Influence of Breast Cancer and Metastases on Incidence of Diabete

Purpose: Diabetes increases the risk of subsequent breast cancer. However, the inverse relationship of breast cancer to incident diabetes development is unclear. In preclinical models increased bone turnover due to bone metastases or endocrine therapies impacts insulin secretion. This analysis was conducted to estimate the incidence of diabetes after breast cancer and the influence of metastases and therapeutic agents. Methods: This retrospective case-control study combined data from a large electronic health data exchange and the Indiana State Cancer Registry on breast cancer patients and controls between 2007 and 2017. Primary exposure was presence of breast cancer and bone or non-bone metastases. The primary outcome was frequency of incident diabetes detected by ICD codes, medication use, or laboratory results, compared between breast cancer cases and controls using conditional or ordinary logistic regressions. Results: 36,083 cases and 36,083 matched controls were detected. Incident diabetes was higher in early stage breast cancer (OR 1.17, 95%CI 1.11-1.23, p<0.0001) and metastatic breast cancer (OR 1.62, 95% CI 1.25-2.09, p=0.0002), compared to controls. Bone metastases conferred higher odds of both pre-existing (OR 1.20, 95% CI 1.03-1.63, p=0.0272) and incident diabetes (OR 1.64, 95% CI 1.19-2.25, p=0.0021). Endocrine therapy was associated with reduced diabetes (OR 0.86, 95% CI 0.79-0.83, p=0.002). Anti-resorptives reduced incident diabetes in those with bone metastases (OR 0.44, 95% CI 0.25-0.78, p=0.005). Conclusion: Breast cancer, especially with metastases, increases subsequent risk of diabetes. As patients with breast cancer live longer, identifying and managing diabetes may impact treatment delivery, cost, survival, and quality of life

Provider Adherence to Syphilis Testing Guidelines Among Stillbirth Cases

Background The Centers for Disease Control and Prevention (CDC) recommends that all women with a stillbirth have a syphilis test after delivery. Our study seeks to evaluate adherence to CDC guidelines for syphilis screening among women with a stillbirth delivery. Methods We utilized data recorded in electronic health records for women who gave birth between January 1, 2014 and December 31, 2016. Patients were included if they were 18-44 years old and possessed an ICD-9-CM or ICD-10-CM diagnosis of stillbirth. Stillbirth diagnoses were confirmed through a random sample of medical chart reviews. To evaluate syphilis screening, we estimated the proportion of women who received syphilis testing within 300 days before stillbirth, within 30 days after a stillbirth delivery, and women who received syphilis testing both before and after stillbirth delivery. Results We identified 1,111 stillbirths among a population of 865,429 unique women with encounter data available from electronic health records. Among a sample of 127 chart reviewed cases, only 35 (27.6%) were confirmed stillbirth cases, 45 (35.4%) possible stillbirth cases, 39 (30.7%) cases of miscarriage, and 8 (6.3%) cases of live births. Among confirmed stillbirth cases, 51.4% had any syphilis testing conducted, 31.4% had testing before their stillbirth delivery, 42.9% had testing after the delivery, and only 22.9% had testing before and after delivery. Conclusions A majority of women with a stillbirth delivery do not receive syphilis screening adherent to CDC guidelines. Stillbirth ICD codes do not accurately identify cases of stillbirth

Sarcopenia, frailty and cachexia patients detected in a multisystem electronic health record database

Background: Sarcopenia, cachexia and frailty have overlapping features and clinical consequences, but often go unrecognized. The objective was to detect patients described by clinicians as having sarcopenia, cachexia or frailty within electronic health records (EHR) and compare clinical variables between cases and matched controls. Methods: We conducted a case-control study using retrospective data from the Indiana Network for Patient Care multi-health system database from 2016 to 2017. The computable phenotype combined ICD codes for sarcopenia, cachexia and frailty, with clinical note text terms for sarcopenia, cachexia and frailty detected using natural language processing. Cases with these codes or text terms were matched to controls without these codes or text terms matched on birth year, sex and race. Two physicians reviewed EHR for all cases and a subset of controls. Comorbidity codes, laboratory values, and other coded clinical variables were compared between groups using Wilcoxon matched-pair sign-rank test for continuous variables and conditional logistic regression for binary variables. Results: Cohorts of 9594 cases and 9594 matched controls were generated. Cases were 59% female, 69% white, and a median (1st, 3rd quartiles) age 74.9 (62.2, 84.8) years. Most cases were detected by text terms without ICD codes n = 8285 (86.4%). All cases detected by ICD codes (total n = 1309) also had supportive text terms. Overall 1496 (15.6%) had concurrent terms or codes for two or more of the three conditions (sarcopenia, cachexia or frailty). Of text term occurrence, 97% were used positively for sarcopenia, 90% for cachexia, and 95% for frailty. The remaining occurrences were negative uses of the terms or applied to someone other than the patient. Cases had lower body mass index, albumin and prealbumin, and significantly higher odds ratios for diabetes, hypertension, cardiovascular and peripheral vascular diseases, chronic kidney disease, liver disease, malignancy, osteoporosis and fractures (all p < 0.05). Cases were more likely to be prescribed appetite stimulants and caloric supplements. Conclusions: Patients detected with a computable phenotype for sarcopenia, cachexia and frailty differed from controls in several important clinical variables. Potential uses include detection among clinical cohorts for targeting recruitment for research and interventions

Validation of ICD-10-CM Codes for Identifying Cases of Chlamydia and Gonorrhea

Background While researchers seek to use administrative health data to examine outcomes for individuals with sexually transmitted infections, the ICD-CM-10 codes used to identify persons with chlamydia and gonorrhea have not been validated. Objectives were to determine the validity of using ICD-10-CM codes to identify individuals with chlamydia and gonorrhea. Methods We utilized data from electronic health records gathered from public and private health systems from October 1, 2015 to December 31, 2016. Patients were included if they were aged 13-44 years and received either 1) laboratory testing for chlamydia or gonorrhea or 2) an ICD-10-CM diagnosis of chlamydia, gonorrhea, or an unspecified STI. To validate ICD-10-CM codes, we calculated positive and negative predictive values, sensitivity, and specificity based on the presence of a laboratory test result. We further examined the timing of clinical diagnosis relative to laboratory testing. Results The positive predictive values for chlamydia, gonorrhea, and unspecified STI ICD-10-CM codes were 87.6%, 85.0%, and 32.0%, respectively. Negative predictive values were high (>92%). Sensitivity for chlamydia diagnostic codes was 10.6% and gonorrhea was 9.7%. Specificity was 99.9% for both chlamydia and gonorrhea. The date of diagnosis occurred on or after the date of the laboratory result for 84.8% of persons with chlamydia, 91.9% for gonorrhea, and 23.5% for unspecified STI. Conclusions Disease specific ICD-10-CM codes accurately identify persons with chlamydia and gonorrhea. However, low sensitivities suggest that most individuals could not be identified in administrative data alone without laboratory test results

Leveraging Data Visualization and a Statewide Health Information Exchange to Support COVID-19 Surveillance and Response: Application of Public Health Informatics

Objective We sought to support public health surveillance and response to coronavirus disease 2019 (COVID-19) through rapid development and implementation of novel visualization applications for data amalgamated across sectors. Materials and Methods We developed and implemented population-level dashboards that collate information on individuals tested for and infected with COVID-19, in partnership with state and local public health agencies as well as health systems. The dashboards are deployed on top of a statewide health information exchange. One dashboard enables authorized users working in public health agencies to surveil populations in detail, and a public version provides higher-level situational awareness to inform ongoing pandemic response efforts in communities. Results Both dashboards have proved useful informatics resources. For example, the private dashboard enabled detection of a local community outbreak associated with a meat packing plant. The public dashboard provides recent trend analysis to track disease spread and community-level hospitalizations. Combined, the tools were utilized 133 637 times by 74 317 distinct users between June 21 and August 22, 2020. The tools are frequently cited by journalists and featured on social media. Discussion Capitalizing on a statewide health information exchange, in partnership with health system and public health leaders, Regenstrief biomedical informatics experts rapidly developed and deployed informatics tools to support surveillance and response to COVID-19. Conclusions The application of public health informatics methods and tools in Indiana holds promise for other states and nations. Yet, development of infrastructure and partnerships will require effort and investment after the current pandemic in preparation for the next public health emergency

Spaces of Yoga – Towards a Non-Essentialist Understanding of Yoga

This chapter will examine some of the spaces that yoga occupies in the contemporary world, both physical and social. By looking at yoga through the focus of particular, contested spaces and locations, it will be argued that overarching essentialist definitions of yoga are impossible, although individuals and social groups can and do create essentialist definitions that are more or less useful for particular purposes. By exploring these narratives and boundaries in the context of specific locations, we can better understand what people are doing with the collection of beliefs and practices known as yoga

A 27-country test of communicating the scientific consensus on climate change.

peer reviewedCommunicating the scientific consensus that human-caused climate change is real increases climate change beliefs, worry and support for public action in the United States. In this preregistered experiment, we tested two scientific consensus messages, a classic message on the reality of human-caused climate change and an updated message additionally emphasizing scientific agreement that climate change is a crisis. Across online convenience samples from 27 countries (n = 10,527), the classic message substantially reduces misperceptions (d = 0.47, 95% CI (0.41, 0.52)) and slightly increases climate change beliefs (from d = 0.06, 95% CI (0.01, 0.11) to d = 0.10, 95% CI (0.04, 0.15)) and worry (d = 0.05, 95% CI (-0.01, 0.10)) but not support for public action directly. The updated message is equally effective but provides no added value. Both messages are more effective for audiences with lower message familiarity and higher misperceptions, including those with lower trust in climate scientists and right-leaning ideologies. Overall, scientific consensus messaging is an effective, non-polarizing tool for changing misperceptions, beliefs and worry across different audiences

Early Diagnosis of HIV Infection in Infants - One Caribbean and Six Sub-Saharan African Countries, 2011-2015.

Pediatric human immunodeficiency virus (HIV) infection remains an important public health issue in resource-limited settings. In 2015, 1.4 million children aged 50% decline. The most common challenges for access to testing for early infant diagnosis included difficulties in specimen transport, long turnaround time between specimen collection and receipt of results, and limitations in supply chain management. Further reductions in HIV mortality in children can be achieved through continued expansion and improvement of services for early infant diagnosis in PEPFAR-supported countries, including initiatives targeted to reach HIV-exposed infants, ensure access to programs for early infant diagnosis of HIV, and facilitate prompt linkage to treatment for children diagnosed with HIV infection

Using machine learning to detect sarcopenia from electronic health records

Introduction: Sarcopenia (low muscle mass and strength) causes dysmobility and loss of independence. Sarcopenia is often not directly coded or described in electronic health records (EHR). The objective was to improve sarcopenia detection using structured data from EHR. Methods: Adults undergoing musculoskeletal testing (December 2017-March 2020) were classified as meeting sarcopenia thresholds for 0 (controls), ≥1 (Sarcopenia-1), or ≥2 (Sarcopenia-2) tests. Electronic health record diagnoses, medications, and laboratory testing were extracted from the Indiana Network for Patient Care. Five machine learning models were applied to EHR data for predicting sarcopenia. Results: Of 1304 participants, 1055 were controls, 249 met Sarcopenia-1 and 76 met Sarcopenia-2. Sarcopenic participants were older, with higher fat mass, Charlson Comorbidity Index, and more chronic diseases. All models performed better for Sarcopenia-2 than Sarcopenia-1. The top performing models for Sarcopenia-1 were Logistic Regression [area under the curve (AUC) 71.59 (95% confidence interval [CI], 71.51-71.66)] and Multi-Layer Perceptron [AUC 71.48 (95%CI, 71.00-71.97)]. The top performing models for Sarcopenia-2 were Logistic Regression [AUC 91.44 (95%CI, 91.28-91.60)] and Support Vector Machine [AUC 90.81 (95%CI, 88.41-93.20)]. For the best Logistic Regression Model, important sarcopenia predictors included diabetes mellitus, digestive system complaints, signs and symptoms involving the nervous, musculoskeletal and respiratory systems, metabolic disorders, and kidney or urinary tract disorders. Opioids, corticosteroids, and antihyperlipidemic drugs were also more common among sarcopenic participants. Conclusions: Applying machine learning models, sarcopenia can be predicted from structured data in EHR, which may be developed through future studies to facilitate large-scale early detection and intervention in clinical populations