Epidemiologic Inference From the Distribution of Tuberculosis Cases in Households in Lima, Peru

Tuberculosis (TB) is a debilitating infectious disease that is responsible for ∼2 million deaths worldwide annually [1, 2]. Infection occurs through the transmission of respiratory droplets from individuals with TB; therefore persons in close and sustained proximity to TB cases are at high risk of infection. Accordingly, much of our basic understanding of TB epidemiology is based on household studies of disease transmission that typically measure the risk of infection [3–5] or disease [6–8] among those living with an index case. Such studies have estimated the burden of TB among close contacts of TB cases [9], the effects of putative risk factors for infection and disease conditional on exposure [10–12], and the time course over which secondary cases arise [9, 13]. Epidemiologic inference from household studies of TB, however, is complicated by the need to differentiate between transmission that occurs within the household of an index case and transmission from community exposure [14, 15]. In addition, exposure to Mycobacterium tuberculosis that does not result in disease may provide protective immunity from future infection or disease [16–18], further complicating the measurement of risk in exposed contacts. Researchers have addressed such methodologic issues by using molecular fingerprinting to identify transmission chains [15] and by nesting household studies within prospective studies that ascertain infection status at baseline [9, 19]. Although these studies have helped to elucidate TB transmission dynamics, they can be costly and take years to complete. We propose that program data may inform estimates of the relative risks of infection from household and community exposures, the degree of partial immunity conferred by a past infection, and the time between cases within households. This type of easily available data could be routinely collected and would provide alternative, cost-efficient methods to improve our understanding of disease dynamics and our ability to select disease control strategies. The cross-sectional distribution of infection within households has been used to study the dynamics of acute respiratory diseases, such as influenza [20–23] and other diseases [24, 25]. Despite the limitations of cross-sectional data, they are more readily obtained than those from prospective studies of exposed cohorts. In the absence of household transmission, the number of cases within a household should reflect a random sample of community prevalence. Deviations from this pattern may be associated with household structure resulting from, for example, clustering of risk factors or household transmission. In this study, we extend the methods of Longini et al [20, 21] to study the distribution of cases of chronic infectious TB occurring in households in Lima, Peru, an area of high disease burden and a growing epidemic of highly drug-resistant disease [1, 26]. We use data from household contacts of index cases with multidrug-resistant TB (disease resistant to at least isoniazid and rifampin, the 2 most powerful antibiotics used to treat TB) to estimate the risk and time course of nonindex cases given a household exposure, the risk of disease acquired through community rather than household transmission, and the protective effect of previous exposure to TB.