Case-finding for common mental disorders in primary care using routinely collected data: a systematic review

Abstract

Purpose Case-finding for common mental disorders (CMD) in routine data unobtrusively identifies patients for mental health research. There is absence of a review of studies examining CMD-case-finding accuracy in routine primary care data. CMDcase definitions include diagnostic/prescription codes, signs/symptoms, and free text within electronic health records. This systematic review assesses evidence for case-finding accuracy of CMD-case definitions compared to reference standards. Methods PRISMA-DTA checklist guided review. Eligibility criteria were outlined prior to study search; studies compared CMD-case definitions in routine primary care data to diagnostic interviews, screening instruments, or clinician judgement. Studies were quality assessed using QUADAS-2. Results Fourteen studies were included, and most were at high risk of bias. Nine studies examined depressive disorders and seven utilised diagnostic interviews as reference standards. Receiver operating characteristic (ROC) planes illustrated overall variable case-finding accuracy across case definitions, quantified by Youden's index. Forest plots demonstrated most case definitions provide high specificity. Conclusion Case definitions effectively identify cases in a population with good accuracy and few false positives. For 100 anxiety cases, identified using diagnostic codes, between 12 and 20 will be false positives; 0-47 cases will be missed. Sensitivity is more variable and specificity is higher in depressive cases; for 100 cases identified using diagnostic codes, between 0 and 87 will be false positives; 4-18 cases will be missed. Incorporating context to case definitions may improve overall case-finding accuracy. Further research is required for meta-analysis and robust conclusions.

Identifying and treating maternal mental health difficulties in Afghanistan: A feasibility study

“…This works well in areas that have sufficient resources and an established referral protocol. However, where there is extremely high prevalence coupled with poor health infrastructure, screening for case-finding may result in “positive” cases flooding a fragile, under-resourced health care system [ 11 , 34 ]. In this study, for example, the majority of women screened (87%) were experiencing high levels of distress.…”

Digital Data Sources and Their Impact on People's Health: A Systematic Review of Systematic Reviews

“…In contrast, EHRs have a more positive role to play in this group, as shown in Figure 10 . It mainly manifests in assessment, support, monitoring, and identification of high-risk people dealing with abuse ( 122 ), mental health problems ( 123 , 124 ), and obesity ( 125 ). Nevertheless, since patients always underreport their experiences or symptoms and some of the high-risk groups cannot communicate ( 122 ), and technique issues result in missed diagnoses or misclassifications ( 122 , 123 ), its effectiveness still needs to be enhanced by more studies ( 123 , 126 ).…”

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

“…Rates of CMDs have increased over the past few decades [2], including in adolescence and early adulthood [3], where these conditions frequently first appear [4,5]. The prevalence of CMDs in childhood and adolescence (age [5][6][7][8][9][10][11][12][13][14][15][16] in the UK is estimated to be 4% [6], rising to 16% among 16-24 year-olds [7]; these can have significant longterm consequences, including on education, quality-of-life, employment and physical and mental health [5,8,9].…”

“…Assessing the prevalence of CMDs in the population, especially in adolescence, is essential for monitoring, research and planning of appropriate public health services. Estimates of prevalence could be from population studies (which are expensive and time-consuming to conduct), or using primary (General Practitioner; GP) and secondary (hospital and specialised healthcare services) care records [10][11][12][13]. However, CMDs are often under-diagnosed in routine primary care data (the socalled 'clinical iceberg' phenomenon), with over half of all depressed patients with clinical symptoms of depression not recognised as such [14,15].…”

“…To assess the accuracy of primary care-derived CMD rates, these must be compared against a reference standard [16]. A systematic review in adults found that, relative to a reference standard, specificity is generally high (few false positives) but sensitivity is rather low (many false negatives; [12]). Previous research from the Avon Longitudinal Study of Parents and Children (ALSPAC) compared linked primary care records at age 17/18 against CMDs measured on 1,562 participants via the revised Clinical Interview Schedule (CIS-R) [10].…”