Faculty Bibliography

Social policies have great potential to improve population health and reduce health disparities. Thus, increasing empirical research seeks to quantify the health effects of social policies by exploiting variation in the timing of policy changes across places. Multiple social policies are often adopted simultaneously or in close succession in the same locations, creating co-occurrence which must be handled analytically for valid inferences. Although this is a substantial methodological challenge for studies aiming to isolate social policy effects, limited prior work has systematically considered analytic solutions within a causal framework or assessed whether these solutions are being adopted. We designated seven analytic solutions to policy co-occurrence, including efforts to disentangle individual policy effects and efforts to estimate the combined effects of co-occurring policies. We leveraged an existing systematic review of social policies and health to evaluate how often policy co-occurrence is identified as a threat to validity and how often each analytic solution is applied in practice. Of the 55 studies, only 17 (31%) reported checking for any co-occurring policies, although 36 (67%) used at least one approach that helps address policy co-occurrence. The most common approaches were: adjusting for measures of co-occurring policies; defining the outcome on subpopulations likely to be affected by the policy of interest (but not other co-occurring policies); and selecting a less-correlated measure of policy exposure. As health research increasingly focuses on policy changes, we must systematically assess policy co-occurrence and apply analytic solutions to strengthen future studies on the health effects of social policies.

Extensive empirical health research leverages variation in the timing and location of policy changes as quasi-experiments. Multiple social policies may be adopted simultaneously in the same locations, creating co-occurrence which must be addressed analytically for valid inferences. The pervasiveness and consequences of co-occurring policies have received limited attention. We analyzed a systematic sample of 13 social policy databases covering diverse domains including poverty, paid family leave, and tobacco. We quantified policy co-occurrence in each database as the fraction of variation in each policy measure across different jurisdictions and times that could be explained by co-variation with other policies (R2). We used simulations to estimate the ratio of the variance of effect estimates under the observed policy co-occurrence to variance if policies were independent. Policy co-occurrence ranged from very high for state-level cannabis policies to low for country-level sexual minority rights policies. For 65% of policies, greater than 90% of the place-time variation was explained by other policies. Policy co-occurrence increased the variance of effect estimates by a median of 57-fold. Co-occurring policies are common and pose a major methodological challenge to rigorously evaluating health effects of individual social policies. When uncontrolled, co-occurring policies confound one another, and when controlled, resulting positivity violations may substantially inflate the variance of estimated effects. Tools to enhance validity and precision for evaluating co-occurring policies are needed.

PURPOSE/OBJECTIVE:Population-based surveys are possible sources from which to draw representative control data for case-control studies. However, these surveys involve complex sampling that could lead to biased estimates of measures of association if not properly accounted for in analyses. Approaches to incorporating complex-sampled controls in density-sampled case-control designs have not been examined. METHODS:We used a simulation study to evaluate the performance of different approaches to estimating incidence density ratios (IDR) from case-control studies with controls drawn from complex survey data using risk-set sampling. In simulated population data, we applied four survey sampling approaches, with varying survey sizes, and assessed the performance of four analysis methods for incorporating survey-based controls. RESULTS:Estimates of the IDR were unbiased for methods that conducted risk-set sampling with probability of selection proportional to survey weights. Estimates of the IDR were biased when sampling weights were not incorporated, or only included in regression modeling. The unbiased analysis methods performed comparably and produced estimates with variance comparable to biased methods. Variance increased and confidence interval coverage decreased as survey size decreased. CONCLUSIONS:Unbiased estimates are obtainable in risk-set sampled case-control studies using controls drawn from complex survey data when weights are properly incorporated.

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.

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Latino people in the US are experiencing higher excess deaths during the COVID-19 pandemic than any other racial/ethnic group, but it is unclear which sociodemographic subgroups within this diverse population are most affected. Such information is necessary to target policies that prevent further excess mortality and reduce inequities. Using death certificate data for January 1, 2016 through February 29, 2020 and time-series models, we estimated the expected weekly deaths among Latino people in California from March 1 through October 3, 2020. We quantified excess mortality as observed minus expected deaths and risk ratios (RR) as the ratio of observed to expected deaths. We considered subgroups categorized by age, sex, nativity, country of birth, educational attainment, occupation, and combinations of these factors. Our results indicate that during the first seven months of the pandemic, Latino deaths in California exceeded expected deaths by 10,316, a 31% increase. Excess death rates were greatest for individuals born in Mexico (RR 1.44; 95% PI, 1.41, 1.48) or a Central American country (RR 1.49; 95% PI, 1.37, 1.64), with less than a high school degree (RR 1.41; 95% PI, 1.35, 1.46), or in food-and-agriculture (RR 1.60; 95% PI, 1.48, 1.74) or manufacturing occupations (RR 1.59; 95% PI, 1.50, 1.69). Immigrant disadvantages in excess death were magnified among working-age Latinos in essential occupations. In sum, the COVID-19 pandemic has disproportionately impacted mortality among Latino immigrants, especially those in unprotected essential jobs. Interventions to reduce these inequities should include targeted vaccination, workplace safety enforcement, and expanded access to medical care and economic support.

COVID-19 mortality increases dramatically with age and is also substantially higher among Black, Indigenous, and People of Color (BIPOC) populations in the United States. These two facts can have conflicting implications because BIPOC populations are younger than white populations. In analyses of California and Minnesota--demographically divergent states--we show that COVID vaccination schedules based solely on age benefit the older white populations at the expense of younger BIPOC populations with higher risk of death from COVID-19. We find that strategies that prioritize high-risk geographic areas for vaccination at all ages better target mortality risk than age-based strategies alone, although they do not always perform as well as direct prioritization of high-risk racial/ethnic groups. Vaccination schemas directly implicate equitability of access, both domestically and globally.

BACKGROUND:Despite the widespread institution of modern massive transfusion protocols with balanced blood product ratios, survival for patients with traumatic hemorrhage receiving ultramassive transfusion (UMT) (defined as ≥20 U of packed red blood cells [RBCs]) in 24 hours) remains low and resource consumption remains high. Therefore, we aimed to identify factors associated with mortality in trauma patients receiving UMT in the modern resuscitation era. METHODS:An Eastern Association for the Surgery of Trauma multicenter retrospective study of 461 trauma patients from 17 trauma centers who received ≥20 U of RBCs in 24 hours was performed (2014-2019). Multivariable logistic regression and Classification and Regression Tree analysis were used to identify clinical characteristics associated with mortality. RESULTS:The 461 patients were young (median age, 35 years), male (82%), severely injured (median Injury Severity Score, 33), in shock (median shock index, 1.2; base excess, -9), and transfused a median of 29 U of RBCs, 22 U of fresh frozen plasma (FFP), and 24 U of platelets (PLT). Mortality was 46% at 24 hours and 65% at discharge. Transfusion of RBC/FFP ≥1.5:1 or RBC/PLT ≥1.5:1 was significantly associated with mortality, most pronounced for the 18% of patients who received both RBC/PLT and RBC/FFP ≥1.5:1 (odds ratios, 3.11 and 2.81 for mortality at 24 hours and discharge; both p < 0.01). Classification and Regression Tree identified that age older than 50 years, low initial Glasgow Coma Scale, thrombocytopenia, and resuscitative thoracotomy were associated with low likelihood of survival (14-26%), while absence of these factors was associated with the highest survival (71%). CONCLUSION:Despite modern massive transfusion protocols, one half of trauma patients receiving UMT are transfused with either RBC/FFP or RBC/PLT in unbalanced ratios ≥1.5:1, with increased associated mortality. Maintaining focus on balanced ratios during UMT is critical, and consideration of advanced age, poor initial mental status, thrombocytopenia, and resuscitative thoracotomy can aid in prognostication. LEVEL OF EVIDENCE:Prognostic, level III.

Interpersonal violence is a major global public health problem, and the burden of nonfatal assault injuries is far greater than that of homicides. To understand trends and inform prevention priorities, we sought to describe nonfatal assault injury trends across demographic groups from 2005 to 2015 in California, USA. Comprehensive hospitalization and emergency department discharge records were used to estimate annual rates of nonfatal assault injury overall and by means and age group and age-standardized annual rates by race/ethnicity, gender, and county. The overall rate of assault injury was stable in California from 2005 to 2015 (mean = 364 per 100,000), but there was substantial heterogeneity across demographic groups, including increases among African Americans (900 to 1,194), American Indian/Alaskan Natives (423 to 572), older individuals (age 25-29 = 697 to 727; 30-39 = 495 to 557; 40-49 = 352 to 404; 50-59 = 194 to 313; 60+ = 66 to 106), and women (199 to 252). Assault injury rates increased among several demographic groups, warranting the attention of professionals involved in violence prevention efforts. Epidemiologic examination to better understand causes of increases can inform prevention efforts. Similar analyses should be applied to other settings to determine how broadly these patterns are observed.

Evidence for Action (E4A), a signature program of the Robert Wood Johnson Foundation, funds investigator-initiated research on the impacts of social programs and policies on population health and health inequities. Across thousands of letters of intent and full proposals E4A has received since 2015, one of the most common methodological challenges faced by applicants is selecting realistic effect sizes to inform calculations of power, sample size, and minimum detectable effect (MDE). E4A prioritizes health studies that are both (1) adequately powered to detect effect sizes that may reasonably be expected for the given intervention and (2) likely to achieve intervention effects sizes that, if demonstrated, correspond to actionable evidence for population health stakeholders. However, little guidance exists to inform the selection of effect sizes for population health research proposals. We draw on examples of five rigorously evaluated population health interventions. These examples illustrate considerations for selecting realistic and actionable effect sizes as inputs to calculations of power, sample size and MDE for research proposals to study population health interventions. We show that plausible effects sizes for population health interventions may be smaller than commonly cited guidelines suggest. Effect sizes achieved with population health interventions depend on the characteristics of the intervention, the target population, and the outcomes studied. Population health impact depends on the proportion of the population receiving the intervention. When adequately powered, even studies of interventions with small effect sizes can offer valuable evidence to inform population health if such interventions can be implemented broadly. Demonstrating the effectiveness of such interventions, however, requires large sample sizes.