Predictive Model to Identify Positive Tuberculosis Skin Test Results During Contact Investigations FREE

Recent tuberculosis (TB) research has focused on case identification and treatment with little attention given to contact investigation. The traditional concentric circle approach of defining contacts as either close or casual based on risk assessments presents difficulties in defining a close contact and in determining when to end the search for contacts.^{1 - 3} Past research pertaining to contact infection has focused on single variables as risk factors: patient factors, such as smear and culture status or cavitary disease^{1 ,3 - 9} ; contact factors, such as age,¹⁰ immunosuppression (or human immunodeficiency virus [HIV]),^{11 - 13} and poverty status¹⁴ ; and environmental exposure factors, such as shared household contact,^{9 ,15} ventilation of the exposure environment,^{16 - 19} and duration of exposure.^{1 ,3} No study of TB transmission has simultaneously evaluated information on case, contact, and environmental exposure factors.

Recent reports by the Institute of Medicine and the Advisory Council for the Elimination of Tuberculosis cited the importance of developing more effective methods of identifying contacts with a high risk of infection.^{20 - 21} The University of Alabama at Birmingham and the Alabama Department of Public Health conducted such a study and developed a model of TB transmission to show which variables best predict a positive tuberculin skin test (TST) result among contacts to active TB cases.

Prior to the study, we formulated standardized operational definitions of all variables associated with contact investigation.²² Investigators recorded and observed contact screening interviews and held focus group discussions with TB field staff and area managers of the Alabama Department of Public Health. These activities showed significant disagreement on definitions of variables related to contact investigation. A behavioral intervention was developed to train TB staff to gather consistent data on each of the precisely defined variables collected during patient treatment and contact investigation.^{22 - 24} The behavioral intervention was developed using social cognitive theory in the context of health education and included instruction, demonstration, and practice with feedback and assessment. The primary training mechanism of the behavioral intervention was a task-oriented workshop. Training included a review of the risk factors for TB infection, an overview of interviewing skills, the introduction of the standardized contact screening protocols and the computer module used to collect the data, as well as practice scenarios for contact investigation and screening. Quality control was ensured by monthly review of field staff reports by area managers and review of the computer modules to determine the extent of missing data, the number of errors made by staff entering the data, and the number of prompts required during the data-entry process. In addition, monthly discussions were held with area managers and select TB field staff to determine their adherence to the use of the standardized definitions. Follow-up educational interventions were performed as necessary.

Staff were trained to enter data on a laptop computer, which was transferred weekly to area servers and then via modem to the University of Alabama at Birmingham.²² Cases were defined according to Centers for Disease Control and Prevention criteria.²⁵ Only confirmed TB cases were used in the model. Case and contact demographics, characteristics of the exposure environment, all field investigation activities, as well as laboratory and clinical data were entered into the database.

The state of Alabama has 11 public health areas. Each area has a manager responsible for all TB-related activities. The sample used to develop the model included 292 consecutive cases with a total of 2941 contacts identified from January 1 through October 15, 1998. Data were collected from 366 new consecutive TB cases and their 3162 contacts identified from October 16, 1998, through April 2000 for a validation sample. Mass screenings at prisons and nursing homes and school screenings unrelated to specific contact investigations were excluded. This study was approved by the University of Alabama at Birmingham institutional review board and the Alabama Department of Public Health.

The TST result was the primary outcome variable and provided a surrogate measure for recent transmission of disease. While not a perfect measure of recent exposure with transmission, it is the primary measure used in all epidemiological contact investigations.³ The TST results were considered positive with induration of 5 mm or more, which is standard for contact investigation. There were 47 readings between 5 and 9 mm and the analysis was similar using either 5 or 10 mm as a positive reaction. The test is administered by injecting 0.1 mL of 5 TU (tuberculin) intradermally (Mantoux method) into the volar aspect of the forearm, reading the millimeters of induration between 48 and 72 hours after injection.²⁶ Contacts whose initial TST result was negative were given a second test 10 to 12 weeks later. If either the first or second test result was positive, contacts were considered positive. Persons known to have a positive skin test result 60 days or more prior to the date the case was reported were considered not to represent recent infection from the case in question and were eliminated from the analysis (98 contacts). When contacts were exposed to more than 1 case, the area manager determined the primary case for that contact.

Explanatory variables with multiple outcome categories were often collapsed for analytical purposes. For example, smear and culture status were both defined as dichotomous variables (negative vs positive) rather than grading the degree of positivity. Case age was grouped into 3 categories (<15, 15-65, and >65 years) to determine if transmission differences existed between children and adolescents, adults, and older adults. Contact age was grouped into 5 categories (≤4, 5-14, 14-24, 25-64, and ≥65 years) defined by the clinicians prior to analysis according to differences thought to exist regarding infection rates among age groups. Cases were considered to have cavitation by radiograph result, which was confirmed by film review. Ventilation of the exposure environment was rated on an ordinal scale defined as follows: 1 = ventilation situation of closed windows and doors; 2 = window/fan exhaust; 3 = window air conditioner unit; 4 = central air conditioner/heat; 5 = completely open to the outside. Because a contact can be exposed in multiple environments, the lowest ventilation rating of all environments in which the contact was exposed was used in the model. The size of the exposure environment was also rated on an ordinal scale (1 = size of a vehicle or car; 2 = size of a bedroom; 3 = size of a house; and 4 = size larger than a house). The total number of times per month the contact was exposed to the case (no matter what the duration of each time) as well as the number of hours per month (accounting for each separate time and duration) were collected.

Certain variables (including positive HIV status for contact and whether the case was homeless) were thought to be important in determining the probability of infection of individual contacts; however, there were too few contacts with these traits to use these variables in the model. Current smoking status of the case was significant but not included due to the large amount of missing data. There was almost no missing data for all the other variables.

Generalized estimating equations (GEEs)²⁷ were used to obtain a model for predicting a positive TST result in contacts of active TB cases. The GEE analysis is necessary to analyze this data because outcomes for the contacts having a TB case in common are not independent of each other. Thus, outcomes within clusters of contacts are correlated, which is taken into account by the GEE. The sample used to develop the model included 292 consecutive cases with a total of 2941 contacts identified during January to October 1998. The selection of predictors began with a univariate analysis of all variables. Those variables significant at the .10 level were retained for inclusion in the generalized estimating equation analysis (Table 1a). To obtain our model, we used a backward elimination method with a significance level of .10. Examination of influential observations and clusters within the GEE model was performed.²⁸ All analyses were performed using SAS software.²⁹

To calculate the predicted probability of a positive skin test result, one must first use the GEE to calculate a given contact's log odds of a positive skin test result (Table 2). This log odds can then be converted back to a predicted probability using the following formula:

To use the model, one must choose a predicted probability level above which all contacts will be examined. To determine this probability level cut point, we compared the sensitivity and specificity of different cut points using the classification table shown in Table 3.

Data were collected from 366 new consecutive TB cases and their 3162 contacts from October 16, 1998, through April 2000 for a validation sample. Since this data set was significantly larger than that used to develop the model, it was divided into 3 data sets using random sampling without replacement to compare results for consistency. The data sets were created by randomizing contacts; therefore, cases could be included in more than 1 of these smaller data sets. Using several data sets to validate our model more efficiently examines its generalizability. The 3 data sets included 1030, 1052, and 1080 contacts, respectively (Table 4). The model was tested in these data sets and the sensitivity, specificity, positive predictive value, false-negative rate, and false-positive rate were calculated.

During the period in which the data were collected, Alabama had a TB incidence rate of 8.8 per 100 000, which was the sixth highest rate in the country. Characteristics of the TB cases and their contacts used to create the model are shown in Table 5. The mean number of contacts per case was 10, but this is quite variable from case to case and ranged from 1 to 181. The median number of contacts per case was 4 with no statistical differences in the number of contacts investigated by sex, race, or age of the active case. Significant differences did exist in the number of contacts investigated by clinical characteristics of the case.³⁰ The overall infection rate among all contacts was approximately 20%.

Table 1 shows the univariate analysis and Table 6 shows the results of the GEE model. Variables are displayed in 3 characteristic domains: case, contact, and environmental exposure. Three hundred seventy-seven contacts were eliminated from the GEE analysis due to missing data. If any variable was missing for a contact or its associated case, the contact's information was not used in model development. Ten cases and their contacts were considered highly influential in the fit of the GEE model. These influential cases were removed from the modeling process. For the final model, collinearity was examined and no significant concerns existed. Interactions were examined and none improved the predictive ability of the model. Using the model outlined in Table 6, we evaluated different probability levels predicting risk of transmission in a classification table showing their test characteristics such as sensitivity, specificity, false-positive, and false-negative rates (Table 3). A cut-point value with a higher sensitivity avoids missing infected individuals but sacrifices specificity. Cut points with higher specificity miss greater numbers of infected individuals but require fewer public health resources. Thus a cut point can be chosen depending on the characteristics of patient populations and availability of public health resources. Our goal was to improve the efficiency of our contact investigations with minimal sacrifice of efficacy. Most importantly, we did not want to miss contacts likely to have been recently infected. Based on our current knowledge 2 assumptions seemed reasonable:

Considering these assumptions, we chose a cut point in which the false-negative rate was close to the presumed background rate, yet allowed for a substantial reduction in the number of contacts examined. A cut point of 0.10 reduced the number of contacts to be investigated by 40% ([783 + 54]/2118) while maintaining a false-negative rate of less than 7% (Table 3). Using this cut point results in a false-positive rate of 80%, which is consistent with an infection rate of between 20% and 30%.

The cut point of 0.10 was used to test our original model in 3 prospective samples of cases and contacts (Table 4). Results were consistent among the new data sets with an average sensitivity, specificity, and positive predictive value of 89%, 36%, and 26%, respectively. The sensitivity increased by approximately 7% for the new data sets and the specificity decreased by about 7%. In all 3 data sets, the false-negative rate remained between 5% and 10%.

To illustrate the sensitivity and specificity of other cut points, we have included receiver operating characteristic curves for both the model-building data set and the validation data sets (Figure 1).

Our results show that specific case, contact, and environmental exposure characteristics can predict which contacts of TB cases are most likely to have a positive TST result. In our model, 7 variables were determined to be statistically significant. The mean sensitivity was 89% and the mean false-negative rate was 7% when tested prospectively in 3 new populations. This analysis indicated there are 3 variables that we deem to be particularly clinically relevant: case has a positive smear, case has cavitary disease, and total hours exposed to the contact each month. These variables are almost immediately available to the field worker on identification of a TB case and indicate that the case is likely to transmit TB to his/her contacts.

To choose an appropriate cut point for determining which contacts to investigate, one must examine the model's sensitivity and specificity at each probability level (Table 3) and assess characteristics of the local population, local priorities, and available resources. Sensitivity represents the probability of the model to correctly predict a positive TST result; whereas specificity denotes the probability of the model to correctly predict a negative TST result. Altering the cut point at which you choose to investigate a contact will influence both the sensitivity and specificity of the model. Lowering the cut point means more people who actually have a positive TST result will be predicted by the model to be positive (increased sensitivity); however, you would also spend additional resources investigating false-positives (persons incorrectly predicted by the model to be TST positive). For example, a state with large resources to allocate to contact investigation might choose a cut point of 0.06, which would allow them to investigate approximately 10% fewer contacts but maintain a sensitivity of 97% and a false-negative rate of 5% (Table 3). This approach might be particularly appropriate in a state with a low infection rate. On the other hand, increasing the cut point improves the specificity, but one would fail to investigate larger numbers of infected contacts (false-negatives). A state with fewer resources to devote to contact investigation might choose a cut point of 0.20 allowing them to investigate 78% fewer contacts, but yeilding a model with lower sensitivity (42%) and a higher false-negative rate (11%) (Table 3).

While trade-offs always exist, sensitivity of a test should be increased at the expense of specificity when the consequences associated with missing a positive test result are high.³² The consequences of missing a positive TST result representing recent infection with TB may lead to spread of disease. This is particularly important if the contact is an infant or is HIV-positive. Therefore, although the cut point we chose results in a low specificity, missing recently infected contacts is less likely.

Another important consideration in determining an appropriate probability level cut point is the background rate of positive TST reactors. The background rate (the prevalence of TB or non-TB mycobacterial infection endemic in the population) is not related to recent TB transmission and will vary with age, geographic area, socioeconomic status, and country of origin. In the absence of recent skin testing survey data, the true background rate is not known. However, we do know atypical mycobacteria infection is relatively high in Alabama.³¹ Ideally we would choose a cut point in which the false-negative rate was equivalent to a precisely known background rate and unlikely to represent recent transmission. A cut point producing a false-negative rate of approximately 9% will mean the proportion of false-negative results due to recent infection is less than 9%—perhaps appreciably less. Therefore, such a model is unlikely to miss many positive reactors representing recent transmission.

The clarification and standardization of terminology on contact tracing and interview skills²² coupled with training courses minimized interobserver variation. Current work is focusing on using alternative methods of analysis to create an algorithm for field workers to use in prioritizing investigation of contacts. In addition, we anticipate this model to serve as a tool for studying host genetic susceptibility and resistance, as well as bacterial virulence and infectiousness, since it precisely characterizes the pheonotypic and environmental aspects of recent transmission.

One limitation of this study is the large amount of missing data on current smoking status for cases. Due to the limited amount of data available, this variable was not included in the analysis. In addition, Alabama had few TB cases in which the individual was homeless or had HIV or AIDS. States with high rates of homelessness or cases of HIV or AIDS among TB cases need to consider this limitation of our study in their contact investigations.

We believe our TB transmission model is a valuable tool for public health. The model can be adapted to different disease and population conditions, reducing the number of contacts public health officials need to investigate while maintaining excellent disease control. The use of this model should allow public health workers to substantially reduce the number of contacts investigated and save valuable resources, which can be devoted to directly observed therapy and other important disease-control activities. While this article emphasizes the science of transmission, it is important to remember that contact tracing is also an art requiring other forms of expertise and intuition. The use of this method should in no way preclude the concept of extending contact tracing in individual cases when a high percentage of contacts are found to be positive reactors. Rather, the use of this model combined with the intuition and experience of TB field workers can assist in reaching the goal of TB elimination while ensuring efficient and effective use of public health resources.