School of Public Health

California’s state government, employers and households are concerned about the future affordability of healthcare. We use health expenditure data from the Centers for Medicare & Medicaid Services’ Office of the Actuary to forecast California’s health expenditures from 2013 to 2022 and identify factors driving expenditure increases. Real health expenditures per capita (2013$) are forecasted to increase from $8,398 to $11,421 (or 36%), resulting in health expenditures increasing from 14.5% to 16.0% of California’s economy. Expenditure increases are mostly driven by gains in real income per capita (40-60%), followed by medical-specific inflation (23%), an aging population (14%), and insurance coverage gains (8%). The -4% to 16% residual is attributable to changes in the volume and mix of services and technology. Several innovations could potentially dampen these increases, such as shared-risk, value-based payment models, practice redesign initiatives, lower cost settings and healthcare professionals, many of which are found in accountable care organizations.

Adult salmon that stray when they escape into non-natal streams to spawn is a natural phenomenon that promotes population growth and genetic diversity, but excessive stray rates impede adult abundance restoration efforts. Adult San Joaquin River (SJR) Basin fall-run Chinook salmon (Oncorhynchus tshawytscha) that return to freshwater to spawn migrate through the San Francisco Bay and Sacramento–San Joaquin River Delta (Delta). The Delta has been heavily affected by land development and water diversion. During the fall time-period for the years 1979 to 2007 Delta pumping facilities diverted on average 340% of the total inflow volume that entered the Delta from the SJR. The hypothesis tested in this paper is that river flow and Delta exports are not significantly correlated with SJR salmon stray rates. Adult coded-wire-tagged salmon recoveries from Central Valley rivers were used to estimate the percentage of SJR Basin salmon that strayed to the Sacramento River Basin. SJR salmon stray rates were negatively correlated (P = 0.05) with the average magnitude of pulse flows (e.g., 10 d) in mid- to late-October and positively correlated (P = 0.10) with mean Delta export rates. It was not possible to differentiate between the effects of pulse flows in October and mean flows in October and November on stray rates because of the co-linearity between these two variables. Whether SJR-reduced pulse flow or elevated exports causes increased stray rates is unclear. Statistically speaking the results indicate that flow is the primary factor. However empirical data indicates that little if any pulse flow leaves the Delta when south Delta exports are elevated, so exports in combination with pulse flows may explain the elevated stray rates. For management purposes, we developed two statistical models that predict SJR salmon stray rate: (1) flow and export as co-independent variables; and (2) south Delta Export (E) and SJR inflow (I) in the form of an E:I ratio.

In this article, we describe an elective course using readers’ theater with students in the health care professions and the arts. Readers' theater is a technique used for the performance of literature in which texts are staged with minimal production values and scripts are not fully memorized. These techniques are drawn upon more commonly in theater and performance studies classrooms, but we found them to be effective as tools for connecting future health care providers with their local communities. With a central theme of age and aging, we chose non-dramatic works of literature and adapted them for dramatic readings at retirement communities in Berkeley and Oakland, California.

INTRODUCTION: Elevated glycosylated hemoglobin (HbA1c) in individuals with type 2 diabetes is associated with increased risk of hospitalization and death after acute COVID-19, however the effect of HbA1c on Long COVID is unclear. OBJECTIVE: Evaluate the association of glycemic control with the development of Long COVID in patients with type 2 diabetes (T2D). RESEARCH DESIGN AND METHODS: We conducted a retrospective cohort study using electronic health record data from the National COVID Cohort Collaborative. Our cohort included individuals with T2D from eight sites with longitudinal natural language processing (NLP) data. The primary outcome was death or new-onset recurrent Long COVID symptoms within 30-180 days after COVID-19. Symptoms were identified as keywords from clinical notes using NLP in respiratory, brain fog, fatigue, loss of smell/taste, cough, cardiovascular and musculoskeletal symptom categories. Logistic regression was used to evaluate the risk of Long COVID by HbA1c range, adjusting for demographics, body mass index, comorbidities, and diabetes medication. A COVID-negative group was used as a control. RESULTS: Among 7430 COVID-positive patients, 1491 (20.1%) developed symptomatic Long COVID, and 380 (5.1%) died. The primary outcome of death or Long COVID was increased in patients with HbA1c 8% to <10% (OR 1.20, 95% CI 1.02 to 1.41) and ≥10% (OR 1.40, 95% CI 1.14 to 1.72) compared with those with HbA1c 6.5% to <8%. This association was not seen in the COVID-negative group. Higher HbA1c levels were associated with increased risk of Long COVID symptoms, especially respiratory and brain fog. There was no association between HbA1c levels and risk of death within 30-180 days following COVID-19. NLP identified more patients with Long COVID symptoms compared with diagnosis codes. CONCLUSION: Poor glycemic control (HbA1c≥8%) in people with T2D was associated with higher risk of Long COVID symptoms 30-180 days following COVID-19. Notably, this risk increased as HbA1c levels rose. However, this association was not observed in patients with T2D without a history of COVID-19. An NLP-based definition of Long COVID identified more patients than diagnosis codes and should be considered in future studies.

BACKGROUND: Infection with the COVID-19-causing pathogen SARS-CoV-2 is associated with disruption in the human gut microbiome. The gut microbiome enables protection against diverse pathogens and exhibits dysbiosis during infectious and autoimmune disease. Studies based in the United States and China have found that severe COVID-19 cases have altered gut microbiome composition when compared to mild COVID-19 cases. We present the first study to investigate the gut microbiome composition of COVID-19 cases in a population from Sub-Saharan Africa. Given the impact of geography and cultural traditions on microbiome composition, it is important to investigate the microbiome globally and not draw broad conclusions from homogenous populations. RESULTS: We used stool samples in a Ugandan biobank collected from COVID-19 cases during 2020-2022. We profiled the gut microbiomes of 83 symptomatic individuals who tested positive for SARS-CoV-2 along with 43 household contacts who did not present any symptoms of COVID-19. The inclusion of healthy controls enables us to generate hypotheses about bacterial strains potentially related to susceptibility to COVID-19 disease, which is highly heterogeneous. Comparison of the COVID-19 patients and their household contacts revealed decreased alpha diversity and blooms of Enterococcus and Eggerthella in COVID-19 cases. CONCLUSIONS: Our study finds that the microbiome of COVID-19 individuals is more likely to be disrupted, as indicated by decreased diversity and increased pathobiont levels. This is either a consequence of the disease or may indicate that certain microbiome states increase susceptibility to COVID-19 disease. Our findings enable comparison with cohorts previously published in the Global North, as well as support new hypotheses about the interaction between the gut microbiome and SARS-CoV-2 infection.

Extreme weather events, including wildfires, are becoming more intense, frequent, and expansive due to climate change, thus increasing negative health outcomes. However, such effects can vary across space, time, and population subgroups, requiring methods that can handle multiple exposed units, account for time-varying confounding, and capture heterogeneous treatment effects. In this article, we proposed an approach based on staggered generalized synthetic control methods to study heterogeneous health effects, using the 2018 California wildfire season as a case study. This study aimed to estimate the effects of the November 2018 California wildfires, one of the states deadliest and most destructive wildfire seasons, on respiratory and circulatory health, document heterogeneity in health impacts, and investigate drivers of this heterogeneity. We applied a two-stage generalized synthetic control method to compare health outcomes in exposed (from 8 November to 5 December 2018) versus unexposed counties and used random-effects meta-regression to evaluate the effect modification of county-level socioeconomic variables on the observed health effects of the November 2018 wildfires. We observed an increase in respiratory hospitalizations for most exposed counties when compared with unexposed counties, with significant increases in Fresno, San Francisco, San Joaquin, San Mateo, and Santa Clara counties. No effect on circulatory hospitalizations was observed. County-level sociodemographic characteristics seem to not modulate the effects of wildfire smoke on respiratory hospitalizations. This novel two-stage framework can be applied in broader settings to understand spatially and temporally compounded health impacts of climate hazards. We provide codes in R for reproducibility and replication purposes.

BACKGROUND: DNA methylation (DNAm) provides a window to characterize the impacts of environmental exposures and the biological aging process. Epigenetic clocks are often trained on DNAm using penalized regression of CpG sites, but recent evidence suggests potential benefits of training epigenetic predictors on principal components. METHODOLOGY/FINDINGS: We developed a pipeline to simultaneously train three epigenetic predictors; a traditional CpG Clock, a PCA Clock, and a SuperLearner PCA Clock (SL PCA). We gathered publicly available DNAm datasets to generate i) a novel childhood epigenetic clock, ii) a reconstructed Hannum adult blood clock, and iii) as a proof of concept, a predictor of polybrominated biphenyl exposure using the three developmental methodologies. We used correlation coefficients and median absolute error to assess fit between predicted and observed measures, as well as agreement between duplicates. The SL PCA clocks improved fit with observed phenotypes relative to the PCA clocks or CpG clocks across several datasets. We found evidence for higher agreement between duplicate samples run on alternate DNAm arrays when using SL PCA clocks relative to traditional methods. Analyses examining associations between relevant exposures and epigenetic age acceleration (EAA) produced more precise effect estimates when using predictions derived from SL PCA clocks. CONCLUSIONS: We introduce a novel method for the development of DNAm-based predictors that combines the improved reliability conferred by training on principal components with advanced ensemble-based machine learning. Coupling SuperLearner with PCA in the predictor development process may be especially relevant for studies with longitudinal designs utilizing multiple array types, as well as for the development of predictors of more complex phenotypic traits.

Antimicrobial resistance (AMR) is a major threat to global public health that continues to grow owing to selective pressure caused by the use and overuse of antimicrobial drugs. Resistance spread by plasmids is of special concern, as they can mediate a wide distribution of AMR genes, including those encoding extended-spectrum β-lactamases (ESBLs). The CTX-M family of ESBLs has rapidly spread worldwide, playing a large role in the declining effectiveness of third-generation cephalosporins. This rapid spread across the planet is puzzling given that plasmids carrying AMR genes have been hypothesized to incur a fitness cost to their hosts in the absence of antibiotics. Here, we focus on a WT plasmid that carries the bla _{CTX-M 55} ESBL gene. We examine its conjugation rates and use head-to-head competitions to assay its associated fitness costs in both laboratory and wild Escherichia coli strains. We found that the wild strains exhibit intermediate conjugation levels, falling between two high-conjugation and two low-conjugation laboratory strains, the latter being older and more ancestral. We also show that the plasmid increases the fitness of both WT and lab strains when grown in lysogeny broth and Davis-Mingioli media without antibiotics, which might stem from metabolic benefits conferred on the host, or from interactions between the host and the rifampicin-resistant mutation we used as a selective marker. Laboratory strains displayed higher conjugation frequencies compared to WT strains. The exception was a low-passage K-12 strain, suggesting that prolonged laboratory cultivation may have compromised bacterial defences against plasmids. Despite low transfer rates among WT E. coli, the plasmid carried low fitness cost in minimal medium but conferred improved fitness in enriched medium, indicating a complex interplay between plasmids, host genetics and environmental conditions. Our findings reveal an intricate relationship between plasmid carriage and bacterial fitness. Moreover, they show that resistance plasmids can confer adaptive advantages to their hosts beyond AMR. Altogether, these results highlight that a closer study of plasmid dynamics is critical for developing a secure understanding of how they evolve and affect bacterial adaptability that is necessary for combating resistance spread.

BACKGROUND: Cardiovascular disease (CVD) morbidity and mortality is increasing in Africa, largely due to undiagnosed and untreated hypertension. Approaches that leverage existing primary health systems could improve hypertension treatment and reduce CVD, but cost-effectiveness is unknown. We evaluated the cost-effectiveness of population-level hypertension screening and implementation of chronic care clinics across eastern, southern, central, and western Africa. METHODS AND FINDINGS: We conducted a modeling study to simulate hypertension and CVD across 3,000 scenarios representing a range of settings across eastern, southern, central, and western Africa. We evaluated 2 policies compared to current hypertension treatment: (1) expansion of HIV primary care clinics into chronic care clinics that provide hypertension treatment for all persons regardless of HIV status (chronic care clinic or CCC policy); and (2) CCC plus population-level hypertension screening of adults ≥40 years of age by community health workers (CHW policy). For our primary analysis, we used a cost-effectiveness threshold of US $500 per disability-adjusted life-year (DALY) averted, a 3% annual discount rate, and a 50-year time horizon. A strategy was considered cost-effective if it led to the lowest net DALYs, which is a measure of DALY burden that takes account of the DALY implications of the cost for a given cost-effectiveness threshold. Among adults 45 to 64 years, CCC implementation would improve population-level hypertension control (the proportion of people with hypertension whose blood pressure is controlled) from mean 4% (90% range 1% to 7%) to 14% (6% to 26%); additional CHW screening would improve control to 44% (35% to 54%). Among all adults, CCC implementation would reduce ischemic heart disease (IHD) incidence by 10% (3% to 17%), strokes by 13% (5% to 23%), and CVD mortality by 9% (3% to 15%). CCC plus CHW screening would reduce IHD by 28% (19% to 36%), strokes by 36% (25% to 47%), and CVD mortality by 25% (17% to 34%). CHW screening was cost-effective in 62% of scenarios, CCC in 31%, and neither policy was cost-effective in 7% of scenarios. Pooling across setting-scenarios, incremental cost-effectiveness ratios were $69/DALY averted for CCC and $389/DALY averted adding CHW screening to CCC. CONCLUSIONS: Leveraging existing healthcare infrastructure to implement population-level hypertension screening by CHWs and hypertension treatment through integrated chronic care clinics is expected to reduce CVD morbidity and mortality and is likely to be cost-effective in most settings across Africa.