Mitchell H. Katz

Attempted Suicide Among Transgender Persons

Abstract To determine the independent predictors of attempted suicide among transgender persons we interviewed 392 male-to-female (MTF) and 123 female-to-male (FTM) individuals. Participants were recruited through targeted sampling, respondent-driven sampling, and agency referrals in San Francisco. The prevalence of attempted suicide was 32% (95% CI = 28% to 36%). In multivariate logistic regression analysis younger age (<25 years), depression, a history of substance abuse treatment, a history of forced sex, gender-based discrimination, and gender-based victimization were independently associated with attempted suicide. Suicide prevention interventions for transgender persons are urgently needed, particularly for young people. Medical, mental health, and social service providers should address depression, substance abuse, and forced sex in an attempt to reduce suicidal behaviors among transgender persons. In addition, increasing societal acceptance of the transgender community and decreasing gender-based prejudice may help prevent suicide in this highly stigmatized population.

Impact of Highly Active Antiretroviral Treatment on HIV Seroincidence Among Men Who Have Sex With Men: San Francisco

OBJECTIVES This study assessed the countervailing effects on HIV incidence of highly active antiretroviral treatment (HAART) among San Francisco men who have sex with men (MSM). METHODS Behavioral risk was determined on the basis of responses to cross-sectional community interviews. HIV incidence was assessed through application of an enzyme-linked immunoassay testing strategy. RESULTS Use of HAART among MSM living with AIDS increased from 4% in 1995 to 54% in 1999. The percentage of MSM who reported both unprotected anal intercourse and multiple sexual partners increased from 24% in 1994 to 45% in 1999. The annual HIV incidence rate increased from 2.1% in 1996 to 4.2% in 1999 among MSM who sought anonymous HIV testing, and the rate was high (5.3%) but stable in a blinded survey of MSM seeking sexually transmitted disease services. CONCLUSIONS Any decrease in per contact risk of HIV transmission due to HAART use appears to have been counterbalanced or overwhelmed by increases in the number of unsafe sexual episodes.

Long-term HIV-1 infection without immunologic progression.

Objective:To identify and describe a subgroup of men infected with HIV for 10–15 years without immunologic progression, and to evaluate the effect of sexually transmitted diseases (STD) and recreational drug use on delayed HIV disease progression. Design: Inception cohort study. Setting: Municipal STD clinic. Participants:A total of 588 men with well documented dates of HIV seroconversion and 197 HIV-seronegative controls. Main outcome measures:AIDS, CD4+ count, rate of CD4+ cell loss, CD8+ count, β2-microglobulin, complete blood count, p24 antigen and HIV-related symptoms. Results:Of 588 men, 69% had developed AIDS by 14 years after HIV seroconversion (95% confidence interval, 64–73%). Of 539 men with HIV seroconversion dates prior to 1983, 42 men (8%) were healthy long-term HIV-positives (HLP), HIV-infected > 10 years without AIDS and with CD4+ counts >500 × 106/l. When compared with progressors (men with HIV seroconversion prior to 1983 but with AIDS or CD4+ counts <200 × 106/l, HLP had a significantly slower rate of CD4+ decline (6 versus 85x106/l cells/year), and less abnormal immunologic, hematologic and clinical parameters. However, when compared with HIV-uninfected controls, HLP demonstrated lower CD4+ counts and mild hematologic abnormalities. There were no consistent differences between HLP and progressors in prior exposure to recreational drugs or STD. Conclusion:There are individuals with long-term HIV infection who appear clinically and immunologically healthy 10–15 years after HIV seroconversion, with stable CD4+ counts. Lack of exposure to STD or recreational drugs does not appear to explain the delayed course of disease progression in HLP.

Multivariable Analysis: A Primer for Readers of Medical Research

Most published medical research uses multivariable analysis. Unfortunately, many readers, especially those uncomfortable with mathematics, treat multivariable models as a black box, accepting the authors explanation of the results without independently assessing whether the models are correctly constructed or interpreted. However, multivariable models can be understood without undue concern for the underlying mathematics. I review the basics of multivariable analysis, including why multivariable models are used, what types exist, what assumptions underlie them, how they should be interpreted, and how they can be evaluated. What Is Multivariable Analysis? Multivariable analysis is a statistical tool for determining the unique contributions of various factors to a single event or outcome. For example, numerous factors are associated with the development of coronary heart disease, including smoking, obesity, sedentary lifestyle, diabetes, elevated cholesterol level, and hypertension. These factors are called risk factors, independent variables, or explanatory variables. Multivariable analysis allows us to determine the independent contribution of each of these risk factors to the development of coronary heart disease (called the outcome, the dependent variable, or the response variable). Why Is Multivariable Analysis Needed? In many clinical situations, experimental manipulation of study groups would be unfeasible, unethical, or impractical. In these circumstances, multivariable analysis can be used to assess the association between multiple risk factors and outcomes. For example, we cannot test whether smoking increases the likelihood of coronary heart disease by randomly assigning persons to groups who smoke and groups who do not smoke. Although bivariate analysis of longitudinal data demonstrates that smokers are more likely than nonsmokers to develop coronary heart disease, this is weak evidence of a causal association. Perhaps the only reason smokers are more likely to develop coronary heart disease is that they are more likely to be male, live in poverty, and have a sedentary lifestyle. In other words, the relationship between smoking and coronary artery disease may be confounded by these other variables. Confounding occurs when the apparent association between a risk factor and an outcome is affected by the relationship of a third variable to the risk factor and to the outcome; the third variable is a confounder. For a variable to be a confounder, the variable must be associated with the risk factor and causally related to the outcome (Figure 1). Male sex, poverty, and sedentary lifestyle could be confounders because they are associated with both smoking and coronary heart disease. With multivariable analysis, we can demonstrate that even after adjusting for male sex, poverty, and sedentary lifestyle, smoking has an independent relationship with coronary artery disease (Figure 2). Figure 1. Relationship among risk factor, confounder, and outcome. Figure 2. Multivariable association between four risk factors and coronary artery disease. A study of the association between periodontal disease and coronary heart disease illustrates how multivariable analysis can be used to identify confounders (2). Bivariate analysis demonstrates that persons with periodontitis have a markedly increased rate of coronary heart disease (relative hazard, 2.66 [95% CI, 2.34 to 3.03]). If this relationship were independent and causal, then interventions that would reduce periodontitis would decrease the occurrence of coronary heart disease. However, periodontitis is also associated with several factors known to be related to coronary heart disease, including older age, male sex, poverty, smoking, increased body mass index, and hypertension, raising the question of whether the association between periodontitis is due to confounding by these factors (Figure 3). With multivariable adjustment for these variables, sampling design, and sampling weights, the association between periodontitis and coronary heart disease weakens substantially: the relative hazard decreases to 1.21; the 95% CIs for the relative hazard (0.98 to 1.50) crosses 1.0; and the association between periodontitis and coronary artery disease is no longer statistically significant. Figure 3. Potential confounders of the relationship between periodontitis and coronary heart disease. Although one can theoretically distinguish independent associations from confounding, a variable may have both an independent effect on outcome and be a confounder of another variables relationship to outcome. For example, poverty is a confounder of the relationship between smoking and coronary artery disease (poor people are more likely to smoke and to develop coronary artery disease), but poverty also has an independent effect on development of coronary artery disease (after adjustment for smoking, cholesterol level, and other known risk factors, poor persons are more likely to develop coronary artery disease). Multivariable analysis is not the only statistical method for eliminating confounding. Stratified analysis can also assess the effect of a risk factor on an outcome while holding other variables constant, thereby eliminating confounding. For example, the effect of periodontitis on coronary heart disease can be examined separately for men and women, which removes the effect of sex on the relationship between these diseases. If periodontitis is no longer significantly associated with coronary heart disease when men and women are looked at separately, then sex was confounding the relationship between the two. If periodontitis is still associated with coronary heart disease when men and women are assessed separately, then the effect of periodontitis on coronary heart disease is independent of sex. Stratification works well when there are only two or three confounders. However, when there are many potential confounders, stratifying for all of them will create literally hundreds of groups in which the investigators would need to determine the relationship between periodontitis and coronary heart disease. Because the sample sizes would be small, the estimates of risk would be unstable. Whether investigators use multivariable analysis or stratification, it is important to remember that they can only adjust for measured variables. Results may still be confounded by known and unknown unmeasured factors. What Types of Multivariable Analysis Are Commonly Used in Clinical Research? The three types of multivariable analysis that are commonly used in clinical research are multiple linear regression, multiple logistic regression, and proportional hazards (Cox) regression (Table). Linear regression is used with interval (also called continuous) outcomes (such as blood pressure). With interval variables, equally sized differences on all parts of the scale are equal. Blood pressure is an interval variable because the difference between a blood pressure of 140 and 143 mm Hg (3 mm Hg) is the same as the difference between a blood pressure of 150 and 153 mm Hg (3 mm Hg). Logistic regression is used with dichotomous outcomes (yes or no; for example, death). Proportional hazards regression is used when the outcome is the length of time to reach a discrete event (such as time from baseline visit to death). Table. Types of Multivariable Analysis How Is the Effect of an Individual Variable on Outcome Assessed in a Multivariable Analysis? The regression coefficient for each variable must be estimated by fitting the model to the data and adjusting for all other variables in the model. With logistic regression and proportional hazards regression, the coefficients have a special meaning. The antilogarithm of the coefficient equals the odds ratio (for logistic regression) and the relative hazard (for proportional hazards regression). The hazard is the probability that a person experiences an outcome in a short time interval, given that the person has survived to the beginning of the interval. When the outcome is uncommon (<15%), the odds ratio and relative hazard are reasonable estimates of the relative risk. For example, if the odds ratio or relative hazard for the association between smoking and fatal heart attacks is 3.0 (assuming that fatal heart attacks occurred in <15% of patients), then smoking roughly triples the risk for a fatal heart attack. If the odds ratio or relative hazard for the association between estrogen use and development of a pathologic fracture is 0.33, then persons who take estrogen have roughly a third of the risk for fracture as persons who do not take estrogen. When the outcome is common, the odds ratio remains a useful measure of association, but it does not approximate the relative risk. For example, a randomized trial of persons with bronchopulmonary aspergillosis showed better response to itraconazole (13 of 28 patients) than to placebo (5 of 27 patients) (3). The odds ratio is 4.7 [(13 27)/(15 5)], but the relative risk is only 3.0 [(13/28)/(5/32)]. With interval-independent variables, the coefficient and the resulting odds ratio or relative hazard can be misunderstood. For example, an observational study reported that the odds ratio for the effect of low-density lipoprotein cholesterol on coronary artery calcification was 1.01 (CI, 1.00 to 1.02) (4). This may seem like a trivial effect until you notice that the odds ratio of 1.01 is for each increase of 1 mg/dL of low-density lipoprotein cholesterol. An increase of 40 mg/dL of cholesterol would produce an odds ratio of 1.49 (1.01) 40. This example demonstrates that the size of the coefficient of an interval variable is entirely dependent on the units being used. With interval-independent variables, readers must also assess whether the model accurately captures the relationship between the variable and the outcome. Multivariable models assume that increases (or decreases) in an interval-independent variable will be associated with

The Impact of Competing Subsistence Needs and Barriers on Access to Medical Care for Persons with Human Immunodeficiency Virus Receiving Care in the United States

OBJECTIVES To examine whether competing subsistence needs and other barriers are associated with poorer access to medical care among persons infected with human immunodeficiency virus (HIV), using self-reported data. DESIGN Survey of a nationally representative sample of 2,864 adults receiving HIV care. MAIN INDEPENDENT VARIABLES Going without care because of needing the money for food, clothing, or housing; postponing care because of not having transportation; not being able to get out of work; and being too sick. MAIN OUTCOME MEASURES Having fewer than three physician visits in the previous 6 months, visiting an emergency room without being hospitalized; never receiving antiretroviral agents, no prophylaxis for Pneumocystis carinii pneumonia in the previous 6 months for persons at risk, and low overall reported access on a six-item scale. RESULTS More than one third of persons (representing >83,000 persons nationally) went without or postponed care for one of the four reasons we studied. In multiple logistic regression analysis, having any one or more of the four competing needs independent variables was associated with significantly greater odds of visiting an emergency room without hospitalization, never receiving antiretroviral agents, and having low overall reported access. CONCLUSIONS Competing subsistence needs and other barriers are prevalent among persons receiving care for HIV in the United States, and they act as potent constraints to the receipt of needed medical care. For persons infected with HIV to benefit more fully from recent advances in medical therapy, policy makers may need to address nonmedical needs such as food, clothing, and housing as well as transportation, home care, and employment support.

The prevalence of oral lesions in HIV-infected homosexual and bisexual men : three San Francisco epidemiological cohorts

To establish the prevalence of HIV-related oral lesions, we performed oral examinations of members of three San Francisco epidemiological cohorts of homosexual and bisexual men over a 3-year period. Hairy leukoplakia, pseudomembranous and erythematous candidiasis, angular cheilitis, Kaposis sarcoma, and oral ulcers were more common in HIV-infected subjects than in HIV-negative subjects. Among HIV-infected individuals, hairy leukoplakia was the most common lesion [20.4%, 95% confidence interval (CD 17.5–23.3%] and pseudomembranous candidiasis was the next most common (5.8%, 95% Cl 4.1–7.5%). Hairy leukoplakia, pseudomembranous candidiasis, angular cheilitis and Kaposis sarcoma were significantly more common in patients with lower CD4 lymphocyte counts (P < 0.05). The prevalence of erythematous candidiasis and Kaposis sarcoma increased during the 3-year period. Careful oral examinations may identify infected patients and provide suggestive information concerning their immune status.

Trends in Causes of Death among Persons with Acquired Immunodeficiency Syndrome in the Era of Highly Active Antiretroviral Therapy, San Francisco, 1994–1998

To understand recent temporal trends in acquired immunodeficiency syndrome (AIDS) mortality in the era of highly active antiretroviral therapy (HAART), trends in causes of death among persons with AIDS in San Francisco who died between 1994 and 1998 were analyzed. Among 5234 deaths, the mortality rate for human immunodeficiency virus (HIV)-related or AIDS-related deaths declined after 1995 (P<.01), whereas the mortality rate for non-HIV- or non-AIDS-related deaths remained stable. The proportion of deaths of persons with AIDS associated with septicemia, non-AIDS-defining malignancy, chronic liver disease, viral hepatitis, overdose, obstructive lung disease, coronary artery disease, and pancreatitis increased (P<.05). The standardized mortality ratio was high for these causes in both pre- and post-HAART periods, except for pancreatitis, a possible complication of HAART, which demonstrated an increasing standardized mortality ratio trend after 1996. With increasing AIDS survival, prevention of chronic diseases, assessment of long-term toxicity from HAART, and surveillance for additional causes of mortality will become increasingly important.

Effect of highly active antiretroviral therapy on diagnoses of sexually transmitted diseases in people with AIDS

BACKGROUND There has been an increase in high-risk sexual behaviour and sexually transmitted diseases (STD) during the time period when highly active antiretroviral therapy (HAART) became widely available. We examined whether taking HAART increased the risk of acquiring an STD--an epidemiological marker of unsafe sex--in people with AIDS. METHODS We did a computerised match of people in the San Francisco STD and AIDS registries. People with AIDS who were diagnosed before 1999 and alive in November, 1995, or later, were classified as having had an STD after AIDS diagnosis or not having had an STD after AIDS diagnosis. We used a Cox proportional hazards model to see whether use of antiretroviral therapy was associated with acquiring an STD after AIDS, after adjustment for sex, age, race, HIV-1 risk category, and CD4 count at AIDS diagnosis. FINDINGS People with AIDS who had had HAART showed an independent increase in the risk of developing an STD (hazard ratio 4.10; 95% CI 2.84-5.94). Americans of African origin, younger age, and higher CD4 count at AIDS diagnosis were also associated with acquiring an STD after AIDS. The number of people living with AIDS who acquired an STD increased over time from 60 (0.66%) in 1995 to 113 (1.32%) in 1998 (p<0.001). INTERPRETATION We have shown that people on HAART are more likely to develop an STD, an epidemiological marker of unsafe sex. More intensive risk-reduction counselling and STD screening for people with AIDS is needed.

Feasibility of Postexposure Prophylaxis (PEP) against Human Immunodeficiency Virus Infection after Sexual or Injection Drug Use Exposure: The San Francisco PEP Study

The feasibility of providing postexposure prophylaxis (PEP) after sexual or injection drug use exposures to human immunodeficiency virus (HIV) was evaluated. PEP was provided within 72 h to individuals with exposures from partners known to have or to be at risk for HIV infection. PEP consisted of 4 weeks of antiretroviral medications and individually tailored risk-reduction and medication-adherence counseling. Among 401 participants seeking PEP, sexual exposures were most common (94%; n=375). Among sexual exposures, receptive (40%) and insertive (27%) anal intercourse were the most common sexual acts. The median time from exposure to treatment was 33 h. Ninety-seven percent of participants were treated exclusively with dual reverse-transcriptase inhibitors, and 78% completed the 4-week treatment. Six months after the exposure, no participant developed HIV antibodies, although a second PEP course for a subsequent exposure was provided to 12%. PEP, after nonoccupational HIV exposure, is feasible for persons at risk for HIV infection.

Increased Incidence of Hodgkin Disease in Homosexual Men with HIV Infection

OBJECTIVE To evaluate the incidence of Hodgkin disease and non-Hodgkin lymphoma among homosexual men infected with human immunodeficiency virus (HIV). DESIGN Cohort study with computer-matched identification of participants with the Northern California Cancer Center registry. Population rate comparisons were made with data from the Surveillance, Epidemiology, and End Results (SEER) cancer registry. PARTICIPANTS The 6704 homosexual men in the San Francisco City Clinic Cohort study. MEASUREMENTS Incidence of Hodgkin disease, non-Hodgkin lymphoma, HIV infection, and the acquired immunodeficiency syndrome (AIDS); calculation of sex and age-adjusted standardized morbidity ratios and attributable risk. RESULTS Eight cases of Hodgkin disease and 90 cases of non-Hodgkin lymphoma were identified through computer matching among cohort members residing in the San Francisco Bay area from 1978 through 1989. Among the HIV-infected men, the age-adjusted standardized morbidity ratio was 5.0 (95% CI, 2.0 to 10.3) for Hodgkin disease and 37.7 (CI, 30.3 to 46.7) for non-Hodgkin lymphoma. The excess risk attributable to HIV infection was 19.3 cases of Hodgkin disease per 100,000 person-years and 224.9 cases of non-Hodgkin lymphoma per 100,000 person-years. CONCLUSION An excess incidence of Hodgkin disease was found in HIV-infected homosexual men. Additional well-designed epidemiologic studies are needed to determine whether Hodgkin disease should be considered an HIV-related malignancy.