Regarding: a cohort study of systemic and local complications following implantation of testicular prostheses.
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Biomedical subjects
Publications and source records attributed to Sander Greenland.
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Using data from 1695 respondents aged 15 to 24 years to the 1995 National Survey of Family Growth, we examined black/white differences in marital history and sex with older, casual, and nonmonogamous partners, as well as the associations of these differences with self-reported bacterial sexually transmitted disease (STD) history. Although characteristics of sexual partners and relationships often differed by race, this did not explain racial disparities in STDs.
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BACKGROUND: Men who have sex with men (MSM) attending sexually transmitted disease (STD) clinics should be considered candidates for hepatitis A virus (HAV) and hepatitis B virus (HBV) vaccination. However, vaccination rates in STD clinics remain less than optimal. GOAL: The goal was to identify factors that affect HAV and HBV vaccination refusals. STUDY DESIGN: A survey was administered to MSM eligible for the vaccinations attending an STD clinic. Vaccines were offered at the end of the clinic visit. RESULTS: Rates of refusal of HAV (RefuseA) and HBV (RefuseB) vaccinations were 36% and 38%. Health motivation was associated with acceptance, while clinical barriers such as "want to test first for immunity," and "want to talk to own doctor first" were associated with refusal. "Not enough time this evening" was most strongly predictive of refusal, relative to strongly disagree (risk ratios [RRstrongly agree] and 95% confidence limits for RefuseA and RefuseB were 2.69 [1.43, 5.05] and 2.02 [1.05, 3.87], respectively). CONCLUSIONS: To increase acceptance, patients less health-motivated should be identified for prevaccination counseling. Some perceived barriers such as time may be a partial excuse; staff should identify and address other perceptions that may be influencing patients' decisions.
Conjugate priors for Bayesian analyses of relative risks can be quite restrictive, because their shape depends on their location. By introducing a separate location parameter, however, these priors generalize to allow modeling of a broad range of prior opinions, while still preserving the computational simplicity of conjugate analyses. The present article illustrates the resulting generalized conjugate analyses using examples from case-control studies of the association of residential wire codes and magnetic fields with childhood leukemia.
In a recent article on the efficacy of antihypertensive therapy, Berlowitz et al. (1998, New England Journal of Medicine 339, 1957-1963) introduced an ad hoc method of adjusting for serial confounding assessed via an intensity score, which records cumulative differences over time between therapy actually received and therapy predicted by prior medical history. Outcomes are subsequently regressed on the intensity score and baseline covariates to determine whether intense treatment or exposure predicts a favorable response. We use a structural nested mean model to derive conditions sufficient for interpreting the Berlowitz results causally. We also consider a modified approach that scales the intensity at each time by the inverse expected treatment given prior medical history. This leads to a simple, two-step implementation of G-estimation if we assume a nonstandard but useful structural nested mean model in which subjects less likely to receive treatment are more likely to benefit from it. These modeling assumptions apply, for example, to health services research contexts in which differential access to care is a primary concern. They are also plausible in our analysis of the causal effect of potent antiretroviral therapy on change in CD4 cell count, because men in the sample who are less likely to initiate treatment when baseline CD4 counts are high are more likely to experience large positive changes. We further extend the methods to accomodate repeated outcomes and time-varying effects of time-varying exposures.
It has long been known that stratifying on variables affected by the study exposure can create selection bias. More recently it has been shown that stratifying on a variable that precedes exposure and disease can induce confounding, even if there is no confounding in the unstratified (crude) estimate. This paper examines the relative magnitudes of these biases under some simple causal models in which the stratification variable is graphically depicted as a collider (a variable directly affected by two or more other variables in the graph). The results suggest that bias from stratifying on variables affected by exposure and disease may often be comparable in size with bias from classical confounding (bias from failing to stratify on a common cause of exposure and disease), whereas other biases from collider stratification may tend to be much smaller.
This paper reviews multilevel and conventional models for the analysis of ecologic (group, aggregate) data. It emphasizes the non-separability of contextual (group-level) effects and individual-level effects that arises from the multilevel structure of the underlying effects. Contrary to common misperceptions, this problem afflicts ecologic studies in which the sole objective is to estimate contextual effects, as well as studies in which the objective is to estimate individual effects. Multilevel effects also severely complicate causal interpretations of model coefficients.
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BACKGROUND: Diphenhydramine may be associated with excess risk of injury relative to nonsedating H1-receptor antagonists. OBJECTIVE: This study sought to compare the risk of injury in patients exposed to diphenhydramine with the risk of injury in patients exposed to loratadine. METHODS: A retrospective cohort study of injury was carried out in 12,106 patients whose initial antihistamine prescription was for diphenhydramine and in 24,968 patients whose initial antihistamine prescription was for loratadine. Data were taken from a health care claims database that included employees, dependents, and retirees who filed claims from January 1991 through December 1998. Rates of six serious injuries in the diphenhydramine cohort after and before the first prescription were compared with rates in the loratadine cohort after and before the first prescription. RESULTS: In the 30 days after the first antihistamine prescription, the rate of all injuries was 308 per 1,000 person-years in the diphenhydramine cohort versus 137 per 1,000 person-years in the loratadine cohort. The rate ratio estimate adjusted for age and gender using Poisson regression was 2.27 (95% confidence limits [CL] 1.93, 2.66). In the corresponding 30 days of the preceding year, the injury rates in the diphenhydramine and loratadine cohorts were 128 and 125 per 1,000 person-years, and the adjusted rate ratio was 1.02 (CL 0.83, 1.26). Thus, the cohorts appeared to have similar preprescription injury rates. The differences between the cohorts declined with time from prescription: For all injuries, the estimated percentage decline in the rate ratio was 4.1% per day (CL 3.3, 4.9), and the estimated time from the initial prescription until the diphenhydramine cohort returned to baseline risk was 32.3 days (CL 26.9, 37.6). CONCLUSIONS: If these associations are causal, the percentage of the injuries attributable to diphenhydramine was 55% (CL 41, 65), implying a substantial number of excess injuries and costs incurred as the result of diphenhydramine use. The high use rates of this drug and the high incidence of injury suggest that further study of the association between injury and type of antihistamine is needed.
This paper provides a brief overview to four major types of causal models for health-sciences research: Graphical models (causal diagrams), potential-outcome (counterfactual) models, sufficient-component cause models, and structural-equations models. The paper focuses on the logical connections among the different types of models and on the different strengths of each approach. Graphical models can illustrate qualitative population assumptions and sources of bias not easily seen with other approaches; sufficient-component cause models can illustrate specific hypotheses about mechanisms of action; and potential-outcome and structural-equations models provide a basis for quantitative analysis of effects. The different approaches provide complementary perspectives, and can be employed together to improve causal interpretations of conventional statistical results.
Meta-analytic reviews of placebo-controlled studies for obsessive-compulsive disorder have found that clomipramine is more effective than drugs with more selective actions on serotonin reuptake, whereas in most direct comparisons, clomipramine's superiority has been less obvious. The authors used metaregression to identify sources of he-terogeneity in placebo-controlled trials of clomipramine, fluvoxamine, sertraline, and paroxetine. They evaluated such patient characteristics as age, gender, age of obsessive-compulsive disorder (OCD) onset, and baseline severity of OCD and depression, and such study characteristics as exclusion or inclusion criteria, length of single-blind prerandomization period, length of trial, number of subjects, and publication year. We found considerable heterogeneity across studies that was associated, in part, with publication year, length of single-blind prerandomization period, length of trial, and severity of patients' OCD. The apparent superiority of clomipramine persisted after controlling for these factors. The authors also confirmed previous reports that placebo response is higher in more recent studies. Meta-analyses can help characterize responders and nonresponders. The authors urge investigators to provide summaries of patient characteristics, especially baseline severity, age at onset, and duration of OCD, by patients' response.
A cluster randomized trial was used to assess the effect of an active group intervention in promoting utilization of voluntary HIV testing and counseling (HIV-TC). Villagers from 40 clusters were sampled to represent the premarital age population and assigned into two groups, intervention and comparison. The intervention was designed to enhance risk perception and increase knowledge about HIV testing. Interviews were performed before and after the intervention. At baseline, 23% of 398 participants had been tested for HIV at least once and 90% reported testing positive. Most participants perceived that they had no chance of being infected with HIV. Among the intervention group, 71% participated in the intervention activities. The risk ratio of HIV-TC acceptance among the intervention group was 2.92, but the risk difference was only 8.11%. Factors associated with HIV-TC acceptance were ever having had a sexually transmitted disease, being previously married, intention to get tested, and having partici pated in AIDS-related activities.
OBJECTIVES: We examined differences in HIV seroprevalence and the likely timing of HIV infection by birth region. METHODS: We analyzed unlinked HIV antibody data on 61 120 specimens from 7 public health centers in Los Angeles County from 1993 to 1999. RESULTS: Most (87%) immigrant clients were Central American/Mexican-born. HIV prevalence was similar for US- and foreign-born clients (1.8% [95% confidence interval (CI) = 1.7%, 1.9%] and 1.6% [95% CI = 1.5%, 1.8%], respectively). Seroprevalence was high among sub-Saharan African females and low among Asian/Pacific Islander males and females. For HIV-positive immigrants, the average age at and time since immigration were 20.6 years and 12.3 years, respectively. CONCLUSIONS: The relatively young age at arrival and long time since arrival for HIV-positive foreign-born clients suggest that most were infected after immigration.
OBJECTIVE: We evaluated inpatient treatment of depression, prescribing patterns for antidepressants, and associated hospital charges. METHODS: We reviewed administrative data of the UCLA Neuropsychiatric Hospital between July 1994 and July 1997 for all 1698 hospitalizations for mood disorders. We evaluated drug utilization patterns and hospital charges by analysis of variance and multiple regression, and by stratifying on diagnosis, severity, age, and other factors. RESULTS: Length of stay was the major contributor to total charges, which included room charges and charges for services, procedures, supplies, and tests. The selective serotonin reuptake inhibitors (SSRIs) were prescribed most often (to 47% of patients), followed by the atypicals (heterocyclics, 12%), the tricyclics (TCAs, 7%), venlafaxine (7%) and the monoamine oxidase inhibitors (MAOIs, < 1%). The atypicals were given to the oldest patients. After controlling for length of stay, patient age, genders and comorbidity, the atypicals were associated with the highest total inpatient charges: $2000 more than MAOIs, $600 more than SSRIs, and $600 more than venlafaxine. Higher charges were the result of more expensive procedures, especially ECT. DISCUSSION: The SSRIs were the most commonly used antidepressant. Charges for antidepressant medications contributed only 0.5% of total inpatient charges. Patients receiving atypicals had among the highest total charges, partly because of the higher use of ECT. They may represent a more severely depressed group who have not responded to other antidepressants.