Epidemiology最新文献

Purpose: Breast cancer has an average 10-year relative survival reaching 84%. This favorable survival is due, in part, to the introduction of biomarker-guided therapies. We estimated the population-level effect of the introduction of two adjuvant therapies-tamoxifen and trastuzumab-on recurrence using the trend-in-trend pharmacoepidemiologic study design.

Methods: We ascertained data on women diagnosed with nonmetastatic breast cancer who were registered in the Danish Breast Cancer Group clinical database. We used the trend-in-trend design to estimate the population-level effect of the introduction of (1) tamoxifen for postmenopausal women with estrogen receptor (ER)-positive breast cancer in 1982, (2) tamoxifen for premenopausal women diagnosed with ER-positive breast cancer in 1999, and (3) trastuzumab for women <60 years diagnosed with human epidermal growth factor receptor 2-positive breast cancer in 2007.

Results: For the population-level effect of the introduction of tamoxifen among premenopausal women diagnosed with ER-positive breast cancer in 1999, the risk of recurrence decreased by nearly one-half (OR = 0.52), consistent with evidence from clinical trials; however, the estimate was imprecise (95% confidence interval [CI] = 0.25, 1.85). We observed an imprecise association between tamoxifen use and recurrence from the time it was introduced in 1982 (OR = 1.24 95% CI = 0.46, 5.11), inconsistent with prior knowledge from clinical trials. For the introduction of trastuzumab in 2007, the estimate was also consistent with trial evidence, though imprecise (OR = 0.51; 95% CI = 0.21, 22.4).

Conclusions: We demonstrated how novel pharmacoepidemiologic analytic designs can be used to evaluate the routine clinical care and effectiveness of therapeutic advancements in a population-based setting while considering some limitations of the approach.

Background: Spatial epidemiology has emerged as an important subfield of epidemiology over the past quarter century. We trace the origins of spatial epidemiology and note that its emergence coincided with technological developments in spatial statistics and geography. We hypothesize that spatial epidemiology makes important contributions to descriptive epidemiology and analytic risk-factor studies but is not yet aligned with epidemiology's current focus on causal inference and intervention.

Methods: We conducted a systematic review of studies indexed in PubMed that used the term "spatial epidemiolog*" in the title, abstract, or keywords. Excluded articles were not written in English, examined disease in animals, or reported biologic pathogen distribution only. We coded the included papers into five categories (review, demonstration of method, descriptive, analytic, and intervention) and recorded the unit of analysis (i.e., individual vs. ecological). We additionally examined articles coded as analytic ecologic studies using scales for lexical content.

Results: A total of 482 articles met the inclusion criteria, including 76 reviews, 117 demonstrations of methods, 122 descriptive studies, 167 analytic studies, and 0 intervention studies. Demonstration studies were most common from 2006 to 2014, and analytic studies were most common after 2015. Among the analytic ecologic studies, those published in later years used more terms relevant to spatial statistics (incidence rate ratio =1.3; 95% confidence interval [CI] = 1.1, 1.5) and causal inference (incidence rate ratio =1.1; 95% CI = 1.1, 1.2).

Conclusions: Spatial epidemiology is an important and growing subfield of epidemiology. We suggest a re-orientation to help align its practice with the goals of contemporary epidemiology.

Background: The UK delivered its first "booster" COVID-19 vaccine doses in September 2021, initially to individuals at high risk of severe disease, then to all adults. The BNT162b2 Pfizer-BioNTech vaccine was used initially, then also Moderna mRNA-1273.

Methods: With the approval of the National Health Service England, we used routine clinical data to estimate the effectiveness of boosting with BNT162b2 or mRNA-1273 compared with no boosting in eligible adults who had received two primary course vaccine doses. We matched each booster recipient with an unboosted control on factors relating to booster priority status and prior COVID-19 immunization. We adjusted for additional factors in Cox models, estimating hazard ratios up to 182 days (6 months) following booster dose. We estimated hazard ratios overall and within the following periods: 1-14, 15-42, 43-69, 70-97, 98-126, 127-152, and 155-182 days. Outcomes included a positive SARS-CoV-2 test, COVID-19 hospitalization, COVID-19 death, non-COVID-19 death, and fracture.

Results: We matched 8,198,643 booster recipients with unboosted controls. Adjusted hazard ratios over 6-month follow-up were: positive SARS-CoV-2 test 0.75 (0.74, 0.75); COVID-19 hospitalization 0.30 (0.29, 0.31); COVID-19 death 0.11 (0.10, 0.14); non-COVID-19 death 0.22 (0.21, 0.23); and fracture 0.77 (0.75, 0.78). Estimated effectiveness of booster vaccines against severe COVID-19-related outcomes peaked during the first 3 months following the booster dose. By 6 months, the cumulative incidence of positive SARS-CoV-2 test was higher in boosted than unboosted individuals.

Conclusions: We estimate that COVID-19 booster vaccination, compared with no booster vaccination, provided substantial protection against COVID-19 hospitalization and COVID-19 death but only limited protection against positive SARS-CoV-2 test. Lower rates of fracture in boosted than unboosted individuals may suggest unmeasured confounding. Observational studies should report estimated vaccine effectiveness against nontarget and negative control outcomes.

Background: Cannabis exposures reported to the California Poison Control System increased following the initiation of recreational cannabis sales on 1 January 2018 (i.e., "commercialization"). We evaluated whether local cannabis control policies adopted by 2021 were associated with shifts in harmful cannabis exposures.

Methods: Using cannabis control policies collected for all 539 California cities and counties in 2020-2021, we applied a differences-in-differences design with negative binomial regression to test the association of policies with harmful cannabis exposures reported to California Poison Control System (2011-2020), before and after commercialization. We considered three policy categories: bans on storefront recreational retail cannabis businesses, overall restrictiveness, and specific recommended provisions (restricting product types or potency, packaging and labeling restrictions, and server training requirements).

Results: Localities that ultimately banned storefront recreational retail cannabis businesses had fewer harmful cannabis exposures for children aged <13 years (rate ratio = 0.82; 95% confidence interval = 0.65, 1.02), but not for people aged >13 years (rate ratio = 0.97; 95% confidence interval = 0.85, 1.11). Of 167 localities ultimately permitting recreational cannabis sales, overall restrictiveness was not associated with harmful cannabis exposures among children aged <13 years, but for people aged >13 years, a 1-standard deviation increase in ultimate restrictiveness was associated with fewer harmful cannabis exposures (rate ratio = 0.93; 95% confidence interval = 0.86, 1.01). For recommended provisions, estimates were generally too imprecise to detect associations with harmful cannabis exposures.

Conclusion: Bans on storefront retail and other restrictive approaches to regulating recreational cannabis may be associated with fewer harmful cannabis exposures for some age groups following statewide commercialization.

Estimating the instantaneous reproduction number ( ) in near real time is crucial for monitoring and responding to epidemic outbreaks on a daily basis. However, such estimates often suffer from bias due to reporting delays inherent in surveillance systems. We propose a fast and flexible Bayesian methodology to overcome this challenge by estimating while taking into account reporting delays. Furthermore, the method naturally takes into account the uncertainty associated with the nowcasting of cases to get a valid uncertainty estimation of the nowcasted reproduction number. We evaluate the proposed methodology through a simulation study and apply it to COVID-19 incidence data in Belgium.

Background: Intervention packages may result in a greater public health impact than single interventions. Understanding the separate impact of each component on the overall package effectiveness can improve intervention delivery.

Methods: We adapted an approach to evaluate the effects of a time-varying intervention package in a network-randomized study. In some network-randomized studies, only a subset of participants in exposed networks receive the intervention themselves. The spillover effect contrasts average potential outcomes if a person was not exposed to themselves under intervention in the network versus no intervention in a control network. We estimated the effects of components of the intervention package in HIV Prevention Trials Network 037, a Phase III network-randomized HIV prevention trial among people who inject drugs and their risk networks using marginal structural models to adjust for time-varying confounding. The index participant in an intervention network received a peer education intervention initially at baseline, then boosters at 6 and 12 months. All participants were followed to ascertain HIV risk behaviors.

Results: There were 560 participants with at least one follow-up visit, 48% of whom were randomized to the intervention, and 1,598 participant visits were observed. The spillover effect of the boosters in the presence of initial peer education training was a 39% rate reduction (rate ratio = 0.61; 95% confidence interval = 0.43, 0.87).

Conclusions: These methods will be useful for evaluating intervention packages in studies with network features.

Background: We evaluated whether participants in the landmark Action to Control Cardiovascular Risk in Diabetes (ACCORD) trial represent US adults aged ≥40 with diabetes.

Methods: Using the nationally representative 2017-2020 prepandemic National Health and Nutrition Examination Survey data, we made operational definitions of ACCORD eligibility criteria. We calculated the percentage of individuals aged ≥40 with diabetes and HbA1c ≥ 6.0% or ≥ 7.5% who met operational ACCORD eligibility criteria.

Results: Applying survey sampling weights to 715 National Health and Nutrition Examination Survey participants aged ≥40 with diabetes and HbA1c ≥ 6.0% (representing 29,717,406 individuals), 12% (95% confidence interval [CI] = 8%, 18%) met the operational ACCORD eligibility criteria. Restricting to HbA1c ≥ 7.5%, 39% (95% CI = 28%, 51%) of respondents met the operational ACCORD eligibility criteria.

Conclusions: ACCORD represented a minority of US middle-aged and older adults with diabetes. Given the differential risk profile between ACCORD participants and the general population with diabetes, extrapolating the trial findings may not be appropriate.

Background: The largest case-control study (Interphone study) investigating glioma risk related to mobile phone use showed a J-shaped relationship with reduced relative risks for moderate use and a 40% increased relative risk among the 10% heaviest regular mobile phone users, using a categorical risk model based on deciles of lifetime duration of use among ever regular users.

Methods: We conducted Monte Carlo simulations examining whether the reported estimates are compatible with an assumption of no effect of mobile phone use on glioma risk when the various forms of biases present in the Interphone study are accounted for. Four scenarios of sources of error in self-reported mobile phone use were considered, along with selection bias. Input parameters used for simulations were those obtained from Interphone validation studies on reporting accuracy and from using a nonresponse questionnaire.

Results: We found that the scenario simultaneously modeling systematic and random reporting errors produced a J-shaped relationship perfectly compatible with the observed relationship from the main Interphone study with a simulated spurious increased relative risk among heaviest users (odds ratio = 1.91) compared with never regular users. The main determinant for producing this J shape was higher reporting error variance in cases compared with controls, as observed in the validation studies. Selection bias contributed to the reduced risks as well.

Conclusions: Some uncertainty remains, but the evidence from the present simulation study shifts the overall assessment to making it less likely that heavy mobile phone use is causally related to an increased glioma risk.