Longitudinal studies are often viewed as the “gold standard” of observational epidemiologic research. Establishing a temporal association is a necessary criterion to identify causal relations. However, when covariates in the causal system vary over time, a temporal association is not straightforward. Appropriate analytical methods may be necessary to avoid confounding and reverse causality. These issues come to light in 2 studies of breastfeeding described in the articles by Al-Sahab et al. (Am J Epidemiol. 2011;173(9):971–977) and Kramer et al. (Am J Epidemiol. 2011;173(9):978–983) in this issue of the Journal. Breastfeeding has multiple time points and is a behavior that is affected by multiple factors, many of which themselves vary over time. This creates a complex causal system that requires careful scrutiny. The methods presented here may be applicable to a wide range of studies that involve time-varying exposures and time-varying confounders.
breast feeding; causality; confounding
The conditions under which children are raised have a long-term impact on health throughout the life course. Because childhood conditions can have such a strong influence on adult risk factors for disease, failure to account for their influences could distort observed associations between adult risk factors and subsequent health outcomes. In other words, childhood conditions could confound the association between every X and Y when X is measured in adulthood. Comparisons of health outcomes between exposed and unexposed siblings have the potential to eliminate confounding effects due to vulnerability factors shared between siblings (i.e., 50% of their genes and aspects of the childhood environment that affect siblings equally). In a large, population-based study of siblings in Denmark, Søndergaard et al. (Am J Epidemiol. 2012;176(8):675–683) found that individuals with higher educational qualifications lived longer than did their siblings with lower educational qualifications. Their results provide evidence for the returns to health resulting from investment in expanded educational opportunities. However, even sibling designs are not conclusive regarding causality; they remain subject to the unmeasured confounding influences of factors that vary within families. Nonetheless, sibling-based approaches should be used more often in studies of adult risk factors to address the long-term influences of the childhood environment on health.
causal inference; education; health; mortality; siblings; social epidemiology
In this issue of the Journal, Pencina and et al. (Am J Epidemiol. 2012;176(6):492–494) examine the operating characteristics of measures of incremental value. Their goal is to provide benchmarks for the measures that can help identify the most promising markers among multiple candidates. They consider a setting in which new predictors are conditionally independent of established predictors. In the present article, the authors consider more general settings. Their results indicate that some of the conclusions made by Pencina et al. are limited to the specific scenarios the authors considered. For example, Pencina et al. observed that continuous net reclassification improvement was invariant to the strength of the baseline model, but the authors of the present study show this invariance does not hold generally. Further, they disagree with the suggestion that such invariance would be desirable for a measure of incremental value. They also do not see evidence to support the claim that the measures provide complementary information. In addition, they show that correlation with baseline predictors can lead to much bigger gains in performance than the conditional independence scenario studied by Pencina et al. Finally, the authors note that the motivation of providing benchmarks actually reinforces previous observations that the problem with these measures is they do not have useful clinical interpretations. If they did, researchers could use the measures directly and benchmarks would not be needed.
area under curve; biomarkers; bivariate binomial distribution; receiver operating characteristic; risk assessment; risk factors
In a 1993 paper (Am J Epidemiol. 1993;137(1):1–8), Weinberg considered whether a variable that is associated with the outcome and is affected by exposure but is not an intermediate variable between exposure and outcome should be considered a confounder in etiologic studies. As an example, she examined the common practice of adjusting for history of spontaneous abortion when estimating the effect of an exposure on the risk of spontaneous abortion. She showed algebraically that such an adjustment could substantially bias the results even though history of spontaneous abortion would meet some definitions of a confounder. Directed acyclic graphs (DAGs) were introduced into epidemiology several years later as a tool with which to identify confounders. The authors now revisit Weinberg's paper using DAGs to represent scenarios that arise from her original assumptions. DAG theory is consistent with Weinberg's finding that adjusting for history of spontaneous abortion introduces bias in her original scenario. In the authors' examples, treating history of spontaneous abortion as a confounder introduces bias if it is a descendant of the exposure and is associated with the outcome conditional on exposure or is a child of a collider on a relevant undirected path. Thoughtful DAG analyses require clear research questions but are easily modified for examining different causal assumptions that may affect confounder assessment.
bias (epidemiology); causality; confounding factors (epidemiology); reproductive history
Risk reclassification methods have become popular in the medical literature as a means of comparing risk prediction models. In this issue of the Journal, Pencina et al. (Am J Epidemiol. 2012;176(6):492–494) present further results for continuous measures of model discrimination and describe their characteristics in nested models with normally distributed variables. Measures include the change in the area under the receiver operating characteristic curve, the integrated discrimination improvement, and the continuous net reclassification improvement. Although theoretically interesting, these continuous measures may not be the most appropriate to assess clinical utility. The continuous net reclassification improvement, in particular, is a measure of effect rather than model improvement and can sometimes exhibit erratic behavior, as illustrated in 2 examples. Caution is needed before using this as a measure of improvement. Further, the test of the continuous net reclassification improvement and that for the integrated discrimination improvement are similar to the likelihood ratio test in nested models and may be overinterpreted. Reclassification in risk strata, while requiring thresholds, may be more relevant clinically with its ability to examine potential changes in treatment decisions.
calibration; discrimination; model fit; risk prediction
The interaction estimates from Bhavnani et al. (Am J Epidemiol. 2012;176(5):387–395) are used to evaluate evidence for mechanistic interaction between coinfecting pathogens for diarrheal disease. Mechanistic interaction is said to be present if there are individuals for whom the outcome would occur if both of 2 exposures are present but would not occur if 1 or the other of the exposures is absent. In the epidemiologic literature, mechanistic interaction is often conceived of as synergism within Rothman's sufficient-cause framework. Tests for additive interaction are sometimes used to assess such synergism or mechanistic interaction, but testing for positive additive interaction only allows for the conclusion of mechanistic interaction under fairly strong “monotonicity” assumptions. Alternative tests for mechanistic interaction, which do not require monotonicity assumptions, have been developed more recently but require more substantial additive interaction to draw the conclusion of the presence of mechanistic interaction. The additive interaction reported by Bhavnani et al. is of sufficient magnitude to provide strong evidence of mechanistic interaction between rotavirus and Giardia and between rotavirus and Escherichia. coli/Shigellae, even without any assumptions about monotonicity.
coinfecting pathogens; diarrhea; interaction; mechanism; synergism
Because of the aging of the population, dementia has become a major public health problem. There has been growing evidence for a possible association between lipids and dementia. A large body of literature has demonstrated multiple hypothesized biologic links between lipids and neurodegenerative or other biologic pathways connected to dementing processes. However, the epidemiologic associations have been conflicting: dyslipidemia at middle age, but not in later life, seems to be associated with higher dementia risk in some but not all studies. Results from the Honolulu-Asia Aging Study reported by Saczynski et al. (Am J Epidemiol 2007;165:000–00) suggest that lipoprotein constituents, such as apolipoprotein A-I, a major component of the high density lipoprotein, may be more informative in enlightening the association between lipids and dementia. In this commentary, the epidemiology and biology of apolipoprotein A-I in relation to dementia is reviewed.
Alzheimer disease; apolipoproteins; dementia; lipids
Pleiotropy across the 8q24 region is perhaps the most intriguing of the genome-wide association findings relating to cancer. This region of chromosome 8 is a gene desert, far from any recognized genes. Guarrera et al., whose work is reported in this issue (Am J Epidemiol. 2012;175(6):479–487), took an epidemiologic approach to learn more about the 8q24 region. They capitalized on their ascertainment of other endpoints in members of the cohort at the Turin site of the European Prospective Investigation Into Cancer and Nutrition to investigate multiple outcomes for additional pleiotropic effects in the 8q24 region. Alternative design options might involve genotyping of more variants, incorporation of more cases, or use of a single control group close to the size of the most common case group. Their analytic methods reflect the uncertainty of the underlying biology. The findings sharpen the scientific question about how variation in the 8q24 region affects pathogenesis. The genome-wide association effort is possible because of the economy of scale afforded by extremely dense genotyping. Strict adherence to the hypothesis-driven approach would ignore information that is obtainable at a trivial cost. The genome-wide association strategy tests whether agnostic data-mining methods can advance knowledge alongside or even in place of the standard hypothesis-driven approach, which is the conventional scientific method children learn in kindergarten and onward, even through graduate school and beyond.
neoplasms; chromosomes, human, pair 8; diabetes mellitus; DNA, intergenic; genetic pleiotropy; mortality
Arsenic exposure affects millions of people worldwide, causing substantial mortality and morbidity from cancers and cardiovascular and respiratory diseases. An article in the current issue (Am J Epidemiol. 2013;177(3):202–212) reports that classic dermatological manifestations, typically associated with chronic arsenic exposure, are predictive of internal cancers among Taiwanese decades after the cessation of exposure. Specifically, the risk of lung and urothelial cancers was elevated, which was evident regardless of arsenic dose, smoking, and age. There was also an unexpected elevated risk of prostate cancer. Despite some methodological limitations, these findings underscore the need for assessing whether dermatological manifestations are also predictive of cardiovascular, respiratory, and other arsenic-related, long-term health consequences. Given the emerging evidence of arsenic exposure from dietary sources beyond contaminated drinking water and occupational and environmental settings, and also because the vast majority of diseases and deaths among exposed populations do not show classic dermatological manifestations, larger and more comprehensive investigations of the health effects of arsenic exposure, especially at lower doses, are needed. In parallel, because the risk of known arsenic-related health outcomes remains elevated decades after exposure cessation, research toward identification of early clinical and biological markers of long-term risk as well as avenues for prevention, in addition to policy actions for exposure reductions, is warranted.
arsenic; cancer; prevention; skin lesions
The typical dilemma with sex-ratio findings is that when they are real, they aren’t interesting, and when they are interesting, they aren’t real. In this issue of the Journal, Fernández et al. (Am J Epidemiol. 2011;174(12):1327–1331) describe a deviation of the sex ratio that is apparently both large and real. There was a temporary but distinct spike in the proportion of boys born in Cuba around the time of the collapse of the national economy during the 1990s. Although an excess of boys does not fit the prevailing biologic theory regarding maternal stress and the sex ratio, the data are consistent with results from the Dutch famine (where population-level deprivation was even more extreme). A new quandary arises in the modern era with interpretation of the sex ratio: If the decision to abort a pregnancy is influenced by the sex of the fetus, a change in the behavior of even a small proportion of women could influence the sex ratio at birth. The possible role of sex selection in the Cuban context is discussed.
abortion; sex ratio
In choosing covariates for adjustment or inclusion in propensity score analysis, researchers must weigh the benefit of reducing confounding bias carried by those covariates against the risk of amplifying residual bias carried by unmeasured confounders. The latter is characteristic of covariates that act like instrumental variables—that is, variables that are more strongly associated with the exposure than with the outcome. In this issue of the Journal (Am J Epidemiol. 2011;174(11):1213–1222), Myers et al. compare the bias amplification of a near-instrumental variable with its bias-reducing potential and suggest that, in practice, the latter outweighs the former. The author of this commentary sheds broader light on this comparison by considering the cumulative effects of conditioning on multiple covariates and showing that bias amplification may build up at a faster rate than bias reduction. The author further derives a partial order on sets of covariates which reveals preference for conditioning on outcome-related, rather than exposure-related, confounders.
bias (epidemiology); confounding factors (epidemiology); epidemiologic methods; instrumental variable; precision; simulation; variable selection
The Icelandic study of melanoma trends by Héry et al. in this issue of the Journal (Am J Epidemiol. 2010;172(7):762–767) is a fascinating analysis of an ecologic association. The authors noted a sharp increase in melanoma incidence that appeared to lag a few years behind the increased prevalence of sunbeds in Iceland. Caution, however, must be exercised in interpreting the data because of the lack of understanding of emissions of ultraviolet radiation from sunbeds and the ecologic nature of the data.
Iceland; melanoma; ultraviolet rays
Metrics such as relative hazards and relative risks do not account for the prevalence of a marker over time and its relation to whether and when an outcome occurs. Uncommon markers that have good predictive values and common markers that are poorly predictive may not be (clinically) useful in predicting disease and other health outcomes. Recent work by Little et al. (Am J Epidemiol. 2011;173(12):1380–1387) highlights the development of a new method that considers both factors in predicting outcomes. Measures that incorporate both marker prevalence and predictive values and therefore are measures of “effectiveness” may be broadly helpful in deciding which markers or exposures are useful in disease screening or should be targeted by health interventions.
biological markers; censored data; life change events; menopause; premenopause; survival analysis
The very insightful and clear paper by VanderWeele and Vansteelandt in this issue of the Journal (Am J Epidemiol. 2010;172(12):1339–1348) bridges the gap between biostatistics methodologists focusing on causal methods for mediation analyses and the practitioners of mediational analyses to the benefit of both groups. In an effort to continue the bridging of this gap, this invited commentary relates the important issue of “natural direct effects” to the well-known epidemiologic method of direct standardization. Additionally, attention is paid to the importance of temporal sequencing to help substantiate the mediation relations among the exposure, mediation, and outcome. A crucial mathematical distortion under the logistics model, called “absence of collapsibility,” is noted in motivating VanderWeele and Vansteelandt's use of the log-linear model for comparing the effect of exposure adjusted for the mediator with the effect of exposure unadjusted for the mediator. It is also noted that this issue applies to one approach to assessing confounding. Finally, some issues are raised for consideration when testing the interaction between the exposure and mediator before assessing mediation.
collapsibility; confounding; epidemiologic methods; logistic regression; log-linear models; standardization
In this issue of the Journal, VanderWeele and Vansteelandt (Am J Epidemiol. 2010;172(12):1339–1348) provide simple formulae for estimation of direct and indirect effects using standard logistic regression when the exposure and outcome are binary, the mediator is continuous, and the odds ratio is the chosen effect measure. They also provide concisely stated lists of assumptions necessary for estimation of these effects, including various conditional independencies and homogeneity of exposure and mediator effects over covariate strata. They further suggest that this will allow effect decomposition in case-control studies if the sampling fractions and population outcome prevalence are known with certainty. In this invited commentary, the author argues that, in a well-designed case-control study in which the sampling fraction is known, it should not be necessary to rely on the odds ratio. The odds ratio has well-known deficiencies as a causal parameter, and its use severely complicates evaluation of confounding and effect homogeneity. Although VanderWeele and Vansteelandt propose that a rare disease assumption is not necessary for estimation of controlled direct effects using their approach, collapsibility concerns suggest otherwise when the goal is causal inference rather than merely measuring association. Moreover, their clear statement of assumptions necessary for the estimation of natural/pure effects suggests that these quantities will rarely be viable estimands in observational epidemiology.
causal inference; conditional independence; confounding; decomposition; estimation; interaction; logistic regression; odds ratio
Weathering—the cumulative burden of adverse psychosocial and economic circumstances on the bodies of minority women—has been repeatedly described in epidemiologic studies. The most common application has been the documentation of rapidly increasing risks of adverse birth outcomes as African-American women age. Previous work has been based largely on cross-sectional data that aggregate women across a variety of socioeconomic circumstances. When more specific information about women's life-course socioeconomic status is taken into account, however, heterogeneity in the weathering experience of African-American women becomes more readily apparent. Adverse birth outcome risk trajectories with advancing age for African-American women who reside in wealthier neighborhoods look much more similar to those of white women. The accompanying article by Love et al. (Am J Epidemiol. 2010;172(2):127–134) provides a more nuanced investigation of the social conditions that contribute to the weathering of African-American women and points to the critical role played by social and economic conditions over the life course in producing adverse birth outcome disparities.
African Americans; infant, premature; infant, small for gestational age; maternal age; poverty; preterm birth; residence characteristics
Epidemiologists are well aware of the negative consequences of measurement error in exposure and outcome variables to their ability to detect putative causal associations. However, empirical proof that remedying the misclassification problem improves estimates of epidemiologic effect is seldom examined in detail. Of all areas in cancer epidemiology, perhaps the best example of the consequences of misclassification and of the steps taken to circumvent them was the pursuit, beginning in the mid-1980s, of the human papillomavirus (HPV) infection–cervical cancer association. The stakes were high: Had the wrong conclusions been reached epidemiologists would have been led astray in the search for competing hypotheses for the sexually transmissible agent causing cervical cancer or in ascribing to HPV infection a mere ancillary role among many lifestyle, hormonal, and environmental factors. The article by Castle et al. in this issue of the Journal (Am J Epidemiol. 2010;171(2):155–163) provides a detailed account of the joint influences of improved HPV and cervical precancer measurements in gradually unveiling the strong magnitude of the underlying association between viral exposure and cervical lesion risk. In this commentary, the authors extend the findings of Castle et al. by providing additional empirical evidence in support of their arguments.
cytology; measurement error; misclassification; papillomavirus infections; uterine cervical neoplasms; vaginal smears
Genomic data will become an increasingly important component of epidemiologic studies in coming years. The authors of the accompanying Journal article, van Ballegooijen et al. (Am J Epidemiol. 2009;170(12):1455–1463), are to be commended for attempting to use the coalescent analysis of viral sequence data to evaluate a hepatitis B vaccination program. Coalescent theory attempts to link the phylogenetic history of populations with rates of population growth and decline. In particular, under certain assumptions, a reduction in genetic diversity can be interpreted as a reduction in disease incidence. However, the authors of this commentary contend that van Ballegooijen et al.’s interpretation of changes in viral genetic diversity as a measure of hepatitis B vaccine effectiveness has major limitations. Because of the potential use of these methods in future vaccination studies, the authors discuss the utility of these methods and the data requirements needed for them to be convincing. First, data sets should be large enough to provide sufficient epidemiologic-scale resolution. Second, data need to reflect sufficiently fine-grained temporal sampling. Third, other processes that can potentially influence genetic diversity and confuse demographic inferences should be considered.
communicable diseases; disease notification; disease transmission, infectious; genetic variation; hepatitis B virus; molecular sequence data; vaccination
Making decisions about medical treatments based upon valid evidence is critical to improve health-care quality, outcomes, and value. Although such research commonly connotes the use of randomized controlled trials, experimental methods are not always feasible, and research using observational, quasi-experimental, and other nonexperimental methods may also be important. At the same time, nonexperimental methods are inherently susceptible to various types of bias and thus present special challenges in the search for valid and generalizable evidence. The study by Gardarsdottir et al. (Am J Epidemiol. 2009;170(3):280–285), on which this commentary is based, addresses a key potential source of bias—mismeasurement of patients’ duration of treatment—in previous research on pharmacotherapy for depression. However, the authors’ study is unlikely to address other potential sources of bias, which may make interpretation of their findings more difficult.
bias (epidemiology); depression; observation; research design; treatment outcome
Many neural tube defects can be prevented if women take folic acid around the time of conception. However, the majority of women do not take folic acid at the critical time, so the US government required that food be fortified with folic acid effective January 1, 1998. Whether the amount being added was sufficient to prevent all folate-related neural tube defects has been hotly debated. Mosley et al. (Am J Epidemiol. 2008;169(1):9–17) found no evidence that folic acid supplement use or dietary folate intake was related to neural tube defects, indicating that fortified food is probably providing sufficient folic acid to prevent folate-related defects. Because data on the effectiveness of fortification in the United States are scarce, this is an important contribution. There is great interest in the other effects of fortification. Folic acid reduces homocysteine levels, and homocysteine has been linked to cardiovascular disease and cancer. On the basis of current evidence, however, it seems unlikely that fortification will reduce cardiovascular disease rates. Its effect on cancer remains unclear. Folic acid may be useful in primary prevention but may also stimulate the growth of existing malignancies or premalignant lesions. Although these issues remain unresolved, Mosley et al. have provided important data to address the primary question: Does fortification prevent folate-related neural tube defects?
anencephaly; folic acid; food, fortified; neural tube defects; spinal dysraphism
Obesity is more prevalent and its consequences severe among middle-aged and older adults. Efforts to understand and address neighborhood-level causes of obesity in this population offer the potential to enhance health and reduce the costs of obesity for everyone. The accompanying paper by Li et al. (Am J Epidemiol. 2009;169(4):401–408) presents new data on the apparently significant interaction between neighborhood and individual characteristics on 1-year change in body weight and waist circumference. Despite methodological limitations in measurement, this paper supports the importance of future research that considers the complex relation between people and where they live. Efforts to design neighborhood-level policy interventions to effectively address the problem of obesity will require greater interdisciplinary collaboration.
aged; obesity; residence characteristics
In this issue of the Journal, Nielsen et al. (Am J Epidemiol 2008;168:481−91) use data from a large Danish study to provide evidence that self-reported stress is associated with increased all-cause mortality over the next 20 years. The finding is remarkable. In this commentary, the authors explore what is really meant by stress; they argue that it would be naïve to view stress as reported in this way, with some external exposure. It has to be seen through the lens of the participant's personal experience, and this lens is likely to be clouded by personality, coping styles, and the common mental disorders—depression and anxiety. The authors discuss a wider literature concerning similar findings associating depression with mortality, suggesting three broad reasons for the association. First, the findings might be explained by the impact of stress or distress on well-established risk factors for cardiovascular disease and cancer. Second, there might be direct, underlying psychosomatic pathways by which stress or distress can affect immune or autonomic function. Third, there might be common causal pathways—shared genes or early adversities that predict both stress and mortality from other causes independently. The authors suggest that life course epidemiologic research is required to test these competing hypotheses.
cause of death; depressive disorder; mortality; prospective studies; stress, psychological
Persistent cervical infections by approximately 15 carcinogenic genotypes of human papillomavirus (HPV) cause virtually all cases of cervical cancer and its immediate precancerous precursor, cervical intraepithelial neoplasia grade 3 or carcinoma in situ. As is shown in a meta-analysis by Koshiol et al. (Am J Epidemiol 2008;168:123–137), detection of carcinogenic HPV viral persistence could be used to identify women at the greatest risk of cervical precancer. Specifically, women who have carcinogenic HPV infection that persists for at least 1 year versus those whose infections clear are at significantly elevated risk of having or developing cervical precancer. However, before detection of HPV persistence can be used in cervical cancer screening, several considerations need to be addressed: 1) validation and Food and Drug Administration approval of a reliable HPV genotyping test, 2) rational clinical algorithms based on risk of precancer and cancer for the clinical management of HPV persistence, 3) clinician and patient acceptability of monitoring of HPV infections (including not responding excessively to the first positive HPV test and waiting 1–2 years for infections to either persist or resolve), and 4) patient compliance with recommended follow-up. Investigators will need to address these and other key issues in order to realize the potential utility of HPV viral monitoring for improving the accuracy of cervical cancer screening.
human papillomavirus 16; human papillomavirus 18; longitudinal studies; papillomavirus infections; uterine cervical neoplasms
Pregnancy hormones are believed to be involved in the protection against breast cancer conferred by pregnancy. We explored the association of maternal breast cancer with chorionic gonadotropin (hCG) and alpha fetoprotein (AFP).
In 2001, a case-control study was nested within the Northern Sweden Maternity Cohort, an ongoing study collecting blood samples from first-trimester pregnant women since 1975. Cases (210) and controls (357) were matched for age, parity and date of blood donation. hCG and AFP were measured by immunoassays.
No overall significant association of breast cancer with either hCG or AFP was observed. However, women with hCG in the top tertile tended to be at lower risk of breast cancer than women with hCG in the lowest tertile in the whole study population and in subgroups of age at sampling, parity and age at cancer diagnosis. A borderline significant decrease in risk with high hCG was observed in women who developed breast cancer after the median lag-time to cancer diagnosis, [0.53 (0.27, 1.03), p = 0.06].
Although very preliminary, our findings are consistent with a possible long-term protective association of breast cancer risk with elevated circulating hCG in the early stages of pregnancy.
AFP; breast cancer; human chorionic gonadotropin; pregnancy; prospective study
The authors examined associations of health behaviors over a 17-year period, separately and in combination, with cognition in late midlife in 5123 men and women from the Whitehall II study (United Kingdom). Health behaviors were assessed in early midlife (mean age=44 years, Phase 1, 1985–1988), in midlife (mean age=56 years, Phase 5, 1997–1999) and in late midlife (mean age=61 years, Phase 7, 2002–2004). A score of the number of unhealthy behaviors (smoking, alcohol abstinence, low physical activity, and low fruit and vegetable consumption) was defined as ranging from 0 to 4. Poor (defined as scores in the worst sex-specific quintile) executive function and memory in late midlife (Phase 7) were analyzed as outcomes. Compared to those with no unhealthy behaviors, those with 3–4 unhealthy behaviors at Phase 1 (Odds Ratio (OR)=1.84; 95% Confidence Interval: 1.27,2.65), Phase 5 (OR=2.38; 1.76, 3.22) and Phase 7 (OR=2.76;2.04,3.73) were more likely to have poor executive function. A similar association was observed for memory. Odds of poor executive function and memory were the greater the more times the participant reported unhealthy behaviors over the three phases. This study suggests that both the number of unhealthy behaviors and their duration is associated with subsequent cognitive function in later life.
Adult; Alcohol Drinking; epidemiology; Cognition Disorders; epidemiology; Exercise; Female; Food Habits; Great Britain; epidemiology; Health Behavior; Humans; Life Style; Longitudinal Studies; Male; Memory Disorders; epidemiology; Middle Aged; Risk; Smoking; epidemiology; cognition, health behaviors, longitudinal studies, middle aged.