Dataset Information

Medical Conditions Predictive of Self-Reported Poor Health: Retrospective Cohort Study.

ABSTRACT: BACKGROUND:Identifying the medical conditions that are associated with poor health is crucial to prioritize decisions for future research and organizing care. However, assessing the burden of disease in the general population is complex, lengthy, and expensive. Claims databases that include self-reported health status can be used to assess the impact of medical conditions on the health in a population. OBJECTIVE:This study aimed to identify medical conditions that are highly predictive of poor health status using claims databases. METHODS:To determine the medical conditions most highly predictive of poor health status, we used a retrospective cohort study using 2 US claims databases. Subjects were commercially insured patients. Health status was measured using a self-report health status response. All medical conditions were included in a least absolute shrinkage and selection operator regression model to assess which conditions were associated with poor versus excellent health. RESULTS:A total of 1,186,871 subjects were included; 61.64% (731,587/1,186,871) reported having excellent or very good health. The leading medical conditions associated with poor health were cancer-related conditions, demyelinating disorders, diabetes, diabetic complications, psychiatric illnesses (mood disorders and schizophrenia), sleep disorders, seizures, male reproductive tract infections, chronic obstructive pulmonary disease, cardiomyopathy, dementia, and headaches. CONCLUSIONS:Understanding the impact of disease in a commercially insured population is critical to identify subjects who may be at risk for reduced productivity and job loss. Claims database studies can measure the impact of medical conditions on the health status in a population and to assess changes overtime and could limit the need to collect prospective collection of information, which is slow and expensive, to assess disease burden. Leading medical conditions associated with poor health in a commercially insured population were the ones associated with high burden of disease such as cancer-related conditions, demyelinating disorders, diabetes, diabetic complications, psychiatric illnesses (mood disorders and schizophrenia), infections, chronic obstructive pulmonary disease, cardiomyopathy, and dementia. However, sleep disorders, seizures, male reproductive tract infections, and headaches were also part of the leading medical conditions associated with poor health that had not been identified before as being associated with poor health and deserve more attention.

SUBMITTER: Cepeda MS

PROVIDER: S-EPMC6996740 | biostudies-literature | 2020 Jan

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Medical Conditions Predictive of Self-Reported Poor Health: Retrospective Cohort Study.

Cepeda M Soledad MS Reps Jenna J Kern David M DM Stang Paul P

JMIR public health and surveillance 20200108 1

<h4>Background</h4>Identifying the medical conditions that are associated with poor health is crucial to prioritize decisions for future research and organizing care. However, assessing the burden of disease in the general population is complex, lengthy, and expensive. Claims databases that include self-reported health status can be used to assess the impact of medical conditions on the health in a population.<h4>Objective</h4>This study aimed to identify medical conditions that are highly predi ...[more]

PMID: 31913130

Similar Datasets

Project description:BackgroundAlthough used widely, there is limited evidence of the BioMedical Admissions Test's (BMAT) predictive validity and incremental validity over prior educational attainment (PEA). We investigated BMAT's predictive and incremental validity for performance in two undergraduate medical schools, Imperial College School of Medicine (ICSM), UK, and Lee Kong Chian School of Medicine (LKCMedicine), Singapore. Our secondary goal was to compare the evidence collected with published evidence relating to comparable tools.MethodsThis was a retrospective cohort study of four ICSM (1188 students, entering 2010-2013) and three LKCMedicine cohorts (222 students, 2013-2015). We investigated associations between BMAT Section 1 ('Thinking Skills'), Section 2 ('Scientific Knowledge and Applications') and Section 3a ('Writing Task') scores, with written and clinical assessment performance across all programme years. Incremental validity was investigated over PEA (A-levels) in a subset of ICSM students.ResultsWhen BMAT sections were investigated independently, Section 2 scores predicted performance on all written assessments in both institutions with mainly small effect sizes (standardised coefficient ranges: ICSM: 0.08-0.19; LKCMedicine: 0.22-0.36). Section 1 scores predicted Years 5 and 6 written assessment performance at ICSM (0.09-0.14) but nothing at LKCMedicine. Section 3a scores only predicted Year 5 clinical assessment performance at ICSM with a coefficient <0.1. There were no positive associations with standardised coefficients >0.1 between BMAT performance and clinical assessment performance. Multivariable regressions confirmed that Section 2 scores were the most predictive. We found no clear evidence of incremental validity for any BMAT section scores over A-level grades.DiscussionSchools who wish to assess scientific knowledge independently of A-levels may find BMAT Section 2 useful. Comparison with previous studies indicates that, overall, BMAT seems less useful than comparable tools. Larger scale studies are needed. Broader questions regarding why institutions adopt certain admissions tests, including those with little evidence, need consideration.

Project description:BACKGROUND:Adverse childhood experiences, including physical, sexual or emotional abuse, can have detrimental impacts on child and adult health. However, little research has explored the impact that such early life experiences have on oral health. This study examines whether experiencing adverse childhood experiences before the age of 18?years is associated with self-reported poor dental health in later life. METHODS:Using stratified random probability sampling, a household survey (N?=?5307; age range 18-69?years) was conducted in the South of England (Hertfordshire, Luton and Northamptonshire). Data were collected at participants' homes using face-to-face interviews. Measures included exposure to nine adverse childhood experiences, and two dental outcomes: tooth loss (>?8 teeth lost due to dental caries or damage) and missing or filled teeth (direct or indirect restorations; >?12 missing or filled teeth). RESULTS:Strong associations were found between exposure to childhood adversity and poor dental health. The prevalence of tooth loss was significantly higher (8.3%) in those with 4+ adverse childhood experiences compared to those who had experienced none (5.0%; p?<?0.05). A similar relationship was found for levels of missing or filled teeth (13.4%, 4+ adverse childhood experiences; 8.1%, none; p?<?0.001). Exposure to 4+ adverse childhood experiences was associated with a higher level of tooth loss and restorations at any age, compared to individuals who had not experienced adversity. Demographically adjusted means for tooth loss increased with adverse childhood experience count in all age groups, rising from 1.0% (18-29?years) and 13.0% (60-69?years) in those with none, to 3.0% and 26.0%, respectively in those reporting 4+. CONCLUSIONS:Exposure to childhood adversity could be an important predictive factor for poor dental health. As oral health is an important part of a child's overall health status, approaches that seek to improve dental health across the life-course should start with safe and nurturing childhoods free from abuse and neglect. Given the growing role that dental professionals have in identifying violence and abuse, it seems appropriate to raise awareness in the field of dentistry of the potential for individuals to have suffered adverse childhood experiences, and the mechanisms linking childhood adversity to poor dental health.

Project description:BackgroundHarnessing health-related data posted on social media in real time can offer insights into how the pandemic impacts the mental health and general well-being of individuals and populations over time.ObjectiveThis study aimed to obtain information on symptoms and medical conditions self-reported by non-Twitter social media users during the COVID-19 pandemic, to determine how discussion of these symptoms and medical conditions changed over time, and to identify correlations between frequency of the top 5 commonly mentioned symptoms post and daily COVID-19 statistics (new cases, new deaths, new active cases, and new recovered cases) in the United States.MethodsWe used natural language processing (NLP) algorithms to identify symptom- and medical condition-related topics being discussed on social media between June 14 and December 13, 2020. The sample posts were geotagged by NetBase, a third-party data provider. We calculated the positive predictive value and sensitivity to validate the classification of posts. We also assessed the frequency of health-related discussions on social media over time during the study period, and used Pearson correlation coefficients to identify statistically significant correlations between the frequency of the 5 most commonly mentioned symptoms and fluctuation of daily US COVID-19 statistics.ResultsWithin a total of 9,807,813 posts (nearly 70% were sourced from the United States), we identified a discussion of 120 symptom-related topics and 1542 medical condition-related topics. Our classification of the health-related posts had a positive predictive value of over 80% and an average classification rate of 92% sensitivity. The 5 most commonly mentioned symptoms on social media during the study period were anxiety (in 201,303 posts or 12.2% of the total posts mentioning symptoms), generalized pain (189,673, 11.5%), weight loss (95,793, 5.8%), fatigue (91,252, 5.5%), and coughing (86,235, 5.2%). The 5 most discussed medical conditions were COVID-19 (in 5,420,276 posts or 66.4% of the total posts mentioning medical conditions), unspecified infectious disease (469,356, 5.8%), influenza (270,166, 3.3%), unspecified disorders of the central nervous system (253,407, 3.1%), and depression (151,752, 1.9%). Changes in posts in the frequency of anxiety, generalized pain, and weight loss were significant but negatively correlated with daily new COVID-19 cases in the United States (r=-0.49, r=-0.46, and r=-0.39, respectively; P<.05). Posts on the frequency of anxiety, generalized pain, weight loss, fatigue, and the changes in fatigue positively and significantly correlated with daily changes in both new deaths and new active cases in the United States (r ranged=0.39-0.48; P<.05).ConclusionsCOVID-19 and symptoms of anxiety were the 2 most commonly discussed health-related topics on social media from June 14 to December 13, 2020. Real-time monitoring of social media posts on symptoms and medical conditions may help assess the population's mental health status and enhance public health surveillance for infectious disease.

Project description:BackgroundPopulation health studies often use existing databases that are not necessarily constituted for research purposes. The question arises as to whether different data sources such as in administrative health data (AHD) and self-report questionnaires are equivalent and lead to similar information.ObjectivesThe main objective of this study was to assess the level of agreement between self-reported medical conditions and medical diagnosis captured in AHD. A secondary objective was to identify predictors of agreement among medical conditions between the two data sources. Therefore, the purposes of the study were to explore the extent to which these two methods of commonly used public health data collection provide concordant records and identify the main predictors of statistical variations.MethodsData were extracted from CARTaGENE, a population-based cohort in Québec, Canada, which was linked to the provincial health insurance records of the same individuals, namely the MED-ÉCHO database from the Régie de l'assurance maladie du Québec (RAMQ) and the fee-for-service billing records provided by the physician, for the time period 1998-2012. Agreement statistics (kappa coefficient) along with sensitivity, specificity and predictive positive value were calculated for 19 chronic conditions and 12 types of cancers. Logistic regressions were used to identify predictors of concordance between self-report and AHD from significant covariates (sex, age groups, education, region, income, heavy utilization of health care system and Charlson comorbidity index).ResultsAgreement between self-reported data and AHD across diseases ranged from kappa of 0.09 for chronic renal failure to 0.86 for type 2 diabetes. Sensitivity of self-reported data was higher than 50% for 14 out of the 31 medical conditions studied, especially for myocardial infarction (88.62%), breast cancer (86.28%), and diabetes (85.06%). Specificity was generally high with a minimum value of 89.70%. Lower concordance between data sources was observed for higher frequency of health care utilization and higher comorbidity scores.ConclusionOverall, there was moderate agreement between the two data sources but important variations were found depending on the type of disease. This suggests that CARTaGENE's participants were generally able to correctly identify the kind of diseases they suffer from, with some exceptions. These results may help researchers choose adequate data sources according to specific study objectives. These results also suggest that Québec's AHD seem to underestimate the prevalence of some chronic conditions, which might result in inaccurate estimates of morbidity with consequences for public health surveillance.

Dataset Information

Medical Conditions Predictive of Self-Reported Poor Health: Retrospective Cohort Study.

Publications

Medical Conditions Predictive of Self-Reported Poor Health: Retrospective Cohort Study.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets