These analyses were intended to explore the potential value of data sources and a research strategy for clarifying the contribution of contextual and social influences on patterns of infectious disease spread among people with SMI. Applying existing research tools to Medicaid data from eight states, we found that rates of HIV among patients with schizophrenia differed sharply across MSAs.
Not surprisingly, it appears that one important source of the variability of HIV prevalence among people with schizophrenia was the underlying variability in prevalence of HIV across the MSAs. As suggested by findings in the literature, we found an association between an MSA's estimated rate of HIV among IDUs within the MSA and the MSA-level treated prevalence rates for Medicaid patients diagnosed with schizophrenia, even after controlling for underlying AIDS prevalence. This association was not eliminated after controlling for a range of environmental covariates. Multiple MSA-level predictors suggested by the -literature (e.g., crime rates and poverty) did not appear to contribute to an explanation of variation in the treated prevalence of HIV among people with schizophrenia over and above the association with HIV among IDUs and HIV in the general population.
There have been some efforts to explain geographical variation in HIV. One study found higher rates of HIV among IDUs in East Coast vs. West Coast cities, but found that this difference could not be adequately explained by differences in self-reported injection or sexual practices.71
Pioneering work by Holmberg calculated figures for HIV incidence and prevalence, and sizes of populations at risk, for 96 MSAs in the early 1990s.72
More recently, the National Development and Research Institutes group has produced a body of work examining associations between various social and structural characteristics and variations in local HIV epidemic characteristics among IDUs. Community-level associations have been found between HIV prevalence among IDUs and the presence of laws against over-the-counter purchasing of syringes and income inequality.73
The population density of IDUs in metropolitan areas may correlate with poverty rates or other indicators of socioeconomic distress and/or laws limiting syringe access in a locality.74
It seems possible that differences in MSA-level approaches to the medical, psychiatric, and social welfare needs of people with SMI will influence the risk environment they encounter and, ultimately, incidence and prevalence of HIV. Future research using ethnographic and social network methods can examine the nature and extent of overlap and contact between IDUs and people with SMI. Given the evidence for possible linkages between drug use and sexual risk,31
it may be valuable to extend work on existing sexual networks of people with SMI.75,76
Additional quantitative comparisons across MSAs are needed but will be challenging. Prior research, particularly in the 1990s, demonstrated that it is feasible to estimate rates of SMI, including schizophrenia, as well as major mood disorders among both in-treatment and out-of-treatment IDUs.77–80
These studies suggest that ongoing research with IDUs, including work with populations using syringe-exchange programs, can be an important potential source of information on major psychiatric illness and HIV dynamics, particularly when they permit comparisons across multiple MSAs.
Additional sources of data may increase the explanatory power of models. Data sources with the potential to provide additional insights into the co-occurrence of SMI and HIV may include Medicare and private insurance files, data from CARE Act81
-funded programs such as the AIDS Drug Assistance Programs,82
and possibly enhanced and/or linked HIV/AIDS surveillance data. MSAs may also differ in the proportion of the -population with an SMI that spends some time in prison or local jails, which may pose HIV risk, suggesting that estimates of these figures may hold promise.
Heuristically, research in this area can benefit from examination of the ways in which the epidemic of HIV among people with SMI is embedded in local epidemics and reflects their dynamics, as well as the distinctive features of psychiatric illnesses. Much research has understandably focused on identifying and describing this group; assessing its needs; adapting clinical practices; and developing, testing, and diffusing psychosocial programs able to accommodate its distinctive needs.83
However, searching for ways in which people with SMI do not differ from others can also call attention to fruitful areas of investigation. For example, multiple studies have already found that contrary to early expectations, people with SMI can often achieve levels of antiretroviral therapy adherence at least as good as others with HIV.56,57,84,85
Similarly, it may be the case that community-level changes relevant to HIV prevention have a significant impact on those with SMI. For example, declines in prevalence rates of HIV among IDUs in a number of cities that have been noted by some authors61,86,87
may have implications for HIV incidence among people with SMI. Generally, our results suggest that harm-reduction and other policies that address the spread of HIV among drug abusers may not only affect those people with SMI who abuse drugs, but also serve as effective strategies for limiting new infections in other “downstream” populations who may interact with drug-abusing populations, such as people with SMI.
In evaluating the suitability of Medicaid claims for examination of SMI morbidity/mortality issues, it is important to be aware of both the general limitations of claims-based research, as well as the specific limitations of our study. Regarding general limitations, our impression is that although claims-based research was once viewed with intense suspicion, its potential is now more widely recognized. However, this new acceptance should not tempt researchers or policy makers to oversell what can be accomplished. Diagnoses found in administrative claims are of uneven quality, although most research finds high rates of concordance with chart diagnosis for more severe disorders such as schizophrenia; some studies have reported 100% agreement.52
While psychiatric diagnoses based on standardized interviews typically produce high-quality diagnoses, clinicians are aware that diagnoses based on these standardized interviews do not possess every advantage over the provider-based diagnoses found in medical charts and administrative claims. Often, the treating clinician has observed the patient over time, knows relatives and other providers who can give clinically important information, and is able to solicit information from a patient based on a treatment alliance, which can motivate disclosures not provided in a paid interview.
Claims provide no information about undiagnosed conditions, so interpretations must gauge the scope of missed cases. In one of the few studies of its kind, we estimated concordance between claims indicative of HIV and state HIV and AIDS registry information.57
We found sensitivity was generally high, but particularly so in groups that use services frequently. While we suspect that this is a good rule of thumb, further research on validity issues is needed.
A further significant limitation was dependence on the availability of additional data sources. We were fortunate to be able to rely on the publications by Tempalski and colleagues61
of their high-quality, carefully constructed estimates of MSA-level rates of HIV among IDUs. Had these not been available, impressions regarding links between MSA-level rates of HIV among IDUs and HIV among people with SMI could not have been explored empirically.
Our findings may also have implications for an expanded view of the role of research development and diffusion. To date, much research on HIV infection rates among people with SMI has been conducted in major medical centers in urban areas and has performed a vital sentinel function, calling the attention of local providers to the emerging, overlooked clinical and public health challenge of HIV among people with SMI, and spurring the development and diffusion of prevention-focused technologies to local communities. But improved understanding and effective response require strengthening the information and influence flow from multiple local communities to the research enterprise and developing community- and provider-based partnerships able to contextualize local disease risks.