Knowledge and Behavior of Primary Care Physicians Regarding Utilization of Standardized Tools in Screening and Assessment of Anxiety,Depression,and Mood Disorders at a Large Integrated Health System

Abstract

Introduction:

Standardized screening, objective evaluation, and management of behavioral health conditions are major challenges in primary care. The Generalized Anxiety Disorder Scale (GAD-7), Patient Health Questionnaire (PHQ-9), and Mood Disorder Questionnaire (MDQ) provide standardized screening and symptom management tools for generalized anxiety disorder (GAD), major depressive disorder (MDD), and Mood Disorders (MD), respectively. This study explores family physicians’ knowledge, attitudes, and practices regarding the utilization of GAD-7, PHQ-9, and MDQ in outpatient primary care offices.

Methods:

The study method was a cross-sectional electronic and paper survey utilizing a self-administered questionnaire that assessed primary care physicians’ demographics, knowledge, attitudes, and practices in rural and urban outpatient clinical settings regarding GAD-7, PHQ-9, and MDQ. Statistical software SAS 9.4 was used for descriptive and Chi-Square statistics.

Results:

Out of 320 total participants,145 responded (45.3%). Responding family physicians demonstrated a high level of familiarity with the GAD-7 (97.9%), PHQ-9 (97.9%), and MDQ (81.3%) assessment tools. However, the reported utilization rates were relatively lower than knowledge, with 62.7%, 73.1%, and 31.9% extremely likely or likely to utilize the GAD-7, PHQ-9, and MDQ as screening and monitoring tools, respectively. Less than a quarter of the total respondents use the objective score for the future management of GAD, with significantly more residents utilizing the score for GAD-7 compared to attendings (P < .05). There was no statistical significance difference between residents and attendings for the objective evaluation of Major Depressive Disorder (P = .26) and Mood Disorders (P = .05).

Conclusions:

Despite being knowledgeable of the utility of GAD-7, PHQ-9, and MDQ, the primary care physicians in a large integrated health system in Central Pennsylvania and Northern Maryland report inconsistent utilization in their practice. Further studies are needed to determine the underlying factors contributing to the suboptimal usage of these screening tools and ways to increase it.

Keywords

major depressive disorder (MDD)generalized anxiety disorder (GAD)bipolar disorder (BD)mood disorders (MD)PHQ-9 GAD-7 MDQ measurement based care (MBC)

Introduction

Behavioral health conditions, including generalized anxiety disorder (GAD), major depressive disorder (MDD), and mood disorders (MD), are common among the US adult population, with a prevalence of 6.2%, 18.3%, and 2.5%, respectively.¹ These conditions are known to be associated with lower quality of life and socioeconomic burden.^2
-4 Behavioral health conditions cumulatively are a leading contributor to disability-adjusted life years (DALYs) worldwide.³ Between 2010 and 2030, it is estimated that behavioral health conditions will have accounted for a staggering loss of USD 16.1 trillion in global economic output.³ Anxiety and mood disorders affect patients’ subjective well-being, overall quality of life, and occupational functioning.^5,6

Globally, more than half of those who require care for GAD (57.5%), MDD (56.3%), and BD (50.2%) do not receive treatment.⁷ Ansseau et al⁸ found that only 5.4% of patients sought help from their primary care physician for a psychiatric condition despite over 42.5% meeting the criteria for a behavioral health condition. If left untreated, milder symptoms of mental illness can lead to hospitalization, suicide attempts, and disability.⁹

Primary care physicians face many challenges when managing behavioral conditions including time consumed in screening and concluding on symptoms management. Measurement-based care (MBC) have been proposed as possible solution to aid in screening and symptoms management of common behavioral health conditions. MBC involves systematically administering symptom rating scales to guide clinical decision-making, has improved healthcare outcomes and treatment efficacy.^10
-12 These measures assist diagnosis and personalize treatment interventions to objective outcome measures, such as symptom reduction. Screening tools, such as the Generalized Anxiety Disorder Scale (GAD-7), Patient Health Questionnaire (PHQ-9), and Mood Disorder Questionnaire (MDQ), are commonly used methods to aid in the diagnosis and assessment of severity for Generalized Anxiety Disorder, Major Depressive Disorder/Post-partum Depression, and Mood Disoder, respectively.^13
-15 For example, Guo et al¹⁶ reported a randomized controlled trial showing significantly more response to therapy, including a shorter time to response and remission with MBC than standard care. However, the utility of MBC for common behavioral health conditions remains low in community-based physician practices.^17,18

Specifically, while there is some data on the utilization of PHQ-9 as an MBC tool, it is limited for GAD-7 and MDQ in primary care.^11,19,20 This study explores family physicians’ knowledge, attitudes, and practices regarding the utilization of GAD-7, PHQ-9, and MDQ in primary care offices for screening and assessment of Generalized Anxiety Disorder, Major Depressive Disorder/Post-partum Depression, and Mood Disorder respectively. The sub-group analysis detects any significant differences between resident physicians, core teaching faculty physicians who work regularly with residents, and other attending family medicine physicians. We hypothesized that residents and core faculty might show higher knowledge and utilization of standardized tools as compared to other attending family physicians.

Methods

Study Type

The present investigation was a cross-sectional self-administered electronic and paper survey that assessed demographics, knowledge, attitudes, and practices of primary care physicians regarding the utilization of GAD-7, PHQ-9, and MDQ.

Study Site

The study was conducted in an large, integrated health system spanning Central Pennsylvania and Northern Maryland. It includes 8 hospitals (6 acute care and 2 specialty hospitals), 20 000 employees, and 2600 physicians. The survey was sent to all 320 active primary care physicians and family medicine residents working in the health system at the time of the survey.

Study Tool

The study investigators did not find previously published questionnaires to assess the knowledge and behavior of primary care physicians regarding the utilization of the GAD-7, PHQ-9, and MDQ. The investigators created a survey named Utilization of Standardized Tools (UST) in Primary Care. It is a self-administered questionnaire (Supplemental Figure 1) with 15 questions, with 5 being demographics. The knowledge assessment in the first question uses a multiple-choice format for GAD-7, PHQ-9, and MDQ. The 3 attitude questions are based on a 5-point Likert scale from extremely likely to extremely unlikely, while the rest of the practice questions are based on dichotomous or more choices. Since this was conducted among family physicians, the practice questions included the behavioral health conditions across the lifespan, including prenatal and post-partum visits. Face validity was established by a review of content and outcomes by 8 family physicians and residents, including the study investigators. Cronbach’s alpha was calculated using the observations. All standardized values exceeded the threshold of .7, confirming internal consistency (Supplemental Table 1). Construct validity was established by the authors’ judgment and experience on the pilot data.

Ethical Approval

This study was determined by our Institutional Review Board (IRB) as IRB exempt. The first page of the survey contained brief information on background, objectives, and anonymity of this investigation to allow informed consent from the participants before starting the survey.

Data Collection and Analysis

The survey was sent electronically and in paper format from July to November 2021. The electronic format used a secure, anonymous platform in SurveyMonkey, while the paper form was mailed with a self-addressed envelope for return. The study investigators collected and entered the data from both electronic and paper responses into Microsoft Excel. This was then imported into Statistical software SAS 9.4 for descriptive and Chi-square statistics to detect significant differences among residents, core teaching faculty, and other attending family physicians. A P-value of <.05 was considered statistically significant.

Results

Demographics and Response Data

The survey response rate was 45.3% (145 of 320). Of the 145 participants who responded, 40 were paper while 102 were via electronic format. Table 1 describes the demographics of the respondents. Most respondents (63.2%, n = 84) were Attending Family Medicine Physicians, excluding Core Teaching Faculty. Resident Family Physicians comprised 21.8% (n = 29) of respondents, the rest being Core Teaching Faculty (15.0%, n = 20). Most were females (60.7%, n = 88), and a majority were 30 to 45 years (55%, n = 80). There was a similar representation of both urban (42.6%, n = 61) and rural (57.3%, n = 82) clinical settings.

Table 1.

Demographics of Surveyed Physicians.

	Survey respondents (n = 145)
Characteristics	n (%)
Age (years)
<30	12 (8.28)
30-45	80 (55.17)
>45	53 (36.55)
Gender
Male	55 (37.93)
Female	88 (60.69)
Non-binary	0 (0.00)
Prefer not to disclose	2 (1.38)
Position^a (missing 12)
Resident PGY1	12 (9.02)
Resident PGY2	9 (6.77)
Resident PGY3	8 (6.02)
Attending physician—core family medicine	20 (15.04)
Attending physician—(others)	84 (63.16)
Clinic settings
Urban	61 (42.66)
Rural	82 (57.34)
Years of medical practice (years)^a missing (2)
<3	37 (25.52)
3-10	47 (32.41)
10-20	21 (14.48)
>20	40 (27.59)

Data presented as n (%) unless stated otherwise.

Data missing for some variables.

Knowledge of the Participants

Most of the respondents were knowledgeable of the GAD-7 (97.9%, n = 142), PHQ-9 (97.9%, n = 142), and MDQ (81.3%, n = 118) tools for screening and symptom monitoring for Generalized Anxiety Disorder, Major Depressive Disorder/Post-partum Depression, and Mood Disoder respectively. Despite most physicians being knowledgeable, only 62.7% were extremely likely to utilize the GAD-7 and 73.1% for the PHQ-9. Even fewer respondents were extremely likely to use the MDQ to monitor BD (31.9%). There was no statistically significant different between residents and attending physicians regarding knowledge about screening and treatment monitoring for Generalized Anxiety Disorder (P = 1.0), Major Depressive Disorder/Post-partum Depression (P = .75), and BD (P = .12). The difference between attendings’ and residents’ opportunities to utilize screening and treatment monitoring tools for GAD (P = .18), PHQ-9 (P = .35), and MDQ (P = .30) was also not statistically significant.

Attitude and Practices

Respondents were asked if their healthcare organization, clinical setting, and clinical management would likely encourage screening and treatment monitoring tools for behavioral health conditions and if those organizations currently encourage its use. Only half of the respondents stated that if given the opportunity, their healthcare organization (51.4%) and clinical setting/management (51.7%) are extremely likely or likely to encourage the use of the GAD-7 tool. For the PHQ-9, more respondents agreed that their healthcare organization (67.4%) and clinical setting/management (62.1%) would be extremely likely or likely to back the use of the tool. However, a minority thought that using the MQD tool would be supported by their healthcare organization (25.8%) or clinical setting/management (24.5%).

Figure 1 shows physicians’ responses if their healthcare organization, clinical setting, and clinical management currently encourage screening and monitoring tools. There was statistically significant difference (P = .046) between attending physicians and residents (36.89% versus 17.24%) in them feeling that their clinical setting/management did not support the use of the GAD-7. Similarly, the difference between attendings and residents was statistically significant in feeling that MDQ usage was not encouraged by clinical settings (P = .0006) and healthcare organizations (P = .0005).

Figure 1.

Distribution of physicians’ responses regarding the encouragement of screening and treatment monitoring tools within respective healthcare organizations, clinical settings, and clinical management.

The integration of these tools into the Electronic Medical Record (EMR) is shown in Figure 2. There was no statistical difference between resident’s and attendings responses that their EMR allows for the integration of the screening and treatment tool for the GAD-7 (P = .17) or the PHQ-9 (P = .85); however, significantly more attendings (87.0%, n = 87) compared to the residents (65.5%, n = 19) stated that the EMR does not allow for the integration of the screening and treatment tools for the MDQ (P = .078).

Figure 2.

Distribution of physicians’ responses regarding the integration of screening and treatment monitoring tools within the EMR.

Figure 3 shows respondents’ utilization of more subjective symptoms or more of an objective screening tool to guide the future management of a patient’s condition. The difference between attendings and residents in the likelihood of using subjective only or adding objective symptom monitoring in the management for Generalized Anxiety Disorder was seen to be statistically significant, with residents incorporating objective symptom assessments (44.0%, n = 13) compared to attendings (21.2%, n = 22; P = .01). Statistical significance between attendings and residents was not seen for Major Depressive Disorder (P = .26) and Mood Disorder (P = .05).

Figure 3.

Distribution of physicians’ responses regarding their reliance more on subjective vs. objective assessments to guide management.

When asked about their practices regarding depression screening of their patients in prenatal and post-partum phases, only 51.9% reported utilizing the PHQ-9 for prenatal visits and 60.7% for postpartum visits. The use of PHQ-9 for prenatal visits was not statistically significant between residents and attendings (P = .08). However, the use of the PHQ-9 for postpartum visits was found to be statistically significant between residents and attendings with (P = .0057), with a majority of residents (82.7%, n = 24) utilizing it as compared to attendings (54.0%, n = 52).

It is interesting to note that attendings would refer a patient with complicated Generalized Anxiety Disorder (55.8%, n = 58) more than the residents (34.5%, n = 10) (P = 0.0096). Similar findings were noted for complex Major Depressive Disorder cases, with 57.7% (n = 60) attendings willing to refer in contrast to residents (34.5%, n = 10) (P = .017). However, there was no difference between residents and attendings in managing patients with Mood Disorder (P = .31).

Discussion

This study reports the knowledge, attitude, and practices of residents, core teaching faculty and attending family physicians in a large integrated health system in Central Pennsylvania and Northern Maryland regarding the utilization of GAD-7, PHQ-9, and MDQ questionnaires in screening and symptom monitoring of Generalized Anxiety Disorder (GAD), Major Depressive Disorder (MDD), and Mood Disorders (MD) respectively. One hundred and forty-five family physicians completed the survey, resulting in a response rate of 45.3%. The response rate and demographics of the participants of this study, as shown in the results section above, are comparable to similar large-scale national surveys.²¹

This study underscores the inconsistency between good knowledge of GAD-7 (97.9%), PHQ-9 (97.9%), and MDQ (81.3%) and attitude and practices, with only 62.7% indicating willingness to use GAD-7 (62.7%) and 73.1% and 31.9% to use PHQ-9 and MDQ respectively. Anxiety and mood disorders also often co-occur and are frequently accompanied by comorbidities, such as substance abuse, chronic physical illness, and somatic disorders.^22,23 It shows that attitude and practices do not solely depend upon knowledge. The programmatic and system-level implementation might be needed more than the mere knowledge of clinicians.

Although Measurement-Based Care (MBC) has been suggested to be the way to treat behavioral health conditions, including anxiety and mood disorders, implementation can be challenging, with 1 study reporting that less than 20% of practitioners implement it in their practice.²⁴ Our study concurs with that while showing a slightly higher percentage of our respondents utilizing objective scores for the future management of GAD (24.1%), Major Depressive Disoder (33.1%), and Mood Disorder (34.27%). Residents were found to rely more on objective assessments than attendings, while the attending physicians relied more on experience and subjective clinical judgment. Additionally, only half the attendings in our study were likely to refer a complicated Generalized Anxiety Disorder (55.8%) or Major Depressive Disorder (57.7%) patient and a significantly fewer number of residents, with only 34.5% for both Generalized Anxiety Disorder and Major Depressive Disorder (P = .017). Barriers to appropriate referrals include subconscious subjective factors such as economic status, gender, ethnicity, and objective tools that may improve referral systems.^25,26 In our study, all other factors, including access to specialty psychiatric care and patient-related factors, were the same for residents and faculty.

Although some reported brief educational initiatives to be effective,¹⁸, Lewis et al²⁷ concluded that negative physician attitudes toward MBC compared to gestalt clinical judgment, administrative burden, cost, and lack of clarity in the system were the main barriers. Others have proposed methods to support increased MBC use in clinical care, including fidelity monitoring via the EMR, measurement feedback systems, evidence-based policy decisions, and pay-for-performance structures.^27,28

This study highlights physicians’ perception of the lack of admin/management support for using the GAD-7 and MDQ (P < .05) compared to PHQ-9. As shown in Figure 2, the reported availability of these tools integrated into the Electronic Medical Record (EMR) was also low (P < .05).

Physicians in our study did not widely support the use of the MDQ. It was found that less than one-third of respondents were highly likely to use the MDQ. Additionally, only one-fourth assumed that the MDQ would be supported by their healthcare organization (25.8%) or clinical management (24.5%). Mood Disoder is notoriously unrecognized, with 73% of Mood Disorder patients being initially misdiagnosed by a healthcare professional and over one-third waiting 10 years or more for an accurate diagnosis.²⁸ Das et al²⁹ reported that 49.0% of patients with Mood Disorder were documented as having current depression by physicians and had no record of an Mood Disorder diagnosis in administrative billing or the medical record.

About 10% to 20% of women get diagnosed with Postpartum depression (PPD), which can affect both mother and child at a time critical to human development.^30,31 In patients with pre-existing anxiety or depression, prenatal, and perinatal screening for Postpartum Depression is crucial as the risk of developing Postpartum Depression is higher.³² The PHQ-9 is recommended by the American College of Obstetricians and Gynecologists (ACOG) as a screening tool for perinatal and postpartum depression. However, only a small percentage of women are screened appropriately.^33,34 According to Goldin Evans et al³⁵, one-fourth of physicians were likely to use a screening tool for diagnosing Postpartum Depression, and 62% were likely to use screening tools more often consistent with the present study.

This study has several limitations. The first set of limitations include those which are inherent to the cross-sectional survey design. For example, the cross-sectional surveys provide a snapshot of a time when the natural environment is dynamic. The second set of limitations include those related to study tool. For example, the question of self-administered questionnaires could be interpreted differently by different individuals. In this current questionnaire, question number 5 can introduce dichotomy that can be misleading. The third set of limitations include those related to generalizability. Although the response rate and demographics of the participants are comparable to other published national surveys, the sample is based on 1 integrated health system in Central Pennsylvania and Northern Maryland.

Conclusion

Despite the good knowledge of standardized tools like GAD-7, PHQ-9, and MDQ for screening and symptom monitoring, their actual utilization is inconsistent among primary care physicians in a large integrated health system in Central Pennsylvania and Northern Maryland with a perceived lack of support from admin, and integration in EMR, among others. Future studies are recommended to focus on evaluating different approaches, including systematic changes to improve the utility of these tools.

Supplemental Material

sj-docx-1-jpc-10.1177_21501319231224711 – Supplemental material for Knowledge and Behavior of Primary Care Physicians Regarding Utilization of Standardized Tools in Screening and Assessment of Anxiety, Depression, and Mood Disorders at a Large Integrated Health System

Supplemental material, sj-docx-1-jpc-10.1177_21501319231224711 for Knowledge and Behavior of Primary Care Physicians Regarding Utilization of Standardized Tools in Screening and Assessment of Anxiety, Depression, and Mood Disorders at a Large Integrated Health System by Abdul Waheed, Asif Khan Afridi, Masooma Rana, Mobeena Arif, Trajan Barrera, Feroza Patel, Muhammad Nausherwan Khan and Erum Azhar in Journal of Primary Care & Community Health

Footnotes

Authors’ Note

Presentations: (1) Pennsylvania Academy of Family Physicians (PAPF) Annual Spring Conference, April 02, 2022, Lancaster, PA. (2) WellSpan Discovery Day, May 19, 2022, York, PA.

Author Contributions

The authors confirm contribution to the paper as follows: A.A., T.B., and M.N.K. contributed to study conception. A.A., T.B., M.N.K., E.A., and A.W. designed the study protocol and submitted it to the Institutional Review Board. A.A., M.A., T.B., F.P., and M.N.K. participated in data collection and entry. Data analysis was performed by E.A. and A.W. All authors were involved in interpretation of the results. The manuscript was written by M.R., M.A., E.A., and A.W. All authors contributed intellectually and edited the manuscript. All authors read and approved the final manuscript. AW is the corresponding author and responsible for the integrity of the work.

Declaration of Conflicting Interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) received no financial support for the research, authorship, and/or publication of this article.

Ethical Approval

This study was determined by WellSpan Health Institutional Review Board (IRB) as IRB exempt with study approval number 1720503-2. The first page of the survey contained brief information on background, objectives, and anonymity of this investigation to allow informed consent from the participants before starting the survey.

ORCID iDs

Abdul Waheed

Masooma Rana

Supplemental Material

Supplemental material for this article is available online.

References

Kessler

Petukhova

Sampson

Zaslavsky

Wittchen

. Twelve-month and lifetime prevalence and lifetime morbid risk of anxiety and mood disorders in the United States. Int J Methods Psychiatr Res. 2012;21(3):169-184. doi:10.1002/mpr.1359

Spitzer

Kroenke

Linzer

, et al. Health-related quality of life in primary care patients with mental disorders. Results from the PRIME-MD 1000 Study. JAMA. 1995;274(19):1511-1517.

Bloom

Cafiero

Jané-Llopis

, et al. The Global Economic Burden of Noncommunicable Diseases. World Economic Forum; 2012: 6.

Patel

Saxena

Lund

, et al. The Lancet Commission on global mental health and sustainable development. Lancet. 2018;392(10157):1553-1598. doi:10.1016/s0140-6736(18)31612-x

Cramer

Torgersen

Kringlen

. Mood disorders and quality of life. A community study. Nord J Psychiatry. 2010;64(1):58-62. doi:10.3109/08039480903287565

Cramer

Torgersen

Kringlen

. Quality of life and anxiety disorders: a population study. J Nerv Ment Dis. 2005;193(3): 196-202. doi:10.1097/01.nmd.0000154836.22687.13

Kohn

Saxena

Levav

Saraceno

. The treatment gap in mental health care. Bull World Health Organ. 2004;82(11): 858-866.

Ansseau

Dierick

Buntinkx

, et al. High prevalence of mental disorders in primary care. J Affect Disord. 2004; 78(1):49-55. doi:10.1016/s0165-0327(02)00219-7

Bijl

de Graaf

Hiripi

, et al. The prevalence of treated and untreated mental disorders in five countries. Health Aff (Millwood). 2003;22(3):122-133. doi:10.1377/hlthaff.22.3.122

10.

Fortney

Unützer

Wrenn

, et al. A tipping point for measurement-based care. Psychiatr Serv. 2017;68(2):179-188. doi:10.1176/appi.ps.201500439

11.

Katzelnick

Duffy

Chung

Regier

Rae

Trivedi

. Depression outcomes in psychiatric clinical practice: using a self-rated measure of depression severity. Psychiatr Serv. 2011;62(8):929-935. doi:10.1176/ps.62.8.pss6208_0929

12.

Dowrick

Leydon

McBride

, et al. Patients’ and doctors’ views on depression severity questionnaires incentivised in UK quality and outcomes framework: qualitative study. BMJ. 2009;338:b663. doi:10.1136/bmj.b663

13.

Spitzer

Kroenke

Williams

. Validation and utility of a self-report version of PRIME-MD: the PHQ primary care study. Primary care evaluation of mental disorders. Patient Health Questionnaire. JAMA. 1999;282(18):1737-1744. doi:10.1001/jama.282.18.1737

14.

Spitzer

Kroenke

Williams

Löwe

. A brief measure for assessing generalized anxiety disorder: the GAD-7. Arch Intern Med. 2006;166(10):1092-1097. doi:10.1001/archinte.166.10.1092

15.

Hirschfeld

Williams

Spitzer

, et al. Development and validation of a screening instrument for bipolar spectrum disorder: the Mood Disorder Questionnaire. Am J Psychiatry. 2000;157(11):1873-1875. doi:10.1176/appi.ajp.157.11.1873

16.

Guo

Xiang

Xiao

, et al. Measurement-based care versus standard care for major depression: a randomized controlled trial with blind raters. Am J Psychiatry. 2015; 172(10):1004-1013. doi:10.1176/appi.ajp.2015.14050652

17.

Boswell

Hepner

Lysell

, et al. The need for a measurement-based care professional practice guideline. Psychotherapy (Chic). 2023;60(1):1-16. doi:10.1037/pst0000439

18.

Fortney

J SR

Unützer

Kennedy

Harbin

Emmet

Alfred

Carneal

. Fixing Behavioral Health Care in America: A National Call for Measurement-Based Care in the Delivery of Behavioral Health Services. The Kennedy Forum; 2015.

19.

Brown

Adams

Vanderhoef

Schipani

Taylor

. Improving PHQ9 utilization rates in a primary care-mental health integration setting. J Am Psychiatr Nurses Assoc. 2020;26(2):206-211. doi:10.1177/1078390319865331

20.

Hawkins

. Improving the rates of objective monitoring of patients with depression with the PHQ-9 in an outpatient psychiatry clinic: a quality improvement initiative. Jt Comm J Qual Patient Saf. 2023;49(3):149-155. doi:10.1016/j.jcjq.2023.01.001

21.

Ericson

Bonuck

Green

Conry

Martin

Carney

. Optimizing survey response rates in graduate medical education research studies. Fam Med. 2023;55(5):304-310. doi:10.22454/FamMed.2023.750371

22.

Kaufman

Charney

. Comorbidity of mood and anxiety disorders. Depress Anxiety. 2000;12 Suppl 1:69-76. doi:10.1002/1520-6394(2000)12:1+<69::Aid-da9>3.0.Co;2-k

23.

Emmanuel

Simmonds

Tyrer

. Systematic review of the outcome of anxiety and depressive disorders. Br J Psychiatry Suppl. 1998;(34):35-41.

24.

Waldrop

McGuinness

. Measurement-based care in psychiatry. J Psychosoc Nurs Ment Health Serv. 2017;55(11):30-35. doi:10.3928/02793695-20170818-01

25.

Shepherd

Gunnell

Maxwell

Mumford

. Development and evaluation of an inner city mental health team. Soc Psychiatry Psychiatr Epidemiol. 1998;33(3):129-135. doi:10.1007/s001270050033

26.

Ross

Moffat

McConnachie

Gordon

Wilson

. Sex and attitude: a randomized vignette study of the management of depression by general practitioners. Br J Gen Pract. 1999;49(438):17-21.

27.

Lewis

Boyd

Puspitasari

, et al. Implementing measurement-based care in behavioral health: a review. JAMA Psychiatry. 2019;76(3):324-335. doi:10.1001/jamapsychiatry.2018.3329

28.

Lish

Dime-Meenan

Whybrow

Price

Hirschfeld

. The National Depressive and Manic-depressive Association (DMDA) survey of bipolar members. J Affect Disord. 1994;31(4):281-294. doi:10.1016/0165-0327(94)90104-x

29.

Das

Olfson

Gameroff

, et al. Screening for bipolar disorder in a primary care practice. Jama. 2005;293(8):956-963. doi:10.1001/jama.293.8.956

30.

Santoro

Peabody

. Identifying and treating maternal depression: Strategies and considerations for health plans. Washington, DC: National Institute of Health Care Management, 2010 June, pp.1-28.

31.

Slomian

Honvo

Emonts

Reginster

Bruyère

. Consequences of maternal postpartum depression: a systematic review of maternal and infant outcomes. Womens Health (Lond). 2019;15:1745506519844044. doi:10.1177/1745506519844044

32.

Hutchens

Kearney

. Risk factors for postpartum depression: an umbrella review. J Midwifery Womens Health. 2020; 65(1):96-108. doi:10.1111/jmwh.13067

33.

Georgiopoulos

Bryan

Wollan

Yawn

. Routine screening for postpartum depression. J Fam Pract. 2001;50(2):117-122.

34.

The American College of Obstetricians and Gynecologists Committee Opinion no. 630. Screening for perinatal depression. Obstet Gynecol. 2015;125(5):1268-1271. doi:10.1097/01.AOG.0000465192.34779.dc

35.

Goldin Evans

Phillippi

Gee

. Examining the screening practices of physicians for postpartum depression: implications for improving health outcomes. Womens Health Issues. 2015;25(6):703-710. doi:10.1016/j.whi.2015.07.003

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