Dataset Information

The diagnosis of depression and its treatment in Canadian primary care practices: an epidemiological study.

ABSTRACT: BACKGROUND: A diagnosis of depression is common in primary care practices, but data are lacking on the prevalence in Canadian practices. We describe the prevalence of the diagnosis among men and women, patient characteristics and drug treatment in patients diagnosed with depression in the primary care setting in Canada. METHODS: Using electronic medical record data from the Canadian Primary Care Sentinel Surveillance Network, we examined whether the prevalence of a depression diagnosis varied by patient characteristics, the number of chronic conditions and the presence of the following chronic conditions: hypertension, diabetes, chronic obstructive pulmonary disease, osteoarthritis, dementia, epilepsy and parkinsonism. We used regression models to examine whether patient characteristics and type of comorbidity were associated with a depression diagnosis. RESULTS: Of the 304 412 patients who had at least 1 encounter with their primary care provider between Jan. 1, 2011, and Dec. 31, 2012, 14% had a diagnosis of depression. Current or past smokers and women with a high body mass index had higher rates of depression. One in 4 patients with a diagnosis of depression also had another chronic condition; those with depression had 1.5 times more primary care visits. About 85% of patients with depression were prescribed medication, most frequently selective serotonin reuptake inhibitors, followed by atypical antipsychotics. INTERPRETATION: Our data provide information on the prevalence of a depression diagnosis in primary care and associations with being female, having a chronic condition, smoking history and obesity in women. Our findings may inform research and assist primary care providers with early detection and interventions in at-risk patient populations.

SUBMITTER: Wong ST

PROVIDER: S-EPMC4251512 | biostudies-literature | 2014 Oct

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

The diagnosis of depression and its treatment in Canadian primary care practices: an epidemiological study.

Wong Sabrina T ST Manca Donna D Barber David D Morkem Rachael R Khan Shahriar S Kotecha Jyoti J Williamson Tyler T Birtwhistle Richard R Patten Scott S

CMAJ open 20141001 4

<h4>Background</h4>A diagnosis of depression is common in primary care practices, but data are lacking on the prevalence in Canadian practices. We describe the prevalence of the diagnosis among men and women, patient characteristics and drug treatment in patients diagnosed with depression in the primary care setting in Canada.<h4>Methods</h4>Using electronic medical record data from the Canadian Primary Care Sentinel Surveillance Network, we examined whether the prevalence of a depression diagno ...[more]

PMID: 25485260

Similar Datasets

Project description:IntroductionFrailty is a medical syndrome, commonly affecting people aged 65 years and over and is characterized by a greater risk of adverse outcomes following illness or injury. Electronic medical records contain a large amount of longitudinal data that can be used for primary care research. Machine learning can fully utilize this wide breadth of data for the detection of diseases and syndromes. The creation of a frailty case definition using machine learning may facilitate early intervention, inform advanced screening tests, and allow for surveillance.ObjectivesThe objective of this study was to develop a validated case definition of frailty for the primary care context, using machine learning.MethodsPhysicians participating in the Canadian Primary Care Sentinel Surveillance Network across Canada were asked to retrospectively identify the level of frailty present in a sample of their own patients (total n = 5,466), collected from 2015-2019. Frailty levels were dichotomized using a cut-off of 5. Extracted features included previously prescribed medications, billing codes, and other routinely collected primary care data. We used eight supervised machine learning algorithms, with performance assessed using a hold-out test set. A balanced training dataset was also created by oversampling. Sensitivity analyses considered two alternative dichotomization cut-offs. Model performance was evaluated using area under the receiver-operating characteristic curve, F1, accuracy, sensitivity, specificity, negative predictive value and positive predictive value.ResultsThe prevalence of frailty within our sample was 18.4%. Of the eight models developed to identify frail patients, an XGBoost model achieved the highest sensitivity (78.14%) and specificity (74.41%). The balanced training dataset did not improve classification performance. Sensitivity analyses did not show improved performance for cut-offs other than 5.ConclusionSupervised machine learning was able to create well performing classification models for frailty. Future research is needed to assess frailty inter-rater reliability, and link multiple data sources for frailty identification.

Dataset Information

The diagnosis of depression and its treatment in Canadian primary care practices: an epidemiological study.

Publications

The diagnosis of depression and its treatment in Canadian primary care practices: an epidemiological study.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets