Journal of chronic diseases最新文献

The forced expiratory volume in one second (FEV₁) is routinely used in epidemiologic studies of lung function to assess the presence and severity of obstructive airways disease. Normative prediction equations developed using data from healthy, asymptomatic individuals may then be used both in a clinical setting and to adjust comparisons among risk subgroups for known demographic differences. Unfortunately no concensus has yet developed as to how best to model lung function data. This paper addresses this issue in a systematic manner using data derived from two cohorts followed over a period of 9–11 years. We compare a variety of cross-sectional and longitudinal models for FEV₁, show how they may be expressed as members of a larger class of general linear models, and discuss goodness-of-fit procedures for comparing them. We found little objective evidence for discriminating among these models; only those fit to FEV₁/ht³ performed poorly. We argue on subjective grounds for the use of models based on FEV₁, as a function of age, height and their interactions.

This paper reviews the sources of apparent time trends in effect. Apparent changes in effect may arise from changes in covariate distributions, background rates, exposure distribution, measurement quality, or selection factors. As with time trends in rates, time trends in effect must have at least one of these sources, since time itself has no effect. If background incidence is changing, however, time trends in effect become dependent on choice of effect measure, and interpretation must take this into account. Evidence that a trend arises from age-, cohort-, or period-related phenomena can indicate the relative plausibility of different explanations of the trend. Conversely, the relative plausibility of each explanation may indicate whether the trend is most appropriately viewed over the axis of age, birth cohort, or calendar time. Nevertheless, studies of short duration relative to an apparent trend (such as most case-control studies) must invoke strong assumptions to justify focusing on a particular axis. Illustrations are given from studies of electronic fetal monitoring and of smoking and lung cancer.

This paper uses a detailed example to illustrate how to detect individual observations that disproportionately influence the results of hypothesis testing with relative risk regression models.

Little is known about how generosity of insurance and population characteristics affect quantity or appropriateness of antibiotic use. Using insurance claims for antibiotics from 5765 non-elderly people who lived in six sites in the United States and were randomly assigned to insurance plans varying by level of cost-sharing, we describe how antibiotic use varies by insurance plan, diagnosis and health status, geographic area, and demographic characteristics. People with free medical care used 85% more antibiotics than those required to pay some portion of their medical bills (controlling for all other variables). Antibiotic use was significantly more common among women, the very young, patients with poorer health, and persons with higher income. Use of antibiotics for viral, viral-bacterial, and bacterial conditions did not differ between free and cost-sharing insurance plans, given antibiotics were the treatment of choice. Cost sharing reduced inappropriate and appropriate antibiotic use to a similar degree.

This paper analyzes the mortality of Mexican Americans and Puerto Ricans residing in the Chicago metropolitan area. In a comparison of these two groups with nonHispanic whites in the same area, it was found that Hispanic mortality is unusually low for the two leading causes of death in the mainstream U.S. population. Explanations discussed include underdiagnosis, underreporting of illness, the “healthy migrant” thesis and possible return migration of elderly ill.

The accuracy of routinely collected mortality data for ischemic heart disease (IHD) as indicators of death from acute myocardial infarction (AMI) was assessed in ages 25–64 years, according to the WHO criteria defined in 1983. Cases were identified from computer records (linked for individuals) of all death certificates and hospital discharges in Western Australia between 1971 and 1982. Where the official cause was IHD about 90% of deaths fulfilled the WHO criteria for definite or possible AMI. Up to 10% of fatal cases of definite or possible AMI were coded to other causes in the official death statistics, however it appeared that variations in this figure with changes in coding practices could cause appreciable bias in the estimation of secular trends in IHD mortality. This problem could largely be overcome by reviewing fatal events where the death certificate was coded to one of a limited number of other ICD rubrics.