This lack of consistency is exacerbated by the use of different reporting periods in different studies. "High risk" can refer to behaviors during the previous month, the previous year, or a person's lifetime. Many of these same methodologic concerns are also present in studies of hepatitis B and hepatitis C. Furthermore, relative rates of infection with HIV, hepatitis B, and hepatitis C among persons with severe mental illness can be difficult to estimate across studies.
In this study we used data from this same large and diverse sample to clarify relationships among demographic, psychiatric, psychosocial, and behavioral predictors and HIV, hepatitis B, and hepatitis C infection status. We also examined whether HIV, hepatitis B, and hepatitis C infection were each predicted by the same risk factors.
A total of 969 persons with severe mental illness from five sites were approached to take part in an assessment involving testing for blood-borne infections and a one-time standardized interview containing questions about sociodemographic characteristics, substance use, risk behaviors for sexually transmitted diseases, history of sexually transmitted diseases, and health care. A detailed description of the sample and data collection methods can be found in the article by Rosenberg and colleagues in this issue of
Psychiatric Services (
+22) and in an earlier report by Rosenberg and colleagues (
+3).
Demographic characteristics. As part of the interview, information was gathered about participants' demographic characteristics, including age, gender, race (African American, white, Hispanic, or other), marital status and number of children, residential information (current living situation and current and lifetime homelessness), and educational, employment, income, and health insurance status (no insurance, private insurance, or Medicaid).
Infection status. A detailed description of procedures for blood collection and analysis can be found in the earlier report by Rosenberg and colleagues (
+3). Four infection-status variables were created from the results of blood assays. The first three variables were the positive or negative status of HIV infection or AIDS, hepatitis B infection, and hepatitis C infection, separately. A fourth variable— "any of the three infections"—was created to compare persons who tested positive for at least one of the three infections with persons who tested negative for each infection. Because the rural North Carolina site did not collect information on hepatitis, study participants from that site were deemed to be positive on the variable "any of the three infections" solely on the basis of their HIV status.
Clinical characteristics. A description of measures of clinical characteristics can be found in the accompanying article by Rosenberg and colleagues (
+22). Diagnostic comparisons were between persons with the psychiatric diagnoses of schizophrenia, schizoaffective disorder, bipolar disorder, major depression, and other diagnoses. Comparisons were also made between participants who did and those who did not have an alcohol use disorder, a substance use disorder, and posttraumatic stress disorder (PTSD).
AIDS Risk Inventory. The AIDS Risk Inventory, a semistructured interview measuring HIV risk behaviors associated with drug use and sexual activity (
+23), is described in the accompanying article by Rosenberg and colleagues (
+22).
Univariate relationships between predictive variables and serologic results by site were examined for any clear patterns and to reduce the size of the set of predictor variables. Next, to examine the consistency of these relationships across sites, we conducted meta-analyses of the t test and chi square results, again using serologic test results as the outcome variables. We then conducted factor analyses to identify clusters of variables and used these results in regression analyses, the results of which are presented below.
+
Univariate relationships
Univariate relationships among 38 demographic, psychiatric, psychosocial, and behavioral predictors and four infection-status outcome variables (presence of HIV, hepatitis B, hepatitis C, or any one of the three infections) were examined separately for each of the five sites by using chi square and t tests. Some relationships emerged as significant in each of the five sites (for example, total overall risk behavior was significantly related to the variable "any of the three infections" in Connecticut (t=−2.28, df=147, p=.024), Maryland (t=−3.36, df=130, p=.001), the public site in North Carolina (t=−2.92, df=175, p=.004), the Department of Veterans Affairs (VA) site in North Carolina (t=−6.16, df=182, p<.001), and New Hampshire (t=−3.62, df=281, p<.001). However, other relationships were significant at some sites but not others. For example, having a current substance use diagnosis was significantly related to HIV infection among study participants in Connecticut (χ2= 4.71, df=1, p=.030), Maryland (χ2= 8.13, df=1, p=.004), and the public site in North Carolina (χ2=4.61, df=1, p=.032) but not the VA site in North Carolina or in New Hampshire.
Because no clear pattern emerged across or between sites for most variables, we conducted meta-analyses of the t test and chi square results to look for stable relationships across sites. The results of these meta-analyses are presented in Tables 1 and 2, along with the results when all sites were merged and analyzed as a single sample, for comparison purposes. The categorical variables by infection status are shown in
+Table 1, and the continuous variables are shown in
+Table 2. (Possible scores on the community violence scale range from 0 to 14; on the current and lifetime adult physical abuse scales range from 0 to 9; on the violence scale (Violence Factor Score) range from 0 to 1; and on the infection risk behavior scale range from 0 to 1.)
As can be seen in
+Table 1, of the demographic variables, race, gender, and number of children showed some relationship to infection outcomes. Hispanic persons were most likely to test positive for at least one infection. However, race was not significantly related to any single infection. Persons who tested positive for hepatitis B and for at least one of the infections had significantly more children than those who tested negative (p=.025). The relationship between number of children and infection status approached significance (p=.070) for hepatitis C and was not significant for HIV. Finally, in terms of demographic variables, the relationship between HIV and gender approached significance (p=.053), with men more likely than women to be positive for HIV.
Across sites, participants who were HIV-positive were significantly more likely to have been homeless in the previous six months. However, homelessness was not significantly related to the variable "any of the three infections." Study participants who tested positive for at least one of the three infections were significantly less likely to have private health insurance (p=.008). This relationship was similarly significant for each of the infections separately (HIV, p= .008; hepatitis B, p=.036; hepatitis C, p=.045). Furthermore, persons who tested positive for hepatitis B (p=.035) but not for HIV or hepatitis C were more likely to have Medicaid insurance.
Persons who tested positive for hepatitis B, hepatitis C, or at least one infection were more likely to have a substance use disorder (p=.039, p=.048, and p=.028, respectively). Persons with HIV, hepatitis C, or any of the infections were more likely to have an alcohol use disorder (p=.003, p=.020, and p=.041, respectively). This finding was observed across sites, except for Connecticut, where persons who tested negative for HIV were more likely to have an alcohol use disorder. Having a diagnosis of PTSD was significantly related to only one of the infection outcome measures—HIV (p=.043). Persons who were HIV-positive were more likely to have PTSD than those who were not.
Study participants who tested positive for at least one of the three infections reported more severe trauma exposure as adults, both currently (p=.048) and across their adult lifetime (p=.027). Trauma exposure was not a significant predictor of each of the three infections separately, although, for lifetime severity of trauma exposure, the relationship approached significance among persons with hepatitis C (p=.058) and HIV (p=.090). For lifetime community violence, persons who tested positive for hepatitis C reported more violence than those who tested negative (p=.048). Similarly, those who were positive for at least one of the infections, or for HIV, hepatitis B, or hepatitis C, separately, were significantly more likely to have been arrested at least once (p=.004, p=.023, p=.035, and p=.001, respectively).
Finally, as would be expected, engaging in risk behaviors for sexually transmitted diseases was significantly related to the variable "any of the three infections" (p=.005), hepatitis B (p=.012), and hepatitis C (p=.020)—persons who were positive for infection reported more risk behaviors. In terms of the relationship between infection risk behaviors and HIV status, site-specific differences were observed. In Maryland and in the public mental health system in North Carolina, this relationship was significant (p<.001), whereas it was not significant in Connecticut, the VA system in North Carolina, or New Hampshire.
In an attempt to reduce the number of variables used in subsequent analyses, a factor analysis was performed on lifetime community violence, current adult physical abuse, lifetime adult physical abuse, and history of arrest. One factor emerged (eignenvalue=2.209), and all four of the variables loaded onto this factor. These factor scores were then used to create a new "violence" variable, which succinctly summarized the original four variables.
+
Multivariate predictors across sites
On the basis of the results of these meta-analyses, only variables that were significantly related to at least one of the three infections were used in subsequent multivariate analyses that pooled data across sites. Multiple logistic regressions were used to examine how infection status was related to gender, race, number of children, private health insurance, Medicaid insurance, homelessness, alcohol abuse diagnosis, substance abuse diagnosis, PTSD diagnosis, violence, and behavioral risk for sexually transmitted diseases.
Site was also used as a predictor to capture site-specific differences. The significant predictors of having at least one of the three infections were site, gender, number of children, private health insurance, substance use disorder, and risk behaviors for sexually transmitted diseases. As can be seen in
+Table 3, these variables were also significant predictors of hepatitis B and hepatitis C, except that gender was not a significant predictor of hepatitis B infection. PTSD emerged as a significant predictor of hepatitis B and the only significant predictor of HIV.
To examine the importance of predictors, we ran multiple versions of sequential regressions predicting serologic test results. The variables were entered in five steps: first, site; second, gender and race; third, number of children, risk total, PTSD, and violence; fourth, private health insurance, Medicaid, and employment; and fifth, alcohol abuse diagnosis, drug abuse diagnosis, and homelessness. The strategy for selecting the order of entry was to rank clusters of variables—with the exception of site—from the least modifiable by means of treatment interventions to the most modifiable. These regressions were then run again with site entered last rather than first. Regardless of when site was entered into the model, it was one of the strongest predictors.
Generally, few differences were found between the standard and sequential regressions, and these differences highlighted the same variables found to be important in the meta-analysis. The one difference between the two analyses was that the marginally significant relationship between race and the variable "any of the three infections" found in the meta-analysis was not significant in the regression analysis. This result suggests that the race variable was confounded with one or more of the stronger predictors, such as site. When race was examined in the regression analyses, it accounted for no significant unique effect.
In contrast with identifying variables that could be used to predict behaviors that put an individual at greater risk of hepatitis C infection as opposed to the other infections, these analyses indicated that the sole robust predictor was the total risk variable. Greater total risk predicted a greater chance of infection with any one of the three infections, with hepatitis C alone, and with hepatitis B alone. For HIV, no significant predictors were identified, perhaps because of the low number of positive cases entered into the analyses. The relationship between the most robust predictor of infection—infection-risk behaviors—is shown by site in
+Table 4. (Data for hepatitis B and C from the public mental health system in North Carolina are absent from the table because information on hepatitis was not collected at that site.)
The rates of infection with HIV, hepatitis B, and hepatitis C varied widely across the study sites, yet the patterns of predictors of infection were largely consistent across sites. Namely, the greater the number of risk behaviors, the greater the likelihood of infection. This observation held regardless of whether a person lived in lower-prevalence, more rural locations (from where the samples in New Hampshire and the public mental health system in North Carolina were drawn) or in higher-prevalence, urban locations (Connecticut and Maryland). The take-home lesson for persons with severe mental disorders—and the clinicians working with them—is that engaging in certain behaviors increases one's risk of infection regardless of local infection rates. Although we did not find evidence that certain behaviors put a person at a greater risk of one infection while other behaviors put the person at a greater risk of a different infection, it is conceivable that some significant differences among the risks—whether clinically important or not—may be found with more powerful research designs. Nevertheless, the message from our analysis is straightforward and uncomplicated: The likelihood of being seropositive for any one of the infections increased with a greater number of risk behaviors.
As noted in the earlier report by Rosenberg and colleagues (
+3), persons with severe mental illnesses have an elevated risk of each of the infections examined in this study. The results reported here extend previous findings by identifying factors that are most predictive of serologic status among these same individuals. Such risk factors for these sexually transmitted diseases include geographic location, gender, and substance abuse in addition to behavioral risk factors, such as having multiple sexual partners and sharing needles. Additional studies reported in this issue of
Psychiatric Services elaborate on the interrelationships among some of these risk factors and serological status. The core finding is that clinicians should be attentive to these risk factors so as to encourage appropriate testing, counseling, and treatment.