Journal of chronic diseases最新文献

The most central epidemiological feature of cancers of the breast, ovary and endometrium is the enormous protective effect of menopause. Simple mathematical “models” of the age-incidence of these tumours suggests that their incidence by age 70 would be between 4 and 8-fold increased if women continued to menstruate into old age. Using the notion of “effective cell-cycle time” to explain this phenomenon, different “models” are constructed for each of these three tumour sites and shown to provide an excellent description of most of the known epidemiology of each of the cancers, and to provide a basis for predicting the long-term effects of various factors on the risk of these diseases. In particular, the model suggests that 5 years of combination-type oral contraceptive use will approximately halve the lifelong risk of both ovarian cancer and endometrial cancer.

Assessing latency for neoplastic diseases is crucial for determining the causal effects of a complex mix of carcinogenic exposures. An initial assessment of cancer risks in a U.S. tire and rubber plant revealed a significant SMR of 140 for prostatic cancer. Using an industry-based, case-control death certificate study of prostatic malignancies, we found matched odds ratios of about 3 (p < 0.025) for Batch Preparation, the work area with the greatest exposure to carbon black, solvents, and heavy metal oxides. To assess latency, we used the matched case-control series to calculate annual estimates of the odds ratio by determining the proportion of cases and controls employed for greater than 1 month in Batch Preparation during each year under study. This approach produced a plot with great fluctuations. To reduce variability in the resulting curve, a method was developed that measured the “etiologic fraction,” which is its highest point represents an estimate of the peak of the latency distribution. For Batch Preparation the modal point was 29 years before death with the greatest risk occurring from employment in the mid-1940's. The latency method allows risk assessment for time and year of greatest exposure difference, thus suggesting appropriate prevention strategies. Applications of this method for other types of studies and exposures are discussed.

This paper presents at an instructive level detailed design, decision and computational aspects of p-values and power for clinical trials in which interim looks at accumulating data are planned. Background theory and an application are presented. In addition, a definition of the term p-value, appropriate for a trial in which interim looks are planned is proposed. The definition is intuitively consistent with that for a fixed sample size trial. That is, the p-value reflects the strength of the data against the null hypothesis. For trials incorporating group sequential methods, the p-value at a particular look reflects the conditional nature of the data collected through that look on the data collected through earlier looks.

Four cross-sectional epidemiologic studies of children 5–17 years of age were conducted between 1973 and 1982 in the community of Bogalusa, La. Anthropometric, blood pressure and serum lipid variables were measured. Measurement error (precision) and reliability (reproducibility) were estimated for these risk factor variables of coronary artery disease. Precision was less than 0.8 cm for height, 0.8 kg for weight, and 1 mm for triceps and subscapular skinfold over the four surveys. Measurement error for the mercury sphygmomanometer and automatic blood pressure instruments was between 4 and 5 mmHg during this same period. Except for triglycerides and total and α-lipoprotein cholesterols that stabilized at lower levels after the first survey, precision of laboratory variables remained nearly constant over the 9 years of survey. Anthropometric measurements were found to be the most reliable followed next by the laboratory and then blood pressure measurements. These estimates offer guidelines applicable to data collection and processing in future investigations of risk factor variables for coronary artery disease.

Cross-sectional and prospective associations between lung function and potential determinants were examined in the Framingham Study. Personal characteristics of interest were body height and weight, hand grip strength, smoking habits, history of respiratory symptoms and disease, and hematocrit.

Baseline pulmonary function was the most powerful indicator of future FEV₁ and cigarette smoking was the most important correctable influence. In addition to baseline FEV₁, FVC and smoking habits, the important predictors of future lung function were age, relative weight, respiratory symptoms and hematocrit. There was a higher follow-up FEV₁ in those who quit smoking compared to those who continued even after adjustment for other associated variables. Relative weight and hematocrit were associated with FEV₁ measured 14 years after baseline (directly for relative weight and inversely for hematocrit).

The effect of measurement error on the accuracy of results in two epidemiological study designs involving longitudinal lung function data was assessed using computer modelling. Five realistic data sets (cohorts) were created, each of 400 subjects, half of whom were exposed to an agent whose effects approximated in magnitude that of cigarette smoking. In each cohort, FEV₁ decline was modelled after 6, 4 and 2 years of observation with and without error in the measurement of level of FEV₁. For each length of observation the effect of exposure on decline was estimated using a follow-up design comparing the FEV₁ decline between exposure groups, and a case-control design comparing risk of exposure in subjects in the top 20th percentile of FEV₁ decline (cases) to exposure in those in the bottom 20th percentile (controls). For both study designs an exposure effect at $\text{p}\textcolor{red}{}\text{0.01}$ could only be consistently detected after 6 years of observation.

Several methods have been proposed for standardizing risks, risk ratios, and risk differences based on the results of logistic regression. These methods provide an alternative to direct standardization, a particularly useful approach when there are many covariates. In this paper, methods for calculating approximate confidence limits for these standardized measures are presented. A simple example, in which published data are used, illustrates the techniques and allows comparison with confidence limits calculated from the directly standardized risk ratio.

Six thousand six hundred thirty two subjects, employed in 420 small and medium-sized companies in the Paris region were examined in a cross-sectional study. Their alcohol consumption, as obtained by interview was found to be higher among males than among females, among workers than among managers, executives, and clerks. Alcohol consumption was positively associated with age, body mass index, coffee and cigarette consumption, occupational exposure to noise and working nights or alternating shifts. A positive, continuous, relationship was observed, for men and women, between alcohol intake and both systolic and diastolic blood pressure. This association was highly significant in the multivariate analysis (multiple linear regression) where alcohol intake, following age and body mass index, was the third predictive factor of blood pressure level in the stepwise regression. The positive association between alcohol consumption and prevalence of arterial hypertension was aggravated by the poor control of hypertension which was found among drinkers. Awareness of hypertension, compliance with an antihypertensive treatment and its efficacy, were negatively associated withg alcohol intake. The findings stress the importance of alcohol consumption which was found to be a major risk factor for arterial hypertension and noncompliance with antihypertensive treatment in this population.