Neighborhood socioeconomic position and tuberculosis transmission: a retrospective cohort study

The study population consisted of all incident reported culture-TB cases with available genotyping with block group-level geocodes recorded in King County, Washington between January 1, 2004 and December 31, 2008. An incident case of TB was defined according to Centers for Disease Control and Prevention (CDC) surveillance criteria, where TB was either diagnosed for the first time or more than 12 months had elapsed since the patient previously completed TB therapy [11]. A culture-positive sample was defined as isolation of M. tuberculosis from a clinical specimen. Patients who did not have both spoligotyping and mycobacterial interspersed repetitive unit-variable-number repeat (MIRU-VNTR) analysis performed on their isolate or did not live in King County at the time of specimen collection were excluded from the analysis. The analysis merged reporting, medical record and genotyping data for TB cases and US census data. Subsequently, only cases with available genotyping results and geocoded addresses were included in the final study population. Approval was granted for this study in May 2009 from the University of Washington and Washington State Institutional Review Boards and final project analysis completed October 2010.

Individual-level case variables were collected at the local level from the Tuberculosis Information Management System (TIMS) and follow standard surveillance definitions [10]. Individual-level variables were subsequently aggregated by block group. Residential address at the time of diagnosis was obtained from patient medical records. Using a geographic information system and latitude/longitude coordinate data, TB cases were geocoded to the corresponding block group of residence. Only block groups with diagnosed TB cases were included in the analyses.

SES was defined at the block group level using census-based indicators of socio-economic disadvantage. A socioeconomic position (SEP) index, was constructed consisting of a standardized z-score combining data on percent working class, unemployed, poverty, high school, expensive homes and median household income. To construct the score, each variable was given a standardized score, which was the sum of all block group values with SEP data (n = 1,576), minus the mean sum, divided by the standard deviation, and then summed up the individual z-scores. Although high inter-correlations and reliability were noted (Cronbach’s α coefficient 0.78), these measures, along with the index, have previously been used to assess US small area differences in health, with the latter developed based on a factor analysis of eleven single SES factors using rank values of the census data [12]. All socio-economic data as well as area-based data were derived from the US Population Census 2000, SF1 and SF3 [13, 14]. All culture-positive patients were genotyped using spoligotyping and 12-locus MIRU-VNTR genotype results obtained through the National TB Genotyping Service. Genotype results were subsequently linked to National TB Surveillance System data using a standardized state case identification number. A cluster was defined as two or more patients with identical TB genotypes within King County. Given the study scope, if cases were part of a Washington cluster designation but unique within King County, they were considered to have a unique TB genotype.

Descriptive statistics were applied to included block groups to assess poverty distributions as well as deviation from King County as a whole. The proportion of TB patients considered to belong to a chain of recent transmission was calculated as the number of subjects belonging to a cluster divided by total number of individuals genotyped [15]. Additionally, the proportion of cases caused by ongoing transmission was estimated using the n-1 method, where the source case of each cluster was not considered to have recent disease [16]. Incidence rates over time were calculated for both clustered and non-clustered (unique genotype) patients. Univariate associations of independent variables and genotype clustering were assessed using Pearson χ². SaTScan was used to generate a spatial scan statistic identifying geographic areas with a higher-than-expected clustering rate. TB incidence rates were calculated for each SEP stratum by dividing the number of TB cases in a particular quartile by the corresponding stratum population, multiplied by the five years in the reporting period. Cuzick’s nonparametric test for trend across ordered SEP groups was assessed as a summary test of statistical significance [17].

To examine area-level influences on disease clustering in addition to individual attributes, multilevel regression models were used to assess the association between SEP and TB clustering. A two-level hierarchical model with binary clustering outcome was estimated with the high SEP quartile serving as the referent. Hierarchical models have the advantage of yielding accurate parameter estimates and sampling variances in the presence of correlated errors [18]. Prevalence ratios and 95% confidence intervals were estimated by binomial regression with the log link function [19]. Model 1 consisted of an empty two-level model to examine log-odds of genotypic clustering in an ‘average’ block group and to quantify block-group-level variance. Model 2 added socioeconomic quartiles as exposure variables. Model 3 controlled for the individual demographic variables of age, race (modeled as dummy variables with white serving as referent), sex (males as referent) and country of origin (US-born as referent) in addition to SEP index. Model 4 included individual socioeconomic variables (homelessness with non-homeless referent, employment with employed referent, provider type modeled with dummy variables with public service provision as referent) in addition to demographics and SEP index. Model 5 added area-level variables of race, ethnicity and foreign birth in addition to individual-level variables and SEP index. Complete case analysis was used such that the number of patients with missing covariates (n = 12) excluded from each model was the same.

The study consisted of 327 block groups in King County with at least one case residing in each (20.7% of block groups with SES data) (Table 1). Block groups included in the study were largely of white (60%), US-born (78%) composition. Hispanic ethnicity made up approximately eight percent of the population, about 10% of individuals were under the federal poverty line and 4% were unemployed. The average five-year incidence rate of TB was 15.6 per 100 000 across all included block groups. In comparison to other block groups in King County (N = 1,249), those included in the study were more likely to contain individuals reporting as black or asian race as well as of Hispanic ethnicity. Additionally, the median proportion foreign-born in these block groups was almost twice as high as that of King County.

Of 686 incident TB cases reported in King County from 2004–2008, 577 (84%) were culture positive, excluding relapses, interjurisdictional transfers, and individuals with missing TB treatment date. Of reported culture-positive cases, 547 (95%) had a reported genotype and 519 (95%) of these cases had both genotyping and block group geocoding available, and therefore were included in the analysis. TB patients were mostly of asian (44%) and black (28%) race, and were largely (81%) foreign-born. Approximately one third of foreign-born patients were identified within five years of arrival in the US.

Of those with a known genotyping result, 212 (41%) of isolates clustered genotypically. Forty-six distinct clusters were identified. The number of patients per cluster ranged from 2 to 32 (Figure 1). A median of 3 and mean of 7 patients were identified per cluster. 52 clustered patients (25%) belonged to 2-case clusters and 160 (75%) belonged to clusters with 3 patients or more. Individual clusters ranged in duration from 1 year to the full 5 years of the study period. Based on spoligotype/MIRU match, 336 unique TB genotype strains were identified in King County during this time period. Assuming that 1 patient per cluster resulted from reactivation of remote infection and that the remainder resulted from the spread of recently transmitted disease (n-1 method), 166 (32%) of isolates could be defined as recently transmitted tuberculosis. Further analysis showed that of patients identified after subtracting out the index case and unique genotypes, 134 (83%) matched the isolate of a patient identified within the 1-year period prior to diagnosis date, suggesting potential recent transmission from individual to another. Clustered TB disease was not spatially homogenously distributed throughout the included block groups with significant spatial aggregation of the clustered patients (P = .047 for most likely cluster, Figure 2).

In unadjusted clustering analyses, patients with unique genotyping results were compared to those patients in clusters (Table 2). Clustering was positively associated with female gender, non-Hispanic ethnicity, US birth, homelessness and substance abuse and with indicators of patient infectivity, including pulmonary TB and cavitary TB disease, although not with HIV infection. On average, patients were identified 397 days apart in 2-person clusters, compared with 155 days’ apart among 3-person or greater clusters (P < 0.001).

Among foreign-born patients, average clustered patient incidence rates (5.10/100 000) were lower than average non-clustered (8.93/100 000). The reverse was true among US-born patients, where clustered rates were almost twice as high as non-clustered (7.04/100 000 vs. 4.81/100 000). Greater proportions of foreign-born patients clustered as time between arrival and diagnosis increased (data not shown).

In unadjusted analyses, as SEP decreased, so the proportion clustering increased. A significant linear trend for increased clustering occurred from high to low SEP quartiles (P = 0.001) (Table 3). Clustered case incidence rates increased with lower SEP index, with the greatest increases in rates when going from low to very low SEP quartiles among both clustered and non-clustered cases and with low incidence rates observed among clustered patients living in the highest SEP quartile. Clustered rates were lower than non-clustered for all quartiles, but much more alike in each progressively lower SEP quartile. Unadjusted fitted log odds of clustering for the continuous SES z-score are shown in Figure 3. Patients residing in block groups in the lowest 10% of all z-scores were even more likely to cluster (56%).

The majority (73%) of US-born patients clustered at the lowest socioeconomic quartile. Within the low and lowest SEP index quartile block groups, US-born patients were significantly more likely to cluster than foreign-born. Clustering increased significantly with residence in progressively lower SEP block groups among both US- (P-trend 0.005) and foreign-born TB patients (P-trend 0.016).

When stratified by SEP index quartiles, the only significant difference between patients stratified by time from arrival to TB diagnosis was seen among those living in the highest SEP group, where clustering peaked among individuals who had been in the US between 10–19 years from arrival to TB diagnosis (data not shown). Individuals who arrived more recently (0–4 years) were more likely to cluster if they lived in lower SES quartile block groups (P-trend 0.035).

Multilevel models in which community-level characteristics were measured at the block group level demonstrated that lower SEP index was positively associated with TB genotypic clustering after controlling for individual covariates, but the trend of higher clustering risk with lower SEP quartile was diminished when adding additional block-group covariates. In an unadjusted model, a large change in between-community variance (25% decrease) suggested the distribution of SEP quartiles was different across block groups. With progressively lower SEP index quartiles, odds of TB clustering increased compared to the next highest quartile (Table 4, model 2). A positive linear trend was observed (P = 0.005). Once individual demographic variables were included in the model (model 3), the association between SEP and TB clustering did not change. Foreign-born patients were significantly less likely to have clustered disease when compared to US-born patients. Addition of individual-level SES measures did not affect the SEP-clustering association (model 4).

When area-level demographic variables were added, SEP-TB clustering odds ratios decreased in the lowest SEP quartile and the significant linear trend showing increasing with decreasing SEP disappeared (P = 0.244). Areas with larger proportions of black inhabitants were more likely to have TB clusters (Adjusted OR = 1.25; 95% CI: 1.01, 1.29) (model 5). In this multilevel analysis, the only individual-level variables to remain independently associated with TB clustering were foreign-born and race after inclusion of all covariates. These findings suggest that area-level demographic measures, and hence factors related to the area of residence, may substantially affect genotyping clustering among TB patients in the lowest SEP quartile.