OBJECTIVES. The purpose of this study was to identify circumstances in which biochemical assessments of smoking produce systematically higher or lower estimates of smoking than self-reports. A secondary aim was to evaluate different statistical approaches to analyzing variation in validity estimates. METHODS. Literature searches and personal inquiries identified 26 published reports containing 51 comparisons between self-reported behavior and biochemical measures. The sensitivity and specificity of self-reports of smoking were calculated for each study as measures of accuracy. RESULTS. Sensitivity ranged from 6% to 100% (mean = 87.5%), and specificity ranged from 33% to 100% (mean = 89.2%). Interviewer-administered questionnaires, observational studies, reports by adults, and biochemical validation with cotinine plasma were associated with higher estimates of sensitivity and specificity. CONCLUSIONS. Self-reports of smoking are accurate in most studies. To improve accuracy, biochemical assessment, preferably with cotinine plasma, should be considered in intervention studies and student populations.

It is widely but incorrectly believed that the t-test and linear regression are valid only for Normally distributed outcomes. The t-test and linear regression compare the mean of an outcome variable for different subjects. While these are valid even in very small samples if the outcome variable is Normally distributed, their major usefulness comes from the fact that in large samples they are valid for any distribution. We demonstrate this validity by simulation in extremely non-Normal data. We discuss situations in which in other methods such as the Wilcoxon rank sum test and ordinal logistic regression (proportional odds model) have been recommended, and conclude that the t-test and linear regression often provide a convenient and practical alternative. The major limitation on the t-test and linear regression for inference about associations is not a distributional one, but whether detecting and estimating a difference in the mean of the outcome answers the scientific question at hand.

Important questions about health care are often addressed by studying health care utilization. Utilization data have several characteristics that make them a challenge to analyze. In this paper we discuss sources of information, the statistical properties of utilization data, common analytic methods including the two-part model, and some newly available statistical methods including the generalized linear model. We also address issues of study design and new methods for dealing with censored data. Examples are presented.

Higher depressive symptom scores in primary care patients were consistently associated with poorer health, functional status and QoL, and increased health care use, but not with demographic variables. The likelihood of treatment for depression was associated with perceptions of health, as well as severity of the depression.

We used data from a4-year prospective study of 2,558 primary care patients age 65 and older in a large staff model health maintenance organization to examine the association of clinically significant depressive symptoms and eight other chronic medical conditions with quality adjusted life years (QALYs). We developed linear regression models to examine the association of clinically significant depressive symptoms as defined by a score of 16 or greater on the Center for Epidemiological Studies Depression Scale and eight common chronic medical disorders at baseline with QALYs over the 4-year study period. Estimates of QALYs were derived from Quality of Well-Being Scale scores at baseline, at 2-year follow-up, and at 4-year follow-up. Individuals with clinically significant depressive symptoms at baseline had signrficantly lower QALYs over the 4year study period thannondepressed subjects, even after adjusting for differences in age, gender, and the eight other chronic medical conditions. In terms of the entire study population, only arthritis and heart disease were more strongly associated with QALYs than depression.

The authors examined differences between students with and without written parental consent to take part in a sensitive health survey. The data were collected using a consent procedure combining "active" and "passive" response options. Two thousand seven hundred five 9th and 12th graders whose parents provided written consent completed a full survey. An identical survey, without sex-related questions, was completed by 3,533 students whose parents gave "passive" consent to this less sensitive version. Students with written consent were more likely to be White, to live in two-parent households, to have a grade point average of B or above, and to be involved in extracurricular activities. They were also more likely to have been exposed to health promotion interventions. Irregular seat belt use was lower in the written-consent group at both grade levels. Among 9th graders, cigarette smoking was less prevalent in the written-consent group. There were no significant differences in alcohol or illicit drug use.

OBJECTIVE:To determine the associations between managed care, physician job satisfaction, and the quality of primary care, and to determine whether physician job satisfaction is associated with health outcomes among primary care patients with pain and depressive symptoms.DESIGN: Prospective cohort study. SETTING:Offices of 261 primary physicians in private practice in Seattle.PATIENTS: We screened 17,187 patients in waiting rooms, yielding a sample of 1,514 patients with pain only, 575 patients with depressive symptoms only, and 761 patients with pain and depressive symptoms; 2,004 patients completed a 6-month follow-up survey. MEASUREMENTS AND RESULTS:For each patient, managed care was measured by the intensity of managed care controls in the patient's primary care office, physician financial incentives, and whether the physician read or used back pain and depression guidelines. Physician job satisfaction at baseline was measured through a 6-item scale. Quality of primary care at follow-up was measured by patient rating of care provided by the primary physician, patient trust and confidence in primary physician, quality-of-care index, and continuity of primary physician. Outcomes were pain interference and bothersomeness, Symptom Checklist for Depression, and restricted activity days. Pain and depression patients of physicians with greater job satisfaction had greater trust and confidence in their primary physicians. Pain patients of more satisfied physicians also were less likely to change physicians in the follow-up period. Depression patients of more satisfied physicians had higher ratings of the care provided by their physicians. These associations remained after controlling statistically for managed care. Physician job satisfaction was not associated with health outcomes. CONCLUSIONS:For primary care patients with pain or depressive symptoms, primary physician job satisfaction is associated with some measures of patient-rated quality of care but not health outcomes.

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