Transitions From Heart Disease to Cancer as the Leading Cause of Death in US States, 1999-2016.

INTRODUCTION: Heart disease has been the leading cause of death in the United States since 1910 and cancer the second leading cause of death since 1933. However, cancer emerged recently as the leading cause of death in many US states. The objective of this study was to provide an in-depth analysis of age-standardized annual state-specific mortality rates for heart disease and cancer. METHODS: We used population-based mortality data from 1999 through 2016 to compare 2 underlying cause-of-death categories: diseases of heart (International Classification of Diseases, 10th Revision [ICD-10] codes I00-I09, I11, I13, and I20-I51) and malignant neoplasms (ICD-10 codes C00-C97). We calculated age-standardized annual state-specific mortality rate ratios (MRRs) as heart disease mortality rate divided by cancer mortality rate. RESULTS: In 1999, age-standardized heart disease mortality exceeded that for cancer in all 50 states. Median state-specific MRR in 1999 was 1.26 (interquartile range [IQR], 1.17-1.34; range, 1.03-1.56), indicating predominance of heart disease mortality nationwide. Median state-specific MRR decreased annually through 2010, reaching a low of 1.00 (IQR, 0.95-1.07; range, 0.71-1.25), indicating that predominance of heart disease mortality prevailed in approximately half of states. Median state-specific MRR increased to 1.03 (IQR, 0.97-1.12; range, 0.77-1.31) in 2016. In 2016, age-standardized cancer mortality exceeded that for heart disease in 19 states. State-level transitions were most apparent for people aged 65 to 84 and affected men, women, and all racial/ethnic groups. CONCLUSION: State-level data indicated heterogeneity across US states in the predominance of heart disease mortality relative to cancer mortality. Timing and magnitude of transitions toward cancer mortality predominance varied by state.

Seasonal Timing of Infant Bronchiolitis, Apnea and Sudden Unexplained Infant Death.

Rates of Sudden Unexplained Infant Death (SUID), bronchiolitis, and central apnea increase in winter in temperate climates. Though associations between these three conditions are suggested, more work is required to establish if there is a causal pathway linking bronchiolitis to SUID through inducing central apnea. Utilizing a large population-based cohort of infants studied over a 20-year period (n = 834,595, from birth years 1989-2009)), we analyzed ecological associations between timing of SUID cases, bronchiolitis, and apnea healthcare visits. Data were analyzed between 2013 and 2015. We used a Cox Proportional Hazards model to analyze possible interactions between maternal smoking and maternal asthma with infant bronchiolitis on time to SUID. SUID and bronchiolitis both occurred more frequently in winter. An increase in bronchiolitis clinical visits occurred within a few days prior to apnea visits. We found a temporal relationship between infant bronchiolitis and apnea. In contrast, no peak in SUID cases was seen during peaks of bronchiolitis. Among those without any bronchiolitis visits, maternal smoking was associated with an increased risk of SUID: Hazard Ratio (HR) of 2.38 (95% CI: 2.11, 2.67, p-value <0.001). Maternal asthma was associated with an increased risk of SUID among infants with at least one bronchiolitis visit: HR of 2.40 (95% CI: 1.04, 5.54, p-value = 0.04). Consistent trends between bronchiolitis, apnea, and SUID were not established due to small numbers of SUID cases. However, interaction analysis revealed potential differential associations of bronchiolitis and SUID by maternal smoking, maternal asthma status.

Investigation of spatio-temporal cancer clusters using residential histories in a case-control study of non-Hodgkin lymphoma in the United States.

BACKGROUND: Non-Hodgkin lymphoma (NHL) is an enigmatic disease with few known risk factors. Spatio-temporal epidemiologic analyses have the potential to reveal patterns that may give clues to new risk factors worthy of investigation. We sought to investigate clusters of NHL through space and time based on life course residential histories. METHODS: We used residential histories from a population-based NHL case-control study of 1300 cases and 1044 controls with recruitment centers in Iowa, Detroit, Seattle, and Los Angeles, and diagnosed in 1998-2000. Novel methods for cluster detection allowing for residential mobility, called Q-statistics, were used to quantify nearest neighbor relationships through space and time over the life course to identify cancer clusters. Analyses were performed on all cases together and on two subgroups of NHL: Diffuse large B-cell lymphoma and follicular lymphoma. These more homogenous subgroups of cases might have a more common etiology that could potentially be detected in cluster analysis. Based on simulation studies designed to help account for multiple testing across space and through time, we required at least four significant cases nearby one another to declare a region a potential cluster, along with confirmatory analyses using spatial-only scanning windows (SaTScan). RESULTS: Evidence of a small cluster in southeastern Oakland County, MI was suggested using residences 10-18 years prior to diagnosis, and confirmed by SaTScan in a time-slice analysis 20 years prior to diagnosis, when all cases were included in the analysis. Consistent evidence of clusters was not seen in the two histologic subgroups. CONCLUSIONS: Suggestive evidence of a small space-time cluster in southeastern Oakland County, MI was detected in this NHL case-control study in the USA.

Space-time analysis of testicular cancer clusters using residential histories: a case-control study in Denmark.

Though the etiology is largely unknown, testicular cancer incidence has seen recent significant increases in northern Europe and throughout many Western regions. The most common cancer in males under age 40, age period cohort models have posited exposures in the in utero environment or in early childhood as possible causes of increased risk of testicular cancer. Some of these factors may be tied to geography through being associated with behavioral, cultural, sociodemographic or built environment characteristics. If so, this could result in detectable geographic clusters of cases that could lead to hypotheses regarding environmental targets for intervention. Given a latency period between exposure to an environmental carcinogen and testicular cancer diagnosis, mobility histories are beneficial for spatial cluster analyses. Nearest-neighbor based Q-statistics allow for the incorporation of changes in residency in spatial disease cluster detection. Using these methods, a space-time cluster analysis was conducted on a population-wide case-control population selected from the Danish Cancer Registry with mobility histories since 1971 extracted from the Danish Civil Registration System. Cases (N=3297) were diagnosed between 1991 and 2003, and two sets of controls (N=3297 for each set) matched on sex and date of birth were included in the study. We also examined spatial patterns in maternal residential history for those cases and controls born in 1971 or later (N= 589 case-control pairs). Several small clusters were detected when aligning individuals by year prior to diagnosis, age at diagnosis and calendar year of diagnosis. However, the largest of these clusters contained only 2 statistically significant individuals at their center, and were not replicated in SaTScan spatial-only analyses which are less susceptible to multiple testing bias. We found little evidence of local clusters in residential histories of testicular cancer cases in this Danish population.

Performance of cancer cluster Q-statistics for case-control residential histories.

Few investigations of health event clustering have evaluated residential mobility, though causative exposures for chronic diseases such as cancer often occur long before diagnosis. Recently developed Q-statistics incorporate human mobility into disease cluster investigations by quantifying space- and time-dependent nearest neighbor relationships. Using residential histories from two cancer case-control studies, we created simulated clusters to examine Q-statistic performance. Results suggest the intersection of cases with significant clustering over their life course, Q(i), with cases who are constituents of significant local clusters at given times, Q(it), yielded the best performance, which improved with increasing cluster size. Upon comparison, a larger proportion of true positives were detected with Kulldorf's spatial scan method if the time of clustering was provided. We recommend using Q-statistics to identify when and where clustering may have occurred, followed by the scan method to localize the candidate clusters. Future work should investigate the generalizability of these findings.

Indoor and Outdoor Air Pollution and Lung Cancer in New Hampshire and Vermont.

Indoor and outdoor air pollution is known to contribute to increased lung cancer incidence. This study is the first to address the contribution of home heating fuel and geographical course particulate matter (PM10) concentrations to lung cancer rates in New Hampshire, U.S. First, Pearson correlation analysis and Geographically weighted regression were used to investigate spatial relationships between outdoor PM10 and lung cancer rates. While the aforementioned analyses did not indicate a significant contribution of PM10 to lung cancer in the state, there was a trend towards a significant association in the northern and southwestern regions of the state. Second, case-control data were used to estimate the contributions of indoor pollution and second hand smoke to risk of lung cancer with adjustment for confounders. Increased risk was found among those who used wood or coal to heat their homes for more than 10 winters before the age of 18, with a significant increase in risk per winter. Resulting data suggest that further investigation of the relationship between heating-related air pollution levels and lung cancer risk is needed.

Ecogeographic genetic epidemiology.

Complex diseases such as cancer and heart disease result from interactions between an individual's genetics and environment, i.e. their human ecology. Rates of complex diseases have consistently demonstrated geographic patterns of incidence, or spatial "clusters" of increased incidence relative to the general population. Likewise, genetic subpopulations and environmental influences are not evenly distributed across space. Merging appropriate methods from genetic epidemiology, ecology and geography will provide a more complete understanding of the spatial interactions between genetics and environment that result in spatial patterning of disease rates. Geographic information systems (GIS), which are tools designed specifically for dealing with geographic data and performing spatial analyses to determine their relationship, are key to this kind of data integration. Here the authors introduce a new interdisciplinary paradigm, ecogeographic genetic epidemiology, which uses GIS and spatial statistical analyses to layer genetic subpopulation and environmental data with disease rates and thereby discern the complex gene-environment interactions which result in spatial patterns of incidence.