Showing papers in "Epidemiology in 2019"
TL;DR: This research presents a novel probabilistic approach that allows us to assess the importance of knowing the carrier and removal status of canine coronavirus as a source of infection for other animals.
Abstract: Background:Researchers increasingly use social contact data to inform models for infectious disease spread with the aim of guiding effective policies about disease prevention and control In this article, we undertake a systematic review of the study design, statistical analyses, and outcomes of the
147 citations
TL;DR: A methodologic framework to estimate future health impacts under climate change scenarios based on a defined set of assumptions and advanced statistical techniques developed in time-series analysis in environmental epidemiology is presented.
Abstract: Reliable estimates of future health impacts due to climate change are needed to inform and contribute to the design of efficient adaptation and mitigation strategies. However, projecting health burdens associated to specific environmental stressors is a challenging task because of the complex risk patterns and inherent uncertainty of future climate scenarios. These assessments involve multidisciplinary knowledge, requiring expertise in epidemiology, statistics, and climate science, among other subjects. Here, we present a methodologic framework to estimate future health impacts under climate change scenarios based on a defined set of assumptions and advanced statistical techniques developed in time-series analysis in environmental epidemiology. The proposed methodology is illustrated through a step-by-step hands-on tutorial structured in well-defined sections that cover the main methodological steps and essential elements. Each section provides a thorough description of each step, along with a discussion on available analytical options and the rationale on the choices made in the proposed framework. The illustration is complemented with a practical example of study using real-world data and a series of R scripts included as Supplementary Digital Content; http://links.lww.com/EDE/B504, which facilitates its replication and extension on other environmental stressors, outcomes, study settings, and projection scenarios. Users should critically assess the potential modeling alternatives and modify the framework and R code to adapt them to their research on health impact projections.
77 citations
TL;DR: It is concluded that selection bias can have a major effect on an IV analysis, and further research is needed on how to conduct sensitivity analyses when selection depends on unmeasured data.
Abstract: Participants in epidemiologic and genetic studies are rarely true random samples of the populations they are intended to represent, and both known and unknown factors can influence participation in a study (known as selection into a study). The circumstances in which selection causes bias in an inst
73 citations
TL;DR: In this paper, the authors show that the magnitude of bias due to selection can be bounded by simple expressions defined by parameters characterizing the relationships between unmeasured factors responsible for the bias and the measured variables.
Abstract: When epidemiologic studies are conducted in a subset of the population, selection bias can threaten the validity of causal inference. This bias can occur whether or not that selected population is the target population and can occur even in the absence of exposure-outcome confounding. However, it is often difficult to quantify the extent of selection bias, and sensitivity analysis can be challenging to undertake and to understand. In this article, we demonstrate that the magnitude of the bias due to selection can be bounded by simple expressions defined by parameters characterizing the relationships between unmeasured factor(s) responsible for the bias and the measured variables. No functional form assumptions are necessary about those unmeasured factors. Using knowledge about the selection mechanism, researchers can account for the possible extent of selection bias by specifying the size of the parameters in the bounds. We also show that the bounds, which differ depending on the target population, result in summary measures that can be used to calculate the minimum magnitude of the parameters required to shift a risk ratio to the null. The summary measure can be used to determine the overall strength of selection that would be necessary to explain away a result. We then show that the bounds and summary measures can be simplified in certain contexts or with certain assumptions. Using examples with varying selection mechanisms, we also demonstrate how researchers can implement these simple sensitivity analyses. See video abstract at, http://links.lww.com/EDE/B535.
68 citations
TL;DR: Higher levels of some airborne metals, specifically mercury, cadmium, and lead, were associated with a higher risk of postmenopausal breast cancer.
Abstract: Background:Toxic metals show evidence of carcinogenic and estrogenic properties. However, little is known about the relationship between airborne metals and breast cancer. We evaluated the risk of breast cancer in relation to exposure to toxic metallic substances in air, individually and combined, i
66 citations
TL;DR: The literature evidence showed that smoking was consistently associated with higher risk of hospital admissions after influenza infection, but the results for ICU admissions and deaths were less conclusive because of the limited number of studies.
Abstract: Background Although smoking has been recognized as a risk factor for many respiratory diseases, its effects of influenza-associated morbidity and mortality remain controversial. We conducted a systematic review and meta-analysis to assess the impact of smoking on influenza-associated hospital admissions, intensive care unit (ICU) admissions, and deaths. Methods We searched the databases of PubMed, CINAHL, EMBASE, and the China National Knowledge Infrastructure for all observational studies published between 1 January 2000 and 30 November 2017 on ever-active/secondhand smoking and influenza-associated hospital admissions, ICU admissions, and deaths. We pooled data using random effect models. Results The initial search retrieved 7495 articles, of which 20 studies were included for systematic review, and 12 studies (eight case-control studies, two cohort studies, and two cross-sectional studies) with 18612 subjects were included in meta-analysis. The overall quality of selected studies was moderate. Ever-active smokers had higher odds of hospital admissions (odds ratio [OR] = 1.5; 95% confidence interval [CI] = 1.3, 1.7) and ICU admissions (OR 2.2; 95% CI = 1.4, 3.4) after influenza infections, as compared with never smokers. No association was observed between ever-active smoking and influenza-associated deaths. We found a positive association between secondhand smoking and influenza-associated hospital admissions, but only in children below 15 years of age. Conclusions The literature evidence showed that smoking was consistently associated with higher risk of hospital admissions after influenza infection, but the results for ICU admissions and deaths were less conclusive because of the limited number of studies.
65 citations
TL;DR: The use of test-negative designs may not completely resolve all potential biases, but they are a valid study design option, and will in some circumstances lead to less bias, as well as often being the most practical option.
Abstract: Test-negative studies recruit cases who attend a healthcare facility and test positive for a particular disease; controls are patients undergoing the same tests for the same reasons at the same healthcare facility and who test negative. The design is often used for vaccine efficacy studies, but not exclusively, and has been posited as a separate type of study design, different from case-control studies because the controls are not sampled from a wider source population. However, the design is a special case of a broader class of case-control designs that identify cases and sample "other patient" controls from the same healthcare facilities. Therefore, we consider that new insights into the test-negative design can be obtained by viewing them as case-control studies with "other patient" controls; in this context, we explore differences and commonalities, to better define the advantages and disadvantages of the test-negative design in various circumstances. The design has the advantage of similar participation rates, information quality and completeness, referral/catchment areas, initial presentation, diagnostic suspicion tendencies, and preferences by doctors. Under certain assumptions, valid population odds ratios can be estimated with the test-negative design, just as with case-control studies with "other patient" controls. Interestingly, directed acyclic graphs (DAGs) are not completely helpful in explaining why the design works. The use of test-negative designs may not completely resolve all potential biases, but they are a valid study design option, and will in some circumstances lead to less bias, as well as often the most practical one.
65 citations
TL;DR: High total metals were associated with lower HbA1c, leptin, and systolic blood pressure, and with higher adiponectin and non-HDL cholesterol, while essential metals were more strongly associated with cardio-metabolic risk than were nonessential metals.
Abstract: Background:Trace metal concentrations may affect cardiometabolic risk, but the role of prenatal exposure is unclear. We examined (1) the relation between blood metal concentrations during pregnancy and child cardiometabolic risk factors; (2) overall effects of metals mixture (essential vs. nonessent
63 citations
TL;DR: Algorithmic diagnoses provide a cost-effective way to conduct dementia research, however, naïve use of existing algorithms in disparities or risk factor research may induce nonconservative bias.
Abstract: Background:Dementia ascertainment is time-consuming and costly. Several algorithms use existing data from the US-representative Health and Retirement Study (HRS) to algorithmically identify dementia. However, relative performance of these algorithms remains unknown.Methods:We compared performance ac
60 citations
TL;DR: This research presents a novel probabilistic approach that allows us to assess the importance of knowing the carrier and removal status of canine coronavirus as a source of infection for other animals.
Abstract: It has been argued that survival bias may distort results in Mendelian randomization studies in older populations. Through simulations of a simple causal structure we investigate the degree to which instrumental variable (IV)-estimators may become biased in the context of exposures that affect survival. We observed that selecting on survival decreased instrument strength and, for exposures with directionally concordant effects on survival (and outcome), introduced downward bias of the IV-estimator when the exposures reduced the probability of survival till study inclusion. Higher ages at study inclusion generally increased this bias, particularly when the true causal effect was not equal to null. Moreover, the bias in the estimated exposure-outcome relation depended on whether the estimation was conducted in the one- or two-sample setting. Finally, we briefly discuss which statistical approaches might help to alleviate this and other types of selection bias. See video abstract at, http://links.lww.com/EDE/B589.
52 citations
TL;DR: In this article, early thyroid hormone disruption may contribute to the development of ADHD, and disrupted maternal thyroid hormone function has been associated with attention deficit hyperactivity disorder (ADHD) is the most common neurobehavioral disorder in children.
Abstract: Background:Attention deficit hyperactivity disorder (ADHD) is the most common neurobehavioral disorder in children, yet its etiology is poorly understood. Early thyroid hormone disruption may contribute to the development of ADHD. Disrupted maternal thyroid hormone function has been associated with
TL;DR: For nonpersistent chemicals, collecting and pooling three samples per day instead of all daily samples efficiently estimates exposures over a week or more, and can strongly limit attenuation bias for nonpersistant chemicals such as bisphenol A.
Abstract: Background:Within-subject biospecimens pooling can theoretically reduce bias in dose–response functions from biomarker-based studies when exposure assessment suffers from classical-type error. However, collecting many urine voids each day is cumbersome. We evaluated the empirical validity of a withi
TL;DR: This research presents a novel probabilistic approach that allows us to assess the importance of knowing the carrier and removal status of canine coronavirus, as a source of infection for other animals.
Abstract: During an infectious disease outbreak, timely information on the number of new symptomatic cases is crucial. However, the reporting of new cases is usually subject to delay due to the incubation period, time to seek care, and diagnosis. This results in a downward bias in the numbers of new cases by the times of symptoms onset towards the current day. The real-time assessment of the current situation while correcting for underreporting is called nowcasting. We present a nowcasting method based on bivariate P-spline smoothing of the number of reported cases by time of symptoms onset and delay. Our objective is to predict the number of symptomatic-but-not-yet-reported cases and combine these with the already reported symptomatic cases into a nowcast. We assume the underlying two-dimensional reporting intensity surface to be smooth. We include prior information on the reporting process as additional constraints: the smooth surface is unimodal in the reporting delay dimension, is (almost) zero at a predefined maximum delay and has a prescribed shape at the beginning of the outbreak. Parameter estimation is done efficiently by penalized iterative weighted least squares. We illustrate our method on a large measles outbreak in the Netherlands. We show that even with very limited information the method is able to accurately predict the number of symptomatic-but-not-yet-reported cases. This results in substantially improved monitoring of new symptomatic cases in real time.
TL;DR: It is suggested that short-term changes in ambient air pollution may be associated with greater risk of violent behavior, regardless of community type.
Abstract: Background:Violence is a leading cause of death and an important public health threat, particularly among adolescents and young adults. However, the environmental causes of violent behavior are not well understood. Emerging evidence suggests exposure to air pollution may be associated with aggressiv
TL;DR: A large validation of >3,000 patients in the register using clinical chart review in the context of the COMBAT-MS study improves the data quality for a central cohort of patients available for future epidemiologic research.
Abstract: The Swedish Multiple Sclerosis Register is a national register monitoring treatment and clinical course for all Swedish multiple sclerosis (MS) patients, with high coverage and close integration wi ...
TL;DR: Differing responses associated with changes in the two exposure metrics underscore the importance of isolating source-specific impacts from those due to total PM2.5 exposure.
Abstract: Background National, state, and local policies contributed to a 65% reduction in sulfur dioxide emissions from coal-fired power plants between 2005 and 2012 in the United States, providing an opportunity to directly quantify public health benefits attributable to these reductions under an air pollution accountability framework. Methods We estimate ZIP code-level changes in two different-but related-exposure metrics: total PM2.5 concentrations and exposure to coal-fired power plant emissions. We associate changes in 10 health outcome rates among approximately 30 million US Medicare beneficiaries with exposure changes between 2005 and 2012 using two difference-in-difference regression approaches designed to mitigate observed and unobserved confounding. Results Rates per 10,000 person-years of six cardiac and respiratory health outcomes-all cardiovascular disease, chronic obstructive pulmonary disorder, cardiovascular stroke, heart failure, ischemic heart disease, and respiratory tract infections-decreased by between 7.89 and 1.95 per (Equation is included in full-text article.)decrease in PM2.5, with comparable decreases in coal exposure leading to slightly larger rate decreases. Results for acute myocardial infarction, heart rhythm disorders, and peripheral vascular disease were near zero and/or mixed between the various exposure metrics and analyses. A secondary analysis found that nonlinearities in relationships between changing health outcome rates and coal exposure may explain differences in their associations. Conclusions The direct analyses of emissions reductions estimate substantial health benefits via coal power plant emission and PM2.5 concentration reductions. Differing responses associated with changes in the two exposure metrics underscore the importance of isolating source-specific impacts from those due to total PM2.5 exposure.
TL;DR: The observed results suggest that higher traffic-related air pollution levels are associated with pregnancy loss, with strongest estimates between the 10th and 20th gestational weeks.
Abstract: Background:Traffic-related air pollution has been linked to multiple adverse pregnancy outcomes. However, few studies have examined pregnancy loss, targeting losses identified by hospital records, a large limitation as it does not capture events not reported to the medical system.Methods:We used a n
TL;DR: Overall, there was not a specifically high period of susceptibility during pregnancy for preterm birth associated with heat wave exposure, but earlier gestational months might be key exposure windows for heat-wave-affected stillbirth.
Abstract: Background:Several studies have investigated the acute effects of high ambient temperature or extreme weather on preterm birth and stillbirth. However, little was known about whether there are any particular stages during which high ambient temperature or heat wave exposure is most harmful to fetal
TL;DR: A positive association between ambient PM2.5 and BP and hypertension in women was observed and no associations were observed with BC.
Abstract: Background:Evidence linking long-term exposure to particulate air pollution to blood pressure (BP) in high-income countries may not be transportable to low- and middle-income countries. We examined cross-sectional associations between ambient fine particulate matter (PM2.5) and black carbon (BC) wit
TL;DR: An exhaustive numerical search over simulated mediation effects found the bound for the bias held when the strength of confounding was described directly via the confounder-mediator relationship instead of via the conditional exposure-confounder relationship.
Abstract: Background Mediation analysis is a powerful tool for understanding mechanisms, but conclusions about direct and indirect effects will be invalid if there is unmeasured confounding of the mediator-outcome relationship. Sensitivity analysis methods allow researchers to assess the extent of this bias but are not always used. One particularly straightforward technique that requires minimal assumptions is nonetheless difficult to interpret, and so would benefit from a more intuitive parameterization. Methods We conducted an exhaustive numerical search over simulated mediation effects, calculating the proportion of scenarios in which a bound for unmeasured mediator-outcome confounding held under an alternative parameterization. Results In over 99% of cases, the bound for the bias held when we described the strength of confounding directly via the confounder-mediator relationship instead of via the conditional exposure-confounder relationship. Conclusions Researchers can conduct sensitivity analysis using a method that describes the strength of the confounder-outcome relationship and the approximate strength of the confounder-mediator relationship that, together, would be required to explain away a direct or indirect effect.
TL;DR: Among women from an infertility clinic, higher PM2.5 exposure was associated with lower ovarian reserve, raising concern that air pollution may accelerate reproductive aging.
Abstract: Background:An increasing number of studies have linked air pollution to decreased fertility. Whether this is due to an effect on ovarian reserve is unknown.Method:Our study included 632 women attending the Massachusetts General Hospital Fertility Center (2004–2015) who had a measured antral follicle
TL;DR: This research presents a novel probabilistic approach that allows us to assess the importance of knowing the carrier and removal status of canine coronavirus as a source of infection for other animals.
Abstract: BACKGROUND Super learning is an ensemble machine learning approach used increasingly as an alternative to classical prediction techniques. When implementing super learning, however, not tuning the hyperparameters of the algorithms in it may adversely affect the performance of the super learner. METHODS In this case study, we used data from a Canadian electronic prescribing system to predict when primary care physicians prescribed antidepressants for indications other than depression. The analysis included 73,576 antidepressant prescriptions and 373 candidate predictors. We derived two super learners: one using tuned hyperparameter values for each machine learning algorithm identified through an iterative grid search procedure and the other using the default values. We compared the performance of the tuned super learner to that of the super learner using default values ("untuned") and a carefully constructed logistic regression model from a previous analysis. RESULTS The tuned super learner had a scaled Brier score (R) of 0.322 (95% [confidence interval] CI = 0.267, 0.362). In comparison, the untuned super learner had a scaled Brier score of 0.309 (95% CI = 0.256, 0.353), corresponding to an efficiency loss of 4% (relative efficiency 0.96; 95% CI = 0.93, 0.99). The previously-derived logistic regression model had a scaled Brier score of 0.307 (95% CI = 0.245, 0.360), corresponding to an efficiency loss of 5% relative to the tuned super learner (relative efficiency 0.95; 95% CI = 0.88, 1.01). CONCLUSIONS In this case study, hyperparameter tuning produced a super learner that performed slightly better than an untuned super learner. Tuning the hyperparameters of individual algorithms in a super learner may help optimize performance.
TL;DR: In this population-based cohort of middle-aged and elderly participants, a higher adherence to a more plant-based, less animal-based diet was associated with less adiposity over time, irrespective of general healthfulness of the specific plant- and animal- based foods.
Abstract: Background:We aimed to explore whether adhering to a more plant-based diet, beyond strict vegan or vegetarian diets, may help prevent adiposity in a middle-aged and elderly population.Methods:We included 9,633 participants from the Rotterdam Study, a prospective cohort in the Netherlands. Dietary da
TL;DR: Behavior and dietary patterns can be used to identify lifestyle patterns that influence survival patterns following breast cancer diagnosis and to examine their association with subsequent survival.
Abstract: Background Few studies have examined the impact of lifestyle patterns on survival following breast cancer. We aimed to identify distinct lifestyle patterns based on five behavior/dietary exposures among a population-based sample of women diagnosed with breast cancer and to examine their association with subsequent survival. Methods In the Carolina Breast Cancer Study Phases I/II, we interviewed 1,808 women 20-74 years of age following diagnosis of invasive breast cancer. We determined vital status using the National Death Index (717 deaths, 427 from breast cancer; median follow-up 13.56 years). We assessed lifestyle patterns using a latent class analysis based on five behavioral and dietary exposures: current versus never/former smokers; low versus high vegetable and fruit intake; high and low/moderate, versus no alcohol consumption; and no and low/moderate, versus high regular physical activity. We used Cox regression to estimate covariate-adjusted hazard ratios (HRs) and 95% confidence intervals (CIs) for all-cause mortality, and cause-specific and subdistribution HRs for breast cancer-specific mortality within 5 years and 13 years postdiagnosis conditional on 5-year survival. Results We identified three distinct lifestyle patterns: healthy behavior and diet (n = 916); healthy behavior and unhealthy diet (n = 624); and unhealthy behavior and diet (n = 268). The unhealthy (vs. healthy) behavior and diet pattern was associated with a 13-year conditional all-cause mortality HR of 1.4 (95% CI = 1.1, 1.9) and with 13-year conditional breast cancer-specific and subdistribution HRs of 1.2 (95% CI = 0.79, 1.9) and 1.2 (95% CI = 0.77, 1.8), respectively. Conclusions Behavioral and dietary patterns can be used to identify lifestyle patterns that influence survival patterns following breast cancer diagnosis.
TL;DR: These results are consistent with the hypothesis that prescription drug monitoring programs are most effective in areas where people are likely to access opioids through medical providers, and varied across county levels of poverty and unemployment.
Abstract: Background:Prescription drug monitoring program are designed to reduce harms from prescription opioids; however, little is known about what populations benefit the most from these programs. We investigated how the relation between implementation of online prescription drug monitoring programs and ra
TL;DR: The future worldwide breast cancer burden will be strongly influenced by large predicted rises in incidence throughout parts of Asia due to an increasingly westernised lifestyle.
Abstract: Background: this communication presents the latest international descriptive epidemiological data for invasive breast cancer amongst women, including incidence and survival in the worldwide. Methods: the incidence statistics presented here for cancers worldwide were taken from the International Agency for Research on Cancer IARC: * The Cancer Incidence in five Continents Vol XI.* GLOBOCAN 2018. *The datas of Cancer survival are taken from: * Cancer survival in five continents, a worldwide population-based study or Concord Study Version 1, 2 and 3. These Concord studies included 101 population-based cancer registries in 31 countries for the period 1990-1994 and followed up to 1999 for the Concord Study 1, 279 population-based cancer registries in 67 countries for the period 1995-2009 for the Concord Study 2 and 412 cancer registries in 85 countries for the period 2000-14 for the Concord Study 3 Results: breast cancer is by far the most frequent cancer among women with an estimated. 2 million new cancer cases diagnosed in 2018 (23% of all cancers), and ranks second overall (10.9% of all cancers). It is now the most common cancer both in developed and developing regions.* Incidence rates vary from 19.3 per 100,000 women in Eastern Africa to 89.7 per 100,000 women in Western Europe, and are high (greater than 80 per 100,000) in developed regions of the world (except Japan) and low (less than 40 per 100,000) in most of the developing regions.For women diagnosed during 2010-14, the range of survival estimates is still wide in each continent, apart from North America and Oceania with 5-year net survival approached 90%. **Age-standardised 5-year net survival was 85% or higher in 25 countries, Costa Rica, Martinique, Canada and the USA, Israel, Japan and 16 European countries, Denmark, Finland, Iceland, Norway, Sweden, UK, Austria, Belgium, France, Germany, the Netherlands, and Switzerland and Italy, Malta, Portugal and Spain. **5-year survival was in the range 80-84% in 12 countries, three countries in Central and South America Argentina, Peru, and Puerto Rico, five Asian countries (Singapore, China, Hong Kong and Taiwan and Turkey and four European countries the Czech Republic and Latvia and Slovenia.**Survival was in the range 70-79% in 12 countrie, Cuba and Ecuador, Kuwait and Mongolia and eight countries in Europe, Estonia, Lithuania, Croatia and Bulgaria and Poland. **Breast cancer survival remains lower in Eastern Europe and Africa. Conclusion: the future worldwide breast cancer burden will be strongly influenced by large predicted rises in incidence throughout parts of Asia due to an increasingly westernised lifestyle. Efforts are underway to reduce the global disparities in survival for women with breast cancer using cost-effective interventions. Note: This abstract was not presented at the meeting. Citation Format: Zoubida Zaidi, Hussain Adlane Dib. The worldwide female breast cancer incidence and survival, 2018 [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr 4191.
TL;DR: The process used to develop and refine the initial three surveys for the All of Us Research Program, building a national longitudinal cohort and collecting data from multiple information sources to advance precision medicine is reported.
Abstract: Background:The All of Us Research Program is building a national longitudinal cohort and collecting data from multiple information sources (e.g., biospecimens, electronic health records, and mobile/wearable technologies) to advance precision medicine. Participant-provided information, collected via
TL;DR: The results demonstrate that the BRFSS methodology introduces substantial uncertainty into reproductive health measures, which could bias population-based estimates, and emphasize the importance of implementing validated sex and gender questions in health surveillance surveys.
Abstract: Background National surveys based on probability sampling methods, such as the Behavioral Risk Factor and Surveillance System (BRFSS), are crucial tools for unbiased estimates of health disparities. In 2014, the BRFSS began offering a module to capture transgender and gender nonconforming identity. Although the BRFSS provides much needed data on the this population, these respondents are vulnerable to misclassification of sex assigned at birth. Methods We applied quantitative bias analysis to explore the magnitude and direction of the systematic bias present as a result of this misclassification. We use multivariate Poisson regression with robust standard errors to estimate the association between gender and four sex-specific outcomes: prostate-specific antigen testing, Pap testing, hysterectomy, and pregnancy. We applied single and multiple imputation methods, and probabilistic adjustments to explore bias present in these estimates. Results Combined BRFSS data from 2014, 2015, and 2016 included 1078 transgender women, 701 transgender men, and 450 gender nonconforming individuals. Sex assigned at birth was misclassified among 29.6% of transgender women and 30.2% of transgender men. Transgender and gender nonconforming individuals excluded due to sex-based skip patterns are demographically distinct from those who were asked reproductive health questions, suggesting that there is noteworthy selection bias present in the data. Estimates for gender nonconforming respondents are vulnerable to small degrees of bias, while estimates for cancer screenings among transgender women and men are more robust to moderate degrees of bias. Conclusion Our results demonstrate that the BRFSS methodology introduces substantial uncertainty into reproductive health measures, which could bias population-based estimates. These findings emphasize the importance of implementing validated sex and gender questions in health surveillance surveys. See video abstract at, http://links.lww.com/EDE/B562.
TL;DR: It is estimated that the effect of the permit-to-purchase repeal on Missouri’s firearm homicide rate is bracketed between 0.9 and 1.3 homicides per 100,000 people, corresponding to a percentage increase of 17% to 27% (95% confidence interval: 0.6, 1.7 or 11%, 35%).
Abstract: In the comparative interrupted time series design (also called the method of difference-in-differences), the change in outcome in a group exposed to treatment in the periods before and after the exposure is compared with the change in outcome in a control group not exposed to treatment in either period. The standard difference-in-difference estimator for a comparative interrupted time series design will be biased for estimating the causal effect of the treatment if there is an interaction between history in the after period and the groups; for example, there is a historical event besides the start of the treatment in the after period that benefits the treated group more than the control group. We present a bracketing method for bounding the effect of an interaction between history and the groups that arises from a time-invariant unmeasured confounder having a different effect in the after period than the before period. The method is applied to a study of the effect of the repeal of Missouri's permit-to-purchase handgun law on its firearm homicide rate. We estimate that the effect of the permit-to-purchase repeal on Missouri's firearm homicide rate is bracketed between 0.9 and 1.3 homicides per 100,000 people, corresponding to a percentage increase of 17% to 27% (95% confidence interval: 0.6, 1.7 or 11%, 35%). A placebo study provides additional support for the hypothesis that the repeal has a causal effect of increasing the rate of state-wide firearm homicides.