Faculty Bibliography

IMPORTANCE:Medicaid patients with mental illness comprise one of the most high-need and complex patient populations. Value-based reforms aim to improve care, but their efficacy in the Medicaid program is unclear. OBJECTIVE:To investigate if New York state's Medicaid value-based payment reform was associated with improved utilization patterns for patients with mental illness. DESIGN, SETTING, AND PARTICIPANTS:This retrospective cohort study used a difference-in-differences analysis to compare changes in utilization between Medicaid beneficiaries whose outpatient practices participated in value-based payment reform and beneficiaries whose practices did not participate from before (July 1, 2013-June 30, 2015) to after reform (July 1, 2015-June 30, 2019). Participants were Medicaid beneficiaries in New York state aged 18 to 64 years with major depression disorder, bipolar disorder, and/or schizophrenia. Data analysis was performed from April 2021 to July 2023. EXPOSURE:Beneficiaries were exposed to value-based payment reforms if their attributed outpatient practice participated in value-based payment reform at baseline (July 1, 2015). MAIN OUTCOMES AND MEASURES:Primary outcomes were the number of outpatient primary care visits and the number of behavioral health visits per year. Secondary outcomes were the number of mental health emergency department visits and hospitalizations per year. RESULTS:The analytic population comprised 306 290 individuals with depression (67.4% female; mean [SD] age, 38.6 [11.9] years), 85 105 patients with bipolar disorder (59.6% female; mean [SD] age, 38.0 [11.6] years), and 71 299 patients with schizophrenia (45.1% female; mean [SD] age, 40.3 [12.2] years). After adjustment, analyses estimated a statistically significant, positive association between value-based payments and behavioral health visits for patients with depression (0.91 visits; 95% CI, 0.51-1.30) and bipolar disorder (1.01 visits; 95% CI, 0.22-1.79). There was no statistically significant changes to primary care visits for patients with depression and bipolar disorder, but value-based payments were associated with reductions in primary care visits for patients with schizophrenia (-1.31 visits; 95% CI, -2.51 to -0.12). In every diagnostic population, value-based payment was associated with significant reductions in mental health emergency department visits (population with depression: -0.01 visits [95% CI, -0.02 to -0.002]; population with bipolar disorder: -0.02 visits [95% CI, -0.05 to -0.001]; population with schizophrenia: -0.04 visits [95% CI, -0.07 to -0.01]). CONCLUSIONS AND RELEVANCE:In this cohort study, Medicaid value-based payment reform was statistically significantly associated with an increase in behavioral health visits and a reduction in mental health emergency department visits for patients with mental illness. Medicaid value-based payment may be effective at altering health care utilization in patients with mental illness.

BACKGROUND:There are conflicting data on whether nonalcoholic fatty liver disease (NAFLD) is associated with susceptibility to pancreatic cancer (PC). Using Mendelian randomization (MR), we investigated the relationship between genetic predisposition to NAFLD and risk for PC. METHODS:Data from genome-wide association studies within the Pancreatic Cancer Cohort Consortium (PanScan; cases n=5090, controls n=8733) and the Pancreatic Cancer Case Control Consortium (PanC4; cases n=4,163, controls n=3,792) were analyzed. We used data on 68 genetic variants with four different MR methods (inverse variance weighting [IVW], MR-Egger, simple median, and penalized weighted median) separately to predict genetic heritability of NAFLD. We then assessed the relationship between each of the four MR methods and PC risk, using logistic regression to calculate odds ratios (ORs) and 95% confidence intervals (CIs), adjusting for PC risk factors, including obesity and diabetes. RESULTS:No association was found between genetically predicted NAFLD and PC risk in the PanScan or PanC4 samples (e.g., PanScan, IVW OR=1.04, 95% CI: 0.88-1.22, MR-Egger OR=0.89, 95% CI: 0.65-1.21; PanC4, IVW OR=1.07, 95% CI: 0.90-1.27, MR-Egger OR=0.93, 95% CI: 0.67-1.28). None of the four MR methods indicated an association between genetically predicted NAFLD and PC risk in either sample. CONCLUSIONS:Genetic predisposition to NAFLD is not associated with PC risk. IMPACT/CONCLUSIONS:Given the close relationship between NAFLD and metabolic conditions, it is plausible that any association between NAFLD and PC might reflect host metabolic perturbations (e.g., obesity, diabetes, or metabolic syndrome) and does not necessarily reflect a causal relationship between NAFLD and PC.

BACKGROUND:Fetal exposure to bisphenols and phthalates may lead to alterations in the respiratory and immune system development in children, and to adverse respiratory health. AIM/OBJECTIVE:To study the associations of fetal bisphenols and phthalates exposure with lung function and asthma at age 13 years. STUDY DESIGN AND METHODS/METHODS:This study among 1020 children was embedded in a population-based prospective cohort study. We measured maternal urine bisphenol and phthalate concentrations in the first, second and third trimester of pregnancy, and lung function by spirometry and asthma by questionnaires at age 13 years. Multivariable linear and logistic regression models were applied. RESULTS:in boys and girls, and of higher first trimester bisphenol S with a decreased risk of asthma in boys and an increased risk of asthma in girls, these results did not remain significant after correction for multiple testing. Results were not modified by maternal history of asthma or atopy. CONCLUSIONS:Maternal urine bisphenol and phthalate concentrations averaged or in specific trimesters during pregnancy were not strongly associated with childhood lung function and asthma at age 13 years. BPS, as a BPA substitute, tended to be associated with impaired lung function and altered risk of asthma, partly sex-dependent, but its strength was limited by a relatively low detection rate and should be queried in contemporary cohorts.

Evaluating causal effects in the presence of interference is challenging in network-based studies of hard-to-reach populations. Like many such populations, people who inject drugs (PWID) are embedded in social networks and often exert influence on others in their network. In our setting, the study design is observational with a non-randomized network-based HIV prevention intervention. Information is available on each participant and their connections that confer possible HIV risk through injection and sexual behaviors. We considered two inverse probability weighted (IPW) estimators to quantify the population-level spillover effects of non-randomized interventions on subsequent health outcomes. We demonstrated that these two IPW estimators are consistent, asymptotically normal, and derived a closed-form estimator for the asymptotic variance, while allowing for overlapping interference sets (groups of individuals in which the interference is assumed possible). A simulation study was conducted to evaluate the finite-sample performance of the estimators. We analyzed data from the Transmission Reduction Intervention Project, which ascertained a network of PWID and their contacts in Athens, Greece, from 2013 to 2015. We evaluated the effects of community alerts on subsequent HIV risk behavior in this observed network, where the connections or links between participants were defined by using substances or having unprotected sex together. In the study, community alerts were distributed to inform people of recent HIV infections among individuals in close proximity in the observed network. The estimates of the risk differences for spillover using either IPW estimator demonstrated a protective effect. The results suggest that HIV risk behavior could be mitigated by exposure to a community alert when an increased risk of HIV is detected in the network.

Background A number of studies have shown an association between social determinants of health and the emergence of obesity and diabetes, but whether the relationship is causal is not clear. Objective To test whether social, environmental, and medical determinants directly or indirectly affect population-level diabetes prevalence after controlling for mediator-mediator interactions. Methods Data were obtained from the CDC and supplemented with nine other data sources for 3,109 US counties. The dependent variable was the prevalence of diabetes in 2017. Independent variables were a given county's 30 social, environmental, and medical characteristics in 2015 and 2016. A network multiple mediation analysis was conducted. First, we used Least Absolute Shrinkage and Selection Operator (LASSO) regression to relate the 2017 diabetes rate in each county to 30 predictors measured in 2016, identifying statistically significant and robust predictors as the mediators within the network model and as direct determinants of 2017 diabetes. Second, each of the direct causes of diabetes was taken as a new response variable and LASSO-regressed on the same 30 independent variables measured in 2015, identifying the indirect (mediated) causes of diabetes. Subsequently, these direct and indirect predictors were used to construct a network model. The completed network was then employed to estimate the direct and mediated impact of variables on diabetes. Results For 2017 diabetes rates, 63% of the variation was explained by five variables measured in 2016: the percentage of residents who were (1) obese, (2) African American, (3) physically inactive, (4) in poor health condition, and (5) had a history of diabetes. These five direct predictors, measured in 2016, mediated the effect of indirect variables measured in 2015, including the percentage of residents who were (1) Hispanic, (2) physically distressed, (3) smokers, (4) living with children in poverty, (5) experiencing limited access to healthy foods, and (6) had low income. Conclusion All of the direct predictors of diabetes prevalence, except the percentage of residents who were African American, were medical conditions potentially influenced by lifestyles. Counties characterized by higher levels of obesity, inactivity, and poor health conditions exhibited increased diabetes rates in the following year. The impact of social determinants of illness, such as low income, children in poverty, and limited access to healthy foods, had an indirect effect on the health of residents and, consequently, increased the prevalence of diabetes.

BACKGROUND:Current clinical guidelines recommend a coronary artery calcium (CAC) score of 100 Agatston Units or demographic-specific 75th percentile as high-risk thresholds for guiding atherosclerotic cardiovascular disease preventive therapy. Meanwhile, low CAC can help derisk individuals who may safely defer statin therapy. However, limited data from the early 2000s, including just 208 older Black individuals, inform CAC percentiles for adults aged 75 to 85 years, and none have been established in adults aged ≥85 years. This study aims to characterize the distribution of CAC and establish demographic-specific CAC percentiles in the population aged ≥75 years. METHODS:We assessed 2886 participants aged ≥75 years without clinical coronary heart disease from the ARIC study (Atherosclerosis Risk in Communities) visit 7 (2018-2019; n=2217) and the MESA (Multi-Ethnic Study of Atherosclerosis) visit 5 (2010-2011; n=669). Prevalence of any CAC >0 and sex- and race-specific CAC percentiles across age were estimated nonparametrically with locally weighted regression models and pooled residual ranking. RESULTS:The median age was 80 (interquartile interval, 77-83) years, and 60% were female. The prevalence of zero CAC was lowest in White males (4%), followed by Black males (13%), White females (14%), and highest in Black females (18%). Regardless of sex and race, most participants had CAC>100 (62.5%). CAC scores increased with age, with CAC identified in ≈95% of participants aged ≥90 years across sex-race subgroups. The 75th percentile corresponded to higher CAC scores for Black older adults (n=741), especially females, than currently used thresholds. CONCLUSIONS:In community-dwelling adults aged ≥75 years free of clinical coronary heart disease, the prevalence of zero CAC was 11%, and CAC >100 as a threshold for high ASCVD risk would categorize most of this older population as high risk. Demographic-specific CAC percentiles from this study are a valuable tool for interpreting CAC in the population aged ≥75 years.

OBJECTIVES:There are inconsistencies in the South Africa HIV mortality data reported by Institute of Health Metrics and Evaluation (IHME), Joint United Nations Programme on HIV/AIDS (UNAIDS), and Statistics South Africa (StatsSA) platforms. Between 2006 and 2016, these global data sets (IHME and UNAIDS) show that HIV-related mortalities were improving in South Africa, whereas StatsSA argues the opposite. We explain the causes of this differing stands and highlight areas that may be improved to address such inconsistencies. METHODS:This observational analysis uses data from IHME, UNAIDS, and StatsSA platforms. RESULTS:We demonstrate that IHME and UNAIDS data sets are based on a mathematical compartmental model, which is not dynamic to all HIV epidemiological aspects. Such limitation may cause inflated improvement in HIV mortality outcomes that are not in line with HIV mortality evidence recorded at the household level as demonstrated by StatsSA. CONCLUSION:There is a need to streamline the IHME, UNAIDS, and StatsSA data on HIV to improve the quality of HIV research and programming in South Africa.

Longstanding gaps in the detection of Alzheimer's disease and related dementias (ADRD) and biopsychosocial care call for public health action to improve population health. We aim to broaden the understanding of the iterative role state plans have played over the last 20 years in prioritizing improvements in the detection of ADRD, primary care capacity, and equity for disproportionately affected populations. Informed by national ADRD priorities, state plans convene stakeholders to identify local needs, gaps, and barriers and set the stage for development of a national public health infrastructure that can align clinical practice reform with population health goals. We propose policy and practice actions that would accelerate the collaboration between public health, community organizations, and health systems to improve ADRD detection-the point of entry into care pathways that could ultimately improve outcomes on a national scale. HIGHLIGHTS: We systematically reviewed the evolution of state/territory plans for Alzheimer's disease and related dementias (ADRD). Plan goals improved over time but lacked implementation capacity. Landmark federal legislation (2018) enabled funding for action and accountability. The Centers for Disease Control and Prevention (CDC) funds three Public Health Centers of Excellence and many local initiatives. Four new policy steps would promote sustainable ADRD population health improvement.