Suchergebnisse

Although annual data are commonly used to model linear trends and changes in trends of disease incidence, monthly data could provide additional resolution for statistical inferences. Because monthly data may exhibit seasonal patterns, we need to consider seasonally adjusted models, which can be theoretically complex and computationally intensive. We propose a combination of methods to reduce the complexity of modeling seasonal data and to provide estimates for a change in trend when the timing and magnitude of the change are unknown. To assess potential changes in trend, we first used autoregressive integrated moving average (ARIMA) models to analyze the residuals and forecast errors, followed by multiple ARIMA intervention models to estimate the timing and magnitude of the change. Because the variable corresponding to time of change is not a statistical parameter, its confidence bounds cannot be estimated by intervention models. To model timing of change and its credible interval, we developed a Bayesian technique. We avoided the need for computationally intensive simulations by deriving a closed form for the posterior distribution of the time of change. Using a combination of ARIMA and Bayesian methods, we estimated the timing and magnitude of change in trend for tuberculosis cases in the United States. Published 2012. This article is a US Government work and is in the public domain in the USA.

Abstract Background Tuberculosis (TB) in developed countries has historically been associated with poverty and low socioeconomic status (SES). In the past quarter century, TB in the United States has changed from primarily a disease of native-born to primarily a disease of foreign-born persons, who accounted for more than 60% of newly-diagnosed TB cases in 2010. The purpose of this study was to assess the association of SES with rates of TB in U.S.-born and foreign-born persons in the United States, overall and for the five most common foreign countries of origin. Methods National TB surveillance data for 1996–2005 was linked with ZIP Code-level measures of SES (crowding, unemployment, education, and income) from U.S. Census 2000. ZIP Codes were grouped into quartiles from low SES to high SES and TB rates were calculated for foreign-born and U.S.-born populations in each quartile. Results TB rates were highest in the quartiles with low SES for both U.S.-born and foreign-born populations. However, while TB rates increased five-fold or more from the two highest to the two lowest SES quartiles among the U.S.-born, they increased only by a factor of 1.3 among the foreign-born. Conclusions Low SES is only weakly associated with TB among foreign-born persons in the United States. The traditional associations of TB with poverty are not sufficient to explain the epidemiology of TB among foreign-born persons in this country and perhaps in other developed countries. TB outreach and research efforts that focus only on low SES will miss an important segment of the foreign-born population.