Does cigarette smoking modify the association between change in body mass index during young adulthood and risk of coronary mortality during middle-age in men? Twenty-five year follow-up results from the Chicago Western Electric Study

Many persons in the U.S. gain weight during young adulthood, and the prevalence of obesity has been increasing among young adults. Although obesity and physical inactivity are generally recognized as risk factors for coronary heart disease (CHD), the magnitude of their effect on risk may have been seriously underestimated due to failure to adequately handle the problem of cigarette smoking. Since cigarette smoking causes weight loss, physically inactive cigarette smokers may remain relatively lean because they smoke cigarettes. We hypothesize cigarette smoking modifies the association between weight gain during young adulthood and risk of coronary heart disease during middle age, and that the true effect of weight gain during young adulthood on risk of CHD can be assessed only in persons who have not smoked cigarettes. Specifically, we hypothesize that weight gain during young adulthood is positively associated with risk of CHD during middle-age in nonsmokers but that the association is much smaller or absent entirely among cigarette smokers. The purpose of this study was to test this hypothesis. The population for analysis was comprised of 1,934 middle-aged, employed men whose average age at the baseline examination was 48.7 years. Information collected at the baseline examinations in 1958 and 1959 included recalled weight at age 20, present weight, height, smoking status, and other CHD risk factors. To decrease the effect of intraindividual variation, the mean values of the 1958 and 1959 baseline examinations were used in analyses. Change in body mass index ($\Delta$BMI) during young adulthood was the primary exposure variable and was measured as BMI at baseline (kg/m$\sp2)$ minus BMI at age 20 (kg/m$\sp2).$ Proportional hazards regression analysis was used to generate relative risks of CHD mortality by category of $\Delta$BMI and cigarette smoking status after adjustment for age, family history of CVD, major organ system disease, BMI at age 20, and number of cigarettes smoked per day. Adjustment was not performed for systolic blood pressure or total serum cholesterol as these were regarded as intervening variables. Vital status was known for all men on the 25th anniversary of their baseline examinations. 705 deaths (including 319 CHD deaths) occurred over 40,136 person-years of experience. $\Delta$BMI was positively associated with risk of CHD mortality in never-smokers, but not in ever-smokers (p for interaction = 0.067). For never-smokers with $\Delta$BMI of stable, low gain, moderate gain, and high gain, adjusted relative risks were 1.00, 1.62, 1.61, and 2.78, respectively (p for trend = 0.010). For ever-smokers, with $\Delta$BMI of stable, low gain, moderate gain, and high gain, adjusted relative risks were 1.00, 0.74, 1.07, and 1.06, respectively (p for trend = 0.422). These results support the research hypothesis that cigarette smoking modifies the association between weight gain and CHD mortality. Current estimates of the magnitude of effect of obesity and physical inactivity on risk of coronary mortality may have been seriously underestimated due to inadequate handling of cigarette smoking. ^

Effects of air pollution on asthma hospitalization rates in different age groups in metropolitan cities of Korea

Many studies have shown relationships between air pollution and the rate of hospital admissions for asthma. A few studies have controlled for age-specific effects by adding separate smoothing functions for each age group. However, it has not yet been reported whether air pollution effects are significantly different for different age groups. This lack of information is the motivation for this study, which tests the hypothesis that air pollution effects on asthmatic hospital admissions are significantly different by age groups. Each air pollutant's effect on asthmatic hospital admissions by age groups was estimated separately. In this study, daily time-series data for hospital admission rates from seven cities in Korea from June 1999 through 2003 were analyzed. The outcome variable, daily hospital admission rates for asthma, was related to five air pollutants which were used as the independent variables, namely particulate matter <10 micrometers (μm) in aerodynamic diameter (PM10), carbon monoxide (CO), ozone (O3), nitrogen dioxide (NO2), and sulfur dioxide (SO2). Meteorological variables were considered as confounders. Admission data were divided into three age groups: children (<15 years of age), adults (ages 15-64), and elderly (≥ 65 years of age). The adult age group was considered to be the reference group for each city. In order to estimate age-specific air pollution effects, the analysis was separated into two stages. In the first stage, Generalized Additive Models (GAMs) with cubic spline for smoothing were applied to estimate the age-city-specific air pollution effects on asthmatic hospital admission rates by city and age group. In the second stage, the Bayesian Hierarchical Model with non-informative prior which has large variance was used to combine city-specific effects by age groups. The hypothesis test showed that the effects of PM10, CO and NO2 were significantly different by age groups. Assuming that the air pollution effect for adults is zero as a reference, age-specific air pollution effects were: -0.00154 (95% confidence interval(CI)= (-0.0030,-0.0001)) for children and 0.00126 (95% CI = (0.0006, 0.0019)) for the elderly for PM 10; -0.0195 (95% CI = (-0.0386,-0.0004)) for children for CO; and 0.00494 (95% CI = (0.0028, 0.0071)) for the elderly for NO2. Relative rates (RRs) were 1.008 (95% CI = (1.000-1.017)) in adults and 1.021 (95% CI = (1.012-1.030)) in the elderly for every 10 μg/m3 increase of PM10 , 1.019 (95% CI = (1.005-1.033)) in adults and 1.022 (95% CI = (1.012-1.033)) in the elderly for every 0.1 part per million (ppm) increase of CO; 1.006 (95%CI = (1.002-1.009)) and 1.019 (95%CI = (1.007-1.032)) in the elderly for every 1 part per billion (ppb) increase of NO2 and SO2, respectively. Asthma hospital admissions were significantly increased for PM10 and CO in adults, and for PM10, CO, NO2 and SO2 in the elderly.^

MEASURING THE EFFECT OF ILLNESS ON THE EXPECTED REMAINING SERVICE TIME FOR THE ACTIVE DUTY ARMY: A LIFE TABLE APPROACH

A life table methodology was developed which estimates the expected remaining Army service time and the expected remaining Army sick time by years of service for the United States Army population. A measure of illness impact was defined as the ratio of expected remaining Army sick time to the expected remaining Army service time. The variances of the resulting estimators were developed on the basis of current data. The theory of partial and complete competing risks was considered for each type of decrement (death, administrative separation, and medical separation) and for the causes of sick time.^ The methodology was applied to world-wide U.S. Army data for calendar year 1978. A total of 669,493 enlisted personnel and 97,704 officers were reported on active duty as of 30 September 1978. During calendar year 1978, the Army Medical Department reported 114,647 inpatient discharges and 1,767,146 sick days. Although the methodology is completely general with respect to the definition of sick time, only sick time associated with an inpatient episode was considered in this study.^ Since the temporal measure was years of Army service, an age-adjusting process was applied to the life tables for comparative purposes. Analyses were conducted by rank (enlisted and officer), race and sex, and were based on the ratio of expected remaining Army sick time to expected remaining Army service time. Seventeen major diagnostic groups, classified by the Eighth Revision, International Classification of Diseases, Adapted for Use In The United States, were ranked according to their cumulative (across years of service) contribution to expected remaining sick time.^ The study results indicated that enlisted personnel tend to have more expected hospital-associated sick time relative to their expected Army service time than officers. Non-white officers generally have more expected sick time relative to their expected Army service time than white officers. This racial differential was not supported within the enlisted population. Females tend to have more expected sick time relative to their expected Army service time than males. This tendency remained after diagnostic groups 580-629 (Genitourinary System) and 630-678 (Pregnancy and Childbirth) were removed. Problems associated with the circulatory system, digestive system and musculoskeletal system were among the three leading causes of cumulative sick time across years of service. ^

THE EFFECT OF CAFFEINE CONSUMPTION ON MORTALITY IN HYPERTENSIVE INDIVIDUALS

The effect of caffeine consumption on mortality was evaluated in a historical cohort study of 10064 hypertensive individuals participating in the Hypertension Detection and Follow-Up Program (HDFP) from 1973 to 1979. The study cohort was stratified into caffeine consumption groups (none, low, medium and high) based on their total level of caffeine intake from beverages (coffee and tea) and certain medications at the One-year follow-up home visit. Stratification was also made by sex, race, type of care and age. The total relative risks (RRs) when computed across strata for each caffeine consumer group (low, medium and high) were not significantly different when compared to the noncaffeine consumer group for all-cause or cause-specific mortality rates. The point estimates and 95 per cent confidence intervals for relative risks of all-cause mortality when compared to nonconsumers were as follows: Low = 0.82 (0.65-1.03), Medium: = 0.82 (0.62-1.82) and High = 0.90 (0.63-1.28). For all sex, race combinations there was an increase in the per cent of current smokers within each caffeine consumer group as the level of caffeine consumption increased. Cigarette smoking was an important confounder correlated with caffeine consumption and associated with mortality in this cohort. When confounding by cigarette smoking was adjusted for in the analysis, no association was found between the level of caffeine consumption and all-cause or cause-specific mortality. ^

Age -period -cohort analysis: An extension of Cox's lifetable regression with time -dependent covariates

It is well known that an identification problem exists in the analysis of age-period-cohort data because of the relationship among the three factors (date of birth + age at death = date of death). There are numerous suggestions about how to analyze the data. No one solution has been satisfactory. The purpose of this study is to provide another analytic method by extending the Cox's lifetable regression model with time-dependent covariates. The new approach contains the following features: (1) It is based on the conditional maximum likelihood procedure using a proportional hazard function described by Cox (1972), treating the age factor as the underlying hazard to estimate the parameters for the cohort and period factors. (2) The model is flexible so that both the cohort and period factors can be treated as dummy or continuous variables, and the parameter estimations can be obtained for numerous combinations of variables as in a regression analysis. (3) The model is applicable even when the time period is unequally spaced.^ Two specific models are considered to illustrate the new approach and applied to the U.S. prostate cancer data. We find that there are significant differences between all cohorts and there is a significant period effect for both whites and nonwhites. The underlying hazard increases exponentially with age indicating that old people have much higher risk than young people. A log transformation of relative risk shows that the prostate cancer risk declined in recent cohorts for both models. However, prostate cancer risk declined 5 cohorts (25 years) earlier for whites than for nonwhites under the period factor model (0 0 0 1 1 1 1). These latter results are similar to the previous study by Holford (1983).^ The new approach offers a general method to analyze the age-period-cohort data without using any arbitrary constraint in the model. ^