Transient and persistent hypochondriacal worry in primary care

1996 ◽  
Vol 26 (3) ◽  
pp. 575-589 ◽  
Author(s):  
James M. Robbins ◽  
Laurence J. Kirmayer
Keyword(s):  
Primary Care ◽  
Anxiety Disorder ◽  
Physician Visit ◽  
Significant Other ◽  
Attributional Styles ◽  
Emotional Instability ◽  
Primary Care Data ◽  
Emotional Vulnerability ◽  
Bodily Sensations ◽  
Interpersonal Vulnerability

SynopsisWe present a 12-month prospective study of hypochondriacal worry in primary care. Data were obtained from 546 family medicine patients at the time of a physician visit for a new illness and again 1 year later. Patients were divided into four groups based on scores on the Illness Worry Scale: non-hypochondriacal (N = 460), transient hypochondriacal (N = 34); emerging hypochondriacal (N = 21); and persistent hypochondriacal (N = 31). Persistent patients had significantly more serious medical history but no more serious current illness than those low on illness worry. Patients with persistent illness worry were more likely than others to have a diagnosis of major depression or anxiety disorder, were more likely to believe that their most important significant other would pathologize new symptoms, yet were less likely to have been encouraged to see the doctor by them. Patients who became less worried over the year reported corresponding decreases in distress, attentiveness to bodily sensations, emotional vulnerability and pathological symptom attributions. We conclude that depressive or anxiety disorders, fears of emotional instability, pathological symptom attributional styles and interpersonal vulnerability provide the best prognostic evidence for enduring illness worry.

scholarly journals CHARGE-AF in a national routine primary care electronic health records database in the Netherlands: validation for 5-year risk of atrial fibrillation and implications for patient selection in atrial fibrillation screening

Open Heart ◽  
2021 ◽  
Vol 8 (1) ◽  
pp. e001459
Author(s):  
Jelle C L Himmelreich ◽  
Wim A M Lucassen ◽  
Ralf E Harskamp ◽  
Claire Aussems ◽  
Henk C P M van Weert ◽  
...  
Keyword(s):  
Atrial Fibrillation ◽  
Primary Care ◽  
High Risk ◽  
The Netherlands ◽  
Patient Selection ◽  
Low Cost ◽  
Calibration Plot ◽  
Primary Care Data ◽  
C Statistic ◽  
Kaplan Meier

AimsTo validate a multivariable risk prediction model (Cohorts for Heart and Aging Research in Genomic Epidemiology model for atrial fibrillation (CHARGE-AF)) for 5-year risk of atrial fibrillation (AF) in routinely collected primary care data and to assess CHARGE-AF’s potential for automated, low-cost selection of patients at high risk for AF based on routine primary care data.MethodsWe included patients aged ≥40 years, free of AF and with complete CHARGE-AF variables at baseline, 1 January 2014, in a representative, nationwide routine primary care database in the Netherlands (Nivel-PCD). We validated CHARGE-AF for 5-year observed AF incidence using the C-statistic for discrimination, and calibration plot and stratified Kaplan-Meier plot for calibration. We compared CHARGE-AF with other predictors and assessed implications of using different CHARGE-AF cut-offs to select high-risk patients.ResultsAmong 111 475 patients free of AF and with complete CHARGE-AF variables at baseline (17.2% of all patients aged ≥40 years and free of AF), mean age was 65.5 years, and 53% were female. Complete CHARGE-AF cases were older and had higher AF incidence and cardiovascular comorbidity rate than incomplete cases. There were 5264 (4.7%) new AF cases during 5-year follow-up among complete cases. CHARGE-AF’s C-statistic for new AF was 0.74 (95% CI 0.73 to 0.74). The calibration plot showed slight risk underestimation in low-risk deciles and overestimation of absolute AF risk in those with highest predicted risk. The Kaplan-Meier plot with categories <2.5%, 2.5%–5% and >5% predicted 5-year risk was highly accurate. CHARGE-AF outperformed CHA2DS2-VASc (Cardiac failure or dysfunction, Hypertension, Age >=75 [Doubled], Diabetes, Stroke [Doubled]-Vascular disease, Age 65-74, and Sex category [Female]) and age alone as predictors for AF. Dichotomisation at cut-offs of 2.5%, 5% and 10% baseline CHARGE-AF risk all showed merits for patient selection in AF screening efforts.ConclusionIn patients with complete baseline CHARGE-AF data through routine Dutch primary care, CHARGE-AF accurately assessed AF risk among older primary care patients, outperformed both CHA2DS2-VASc and age alone as predictors for AF and showed potential for automated, low-cost patient selection in AF screening.

Patients’ satisfaction after a geriatric assessment into primary care: data from FAP study

2018 ◽  
Vol 16 (4) ◽  
pp. 391-397
Author(s):  
Maxime Renoux ◽  
Bruno Chicoulaa ◽  
Christine Lagourdette ◽  
Emile Escourrou ◽  
Marion Secher ◽  
...  
Keyword(s):  
Primary Care ◽  
Geriatric Assessment ◽  
Patients Satisfaction ◽  
Primary Care Data

Primary care data

2018 ◽  
pp. 54-66
Author(s):  
Marilyn James ◽  
Elizabeth Stokes
Keyword(s):  
Primary Care ◽  
Primary Care Data

Cancer incidence in patients with a high normal platelet count: a cohort study using primary care data

2018 ◽  
Vol 35 (6) ◽  
pp. 671-675 ◽  
Author(s):  
Emily Ankus ◽  
Sarah J Price ◽  
Obioha C Ukoumunne ◽  
William Hamilton ◽  
Sarah E R Bailey
Keyword(s):  
Primary Care ◽  
Cohort Study ◽  
Platelet Count ◽  
Cancer Incidence ◽  
Normal Platelet Count ◽  
Normal Platelet ◽  
Primary Care Data

scholarly journals Assessing and predicting adolescent and early adulthood common mental disorders using electronic primary care data: analysis of a prospective cohort study (ALSPAC) in Southwest England

BMJ Open ◽  
2021 ◽  
Vol 11 (10) ◽  
pp. e053624
Author(s):  
Daniel Smith ◽  
Kathryn Willan ◽  
Stephanie L Prady ◽  
Josie Dickerson ◽  
Gillian Santorelli ◽  
...  
Keyword(s):  
Primary Care ◽  
Cohort Study ◽  
Mental Disorders ◽  
Outcome Measures ◽  
Prospective Cohort Study ◽  
Prospective Cohort ◽  
Common Mental Disorders ◽  
Free Text ◽  
Primary Care Data ◽  
Time Point

ObjectivesWe aimed to examine agreement between common mental disorders (CMDs) from primary care records and repeated CMD questionnaire data from ALSPAC (the Avon Longitudinal Study of Parents and Children) over adolescence and young adulthood, explore factors affecting CMD identification in primary care records, and construct models predicting ALSPAC-derived CMDs using only primary care data.Design and settingProspective cohort study (ALSPAC) in Southwest England with linkage to electronic primary care records.ParticipantsPrimary care records were extracted for 11 807 participants (80% of 14 731 eligible). Between 31% (3633; age 15/16) and 11% (1298; age 21/22) of participants had both primary care and ALSPAC CMD data.Outcome measuresALSPAC outcome measures were diagnoses of suspected depression and/or CMDs. Primary care outcome measure were Read codes for diagnosis, symptoms and treatment of depression/CMDs. For each time point, sensitivities and specificities for primary care CMD diagnoses were calculated for predicting ALSPAC-derived measures of CMDs, and the factors associated with identification of primary care-based CMDs in those with suspected ALSPAC-derived CMDs explored. Lasso (least absolute selection and shrinkage operator) models were used at each time point to predict ALSPAC-derived CMDs using only primary care data, with internal validation by randomly splitting data into 60% training and 40% validation samples.ResultsSensitivities for primary care diagnoses were low for CMDs (range: 3.5%–19.1%) and depression (range: 1.6%–34.0%), while specificities were high (nearly all >95%). The strongest predictors of identification in the primary care data for those with ALSPAC-derived CMDs were symptom severity indices. The lasso models had relatively low prediction rates, especially in the validation sample (deviance ratio range: −1.3 to 12.6%), but improved with age.ConclusionsPrimary care data underestimate CMDs compared to population-based studies. Improving general practitioner identification, and using free-text or secondary care data, is needed to improve the accuracy of models using clinical data.

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