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Introduction: New York City has one of the highest reported death rates from coronary heart disease in the United States. We sought to measure the accuracy of this rate by examining death certificates., Methods: We conducted a cross-sectional validation study by using a random sample of death certificates that recorded in-hospital deaths in New York City from January through June 2003, stratified by neighborhoods with low, medium, and high coronary heart disease death rates. We abstracted data from hospital records, and an independent, blinded medical team reviewed these data to validate cause of death. We computed a comparability ratio (coronary heart disease deaths recorded on death certificates divided by validated coronary heart disease deaths) to quantify agreement between death certificate determination and clinical judgment., Results: Of 491 sampled death certificates for in-hospital deaths, medical charts were abstracted and reviewed by the expert panel for 444 (90%). The comparability ratio for coronary heart disease deaths among decedents aged 35 to 74 years was 1.51, indicating that death certificates overestimated coronary heart disease deaths in this age group by 51%. The comparability ratio increased with age to 1.94 for decedents aged 75 to 84 years and to 2.37 for decedents aged 85 years or older., Conclusion: Coronary heart disease appears to be substantially overreported as a cause of death in New York City among in-hospital deaths.

The Genetic Analysis Workshop 14 simulated dataset was designed 1) To test the ability to find genes related to a complex disease (such as alcoholism). Such a disease may be given a variety of definitions by different investigators, have associated endophenotypes that are common in the general population, and is likely to be not one disease but a heterogeneous collection of clinically similar, but genetically distinct, entities. 2) To observe the effect on genetic analysis and gene discovery of a complex set of gene x gene interactions. 3) To allow comparison of microsatellite vs. large-scale single-nucleotide polymorphism (SNP) data. 4) To allow testing of association to identify the disease gene and the effect of moderate marker x marker linkage disequilibrium. 5) To observe the effect of different ascertainment/disease definition schemes on the analysis. Data was distributed in two forms. Data distributed to participants contained about 1,000 SNPs and 400 microsatellite markers. Internet-obtainable data consisted of a finer 10,000 SNP map, which also contained data on controls. While disease characteristics and parameters were constant, four "studies" used varying ascertainment schemes based on differing beliefs about disease characteristics. One of the studies contained multiplex two- and three-generation pedigrees with at least four affected members. The simulated disease was a psychiatric condition with many associated behaviors (endophenotypes), almost all of which were genetic in origin. The underlying disease model contained four major genes and two modifier genes. The four major genes interacted with each other to produce three different phenotypes, which were themselves heterogeneous. The population parameters were calibrated so that the major genes could be discovered by linkage analysis in most datasets. The association evidence was more difficult to calibrate but was designed to find statistically significant association in 50% of datasets. We also simulated some marker x marker linkage disequilibrium around some of the genes and also in areas without disease genes. We tried two different methods to simulate the linkage disequilibrium.