Epidemiology最新文献

Background: Hepatitis C virus (HCV) infection is a public health concern, with people living with opioid use disorder having a higher risk of infection. Despite the cooccurrence of HCV and opioid use disorder, little is known about the treatment patterns for the disorder in this population. This study characterized opioid agonist therapy adherence trajectories over 15 months following opioid agonist therapy initiation among people living with HCV and opioid use disorder and described the baseline characteristics of the patients within distinct opioid agonist therapy adherence trajectories.

Methods: We used Merative MarketScan healthcare claims data from 2015 to 2019 to identify distinct medication treatment adherence trajectories via growth mixture modeling among 5,495 people who initiated opioid agonist therapy for opioid use disorder and were living with HCV.

Results: Our models identified three distinct opioid agonist therapy adherence trajectories over the 15 months of follow-up. We named these trajectories rapidly declining opioid agonist therapy adherence (class 1; N = 1,904; 35%), steadily declining opioid agonist therapy adherence (class 2; N = 2,150; 39%), and consistently high opioid agonist therapy adherence (N = 1,441; 26%). People in the consistently high adherence group were older, more likely to be women (vs. men), White (vs. Black), had HCV direct-acting antiviral treatment during the baseline period, and had the lowest prevalence of nonopioid substance use diagnoses.

Conclusions: These results may inform support for populations with elevated baseline risk of low opioid agonist therapy adherence during follow-up.

Simulation studies are used to evaluate and compare the properties of statistical methods in controlled experimental settings. In most cases, performing a simulation study requires knowledge of the true value of the parameter, or estimand, of interest. However, in many simulation designs, the true value of the estimand is difficult to compute analytically. Here, we illustrate the use of Monte Carlo integration to compute true estimand values in simple and more complex simulation designs. We provide general pseudocode that can be replicated in any software program of choice to demonstrate key principles in using Monte Carlo integration in two scenarios: a simple three-variable simulation where interest lies in the marginally adjusted odds ratio and a more complex causal mediation analysis where interest lies in the controlled direct effect in the presence of mediator-outcome confounders affected by the exposure. We discuss general strategies that can be used to minimize Monte Carlo error and to serve as checks on the simulation program to avoid coding errors. R programming code is provided illustrating the application of our pseudocode in these settings.

Social contact studies are used in infectious disease epidemiology to infer a contact matrix , having the mean number of contacts between individuals of different age groups as elements. However, does not capture the (often large) variation in the number of contacts within each age group, information is also available in social contact studies. Here, we include such variation by separating each age group into two halves: the socially active (having many contacts) and the socially less active (having fewer contacts). The extended contact matrix and its associated epidemic model show that acknowledging variation in social activity within age groups has a substantial impact on the basic reproduction number, , and the final fraction getting infected if the epidemic takes off, . In fact, variation in social activity is more important for data fitting than allowing for different age groups. A difficulty with variation in social activity, however, is that social contact studies typically lack information on whether mixing with respect to social activity is assortative (when socially active mainly have contact with other socially active individuals) or not. Our analysis shows that accounting for variation in social activity improves model predictability, yielding more accurate expressions for and irrespective of whether such mixing is assortative, but different assumptions on assortativity give rather different outputs. Future social contact studies should, therefore, also try to infer the degree of assortativity (with respect to social activity) between peers and their contacts.

Background: In this article, we test the hypothesis that SARS-CoV-2 infection and the COVID-19 pandemic period had stronger adverse implications for perinatal outcomes among marginalized racial and ethnic groups in California.

Methods: We used California birth certificates and hospital data from 2019 to 2021 to estimate marginal risk differences for SARS-CoV-2 infection and the COVID-19 pandemic period in relation to perinatal outcomes for Asian, Black, Hispanic, Multiracial, and White pregnant people using targeted maximum likelihood estimation.

Results: Among 849,401 deliveries, there were racial and ethnic disparities in the burden of SARS-CoV-2 infection and perinatal outcomes and in the magnitudes of risk associated with SARS-CoV-2 infection and the COVID-19 pandemic. Hispanic pregnant people had the highest incidence of SARS-CoV-2 infection. Asian and Black pregnant people had the greatest marginal risk differences for multiple outcomes, particularly outcomes already disproportionately experienced by these groups.

Conclusions: Risks from SARS-CoV-2 infection and the COVID-19 pandemic period on perinatal outcomes were disproportionately experienced by marginalized racial and ethnic groups. Differential burdens of infection and larger risks experienced with pandemic exposures were associated with worse perinatal outcomes for Asian, Black, and Hispanic pregnant people in California compared with those for White pregnant people.

Background: Although maternal exposure to artificial light at night has shown negative associations with pregnancy outcomes, its impact on the risk of congenital heart disease remains unclear. This study examined the association between maternal exposure to artificial light at night during pregnancy and occurrence of congenital heart disease in offspring, considering potential interactions with sociodemographics.

Methods: We included newborns diagnosed prenatally with congential heart disease and healthy volunteers from 21 cities in southern China. Using satellite data, we estimated annual exposure to artificial light at night at maternal residential addresses during pregnancy. We evaluated associations using marginal structural logistic models and assessed multiplicative and additive interaction between sociodemographics and light exposure.

Results: Each 1-unit increase in light at night during pregnancy was associated with an elevated risk of total congenital heart disease (odds ratio [OR]: 1.2, 95% confidence interval [CI]: 1.2, 1.3), and of almost all specific disease subtypes, in offspring. Using quartiles of light at night confirmed a monotonic dose-response relationship between exposure and disease. The association was more pronounced in severe disease. Some sociodemographic characteristics modified associations between light at night and congenital heart disease, with detrimental associations more pronounced among offspring of mothers with lower education (OR: 1.3, 95% CI: 1.2, 1.3), lower income (OR: 1.2, 95% CI: 1.1, 1.3), or being usual residents (OR: 1.3, 95% CI: 1.2, 1.4), based on the continuous model.

Conclusions: Maternal exposure to artificial light at night during pregnancy was substantially associated with an elevated risk of congenital heart disease in offspring. This association was more pronounced among some sociodemographic groups.

Background: Longevity has stagnated during the last decade in the United States, but this stagnation has not been homogeneous. We aimed to explore the spatial variation of life expectancy by sex across commuting zones in the contiguous United States from 1990 to 2019.

Methods: We computed sex-specific life expectancy at birth for US commuting zones across six 5-year periods (1990-1994 to 2015-2019) and examined the spatial variability of life expectancy and clustering of baseline and changes in life expectancy during the study period.

Results: Overall life expectancy increased over time for both males and females and recently stagnated, while variability has increased for females. Regardless of sex, commuting zones with low baseline life expectancy that worsened over time were concentrated in the Appalachian region and Deep South. Areas with high baseline life expectancy and improved the most over time were scattered throughout the Midwest, Northwest, and West.

Conclusion: The recent stagnation in life expectancy reflects wide spatial heterogeneity in changes in longevity. Growing spatial differences in longevity render males and females in the South, specifically the Appalachia and along the Mississippi River, to consistently live disproportionate short lives. Further studies should explore the contribution of different causes of death and the potential contextual drivers of these patterns.