Case-Control Studies

Reproductive factors are associated with reduced risk of breast cancer, but less is known about whether there is differential protection against subtypes of breast cancer. Assuming reproductive factors act through hormonal mechanisms they should protect predominantly against cancers expressing oestrogen (ER) and progesterone (PR) receptors. We examined the effect of reproductive factors on subgroups of tumours defined by hormone receptor status as well as histology using data from the NIHCD Women's Contraceptive and Reproductive Experiences (CARE) Study, a multicenter case-control study of breast cancer. We estimated odds ratios (ORs) and 95% confidence intervals (CIs) as measures of relative risk using multivariate unconditional logistic regression methods. Multiparity and early age at first birth were associated with reduced relative risk of ER + PR + tumours (P for trend=0.0001 and 0.01, respectively), but not of ER - PR - tumours (P for trend=0.27 and 0.85), whereas duration of breastfeeding was associated with lower relative risk of both receptor-positive (P for trend=0.0002) and receptor-negative tumours (P=0.0004). Our results were consistent across subgroups of women based on age and ethnicity. We found few significant differences by histologic subtype, although the strongest protective effect of multiparity was seen for mixed ductolobular tumours. Our results indicate that parity and age at first birth are associated with reduced risk of receptor-positive tumours only, while lactation is associated with reduced risk of both receptor-positive and -negative tumours. This suggests that parity and lactation act through different mechanisms. This study also suggests that reproductive factors have similar protective effects on breast tumours of lobular and ductal origin.

BackgroundSeasonal variations in the acute exacerbation of chronic obstructive pulmonary disease (COPD) have been reported. However, the influence of air temperature and other meteorological factors on COPD exacerbation remains unclear.MethodsNational Health Insurance registry data from January 1, 1999 to December 1, 2009 and meteorological variables from the Taiwan Central Weather Bureau for the same period were analyzed. A case-crossover study design was used to investigate the association between COPD exacerbation and meteorological variables.ResultsA total of 16,254 cases who suffered from COPD exacerbation were enrolled. We found that a 1°C decrease in air temperature was associated with a 0.8% increase in the exacerbation rate on event-days (95% confidence interval (CI), 1.015-1.138, p = 0.015). With a 5°C decrease in mean temperature, the cold temperature (28-day average temperature) had a long-term effect on the exacerbation of COPD (odds ratio (OR), 1.106, 95% CI 1.063-1.152, pConclusionsThis study demonstrated the effect of cold temperatures on the COPD exacerbation rate. Elderly patients and those without inhaled medicine before the exacerbation event were affected significantly by lower mean temperatures. A more comprehensive program to prevent cold stress in COPD patients may lead to a reduction in the exacerbations rate of COPD.

Heterogeneity is an important concept in psychiatric research and science more broadly. It negatively impacts effect size estimates under case-control paradigms, and it exposes important flaws in our existing categorical nosology. Yet, our field has no precise definition of heterogeneity proper. We tend to quantify heterogeneity by measuring associated correlates such as entropy or variance: practices which are akin to accepting the radius of a sphere as a measure of its volume. Under a definition of heterogeneity as the degree to which a system deviates from perfect conformity, this paper argues that its proper measure roughly corresponds to the size of a system's event/sample space, and has units known as numbers equivalent. We arrive at this conclusion through focused review of more than 100 years of (re)discoveries of indices by ecologists, economists, statistical physicists, and others. In parallel, we review psychiatric approaches for quantifying heterogeneity, including but not limited to studies of symptom heterogeneity, microbiome biodiversity, cluster-counting, and time-series analyses. We argue that using numbers equivalent heterogeneity measures could improve the interpretability and synthesis of psychiatric research on heterogeneity. However, significant limitations must be overcome for these measures-largely developed for economic and ecological research-to be useful in modern translational psychiatric science.

It is well-established that the odds ratio (OR) is non-collapsible and that adjustment of the OR for a risk factor can produce a change in the main effect estimate even when there is no confounding. In contrast, the risk ratio (RR) is collapsible, whereas the rate ratio is non-collapsible, but usually to a lesser extent than is the case with ORs. However, almost all discussions of non-collapsibility have been conducted in the context of effect measures in cohort studies. In this paper, I consider non-collapsibility in case-control studies where the OR inherits the collapsibility or non-collapsibility of the ratio effect measure that the study was designed to estimate. This is most clearly illustrated in the situation of a closed cohort with a fixed follow-up time. In this situation, choosing controls by cumulative incidence sampling estimates the OR in the corresponding cohort study, and therefore the resulting case-control OR is non-collapsible. Choosing controls by case-cohort sampling estimates the cohort study RR, and the resulting case-control OR is collapsible. Choosing controls by density sampling (perhaps the most common approach) estimates the cohort study rate ratio, and the resulting case-control OR is non-collapsible, but to a lesser extent than for cumulative incidence sampling. Moreover, if the disease under study is rare (in the cohort study), all three methods produce case-control ORs that are approximately collapsible.