Study data
The study population was identified from five NHANES cohorts from 2007 to 2016. Conducted by the National Center for Health Statistics (NCHS), NHANES is a continuous cross-sectional survey with data released biannually and is effective in determining the prevalence of major diseases and associated risk factors among adults and children in the U.S. [25, 26]. The NHANES data are rich and unique in two ways. Firstly, it combines information collected from both interviews and physical examinations that are necessary to answer the research questions. The interviews include demographics, socio-economic status, drug use information, and health-related questions; and the physical examinations include medical measurements and results of laboratory tests. Secondly, each survey cycle examines a nationally representative sample, and findings from the study are generalizable to the U.S. Further details are described elsewhere [26].
Study population and sampling procedure
The study population comprised people who reported methamphetamine use in lifetime. The flow chart in Figure 1 illustrates the process of case selection. The study included anyone who completed testing for any of the three sets of tests including HBV, HCV and HIV, and also answered “yes” to both questions “ever used cocaine/heroin/methamphetamine” and “ever used methamphetamine”. The study excluded anyone whose age was not between 20 and 59 years (as they were not eligible to answer drug use question) and anyone who obtained HBV immunity and was not infected by HBV.
Data sources
The primary outcome measure was positive/ negative detection of any of the three BBVs (HBV, HCV, and HIV) which were determined according to the results of a set of serological tests. Three HBV serological markers were tested in the NHANES study: antibody to hepatitis B core antigen (anti-HBc), indicative of previous or ongoing HBV infection; hepatitis B surface antigen (HBsAg), indicative of chronic infection; and antibody to hepatitis B surface antigen (HBsAb), serological evidence of vaccine-induced immunity [27]. Positive HBV detection was defined as a positive result of anti-HBc; while negative HBV detection was defined as negative for all HBV serological markers including anti-HBc, HBsAg and HBsAb. Indeterminate serological test results were coded as negative since we used a conservative definition to determine positive detection. The HBsAg is tested only when the anti-HBc test is positive. Participants who were HBsAb positive but anti-HBc negative and HBsAg negative were excluded from analyses, since they had acquired immunity through vaccination and were not considered as population at risk of HBV infection.
Two HCV serological markers were tested: hepatitis C antibody and hepatitis C RNA [28]. The hepatitis C RNA is tested only when the hepatitis C antibody test is positive. Current HCV infection was indicated by both hepatitis C antibody and RNA positive, and chronic HCV infection was defined as hepatitis C RNA positive 6 months after an acute infection. Positive HCV detection was defined as a positive result for both hepatitis C antibody and hepatitis C RNA; while negative HCV detection was defined as negative for hepatitis C antibody. Similarly, indeterminate serological test results were coded as negative.
Two HIV serological markers were tested: HIV-1 and HIV-2 antibody [29]. Specimens are initially tested by a combo set of HIV-1/2 Enzyme Immunoassay (EIA), and then repeated reactive specimens are tested with HIV-1/2 supplemental assay. Positive HIV detection was defined as positive result from the two rounds of tests. If EIA is positive but following supplemental tests are not positive (e.g., negative, indeterminate), a confirmatory test is performed for a final rule: HIV detection is positive with a positive confirmatory test result, and HIV detection is negative with a negative confirmatory test result.
According to previous literature [4, 5, 12, 30, 31], demographic characteristics (age, gender, race/ethnicity), socio-economic status (poverty index, health insurance, healthcare access, education), sexual activity (number of sexual partners in the past year, sexual identity), and drug use behaviors (number of drug use, IDU, number of times used methamphetamine in lifetime use, age started using methamphetamine) were known factors associated with infection of BBV. Therefore, these variables were included as potential confounders in the analyses.
Demographics including age, gender and race, health insurance and hospital utilization and access to care information were collected through Sample Person Questionnaire. Socio-economic status (poverty index, education) was obtained through Family Questionnaire. Drug use information (e.g., number of drug use, IDU, number of times used methamphetamine in lifetime use, and age started using methamphetamine, etc.) was obtained through Audio Computer Assisted Personal Self Interview (ACASI) Questionnaire. Sexual behaviors (number of sexual partners, sexual identity) were collected through both ACASI and computer assisted personal interview (CAPI) questionnaires during participants’ visit to the examination center. All three BBVs related measures were obtained from corresponding laboratory tests. The specific laboratory methods can be found elsewhere [25]. Responses to questions including education, drug use, and sexual activity were limited to participants aged 20 to 59 years.
Statistical analysis
Descriptive analyses include both raw and weighted frequency and percent of all covariates mentioned above. Weighted frequencies and percentage for the combined ten years of data were calculated by multiplying the sample weight WTMEC2YR by 0.2. The Rao Scott Chi-squared statistic was calculated to assess the association between each covariate and outcome measure. Bivariate logistic regression and three multivariable logistic regression models were developed to examine the risk factors associated with BBV positive results among people who used methamphetamine. The outcome was tested positive for BBV or negative to BBV as identified from laboratory tests. The main risk factors of interest were drug use behaviors (number of drug use, IDU, number of times used methamphetamine in lifetime use, and age started using methamphetamine).
Model I, which only includes demographics, evaluated the effect of demographic characteristics on the BBV positive result. Model II further added a set of socio-economic characteristics and sexual behavior information into the modelling to evaluate their effect on the BBV positive result, controlling for demographics. Although health insurance, healthcare access, and number of sexual partners were not statistical significant in our model, they are, in general, confirmed risk factors for BBV infection according to previous literatures, so included them in the model to adjust for their effects. Model III further explored how drug use affects the BBV positive result while taking into consideration all previous variables, which is our key research interest. The rationale to include them are two-fold: i), they are statistically significantly associated (p<0.05) with the BBV positive result in the unadjusted analyses; ii), they are suggested to have influence on the likelihood of being tested BBV positive.
Unadjusted odds ratios (uORs) and their 95% confidence intervals (CIs) were reported from bivariate logistic regression models, and adjusted odds ratios (aORs) and their 95% CIs were reported from the three multivariable logistic regression models. Missing data was not included in the analyses. For all ORs reported, statistical significance was considered as CI not crossing 1 and corresponding p-value being less than 0.05.
R programming (RStudio, version 3.6) was used for all analyses. Library “tidyverse” was used to clean data and generate appropriate subset for statistical analyses. Library “survey” and “srvyr” were used to analyze weighted NHANES data. Survey functions “svytotal”, “svymean”, “svychisq” and “svyCreateTableOne” were used to perform descriptive analyses; “svyglm” was used to perform logistic regression modeling, and “jtools” was used to draw figure 2.