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Open Access Publications from the University of California

Scholarly Works (23 results)

  • Thesis
  • Peer Reviewed
UC San Francisco Electronic Theses and Dissertations (2018)

In chapter 1, using a Galton-Watson branching-process analysis, we characterize differences in measles transmission by estimating the association between genotype and the reproduction number among post-elimination California measles cases. Genotype B3 is found to be a significant predictor of transmissibility.

In chapter 2, we determine whether data from FDA-cleared wired and Bluetooth smart thermometers sold by a San Francisco-based company aid influenza forecasting efforts. We compare this smart thermometer data to regional influenza and ILI surveilance data from the California Department of Public Health. We evaluated the correlation between the regional California surveillance data and smart thermometer data, tested the hypothesis that smart thermometer readings and symptom reports provide regionally specific predictions, and determined whether smart thermometer and mobile application improved disease forecasts. Our results are consistent with the hypothesis that smart thermometer readings and symptom reports reflect underlying disease transmission in California. Data from such cloud-based devices could supplement syndromic influenza surveillance data.

In chapter 3, we examine whether changes in varicella transmissibility may have occurred following the change from one- to two-dose vaccination scheduling in 2007. Following the change in ACIP recommendations for varicella vaccination in 2007, the median outbreak size decreased. However, while the number of outbreaks has continued to decrease following 2008, we do not find evidence that the distribution of sizes has changed since 2008. Using insights from branching process models assuming both subcritical and supercritical transmission and with and without depletion of susceptibles, we cannot rule out that varicella transmission is supercritical in school-based settings.

    Cover page: Mathematical Models of Vaccine-Preventable Disease in California
    • Article
    • Peer Reviewed
    UC San Francisco Previously Published Works (2015)
    Late 19th century epidemics of tuberculosis (TB) in Western Canadian First Nations resulted in peak TB mortality rates more than six times the highest rates recorded in Europe. Using a mathematical modeling approach and historical TB mortality time series, we investigate potential causes of high TB mortality and rapid epidemic decline in First Nations from 1885 to 1940. We explore two potential causes of dramatic epidemic dynamics observed in this setting: first, we explore effects of famine prior to 1900 on both TB and population dynamics. Malnutrition is recognized as an individual-level risk factor for TB progression and mortality; its population-level effects on TB epidemics have not been explored previously. Second, we explore effects of heterogeneity in susceptibility to TB in two ways: modeling heterogeneity in susceptibility to infection, and heterogeneity in risk of developing disease once infected. Our results indicate that models lacking famine-related changes in TB parameters or heterogeneity result in an implausibly poor fit to both the TB mortality time series and census data; the inclusion of these features allows for the characteristic decline and rise in population observed in First Nations during this time period and confers improved fits to TB mortality data.
      Cover page: Modeling historical tuberculosis epidemics among Canadian First Nations: effects of malnutrition and genetic variation
      • Article
      • Peer Reviewed
      UC San Francisco Previously Published Works (2017)
      We extend a technique of approximation of the long-term behavior of a supercritical stochastic epidemic model, using the WKB approximation and a Hamiltonian phase space, to the subcritical case. The limiting behavior of the model and approximation are qualitatively different in the subcritical case, requiring a novel analysis of the limiting behavior of the Hamiltonian system away from its deterministic subsystem. This yields a novel, general technique of approximation of the quasistationary distribution of stochastic epidemic and birth-death models and may lead to techniques for analysis of these models beyond the quasistationary distribution. For a classic SIS model, the approximation found for the quasistationary distribution is very similar to published approximations but not identical. For a birth-death process without depletion of susceptibles, the approximation is exact. Dynamics on the phase plane similar to those predicted by the Hamiltonian analysis are demonstrated in cross-sectional data from trachoma treatment trials in Ethiopia, in which declining prevalences are consistent with subcritical epidemic dynamics.
        Cover page: Hamiltonian Analysis of Subcritical Stochastic Epidemic Dynamics.
        • Article
        • Peer Reviewed
        UC Santa Cruz Previously Published Works (2023)
        There is growing need to distinguish between sex and gender. While sex is assigned at birth, gender is socially constructed and may not correspond to one's assigned sex. However, in most research studies, sex or gender is assessed in isolation or the terms are used interchangeably, which has implications for research accuracy and inclusivity. We used data from the UK Biobank to quantify the prevalence of disagreement between chromosomal and self-reported sex and identify potential reasons for discordance. Among approximately 200 individuals with sex discordance, 71% of discordances were potentially explained by the presence of intersex traits or transgender identity. The findings indicate that when describing sex- and/or gender-specific differences in health, researchers may be limited in their ability to draw conclusions regarding specific sex and/or gender health information.
          Cover page: Discordance in chromosomal and self-reported sex in the UK Biobank: Implications for transgender- and intersex-inclusive data collection
          • Article
          • Peer Reviewed
          UC San Francisco Previously Published Works (2021)
          Growing evidence has suggested an association between sleep duration and Alzheimer's disease (AD), but it is unclear if sleep duration is a manifestation of the AD disease process. We studied whether genetic liability for AD predicts sleep duration using a genetic risk score (GRS) for AD (AD-GRS), in 406,536 UK Biobank participants with European ancestry and without dementia at enrollment. Higher AD-GRS score was associated with shorter sleep (b = -0.014, 95% confidence interval [CI] = -0.022 to -0.006), especially in those aged 55+. Using AD-GRS as an instrumental variable for AD diagnosis, incipient AD reduced sleep duration by 1.87 hours (95% CI = 0.96, 2.78). Short sleep duration might be an early marker of AD. ANN NEUROL 2021;89:177-181.
            • Article
            • Peer Reviewed
            UC San Francisco Previously Published Works (2017)
            Compartmental model diagrams have been used for nearly a century to depict causal relationships in infectious disease epidemiology. Causal directed acyclic graphs (DAGs) have been used more broadly in epidemiology since the 1990s to guide analyses of a variety of public health problems. Using an example from chronic disease epidemiology, the effect of type 2 diabetes on dementia incidence, we illustrate how compartmental model diagrams can represent the same concepts as causal DAGs, including causation, mediation, confounding, and collider bias. We show how to use compartmental model diagrams to explicitly depict interaction and feedback cycles. While DAGs imply a set of conditional independencies, they do not define conditional distributions parametrically. Compartmental model diagrams parametrically (or semiparametrically) describe state changes based on known biological processes or mechanisms. Compartmental model diagrams are part of a long-term tradition of causal thinking in epidemiology and can parametrically express the same concepts as DAGs, as well as explicitly depict feedback cycles and interactions. As causal inference efforts in epidemiology increasingly draw on simulations and quantitative sensitivity analyses, compartmental model diagrams may be of use to a wider audience. Recognizing simple links between these two common approaches to representing causal processes may facilitate communication between researchers from different traditions.
              Cover page: Compartmental Model Diagrams as Causal Representations in Relation to DAGs
              • Article
              • Peer Reviewed
              UC San Francisco Previously Published Works (2020)
              A large measles outbreak in 2014-2015, linked to Disneyland theme parks, attracted international attention, and led to changes in California vaccine policy. We use dates of symptom onset and known epidemic links for California cases in this outbreak to estimate time-varying transmission in the outbreak, and to estimate generation membership of cases probabilistically. We find that transmission declined significantly during the course of the outbreak (p = 0.012), despite also finding that estimates of transmission rate by day or by generation can overestimate temporal decline. We additionally find that the outbreak size and duration alone are sufficient in this case to distinguish temporal decline from time-invariant transmission (p = 0.014). As use of a single large outbreak can lead to underestimates of immunity, however, we urge caution in interpretation of quantities estimated from this outbreak alone. Further research is needed to distinguish causes of temporal decline in transmission rates.
                Cover page: Measles transmission during a large outbreak in California
                • Article
                • Peer Reviewed
                UC San Francisco Previously Published Works (2018)
                Background:Substantial heterogeneity in measles outbreak sizes may be due to genotype-specific transmissibility. Using a branching process analysis, we characterize differences in measles transmission by estimating the association between genotype and the reproduction number R among postelimination California measles cases during 2000-2015 (400 cases, 165 outbreaks). Methods:Assuming a negative binomial secondary case distribution, we fit a branching process model to the distribution of outbreak sizes using maximum likelihood and estimated the reproduction number R for a multigenotype model. Results:Genotype B3 is found to be significantly more transmissible than other genotypes (P = .01) with an R of 0.64 (95% confidence interval [CI], .48-.71), while the R for all other genotypes combined is 0.43 (95% CI, .28-.54). This result is robust to excluding the 2014-2015 outbreak linked to Disneyland theme parks (referred to as "outbreak A" for conciseness and clarity) (P = .04) and modeling genotype as a random effect (P = .004 including outbreak A and P = .02 excluding outbreak A). This result was not accounted for by season of introduction, age of index case, or vaccination of the index case. The R for outbreaks with a school-aged index case is 0.69 (95% CI, .52-.78), while the R for outbreaks with a non-school-aged index case is 0.28 (95% CI, .19-.35), but this cannot account for differences between genotypes. Conclusions:Variability in measles transmissibility may have important implications for measles control; the vaccination threshold required for elimination may not be the same for all genotypes or age groups.
                  • Article
                  • Peer Reviewed
                  UC San Francisco Previously Published Works (2019)
                  Social media posts regarding measles vaccination were classified as pro-vaccination, expressing vaccine hesitancy, uncertain, or irrelevant. Spearman correlations with Centers for Disease Control and Prevention-reported measles cases and differenced smoothed cumulative case counts over this period were reported (using time series bootstrap confidence intervals). A total of 58,078 Facebook posts and 82,993 tweets were identified from 4 January 2009 to 27 August 2016. Pro-vaccination posts were correlated with the US weekly reported cases (Facebook: Spearman correlation 0.22 (95% confidence interval: 0.09 to 0.34), Twitter: 0.21 (95% confidence interval: 0.06 to 0.34)). Vaccine-hesitant posts, however, were uncorrelated with measles cases in the United States (Facebook: 0.01 (95% confidence interval: -0.13 to 0.14), Twitter: 0.0011 (95% confidence interval: -0.12 to 0.12)). These findings may result from more consistent social media engagement by individuals expressing vaccine hesitancy, contrasted with media- or event-driven episodic interest on the part of individuals favoring current policy.
                    Cover page: Facebook and Twitter vaccine sentiment in response to measles outbreaks
                    • Article
                    • Peer Reviewed
                    UC Davis Previously Published Works (2021)

                    Objectives

                    Evidence on adverse childhood experiences (ACEs) and late-life cognitive outcomes is inconsistent, with little research among diverse racial/ethnic groups. We investigated whether ACE exposures were associated with worse late-life cognition for all racial/ethnic groups and at different ages of exposure.

                    Design

                    Covariate-adjusted mixed-effects linear regression models estimated associations of: (1) total number of ACEs experienced, (2) earliest age when ACE occurred and (3) type of ACE with overall cognition.

                    Setting

                    Kaiser Permanente Northern California members aged 65 years and older, living in Northern California.

                    Participants

                    Kaiser Healthy Aging and Diverse Life Experiences study baseline participants, aged 65 years and older (n=1661; including 403 Asian-American, 338 Latino, 427 Black and 493 white participants).

                    Results

                    Most respondents (69%) reported one or more ACE, most frequently family illness (36%), domestic violence (23%) and parental divorce (22%). ACE count was not adversely associated with cognition overall (β=0.01; 95% CI -0.01 to 0.03), in any racial/ethnic group or for any age category of exposure. Pooling across all race/ethnicities, parent's remarriage (β=-0.11; 95% CI -0.20 to -0.03), mother's death (β=-0.18; 95% CI -0.30 to -0.07) and father's death (β=-0.11; 95% CI -0.20 to -0.01) were associated with worse cognition.

                    Conclusion

                    Adverse childhood exposures overall were not associated with worse cognition in older adults in a diverse sample, although three ACEs were associated with worse cognitive outcomes.
                      Cover page: Are adverse childhood experiences associated with late-life cognitive performance across racial/ethnic groups: results from the Kaiser Healthy Aging and Diverse Life Experiences study baseline
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