Mendelian randomization uses genetic variants to make causal inferences about a modifiable exposure. Subject to a genetic variant satisfying the instrumental variable assumptions, an association between the variant and outcome implies a causal effect of the exposure on the outcome. Complications arise with a binary exposure that is a dichotomization of a continuous risk factor (for example, hypertension is a dichotomization of blood pressure). This can lead to violation of the exclusion restriction assumption: the genetic variant can influence the outcome via the continuous risk factor even if the binary exposure does not change. Provided the instrumental variable assumptions are satisfied for the underlying continuous risk factor, causal inferences for the binary exposure are valid for the continuous risk factor. Causal estimates for the binary exposure assume the causal effect is a stepwise function at the point of dichotomization. Even then, estimation requires further parametric assumptions. Under monotonicity, the causal estimate represents the average causal effect in ‘compliers’, individuals for whom the binary exposure would be present if they have the genetic variant and absent otherwise. Unlike in randomized trials, genetic compliers are unlikely to be a large or representative subgroup of the population. Under homogeneity, the causal effect of the exposure on the outcome is assumed constant in all individuals; rarely a plausible assumption. We here provide methods for causal estimation with a binary exposure (although subject to all the above caveats). Mendelian randomization investigations with a dichotomized binary exposure should be conceptualized in terms of an underlying continuous variable.

OBJECTIVE To update estimates of cancer risk in SLE relative to the general population. METHODS A multisite international SLE cohort was linked with regional tumor registries. Standardized incidence ratios (SIRs) were calculated as the ratio of observed to expected cancers. RESULTS Across 30 centres, 16,409 patients were observed for 121,283 (average 7.4) person-years. In total, 644 cancers occurred. Some cancers, notably hematologic malignancies, were substantially increased (SIR 3.02, 95% confidence interval, CI, 2.48, 3.63), particularly non-Hodgkin’s lymphoma, NHL (SIR 4.39, 95% CI 3.46, 5.49) and leukemia. In addition, increased risks of cancer of the vulva (SIR 3.78, 95% CI 1.52, 7.78), lung (SIR 1.30, 95% CI 1.04, 1.60), thyroid (SIR 1.76, 95% CI 1.13, 2.61) and possibly liver (SIR 1.87, 95% CI 0.97, 3.27) were suggested. However, a decreased risk was estimated for breast (SIR 0.73, 95% CI 0.61–0.88), endometrial (SIR 0.44, 95% CI 0.23–0.77), and possibly ovarian cancers (0.64, 95% CI 0.34–1.10). The variability of comparative rates across different cancers meant that only a small increased risk was estimated across all cancers (SIR 1.14, 95% CI 1.05, 1.23). CONCLUSION These data estimate only a small increased risk in SLE (versus the general population) for cancer over-all. However, there is clearly an increased risk of NHL, and cancers of the vulva, lung, thyroid, and possibly liver. It remains unclear to what extent the association with NHL is mediated by innate versus exogenous factors. Similarly, the etiology of the decreased breast, endometrial, and possibly ovarian cancer risk is uncertain, though investigations are ongoing.

Mendelian randomization (MR) is used to answer a variety of epidemiologic questions. One stated advantage of MR is that it estimates a "lifetime effect" of exposure though this term remains vaguely-defined. Instrumental variable analysis, on which MR is based, has focused on estimating the effects of point or time-fixed exposures rather than "lifetime effects". We use an empirical example with data from the Rotterdam Study to demonstrate how confusion can arise when estimating "lifetime effects". We provide one possible definition of a lifetime effect: the average change in outcome measured at time t when the entire exposure trajectory from conception to time t is shifted by one unit. We show that MR only estimates this type of lifetime effect under specific conditions, for example when the effect of the genetic variants used on exposure do not change over time (which many genetic variants commonly used in MR do). Lastly, we simulate the magnitude of bias that would result in realistic scenarios that use genetic variants with effects that change over time. We recommend future MR studies carefully consider the effect of interest and how genetic variants whose effects change with time may impact the interpretability and validity of their results.

Purpose of ReviewInstrumental variable (IV) methods continue to be applied to questions ranging from genetic to social epidemiology. In the epidemiologic literature, discussion of whether the assumptions underlying IV analyses hold is often limited to only certain assumptions and even then, arguments are mostly made using subject matter knowledge. To complement subject matter knowledge, there exist a variety of falsification strategies and other tools for weighing the plausibility of the assumptions underlying IV analyses.Recent FindingsThere are many tools that can refute the IV assumptions or help estimate the magnitude or direction of possible bias if the conditions do not hold perfectly. Many of these tools, including both recently developed strategies and strategies described decades ago, are underused or only used in specific applications of IV methods in epidemiology.SummaryAlthough estimating causal effects with IV analyses relies on unverifiable assumptions, the assumptions can sometimes be refuted. We suggest that the epidemiologists using IV analyses employ all the falsification strategies that apply to their research question in order to avoid settings that demonstrably violate a core condition for valid inference.

Historically, reforms that have increased the duration of job‐protected paid parental leave have improved women's economic outcomes. By targeting the period around childbirth, access to paid parental leave also appears to reduce rates of infant mortality, with breastfeeding representing one potential mechanism. The provision of more generous paid leave entitlements in countries that offer unpaid or short durations of paid leave could help families strike a balance between the competing demands of earning income and attending to personal and family well‐being. Context Policies legislating paid leave from work for new parents, and to attend to individual and family illness, are common across Organisation for Economic Co‐operation and Development (OECD) countries. However, there exists no comprehensive review of their potential impacts on economic, social, and health outcomes. Methods We conducted a systematic review of the peer‐reviewed literature on paid leave and socioeconomic and health outcomes. We reviewed 5,538 abstracts and selected 85 published papers on the impact of parental leave policies, 22 papers on the impact of medical leave policies, and 2 papers that evaluated both types of policies. We synthesized the main findings through a narrative description; a meta‐analysis was precluded by heterogeneity in policy attributes, policy changes, outcomes, and study designs. Findings We were able to draw several conclusions about the impact of parental leave policies. First, extensions in the duration of paid parental leave to between 6 and 12 months were accompanied by attendant increases in leave‐taking and longer durations of leave. Second, there was little evidence that extending the duration of paid leave had negative employment or economic consequences. Third, unpaid leave does not appear to confer the same benefits as paid leave. Fourth, from a population health perspective, increases in paid parental leave were consistently associated with better infant and child health, particularly in terms of lower mortality rates. Fifth, paid paternal leave policies of adequate length and generosity have induced fathers to take additional time off from work following the birth of a child. How medical leave policies for personal or family illness influence health has not been widely studied. Conclusions There is substantial quasi‐experimental evidence to support expansions in the duration of job‐protected paid parental leave as an instrument for supporting women's labor force participation, safeguarding women's incomes and earnings, and improving child survival. This has implications, in particular, for countries that offer shorter durations of job‐protected paid leave or lack a national paid leave entitlement altogether.

Objective. To evaluate the quality of the methods and reporting of published studies that validate administrative database algorithms for rheumatic disease case ascertainment. Methods. We systematically searched MEDLINE, Embase, and the reference lists of articles published from 1980 to 2011. We included studies that validated administrative data algorithms for rheumatic disease case ascertainment using medical record or patient-reported diagnoses as the reference standard. Each study was evaluated using published standards for the reporting and quality assessment of diagnostic accuracy, which informed the development of a methodologic framework to help critically appraise and guide research in this area. Results. Twenty-three studies met the inclusion criteria. Administrative database algorithms to identify cases were most frequently validated against diagnoses in medical records (83%). Almost two-thirds of the studies (61%) used diagnosis codes in administrative data to identify potential cases and then reviewed medical records to confirm the diagnoses. The remaining studies did the reverse, identifying patients using a reference standard and then testing algorithms to identify cases in administrative data. Many authors (61%) described the patient population, but few (26%) reported key measures of diagnostic accuracy (sensitivity, specificity, and positive and negative predictive values). Only one-third of studies reported disease prevalence in the validation study sample. Conclusion. The methods used in administrative data validation studies of rheumatic diseases are highly variable. Few studies reported key measures of diagnostic accuracy despite their importance for drawing conclusions about the validity of administrative database algorithms. We developed a methodologic framework and recommendations for validation study conduct and reporting.

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Objective. To estimate the population-based prevalence of systemic lupus erythematosus (SLE) and systemic sclerosis (SSc) in Alberta, Canada, stratified by First Nations status. Methods. Physician billing claims and hospitalization data for the province of Alberta (1994 -2007) were used to ascertain cases of SLE and SSc using 3 case definitions. A latent class Bayesian hierarchical regression model was employed to account for the imperfect sensitivity and specificity of billing and hospitalization data in case ascertainment. We accounted for demographic factors, estimating prevalence rates for the First Nations and non-First Nations populations by sex, age group, and location of residence (urban/rural). Results. Our model estimated the prevalence of SLE in Alberta to be 27.3 cases per 10,000 females (95% credible interval [95% CrI] 25.9 -28.8) and 3.2 cases per 10,000 males (95% CrI 2.6 -3.8). The overall prevalence of SSc in Alberta was 5.8 cases per 10,000 females (95% CrI 5.1-6.5) and 1.0 case per 10,000 males (95% CrI 0.7-1.4). First Nations females over 45 years of age had twice the prevalence of either SLE or SSc relative to non-First Nations females. There was also a trend toward higher overall SLE prevalence in urban dwellers, and higher overall SSc prevalence in rural residents. Conclusion. First Nations females older than 45 years of age have an increased prevalence of either SLE or SSc. This may reflect a true predominance of autoimmune rheumatic diseases in this demographic, or may indicate systematic differences in health care delivery.