Dataset Information

Geographic disparities in new onset of internalizing disorders in Pennsylvania adolescents using electronic health records.

ABSTRACT: We evaluated associations of community types and features with new-onset internalizing disorders among Pennsylvania adolescents to identify the location and scale of risk. Using a nested case-control study, we drew subjects from electronic health records 2008-2016, requiring cases (n = 7974) to have two medication orders or diagnoses indicating an internalizing disorder; controls (n = 31,895) were frequency-matched. Subjects were assigned to three community classifications: townships, boroughs, city census tracts; urbanized areas, urban clusters, rural areas; and a combination. Using logistic regression with generalized estimating equations, we found that compared to rural-townships, the highest odds were in urban cluster-city census tracts (odds ratio, 95% confidence interval: 1.78, 1.41-2.26); lowest in urbanized area-city census tracts (0.85, 0.74-0.97). Higher community socioeconomic deprivation was associated with increased odds in urban clusters (1.21, 1.00-1.48) and higher greenness with decreased odds in urban clusters (0.73, 0.62-0.86).

SUBMITTER: Gorski-Steiner I

PROVIDER: S-EPMC9193976 | biostudies-literature | 2022 Jun

REPOSITORIES: biostudies-literature

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Geographic disparities in new onset of internalizing disorders in Pennsylvania adolescents using electronic health records.

Gorski-Steiner Irena I O'Dell Sean S Bandeen-Roche Karen K Volk Heather E HE Goes Fernando S FS Schwartz Brian S BS

Spatial and spatio-temporal epidemiology 20210627

We evaluated associations of community types and features with new-onset internalizing disorders among Pennsylvania adolescents to identify the location and scale of risk. Using a nested case-control study, we drew subjects from electronic health records 2008-2016, requiring cases (n = 7974) to have two medication orders or diagnoses indicating an internalizing disorder; controls (n = 31,895) were frequency-matched. Subjects were assigned to three community classifications: townships, boroughs, ...[more]

PMID: 35691643

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Project description:BackgroundMental disorders are the leading cause of disability for youth worldwide. However, there is a dearth of validated, brief instruments to assess mental health in low- and middle-income countries (LMIC). We aimed to facilitate identification of mental disorders in LMIC contexts by adapting and validating measures of internalizing and externalizing disorders for adolescents in Mozambique, an LMIC in southeastern Africa.MethodsWe selected instruments with good support for validity in high-income and other LMIC settings: the Patient Health Questionnaire Adolescent (PHQ-A), Generalized Anxiety Disorders 7 (GAD-7), and Strengths and Difficulties Questionnaire (SDQ). Instruments were adapted by local and international mental health specialists followed by cognitive interviews (n = 48) with Mozambican adolescents. We administered the instruments along with the Miniature International Neuropsychiatric Interview for Children and Adolescents (MINI-KID)to 485 adolescents aged 12-19 years attending two secondary schools in Maputo City, Mozambique. One week later, we re-administered instruments to a randomly selected sample of 49 adolescents.ResultsParticipants were 66.2% (n = 321) female and the average age was 15.9 (S.D = 1.7).Internal consistency (alpha = 0.80, PHQ-A; 0.84, GAD-7; 0.80, SDQ) and test-retest reliabilty (ICC = 0.74, PHQ-A; 0.70, GAD-7; 0.77, SDQ) were acceptabe for the PHQ-A, GAD-7, and the full SDQ. The SDQ internalizing subscale showed poor test-retest reliability (ICC = 0.63) and the SDQ externalizing subscale showed poor internal consistency (alpha = 0.65). All instruments demonstrated good sensitivity and specificity (> 0.70). Youden's index identified optimal cutoff scores of 8 for the PHQ-A, 5 for the GAD-7, 10 for the SDQ internalizing and 9 for the SDQ externalizing subscales, though a range of scores provided acceptable sensitivity and specificity.ConclusionsOur data supports reliability and validity of the PHQ-A, GAD-7, and SDQ instruments for rapidly assessing mental health problems in Mozambican adolescents. Use of these tools in other contexts with limited specialists may asist with expanding mental health assessment. Specific instrument and cutoff selection should be based on screening goals, treatment resources, and program objectives.

Project description:ObjectiveHypertension has long been recognized as one of the most important predisposing factors for cardiovascular diseases and mortality. In recent years, machine learning methods have shown potential in diagnostic and predictive approaches in chronic diseases. Electronic health records (EHRs) have emerged as a reliable source of longitudinal data. The aim of this study is to predict the onset of hypertension using modern deep learning (DL) architectures, specifically long short-term memory (LSTM) networks, and longitudinal EHRs.Materials and methodsWe compare this approach to the best performing models reported from previous works, particularly XGboost, applied to aggregated features. Our work is based on data from 233 895 adult patients from a large health system in the United States. We divided our population into 2 distinct longitudinal datasets based on the diagnosis date. To ensure generalization to unseen data, we trained our models on the first dataset (dataset A "train and validation") using cross-validation, and then applied the models to a second dataset (dataset B "test") to assess their performance. We also experimented with 2 different time-windows before the onset of hypertension and evaluated the impact on model performance.ResultsWith the LSTM network, we were able to achieve an area under the receiver operating characteristic curve value of 0.98 in the "train and validation" dataset A and 0.94 in the "test" dataset B for a prediction time window of 1 year. Lipid disorders, type 2 diabetes, and renal disorders are found to be associated with incident hypertension.ConclusionThese findings show that DL models based on temporal EHR data can improve the identification of patients at high risk of hypertension and corresponding driving factors. In the long term, this work may support identifying individuals who are at high risk for developing hypertension and facilitate earlier intervention to prevent the future development of hypertension.

Project description:ObjectiveTo characterize the quantity and quality of hospital capacity across the United States.Data sourcesWe combine a 2017 near-census of US hospital inpatient discharges from the Healthcare Cost and Utilization Project (HCUP) with American Hospital Association Survey, Hospital Compare, and American Community Survey data.Study designThis study produces local hospital capacity quantity and care quality measures by allocating capacity to zip codes using market shares and population totals. Disparities in these measures are examined by race and ethnicity, income, age, and urbanicity.Data collection/extraction methodsAll data are derived from pre-existing sources. All hospitals and zip codes in states, including the District of Columbia, contributing complete data to HCUP in 2017 are included.Principal findingsNon-Hispanic Black individuals living in zip codes supplied, on average, 0.11 more beds per 1000 population (SE = 0.01) than places where non-Hispanic White individuals live. However, the hospitals supplying this capacity have 0.36 fewer staff per bed (SE = 0.03) and perform worse on many care quality measures. Zip codes in the most urban parts of America have the least hospital capacity (2.11 beds per 1000 persons; SEM = 0.01) from across the rural-urban continuum. While more rural areas have markedly higher capacity levels, urban areas have advantages in staff and capital per bed. We do not find systematic differences in care quality between rural and urban areas.ConclusionsThis study highlights the importance of lower hospital care quality and resource intensity in driving racial and ethnic, as well as income, disparities in hospital care-related outcomes. This study also contributes an alternative approach for measuring local hospital capacity that accounts for cross-hospital service area flows. Adjusting for these flows is necessary to avoid underestimating the supply of capacity in rural areas and overestimating it in places where non-Hispanic Black individuals tend to live.

Dataset Information

Geographic disparities in new onset of internalizing disorders in Pennsylvania adolescents using electronic health records.

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Geographic disparities in new onset of internalizing disorders in Pennsylvania adolescents using electronic health records.

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