and distance to shoreline were related to Pr. pandalicola shrimp infestations in salt pannes in summer. This study describes the prevalence of the bopyrid isopod infesting daggerblade grass shrimp in salt marshes in New England, with implications for how parasitized shrimp influence salt marsh food webs in which they are found.

Using Parametric g-Computation for Time-to-Event Data and Distributed Lag Models to Identify Critical Exposure Windows for Preterm Birth: An Illustrative Example Using PM2.5 in a Retrospective Birth Cohort Based in Eastern Massachusetts (2011-2016).

BACKGROUND: Parametric g-computation is an attractive analytic framework to study the health effects of air pollution. Yet, the ability to explore biologically relevant exposure windows within this framework is underdeveloped. OBJECTIVES: We outline a novel framework for how to incorporate complex lag-responses using distributed lag models (DLMs) into parametric g-computation analyses for survival data. We call this approach "g-survival-DLM" and illustrate its use examining the association between PM2.5 during pregnancy and the risk of preterm birth (PTB). METHODS: We applied the g-survival-DLM approach to estimate the hypothetical static intervention of reducing average PM2.5 in each gestational week by 20% on the risk of PTB among 9,403 deliveries from Beth Israel Deaconess Medical Center, Boston, Massachusetts, 2011-2016. Daily PM2.5 was taken from a 1-km grid model and assigned to address at birth. Models were adjusted for sociodemographics, time trends, nitrogen dioxide, and temperature. To facilitate implementation, we provide a detailed description of the procedure and accompanying R syntax. RESULTS: There were 762 (8.1%) PTBs in this cohort. The gestational week-specific median PM2.5 concentration was relatively stable across pregnancy at ∼7µg/m3. We found that our hypothetical intervention strategy changed the cumulative risk of PTB at week 36 (i.e., the end of the preterm period) by -0.009 (95% confidence interval: -0.034, 0.007) in comparison with the scenario had we not intervened, which translates to about 86 fewer PTBs in this cohort. We also observed that the critical exposure window appeared to be weeks 5-20. DISCUSSION: We demonstrate that our g-survival-DLM approach produces easier-to-interpret, policy-relevant estimates (due to the g-computation); prevents immortal time bias (due to treating PTB as a time-to-event outcome); and allows for the exploration of critical exposure windows (due to the DLMs). In our illustrative example, we found that reducing fine particulate matter [particulate matter (PM) with aerodynamic diameter ≤2.5µm (PM2.5)] during gestational weeks 5-20 could potentially lower the risk of PTB. https://doi.org/10.1289/EHP13891.

Medication for Opioid Use Disorder After Serious Injection-Related Infections in Massachusetts.

Importance: Serious injection-related infections (SIRIs) cause significant morbidity and mortality. Medication for opioid use disorder (MOUD) improves outcomes but is underused. Understanding MOUD treatment after SIRIs could inform interventions to close this gap. Objectives: To examine rehospitalization, death rates, and MOUD receipt for individuals with SIRIs and to assess characteristics associated with MOUD receipt. Design, Setting, and Participants: This retrospective cohort study used the Massachusetts Public Health Data Warehouse, which included all individuals with a claim in the All-Payer Claims Database and is linked to individual-level data from multiple government agencies, to assess individuals aged 18 to 64 years with opioid use disorder and hospitalization for endocarditis, osteomyelitis, epidural abscess, septic arthritis, or bloodstream infection (ie, SIRI) between July 1, 2014, and December 31, 2019. Data analysis was performed from November 2021 to May 2023. Exposure: Demographic and clinical factors potentially associated with posthospitalization MOUD receipt. Main Outcomes and Measures: The main outcome was MOUD receipt measured weekly in the 12 months after hospitalization. We used zero-inflated negative binomial regression to examine characteristics associated with any MOUD receipt and rates of treatment in the 12 months after hospitalization. Secondary outcomes were receipt of any buprenorphine formulation, methadone, and extended-release naltrexone examined individually. Results: Among 8769 individuals (mean [SD] age, 43.2 [12.0] years; 5066 [57.8%] male) who survived a SIRI hospitalization, 4305 (49.1%) received MOUD, 5919 (67.5%) were rehospitalized, and 973 (11.1%) died within 12 months. Of those treated with MOUD in the 12 months after hospitalization, the mean (SD) number of MOUD initiations during follow-up was 3.0 (1.7), with 956 of 4305 individuals (22.2%) receiving treatment at least 80% of the time. MOUD treatment after SIRI hospitalization was significantly associated with MOUD in the prior 6 months (buprenorphine: adjusted odds ratio [AOR], 16.51; 95% CI, 13.81-19.74; methadone: AOR, 28.46; 95% CI, 22.41-36.14; or naltrexone: AOR, 2.05; 95% CI, 1.56-2.69). Prior buprenorphine (incident rate ratio [IRR], 1.17; 95% CI, 1.11-1.24) or methadone (IRR, 1.89; 95% CI, 1.79-2.01) use was associated with higher treatment rates after hospitalization, and prior naltrexone use (IRR, 0.86; 95% CI, 0.77-0.95) was associated with lower rates. Conclusions and Relevance: This study found that in the year after a SIRI hospitalization in Massachusetts, mortality and rehospitalization were common, and only half of patients received MOUD. Treatment with MOUD before a SIRI was associated with posthospitalization MOUD initiation and time receiving MOUD. Efforts are needed to initiate MOUD treatment during SIRI hospitalizations and subsequently retain patients in treatment.

Morbidity and Mortality of Hospital-Onset SARS-CoV-2 Infections Due to Omicron Versus Prior Variants : A Propensity-Matched Analysis.

BACKGROUND: Many hospitals have scaled back measures to prevent nosocomial SARS-CoV-2 infection given large decreases in the morbidity and mortality of SARS-CoV-2 infections for most people. Little is known, however, about the morbidity and mortality of nosocomial SARS-CoV-2 infections for hospitalized patients in the Omicron era. OBJECTIVE: To estimate the effect of nosocomial SARS-CoV-2 infection on hospitalized patients' outcomes during the pre-Omicron and Omicron periods. DESIGN: Retrospective matched cohort study. SETTING: 5 acute care hospitals in Massachusetts, December 2020 to April 2023. PATIENTS: Adults testing positive for SARS-CoV-2 on or after hospital day 5, after negative SARS-CoV-2 test results on admission and on hospital day 3, were matched to control participants by hospital, service, time period, days since admission, and propensity scores that incorporated demographics, comorbid conditions, vaccination status, primary diagnosis category, vital signs, and laboratory test values. MEASUREMENTS: Primary outcomes were hospital mortality and time to discharge. Secondary outcomes were intensive care unit (ICU) admission, need for advanced oxygen support, discharge destination, hospital-free days, and 30-day readmissions. RESULTS: There were 274 cases of hospital-onset SARS-CoV-2 infection during the pre-Omicron period and 1037 cases during the Omicron period (0.17 vs. 0.49 cases per 100 admissions). Patients with hospital-onset SARS-CoV-2 infection were older and had more comorbid conditions than those without. During the pre-Omicron period, hospital-onset SARS-CoV-2 infection was associated with increased risk for ICU admission, increased need for high-flow oxygen, longer time to discharge (median difference, 4.7 days [95% CI, 2.9 to 6.6 days]), and higher mortality (risk ratio, 2.0 [CI, 1.1 to 3.8]) versus matched control participants. During the Omicron period, hospital-onset SARS-CoV-2 infection remained associated with increased risk for ICU admission and increased time to discharge (median difference, 4.2 days [CI, 3.6 to 5.0 days]). The association with increased hospital mortality was attenuated but still significant (risk ratio, 1.6 [CI, 1.2 to 2.3]). LIMITATION: Residual confounding may be present. CONCLUSION: Hospital-onset SARS-CoV-2 infection during the Omicron period remains associated with increased morbidity and mortality. PRIMARY FUNDING SOURCE: Harvard Medical School Department of Population Medicine.

Receipt of medications for opioid use disorder before and after incarceration in Massachusetts State prisons, 2014-2019.

BACKGROUND: Little is known about how use patterns of medications for opioid use disorder (MOUDs) evolve from pre-incarceration to post-incarceration among incarcerated individuals with opioid use disorder. This article describes pre- and post-incarceration MOUD receipt during a period when naltrexone was the only type of MOUD offered in a state prison system, the Massachusetts Department of Correction (MADOC). METHODS: A retrospective cohort study of individuals with opioid use disorder who had an incarceration episode in MADOC during January 2015 to March 2019. The data source was the Massachusetts Public Health Data Warehouse, a multi-sector data platform that links individual-level data from multiple statewide datasets. We described patterns of MOUD receipt during the four weeks prior to and after an incarceration episode. Multivariable logistic regression models characterized predictors of post-incarceration MOUD receipt. RESULTS: In the male sample (n=691 incarcerations), from the pre- to post-incarceration periods, receipt of buprenorphine increased (14.3 % to 18.3 %), naltrexone increased (5.0 % to 10.5 %), and methadone decreased (4.7 % to 1.7 %). Similarly, in the female sample (n=892 incarcerations), from the pre- to post-incarceration periods, receipt of buprenorphine increased (10.3 % to 12.3 %, naltrexone increased (4.5 % to 9.3 %), and methadone decreased (5.0 % to 2.9 %). Much of the post-release naltrexone receipt occurred among participants in MADOC's pre-release naltrexone program. CONCLUSIONS: MOUD receipt was low but increased slightly in the post-incarceration period. This change was driven by increases in buprenorphine and naltrexone and despite decreases in methadone.

Electronic Health Record Data for Lyme Disease Surveillance, Massachusetts, USA, 2017-2018.

Lyme disease surveillance based on provider and laboratory reports underestimates incidence. We developed an algorithm for automating surveillance using electronic health record data. We identified potential Lyme disease markers in electronic health record data (laboratory tests, diagnosis codes, prescriptions) from January 2017-December 2018 in 2 large practice groups in Massachusetts, USA. We calculated their sensitivities and positive predictive values (PPV), alone and in combination, relative to medical record review. Sensitivities ranged from 57% (95% CI 47%-69%) for immunoassays to 87% (95% CI 70%-100%) for diagnosis codes. PPVs ranged from 53% (95% CI 43%-61%) for diagnosis codes to 58% (95% CI 50%-66%) for immunoassays. The combination of a diagnosis code and antibiotics within 14 days or a positive Western blot had a sensitivity of 100% (95% CI 86%-100%) and PPV of 82% (95% CI 75%-89%). This algorithm could make Lyme disease surveillance more efficient and consistent.

Patterns and correlates of mental healthcare utilization during the COVID-19 pandemic among individuals with pre-existing mental disorder.

How did mental healthcare utilization change during the COVID-19 pandemic period among individuals with pre-existing mental disorder? Understanding utilization patterns of these at-risk individuals and identifying those most likely to exhibit increased utilization could improve patient stratification and efficient delivery of mental health services. This study leveraged large-scale electronic health record (EHR) data to describe mental healthcare utilization patterns among individuals with pre-existing mental disorder before and during the COVID-19 pandemic and identify correlates of high mental healthcare utilization. Using EHR data from a large healthcare system in Massachusetts, we identified three "pre-existing mental disorder" groups (PMD) based on having a documented mental disorder diagnosis within the 6 months prior to the March 2020 lockdown, related to: (1) stress-related disorders (e.g., depression, anxiety) (N = 115,849), (2) serious mental illness (e.g., schizophrenia, bipolar disorders) (N = 11,530), or (3) compulsive behavior disorders (e.g., eating disorder, OCD) (N = 5,893). We also identified a "historical comparison" group (HC) for each PMD (N = 113,604, 11,758, and 5,387, respectively) from the previous year (2019). We assessed the monthly number of mental healthcare visits from March 13 to December 31 for PMDs in 2020 and HCs in 2019. Phenome-wide association analyses (PheWAS) were used to identify clinical correlates of high mental healthcare utilization. We found the overall number of mental healthcare visits per patient during the pandemic period in 2020 was 10-12% higher than in 2019. The majority of increased visits was driven by a subset of high mental healthcare utilizers (top decile). PheWAS results indicated that correlates of high utilization (prior mental disorders, chronic pain, insomnia, viral hepatitis C, etc.) were largely similar before and during the pandemic, though several conditions (e.g., back pain) were associated with high utilization only during the pandemic. Limitations included that we were not able to examine other risk factors previously shown to influence mental health during the pandemic (e.g., social support, discrimination) due to lack of social determinants of health information in EHR data. Mental healthcare utilization among patients with pre-existing mental disorder increased overall during the pandemic, likely due to expanded access to telemedicine. Given that clinical correlates of high mental healthcare utilization in a major hospital system were largely similar before and during the COVID-19 pandemic, resource stratification based on known risk factor profiles may aid hospitals in responding to heightened mental healthcare needs during a pandemic.

Bidirectional Association Between Atrial Fibrillation and Myocardial Infarction, and Relation to Mortality in the Framingham Heart Study.

BACKGROUND: Individuals with both atrial fibrillation (AF) and myocardial infarction (MI) have higher mortality compared with individuals with only 1 condition. Whether mortality differs according to the temporal order of AF and MI is unclear. METHODS AND RESULTS: We included participants from the FHS (Framingham Heart Study) from 1960 and onwards. We assessed the hazard ratio (HR) of new-onset AF and MI, and mortality according to MI and AF status (prevalent and interim) using multivariable-adjusted Cox proportional hazards models. Interim diseases were modeled as time-varying variables. For the analysis of new-onset AF, 10 923 participants (55% women; mean±SD age, 54±8 years) were included. For new-onset MI, 10 804 participants (55% women; mean±SD age, 54±8 years) were included. Compared with no MI, the hazard of new-onset AF was higher in participants with prevalent (HR, 1.60 [95% CI, 1.32-1.94]) and interim MI (HR, 3.96 [95% CI, 3.18-4.91]). Both ST-segment-elevation MI and non-ST-segment-elevation MI were associated with new-onset AF. Interim AF, not prevalent AF, was associated with higher hazard rate of new-onset MI (HR, 2.21 [95% CI, 1.67-2.92]). Interim AF was associated with both ST-segment-elevation MI and non-ST-segment-elevation MI. Mortality was significantly greater among participants with AF and MI compared with participants with 1 of the 2, regardless of temporal order. CONCLUSIONS: We report a bidirectional association between AF and MI, which was observed for both non-ST-segment-elevation MI and ST-segment-elevation MI. Participants with both AF and MI had considerably higher mortality compared with participants with only 1 of the 2 conditions, regardless of order.

Multiple marginalized identities: A qualitative exploration of intersectional perinatal experiences of birthing people of color with substance use disorder in Massachusetts.

INTRODUCTION: Racial and ethnic inequities persist in receipt of prenatal care, mental health services, and addiction treatment for pregnant and postpartum individuals with substance use disorder (SUD). Further qualitative work is needed to understand the intersectionality of racial and ethnic discrimination, stigma related to substance use, and gender bias on perinatal SUD care from the perspectives of affected individuals. METHODS: Peer interviewers conducted semi-structured qualitative interviews with recently pregnant people of color with SUD in Massachusetts to explore the impact of internalized, interpersonal, and structural racism on prenatal, birthing, and postpartum experiences. The study used a thematic analysis to generate the codebook and double coded transcripts, with an overall kappa coefficient of 0.89. Preliminary themes were triangulated with five participants to inform final theme development. RESULTS: The study includes 23 participants of diverse racial/ethnic backgrounds: 39% mixed race/ethnicity (including 9% with Native American ancestry), 30% Hispanic or Latinx, 26% Black/African American, 4% Asian. While participants frequently names racial and ethnic discrimination, both interpersonal and structural, as barriers to care, some participants attributed poor experiences to other marginalized identities and experiences, such as having a SUD. Three unique themes emerged from the participants' experiences: 1) Participants of color faced increased scrutiny and mistrust from clinicians and treatment programs; 2) Greater self-advocacy was required from individuals of color to counteract stereotypes and stigma; 3) Experiences related to SUD history and pregnancy status intersected with racism and gender bias to create distinct forms of discrimination. CONCLUSION: Pregnant and postpartum people of color affected by perinatal SUD faced pervasive mistrust and unequal standards of care from mostly white healthcare staff and treatment spaces, which negatively impacted their treatment access, addiction medication receipt, postpartum pain management, and ability to retain custody of their children. Key clinical interventions and policy changes identified by participants for antiracist action include personalizing anesthetic plans for adequate peripartum pain control, minimizing reproductive injustices in contraceptive counseling, and addressing misuse of toxicology testing to mitigate inequitable Child Protective Services (CPS) involvement and custody loss.

Suicide mortality according to occupation and method of suicide, Massachusetts, 2010-2019.

BACKGROUND: Suicide rates in the United States have been increasing. Work-related factors may contribute to risk for suicide. These work-related factors may be reflected in a varied risk for different suicide methods between occupations. This study sought to assess occupational differences in suicide rates according to the method used. METHODS: Death certificate data about suicide deaths in Massachusetts between 2010 and 2019 were used to calculate mortality rates and rate ratios with univariable and multivariable models controlling for age, sex, race ethnicity, and educational attainment for suicides overall, and for three specific methods of suicide (hanging/strangulation/suffocation, firearms, and poisoning) by occupation. RESULTS: In multivariate models, the risk for suicide was significantly elevated for workers in arts, design, entertainment, sports, and media (relative risk [RR] = 1.84, 95% confidence interval [CI] = 1.53, 2.22); construction trades (RR = 1.68, 95% CI = 1.53, 1.84); protective services (RR = 1.49, 95% CI = 1.26, 1.77); and healthcare support occupations (RR = 1.55, 95% CI = 1.25, 1.93). Occupational risk for suicide differed across different methods. For hanging/strangulation/suffocation, workers in arts, design, entertainment, sports, and media occupations had the highest RR (2.09, 95% CI = 1.61, 2.71). For firearms, workers in protective service occupations had the highest RR (4.20, 95% CI = 3.30, 5.34). For poisoning, workers in life, physical, and social science occupations had the highest RR (2.32, 95% CI = 1.49, 3.60). CONCLUSIONS: These findings are useful for identifying vulnerable working populations for suicide. Additionally, some of the occupational differences in the risk for suicide and for specific methods of suicide may be due to workplace factors. Further research is needed to understand these workplace factors so that interventions can be designed for prevention.

Using decision tree models and comprehensive statewide data to predict opioid overdoses following prison release.

PURPOSE: Identifying predictors of opioid overdose following release from prison is critical for opioid overdose prevention. METHODS: We leveraged an individually linked, state-wide database from 2015-2020 to predict the risk of opioid overdose within 90 days of release from Massachusetts state prisons. We developed two decision tree modeling schemes: a model fit on all individuals with a single weight for those that experienced an opioid overdose and models stratified by race/ethnicity. We compared the performance of each model using several performance measures and identified factors that were most predictive of opioid overdose within racial/ethnic groups and across models. RESULTS: We found that out of 44,246 prison releases in Massachusetts between 2015-2020, 2237 (5.1%) resulted in opioid overdose in the 90 days following release. The performance of the two predictive models varied. The single weight model had high sensitivity (79%) and low specificity (56%) for predicting opioid overdose and was more sensitive for White non-Hispanic individuals (sensitivity = 84%) than for racial/ethnic minority individuals. CONCLUSIONS: Stratified models had better balanced performance metrics for both White non-Hispanic and racial/ethnic minority groups and identified different predictors of overdose between racial/ethnic groups. Across racial/ethnic groups and models, involuntary commitment (involuntary treatment for alcohol/substance use disorder) was an important predictor of opioid overdose.

The Impact of Common Variations in Sequential Organ Failure Assessment Score Calculation on Sepsis Measurement Using Sepsis-3 Criteria: A Retrospective Analysis Using Electronic Health Record Data.

OBJECTIVES: To assess the impact of different methods of calculating Sequential Organ Failure Assessment (SOFA) scores using electronic health record data on the incidence, outcomes, agreement, and predictive validity of Sepsis-3 criteria. DESIGN: Retrospective observational study. SETTING: Five Massachusetts hospitals. PATIENTS: Hospitalized adults, 2015 to 2022. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: We defined sepsis as a suspected infection (culture obtained and antibiotic administered) with a concurrent increase in SOFA score by greater than or equal to 2 points (Sepsis-3 criteria). Our reference SOFA implementation strategy imputed normal values for missing data, used Pa o2 /F io2 ratios for respiratory scores, and assumed normal baseline SOFA scores for community-onset sepsis. We then implemented SOFA scores using different missing data imputation strategies (averaging worst values from preceding and following days vs. carrying forward nonmissing values), imputing respiratory scores using Sp o2 /F io2 ratios, and incorporating comorbidities and prehospital laboratory data into baseline SOFA scores. Among 1,064,459 hospitalizations, 297,512 (27.9%) had suspected infection and 141,052 (13.3%) had sepsis with an in-hospital mortality rate of 10.3% using the reference SOFA method. The percentage of patients missing SOFA components for at least 1 day in the infection window was highest for Pa o2 /F io2 ratios (98.6%), followed by Sp o2 /F io2 ratios (73.5%), bilirubin (68.5%), and Glasgow Coma Scale scores (57.2%). Different missing data imputation strategies yielded near-perfect agreement in identifying sepsis (kappa 0.99). However, using Sp o2 /F io2 imputations yielded higher sepsis incidence (18.3%), lower mortality (8.1%), and slightly lower predictive validity for mortality (area under the receiver operating curves [AUROC] 0.76 vs. 0.78). For community-onset sepsis, incorporating comorbidities and historical laboratory data into baseline SOFA score estimates yielded lower sepsis incidence (6.9% vs. 11.6%), higher mortality (13.4% vs. 9.6%), and higher predictive validity (AUROC 0.79 vs. 0.75) relative to the reference SOFA implementation. CONCLUSIONS: Common variations in calculating respiratory and baseline SOFA scores, but not in handling missing data, lead to substantial differences in observed incidence, mortality, agreement, and predictive validity of Sepsis-3 criteria.

Electronic Health Record-Based Algorithm for Monitoring Respiratory Virus-Like Illness.

Viral respiratory illness surveillance has traditionally focused on single pathogens (e.g., influenza) and required fever to identify influenza-like illness (ILI). We developed an automated system applying both laboratory test and syndrome criteria to electronic health records from 3 practice groups in Massachusetts, USA, to monitor trends in respiratory viral-like illness (RAVIOLI) across multiple pathogens. We identified RAVIOLI syndrome using diagnosis codes associated with respiratory viral testing or positive respiratory viral assays or fever. After retrospectively applying RAVIOLI criteria to electronic health records, we observed annual winter peaks during 2015-2019, predominantly caused by influenza, followed by cyclic peaks corresponding to SARS-CoV-2 surges during 2020-2024, spikes in RSV in mid-2021 and late 2022, and recrudescent influenza in late 2022 and 2023. RAVIOLI rates were higher and fluctuations more pronounced compared with traditional ILI surveillance. RAVIOLI broadens the scope, granularity, sensitivity, and specificity of respiratory viral illness surveillance compared with traditional ILI surveillance.

Association between Combined Sewer Overflow Events and Gastrointestinal Illness in Massachusetts Municipalities with and without River-Sourced Drinking Water, 2014-2019.

BACKGROUND: Combined sewer overflow (CSO) events release untreated wastewater into surface waterbodies during heavy precipitation and snowmelt. Combined sewer systems serve ∼40 million people in the United States, primarily in urban and suburban municipalities in the Midwest and Northeast. Predicted increases in heavy precipitation events driven by climate change underscore the importance of quantifying potential health risks associated with CSO events. OBJECTIVES: The aims of this study were to a) estimate the association between CSO events (2014-2019) and emergency department (ED) visits for acute gastrointestinal illness (AGI) among Massachusetts municipalities that border a CSO-impacted river, and b) determine whether associations differ by municipal drinking water source. METHODS: A case time-series design was used to estimate the association between daily cumulative upstream CSO discharge and ED visits for AGI over lag periods of 4, 7, and 14 days, adjusting for temporal trends, temperature, and precipitation. Associations between CSO events and AGI were also compared by municipal drinking water source (CSO-impacted river vs. other sources). RESULTS: Extreme upstream CSO discharge events (>95th percentile by cumulative volume) were associated with a cumulative risk ratio (CRR) of AGI of 1.22 [95% confidence interval (CI): 1.05, 1.42] over the next 4 days for all municipalities, and the association was robust after adjusting for precipitation [1.17 (95% CI: 0.98, 1.39)], although the CI includes the null. In municipalities with CSO-impacted drinking water sources, the adjusted association was somewhat less pronounced following 95th percentile CSO events [CRR= 1.05 (95% CI: 0.82, 1.33)]. The adjusted CRR of AGI was 1.62 in all municipalities following 99th percentile CSO events (95% CI: 1.04, 2.51) and not statistically different when stratified by drinking water source. DISCUSSION: In municipalities bordering a CSO-impacted river in Massachusetts, extreme CSO events are associated with higher risk of AGI within 4 days. The largest CSO events are associated with increased risk of AGI regardless of drinking water source. https://doi.org/10.1289/EHP14213.