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Little is known about the relationships between demographic and economic social determinants of health and the probability of contracting COVID-19 in American Indian and Alaska Native (AI/AN) peoples. In addition, we do not know if and how tribal payments, unique to AI/AN peoples, are associated with the probability of contracting COVID-19. We surveyed 767 AI/AN patients of five geographically disparate health organizations that primarily served AI/AN peoples in urban settings between January and May of 2021. We used univariate modified Poisson regressions to estimate the influence of age, gender, household composition, education, household income, and tribal payments on risk of contracting COVID-19, with results presented as both risk and risk difference. Fifteen percent of the sample contracted COVID-19, and individuals who lived in households with two or more generations had an 11-percentage point elevated risk of contracting COVID-19 compared to those who lived alone. Twenty-seven percent of participants received tribal payments; receipt was associated with seven percentage points (change from 18% probability to 11% probability) lower risk of contracting COVID-19. Our findings showed interventions specifically designed to reduce the spread of COVID-19 in multigenerational households, and regular tribal payments may help improve health outcomes in urban AI/AN populations. [ABSTRACT FROM AUTHOR]

ABSTRACT Background Objective Methods Results Conclusions Empirically evaluating the potential impact of recall bias on observed associations of prenatal medication exposure is crucial.We sought to assess the effects of exposure misclassification on previous studies of the use of nonsteroidal anti‐inflammatory drugs (NSAIDs) in early pregnancy and increased risk of amniotic band syndrome (ABS).Using data from the National Birth Defects Prevention Study (NBDPS) on births from 1997 to 2011, we included 189 mothers of infants with ABS and 11,829 mothers of infants without congenital anomalies. We identified external studies of medication use during pregnancy to obtain validity parameters for a probabilistic bias analysis to adjust for exposure misclassification. Due to uncertainty about the transportability of these parameters, we conducted multidimensional bias analyses to explore combinations of values on the results.When we assumed higher specificity in cases or higher sensitivity in controls, misclassification‐adjusted estimates suggested confounding‐adjusted estimates were attenuated. However, in a few instances, when we assumed greater sensitivity in the cases than the controls (and Sp ≥ 0.9), the misclassification‐adjusted estimates suggested upward bias in the confounding‐adjusted estimates.Results from our bias analysis highlighted that the magnitude of bias depended on the mechanism and the extent of misclassification. However, the parameters available from the validation studies were not directly applicable to our study. In the absence of reliable validation studies, considering mechanisms of bias and simulation studies to outline combinations of plausible scenarios to better inform conclusions on the effects of these medications on pregnancy outcomes remains important. [ABSTRACT FROM AUTHOR]

Background: Childhood maltreatment is associated with elevated adult weight. It is unclear whether this association extends to pregnancy, a critical window for the development of obesity. Methods: We examined associations of childhood maltreatment histories with prepregnancy body mass index (BMI) and gestational weight gain among women who had participated for >20 years in a longitudinal cohort. At age 26-35 years, participants reported childhood maltreatment (physical, sexual, and emotional abuse; emotional neglect) and, 5 years later, about prepregnancy weight and gestational weight gain for previous pregnancies (n = 656). Modified Poisson regression models were used to estimate associations of maltreatment history with prepregnancy BMI and gestational weight gain z-scores, adjusting for sociodemographics. We used multivariate imputation by chained equations to adjust outcome measures for misclassification using data from an internal validation study. Results: Before misclassification adjustment, results indicated a higher risk of prepregnancy BMI ≥30 kg/m² in women with certain types of maltreatment (e.g., emotional abuse risk ratio = 2.4; 95% confidence interval: 1.5, 3.7) compared with women without that maltreatment type. After misclassification adjustment, estimates were attenuated but still modestly elevated (e.g., emotional abuse risk ratio = 1.7; 95% confidence interval: 1.1, 2.7). Misclassificationadjusted estimates for maltreatment associations with gestational weight gain z-scores were close to the null and imprecise. Conclusions: Findings suggest an association of maltreatment with prepregnancy BMI ≥30 kg/m² but not with high gestational weight gain. Results suggest a potential need for equitable interventions that can support all women, including those with maltreatment histories, as they enter pregnancy. [ABSTRACT FROM AUTHOR]

Background: Prepregnancy body mass index (BMI) and gestational weight gain (GWG) are determinants of maternal and child health. However, many studies of these factors rely on error-prone self-reported measures. Methods: Using data from Life-course Experiences And Pregnancy (LEAP), a US-based cohort, we assessed the validity of prepregnancy BMI and GWG recalled on average 8 years postpartum against medical record data treated as alloyed gold standard (“true”) values. We calculated probabilities of being classified into a self-reported prepregnancy BMI or GWG category conditional on one’s true category (analogous to sensitivities and specificities) and probabilities of truly being in each prepregnancy BMI or GWG category conditional on one’s self-reported category (analogous to positive and negative predictive values). Results: There was a tendency toward under-reporting prepregnancy BMI. Self-report misclassified 32% (95% confidence interval [CI] = 19%, 48%) of those in LEAP with truly overweight and 13% (5%, 27%) with obesity into a lower BMI category. Self-report correctly predicted the truth for 72% (55%, 84%) with self-reported overweight to 100% (90%, 100%) with self-reported obesity. For GWG, both under- and over-reporting were common; self-report misclassified 32% (15%, 55%) with truly low GWG as having moderate GWG and 50% (28%, 72%) with truly high GWG as moderate or low GWG. Self-report correctly predicted the truth for 45% (25%, 67%) with self-reported high GWG to 85% (76%, 91%) with selfreported moderate GWG. Misclassification of BMI and GWG varied across maternal characteristics. Conclusion: Findings can be used in quantitative bias analyses to estimate bias-adjusted associations with prepregnancy BMI and GWG. [ABSTRACT FROM AUTHOR]

Background: Opioid-related mortality is an important public health problem in the United States. Incidence estimates rely on death certificate data generated by health care providers and medical examiners. Opioid overdoses may be underreported when other causes of death appear plausible. We applied physician-elicited death certificate bias parameters to quantitative bias analyses assessing potential age-related differential misclassification in US opioid-related mortality estimates. Methods: We obtained cause-of-death data (US, 2017) from the National Center for Health Statistics and calculated crude opioid-related outpatient death counts by age category (25–54, 55–64, 65+). We elicited beliefs from 10 primary care physicians on sensitivity of opioid-related death classification from death certificates. We summarized elicited sensitivity estimates, calculated plausible specificity values, and applied resulting parameters in a probabilistic bias analysis. Results: Physicians estimated wide sensitivity ranges for classification of opioid-related mortality by death certificates, with lower estimated sensitivities among older age groups. Probabilistic bias analyses adjusting for physician-estimated misclassification indicated 3.1 times more (95% uncertainty interval: 1.2–23.5) opioid-related deaths than the observed death count in the 65+ age group. All age groups had substantial increases in bias-adjusted death counts. Conclusions: We developed and implemented a feasible method of eliciting physician expert opinion on bias parameters for sensitivity of a medical record-based death indicator and applied findings in quantitative bias analyses adjusting for differential misclassification. Our findings are consistent with the hypothesis that opioid-related mortality rates may be substantially underestimated, particularly among older adults, due to misclassification in cause-of-death data from death certificates. [ABSTRACT FROM AUTHOR]

Background: Binge drinking can result in various types of harms including traffic crashes. Bars and restaurants that serve alcohol to patrons who are obviously intoxicated (i.e., overservice) contribute to these crashes. One strategy to address overservice is place of last drink (POLD) where law enforcement officers responding to alcohol‐related incidents inquire about where the individuals last drank alcohol. This information may then be used to identify bars and restaurants that frequently overserve alcohol. There is limited evaluation of the effectiveness of POLD in reducing overservice, traffic crashes, and other harms. Methods: We evaluated the effects of a POLD initiative, developed by some law enforcement agencies in Minnesota (USA), on alcohol‐related traffic crashes from 2010 to 2019. Among 89 intervention (POLD) vs. comparison communities, we fit regression models with participation in POLD as the predictor. As secondary analyses, we fit models with POLD implementation level as the predictor (implementation levels were assessed via a survey of law enforcement agencies). We controlled for relevant community and agency characteristics. Results: In the model with participation in POLD as a predictor, there was little difference in the rate of total alcohol‐related crashes (rate ratio [RR] = 1.07, 95% CI: 0.85–1.34). In the model with level of implementation as a predictor, the rate of total alcohol‐related crashes was comparable between communities with high implementation and those with no implementation (RR = 0.89; 95% CI: 0.71–1.10). Similar results were seen for alcohol‐related crashes with nonfatal injury and property damage outcomes. Conclusions: This study found little evidence that the POLD initiative, as currently implemented, was associated with reductions in traffic crashes across communities in Minnesota. Further research could explore whether specific characteristics of POLD are particularly important and whether POLD could be combined with other strategies to reduce traffic crashes and other alcohol‐related harms. [ABSTRACT FROM AUTHOR]

The goal of this study was to reconstruct the historical respirable silica (RS) and respirable dust (RD) exposures of workers in the Minnesota taconite industry from 1955 to 2010 as part of several epidemiological studies for assessing the association between exposure to components of taconite dusts and the development of respiratory diseases. A job-exposure matrix (JEM) was developed that uses 9127 RS and 19 391 RD occupational hygiene historical measurements. Historical RS and RD data were extracted from several sources and were grouped into seven mines and then into eight departments [Concentrating, Crushing, Janitor, Mining, Office/control room, Pelletizing, Shop (mobile), and Shop (stationary)]. Within each department, we applied a two-level random-intercept regression model which assumes that the natural log of Y (RD or RS concentration) changes over time at a constant rate. Among all predicted RD and RS values, we found that larger RD values were located in the following departments: Crushing, Concentrating, Pelletizing, and Shop (mobile). Larger RS values were located only in either Crushing or Shop (mobile). The annual rates of change for historical RD and RS exposures were between −3.3 and 3.2%. The silica percentage in the dust varied by mine/department with the highest value of 29.3% in Mine F (Crushing) and the lowest value of 2.1% in Mine B (Pelletizing). The predicted historical RD and RS arithmetic mean exposures ranged between <0.075 and 3.14 mg m−3, and between <0.005 and 0.36 mg m−3, respectively. The result of this study is a JEM by mine, department, and year for RD and RS for epidemiological studies. [ABSTRACT FROM AUTHOR]

Objectives: An excess of mesothelioma has been previously observed in iron ore miners in Northeastern Minnesota. This study explored the potential association between mesothelioma and elongate mineral particle (EMP) exposures in the Minnesota taconite mining industry, examining both regulated and non-regulated EMP dimensions., Methods: A nested case-control study design within the Mineral Resources Health Assessment Programme cohort analysed 104 mesothelioma cases and 410 controls. Cumulative EMP exposures were assessed across various dimensional definitions, including regulated (National Institute for Occupational Safety and Health, NIOSH) and non-regulated EMPs. Conditional logistic regression models were applied to estimate mesothelioma risk linked to employment duration and cumulative EMP exposure while adjusting for potential confounding variables., Results: Consistent with earlier assessments, mesothelioma was associated with the number of years employed in the taconite industry (rate ratio (RR) 1.02, 95% CI 1.00 to 1.05) and cumulative NIOSH EMP exposure ((EMP/cc)×years) in taconite mining and processing (RR 1.20, 95% CI 0.99 to 1.46). Positive associations were also observed with mesothelioma and cumulative exposure to non-regulated EMPs., Conclusions: This study supported the main conclusions in the previous study that the data were consistent with an association between mesothelioma and employment duration and with NIOSH EMP exposure in taconite mining and processing. However, the 95% CI indicates the data are also compatible with a null effect. Given the limitations we cite in the manuscript, additional study is needed to clarify the effect. Additionally, this study found possible evidence of a positive association between mesothelioma and cumulative exposure to Chatfield EMP, Suzuki EMP and Cleavage Fragments in the Minnesota taconite worker population., Competing Interests: Competing interests: None declared., (© Author(s) (or their employer(s)) 2025. No commercial re-use. See rights and permissions. Published by BMJ Group.)

Background: We evaluated the impact of systematic bias due to loss of heterozygosity (LOH) and incomplete phenotyping in studies examining the relationship between CYP2D6 variants and breast cancer recurrence among women treated with tamoxifen., Methods: We performed a systematic review of the literature on tamoxifen, CYP2D6 variants, and breast cancer recurrence. A quantitative bias analysis was performed to adjust for LOH and incomplete phenotyping. Bias-adjusted results were then combined in a meta-analysis., Results: Thirty-three studies informed the bias analysis and meta-analysis on CYP2D6 variants and breast cancer recurrence and/or mortality. An unadjusted meta-analysis suggested increased risk of recurrence and/or mortality for poor relative to normal metabolizers [RR = 1.28; 95% simulation interval (SI), 1.04-1.58] with substantial heterogeneity (I2 = 27%; P for heterogeneity = 0.07). Adjusting for LOH and incomplete genotyping resulted in a slight change in the effect estimate and a decrease in heterogeneity (RR = 1.34; 95% SI, 1.10-1.63; I2 = 0%; P for heterogeneity = 0.17). Intermediate metabolizers had a slightly increased risk of recurrence and/or mortality relative to normal metabolizers (RR = 1.15; 95% SI, 1.00-1.34; I2 = 0%; P for heterogeneity = 0.89)., Conclusions: Adjusting for biases such as LOH and incomplete genotyping reduced observed heterogeneity between studies. Individuals with poor CYP2D6 phenotypes were at increased risk for breast cancer outcomes compared with those with normal phenotypes., Impact: Reduction in CYP2D6 activity was associated with an increased risk of breast cancer recurrence and/or mortality, and results underscore the importance of quantitatively adjusting for biases when aggregating study results. See related In the Spotlight, p. 221., (©2024 American Association for Cancer Research.)

Background: A 2013 meta-analysis observed a protective association between overweight BMI (versus normal BMI) and all-cause mortality that was particularly strong in people aged ≥65. Estimates informing this meta-analysis were highly heterogeneous, and critics raised insufficient or inappropriate confounder adjustment in many studies as an explanation for the protective summary association. Using this topic as an example, we demonstrate a novel approach for external adjustment of individual studies for a uniform and sufficient confounder set before meta-analysis., Methods: We abstracted summary data on the 33 associations comprising the age ≥65 stratum of the 2013 meta-analysis. Using an external dataset (NHANES III), we derived covariates used in each study's multivariable model of the overweight-mortality association. We then calculated a bias factor to quantify the direction and magnitude of displacement of the ratio measure of association after changing from the original adjustment set to a sufficient adjustment set. After applying bias factors to adjust original associations, we compared summary results from random effects meta-analyses with and without such adjustment., Results: We reproduced the original meta-analysis of overweight-mortality estimates among older participants and found a protective association similar to that reported in 2013 (summary RR=0.88, 95% CI: 0.84, 0.92, I 2 =38.4%). After we simulated uniform adjustment of all 33 associations for a minimally sufficient confounder set (age, sex, and smoking status), the meta-analysis showed a similar summary association (summary RR=0.90, 95% CI: 0.86, 0.94), but with reduced heterogeneity (I 2 =34.6%)., Conclusion: Simulated uniform adjustment for a sufficient confounder set may improve rigor and promote consensus in meta-analysis., Competing Interests: Conflict of interest: No author declares a conflict of interest., (Copyright © 2024 Wolters Kluwer Health, Inc. All rights reserved.)

Mismeasurement of a dichotomous outcome yields an unbiased risk ratio estimate when there are no false positive cases (perfect specificity) and when sensitivity is non-differential with respect to exposure status. In studies where these conditions are expected, quantitative bias analysis may be considered unnecessary. We conducted a simulation study to explore the robustness of this special case to small departures from perfect specificity and stochastic departures from non-differential sensitivity. We observed substantial bias of the risk ratio with specificity values as high at 99.8%. The magnitude of bias increased directly with the true underlying risk ratio and was markedly stronger at lower baseline risk. Stochastic departure from non-differential sensitivity also resulted in substantial bias in most simulated scenarios; downward bias prevailed when sensitivity was higher among unexposed compared with exposed, and upward bias prevailed when sensitivity was higher among exposed compared with unexposed. Our results show that seemingly innocuous departures from perfect specificity (e.g., 0.2%) and from non-differential sensitivity can yield substantial bias of the risk ratio under outcome misclassification. We present a web tool permitting easy exploration of this bias mechanism under user-specifiable study scenarios., (© The Author(s) 2024. Published by Oxford University Press on behalf of the Johns Hopkins Bloomberg School of Public Health. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.)

Direct oral anticoagulants (DOACs; rivaroxaban, apixaban) and warfarin are approved for venous thromboembolism (VTE) treatment. Few direct comparisons of DOACs on risk of mortality among VTE patients exist, and for patients with concomitant conditions (e.g., kidney and liver disease) clinical guidelines are unclear. We evaluated 6-month all-cause mortality by anticoagulant prescribed for primary treatment of VTE. Using Medicare 20% sample data, we created a propensity score matched analytic dataset of 47,860 beneficiaries with non-cancer incident VTE. We used Cox regression to estimate adjusted hazard ratios (HRs) of OACs with 6-month mortality, and tested interactions by liver/kidney disease. There were 3,422 deaths over 6 months of follow-up. In adjusted models, patients prescribed rivaroxaban [HR: 0.82 (95% CI: 0.76-0.90)] had lower rates of mortality versus warfarin. There was no association comparing apixaban to warfarin [HR: 0.96 (95% CI: 0.87-1.07)]. In head-to-head comparisons of apixaban versus rivaroxaban the HR was 1.14 (95% CI: 1.01-1.28)]. Findings were similar among patients with liver and kidney disease. Overall, risk of death was similar by OAC prescribed. Though it is possible residual confounding remained, there was some suggestion of lower risk with rivaroxaban than warfarin. DOACs appear safe among VTE patients with concomitant kidney or liver disease., (© The Author(s) 2024. Published by Oxford University Press on behalf of the Johns Hopkins Bloomberg School of Public Health. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.)

Background: Food insecurity is an important social determinant of health that was exacerbated by the COVID-19 pandemic. Both food insecurity and COVID-19 infection disproportionately affect racial and ethnic minority groups, particularly American Indian and Alaska Native communities; however, there is little evidence as to whether food insecurity is associated with COVID-19 infection or COVID-19 preventive behaviors such as vaccination uptake. The purpose of this study was to evaluate associations between food insecurity, COVID-19 infection, and vaccination status among urban American Indian and Alaska Native adults seen at 5 clinics serving urban Native people., Methods: In partnership with health organizations in Alaska, Colorado, Kansas, Minnesota, and New Mexico, the study team conducted a cross-sectional survey in 2021 to assess food security status and attitudes, barriers, and facilitators for COVID-19 testing and vaccination. Logistic regression was used to examine the association of food security status with sociodemographic factors and COVID-19 infection and vaccination status. Marginal standardization was applied to present results as prevalence differences., Results: Among 730 American Indian and Alaska Native adults, the prevalence of food insecurity measured during the pandemic was 38%. For participants who reported persistent food security status before and during the pandemic (n=588), the prevalence of food insecurity was 25%. Prevalence of COVID-19 infection and vaccination did not vary by food security status after adjustment for confounders., Conclusions: High rates of food insecurity among American Indian and Alaska Native communities likely increased during the COVID-19 pandemic. However, despite the high prevalence of food insecurity, community-led efforts to reduce COVID-19 infection and increase vaccination uptake across Indian Health Service and Tribal healthcare facilities may have mitigated the negative impacts of the pandemic for families experiencing food insecurity. These successful approaches serve as an important reference for future public health efforts that require innovative strategies to improve overall health in AIAN communities., Competing Interests: Competing interests The authors declare that they have no competing interests.