Neighborhood-level Fatal Police Violence and Severe Maternal Morbidity in California.

Police violence is a pervasive issue that may have adverse implications for severe maternal morbidity (SMM). We assessed how the occurrence of fatal police violence (FPV) in one's neighborhood before/during pregnancy may influence SMM risk. Hospital discharge records from California between 2002-2018 were linked with the Fatal Encounters database (N=2,608,682). We identified 2,184 neighborhoods (census-tracts) with at least one FPV incident during the study period and used neighborhood fixed-effects models adjusting for individual sociodemographic characteristics to estimate odds of SMM associated with experiencing FPV in one's neighborhood anytime within the 24-months before childbirth. We did not find conclusive evidence on the link between FPV occurrence before delivery and SMM. However, estimates show that birthing people residing in neighborhoods where one or more FPV events had occurred within the preceding 24-months of giving birth may have a mildly elevated odds of SMM than those residing in the same neighborhoods with no FPV occurrence during the 24-months preceding childbirth (Odds Ratio (OR)=1.02; 95% Confidence Interval (CI): 0.99-1.05), particularly among those living in neighborhoods with fewer (1-2) FPV incidents throughout the study period (OR=1.03; 95% CI:1.00-1.06). Our findings provide evidence for the need to continue to examine the health consequences of police violence.

PROVIDENT: Development and Validation of a Machine Learning Model to Predict Neighborhood-level Overdose Risk in Rhode Island.

BACKGROUND: Drug overdose persists as a leading cause of death in the United States, but resources to address it remain limited. As a result, health authorities must consider where to allocate scarce resources within their jurisdictions. Machine learning offers a strategy to identify areas with increased future overdose risk to proactively allocate overdose prevention resources. This modeling study is embedded in a randomized trial to measure the effect of proactive resource allocation on statewide overdose rates in Rhode Island (RI). METHODS: We used statewide data from RI from 2016 to 2020 to develop an ensemble machine learning model predicting neighborhood-level fatal overdose risk. Our ensemble model integrated gradient boosting machine and super learner base models in a moving window framework to make predictions in 6-month intervals. Our performance target, developed a priori with the RI Department of Health, was to identify the 20% of RI neighborhoods containing at least 40% of statewide overdose deaths, including at least one neighborhood per municipality. The model was validated after trial launch. RESULTS: Our model selected priority neighborhoods capturing 40.2% of statewide overdose deaths during the test periods and 44.1% of statewide overdose deaths during validation periods. Our ensemble outperformed the base models during the test periods and performed comparably to the best-performing base model during the validation periods. CONCLUSIONS: We demonstrated the capacity for machine learning models to predict neighborhood-level fatal overdose risk to a degree of accuracy suitable for practitioners. Jurisdictions may consider predictive modeling as a tool to guide allocation of scarce resources.

Harnessing Google Health Trends Data for Epidemiologic Research.

Interest in using internet search data, such as that from the Google Health Trends Application Programming Interface (GHT-API), to measure epidemiologically relevant exposures or health outcomes is growing due to their accessibility and timeliness. Researchers enter search term(s), geography, and time period, and the GHT-API returns a scaled probability of that search term, given all searches within the specified geographic-time period. In this study, we detailed a method for using these data to measure a construct of interest in 5 iterative steps: first, identify phrases the target population may use to search for the construct of interest; second, refine candidate search phrases with incognito Google searches to improve sensitivity and specificity; third, craft the GHT-API search term(s) by combining the refined phrases; fourth, test search volume and choose geographic and temporal scales; and fifth, retrieve and average multiple samples to stabilize estimates and address missingness. An optional sixth step involves accounting for changes in total search volume by normalizing. We present a case study examining weekly state-level child abuse searches in the United States during the coronavirus disease 2019 pandemic (January 2018 to August 2020) as an application of this method and describe limitations.

Respondent-Driven Sampling for Estimation of the Cumulative Lifetime Incidence of Abortion in Soweto, Johannesburg, South Africa: A Methodological Assessment.

Respondent-driven sampling (RDS) is a potential strategy for addressing challenges in accurate estimation of abortion incidence, but it relies on often untested assumptions. We conducted an RDS study to estimate the cumulative lifetime incidence of abortion in Soweto, Johannesburg, South Africa (April-December 2018), to evaluate whether RDS assumptions were met, and to compare RDS estimates of incidence with estimates adjusted for employment and age based on census data. A total of 849 participants were recruited from 11 seed participants between April and December 2018. The assumption that individuals can identify target population members and the assumption of approximation of sampling with replacement was met. There were minor violations of the assumptions of seed independence from the final sample and reciprocity of ties. Assumptions of accurate degree reporting and random recruitment were not met. Failure to meet assumptions yielded a study sample with different employment characteristics than those of the target population; this could not be resolved by standard RDS methods. The RDS estimate of cumulative lifetime abortion incidence was 12.1% (95% confidence interval: 9.8, 14.3), and the employment-adjusted estimate was 16.9% (95% confidence interval: 12.8, 22.1). We caution researchers in using RDS for representative estimates of abortion incidence. Use of postsurvey weights to adjust for differences in characteristics between the sample and the target population may yield more representative results.

Translating Predictive Analytics for Public Health Practice: A Case Study of Overdose Prevention in Rhode Island.

Prior applications of machine learning to population health have relied on conventional model assessment criteria, limiting the utility of models as decision support tools for public health practitioners. To facilitate practitioners' use of machine learning as a decision support tool for area-level intervention, we developed and applied 4 practice-based predictive model evaluation criteria (implementation capacity, preventive potential, health equity, and jurisdictional practicalities). We used a case study of overdose prevention in Rhode Island to illustrate how these criteria could inform public health practice and health equity promotion. We used Rhode Island overdose mortality records from January 2016-June 2020 (n = 1,408) and neighborhood-level US Census data. We employed 2 disparate machine learning models, Gaussian process and random forest, to illustrate the comparative utility of our criteria to guide interventions. Our models predicted 7.5%-36.4% of overdose deaths during the test period, illustrating the preventive potential of overdose interventions assuming 5%-20% statewide implementation capacities for neighborhood-level resource deployment. We describe the health equity implications of use of predictive modeling to guide interventions along the lines of urbanicity, racial/ethnic composition, and poverty. We then discuss considerations to complement predictive model evaluation criteria and inform the prevention and mitigation of spatially dynamic public health problems across the breadth of practice. This article is part of a Special Collection on Mental Health.

Vital Signs: Health Disparities in Hemodialysis-Associated Staphylococcus aureus Bloodstream Infections - United States, 2017-2020.

INTRODUCTION: Racial and ethnic minorities are disproportionately affected by end-stage kidney disease (ESKD). ESKD patients on dialysis are at increased risk for Staphylococcus aureus bloodstream infections, but racial, ethnic, and socioeconomic disparities associated with this outcome are not well described. METHODS: Surveillance data from the 2020 National Healthcare Safety Network (NHSN) and the 2017-2020 Emerging Infections Program (EIP) were used to describe bloodstream infections among patients on hemodialysis (hemodialysis patients) and were linked to population-based data sources (CDC/Agency for Toxic Substances and Disease Registry [ATSDR] Social Vulnerability Index [SVI], United States Renal Data System [USRDS], and U.S. Census Bureau) to examine associations with race, ethnicity, and social determinants of health. RESULTS: In 2020, 4,840 dialysis facilities reported 14,822 bloodstream infections to NHSN; 34.2% were attributable to S. aureus . Among seven EIP sites, the S. aureus bloodstream infection rate during 2017-2020 was 100 times higher among hemodialysis patients (4,248 of 100,000 person-years) than among adults not on hemodialysis (42 of 100,000 person-years). Unadjusted S. aureus bloodstream infection rates were highest among non-Hispanic Black or African American (Black) and Hispanic or Latino (Hispanic) hemodialysis patients. Vascular access via central venous catheter was strongly associated with S. aureus bloodstream infections (NHSN: adjusted rate ratio [aRR] = 6.2; 95% CI = 5.7-6.7 versus fistula; EIP: aRR = 4.3; 95% CI = 3.9-4.8 versus fistula or graft). Adjusting for EIP site of residence, sex, and vascular access type, S. aureus bloodstream infection risk in EIP was highest in Hispanic patients (aRR = 1.4; 95% CI = 1.2-1.7 versus non-Hispanic White [White] patients), and patients aged 18-49 years (aRR = 1.7; 95% CI = 1.5-1.9 versus patients aged ≥65 years). Areas with higher poverty levels, crowding, and lower education levels accounted for disproportionately higher proportions of hemodialysis-associated S. aureus bloodstream infections. CONCLUSIONS AND IMPLICATIONS FOR PUBLIC HEALTH PRACTICE: Disparities exist in hemodialysis-associated S. aureus infections. Health care providers and public health professionals should prioritize prevention and optimized treatment of ESKD, identify and address barriers to lower-risk vascular access placement, and implement established best practices to prevent bloodstream infections.

Community-Level Risk Factors for Firearm Assault and Homicide: The Role of Local Firearm Dealers and Alcohol Outlets.

BACKGROUND: Identifying community characteristics associated with firearm assault could facilitate prevention. We investigated the effect of community firearm dealer and alcohol outlet densities on individual risk of firearm assault injury. METHODS: In this density-sampled case-control study of Californians, January 2005-September 2015, cases comprised all residents with a fatal or nonfatal firearm assault injury. For each month, we sampled controls from the state population in a 4:1 ratio with cases. Exposures were monthly densities of county-level pawn and nonpawn firearm dealers and ZIP code-level off-premises alcohol outlets and bars and pubs ("bars/pubs"). We used case-control-weighted G-computation to estimate risk differences (RD) statewide and among younger Black men, comparing observed exposure densities to hypothetical interventions setting these densities to low. We estimated additive interactions between firearm and alcohol retailer density. Secondary analyses examined interventions targeted to high exposure density or outcome burden areas. RESULTS: There were 67,850 cases and 268,122 controls. Observed (vs. low) densities of pawn firearm dealers and off-premises alcohol outlets were individually associated with elevated monthly risk of firearm assault per 100,000 people (RD pawn dealers : 0.06, 95% CI: 0.05, 0.08; RD off-premises outlets : 0.01, 95% CI: 0.01, 0.03), but nonpawn firearm dealer and bar/pub density were not; models targeting only areas with the highest outcome burden were similar. Among younger Black men, estimates were larger. There was no interaction between firearm and alcohol retailer density. CONCLUSIONS: Our results are consistent with the hypothesis that limiting pawn firearm dealers and off-premises alcohol outlet densities can reduce interpersonal firearm violence.

Accounting for Misclassification and Selection Bias in Estimating Effectiveness of Self-managed Medication Abortion.

BACKGROUND: Studies on the effectiveness of self-managed medication abortion may suffer from misclassification and selection bias due to self-reported outcomes and loss of follow-up. Monte Carlo sensitivity analysis can estimate self-managed abortion effectiveness accounting for these potential biases. METHODS: We conducted a Monte Carlo sensitivity analysis based on data from the Studying Accompaniment model Feasibility and Effectiveness Study (the SAFE Study), to generate bias-adjusted estimates of the effectiveness of self-managed abortion with accompaniment group support. Between July 2019 and April 2020, we enrolled a total of 1051 callers who contacted accompaniment groups in Argentina and Nigeria for self-managed abortion information; 961 took abortion medications and completed at least one follow-up. Using these data, we calculated measures of effectiveness adjusted for ineligibility, misclassification, and selection bias across 50,000 simulations with bias parameters drawn from pre-specified Beta distributions in R. RESULTS: After accounting for the potential influence of various sources of bias, bias-adjusted estimates of effectiveness were similar to observed estimates, conditional on chosen bias parameters: 92.68% (95% simulation interval: 87.80%, 95.74%) for mifepristone in combination with misoprostol (versus 93.7% in the observed data) and 98.47% (95% simulation interval: 96.79%, 99.39%) for misoprostol alone (versus 99.3% in the observed data). CONCLUSIONS: After adjustment for multiple potential sources of bias, estimates of self-managed medication abortion effectiveness remain high. Monte Carlo sensitivity analysis may be useful in studies measuring an epidemiologic proportion (i.e., effectiveness, prevalence, cumulative incidence) while accounting for possible selection or misclassification bias.

Vital Signs: Health Disparities in Hemodialysis-Associated Staphylococcus aureus Bloodstream Infections - United States, 2017-2020.

Introduction: Racial and ethnic minorities are disproportionately affected by end-stage kidney disease (ESKD). ESKD patients on dialysis are at increased risk for Staphylococcus aureus bloodstream infections, but racial, ethnic, and socioeconomic disparities associated with this outcome are not well described. Methods: Surveillance data from the 2020 National Healthcare Safety Network (NHSN) and the 2017-2020 Emerging Infections Program (EIP) were used to describe bloodstream infections among patients on hemodialysis (hemodialysis patients) and were linked to population-based data sources (CDC/Agency for Toxic Substances and Disease Registry [ATSDR] Social Vulnerability Index [SVI], United States Renal Data System [USRDS], and U.S. Census Bureau) to examine associations with race, ethnicity, and social determinants of health. Results: In 2020, 4,840 dialysis facilities reported 14,822 bloodstream infections to NHSN; 34.2% were attributable to S. aureus. Among seven EIP sites, the S. aureus bloodstream infection rate during 2017-2020 was 100 times higher among hemodialysis patients (4,248 of 100,000 person-years) than among adults not on hemodialysis (42 of 100,000 person-years). Unadjusted S. aureus bloodstream infection rates were highest among non-Hispanic Black or African American (Black) and Hispanic or Latino (Hispanic) hemodialysis patients. Vascular access via central venous catheter was strongly associated with S. aureus bloodstream infections (NHSN: adjusted rate ratio [aRR] = 6.2; 95% CI = 5.7-6.7 versus fistula; EIP: aRR = 4.3; 95% CI = 3.9-4.8 versus fistula or graft). Adjusting for EIP site of residence, sex, and vascular access type, S. aureus bloodstream infection risk in EIP was highest in Hispanic patients (aRR = 1.4; 95% CI = 1.2-1.7 versus non-Hispanic White [White] patients), and patients aged 18-49 years (aRR = 1.7; 95% CI = 1.5-1.9 versus patients aged ≥65 years). Areas with higher poverty levels, crowding, and lower education levels accounted for disproportionately higher proportions of hemodialysis-associated S. aureus bloodstream infections. Conclusions and implications for public health practice: Disparities exist in hemodialysis-associated S. aureus infections. Health care providers and public health professionals should prioritize prevention and optimized treatment of ESKD, identify and address barriers to lower-risk vascular access placement, and implement established best practices to prevent bloodstream infections.

Identifying Predictors of Opioid Overdose Death at a Neighborhood Level With Machine Learning.

Predictors of opioid overdose death in neighborhoods are important to identify, both to understand characteristics of high-risk areas and to prioritize limited prevention and intervention resources. Machine learning methods could serve as a valuable tool for identifying neighborhood-level predictors. We examined statewide data on opioid overdose death from Rhode Island (log-transformed rates for 2016-2019) and 203 covariates from the American Community Survey for 742 US Census block groups. The analysis included a least absolute shrinkage and selection operator (LASSO) algorithm followed by variable importance rankings from a random forest algorithm. We employed double cross-validation, with 10 folds in the inner loop to train the model and 4 outer folds to assess predictive performance. The ranked variables included a range of dimensions of socioeconomic status, including education, income and wealth, residential stability, race/ethnicity, social isolation, and occupational status. The R2 value of the model on testing data was 0.17. While many predictors of overdose death were in established domains (education, income, occupation), we also identified novel domains (residential stability, racial/ethnic distribution, and social isolation). Predictive modeling with machine learning can identify new neighborhood-level predictors of overdose in the continually evolving opioid epidemic and anticipate the neighborhoods at high risk of overdose mortality.

Evaluating the Effects of Opioid Prescribing Policies on Patient Outcomes in a Safety-net Primary Care Clinic.

BACKGROUND: After decades of liberal opioid prescribing, multiple efforts have been made to reduce reliance upon opioids in clinical care. Little is known about the effects of opioid prescribing policies on outcomes beyond opioid prescribing. OBJECTIVE: To evaluate the combined effects of multiple opioid prescribing policies implemented in a safety-net primary care clinic in San Francisco, CA, in 2013-2014. DESIGN: Retrospective cohort study and conditional difference-in-differences analysis of nonrandomized clinic-level policies. PATIENTS: 273 patients prescribed opioids for chronic non-cancer pain in 2013 at either the treated (n=151) or control clinic (n=122) recruited and interviewed in 2017-2018. INTERVENTIONS: Policies establishing standard protocols for dispensing opioid refills and conducting urine toxicology testing, and a new committee facilitating opioid treatment decisions for complex patient cases. MAIN MEASURES: Opioid prescription (active prescription, mean dose in morphine milligram equivalents [MME]) from electronic medical charts, and heroin and opioid analgesics not prescribed to the patient (any use, use frequency) from a retrospective interview. KEY RESULTS: The interventions were associated with a reduction in mean prescribed opioid dose in the first three post-policy years (year 1 conditional difference-in-differences estimate: -52.0 MME [95% confidence interval: -109.9, -10.6]; year 2: -106.2 MME [-195.0, -34.6]; year 3: -98.6 MME [-198.7, -23.9]; year 4: -72.6 MME [-160.4, 3.6]). Estimates suggest a possible positive association between the interventions and non-prescribed opioid analgesic use (year 3: 5.2 absolute percentage points [-0.1, 11.2]) and use frequency (year 3: 0.21 ordinal frequency scale points [0.00, 0.47]) in the third post-policy year. CONCLUSIONS: Clinic-level opioid prescribing policies were associated with reduced dose, although the control clinic achieved similar reductions by the fourth post-policy year, and the policies may have been associated with increased non-prescribed opioid analgesic use. Clinicians should balance the urgency to reduce opioid prescribing with potential harms from rapid change.

US shelter in place policies and child abuse Google search volume during the COVID-19 pandemic.

The COVID-19 pandemic has led to unemployment, school closures, movement restrictions, and social isolation, all of which are child abuse risk factors. Our objective was to estimate the effect of COVID-19 shelter in place (SIP) policies on child abuse as captured by Google searches. We applied a differences-in-differences design to estimate the effect of SIP on child abuse search volume. We linked state-level SIP policies to outcome data from the Google Health Trends Application Programming Interface. The outcome was searches for child abuse-related phrases as a scaled proportion of total searches for each state-week between December 31, 2017 and June 14, 2020. Between 914 and 1512 phrases were included for each abuse subdomain (physical, sexual, and emotional). Eight states and DC were excluded because of suppressed outcome data. Of the remaining states, 38 introduced a SIP policy between March 19, 2020 and April 7, 2020 and 4 states did not. The introduction of SIP generally led to no change, except for a slight reduction in child abuse search volume in weeks 8-10 post-SIP introduction, net of changes experienced by states that did not introduce SIP at the same time. We did not find strong evidence for an effect of SIP on child abuse searches. However, an increase in total search volume during the pandemic that may be differential between states with and without SIP policies could have biased these findings. Future work should examine the effect of SIP at the individual and population level using other data sources.

Examining Mediators of the Relationship Between Community Mobilization and HIV Incidence Among Young South African Women Participating in the HPTN 068 Study Cohort.

We previously demonstrated that village community mobilization (CM) was associated with reduced HIV incidence among adolescent girls and young women (AGYW) in South Africa. Little remains known about the mechanisms linking CM to HIV incidence. Using longitudinal data from 2292 AGYW in the HPTN 068 cohort (2011-2017), we examined whether school attendance, pro-social engagement, and hope for the future mediated the relationship between CM and HIV incidence. CM was measured at the village-level via two population-based surveys (2012 and 2014). Mediators and incident HIV infection were measured through HPTN 068 surveys and HIV testing. Mediation analyses were conducted using Mplus 8.5, adjusting for village-level clustering and covariates. Hope for the future mediated the relationship between CM and HIV incidence (indirect effect-RR 0.98, bias-corrected 95% CI 0.96, 0.99). Pro-social engagement and school attendance did not demonstrate indirect effects. CM reduces AGYW's HIV acquisition risk, in part, by engendering hope.

Evaluating the Validity and Reliability of the Gender Equitable Men's Scale Using a Longitudinal Cohort of Adolescent Girls and Young Women in South Africa.

Inequitable gender norms and beliefs contribute to increased sexual risk behavior, and, among adolescent girls and young women (AGYW), risk of HIV acquisition. We investigated the longitudinal measurement properties of the Gender Equitable Men's Scale (GEMS) when applied to a cohort of AGYW in rural South Africa (2011-2015). We used item response theory [Person-Item maps, Differential Item Functioning (DIF)] and measurement invariance confirmatory factor analysis models to assess the validity and reliability of the GEMS instrument. Item difficulty and endorsement of gender equitable beliefs both shifted over time. DIF analysis identified item bias for over half of the items; influenced by age, pregnancy, sexual debut, and intimate partner violence. Measurement invariance models revealed strong longitudinal invariance properties. GEMS is a reliable longitudinal measurement of gender equitable beliefs, with notable bias for specific items when administered to subgroups. Additional items specific to the adolescent experience are warranted for a more stable assessment of gender equitable beliefs in a population facing shifting norms as they mature.

Cigarettes smoked among daily and non-daily smokers following CVS Health's tobacco-free pharmacy policy.

INTRODUCTION: In September 2014, CVS Health ceased tobacco sales in all of its 7700 pharmacies nationwide. We investigate the impact of the CVS policy on the number of cigarettes smoked per day among metropolitan daily and non-daily smokers, who may respond to the availability of smoking cues in different manners. METHODS: Data are from the US Census Bureau Tobacco Use Supplement to the Current Population Survey 2014-2015 and the Blue Cross and Blue Shield Institute Community Health Management Hub. Adjusted difference-in-difference (DID) regressions assess changes in the number of cigarettes smoked per day among daily smokers (n=10 759) and non-daily smokers (n=3055), modelling core-based statistical area (CBSA) level CVS pharmacy market share continuously. To assess whether the policy had non-linear effects across the distribution of CVS market share, we also examine market share using tertiles. RESULTS: CVS's tobacco-free pharmacy policy was associated with a significant reduction in the number of cigarettes smoked by non-daily smokers in the continuous DID (rate ratio=0.985, p=0.022), with a larger reduction observed among non-daily smokers in CBSAs in the highest third of CVS market share compared with those living in CBSAs with no CVS presence (rate ratio=0.706, p=0.027). The policy, however, was not significantly associated with differential changes in the number of cigarettes by daily smokers. CONCLUSION: The removal of tobacco products from CVS pharmacies was associated with a reduction in the number of cigarettes smoked per day among non-daily smokers in metropolitan CBSAs, particularly those in which CVS had a large pharmacy market share.

Excess Google Searches for Child Abuse and Intimate Partner Violence During the COVID-19 Pandemic: Infoveillance Approach.

BACKGROUND: The COVID-19 pandemic has created environments with increased risk factors for household violence, such as unemployment and financial uncertainty. At the same time, it led to the introduction of policies to mitigate financial uncertainty. Further, it hindered traditional measurements of household violence. OBJECTIVE: Using an infoveillance approach, our goal was to determine if there were excess Google searches related to exposure to child abuse, intimate partner violence (IPV), and child-witnessed IPV during the COVID-19 pandemic and if any excesses are temporally related to shelter-in-place and economic policies. METHODS: Data on relative search volume for each violence measure was extracted using the Google Health Trends application programming interface for each week from 2017 to 2020 for the United States. Using linear regression with restricted cubic splines, we analyzed data from 2017 to 2019 to characterize the seasonal variation shared across prepandemic years. Parameters from prepandemic years were used to predict the expected number of Google searches and 95% prediction intervals (PI) for each week in 2020. Weeks with searches above the upper bound of the PI are in excess of the model's prediction. RESULTS: Relative search volume for exposure to child abuse was greater than expected in 2020, with 19% (10/52) of the weeks falling above the upper bound of the PI. These excesses in searches began a month after the Pandemic Unemployment Compensation program ended. Relative search volume was also heightened in 2020 for child-witnessed IPV, with 33% (17/52) of the weeks falling above the upper bound of the PI. This increase occurred after the introduction of shelter-in-place policies. CONCLUSIONS: Social and financial disruptions, which are common consequences of major disasters such as the COVID-19 pandemic, may increase risks for child abuse and child-witnessed IPV.

Modeling Combination Interventions to Prevent Human Immunodeficiency Virus in Adolescent Girls and Young Women in South Africa (HIV Prevention Trials Network 068).

BACKGROUND: Combination interventions may be an effective way to prevent human immunodeficiency virus (HIV) in adolescent girls and young women. However, current studies are not designed to understand which specific interventions and combinations will be most effective. We estimate the possible impacts of interventions on a combination of factors associated with HIV. METHODS: We used the g-formula to model interventions on combinations of HIV risk factors to identify those that would prevent the most incident HIV infections, including low school attendance, intimate partner violence, depression, transactional sex, and age-disparate partnerships. We used data from the HIV Prevention Trials Network (HPTN) 068 study in rural South Africa from 2011 to 2017. We estimated HIV incidence under a potential intervention that reduced each risk factor and compared this to HIV incidence under the current distribution of these risk factors. RESULTS: Although many factors had strong associations with HIV, potential intervention estimates did not always suggest large reductions in HIV incidence because the prevalence of risk factors was low. When modeling combination effects, an intervention to increase schooling, decrease depression, and decease transactional sex showed the largest reduction in incident infection (risk difference, -1.4%; 95% confidence interval [CI], -2.7% to -.2%), but an intervention on only transactional sex and depression still reduced HIV incidence by -1.3% (95% CI, -2.6% to -.2%). CONCLUSIONS: To achieve the largest reductions in HIV, both prevalence of the risk factor and strength of association with HIV must be considered. Additionally, intervening on more risk factors may not necessarily result in larger reductions in HIV incidence.

Association between Socioeconomic Status and Incidence of Community-Associated Clostridioides difficile Infection - United States, 2014-2015.

We evaluated the association between socioeconomic status (SES) and community-associated Clostridioides difficile infection (CA-CDI) incidence across 2474 census tracts in 10 states. Highly correlated community-level SES variables were transformed into distinct factors using factor analysis. We found low SES communities were associated with higher CA-CDI incidence.

Invited Commentary: The Society for Epidemiologic Research's Commitment to Diversity and Equity-Pathways to Filling the Glass.

In the article by Nobles et al. (Am J Epidemiol. 2021;190(9):1710-1720), characteristics of those epidemiologists selected for various chair and presentation roles at the annual meetings of the Society for Epidemiologic Research (SER) from 2015 through 2017 were examined. Characteristics that were compared included inferred gender, institutional affiliation, subject area, and h-index. Important disparities were observed between session chairs, speakers, and poster presenters. SER leadership considers diversity and equity to be priorities and is committed to positive change. New programs and processes have been used to broaden participation and improve diversity since 2018, but the SER must continue its efforts to change processes and monitor of the experiences of SER members. A diversity of perspectives within the SER membership and at its meetings will improve all aspects of our practice of epidemiology.

California's Mental Health Services Act and Mortality Due to Suicide, Homicide, and Acute Effects of Alcohol: A Synthetic Control Application.

California's Mental Health Services Act (MHSA) substantially expanded funding of county mental health services through a state tax, and led to broad prevention efforts and intensive services for individuals experiencing serious mental disorders. We estimated the associations between MHSA and mortality due to suicide, homicide, and acute effects of alcohol. Using annual cause-specific mortality data for each US state and the District of Columbia from 1976-2015, we used a generalization of the quasi-experimental synthetic control method to predict California's mortality rate for each outcome in the absence of MHSA using a weighted combination of comparison states. We calculated the association between MHSA and each outcome as the absolute difference and percentage difference between California's observed and predicted average annual rates over the postintervention years (2007-2015). MHSA was associated with modest decreases in average annual rates of homicide (-0.81/100,000 persons, corresponding to a 13% reduction) and mortality from acute alcohol effects (-0.35/100,000 persons, corresponding to a 12% reduction). Placebo test inference suggested that the associations were unlikely to be due to chance. MHSA was not associated with suicide. Protective associations with mortality due to homicide and acute alcohol effects provide evidence for modest health benefits of MHSA at the population level.