Depression screening tools are designed to catch the cases of the disorder that go unnoticed by health care providers. While believed to be incredibly effective, a new study suggests available data overestimates the accuracy of these tools.

Study authors Danielle B. Rice and Brett D. Thombs, of the Lady Davis Institute for Medical Research, Jewish General Hospital in Montreal, noted that effective depression treatments are available, but approximately half of depressed patients go unrecognized. The "vast majority" of depression screening is done outside of psychiatric and specialty-care settings, which may account for the varied differences in screening guidelines and policies. Rice and Thombs said to improve upon usual care, screening tools must accurately identify patients who are not currently in treatment or seeking treatment — and according to their research, this isn’t typically the case.

"Our objective was not to conduct a systematic review of screening tool accuracy," Rice and Thombs wrote. "Rather, it was to evaluate inclusion and exclusion criteria in studies likely to influence future research methods, policy and practice."

The duo searched for primary studies and meta-analyses that looked at the accuracy of depression screening tools through MEDLINE, a resources guide that contains journal citations and abstractions from biomedical literature from around the world, according to the website. They included studies published in 2013 or later in order to "obtain recent studies that reflect current clinical practice." In order for a study to be considered primary, it had to have reported on the accuracy of one or more depression screening tools compared to a diagnosis of depression based on an interview.

Though their search yielded 501 results, Rice and Thombs only found 89 were eligible primary studies and 5 were eligible meta-analyses. And the results revealed a mere 5.6 percent of primary studies (five) and 3.4 percent of analyses (three) excluded currently diagnosed or treated patients at the time of the study. In three studies that reported on the number of patients who were excluded because they were being treated, the number of excluded patients was more than double the newly-identified depressed patients.

These findings are on par with prior research, according to the authors. A study conducted in 2011 found that if half of depressed patients in a primary care population (10 percent of whom have major depression), were already receiving treatment, then properly excluding them would result in fewer truly new cases of depressed patients.

"In clinical practice, depression symptom questionnaires, or screening tools, are used by healthcare providers for a number of purposes, including screening to detect previously unidentified cases, tracking treatment progress, or detecting relapse, for instance," Rice and Thombs explained. "For the purpose of screening, however, they are only useful to the extent that they distinguish between disordered and non-disordered states that are not otherwise identified."

That said, the authors acknowledged their study is limited. For example, they only searched MEDLINE for eligible studies and were otherwise unable to precisely determine the effect that "inappropriate inclusion" had on accuracy estimates. They said that several other studies, including the one from 2011, have also noted that already-diagnosed patients can inflate the accuracy of diagnostic tools.

They concluded: "Well-designed studies that exclude patients currently diagnosed or treated for depression are needed to generate realistic estimates of accuracy that reflect what would be achieved in clinical practice. Although depression symptom questionnaires are used for a variety of purposes, including follow-up assessment of patients receiving treatment, studies that seek to evaluate their accuracy for identifying patients with previously unrecognized depression must exclude these patients."

Source: Rice DB, Thombs BD. Risk of Bias from Inclusion of Currently Diagnosed or Treated Patients in Studies of Depression Screening Tool Accuracy: A Cross-Sectional Analysis of Recently Published Primary Studies and Meta-Analyses. PLOS ONE. 2016.