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

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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

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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: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.

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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.

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