Geospatial Analysis Of Violent Crime And Premature Mortality From Chd

Background: Cardiovascular disease (CVD) is the leading cause of death in the United States and many of these deaths are preventable. Studies have shown that neighborhood-level characteristics may contribute to health outcomes, but no study has yet examined whether neighborhood crime contributes to early mortality from CVD. Objective: We examined geographic trends in the association between neighborhood crime rates and premature mortality from coronary heart disease (CHD) using New Haven, CT USA as a model city. Methods: Neighborhoods in New Haven were established by existing census tracts. CHD deaths were identified from the Connecticut Master Death Files and violent crime rates were calculated from the FBI Uniform Crime Reports. We conducted a global ordinary least squares (OLS) analysis and a geographically weighted regression (GWR) analysis to model average years of potential life lost (YPLL) by census tract. Results: Out of 687 CHD deaths in the city of New Haven from 2005-2010, 319, or 46.4%, are considered premature. The OLS model accounted for 30.8% and the GWR model accounted for 48.6% of the variability in premature deaths from CHD. An increase of 10 violent crimes per 1,000 residents was associated with an average of 2.3 additional years of life lost (p=0.043), while holding other neighborhood factors constant. Moreover, the GWR model predicted a 7-fold disparity in premature CHD mortality across census tracts, ranging from 1.73 YPLL to 12.38 YPLL. Conclusion: Our findings suggest that neighborhood violent crime rates may contribute to premature death from CHD. Modeling based on geographic variation is a powerful tool to enhance resolution of previously unidentified environmental factors contributing to preventable death from cardiovascular disease

Using Electronic Health Record Data for Public Health Surveillance of Diabetes among Young Adults

There is growing interest in using electronic health records (EHRs) for the surveillance of chronic diseases because these data contain a wealth of timely, clinical information on large samples of individuals. However, as these data are collected for clinical purposes, they may be prone to a number of biases that could affect their utility for public health practitioners or researchers. First, these data represent convenience samples of individuals who are in-care. These samples may not be representative of target populations for public health surveillance activities (e.g., the general population within a city or jurisdiction) by factors like demographics or health status, which could affect the generalizability of results. Second, key variables, including demographics or disease status, are often susceptible to missing data or misclassification, which could affect estimation of disease prevalence or risk factor associations. The goal of this integrated learning experience (ILE) was to assess the application of EHR data for chronic disease surveillance, focusing on the potential impact of selection and information biases for the case study of diabetes. The first aim characterized the existing literature on defining diabetes status and type using EHRs from a population health perspective. The second aim externally validated diabetes prevalence estimates generated using EHR data from a large academic medical center in New York City (NYC) compared to traditional surveillance estimates from a local health survey. Various statistical methods, including raking, post-stratification, and multilevel regression with post-stratification, were applied to these real-world data and to simulated data to assess the ability to mitigate selection biases. Finally, the third aim externally validated EHR-based associations with potential diabetes risk factors (i.e., race/ethnicity and asthma) compared to estimates from national surveillance systems, including the Behavioral Risk Factor Surveillance System and National Health and Nutrition Examination Survey. Methods from the missing data and causal inference literature were then applied to assess the ability to control for misclassification of health outcomes in the EHR data. Results from the literature review demonstrated that while there was no gold standard for defining diabetes using EHR data, definitions that prioritized sensitivity over specificity may be preferable for population health purposes. Based on this review, a flexible definition that searched for evidence of diabetes across diagnoses, medications, and lab results was used for the second and third aims. In the second aim, using statistical methods to account for demographic differences between the EHR sample and general population helped to remediate biases observed in the crude diabetes prevalence estimates. However, simulation results demonstrated that these methods may be insufficient when data are lacking for variables that are strong predictors of selection into the EHR sample. In the third aim, applying missing data or causal inference methods to control for misclassification of health outcomes greatly reduced the strength of the association between asthma and diabetes status compared to naïve associations observed within the EHR sample, in alignment with observations from national health survey data. Overall, the findings of this ILE suggest that naïve EHR analyses may yield biased estimates of diabetes prevalence or measures of association, driven in part by differences in healthcare utilization patterns across the population. However, applying epidemiologic frameworks can help control for and, importantly, characterize residual biases in these estimates. Future research is needed to assess the potential for selection and information biases across a variety of health outcomes, geographies, and EHR data sources to further inform the utility of these data for population health surveillance

Examination of the Chromatin Structure of Xlr3b Using the Chromosome Conformation Capture Assay

Imprinted genes contain epigenetic modifications that influence expression patterns based on parent-of-origin. Recent studies have shown that imprinted genes contribute to numerous human diseases and disorders. Xlr3b, an imprinted gene on the X chromosome, has been implicated in social and behavioral deficits characteristic of disorders such as Turner syndrome and autism. The imprinting mechanism of this gene is still unknown, and this study analyzed the native chromatin structure of Xlr3b through the chromosome conformation capture assay to determine if there are any long-range interactions that regulate the expression of this gene. Brain tissue from a mouse model of Turner syndrome (39, Xm) was used in this protocol, and the samples were analyzed through PCR amplification with primers designed to capture interacting fragments. No long-range interactions were found with the maternal copy of Xlr3b, indicating that the expression is not promoted by a distant enhancer. However, it remains a possibility that the imprinting mechanism of Xlr3b is regulated by insulating interactions within the paternal chromosome

Developing Phenotypes from Electronic Health Records for Chronic Disease Surveillance

ObjectiveTo utilize clinical data in Electronic Health Records (EHRs) to develop chronic disease phenotypes appropriate for conducting population health surveillance.IntroductionChronic diseases, including hypertension, type 2 diabetes mellitus (diabetes), obesity, and hyperlipidemia, are some of the leading causes of morbidity and mortality in the United States. Monitoring disease prevalence guides public health programs and policies that help prevent this burden. EHRs can supplement traditional sources of chronic disease surveillance, such as health surveys and administrative claims datasets, by offering near real-time data, large sample sizes, and a rich source of clinical data. However, few studies have provided clear, consistent EHR phenotypes that were developed to inform population health surveillance.MethodsRetrospective EHR data were obtained for patients seen at New York University Langone Health in 2017 (n=1,397,446). To better estimate chronic disease burden among New York City (NYC) adults, the patient population was limited to NYC residents aged 20 or older, who were seen in the ambulatory primary care setting (n=153,653). Rule-based algorithms for identifying patients with hypertension, statin-eligibility, diabetes, and obesity were developed based on a combination of diagnostic codes, lab results or vitals, and relevant prescriptions. We compared the performance of our metric definitions to selected phenotypes from the literature using percent agreement and Cohen’s kappa. Patients with discordant disease classifications between the two sets of definitions were analyzed through natural language processing (NLP) on the patients’ 2017 medical notes using a support vector machine model. Statin-eligibility is a novel phenotype and therefore did not have a comparable definition in the literature. Sensitivity analyses were conducted to determine how disease burden changed under alternative rules for each metric.ResultsOf 153,653 adult ambulatory care patients in 2017, an estimated 53.7% had hypertension, 12.4% had diabetes, 27.8% were obese, and 30.0% were statin-eligible under our proposed definitions. The estimated prevalence of hypertension increased from 28.1% to 53.7% when diagnostic codes were supplemented with blood pressure measurements and anti-hypertensive medications, while the estimated prevalence of diabetes increased less than one percentage point with inclusion of diabetes-related medications and elevated A1C measurements. There was high agreement between our obesity (94.5% agreement, k=0.86) and diabetes (96.2% agreement, k=0.81) definitions and selected definitions from the literature and moderate agreement between the hypertension definitions (74.8% agreement, k=0.41). NLP classification of discordant cases had greater alignment with the classification results of our definitions for both hypertension (78.0% agreement) and diabetes (71.2% agreement) but did not show strong agreement with either obesity algorithm. Sensitivity analyses did not have large impacts on prevalence estimates for any of the indicators, with all estimates within two percentage points of the final algorithms.ConclusionsOur proposed rule-based phenotypes using prescriptions, labs, and vitals improved ascertainment of conditions beyond diagnostic codes and were robust to modifications per sensitivity analyses. Results from our algorithms were highly consistent with standard phenotypes from the literature and may improve case capture for surveillance purposes. These algorithms can be replicated across diverse EHR networks and can be weighted to generate population prevalence estimates.

Using electronic health records to enhance surveillance of diabetes in children, adolescents and young adults: a study protocol for the DiCAYA Network

Introduction Traditional survey-based surveillance is costly, limited in its ability to distinguish diabetes types and time-consuming, resulting in reporting delays. The Diabetes in Children, Adolescents and Young Adults (DiCAYA) Network seeks to advance diabetes surveillance efforts in youth and young adults through the use of large-volume electronic health record (EHR) data. The network has two primary aims, namely: (1) to refine and validate EHR-based computable phenotype algorithms for accurate identification of type 1 and type 2 diabetes among youth and young adults and (2) to estimate the incidence and prevalence of type 1 and type 2 diabetes among youth and young adults and trends therein. The network aims to augment diabetes surveillance capacity in the USA and assess performance of EHR-based surveillance. This paper describes the DiCAYA Network and how these aims will be achieved.Methods and analysis The DiCAYA Network is spread across eight geographically diverse US-based centres and a coordinating centre. Three centres conduct diabetes surveillance in youth aged 0–17 years only (component A), three centres conduct surveillance in young adults aged 18–44 years only (component B) and two centres conduct surveillance in components A and B. The network will assess the validity of computable phenotype definitions to determine diabetes status and type based on sensitivity, specificity, positive predictive value and negative predictive value of the phenotypes against the gold standard of manually abstracted medical charts. Prevalence and incidence rates will be presented as unadjusted estimates and as race/ethnicity, sex and age-adjusted estimates using Poisson regression.Ethics and dissemination The DiCAYA Network is well positioned to advance diabetes surveillance methods. The network will disseminate EHR-based surveillance methodology that can be broadly adopted and will report diabetes prevalence and incidence for key demographic subgroups of youth and young adults in a large set of regions across the USA