Vinicius C. Antao

The intersection of big data and artificial intelligence (AI) has resulted in advances in numerous areas, including machine learning, computer vision, and natural language processing. Although there are many potentially transformative applications of AI in health care, including precision medicine, this industry has been slow to adopt these technologies. At the same time, the operations of health care have historically been system-directed and physician-directed rather than patient-centered. The application of AI to patient-reported outcome measures (PROMs), which provide insight into patient-centered health outcomes, could steer research and healthcare delivery toward decisions that optimize outcomes important to patients. Historically, PROMs have only been collected within research registries. However, the increasing availability of PROMs within electronic health records has led to their inclusion in big data ecosystems, where they can inform or be informed by other data elements. The use of big data to analyze PROMs can help establish norms, evaluate data distribution, and determine proportions of patients achieving change or threshold standards. This information can be used for benchmarking, risk adjustment, predictive modeling, and ultimately improving the health of individuals and populations.

Thousands of lower Manhattan residents sustained damage to their homes following the collapse of the Twin Towers on 11 September 2001. Respiratory outcomes have been reported in this population. We sought to describe patterns of home damage and cleaning practices in lower Manhattan and their impacts on respiratory outcomes among World Trade Center Health Registry (WTCHR) respondents. Data were derived from WTCHR Wave 1 (W1) (9/2003–11/2004) and Wave 2 (W2) (11/2006–12/2007) surveys. Outcomes of interest were respiratory symptoms (shortness of breath (SoB), wheezing, persistent chronic cough, upper respiratory symptoms (URS)) first occurring or worsening after 9/11 W1 and still present at W2 and respiratory diseases (asthma and chronic obstructive pulmonary disease (COPD)) first diagnosed after 9/11 W1 and present at W2. We performed descriptive statistics, multivariate logistic regression and geospatial analyses, controlling for demographics and other exposure variables. A total of 6447 residents were included. Mean age on 9/11 was 45.1 years (±15.1 years), 42% were male, 45% had ever smoked cigarettes, and 44% reported some or intense dust cloud exposure on 9/11. The presence of debris was associated with chronic cough (adjusted OR (aOR) = 1.56, CI: 1.12–2.17), and upper respiratory symptoms (aOR = 1.56, CI: 1.24–1.95). A heavy coating of dust was associated with increased shortness of breath (aOR = 1.65, CI: 1.24–2.18), wheezing (aOR = 1.43, CI: 1.03–1.97), and chronic cough (aOR = 1.59, CI: 1.09–2.28). Dusting or sweeping without water was the cleaning behavior associated with the largest number of respiratory outcomes, such as shortness of breath, wheezing, and URS. Lower Manhattan residents who suffered home damage following the 9/11 attacks were more likely to report respiratory symptoms and diseases compared to those who did not report home damage.

The burden of chronic diseases, including occupational respiratory diseases (ORDs), is increasing worldwide. Nevertheless, epidemiological data on these conditions are scarce in most countries. Therefore, it is important to conduct surveillance to monitor ORDs, particularly in developing countries, where the working population is especially vulnerable and the health system infrastructure is usually weak. This article provides a general framework for the implementation of ORD surveillance in developing countries. The main objectives of surveillance are to describe incidence and prevalence of ORDs, as well as to identify sentinel events and new associations between occupational exposures and health outcomes. Diseases with high morbidity and mortality and those in which early diagnosis with standardized tests are available are especially suitable for surveillance activities. Simple strategies, preferably using existing resources and technology, are the best option for surveillance in developing countries. This article offers examples of specific surveillance systems that are in place in Brazil, China, Cuba, India, and South Africa.

Background: The decline in asbestos use in the United States may impact mesothelioma incidence. Objective: This report provides national and state-specific estimates of mesothelioma incidence in the United States using cancer surveillance data for the entire US population. Methods: Data from the National Program for Cancer Registries and the Surveillance, Epidemiology, and End Results program were used to calculate incidence rates and annual percent change. Results: During 2003–2008, an average of 1·05 mesothelioma cases per 100 000 persons were diagnosed annually in the United States; the number of cases diagnosed each year remained level, whereas rates decreased among men and were stable among women. Conclusion: US population-based cancer registry data can be used to determine the burden of mesothelioma and track its decline. Even 30 years after peak asbestos use in the United States, 3200 mesothelioma cases are diagnosed annually, showing that the US population is still at risk.