A Prospective Validation of a Clinical Algorithm to Detect Tuberculosis in Child Contacts.

Over 60% of pediatric tuberculosis cases are undetected by healthcare services in low-income settings (1). Untreated children with tuberculosis have fatality rates of . 20%, reaching above 40% in children ,5 years old (2). Specific, effective, and validated interventions to increase case detection in children are urgently needed. Household contact tracing has been widely recommended to increase detection of childhood tuberculosis cases. Although it can provide high yields (3, 4), child contact tracing has rarely been implemented in high-burden settings (5). Methods are urgently needed to incentivize contact tracing for national tuberculosis programs in low-income settings. A clinical risk score was recently proposed in Taiwan (a high-income, low-prevalence setting) to increase the effectiveness of child contact tracing (6). This algorithm uses a combination of contact, index, and environmental characteristics to allocate child contacts into highand low-risk groups for secondary disease. External validation in high-burden settings has not been performed. To investigate whether this score (6) is useful for detecting tuberculosis in exposed children from Sub-Saharan Africa, we used data from a large Ugandan child contact cohort. This was a prospective cohort study of household contacts of tuberculosis cases; the study has been described previously (7–9). Briefly, we identified adult patients in Kampala, Uganda, with a new diagnosis of tuberculosis. Index cases were microbiologically confirmed through a positive culture test and evaluated through physical examination and medical history. Households were visited by field workers within 2 weeks of diagnosis, and contacts were defined as spending at least 7 consecutive days in the index’s household 3 months before diagnosis. Coprevalent tuberculosis was defined as tuberculosis within 3months of baseline. Contacts were evaluated for tuberculosis through a medical examination, posteroanterior chest radiographs, specimen microscopy, and mycobacterial culture. Contacts without coprevalent tuberculosis were followed for incident tuberculosis for 2 years. Contacts were grouped by age (,13 yr old) and disease status to categorize the data according to the scoring algorithm (6). Contacts were given a point score based on the algorithm (6): contact tuberculin skin test (TST) induration (2 points if 10–14 mm, 3 points if 15–20 mm, and 4 points if >20 mm), index smear result (1 point if smear positive), index sex (1 point if female), and burden of residence area (2 points if index lived in a high-incidence area). Because all index cases in the Ugandan cohort lived in a high-incidence area, all contacts were automatically given 2 points. The score’s discrimination and classification accuracy was compared with results from the original derivation and internal validation populations. Cochran-Armitage tests were used to test for trends between disease outcomes and multiple point categories in the risk score. The institutional review boards at the Uganda National Council for Science and Technology, the Uganda National AIDS Research Subcommittee, University Hospitals Cleveland Medical Center, and Makerere University approved this study. Informed consent was obtained for index cases, and parents of contacts provided verbal assent for their children. Nine months of isoniazid prophylaxis was offered to child contacts if they were ,5 years old, HIV-infected, or TST-positive. Overall, 1,941 household contacts were enrolled; 1,032 of these contacts were ,13 years old and included in this analysis (Figure 1). The median contact age was 6 years. The majority had a positive TST (n = 586), and 63 had tuberculosis (55 subjects [5.3%] had tuberculosis at baseline and 8 [0.8%] developed tuberculosis over 2 yr). Tuberculosis was microbiologically confirmed in 65.1% of these cases. Isoniazid preventive therapy was started in 274 children who were free of tuberculosis at baseline. In a univariate analysis, no variable included in the risk score was a statistically significant predictor of coprevalent, incident, or any tuberculosis. The proposed algorithm had low predictive power (C-statistic, 0.54). Coprevalent tuberculosis prevalence varied from 4.8% for contacts with a score of 4 to 12.9% with a score of 8, but there was no association between the score and disease prevalence (Ptrend = 0.528; Table 1). Similarly, for contacts with any disease, the observed proportion varied from 3.8% for contacts with a score of 7 to 12.9% with a score of 8, and there was no point score trend (Ptrend = 0.212). There were eight incident cases, five of which occurred in contacts with a score of 7. Compared with children with a 0–4 score, children with a 5–8 score did not have statistically more coprevalent (P = 0.405), incident (P = 0.237), or any tuberculosis event (P = 0.227). Making a diagnosis of tuberculosis in children remains a clinical and programmatic challenge. To guide clinicians in identifying high-risk children, a new risk score was recently derived and implemented in Taiwan (6). Although it was highly predictive among Taiwanese child contacts (C-statistic, 0.87), this algorithm had low predictive power in our large, prospective child contact cohort from Uganda. There are several possible reasons why this algorithm performed poorly in our cohort. First, lowand high-incidence areas may have different risk factors for tuberculosis. For example, the prevalence of HIV in Taiwan is significantly different from that in Uganda, and HIV plays a critical role in influencing the tuberculosis epidemic in Africa (7, 9, 10). This score may be most useful in high-income, low-tuberculosis-burden settings where HIV prevalence is low. External, prospective validations in such settings are necessary to address this issue. Second, case ascertainment bias is a concern because the original derivationcohort study used retrospective programmatic data. This study has certain limitations that should be mentioned. First, without molecular genotyping, we are unable to state with certainty that contacts acquired disease owing to household exposure. However, our aim was to evaluate the disease yield in our setting using the specified algorithm, not to measure household transmission. Second, because one variable in the derived score was “high-incidence area,” to evaluate this Supported in part by the National Institute of Allergy and Infectious Diseases (NO1-AI-95383 and HHSN266200700022C/NO1-AI-70022), the Fogarty International Center (TW-00011), and the Center for AIDS Research (AI 36219).