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.

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.

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.

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.

Deployment of the National Notifiable Diseases Surveillance System during the 2022-23 mpox outbreak in the United States-Opportunities and challenges with case notifications during public health emergencies.

Timely case notifications following the introduction of an uncommon pathogen, such as mpox, are critical for understanding disease transmission and for developing and implementing effective mitigation strategies. When Massachusetts public health officials notified the Centers for Disease Control and Prevention (CDC) about a confirmed orthopoxvirus case on May 17, 2023, which was later confirmed as mpox at CDC, mpox was not a nationally notifiable disease. Because existing processes for new data collections through the National Notifiable Disease Surveillance System were not well suited for implementation during emergency responses at the time of the mpox outbreak, several interim notification approaches were established to capture case data. These interim approaches were successful in generating daily case counts, monitoring disease transmission, and identifying high-risk populations. However, the approaches also required several data collection approvals by the federal government and the Council for State and Territorial Epidemiologists, the use of four different case report forms, and the establishment of complex data management and validation processes involving data element mapping and record-level de-duplication steps. We summarize lessons learned from these interim approaches to inform and improve case notifications during future outbreaks. These lessons reinforce CDC's Data Modernization Initiative to work in close collaboration with state, territorial, and local public health departments to strengthen case-based surveillance prior to the next public health emergency.

Medicaid Inmate Exclusion Policy and Infectious Diseases Care for Justice-Involved Populations.

The Medicaid Inmate Exclusion Policy (MIEP) prohibits using federal funds for ambulatory care services and medications (including for infectious diseases) for incarcerated persons. More than one quarter of states, including California and Massachusetts, have asked the federal government for authority to waive the MIEP. To improve health outcomes and continuation of care, those states seek to cover transitional care services provided to persons in the period before release from incarceration. The Massachusetts Sheriffs' Association, Massachusetts Department of Correction, Executive Office of Health and Human Services, and University of Massachusetts Chan Medical School have collaborated to improve infectious disease healthcare service provision before and after release from incarceration. They seek to provide stakeholders working at the intersection of criminal justice and healthcare with tools to advance Medicaid policy and improve treatment and prevention of infectious diseases for persons in jails and prisons by removing MIEP barriers through Section 1115 waivers.

Identifying data-driven subtypes of major depressive disorder with electronic health records.

BACKGROUND: Efforts to reduce the heterogeneity of major depressive disorder (MDD) by identifying subtypes have not yet facilitated treatment personalization or investigation of biology, so novel approaches merit consideration. METHODS: We utilized electronic health records drawn from 2 academic medical centers and affiliated health systems in Massachusetts to identify data-driven subtypes of MDD, characterizing sociodemographic features, comorbid diagnoses, and treatment patterns. We applied Latent Dirichlet Allocation (LDA) to summarize diagnostic codes followed by agglomerative clustering to define patient subgroups. RESULTS: Among 136,371 patients (95,034 women [70 %]; 41,337 men [30 %]; mean [SD] age, 47.0 [14.0] years), the 15 putative MDD subtypes were characterized by comorbidities and distinct patterns in medication use. There was substantial variation in rates of selective serotonin reuptake inhibitor (SSRI) use (from a low of 62 % to a high of 78 %) and selective norepinephrine reuptake inhibitor (SNRI) use (from 4 % to 21 %). LIMITATIONS: Electronic health records lack reliable symptom-level data, so we cannot examine the extent to which subtypes might differ in clinical presentation or symptom dimensions. CONCLUSION: These data-driven subtypes, drawing on representative clinical cohorts, merit further investigation for their utility in identifying more homogeneous patient populations for basic as well as clinical investigation.

LatinX harm reduction capital, medication for opioid use disorder, and nonfatal overdose: A structural equation model analysis among people who use drugs in Massachusetts.

BACKGROUND: We introduce the concept of harm reduction capital (HRCap) as the combination of knowledge, resources, and skills related to substance use risk reduction, which we hypothesize to predict MOUD use and opioid overdose. In this study, we explored the interrelationships between ethnicity, HRCap, nonfatal overdose, and MOUD use among PWUD. METHODS: Between 2017 and 2019, people who currently or in the past used opioids and who lived in Massachusetts completed a one-time survey on substance use history, treatment experiences, and use of harm reduction services. We fit first-order measurement constructs for positive and negative HRCap (facilitators and barriers). We used generalized structural equation models to examine the inter-relationships of the latent constructs with LatinX self-identification, past year overdose, and current use of MOUD. RESULTS: HRCap barriers were positively associated with past-year overdose (b=2.6, p<0.05), and LatinX self-identification was inversely associated with HRCap facilitators (b=-0.49, p<0.05). There was no association between overdose in the past year and the current use of MOUD. LatinX self-identification was positively associated with last year methadone treatment (b=0.89, p<0.05) but negatively associated with last year buprenorphine treatment (b=-0.68, p<0.07). Latinx PWUD reported lower positive HRCap than white non-LatinX PWUD and had differential utilization of MOUD. CONCLUSION: Our findings indicate that a recent overdose was not associated with the current use of MOUD, highlighting a severe gap in treatment utilization among individuals at the highest risk. The concept of HRCap and its use in the model highlight substance use treatment differences, opportunities for intervention, and empowerment.

Increase in Functional Tic Presentations in Sexual Orientation and Gender Identity Minority Youth During Coronavirus Disease 2019.

BACKGROUND: Functional tic disorders are among the least common functional movement disorders, but their prevalence rose during the coronavirus disease 2019 (COVID-19) pandemic. Although female adolescents develop functional neurological disorders at higher rates than males, investigations into sexual orientation and gender identity (SOGI) status of these patients are limited. METHODS: We completed a retrospective, cross-sectional time series examining the incidence of new-onset functional tic disorders in youth presenting to the Massachusetts General Hospital Movement Disorder clinics before and during the COVID-19 pandemic. Data were collected by searching for relevant International Classification of Diseases (ICD)-10 diagnostic codes in youth aged nine to 26 years using a hospital-wide data repository. Individual cases were reviewed for inclusion based on clinical criteria and expert consensus. RESULTS: The prevalence of functional tic presentations in youth rose 8.6-fold from pre- to postpandemic levels (Fisher exact test P < 0.001), whereas the prevalence of developmental tic presentations pre- and postpandemic remained stable (114 vs 112). SOGI minority youth comprised 37% of those with functional tics (total n = 19). Ninety five percent of patients with functional tics identified as female, with 10% of these identifying as transgender. CONCLUSIONS: Our data confirm previously demonstrated dramatic rises in functional tic presentations during the COVID-19 pandemic and, more notably, reveal a strong association with SOGI minority status. We highlight the potential link between functional tic disorders and SOGI minority status. Providing a safe and supportive clinical environment and addressing stress linked to SOGI minority status may help to improve patient prognosis.

COVID-19 mortality among Massachusetts workers and the association with telework ability, 2020.

BACKGROUND: Working outside the home put some workers at risk for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) exposure and might partly explain elevated coronavirus disease 2019 (COVID-19) mortality rates in the first months of the pandemic in certain groups of Massachusetts workers. To further investigate this premise, we examined COVID-19 mortality among Massachusetts workers, with a specific focus on telework ability based on occupation. METHODS: COVID-19-associated deaths between January 1 and December 31, 2020 among Massachusetts residents aged 18-64 years were analyzed. Deaths were categorized into occupation-based quadrants (Q) of telework ability. Age-adjusted rates were calculated by key demographics, industry, occupation, and telework quadrant using American Community Survey workforce estimates as denominators. Rate ratios (RRs) and 95% confidence intervals comparing rates for quadrants with workers unlikely able to telework (Q2, Q3, Q4) to that among those likely able to telework (Q1) were calculated. RESULTS: The overall age-adjusted COVID-19-associated mortality rate was 26.4 deaths per 100,000 workers. Workers who were male, Black non-Hispanic, Hispanic, born outside the US, and with lower than a high school education level experienced the highest rates among their respective demographic groups. The rate varied by industry, occupation and telework quadrant. RRs comparing Q2, Q3, and Q4 to Q1 were 0.99 (95% confidence interval [CI]: 0.8-1.2), 3.2 (95% CI: 2.6-3.8) and 2.5 (95% CI: 2.0-3.0), respectively. CONCLUSION: Findings suggest a positive association between working on-site and COVID-19-associated mortality. Work-related factors likely contributed to COVID-19 among Massachusetts workers and should be considered in future studies of COVID-19 and similar diseases.

Offspring cardiometabolic outcomes and postnatal growth trajectories after exposure to maternal SARS-CoV-2 infection.

OBJECTIVE: The objective of this study is to determine whether in utero exposure to SARS-CoV-2 is associated with increased risk for a cardiometabolic diagnosis by 18 months of age. METHODS: This retrospective electronic health record (EHR)-based cohort study included the live-born offspring of all individuals who delivered during the COVID-19 pandemic (April 1, 2020-December 31, 2021) at eight hospitals in Massachusetts. Offspring exposure was defined as a positive maternal SARS-CoV-2 polymerase chain reaction test during pregnancy. The primary outcome was presence of an ICD-10 code for a cardiometabolic disorder in offspring EHR by 18 months. Weight-, length-, and BMI-for-age z scores were calculated and compared at 6-month intervals from birth to 18 months. RESULTS: A total of 29,510 offspring (1599 exposed and 27,911 unexposed) were included. By 18 months, 6.7% of exposed and 4.4% of unexposed offspring had received a cardiometabolic diagnosis (crude odds ratio [OR] 1.47 [95% CI: 1.10 to 1.94], p = 0.007; adjusted OR 1.38 [1.06 to 1.77], p = 0.01). Exposed offspring had a significantly greater mean BMI-for-age z score versus unexposed offspring at 6 months (z score difference 0.19 [95% CI: 0.10 to 0.29], p < 0.001; adjusted difference 0.04 [-0.06 to 0.13], p = 0.4). CONCLUSIONS: Exposure to maternal SARS-CoV-2 infection was associated with an increased risk of receiving a cardiometabolic diagnosis by 18 months preceded by greater BMI-for-age at 6 months.

Drug poisoning deaths by occupation and drug type, Massachusetts 2010-2019.

OBJECTIVE: To investigate variation in drug poisoning mortality rates by drug type and occupation in Massachusetts. METHODS: Death certificates for deaths by drug poisonings occurring between 2010 and 2019 in Massachusetts were coded based on the decedent's occupation. Mortality rates and rate ratios (with all other occupations as the reference group) were calculated based on the occupation of the workers according to drug type. Poisson regression was used to determine significantly elevated mortality rates and trends in drug poisoning deaths by occupation and drug type. RESULTS: The rate of drug poisoning deaths increased from 2010 to 2016 after which they plateaued. With respect to specific substances, fentanyl- and cocaine-related deaths increased throughout the surveillance period. For drug poisoning deaths overall, workers in construction trades (3,017); food preparation and serving (1,116); transportation and material moving (1,062) occupations had the highest number of drug poisoning deaths. When adjusting for age, sex, race/ethnicity, and educational attainment, workers in 7 occupations had significantly elevated mortality rate ratios for drug poisonings overall: farming, fishing, and forestry (3.42, P < 0.001); construction trades (2.58, P < 0.001); health care support (1.61, P < 0.001); community and social service (1.60, P < 0.001); food preparation and serving related (1.54, P < 0.001); personal care and service (1.37, P < 0.001); and arts, design, entertainment, sports, and media (1.21, P = 0.010). In many cases, workers in these same occupations had elevated mortality rate ratios for poisonings from specific substances. Health care practitioners and technical occupation workers only had elevated rates for methadone-related poisonings (1.73, P = 0.010). CONCLUSIONS: These findings highlight that workers in certain occupations have an elevated risk for drug poisonings and that the patterns differ with respect to the drug type. These findings can be useful for providing services to workers in high-risk occupations and in identifying occupational factors that may be related to the risk of drug poisoning death. While previous research has begun to uncover work-related factors that may contribute to opioid use, further work is needed to identify occupational factors that may contribute to psychostimulant and benzodiazepine use.

Acute exposure to pollen and airway inflammation in adolescents.

INTRODUCTION: Pollen exposure is known to exacerbate allergic asthma and allergic rhinitis symptoms, yet few studies have investigated if exposure to pollen affects lung function or airway inflammation in healthy children. METHODS: We evaluated the extent to which higher pollen exposure was associated with differences in airway inflammation and lung function among 490 early adolescent participants (mean age of 12.9 years) in Project Viva, a prebirth cohort based in Massachusetts. We obtained regional daily total pollen counts, including tree, grass, and weed pollen, from a Rotorod pollen counter. We evaluated associations of 3- and 7-day moving averages of pollen with fractional exhaled nitric oxide (FeNO) and lung function using linear regression models and evaluated the linearity of associations with penalized splines. We tested if associations of pollen with FeNO and lung function were modified by current asthma diagnosis, history of allergic rhinitis, aeroallergen sensitivity, temperature, precipitation, and air pollution. RESULTS: Three- and 7-day median pollen concentrations were 19.0 grains/m3 (IQR: 73.4) and 20.9 grains/m3 (IQR: 89.7). In main models, higher concentrations of total pollen over the preceding 3 and 7 days were associated with a 4.6% (95% CI: 0.1,9.2) and 7.4% (95% CI: 0.9,14.3) higher FeNO per IQR of pollen, respectively. We did not find associations of pollen with lung function in main models. Asthma, allergic rhinitis, precipitation, and air pollution (nitrogen dioxide and ozone) modified associations of pollen with lung function (Pinteraction < 0.1), while temperature, sex, and aeroallergen sensitization did not. CONCLUSION: Short-term exposure to pollen was associated with higher FeNO in early adolescents, even in the absence of allergic sensitization and asthma.

Bundling Colorectal Cancer Screening Outreach with Screening for Social Risk in Federally Qualified Health Centers: A Stepped-Wedge Implementation-Effectiveness Study.

BACKGROUND: Bundling is combining individual interventions to meet quality metrics. Bundling offers of cancer screening with screening for social determinants of health (SDOH) may enable health centers to assist patients with social risks and yield efficiencies. OBJECTIVE: To measure effects of bundling fecal immunochemical testing (FIT) and SDOH screening in federally qualified health centers (FQHCs). DESIGN: Clustered stepped-wedge trial. PARTICIPANTS: Four Massachusetts FQHCs randomized to implement bundled FIT-SDOH over 8-week "steps." INTERVENTION: Outreach to 50-75-year-olds overdue for CRC screening to offer FIT with SDOH screening. The implementation strategy used facilitation and training for data monitoring and reporting. MAIN MEASURES: Implementation process descriptions, data from facilitation meetings, and CRC and SDOH screening rates. Rates were compared between implementation and control FQHCs in each "step" by fitting generalized linear mixed-effects models with random intercepts for FQHCs, patients, and "step" by FQHC. KEY RESULTS: FQHCs tailored implementation processes to their infrastructure, workflows, and staffing and prioritized different groups for outreach. Two FQHCs used population health outreach, and two integrated FIT-SDOH within established programs, such as pre-visit planning. Of 34,588 patients overdue for CRC screening, 54% were female; 20% Black, 11% Latino, 10% Asian, and 47% white; 32% had Medicaid, 16% Medicare, 32% private insurance, and 11% uninsured. Odds of CRC screening completion in implementation "steps" compared to controls were higher overall and among groups prioritized for outreach (overall: adjusted odds ratio (aOR) 2.41, p = 0.005; prioritized: aOR 2.88, p = 0.002). Odds of SDOH screening did not differ across "steps." CONCLUSIONS: As healthcare systems are required to conduct more screenings, it is notable that outreach for a long-standing cancer screening requirement increased screening, even when bundled with a newer screening requirement. This outreach was feasible in a real-world safety-net clinical population and may conserve resources, especially compared to more complex or intensive outreach strategies. CLINICAL TRIALS REGISTRATION: NCT04585919.

Missing data and missed infections: investigating racial and ethnic disparities in SARS-CoV-2 testing and infection rates in Holyoke, Massachusetts.

Routinely collected testing data have been a vital resource for public health response during the COVID-19 pandemic and have revealed the extent to which Black and Hispanic persons have borne a disproportionate burden of SARS-CoV-2 infections and hospitalizations in the United States. However, missing race and ethnicity data and missed infections due to testing disparities limit the interpretation of testing data and obscure the true toll of the pandemic. We investigated potential bias arising from these 2 types of missing data through a case study carried out in Holyoke, Massachusetts, during the prevaccination phase of the pandemic. First, we estimated SARS-CoV-2 testing and case rates by race and ethnicity, imputing missing data using a joint modeling approach. We then investigated disparities in SARS-CoV-2 reported case rates and missed infections by comparing case rate estimates with estimates derived from a COVID-19 seroprevalence survey. Compared with the non-Hispanic White population, we found that the Hispanic population had similar testing rates (476 tested per 1000 vs 480 per 1000) but twice the case rate (8.1% vs 3.7%). We found evidence of inequitable testing, with a higher rate of missed infections in the Hispanic population than in the non-Hispanic White population (79 infections missed per 1000 vs 60 missed per 1000).

Association between social vulnerability and place of death during the first 2 years of COVID-19 in Massachusetts.

We investigated the relationship between individual-level social vulnerability and place of death during the infectious disease emergency of the COVID-19 pandemic in Massachusetts. Our research represents a unique contribution by matching individual-level death certificates with COVID-19 test data to analyse differences in distributions of place of death.

Massachusetts companion program bolsters COVID-19 vaccine rates among seniors.

In parts of the United States, COVID-19 vaccination rates remained low until late in Fall 2021 owing to both limited vaccine access and hesitancy. With colliding epidemics of RSV, flu, and COVID-19 in the winter, the retrospective evaluation of vaccine incentive policies is needed to inform future routine immunization campaigns. The Massachusetts companion program is one example of a policy that could boost vaccine uptake among older populations. Our regression discontinuity analysis suggests that the program was associated with an increase of up to 22 percentage points in the proportion of individuals aged 75 and older who have been fully vaccinated. Going forward, similar intervention strategies could be invaluable in scenarios where household contacts pose the greatest risk of transmission or where social ties can strongly influence individual decision-making.

Cost-effectiveness of expanded latent TB infection testing and treatment: Lynn City, Massachusetts, USA.

BACKGROUND: Between October 2016 and March 2019, Lynn Community Health Center in Massachusetts implemented a targeted latent TB infection testing and treatment (TTT) program, increasing testing from a baseline of 1,200 patients tested to an average of 3,531 patients tested, or 9% of the population per year.METHODS: We compared pre-implementation TTT, represented by the first two quarters of implementation data, to TTT, represented by 12 quarters of data. Time, diagnostic, and laboratory resources were estimated using micro-costing. Other cost and testing data were obtained from the electronic health record, pharmaceutical claims, and published reimbursement rates. A Markov cohort model estimated future health outcomes and cost-effectiveness from a societal perspective in 2020 US dollars. Monte Carlo simulation generated 95% uncertainty intervals.RESULTS: The TTT program exhibited extended dominance over baseline pre-intervention testing and had an incremental cost-effectiveness ratio (ICER) of US$52,603 (US$22,008â-"US$95,360). When compared to baseline pre-TTT testing, the TTT program averted an estimated additional 7.12 TB cases, 3.49 hospitalizations, and 0.16 deaths per lifetime cohort each year.CONCLUSIONS: TTT was more cost-effective than baseline pre-implementation testing. Lynn Community Health Centerâ-™s experience can help inform other clinics considering expanding latent TB infection testing.

Medication Treatment for Youth in Substance Use Disorder Residential Treatment.

OBJECTIVE: Residential is a common treatment setting for youth with high-severity substance use disorders (SUD). This study evaluated the prevalence of psychiatric symptoms and medication for youth in residential SUD treatment. METHODS: Youth in Massachusetts state licensed and funded SUD residential programs completed questionnaires assessing demographics, primary substance of use, and psychopathology symptoms (Youth Self Report [YSR]/Adult Self Report [ASR]). De-identified medication lists were provided by the programs. Descriptive statistics were used to describe the sample. RESULTS: Among the 47 youth who participated, 51.1% were male, 72.3% white, 83% non-Hispanic, mean age 20.7 years. Opioids were the most common primary substance identified by youth (51.1%), and 75% had at least one clinically elevated subscale on the YSR/ASR. Most youth were prescribed at least one medication (89.4%) with a mean of 2.9 medications. CONCLUSION: Youth in SUD residential treatment frequently have clinically elevated psychiatric symptoms, and psychotropic medication was commonly prescribed.