HIV Infection Among People Who Inject Drugs in the United States: Geographically Explained Variance Across Racial and Ethnic Groups

Objectives. We explored how variance in HIV infection is distributed across multiple geographical scales among people who inject drugs (PWID) in the United States, overall and within racial/ethnic groups.Methods. People who inject drugs (n = 9077) were recruited via respondent-driven sampling from 19 metropolitan statistical areas (MSAs) for the Centers for Disease Control and Prevention’s 2009 National HIV Behavioral Surveillance system. We used multilevel modeling to determine the percentage of variance in HIV infection explained by zip codes, counties, and MSAs where PWID lived, overall and for specific racial/ethnic groups.Results. Collectively, zip codes, counties, and MSAs explained 29% of variance in HIV infection. Within specific racial/ethnic groups, all 3 scales explained variance in HIV infection among non-Hispanic/Latino White PWID (4.3%, 0.2%, and 7.5%, respectively), MSAs explained variance among Hispanic/Latino PWID (10.1%), and counties explained variance among non-Hispanic/Latino Black PWID (6.9%).Conclusions. Exposure to potential determinants of HIV infection at zip codes, counties, and MSAs may vary for different racial/ethnic groups of PWID, and may reveal opportunities to identify and ameliorate intraracial inequities in exposure to determinants of HIV infection at these geographical scales.Since the mid-1990s, there has been an increase in studies evaluating whether features of the social, economic, physical, and political environment (i.e., place characteristics) affect health. This focus on place characteristics is evident in the development of theories conceptualizing place characteristics as health determinants,1–3 in the use of geospatial and systematic social observation methods to measure place characteristics,4–10 in the application of multilevel modeling to assess the potential impacts of place characteristics,11–18 and in the recognition that interventions should not solely encourage individual behavior change but also modify environmental features.3,16,19Literature emerging from this field of research demonstrates that place characteristics operationalized at different geographical scales influence psychosocial processes and individual behaviors that increase vulnerability to several health outcomes. With rare exception,20–24 however, studies of place and health typically assess the potential influence of place characteristics at a single geographical scale and do not simultaneously evaluate characteristics of other geographical scales. For example, several studies, including our own,25,26 sample participants from a single metropolitan statistical area (MSA) to assess the relationships of census tract characteristics to health, without sampling participants from multiple MSAs to simultaneously assess the relationships of tract-, county-, and MSA-level characteristics to health.25–32 The decision to focus on characteristics of a single geographical scale may arise because of data availability, cost constraints, or feasibility.Studies of place and health that focus on a single geographical scale, however, may misspecify relationships and hinder the exploration of causal pathways in 2 ways. First, studies that focus on features measured at a single geographical scale may overlook potential health determinants that are operationalized at other geographical scales. For instance, research assessing the relationships of features of neighborhoods (e.g., economic deprivation, racial/ethnic composition, policing practices, and “crackdowns”) cannot determine the influence of policies, laws, and governmental expenditures that are operationalized at county, MSA, and state levels, and shape neighborhood environments. Second, studies of features of a single geographical scale cannot determine whether relationships between characteristics operating at one geographical scale are confounded, mediated, or modified by characteristics of other geographic scales.3,16,33 The possibility that at least 1 of these mechanisms can occur has been demonstrated in research conducted by Warner and Gomez, which suggests that, among Black women diagnosed with breast cancer, residing in census blocks with high concentrations of Black residents is more protective against mortality in more racially segregated metropolitan areas than less racially segregated metropolitan areas.34In addition, research assessing the association of place-based factors with health outcomes rarely highlights the extent to which variance in health outcomes is explained by place and place-based factors. Determining whether health outcomes vary geographically can generate hypotheses about inequities in exposure to potential place-based determinants of health, and thereby inform how interventions and social policies are developed and spatially concentrated.35The present study illustrates the generative possibilities of extending research beyond a single geographical scale by achieving 2 primary aims. The study’s first aim is to determine the share of total variance in HIV infection that is apportioned to zip codes, counties, and MSAs among people who inject drugs (PWID). In the United States, PWID account for 22% of people living with HIV,36 and a growing body of literature demonstrates that features of neighborhoods such as census-tract racial composition and block-level social or physical disorder are associated with HIV-related outcomes among PWID,37,38 as are features of MSAs, including drug-related law enforcement, income inequality, residential segregation, and health service access.39–41 Revealing the geographical scale to which variance in HIV infection is apportioned among PWID can stimulate hypotheses about inequities in exposure to place-based determinants of HIV and inform the development and tailoring of place-based interventions. For example, finding high MSA-level variance in HIV infection may support analyses of whether MSA-level variations in health care service access predict variance in HIV serostatus and, if they do, support interventions to increase health care access in low-access MSAs. In contrast, if little to no variance in HIV infection among PWID is apportioned to MSAs, PWID may encounter a relatively uniform exposure to health care service access.Previous studies have found that variance in some health outcomes vary across racial/ethnic groups.42,43 The second aim of this study therefore tests the hypothesis that variance in HIV infection will differ within each of 3 racial/ethnic groups of PWID: non-Hispanic/Latino Whites, non-Hispanic/Latino Blacks, and Hispanics/Latinos.     

Analysis of transtheoretical model of health behavioral changes in a nutrition intervention study—a continuous time Markov chain model with Bayesian approach

Continuous time Markov chain (CTMC) models are often used to study the progression of chronic diseases in medical research but rarely applied to studies of the process of behavioral change. In studies of interventions to modify behaviors, a widely used psychosocial model is based on the transtheoretical model that often has more than three states (representing stages of change) and conceptually permits all possible instantaneous transitions. Very little attention is given to the study of the relationships between a CTMC model and associated covariates under the framework of transtheoretical model. We developed a Bayesian approach to evaluate the covariate effects on a CTMC model through a log‐linear regression link. A simulation study of this approach showed that model parameters were accurately and precisely estimated. We analyzed an existing data set on stages of change in dietary intake from the Next Step Trial using the proposed method and the generalized multinomial logit model. We found that the generalized multinomial logit model was not suitable for these data because it ignores the unbalanced data structure and temporal correlation between successive measurements. Our analysis not only confirms that the nutrition intervention was effective but also provides information on how the intervention affected the transitions among the stages of change. We found that, compared with the control group, subjects in the intervention group, on average, spent substantively less time in the precontemplation stage and were more/less likely to move from an unhealthy/healthy state to a healthy/unhealthy state. Copyright © 2015 John Wiley & Sons, Ltd.     

Comparison of a Web-Based Dietary Assessment Tool with Software for the Evaluation of Dietary Records

Background

Dietary assessment in clinical practice is performed by means of computer support, either in the form of a web-based tool or software. The aim of the paper is to present the results of the comparison of a Slovenian web-based tool with German software for the evaluation of four-day weighted paper-and-pencil-based dietary records (paper-DRs) in pregnant women.

Methods

A volunteer group of pregnant women (n=63) completed paper-DRs. These records were entered by an experienced research dietitian into a web-based application (Open Platform for Clinical Nutrition, OPEN, http://opkp.si/en, Ljubljana, Slovenia) and software application (Prodi 5.7 Expert plus, Nutri-Science, Stuttgart, Germany, 2011). The results for calculated energy intake, as well as 45 macro- and micronutrient intakes, were statistically compared by using the non-parametric Spearman’s rank correlation coefficient. The cut-off for Spearman’s rho was set at >0.600.

Results

12 nutritional parameters (energy, carbohydrates, fat, protein, water, potassium, calcium, phosphorus, dietary fiber, vitamin C, folic acid, and stearic acid) were in high correlation (>0.800), 18 in moderate (0.600–0.799), 11 in weak correlation (0.400–0.599), while 5 (arachidonic acid, niacin, alpha-linolenic acid, fluoride, total sugars) did not show any statistical correlation.

Conclusion

Comparison of the results of the evaluation of dietary records using a web-based dietary assessment tool with those using software shows that there is a high correlation for energy and macronutrient content.