Assessing and predicting adolescent and early adulthood common mental disorders in the ALSPAC cohort using electronic primary care data

Abstract

AbstractObjectivesThis paper has three objectives: 1) examine agreement between common mental disorders (CMDs) derived from primary health care records and repeated CMD questionnaire data from ALSPAC (the Avon Longitudinal Study of Parents and Children); 2) explore the factors affecting CMD identification in primary care records; and 3) taking ALSPAC as the reference standard, to construct models predicting ALSPAC-derived CMDs using primary care data.Design and SettingProspective cohort study (ALSPAC) with linkage to electronic primary care data.ParticipantsPrimary care records were extracted for 11,807 ALSPAC participants (80% of the 14,731 eligible participants). The number of participants with both linked primary care and ALSPAC CMD data varied between 3,633 (age 15/16) to 1,298 (age 21/22).Outcome measuresOutcome measures from ALSPAC data were diagnoses of suspected depression and/or CMDs. For the primary care data, Read codes for diagnosis, symptoms and treatment were used to indicate the presence of depression and CMDs.For each time point, sensitivities and specificities (using ALSPAC-derived CMDs as the reference standard) were calculated and the factors associated with identification of primary care-based CMDs in those with suspected ALSPAC-derived CMDs explored. Lasso models were then performed to predict ALSPAC CMDs from primary care data.ResultsSensitivities were low for CMDs (range: 3.5 to 19.1%) and depression (range: 1.6 to 34.0%), while specificities were high (nearly all >95%). The strongest predictor of identification in the primary care data was symptom severity. The lasso models had relatively low prediction rates, especially for out-of-sample prediction (deviance ratio range: - 1.3 to 12.6%), but improved with age.ConclusionsEven with predictive modelling using all available information, primary care data underestimate CMD rates compared to estimates from population-based studies. Research into the use of free-text data or secondary care information is needed to improve the predictive accuracy of models using clinical data.Strengths and limitations of this studyWe used a large prospective cohort (ALSPAC) and were able to link these data to individuals’ electronic primary care records, with this linkage data covering ∼80% of the cohort.We used validated mental health questionnaires to assess depression and common mental disorders among the ALSPAC cohort, which we treat as our ‘reference standard’.We were able to assess agreement between ALSPAC data and electronic primary care data for common mental disorders across adolescence and into adulthood, a key life transition and period where mental health problems often emerge.There is a risk of selection bias, as many participants with primary care data did not have ALSPAC mental health measures, while primary care data coverage also decreased with age; continued participation in both cases is likely to be non-random.For this study we assumed that the common mental disorder data from ALSPAC are the ‘reference standard’ against which the primary care data should be compared; however, this data may also be subject to misclassification.The available linkage data consisted of primary care Read codes, which misses data from other clinical sources, such as secondary care or from primary care free-text data.