Association between sociodemographic factors, clinic characteristics and mental health screening rates in primary care

Frank Müller; Alyssa M Abdelnour; Diana N Rutaremara; Judith E Arnetz; Eric D Achtyes; Omayma Alshaarawy; Harland T Holman

doi:10.1371/journal.pone.0301125

. 2024 Mar 28;19(3):e0301125. doi: 10.1371/journal.pone.0301125

Association between sociodemographic factors, clinic characteristics and mental health screening rates in primary care

Frank Müller ^1,^2,^3,^*, Alyssa M Abdelnour ¹, Diana N Rutaremara ¹, Judith E Arnetz ¹, Eric D Achtyes ⁴, Omayma Alshaarawy ^5,^‡, Harland T Holman ^1,^2,^‡

Editor: Swaantje Wiarda Casjens⁶

PMCID: PMC10977679 PMID: 38547139

Abstract

Background

Screening for mental health problems has been shown to be effective to detect depression and initiate treatment in primary care. Current guidelines recommend periodic screening for depression and anxiety. This study examines the association of patient sociodemographic factors and clinic characteristics on mental health screening in primary care.

Design

In this retrospective cohort study, electronic medical record (EMR) data from a 14-month period from 10/15/2021 to 12/14/2022 were analyzed. Data were retrieved from 18 primary care clinics from the Corewell Health healthcare system in West Michigan. The main outcome was documentation of any Patient Health Questionnaire (PHQ-4/PHQ-9/GAD-7) screening in the EMR within the 14-month period at patient level. General linear regression models with logit link function were used to assess adjusted odds ratio (aOR) of having a documented screening.

Results

In total, 126,306 unique patients aged 16 years or older with a total of 291,789 encounters were included. The prevalence of 14-month screening was 79.8% (95% CI, 79.6–80.0). Regression analyses revealed higher screening odds for patients of smaller clinics (<5,000 patients, aOR 1.88; 95% CI 1.80–1.98 vs. clinics >10.000 patients), clinics in areas with mental health provider shortages (aOR 1.69; 95% CI 1.62–1.77), frequent visits (aOR 1.80; 95% CI, 1.78–1.83), and having an annual physical / well child visit encounter (aOR 1.52; 95% CI, 1.47–1.57). Smaller positive effect sizes were also found for male sex, Black or African American race, Asian race, Latinx ethnicity (ref. White/Caucasians), and having insurance through Medicaid (ref. other private insurance).

Discussion

The 14-month mental health screening rates have been shown to be significantly lower among patients with infrequent visits seeking care in larger clinics and available mental health resources in the community. Introducing and incentivizing mandatory mental health screening protocols in annual well visits, are viable options to increase screening rates.

Introduction

Depression is the leading cause of disability and is one of the most common mental illnesses in the United States [1]. While many people with behavioral health concerns have encounters with their primary care providers [2], healthcare professionals in primary care not using validated screening instruments will only identify approximately half of their depressive patients [3, 4] Assessing depression using standardized questionnaire instruments in primary care has been proven effective in initiating mental health care [5]. There is a growing body of evidence indicating that mental health screening decreases clinical morbidity in individuals with depression due to early treatment in response to positive screening results [6]. Early diagnosis and management additionally improve patients’ quality of life, decrease healthcare costs, and reduce exacerbations of co-morbid medical conditions [7–9]. The United States Preventative Service Task Force (USPSTF) recommended screening for depression in primary care settings for adults in 2016 [10]. A recently released update now also recommends screening for clinically relevant anxiety symptoms [11]. A large study showed increased depression screening rates and increased rates of depression diagnosis and treatment after the introduction of a screening regimen [12]. Particular racial and ethnic minorities and uninsured patients tend to benefit from rigorous mental health screening and subsequent treatment [13]. Although the USPSTF recommendation has been in place for 7 years, many primary care providers still perform mental health screening on a “case-by-case” basis rather than consistently [14], and screening rates have been shown to be 59% [12], 67.2% [15], and 88.8% [16] in various clinical settings.

While there is a growing body of evidence highlighting the benefits of regular mental health screening in primary care, studies of sociodemographic factors associated with lower odds of screening have mostly been conducted before [17, 18] or a few years after the release of the first USPSTF recommendations in 2016 [12, 15, 16]. Newer large-scale data on mental health screening prevalence, which reflects the mental health burden associated with the COVID-19 pandemic [19] is lacking. Identifying these factors is important to inform clinical interventions to improve screening and potential diagnosis. This study addresses this in a large cohort of patients.

Methods

This is a retrospective cohort study of 18 primary care clinics in the Corewell Health healthcare system in West Michigan. This study received approval from the institutional review board of Corewell Health and was deemed non-human subject research (Decision #: 2022–342). The report of the study results adheres to the STROBE guidelines for observational studies [20].

Setting

Corewell Health is a non-profit managed healthcare organization with 22 hospitals and more than 300 outpatient facilities and is the largest health care provider across the state of Michigan. This study is based in the West Michigan area with its 1.4 million residents in the Grand Rapids-Kentwood-Muskegon Combined Statistical Area [21] and also includes clinics located in adjacent suburban or rural areas. The median household income in this metropolitan region is similar to the overall United States ($69,643 vs $69,717), however, the foreign-born population is 5.7%, which is considerably lower than the national average (13.6%). The proportion of White/Caucasians among the residents of the metropolitan area Grand Rapids-Kentwood-Muskegon is similar to the overall US population (77% vs. 75.8% US average), however, with a lower proportion of Hispanic (9% vs. 18.9% US average) and Black or African American residents (7% vs 13.6% US average). According to US Census data, the number of uninsured residents was 5.2% in the West Michigan region, lower than the US average (9.8%).

This study used data from 18 primary care clinics employing primary care providers on a total of 143.9 full time equivalent positions. Using the rural-urban commuting area classification introduced by the US Department of Agriculture [22], 83.3% of clinics are located in metropolitan areas, 11.1% in small towns or rural areas, and one clinic (5.6%) in micropolitan areas.

Data sample

Data was retrieved through a Corewell Health honest broker who randomly assigned eight primary care clinics to the study. Ten other clinics were purposefully assigned to roughly match key demographics in the region. Data was extracted by the honest broker from electronic medical records (EMR, Epic HYPERSPACE, Epic Systems Corporation, Verona WI, USA). The number of clinics included in the study were defined together with Corewell Health’s Sensitive Data Sharing Workgroup to ensure a most likely representative sample while reducing the risk that data extraction would impede the operability of the EMR system.

The data set was created using specific criteria: All patients aged 16 or older who completed at least one office visit encounter between 10/15/2021 to 12/14/2022 were included. Patients were excluded if they were not established patients at each respective clinic as well as pure clinical resource visits (e.g. immunization, pap-smear test). Established patients were all patients enrolled with a primary care provider of the respective clinic and having at least one encounter before 10/15/2021.

Besides aggregated data on patients, a dataset listing encounters of the included patients was also retrieved to assess screening probability for each encounter. To control for the respective clinic in multivariable models, we also excluded patients who had encounters at various primary care clinics.

We did not apply any exclusions based on previous or newly diagnosed conditions. We expected annual well visits to be a good opportunity to screen patients for mental health problems, and due to patient preference (e.g. appointment conflicting with work or holidays), we deliberately chose a somewhat longer study time period of 14 months. Requested data was de-identified by the honest broker before being provided to the researchers.

Outcome and covariates

The primary outcome was receiving at least one mental health screening (either PHQ-4/PHQ-9/GAD-7) during the study period. The Patient Health Questionnaire (PHQ-4) is a commonly used ultra-brief screening instrument for depression and anxiety in adults. The PHQ-4 was introduced in the healthcare system as standard of care to screen for clinically relevant depressive and anxiety symptoms in June 2017. This questionnaire consists of 4 questions that can be answered on a 4-point scale. The first two questions cover the two main diagnostic criteria for depressive disorders according to DSM 5-TR [23] and form a subscale for depression (PHQ-2). The last two questions cover the main criteria for generalized anxiety disorder (GAD-2). The PHQ-4 has been studied in various populations showing measurement invariance across different genders, ages, and cross-cultural groups [24] and has a specificity of 94.5% and sensitivity of 51.6% compared to the more comprehensive 53-item Brief Symptom Inventory [25].

Typically, a medical assistant would document PHQ-4 responses as part of rooming the patient before the visit. The EMR system Epic prompts a PHQ-4 for every visit, however providers can skip entering results. Epic provides distinct fields to enter PHQ-4 responses and calculates sum scores automatically. Screened patients with a sum score ≥ 6, indicating clinically relevant depressive or anxious symptoms, were additionally asked to complete the more comprehensive PHQ-9 [26] and GAD-7 [27] as part of standard clinic procedure.

Covariates include the sociodemographic factors patient’s age, sex, self-reported race and ethnicity, and health insurance type. Patient self-reported race/ethnicity was categorized using a two-step approach. Those identifying as Hispanic were labeled "Latina/Latino/Latinx," while non-Hispanic individuals were classified by their specific race (White/Caucasian, American Indian/Alaska Native, Asian, Black or African American, Other). All sociodemographic data were extracted for patients’ first encounter during the study period. Furthermore, illness-related factors such as presence of diabetes mellitus (ICD-10 Codes E10, E11, O24.4), chronic ischemic heart disease (I25), chronic rheumatic/inflammatory disease (D89.9, M30-M36, K50-K52, K75.4), malignancy and cancer diagnosis (C00-D48), and mental diagnoses (F) except F17.2 (tobacco use) during or before the study period were included. Chronic conditions were chosen specifically as they have been linked with higher prevalence of anxiety and depression [28–31]. Encounter-related factors included the type of appointment, differentiating between annual physical appointments (such as well child visits for 16 to 21 year-olds or Medicare Annual Wellness Visits [32]) and regular primary care encounters. To adjust for clinics’ characteristics associated with the mental health needs of the served community, we collected information on mental health provider shortage using the Health Professional Shortage Area (Mental Health Area HPSA) score on a municipality/county level. Mental HPSAs scores can range between 0 and 25 and are calculated considering a variety of factors including population-to-provider ratio, proportion of residents below federal poverty level (FPL), proportion of residents aged >65 or <18, alcohol and substance use disorder prevalence as well as expected travel time to Nearest Source of Care (NSC) outside the HPSA area [33]. We collapsed Mental HPSAs scores into three groups indicating no mental health provider shortage (HPSA Score = 0), moderate mental health provider shortage (HPSA Score = 1–12), and significant mental health shortage (HPSA Score = 13–25).

Furthermore, we included clinic size calculated as the total of unique patients served during the study period. Clinic size was grouped into small clinics (<5,000 patients), mid-sized clinics (5,000–10,000 patients), and large clinics (>10,000 patients). As number of served patients at a clinic was positively correlated with urban clinic location (Spearman’s rho = 0.39) which may introduce collinearity, we deliberately included only clinic size in our multivariable model.

Statistical analyses

We used descriptive statistics including absolute and relative frequencies, mean, and standard deviation (SD) to characterize our sample. Documentation prevalence for mental health screening during the 14-month study period is displayed in forest plots for various subgroups with corresponding 95% confidence intervals (95% CI).

Lastly, we performed multivariable analysis using general linear models with a logit link function to assess the association of sociodemographic (age, sex, race, ethnicity, health insurance), encounter related (having an annual physical), location related (clinic size and mental health provider shortage at municipal/county level) as well as condition related (diabetes mellitus, chronic ischemic heart disease, cancer diagnosis, mental health diagnosis) factors and having at least one mental health screening (PHQ-4/GAD-7/PHQ-9) documented in a 14-month period as a binary outcome. We applied two different models, where model 1 included all aforementioned factors and model 2 additionally adjusted for frequencies of encounters. Results are reported as adjusted odds ratios (aOR) with corresponding 95% confidence intervals (95% CI). Subjects with missing data were excluded from all logistic regressions.

In all analyses, p values <0.05 were considered as statistically significant. All statistical analyses were performed using SPSS 28 (IBM, Armonk, NY, USA). Figures were plotted using GraphPad Prism 9.5 (GraphPad Software, San Diego, CA, USA).

Results

After excluding patients with encounters at multiple primary care clinics and clinical resource encounters, 126,306 patients with 291,789 encounters were included for analysis (Fig 1).

Those patients were between 16 and 104 years old (mean: 50.8, SD 18.9), more often of female sex (57.6%) and predominantly White or Caucasian (85.7%).

The primary care clinics were mainly located in metropolitan areas (83.3%) and provided care to an average of 7,017 (min 816, max 23,288, SD 5,613) unique patients during the 14-month study period. Six clinics were situated in counties/municipalities without a mental health provider shortage, while 4 and 8 clinics were located in areas with a moderate or significant mental health provider shortage, respectively. More detailed data are included in Table 1.

Table 1. Sociodemographics of n = 126,306 patients.

Characteristic			No. (%)
Sex		Male	53,531 (42.2)
Sex		Female	72,771 (57.6)
Age		Mean (SD)	50.8 (18.9)
		16–35	32,731 (25.9)
		36–55	37,965 (30.1)
		56–75	43,265 (34.3)
		75+	12,345 (9.8)
Race / Ethnicity^b		Other	1,788 (1.4)
		American Indian or Alaska Native	368 (0.3)
		Asian	2,856 (2.3)
		Black or African American	5,461 (4.4)
		White or Caucasian	106,924 (85.7)
		Latino/Latina/Latinx	7,304 (5.9)
Insurance		None	1,580 (1.3)
		Medicare	36,111 (28.6)
		Medicaid	12,079 (9.6)
		Private Insurance	76,536 (60.6)
Encounters		Mean (SD)	2.3 (1.7)
		1–2 encounters	85,340 (67.6)
		3–4 encounters	28,796 (22.8)
		5–9 encounters	11,441 (9.1)
		10+ encounters	729 (0.6)
		Annual physical / Well Child Visit	86,114 (68.2)
Diabetes mellitus			17,109 (13.5)
Chronic ischemic heart disease			7,731 (6.1)
Chronic inflammatory / rheumatic condition			5,036 (4.0)
Cancer diagnosis			23,206 (18.4)
Mental health diagnosis			58.646 (46.4)
Clinic size	< 5,000 patients		20,427 (16.2)
	5,000–10,000 patients		44,797 (35,5)
	> 10,000 patients		61,082 (48.4)
Mental health provider shortage	None (HPSA 0)		48,553 (38.4)
	Moderate (HPSA 1–12)		42,909 (34.0)
	Significant (HPSA 13–25)		34,844 (27.6)

Open in a new tab

^a Missing sex n = 4

^b Missing race/ethnicity n = 1,605, “other” includes Native Hawaiian or other Pacific Islander

On an encounter level, the odds of having any documented PHQ-4/PHQ-9/GAD-7 screening was 66.2% (SD 0.47). On a patient level, 79.8% (95% CI, 79.6–80.0) had a documentation of any PHQ-4/PHQ-9/GAD-7 screening during the 14-month period. 14.3% (n = 14,444) of patients with a documented PHQ-4 screening during the study period were also screened with a GAD-7. Similarly, 10.6% (n = 10,718) of PHQ-4 screened patients received an additional screening with the PHQ-9.

The patient level 14-month screening prevalence (any PHQ-4/PHQ-9/GAD-7) ranged between 51.3% and 98.6% among the included clinics.

The majority of patients had only one (29.3%) or two (28.1%) encounters. Screening prevalence for patients having a single encounter during the 14-month study period was 67.8% (95% CI, 67.5–68.2), for those with two encounters 83.4 (95% CI, 83.0–83.8).

The 14-months screening prevalence rate was lowest among 34-year-olds (75.7%, n = 1,940) and highest among 87-year-olds (88.4%, n = 466). Screening rates were above average among 16 to 18-year-olds and–with few exceptions–among people aged 60 and older (S1 Fig).

In unadjusted analyses, odds of receiving a screening within 14 months were higher for patients who were males (80.1%, 95% CI 79.8–80.5), had an annual physical appointment (81.2%, 95% CI 81.0–81.5 vs. those who did not 76.6%, 95% CI 76.2–77.1) and Black or African American patients had higher screening rates (86.2%, 95% CI 85.3–87.1) vs. White or Caucasian patients (79.1%; 95% CI 78.9–79.4). Patients with up to two appointments had lower (73.9%; 95% CI 73.6–74.2) than average chances to receive mental health screening. Patients with no chronic conditions (diabetes mellitus, chronic ischemic heart disease, chronic inflammatory condition, cancer diagnosis or mental health problems) had 14-month screening rates below average. More information is provided in Fig 2.

Fig 2 — ^a Missing sex n = 4, ^b Includes Native Hawaiian or other Pacific Islander, ^c Missing race / ethnicity n = 1,605.

Two different multivariable logistic regression models including a total of n = 124,698 patients were performed. All aORs are depicted in Table 2. Model 2 accounts for frequent encounters, while model 1 does not. Both models revealed sex differences on the outcome “Documented mental health screening (any PHQ-4/GAD-7/PHQ-9) within 14 months” in favor of male patients. Also, Black or African Americans, Asians, and Latinas/Latinos/Latinx showed significantly higher odds to be screened compared to White or Caucasians in both models, respectively. Compared to patients with private insurance, those insured through Medicaid were more likely to have a screening documented. Furthermore, patients having at least one annual physical / wellness visit / well child visit had higher screening odds compared to those who did not have one of these preventive encounters.

Table 2. Results of binary logistic regression models (n = 124,698 patients).

Parameter		Model 1		Model 2
Parameter		aOR (95% CI)	P	aOR (95% CI)	P
Age (years)	16–19	1.11 (1.02–1.21)	0.019	1.24 (1.13–1.35)	<0.001
	20–29	0.84 (0.79–0.89)	<0.001	0.94 (0.89–1.00)	0.050
	30–39	0.86 (0.81–0.90)	<0.001	0.95 (0.89–1.00)	0.053
	40–49	0.95 (0.89–1.00)	0.048	1.01 (0.96–1.07)	0.694
	50–59	0.96 (0.91–1.01)	0.087	0.97 (0.92–1.03)	0.307
	60–69	ref		ref
	70–79	1.08 (1.01–1.14)	0.016	1.03 (0.96–1.09)	0.404
	80–89	1.60 (1.47–1.75)	<0.001	1.37 (1.25–1.50)	<0.001
	90+	1.42 (1.20–1.67)	<0.001	1.26 (1.07–1.49)	0.007
Sex	Male	1.10 (1.06–1.13)	<0.001	1.14 (1.11–1.18)	<0.001
Sex	Female	ref		ref
Race/Ethnicity	Latina/Latino/Latinx	1.74 (1.62–1.86)	<0.001	1.61 (1.50–1.73)	<0.001
	Other^a	1.15 (1.01–1.30)	0.029	1.09 (0.96–1.23)	0.198
	American Indian or Alaska Native	1.25 (0.94–1.64)	0.120	1.18 (0.89–1.57)	0.253
	Asian	1.38 (1.24–1.53)	<0.001	1.33 (1.20–1.48)	<0.001
	Black or African American	1.37 (1.27–1.49)	<0.001	1.20 (1.10–1.31)	<0.001
	White or Caucasian	ref		ref
Insurance	None	1.08 (0.95–1.22)	0.255	1.14 (1.00–1.29)	0.052
	Medicare	1.13 (1.07–1.19)	<0.001	1.03 (0.97–1.09)	0.345
	Medicaid	1.31 (1.24–1.38)	<0.001	1.16 (1.09–1.22)	<0.001
	Other private insurance	ref		ref
Annual Physical / Well Child Visit		1.49 (1.44–1.53)	<0.001	1.35 (1.31–1.39)	<0.001
Encounters	(continous)		1.80 (1.78–1.83)	<0.001
Diabetes mellitus		1.37 (1.31–1.44)	<0.001	1.00 (0.96–1.06)	0.861
Chronic ischemic heart disease		1.06 (0.99–1.14)	0.093	0.94 (0.87–1.00)	0.066
Chronic inflammatory / rheumatic diagnosis		1.02 (0.94–1.09)	0.694	0.89 (0.82–0.96)	0.002
Malignancy / Cancer diagnosis		1.01 (0.97–1.05)	0.527	0.93 (0.89–0.97)	<0.001
Mental health diagnosis		1.37 (1.33–1.42)	<0.001	1.12 (1.08–1.15)	<0.001
Clinic size (unique patients/14 months)	<5,000 patients	1.95 (1.87–2.05)	<0.001	1.88 (1.80–1.98)	<0.001
	5,000–10,000 patients	1.58 (1.51–1.65)	<0.001	1.62 (1.55–1.69)	<0.001
	>10,000 patients	ref		ref
Mental Health Provider Shortage (HPSA)	None	ref		ref
	Moderate	1.95 (1.88–2.02)	<0.001	1.96 (1.89–2.03)	<0.001
	Significant	1.59 (1.52–1.66)	<0.001	1.69 (1.62–1.77)	<0.001

Open in a new tab

^a “Other” includes Native Hawaiian or other Pacific Islander

Patients’ aged 20 to 49 were less likely to have a documented mental health screening in model 1 compared to the reference group of 60-69-year-olds, but when further adjusted to frequency of encounters the effect size decreased and remained only statistically significant for 20-29-year-olds.

The association between clinic size/location and mental health screening prevalence shown in descriptive analyses was revealed in both regression models. Smaller clinics were more likely to perform and document mental health screening compared to larger clinics, and clinics located in areas with a mental health provider shortage performed those screenings significantly more often on their patients. While patients with a mental health diagnosis showed increased screening rates in both regression models compared to those without such a diagnosis, screening prevalence did not differ among patients with diagnosed diabetes or chronic ischemic heart disease, when adjusting for frequent encounters. For cancer patients and patients with chronic inflammatory or rheumatic conditions, screening odds were even lower compared to those without such conditions when adjusted for frequency of encounters.

Discussion

While early detection of mental health disorders has been shown to enhance patients’ quality of life, reduce healthcare costs, and subsequently decrease complications from co-morbidities, our study suggests that providers struggle to screen patients with only occasional encounters, younger patients in their 20s, female sex, White and Caucasian race, having private insurance, and not having preventive annual physical visits. This is concerning as the highest prevalence rates for depression are found among people in their 20s, with women particularly affected at this age [34].

While 14-month screening prevalence in our study was 79.8%, other researchers found considerably lower depression screening rates of 48.6% using a representative sample of US adults aged 35 or older [18]. That study, however, used patient self-reported data and found other demographic factors correlated with mental health screening than in our study. Another study reported similar mental health screening rates, with higher odds in patients aged 65 to 84 and 85+, compared to those aged 18 to 44, however, insurance type was not considered in that study [35].

Our study revealed significant screening rate variations among clinics, despite the health system having a longstanding standard procedure for mental health screening. This heterogeneity in one geographically bounded health system is surprising and concerning. Implementing mental health screening in the work routine was considerably more successful in some clinics than in others. This raises not only questions on structural and organizational barriers that may contribute to under-screening in those clinics but also indicates that a single top-down implementation effort may not be effective among some clinics which may require some more tailored and continuous approaches, e.g. constant monitoring and feedback, incentivizing of screening, highlighting the importance of screening in team meetings, education of office staff, reminder through EMR software, etc. To our surprise, smaller clinics and clinics in areas with mental health provider shortages had substantially higher mental health screening odds. While this phenomenon has to our knowledge not been described before, it is likely that providers in communities lacking mental health providers and with fewer options to refer patients with mental health needs may be more aware of their patients’ mental health problems and perhaps even more keen in treating them. Conversely, providers in areas with sufficient mental health services may be more prone to expect their patients with mental health problems to seek treatment by specialists and thus do not prioritize mental health screening. While scarcity of mental health services is expanding in the U.S. [36], further qualitative research can provide insights on how primary care providers in both provider shortage areas and sufficiently covered areas perceive their professional roles when treating mental health problems. Prior research indicated barriers to primary care providers to screen for mental health, such as lack of training on mental health care [37], anticipated negative reactions to screening from patients, perception of “knowing a patient personally” [38], time constraints [14] and low comfort levels of primary care providers’ comfort in treating psychiatric conditions [39].

Higher screening rates in the elderly may be attributed to Medicare Annual Wellness Visits (AWV) introduced in 2011, offering preventive services that include mental health screening without patient co-payments [32]. The established practice of well child visits, which are regularly offered until the age of 21, and also include screening for mental health, could have also a positive effect on screening prevalence [40]. Unlike AWVs, preventive encounters (e.g. routine annual physicals) in private health insurance plans may either not mandate mental health screenings or propose it but not enforce actual implementation, which may explain lower screening odds.

Furthermore, providers can be prone to bias about who they think may “need” screening, and providers may not be aware of racial and cultural differences in the symptom presentation [41]. However, our study found significantly higher screening rates among Black and African American, Asian and Latinx patients compared to Whites and Caucasian. While this may reflect increased efforts of diversity equity and inclusion initiatives to address mental health disparities in minority populations, other researchers highlighted that implicit bias is a possible explanation that Black people have higher rates of being diagnosed with certain mental diagnoses [42]. Other studies also highlighted lower screening rates among some limited English language proficiency patient groups [16, 43].

Patients with chronic conditions that are associated with higher mental health burdens [28–30] did have higher mental health screening prevalence in unadjusted analyses, but average or even lower odds of receiving mental health screening when adjusted for frequent visits suggesting that encounter frequency is a modifying factor. While our study could show this tendency for various conditions, patients with a known mental health diagnosis were more likely to receive a screening in both regression models.

Quality improvement projects introducing standard of care procedures, treatment algorithms, and/or modified office protocols have shown to increase mental health screening [16, 35, 44]. While these efforts remain on a local level, our study suggested that annual wellness visits correlate with increased screening rates. Encouraging and incentivizing wellness visits, along with incorporating mental health screening as a standard of care procedure, could have a nationwide impact. Timely diagnosis and treatment of depression and anxiety symptoms are beneficial in terms of prognostic outcomes, can prevent worsening of comorbidities, and have been proven cost-effective [7, 8, 45].

Limitations

Our study comes with limitations that need to be considered when interpreting the results. The clinics chosen were not similar to the sociodemographic structure of the overall U.S. population, for example, our patient sample had a higher proportion of White or Caucasian patients. This limits the validity for certain racial and ethnic minorities. Another limitation is that our study only encompassed clinics from one health care system in the Midwest. Furthermore, the data did not specify if patients rejected mental health screening or were not able to be screened, e.g. due to disabilities.

Conclusions

This study highlights clinic size, mental health service availability in the community, and number of encounters of a patient are main determinants for receiving the recommended mental health screening. Primary care providers offer accessible mental health services by assessing and diagnosing problems, provide treatment or referrals if necessary, and support patients in managing their condition. As a prerequisite, providers need to be well trained, aware of their important role and comfortable in treating patients. Mental health screening was performed more often by providers of smaller clinics with scarce mental health treatment resources in their communities, suggesting that these providers were more likely to fulfil this role. On the contrary, healthy and younger patients with less healthcare encounters seeking care in larger urban clinics in areas with generally good availability of mental health services were less likely to receive screening for anxiety and depression. While other factors like sex, race or ethnicity also play a role, this finding is particularly concerning given the high rates of depression and anxiety in these age groups.

Furthermore, our study showed a considerable variety of mental health screening prevalence between clinics. This highlights the necessity for a continuous and tailored implementation effort to foster equitable mental health care.

Supporting information

S1 Fig. 14-month documented mental health screening prevalence with corresponding 95% CI among different ages (patients aged >95 years were grouped into 95-year-olds).

Dotted line depicts mean screening rate.

(DOCX)

pone.0301125.s001.docx^{(58.8KB, docx)}

Data Availability

The analysed dataset is not publicly available due to the decision of the responsible institutional review board. It is however available upon reasonable request within a data sharing agreement. Inquiries can be made to honestbroker@spectrumhealth.org.

Funding Statement

Funding/Support: FM received funding as Peter C. and Pat Cook endowed Clinical Research Fellow for conducting this study. OA received funding from the NIH/NCCIH (R00AT009156) and Michigan State University. Role of the Funder/Sponsor: The funding sources had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication.

References

1.Weinberger AH, Gbedemah M, Martinez AM, Nash D, Galea S, Goodwin RD. Trends in depression prevalence in the USA from 2005 to 2015: widening disparities in vulnerable groups. Psychol Med. 2018;48(8):1308–1315. doi: 10.1017/S0033291717002781 [DOI] [PubMed] [Google Scholar]
2.Narrow WE. Use of Services by Persons With Mental and Addictive Disorders: Findings From the National Institute of Mental Health Epidemiologic Catchment Area Program. Arch Gen Psychiatry. 1993;50(2):95. doi: 10.1001/archpsyc.1993.01820140017002 [DOI] [PubMed] [Google Scholar]
3.Mitchell AJ, Vaze A, Rao S. Clinical diagnosis of depression in primary care: a meta-analysis. The Lancet. 2009;374(9690):609–619. doi: 10.1016/S0140-6736(09)60879-5 [DOI] [PubMed] [Google Scholar]
4.Mitchell AJ, Kakkadasam V. Ability of nurses to identify depression in primary care, secondary care and nursing homes—A meta-analysis of routine clinical accuracy. International Journal of Nursing Studies. 2011;48(3):359–368. doi: 10.1016/j.ijnurstu.2010.05.012 [DOI] [PubMed] [Google Scholar]
5.Haefner J, Daly M, Russell S. Assessing Depression in the Primary Care Setting. J Doct Nurs Pract. 2017;10(1):28–37. doi: 10.1891/2380-9418.10.1.28 [DOI] [PubMed] [Google Scholar]
6.O’Connor E, Rossom RC, Henninger M, et al. Screening for Depression in Adults: An Updated Systematic Evidence Review for the U.S. Preventive Services Task Force. Agency for Healthcare Research and Quality (US); 2016. Accessed March 21, 2023. http://www.ncbi.nlm.nih.gov/books/NBK349027/ [PubMed] [Google Scholar]
7.Birk JL, Kronish IM, Moise N, Falzon L, Yoon S, Davidson KW. Depression and multimorbidity: Considering temporal characteristics of the associations between depression and multiple chronic diseases. Health Psychology. 2019;38(9):802–811. doi: 10.1037/hea0000737 [DOI] [PMC free article] [PubMed] [Google Scholar]
8.Egede LE, Zheng D, Simpson K. Comorbid Depression is Associated With Increased Health Care Use and Expenditures in Individuals With Diabetes. Diabetes Care. 2002;25(3):464–470. doi: 10.2337/diacare.25.3.464 [DOI] [PubMed] [Google Scholar]
9.Mulvaney-Day N, Marshall T, Downey Piscopo K, et al. Screening for Behavioral Health Conditions in Primary Care Settings: A Systematic Review of the Literature. J GEN INTERN MED. 2018;33(3):335–346. doi: 10.1007/s11606-017-4181-0 [DOI] [PMC free article] [PubMed] [Google Scholar]
10.Siu AL, and the US Preventive Services Task Force (USPSTF), Bibbins-Domingo K, et al. Screening for Depression in Adults: US Preventive Services Task Force Recommendation Statement. JAMA. 2016;315(4):380. doi: 10.1001/jama.2015.18392 [DOI] [PubMed] [Google Scholar]
11.Anxiety in Adults: Screening. Published online 2022. Accessed March 21, 2023. https://uspreventiveservicestaskforce.org/uspstf/draft-recommendation/anxiety-adults-screening#fullrecommendationstart
12.Pfoh ER, Janmey I, Anand A, Martinez KA, Katzan I, Rothberg MB. The Impact of Systematic Depression Screening in Primary Care on Depression Identification and Treatment in a Large Health Care System: A Cohort Study. J GEN INTERN MED. 2020;35(11):3141–3147. doi: 10.1007/s11606-020-05856-5 [DOI] [PMC free article] [PubMed] [Google Scholar]
13.Meyers MA, Groh CJ, Binienda J. Depression Screening and Treatment in Uninsured Urban Patients. The Journal of the American Board of Family Medicine. 2014;27(4):520–529. doi: 10.3122/jabfm.2014.04.130254 [DOI] [PubMed] [Google Scholar]
14.Pilipenko N, Vivar-Ramon C. Depression screening perceptions and practices in a primary care clinic: A mixed-methods study. Psychological Services. Published online February 23, 2023. doi: 10.1037/ser0000753 [DOI] [PubMed] [Google Scholar]
15.Rezaeizadeh A, Sanchez K, Zolfaghari K, Madia ND. Depression screening and treatment among uninsured populations in Primary Care. International Journal of Clinical and Health Psychology. 2021;21(3):100241. doi: 10.1016/j.ijchp.2021.100241 [DOI] [PMC free article] [PubMed] [Google Scholar]
16.Garcia ME, Hinton L, Neuhaus J, Feldman M, Livaudais-Toman J, Karliner LS. Equitability of Depression Screening After Implementation of General Adult Screening in Primary Care. JAMA Netw Open. 2022;5(8):e2227658. doi: 10.1001/jamanetworkopen.2022.27658 [DOI] [PMC free article] [PubMed] [Google Scholar]
17.Akincigil A, Matthews EB. National Rates and Patterns of Depression Screening in Primary Care: Results From 2012 and 2013. PS. 2017;68(7):660–666. doi: 10.1176/appi.ps.201600096 [DOI] [PubMed] [Google Scholar]
18.Kato E, Borsky AE, Zuvekas SH, Soni A, Ngo-Metzger Q. Missed Opportunities for Depression Screening and Treatment in the United States. J Am Board Fam Med. 2018;31(3):389–397. doi: 10.3122/jabfm.2018.03.170406 [DOI] [PubMed] [Google Scholar]
19.Gray B, Ommeren M van, Lewis S, et al. Mental Health and COVID-19: Early evidence of the pandemic’s impact. Published online March 2, 2022. Accessed May 8, 2023. https://apps.who.int/iris/rest/bitstreams/1412184/retrieve [Google Scholar]
20.von Elm E, Altman DG, Egger M, et al. The Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) statement: guidelines for reporting observational studies. J Clin Epidemiol. 2008;61(4):344–349. doi: 10.1016/j.jclinepi.2007.11.008 [DOI] [PubMed] [Google Scholar]
21.Metropolitan and Micropolitan Statistical Areas Population Totals and Components of Change: 2020–2021. United States Census Bureau; 2022. Accessed January 12, 2023. https://www.census.gov/data/tables/time-series/demo/popest/2020s-total-metro-and-micro-statistical-areas.html [Google Scholar]
22.Rural-Urban Commuting Area Codes. Published online August 17, 2020. https://www.ers.usda.gov/data-products/rural-urban-commuting-area-codes.aspx
23.American Psychiatric Association, ed. Diagnostic and Statistical Manual of Mental Disorders: DSM-5-TR. Fifth edition, text revision. American Psychiatric Association Publishing; 2022. [Google Scholar]
24.Wicke FS, Krakau L, Löwe B, Beutel ME, Brähler E. Update of the standardization of the Patient Health Questionnaire-4 (PHQ-4) in the general population. Journal of Affective Disorders. 2022;312:310–314. doi: 10.1016/j.jad.2022.06.054 [DOI] [PubMed] [Google Scholar]
25.Kerper LF, Spies CD, Tillinger J, et al. Screening for Depression, Anxiety, and General Psychological Distress in Pre-operative Surgical Patients: A Psychometric Analysis of the Patient Health Questionnaire 4 (Phq-4). clinhp. 2014;4(1):5–14. doi: 10.29102/clinhp.14002 [DOI] [Google Scholar]
26.Kroenke K, Spitzer RL, Williams JBW. The PHQ-9: Validity of a brief depression severity measure. J Gen Intern Med. 2001;16(9):606–613. doi: 10.1046/j.1525-1497.2001.016009606.x [DOI] [PMC free article] [PubMed] [Google Scholar]
27.Spitzer RL, Kroenke K, Williams JBW, Löwe B. A Brief Measure for Assessing Generalized Anxiety Disorder: The GAD-7. Arch Intern Med. 2006;166(10):1092. doi: 10.1001/archinte.166.10.1092 [DOI] [PubMed] [Google Scholar]
28.Karami N, Kazeminia M, Karami A, Salimi Y, Ziapour A, Janjani P. Global prevalence of depression, anxiety, and stress in cardiac patients: A systematic review and meta-analysis. Journal of Affective Disorders. 2023;324:175–189. doi: 10.1016/j.jad.2022.12.055 [DOI] [PubMed] [Google Scholar]
29.Riedl D, Schuessler G. Prevalence of Depression and Cancer–A systematic review. Zeitschrift für Psychosomatische Medizin und Psychotherapie. 2022;68(1):74–86. doi: 10.13109/zptm.2021.67.oa11 [DOI] [PubMed] [Google Scholar]
30.Wang Y, Lopez JMS, Bolge SC, Zhu VJ, Stang PE. Depression among people with type 2 diabetes mellitus, US National Health and Nutrition Examination Survey (NHANES), 2005–2012. BMC Psychiatry. 2016;16:88. doi: 10.1186/s12888-016-0800-2 [DOI] [PMC free article] [PubMed] [Google Scholar]
31.Fiest KM, Hitchon CA, Bernstein CN, et al. Systematic Review and Meta-analysis of Interventions for Depression and Anxiety in Persons With Rheumatoid Arthritis. J Clin Rheumatol. 2017;23(8):425–434. doi: 10.1097/RHU.0000000000000489 [DOI] [PMC free article] [PubMed] [Google Scholar]
32.Moore N, Koenig N, Shakiba K, Heidbrink J, Guinn M. Increasing Medicare Annual Wellness Visits in Accountable Care Organizations. Am J Accountable Care. 2021;9(3):13–18. doi: 10.37765/ajac.2021.88749 [DOI] [Google Scholar]
33.U.S. Health Ressources and Services Administration, Bureau of Health Workforce. Scoring Shortage Designations. Accessed December 27, 2023. https://bhw.hrsa.gov/workforce-shortage-areas/shortage-designation/scoring [Google Scholar]
34.Kessler RC, Birnbaum H, Bromet E, Hwang I, Sampson N, Shahly V. Age differences in major depression: results from the National Comorbidity Survey Replication (NCS-R). Psychol Med. 2010;40(2):225–237. doi: 10.1017/S0033291709990213 [DOI] [PMC free article] [PubMed] [Google Scholar]
35.Blackstone SR, Sebring AN, Allen C, Tan JS, Compton R. Improving Depression Screening in Primary Care: A Quality Improvement Initiative. J Community Health. 2022;47(3):400–407. doi: 10.1007/s10900-022-01068-6 [DOI] [PMC free article] [PubMed] [Google Scholar]
36.Tadmon D, Bearman PS. Differential spatial-social accessibility to mental health care and suicide. Proc Natl Acad Sci USA. 2023;120(19):e2301304120. doi: 10.1073/pnas.2301304120 [DOI] [PMC free article] [PubMed] [Google Scholar]
37.Leigh H, Stewart D, Mallios R. Mental health and psychiatry training in primary care residency programs. General Hospital Psychiatry. 2006;28(3):195–204. doi: 10.1016/j.genhosppsych.2005.10.004 [DOI] [PubMed] [Google Scholar]
38.Hill SK, Cantrell P, Edwards J, Dalton W. Factors Influencing Mental Health Screening and Treatment Among Women in a Rural South Central Appalachian Primary Care Clinic: Mental Health Screening in Rural Primary Care. The Journal of Rural Health. 2016;32(1):82–91. doi: 10.1111/jrh.12134 [DOI] [PubMed] [Google Scholar]
39.Stilwell K, Pelkey L, Platt T, et al. Survey of Primary Care Provider Comfort in Treating Psychiatric Patients in 2 Community Clinics: A Pilot Study. Prim Care Companion CNS Disord. 2022;24(1). doi: 10.4088/PCC.21m03020 [DOI] [PubMed] [Google Scholar]
40.Zuckerbrot RA, Cheung A, Jensen PS, et al. Guidelines for Adolescent Depression in Primary Care (GLAD-PC): Part I. Practice Preparation, Identification, Assessment, and Initial Management. Pediatrics. 2018;141(3):e20174081. doi: 10.1542/peds.2017-4081 [DOI] [PubMed] [Google Scholar]
41.Payne JS. Influence of Race and Symptom Expression on Clinicians’ Depressive Disorder Identification in African American Men. Journal of the Society for Social Work and Research. 2012;3(3):162–177. doi: 10.5243/jsswr.2012.11 [DOI] [Google Scholar]
42.Olbert CM, Nagendra A, Buck B. Meta-analysis of Black vs. White racial disparity in schizophrenia diagnosis in the United States: Do structured assessments attenuate racial disparities? Journal of Abnormal Psychology. 2018;127(1):104–115. doi: 10.1037/abn0000309 [DOI] [PubMed] [Google Scholar]
43.Müller F, Abdelnour AM, Rutaremara DN, et al. Mental Health Screening Differences in Non-English Speaking Patients: Results From a Retrospective Cohort Study. J Prim Care Community Health. 2023;14:21501319231200304. doi: 10.1177/21501319231200304 [DOI] [PMC free article] [PubMed] [Google Scholar]
44.Lindsay M, Decker VB. Improving Depression Screening in Primary Care. J Doct Nurs Pract. Published online May 3, 2022:JDNP-2021-0005.R1. doi: 10.1891/JDNP.2021-0005 [DOI] [PubMed] [Google Scholar]
45.Firth J, Siddiqi N, Koyanagi A, et al. The Lancet Psychiatry Commission: a blueprint for protecting physical health in people with mental illness. The Lancet Psychiatry. 2019;6(8):675–712. doi: 10.1016/S2215-0366(19)30132-4 [DOI] [PubMed] [Google Scholar]

PLoS One. doi: 10.1371/journal.pone.0301125.r001

Decision Letter 0

Swaantje Wiarda Casjens

Transfer Alert

This paper was transferred from another journal. As a result, its full editorial history (including decision letters, peer reviews and author responses) may not be present.

20 Nov 2023

PONE-D-23-16932The Impact of Sociodemographic Factors on Mental Health Screening Rates in Primary CarePLOS ONE

Dear Dr. Müller,

Thank you for submitting your manuscript to PLOS ONE. After careful consideration, we feel that it has merit but does not fully meet PLOS ONE’s publication criteria as it currently stands. Therefore, we invite you to submit a revised version of the manuscript that addresses the points raised during the review process.

Please submit your revised manuscript by Jan 04 2024 11:59PM. If you will need more time than this to complete your revisions, please reply to this message or contact the journal office at plosone@plos.org. When you're ready to submit your revision, log on to https://www.editorialmanager.com/pone/ and select the 'Submissions Needing Revision' folder to locate your manuscript file.

Please include the following items when submitting your revised manuscript:

A rebuttal letter that responds to each point raised by the academic editor and reviewer(s). You should upload this letter as a separate file labeled 'Response to Reviewers'.
A marked-up copy of your manuscript that highlights changes made to the original version. You should upload this as a separate file labeled 'Revised Manuscript with Track Changes'.
An unmarked version of your revised paper without tracked changes. You should upload this as a separate file labeled 'Manuscript'.

If you would like to make changes to your financial disclosure, please include your updated statement in your cover letter. Guidelines for resubmitting your figure files are available below the reviewer comments at the end of this letter.

If applicable, we recommend that you deposit your laboratory protocols in protocols.io to enhance the reproducibility of your results. Protocols.io assigns your protocol its own identifier (DOI) so that it can be cited independently in the future. For instructions see: https://journals.plos.org/plosone/s/submission-guidelines#loc-laboratory-protocols. Additionally, PLOS ONE offers an option for publishing peer-reviewed Lab Protocol articles, which describe protocols hosted on protocols.io. Read more information on sharing protocols at https://plos.org/protocols?utm_medium=editorial-email&utm_source=authorletters&utm_campaign=protocols.

We look forward to receiving your revised manuscript.

Kind regards,

Dr, Swaantje Casjens

Academic Editor

PLOS ONE

Journal Requirements:

When submitting your revision, we need you to address these additional requirements.

1. Please ensure that your manuscript meets PLOS ONE's style requirements, including those for file naming. The PLOS ONE style templates can be found at

https://journals.plos.org/plosone/s/file?id=wjVg/PLOSOne_formatting_sample_main_body.pdf and

https://journals.plos.org/plosone/s/file?id=ba62/PLOSOne_formatting_sample_title_authors_affiliations.pdf

2. Note from Emily Chenette, Editor in Chief of PLOS ONE, and Iain Hrynaszkiewicz, Director of Open Research Solutions at PLOS: Did you know that depositing data in a repository is associated with up to a 25% citation advantage (https://doi.org/10.1371/journal.pone.0230416)? If you’ve not already done so, consider depositing your raw data in a repository to ensure your work is read, appreciated and cited by the largest possible audience. You’ll also earn an Accessible Data icon on your published paper if you deposit your data in any participating repository (https://plos.org/open-science/open-data/#accessible-data).

3. Please expand the acronym “NIH/NCCIH” (as indicated in your financial disclosure) so that it states the name of your funders in full.

This information should be included in your cover letter; we will change the online submission form on your behalf.

4. We note that you have indicated that data from this study are available upon request. PLOS only allows data to be available upon request if there are legal or ethical restrictions on sharing data publicly. For more information on unacceptable data access restrictions, please see http://journals.plos.org/plosone/s/data-availability#loc-unacceptable-data-access-restrictions.

In your revised cover letter, please address the following prompts:

a) If there are ethical or legal restrictions on sharing a de-identified data set, please explain them in detail (e.g., data contain potentially sensitive information, data are owned by a third-party organization, etc.) and who has imposed them (e.g., an ethics committee). Please also provide contact information for a data access committee, ethics committee, or other institutional body to which data requests may be sent.

b) If there are no restrictions, please upload the minimal anonymized data set necessary to replicate your study findings as either Supporting Information files or to a stable, public repository and provide us with the relevant URLs, DOIs, or accession numbers. For a list of acceptable repositories, please see http://journals.plos.org/plosone/s/data-availability#loc-recommended-repositories.

We will update your Data Availability statement on your behalf to reflect the information you provide.

Additional Editor Comments:

Please note in particular the comments on the statistical analysis from both reviewers: modelling of age, clinic-related heterogeneity. I would also like to point out that a p-value <0.05 represents statistical significance and not general significance.

Also make sure that all abbreviations are introduced the first time they are used (e.g., EMR in the abstract) and check the references carefully. It does not conform to PlosOne specifications, e.g., journal abbreviations are required.

[Note: HTML markup is below. Please do not edit.]

Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

1. Is the manuscript technically sound, and do the data support the conclusions?

The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented.

Reviewer #1: Partly

Reviewer #2: Partly

**********

2. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: No

Reviewer #2: Yes

**********

3. Have the authors made all data underlying the findings in their manuscript fully available?

The PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified.

Reviewer #1: No

Reviewer #2: No

**********

4. Is the manuscript presented in an intelligible fashion and written in standard English?

PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here.

Reviewer #1: Yes

Reviewer #2: Yes

**********

5. Review Comments to the Author

Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters)

Reviewer #1: Overall I found this manuscript to be an important addition to the literature on mental health screening. The questions asked are sound and the writing is generally clear. Specific attention should be paid to the analysis of age in the model and the interpretation of the chronic disease data. Detailed comments on both of these along with other minor comments below:

Abstract

In the design section of the abstract the outcome is listed as “documentation of a PHQ-4 screening” and the analysis strategy is stated as “assess odds ratio (aOR) of receiving a screening.” I would recommend changing tis to “assess odds ration (aOR) of having a documented screening” to be consistent.

Also please clarify in the abstract that outcome is the documentation of screening at the patient (rather than visit) level.

Discussion of abstract has the take home point that “Introducing and incentivizing mandatory mental health screening protocols in annual well visits, similar to well child visits or Medicare Annual Wellness Visits, are viable options to increase screening rates.” I am confused by this statement because the the design section states that mental health screening has been a standard procedure in this health system since prior to the start of this study period. These screenings are already incentivized by most insurers.

Introduction

Paragraph one contains the sentence “A large study showed increased depression screening rates and increased rates of depression diagnosis and treatment after the introduction of a screening regime.” I believe the last word of this sentence should be “regimen” rather than “regime.”

Setting

Suggest removing the word “somewhat” from the following sentence: “with a somewhat lower proportion of Hispanic (9% vs. 18.9% US average) and Black or African American residents (7% vs 13.6% US average).” These are both roughly half the proportion which I wouldn’t classify as somewhat.

Data sample

Please define “established patient.”

Outcome and covariates. Last paragraph starts with “Covariates include the sociodemographic factors patient’s age (at first encounter)”. Can delete “(at first encounter)” since you specify in the next sentence that this is true for all covariates.

Results

I am a little confused by the reporting of patient level outcomes and encounter level outcomes. For example on page 8, second paragraph, does the following sentence refer to the prevalence of screening at the encounter level or patient level? “The 14-month screening prevalence ranged between 51.3% and 98.6% among the included clinics.” Can the authors clarify this by adding the words “at the patient level” or “at the encounter level” after the word prevalence?

First sentence of paragraph 3 on page 8 should end with the word “encounters” rather than “encounter.”

I question the authors’ choice to include age as a continuous variable in the model when the unadjusted analysis shows a bimodal distribution (higher screening rates at younger and older ages). I suggest that they work with a statistician to find a more appropriate way to treat age in the multivariate analysis, perhaps categorically as it is presented in Table 1. I think this is especially important given that age does seem to be an important factor in screening rates and that modelling this continuously shows, I believe misleadingly, that it is not (unadjusted model shows significance but with an odds ratio of essentially 1).

Interesting that for all chronic diseases the odds of receiving a PHQ4 decreases when you adjust for frequency of visits, suggesting that the effect is indirect (chronic disease leads to increased number of visits which leads to increased screening rates) rather than direct (chronic disease leads to increased screening). This seems to be the main take home point from the chronic disease analysis and I don’t see it discussed in the paper.

Discussion

Again, I think that the conclusions drawn from the age analysis are misleading based on the treatment of this covariate as a linear variable in the model. This will be important to address given the author’s discussion of the interaction between age and wellness visits in the conclusion (which I think is an interesting point that should be explored through proper analysis of the bimodal age data).

Again, conclusions from chronic disease analysis seem, to me, to be that frequency of visits is a modifying factor (all in the same direction, increased screening with increased frequency of visits) and that, when this is adjusted for, different chronic diseases have different associations with screening rates (none for diabetes and CV disease, decreased rates of screening for inflammatory disease and cancer, and increased screening for mental health).

Figures

It is somewhat disorienting to me that Figure 2 has PHQ4 prevalence on the y axis and Figure 3 has PHQ prevalence on the x axis. Can the authors be consistent in the presentation of their data? Figure 2 seems overly detailed to me given the re-presentation of this data in Figure 3 with categories that tell the same story.

Reviewer #2: This is an interesting article reporting on the important matter of screening for depression and anxiety in primary care settings.

It conducts a retrospective analysis of EMR data from a health system in Western Michigan, aiming to shed light on some of the associations between patient characteristics and their receipt of the PHQ-4 screening questionnaire.

As you will see in my comments, I believe that a set of additional analyses could really elevate this paper and turn it into an important contribution. Mainly, this has to do with the need to separate the associations found between patients’ demographics and their receipt of the screening questionnaire, and the very substantial heterogeneity detected in the rate of questionnaire administration between different clinics (an important finding on its own).

The question whether, upon revision, the authors can disentangle these drivers of the observed association will make or break this article.

Comments for the authors:

1. On p. 8, the authors report that “The 14- month screening prevalence ranged between 51.3% and 98.6% among the included clinics.” This indicates huge variance and should be treated as a key finding on its own that raises important questions concerning the differences between these clinics. The authors need to use their data to investigate this heterogeneity as it might in fact drive the demographic effects reported. What are the sources of this heterogeneity? Does the data allow you to identify individual providers and their likelihoods of administering the questionnaire? The analyses looking at patient characteristics should additionally control for clinic-related covariates (for one, the authors mention that they tag clinics by rurality). It is important to disaggregate observed effects between those having to do with patient characteristics and those driven by place. Additionally, on p. 7 the authors mention a sensitivity analysis that looked at between-clinic differences, but its findings are not reported nor discussed. The clinic-driven heterogeneity of results is germane and should be addressed both descriptively and in the discussion.

2. Adding to the same between-clinic heterogeneity issue, on p. 5 the authors write that “To control for the respective clinic in multivariable models, we also excluded patients who had encounters at various primary care clinics.” I am not sure I understand the logic for excluding these clients. In fact, an analysis that focuses on these patients in particular might be useful to uncover the puzzle of the between-clinic gaps.

3. On p. 6, authors write that “The EMR system Epic prompts a PHQ-4 for every visit, however providers can skip entering results. Epic provides distinct fields to enter PHQ-4 responses and calculates sum scores automatically. Screened patients with a sum score ≥ 6, indicating clinically relevant depressive or anxious symptoms, were additionally asked to complete the more comprehensive PHQ-9 and GAD-7 as part of standard clinic procedure.”

Instead of the PHQ-4, do any patients receive these more comprehensive questionnaires or undergo some other mental health screening during their visits? If so, the outcome variable and analyses need to be amended.

Additionally, of the patients who received the PHQ-4, examining how many scored 6 or over (or, if the scores are inaccessible, how many were given these more comprehensive questionnaires following the PHQ-4) could be an interesting outcome further elucidating the likelihood that different demographics’ mental illness goes undiagnosed.

4. Throughout the article, the use of the term “impact” (e.g. “The Impact of Sociodemographic Factors on Mental Health Screening”) should be changed to “association”. The evidence provided does not establish causality.

5. Given the centrality of race to the framing of the article, I would include the Asian patient odds in the text, in addition to other racial groups (e.g., Abstract and p. 8). I believe the rates for women patients, too, are missing from p. 8.

6. The discussion is currently too long. Precious word-count can be diverted to conducting and reporting further analyses, as suggested above. That being said, I am convinced by the point raised in the discussion that “limited mental health treatment capacities might reduce providers’ willingness to screen for mental health problems as they find themselves in a position where they cannot refer patients to specialists” (p. 11). I think this argument as well as the phenomenon of psychiatrist scarcity indicated by reference 42 could certainly be one of the mechanisms driving the between-clinic disparities this study observes. If indeed more rural clinics have lower rates of questionnaire administration, this would support this theory.

7. On p. 5, the authors mention that “Patients were excluded if they were not established patients at each respective clinic”. Why were these patients excluded? Was it necessary? I don’t believe Figure 1 properly reflects this exclusion.

More minor comments:

1. On p. 3, the sentence starting with “While there is a growing body of evidence” is missing some words.

2. On p. 8, authors report that “The 14-months screening prevalence rate was lowest among 34-year-olds (75.4%, n=1,940) and highest among 87-year-olds (88.4%, n=466).” It might be preferable to report (and statistically test) differences in questionnaire administration frequency on wider, 5- or 10-year age spans.

3. In the Discussion, on p. 10, the authors mention that “Our study showed that within a 14-month period, 79.7% of patients received a PHQ-4 screening with considerably lower rates for patients in their 20’s and 30’s.” This should be statistically tested.

I thank you for sharing this manuscript with me and wish you good luck in continuing your analysis of this interesting data.

**********

6. PLOS authors have the option to publish the peer review history of their article (what does this mean?). If published, this will include your full peer review and any attached files.

If you choose “no”, your identity will remain anonymous but your review may still be made public.

Do you want your identity to be public for this peer review? For information about this choice, including consent withdrawal, please see our Privacy Policy.

Reviewer #1: No

Reviewer #2: No

**********

[NOTE: If reviewer comments were submitted as an attachment file, they will be attached to this email and accessible via the submission site. Please log into your account, locate the manuscript record, and check for the action link "View Attachments". If this link does not appear, there are no attachment files.]

While revising your submission, please upload your figure files to the Preflight Analysis and Conversion Engine (PACE) digital diagnostic tool, https://pacev2.apexcovantage.com/. PACE helps ensure that figures meet PLOS requirements. To use PACE, you must first register as a user. Registration is free. Then, login and navigate to the UPLOAD tab, where you will find detailed instructions on how to use the tool. If you encounter any issues or have any questions when using PACE, please email PLOS at figures@plos.org. Please note that Supporting Information files do not need this step.

PLoS One. 2024 Mar 28;19(3):e0301125. doi: 10.1371/journal.pone.0301125.r002

Author response to Decision Letter 0

20 Jan 2024

Reviewer 1

Overall I found this manuscript to be an important addition to the literature on mental health screening. The questions asked are sound and the writing is generally clear. Specific attention should be paid to the analysis of age in the model and the interpretation of the chronic disease data. Detailed comments on both of these along with other minor comments below:

Thank you for your time and efforts in reviewing our paper.

Abstract

We agree and changed the wording.

Also please clarify in the abstract that outcome is the documentation of screening at the patient (rather than visit) level.

Thank you for this remark. We have added this important aspect.

Thank you for this remark. While some insurers include mental health screening in their wellness visits, not all do so. Furthermore, private insurance providers typically don't invest considerable efforts in ensuring or enforcing the actual implementation of mental health screening, despite proposing its inclusion in coverage. In addition, healthcare systems may establish standard care procedures, yet clinics might deprioritize these protocols, particularly when there are no sanctions for non-compliance. Conversely, Annual Wellness Visits (AWVs) offer a high revenue value unit rate, providing a strong incentive for conducting and documenting mental health screening as an integral part of the AWV process, which is mandatory for billing. We have added this to the discussion:

“Higher screening rates in the elderly may be attributed to Medicare Annual Wellness Visits (AWV) introduced in 2011, offering preventive services that include mental health screening without patient co-payments.32 The established practice of well child visits, which are regularly offered until the age of 21, and also include screening for mental health, could have also a positive effect on screening prevalence.38 Unlike AWVs, preventive encounters (e.g. routine annual physicals) in private health insurance plans may either not mandate mental health screenings or propose it but not enforce actual implementation, which may explain lower screening odds.”

Introduction

Thank you for catching this typo. Corrected.

Setting

We agree and have deleted “somewhat”.

Data sample

Please define “established patient.”

� Thank you for catching this. We have added this information to the Methods section:

“Established patients were all patients enrolled with a primary care provider of the respective clinic and having at least one encounter before 10/15/2021.”

Done.

Results

We agree that this is indeed confusing. We have changed the sentences accordingly.

First sentence of paragraph 3 on page 8 should end with the word “encounters” rather than “encounter.”

Thank you for catching this error. Corrected.

Thank you for this comment. We agree and have added the patient’s age now as a categorical variable to the multivariable model and re-done the analyses. This revealed the bimodal distribution with lower screening odds in patients 20s (both models) and 20s-40s (without adjusting on frequent encounters) compared to reference (60-69-year-olds). Patients aged 80 and above had significantly higher screening odds. We also changed the discussion accordingly.

Of note, we streamlined the race/ethnicity categorization into a single variable. This adjustment was made because Latinx patients frequently left the "race" field blank, resulting in a decrease in the inclusion of Latinx individuals to the multivariable model due to missing values. We have added this to the methods section.

Furthermore, we have added clinic characteristics (size and location) to the regression model as suggested by reviewer #2, which provided further insights.

Thank you for highlighting this important aspect. We have added it to the discussion:

“Patients with chronic conditions that are associated with higher mental health burden did have higher mental health screening prevalence in unadjusted analyses, but average or even lower odds of receiving mental health screening when adjusted for frequent visits suggesting that encounter frequency is a modifying factor. While our study could show this tendency for various conditions, patients with a known mental health diagnosis were more likely to receive a screening in both regression models.”

Discussion

Thank you for this comment, as described above, we have changed the regression model accordingly and now report on the updated results and also discuss new findings.

Thank you for this remark. See response above.

Figures

Thank you for this comment. We believe that figure 2 provides important and detailed information and allows bivariate comparisons, which is of importance to reviewer #2. To keep the presented information concise, we decided however to move figure 2 to the appendix.

Reviewer #2

This is an interesting article reporting on the important matter of screening for depression and anxiety in primary care settings.

Thank you for your time and effort in reviewing our manuscript.

The question whether, upon revision, the authors can disentangle these drivers of the observed association will make or break this article.

Comments for the authors:

Thank you for this important comment. We agree that including respective clinics as covariates in our multivariable analyses does not add any additional explanatory value to the model given the considerable variance of screening rates among the clinics. Analyzing screening rates on provider level can be also misleading, as patient care involves a clinical team, with medical assistants often performing PHQ screenings and other preparatory measures such as vital signs, height/weight during rooming.

We have decided to add two important measures to the analyses, that should allow us to better characterize the involved clinics.

a) We have added metrics on Health Professional Shortage Area (HPSA) to include mental health shortages in the respective county/municipality.

b) Furthermore, we added clinic size measured as they served unique patients during the study period. Of note, clinic size was correlated with the RUCA rurality codes thus we dropped RUCA from analyses to avoid factor collinearity.

We have added this to the methods section:

“To adjust for clinics’ characteristics associated with the mental health needs of the served community, we collected information on mental health provider shortage using the Health Professional Shortage Area (Mental Health Area HPSA) score on municipality/county level. Mental HPSAs scores can range between 0 and 25 and are calculated considering a variety of factors including population-to-provider ratio, proportion of residents below federal poverty level (FPL), proportion of residents aged >65 or <18, alcohol and substance use disorder prevalence as well as expected travel time to Nearest Source of Care (NSC) outside the HPSA area.33 We collapsed Mental HPSAs scores into three groups indicating no mental health provider shortage (HPSA Score = 0), moderate mental health provider shortage (HPSA Score = 1-12), and significant mental health provider shortage (HPSA Score = 13-25).

Furthermore, we included clinic size calculated as the total of unique patients served during the study period. Clinic size was grouped into small clinics (<5,000 patients), mid-sized clinics (5,000-10,000 patients), and large clinics (>10,000 patients). As the number of served patients at a clinic was positively correlated with urban clinic location (Spearman’s rho = 0.39) which may introduce collinearity, we deliberately included only clinic size in our multivariable model.”

Furthermore, we updated table 1 and now report also on the characteristics of the clinics in both text and table:

“Six clinics were situated in counties/municipalities without a mental health provider shortage, while 4 and 8 clinics were located in areas with a moderate or significant mental health provider shortage, respectively.”

“The association between clinic size/location and mental health screening prevalence shown in descriptive analyses was revealed in both regression models. Smaller clinics were more likely to perform and document mental health screening compared to larger clinics, and clinics located in areas with mental health provider shortage performed those screenings significantly more often on their patients.”

Furthermore, we have restructured the entire discussion section, now highlighting the new findings as follows:

“Our study revealed significant screening rate variations among clinics, despite the health system having a longstanding standard procedure for mental health screening. To our surprise, smaller clinics and clinics in areas with mental health provider shortages had substantially higher mental health screening odds. While this phenomenon has to our knowledge not been described before, it is likely that providers in communities lacking mental health providers and with fewer options to refer patients with mental health needs may be more aware of their patients’ mental health problems and perhaps even more keen in treating them. Conversely, providers in areas with sufficient mental health services may be more prone to expect their patients with mental health problems to seek treatment by specialists and thus do not prioritize mental health screening. While scarcity of mental health services is expanding in the U.S.42, further qualitative research can provide insights on how primary care providers in provider shortage areas and sufficiently covered areas perceive their professional roles when treating mental health problems.”

Thank you for this suggestion. We have excluded those patients, as they likely switched providers during the study period, and thus data for a 14-month observation period at one provider is incomplete. A dataset with longer observational periods (e.g. 5 years) would be necessary to identify a sufficient number of patients switching clinics and to observe screening differences. However, such a dataset was not available (see also comment below). We are also unsure if a subsample of patients switching providers is representative of the entire patient population, as those patients may seek another primary care provider for distinct reasons (e.g. provision of specialized services at the new clinic, moving to another city, dissatisfaction with previous provider, etc.).

Thank you for this comment. Indeed, there are a few patients with no documented PHQ-4 but a GAD-7 (n=62) or PHQ-9 (n=84). We have updated the outcome now defined as receiving either PHQ-4, PHQ-9, or GAD-7 in at least one encounter during the 14-month period. However, these changes are rather minor, e.g. 14-month prevalence for documented PHQ-4 is 79.712%, 14-month prevalence for any documented PHQ-4/PHQ-9/GAD-7 is 79.783%. We have updated these minor changes, where applicable throughout the manuscript, and also used this measure as an outcome for the multivariable analyses, and also amended the methods section accordingly.

“The primary outcome was receiving at least one PHQ-4 mental health screening (either PHQ-4/PHQ-9/GAD-7) during the study period.”

While we have not recorded the score values of the respective instruments, 14.3% (n=14,444) of patients with a documented PHQ-4 screening during the study period were also screened with a GAD-7. Similarly, 10.6% (n=10,718) of PHQ-4 screened patients received an additional screening with the PHQ-9. We have added this information to the result section.

“On patient level, 79.8% (95% CI, 79.6 – 80.0) had a documentation of any PHQ-4/PHQ-9/GAD-7 screening during the 14-month period. 14.3% (n=14,444) of patients with a documented PHQ-4 screening during the study period were also screened with a GAD-7. Similarly, 10.6% (n=10,718) of PHQ-4 screened patients received an additional screening with the PHQ-9.”

Differences in comprehensive screening prevalence rates (PHQ-9/GAD-7) are indeed an important topic that needs further investigation. However, it is a prerequisite for those analyses that the sub-sample of PHQ-9/GAD-7 screened patients is representative of the non-PHQ-4 screened population. As we cannot ascertain this, the interpretation of results would be rather speculative.

Thank you for this remark. We have changed the wording throughout the manuscript accordingly.

Thank you for this remark. Due to word count limitations, we were only able to add aORs in the abstract for the factors with considerable effect sizes (aOR >1.5), but commented on this without reporting aORs. Given that we provide 2 models and all aORs are depicted in table 2, we decided not to repeat numbers but rather provide an overview of the important findings.

Thank you for this comment. We have restructured the entire discussion and made it more concise. Please see also responses above.

Thank you for this comment. We have added a sentence defining “established patients”.

“Established patients were all patients enrolled with a primary care provider of the respective clinic and having at least one encounter before 10/15/2021.”

The rationale for excluding non-established patients was that we did not want to mix in urgent-care patients or patients who receive regular primary care services with an established provider somewhere else. However, we agree that mental health screening prevalence among patients seeking urgent care is an important aspect that should be addressed in further investigations.

As an establishment with a provider was an á priori exclusion criteria, our honest broker excluded non-established patients before providing us the data. Thus, figure 1 cannot be amended.

More minor comments:

1. On p. 3, the sentence starting with “While there is a growing body of evidence” is missing some words.

Thank you for catching this error. We have rephrased the sentence.

Thank you for this comment. The healthcare system underwent some restructuring and merging some years ago, thus unified EMR data covering a longer period was not available for this study. Despite these practical considerations, monitoring the impact of the release of screening recommendations or standard of care procedures in a longitudinal design was not objective in our study.

Thank you for this remark. Figure 2 indicates 14-month PHQ-4 screening prevalence with corresponding 95% confidence intervals for respective ages. When error bars don’t overlap with the mean (dotted line), they are independent (p<0.05). When two error bars of two ages don’t overlap, the difference is also statistically significant on a p<0.05 level. It is arguable if further statistical tests would contribute to a better understanding, given the high number of included patients making rather small differences statistically significant.

I thank you for sharing this manuscript with me and wish you good luck in continuing your analysis of this interesting data.

PLoS One. doi: 10.1371/journal.pone.0301125.r003

Decision Letter 1

Swaantje Wiarda Casjens

6 Feb 2024

PONE-D-23-16932R1Association between Sociodemographic Factors, Clinic Characteristics and Mental Health Screening Rates in Primary CarePLOS ONE

Dear Dr. Müller,

Both initial reviewers have reviewed your revised manuscript again. Only reviewer 2 still has a few comments. I would like to ask you to revise your manuscript again with regard to these comments.

A minor comment from the second reviewer concerns the presentation of Table 2. In this context, I would also like to ask you to explain the difference between Model 1 and Model 2 in a footnote to Table 2. The additional adjustment for encounters is not immediately apparent due to the large number of factors examined. I would also like to remind you to check the references carefully. It does not comply with the PlosOne specifications (e.g. journal abbreviations are required, references with more than six authors should list the first six authors, references should be cited in parentheses, etc.).

Please submit your revised manuscript by Mar 22 2024 11:59PM. If you will need more time than this to complete your revisions, please reply to this message or contact the journal office at plosone@plos.org. When you're ready to submit your revision, log on to https://www.editorialmanager.com/pone/ and select the 'Submissions Needing Revision' folder to locate your manuscript file.

Please include the following items when submitting your revised manuscript:

A rebuttal letter that responds to each point raised by the academic editor and reviewer(s). You should upload this letter as a separate file labeled 'Response to Reviewers'.
A marked-up copy of your manuscript that highlights changes made to the original version. You should upload this as a separate file labeled 'Revised Manuscript with Track Changes'.
An unmarked version of your revised paper without tracked changes. You should upload this as a separate file labeled 'Manuscript'.

We look forward to receiving your revised manuscript.

Kind regards,

Swaantje Wiarda Casjens

Academic Editor

PLOS ONE

Journal Requirements:

Please review your reference list to ensure that it is complete and correct. If you have cited papers that have been retracted, please include the rationale for doing so in the manuscript text, or remove these references and replace them with relevant current references. Any changes to the reference list should be mentioned in the rebuttal letter that accompanies your revised manuscript. If you need to cite a retracted article, indicate the article’s retracted status in the References list and also include a citation and full reference for the retraction notice.

[Note: HTML markup is below. Please do not edit.]

Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

1. If the authors have adequately addressed your comments raised in a previous round of review and you feel that this manuscript is now acceptable for publication, you may indicate that here to bypass the “Comments to the Author” section, enter your conflict of interest statement in the “Confidential to Editor” section, and submit your "Accept" recommendation.

Reviewer #1: All comments have been addressed

Reviewer #2: (No Response)

**********

2. Is the manuscript technically sound, and do the data support the conclusions?

Reviewer #1: Yes

Reviewer #2: Yes

**********

3. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: Yes

Reviewer #2: Yes

**********

4. Have the authors made all data underlying the findings in their manuscript fully available?

Reviewer #1: No

Reviewer #2: No

**********

5. Is the manuscript presented in an intelligible fashion and written in standard English?

Reviewer #1: Yes

Reviewer #2: Yes

**********

6. Review Comments to the Author

Reviewer #1: All comments addressed. Thank you in particular for addressing the concerns about age in the analysis which seem to have revealed interesting findings now reported and addressed in the discussion.

Reviewer #2: I am a returning Reviewer (no. 2). I commend the authors for responding to my prior comments and revising the article accordingly. I believe my prior concerns have been addressed. Reading the revised version, I do have one important comment remaining, as well a few more minor comments:

1. I thank the authors for now referring in the Discussion to the huge variability in screening rates they have observed between the clinics (ranging between 51.3% and 98.6%). To me, this still is one of the most important findings of the study, and I would encourage the authors to stress it even more. The existence of such heterogeneity in one, geographically bounded health system is surprising and alarming. Simultaneously, it may be encouraging, too. Namely, it can be seen an indication that beyond patient-level attributes that (as the authors show) contribute to under-screening and that may be more challenging for staff and practitioners to overcome (in terms of bias, cultural competence, etc.), the simple fact that some clinics screen all patients and others only one half indicates that perhaps even a more “blunt force” organizational, top-down, intervention ensuring screens are indeed routinely provided could have a hugely beneficial impact. The scale of this variance is quite shocking, raises interesting questions, and I would encourage the authors to dwell on it and its implications a bit more.

Minor comments:

2. The wording in the second paragraph of the Discussion, referring to reference no. 18 is slightly confusing. When the authors write “According to this study”, I suspect they may have meant “According to that study”. And so on. Further, I am not sure repeating the specific findings of that other study are of much importance. The key thing is that its findings on the demographic correlates of screening quite substantially differed from the current analysis, and that this may be attributable to its different methodology (pt. self-reports). This may be all the authors need to say about it.

3. I very much appreciated the analysis of the mental health professional shortage areas. I think it really adds depth to the discussion and I believe the article benefits from it quite considerably.

4. Lastly, I believe Table 2 is showing multivariate regression results, and so its columns should indicate aOR, and not OR.

Thank you for sharing with me this interesting research and for conducting what I believe to be a successful revision. Congratulations!

**********

7. PLOS authors have the option to publish the peer review history of their article (what does this mean?). If published, this will include your full peer review and any attached files.

If you choose “no”, your identity will remain anonymous but your review may still be made public.

Do you want your identity to be public for this peer review? For information about this choice, including consent withdrawal, please see our Privacy Policy.

Reviewer #1: No

Reviewer #2: No

**********

PLoS One. 2024 Mar 28;19(3):e0301125. doi: 10.1371/journal.pone.0301125.r004

Author response to Decision Letter 1

26 Feb 2024

Reviewer #2:

I am a returning Reviewer (no. 2). I commend the authors for responding to my prior comments and revising the article accordingly. I believe my prior concerns have been addressed. Reading the revised version, I do have one important comment remaining, as well a few more minor comments:

Thank you again for your time and efforts in reviewing our manuscript.

Thank you for this important remark. The underutilization of screening instruments shown in our study stresses questions on how the implementation of this screening regime was carried out and how implementation strategies might be improved. We have now elaborated on this and added two paragraphs to each discussion and conclusion section:

“Our study revealed significant screening rate variations among clinics, despite the health system having a longstanding standard procedure for mental health screening. This heterogeneity in one geographically bounded health system is surprising and concerning. Implementing mental health screening in the work routine was considerably more successful in some clinics than in others. This raises not only questions on structural and organizational barriers that may contribute to under-screening in those clinics but also indicates that a single top-down implementation effort may not be effective among some clinics which may require some more tailored and continuous approaches, e.g. constant monitoring and feedback, incentivizing of screening, highlighting the importance of screening in team meetings, education of office staff, reminder through EMR software, etc.”

“Furthermore, our study showed a considerable variety of mental health screening prevalence between clinics. This highlights the necessity for a continuous and tailored implementation effort to foster equitable mental health care.”

Minor comments:

Thank you for this comment. We have rephrased this section to make it more concise:

“While 14-month screening prevalence in our study was 79.8%, other researchers found considerably lower depression screening rates of 48.6% using a representative sample of US adults aged 35 or older.18 That study, however, used patient self-reported data and found other demographic factors correlated with mental health screening than in our study.”

3. I very much appreciated the analysis of the mental health professional shortage areas. I think it really adds depth to the discussion and I believe the article benefits from it quite considerably.

Thank you!

4. Lastly, I believe Table 2 is showing multivariate regression results, and so its columns should indicate aOR, and not OR.

Thank you for catching this. Indeed, it should read “aOR”. We have changed it.

Thank you for sharing with me this interesting research and for conducting what I believe to be a successful revision. Congratulations!

Thank you again for your helpful comments!

PLoS One. doi: 10.1371/journal.pone.0301125.r005

Decision Letter 2

Swaantje Wiarda Casjens

12 Mar 2024

Association between Sociodemographic Factors, Clinic Characteristics and Mental Health Screening Rates in Primary Care

PONE-D-23-16932R2

Dear Dr. Müller,

We’re pleased to inform you that your manuscript has been judged scientifically suitable for publication and will be formally accepted for publication once it meets all outstanding technical requirements.

Within one week, you’ll receive an e-mail detailing the required amendments. When these have been addressed, you’ll receive a formal acceptance letter and your manuscript will be scheduled for publication.

An invoice will be generated when your article is formally accepted. Please note, if your institution has a publishing partnership with PLOS and your article meets the relevant criteria, all or part of your publication costs will be covered. Please make sure your user information is up-to-date by logging into Editorial Manager at http://www.editorialmanager.com/pone/ and clicking the ‘Update My Information' link at the top of the page. If you have any questions relating to publication charges, please contact our Author Billing department directly at authorbilling@plos.org.

If your institution or institutions have a press office, please notify them about your upcoming paper to help maximize its impact. If they’ll be preparing press materials, please inform our press team as soon as possible -- no later than 48 hours after receiving the formal acceptance. Your manuscript will remain under strict press embargo until 2 pm Eastern Time on the date of publication. For more information, please contact onepress@plos.org.

Kind regards,

Swaantje Wiarda Casjens

Academic Editor

PLOS ONE

Additional Editor Comments (optional):

Reviewers' comments:

PLoS One. doi: 10.1371/journal.pone.0301125.r006

Acceptance letter

Swaantje Wiarda Casjens

19 Mar 2024

PONE-D-23-16932R2

PLOS ONE

Dear Dr. Müller,

I'm pleased to inform you that your manuscript has been deemed suitable for publication in PLOS ONE. Congratulations! Your manuscript is now being handed over to our production team.

At this stage, our production department will prepare your paper for publication. This includes ensuring the following:

* All references, tables, and figures are properly cited

* All relevant supporting information is included in the manuscript submission,

* There are no issues that prevent the paper from being properly typeset

If revisions are needed, the production department will contact you directly to resolve them. If no revisions are needed, you will receive an email when the publication date has been set. At this time, we do not offer pre-publication proofs to authors during production of the accepted work. Please keep in mind that we are working through a large volume of accepted articles, so please give us a few weeks to review your paper and let you know the next and final steps.

Lastly, if your institution or institutions have a press office, please let them know about your upcoming paper now to help maximize its impact. If they'll be preparing press materials, please inform our press team within the next 48 hours. Your manuscript will remain under strict press embargo until 2 pm Eastern Time on the date of publication. For more information, please contact onepress@plos.org.

If we can help with anything else, please email us at customercare@plos.org.

Thank you for submitting your work to PLOS ONE and supporting open access.

Kind regards,

PLOS ONE Editorial Office Staff

on behalf of

Dr. Swaantje Wiarda Casjens

Academic Editor

PLOS ONE

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

S1 Fig. 14-month documented mental health screening prevalence with corresponding 95% CI among different ages (patients aged >95 years were grouped into 95-year-olds).

Dotted line depicts mean screening rate.

(DOCX)

pone.0301125.s001.docx^{(58.8KB, docx)}

Data Availability Statement

[pone.0301125.ref001] 1.Weinberger AH, Gbedemah M, Martinez AM, Nash D, Galea S, Goodwin RD. Trends in depression prevalence in the USA from 2005 to 2015: widening disparities in vulnerable groups. Psychol Med. 2018;48(8):1308–1315. doi: 10.1017/S0033291717002781 [DOI] [PubMed] [Google Scholar]

[pone.0301125.ref002] 2.Narrow WE. Use of Services by Persons With Mental and Addictive Disorders: Findings From the National Institute of Mental Health Epidemiologic Catchment Area Program. Arch Gen Psychiatry. 1993;50(2):95. doi: 10.1001/archpsyc.1993.01820140017002 [DOI] [PubMed] [Google Scholar]

[pone.0301125.ref003] 3.Mitchell AJ, Vaze A, Rao S. Clinical diagnosis of depression in primary care: a meta-analysis. The Lancet. 2009;374(9690):609–619. doi: 10.1016/S0140-6736(09)60879-5 [DOI] [PubMed] [Google Scholar]

[pone.0301125.ref004] 4.Mitchell AJ, Kakkadasam V. Ability of nurses to identify depression in primary care, secondary care and nursing homes—A meta-analysis of routine clinical accuracy. International Journal of Nursing Studies. 2011;48(3):359–368. doi: 10.1016/j.ijnurstu.2010.05.012 [DOI] [PubMed] [Google Scholar]

[pone.0301125.ref005] 5.Haefner J, Daly M, Russell S. Assessing Depression in the Primary Care Setting. J Doct Nurs Pract. 2017;10(1):28–37. doi: 10.1891/2380-9418.10.1.28 [DOI] [PubMed] [Google Scholar]

[pone.0301125.ref006] 6.O’Connor E, Rossom RC, Henninger M, et al. Screening for Depression in Adults: An Updated Systematic Evidence Review for the U.S. Preventive Services Task Force. Agency for Healthcare Research and Quality (US); 2016. Accessed March 21, 2023. http://www.ncbi.nlm.nih.gov/books/NBK349027/ [PubMed] [Google Scholar]

[pone.0301125.ref007] 7.Birk JL, Kronish IM, Moise N, Falzon L, Yoon S, Davidson KW. Depression and multimorbidity: Considering temporal characteristics of the associations between depression and multiple chronic diseases. Health Psychology. 2019;38(9):802–811. doi: 10.1037/hea0000737 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0301125.ref008] 8.Egede LE, Zheng D, Simpson K. Comorbid Depression is Associated With Increased Health Care Use and Expenditures in Individuals With Diabetes. Diabetes Care. 2002;25(3):464–470. doi: 10.2337/diacare.25.3.464 [DOI] [PubMed] [Google Scholar]

[pone.0301125.ref009] 9.Mulvaney-Day N, Marshall T, Downey Piscopo K, et al. Screening for Behavioral Health Conditions in Primary Care Settings: A Systematic Review of the Literature. J GEN INTERN MED. 2018;33(3):335–346. doi: 10.1007/s11606-017-4181-0 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0301125.ref010] 10.Siu AL, and the US Preventive Services Task Force (USPSTF), Bibbins-Domingo K, et al. Screening for Depression in Adults: US Preventive Services Task Force Recommendation Statement. JAMA. 2016;315(4):380. doi: 10.1001/jama.2015.18392 [DOI] [PubMed] [Google Scholar]

[pone.0301125.ref011] 11.Anxiety in Adults: Screening. Published online 2022. Accessed March 21, 2023. https://uspreventiveservicestaskforce.org/uspstf/draft-recommendation/anxiety-adults-screening#fullrecommendationstart

[pone.0301125.ref012] 12.Pfoh ER, Janmey I, Anand A, Martinez KA, Katzan I, Rothberg MB. The Impact of Systematic Depression Screening in Primary Care on Depression Identification and Treatment in a Large Health Care System: A Cohort Study. J GEN INTERN MED. 2020;35(11):3141–3147. doi: 10.1007/s11606-020-05856-5 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0301125.ref013] 13.Meyers MA, Groh CJ, Binienda J. Depression Screening and Treatment in Uninsured Urban Patients. The Journal of the American Board of Family Medicine. 2014;27(4):520–529. doi: 10.3122/jabfm.2014.04.130254 [DOI] [PubMed] [Google Scholar]

[pone.0301125.ref014] 14.Pilipenko N, Vivar-Ramon C. Depression screening perceptions and practices in a primary care clinic: A mixed-methods study. Psychological Services. Published online February 23, 2023. doi: 10.1037/ser0000753 [DOI] [PubMed] [Google Scholar]

[pone.0301125.ref015] 15.Rezaeizadeh A, Sanchez K, Zolfaghari K, Madia ND. Depression screening and treatment among uninsured populations in Primary Care. International Journal of Clinical and Health Psychology. 2021;21(3):100241. doi: 10.1016/j.ijchp.2021.100241 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0301125.ref016] 16.Garcia ME, Hinton L, Neuhaus J, Feldman M, Livaudais-Toman J, Karliner LS. Equitability of Depression Screening After Implementation of General Adult Screening in Primary Care. JAMA Netw Open. 2022;5(8):e2227658. doi: 10.1001/jamanetworkopen.2022.27658 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0301125.ref017] 17.Akincigil A, Matthews EB. National Rates and Patterns of Depression Screening in Primary Care: Results From 2012 and 2013. PS. 2017;68(7):660–666. doi: 10.1176/appi.ps.201600096 [DOI] [PubMed] [Google Scholar]

[pone.0301125.ref018] 18.Kato E, Borsky AE, Zuvekas SH, Soni A, Ngo-Metzger Q. Missed Opportunities for Depression Screening and Treatment in the United States. J Am Board Fam Med. 2018;31(3):389–397. doi: 10.3122/jabfm.2018.03.170406 [DOI] [PubMed] [Google Scholar]

[pone.0301125.ref019] 19.Gray B, Ommeren M van, Lewis S, et al. Mental Health and COVID-19: Early evidence of the pandemic’s impact. Published online March 2, 2022. Accessed May 8, 2023. https://apps.who.int/iris/rest/bitstreams/1412184/retrieve [Google Scholar]

[pone.0301125.ref020] 20.von Elm E, Altman DG, Egger M, et al. The Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) statement: guidelines for reporting observational studies. J Clin Epidemiol. 2008;61(4):344–349. doi: 10.1016/j.jclinepi.2007.11.008 [DOI] [PubMed] [Google Scholar]

[pone.0301125.ref021] 21.Metropolitan and Micropolitan Statistical Areas Population Totals and Components of Change: 2020–2021. United States Census Bureau; 2022. Accessed January 12, 2023. https://www.census.gov/data/tables/time-series/demo/popest/2020s-total-metro-and-micro-statistical-areas.html [Google Scholar]

[pone.0301125.ref022] 22.Rural-Urban Commuting Area Codes. Published online August 17, 2020. https://www.ers.usda.gov/data-products/rural-urban-commuting-area-codes.aspx

[pone.0301125.ref023] 23.American Psychiatric Association, ed. Diagnostic and Statistical Manual of Mental Disorders: DSM-5-TR. Fifth edition, text revision. American Psychiatric Association Publishing; 2022. [Google Scholar]

[pone.0301125.ref024] 24.Wicke FS, Krakau L, Löwe B, Beutel ME, Brähler E. Update of the standardization of the Patient Health Questionnaire-4 (PHQ-4) in the general population. Journal of Affective Disorders. 2022;312:310–314. doi: 10.1016/j.jad.2022.06.054 [DOI] [PubMed] [Google Scholar]

[pone.0301125.ref025] 25.Kerper LF, Spies CD, Tillinger J, et al. Screening for Depression, Anxiety, and General Psychological Distress in Pre-operative Surgical Patients: A Psychometric Analysis of the Patient Health Questionnaire 4 (Phq-4). clinhp. 2014;4(1):5–14. doi: 10.29102/clinhp.14002 [DOI] [Google Scholar]

[pone.0301125.ref026] 26.Kroenke K, Spitzer RL, Williams JBW. The PHQ-9: Validity of a brief depression severity measure. J Gen Intern Med. 2001;16(9):606–613. doi: 10.1046/j.1525-1497.2001.016009606.x [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0301125.ref027] 27.Spitzer RL, Kroenke K, Williams JBW, Löwe B. A Brief Measure for Assessing Generalized Anxiety Disorder: The GAD-7. Arch Intern Med. 2006;166(10):1092. doi: 10.1001/archinte.166.10.1092 [DOI] [PubMed] [Google Scholar]

[pone.0301125.ref028] 28.Karami N, Kazeminia M, Karami A, Salimi Y, Ziapour A, Janjani P. Global prevalence of depression, anxiety, and stress in cardiac patients: A systematic review and meta-analysis. Journal of Affective Disorders. 2023;324:175–189. doi: 10.1016/j.jad.2022.12.055 [DOI] [PubMed] [Google Scholar]

[pone.0301125.ref029] 29.Riedl D, Schuessler G. Prevalence of Depression and Cancer–A systematic review. Zeitschrift für Psychosomatische Medizin und Psychotherapie. 2022;68(1):74–86. doi: 10.13109/zptm.2021.67.oa11 [DOI] [PubMed] [Google Scholar]

[pone.0301125.ref030] 30.Wang Y, Lopez JMS, Bolge SC, Zhu VJ, Stang PE. Depression among people with type 2 diabetes mellitus, US National Health and Nutrition Examination Survey (NHANES), 2005–2012. BMC Psychiatry. 2016;16:88. doi: 10.1186/s12888-016-0800-2 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0301125.ref031] 31.Fiest KM, Hitchon CA, Bernstein CN, et al. Systematic Review and Meta-analysis of Interventions for Depression and Anxiety in Persons With Rheumatoid Arthritis. J Clin Rheumatol. 2017;23(8):425–434. doi: 10.1097/RHU.0000000000000489 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0301125.ref032] 32.Moore N, Koenig N, Shakiba K, Heidbrink J, Guinn M. Increasing Medicare Annual Wellness Visits in Accountable Care Organizations. Am J Accountable Care. 2021;9(3):13–18. doi: 10.37765/ajac.2021.88749 [DOI] [Google Scholar]

[pone.0301125.ref033] 33.U.S. Health Ressources and Services Administration, Bureau of Health Workforce. Scoring Shortage Designations. Accessed December 27, 2023. https://bhw.hrsa.gov/workforce-shortage-areas/shortage-designation/scoring [Google Scholar]

[pone.0301125.ref034] 34.Kessler RC, Birnbaum H, Bromet E, Hwang I, Sampson N, Shahly V. Age differences in major depression: results from the National Comorbidity Survey Replication (NCS-R). Psychol Med. 2010;40(2):225–237. doi: 10.1017/S0033291709990213 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0301125.ref035] 35.Blackstone SR, Sebring AN, Allen C, Tan JS, Compton R. Improving Depression Screening in Primary Care: A Quality Improvement Initiative. J Community Health. 2022;47(3):400–407. doi: 10.1007/s10900-022-01068-6 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0301125.ref036] 36.Tadmon D, Bearman PS. Differential spatial-social accessibility to mental health care and suicide. Proc Natl Acad Sci USA. 2023;120(19):e2301304120. doi: 10.1073/pnas.2301304120 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0301125.ref037] 37.Leigh H, Stewart D, Mallios R. Mental health and psychiatry training in primary care residency programs. General Hospital Psychiatry. 2006;28(3):195–204. doi: 10.1016/j.genhosppsych.2005.10.004 [DOI] [PubMed] [Google Scholar]

[pone.0301125.ref038] 38.Hill SK, Cantrell P, Edwards J, Dalton W. Factors Influencing Mental Health Screening and Treatment Among Women in a Rural South Central Appalachian Primary Care Clinic: Mental Health Screening in Rural Primary Care. The Journal of Rural Health. 2016;32(1):82–91. doi: 10.1111/jrh.12134 [DOI] [PubMed] [Google Scholar]

[pone.0301125.ref039] 39.Stilwell K, Pelkey L, Platt T, et al. Survey of Primary Care Provider Comfort in Treating Psychiatric Patients in 2 Community Clinics: A Pilot Study. Prim Care Companion CNS Disord. 2022;24(1). doi: 10.4088/PCC.21m03020 [DOI] [PubMed] [Google Scholar]

[pone.0301125.ref040] 40.Zuckerbrot RA, Cheung A, Jensen PS, et al. Guidelines for Adolescent Depression in Primary Care (GLAD-PC): Part I. Practice Preparation, Identification, Assessment, and Initial Management. Pediatrics. 2018;141(3):e20174081. doi: 10.1542/peds.2017-4081 [DOI] [PubMed] [Google Scholar]

[pone.0301125.ref041] 41.Payne JS. Influence of Race and Symptom Expression on Clinicians’ Depressive Disorder Identification in African American Men. Journal of the Society for Social Work and Research. 2012;3(3):162–177. doi: 10.5243/jsswr.2012.11 [DOI] [Google Scholar]

[pone.0301125.ref042] 42.Olbert CM, Nagendra A, Buck B. Meta-analysis of Black vs. White racial disparity in schizophrenia diagnosis in the United States: Do structured assessments attenuate racial disparities? Journal of Abnormal Psychology. 2018;127(1):104–115. doi: 10.1037/abn0000309 [DOI] [PubMed] [Google Scholar]

[pone.0301125.ref043] 43.Müller F, Abdelnour AM, Rutaremara DN, et al. Mental Health Screening Differences in Non-English Speaking Patients: Results From a Retrospective Cohort Study. J Prim Care Community Health. 2023;14:21501319231200304. doi: 10.1177/21501319231200304 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0301125.ref044] 44.Lindsay M, Decker VB. Improving Depression Screening in Primary Care. J Doct Nurs Pract. Published online May 3, 2022:JDNP-2021-0005.R1. doi: 10.1891/JDNP.2021-0005 [DOI] [PubMed] [Google Scholar]

[pone.0301125.ref045] 45.Firth J, Siddiqi N, Koyanagi A, et al. The Lancet Psychiatry Commission: a blueprint for protecting physical health in people with mental illness. The Lancet Psychiatry. 2019;6(8):675–712. doi: 10.1016/S2215-0366(19)30132-4 [DOI] [PubMed] [Google Scholar]

PERMALINK

Association between sociodemographic factors, clinic characteristics and mental health screening rates in primary care

Frank Müller

Alyssa M Abdelnour

Diana N Rutaremara

Judith E Arnetz

Eric D Achtyes

Omayma Alshaarawy

Harland T Holman

Roles

Abstract

Background

Design

Results

Discussion

Introduction

Methods

Setting

Data sample

Outcome and covariates

Statistical analyses

Results

Fig 1. Flow chart of patient inclusion.

Table 1. Sociodemographics of n = 126,306 patients.

Fig 2. Unadjusted 14-months mental health screening prevalence with corresponding 95% CI.

Table 2. Results of binary logistic regression models (n = 124,698 patients).

Discussion

Limitations

Conclusions

Supporting information

Data Availability

Funding Statement

References

Decision Letter 0

Swaantje Wiarda Casjens

Roles

Transfer Alert

Author response to Decision Letter 0

Decision Letter 1

Swaantje Wiarda Casjens

Roles

Author response to Decision Letter 1

Decision Letter 2

Swaantje Wiarda Casjens

Roles

Acceptance letter

Swaantje Wiarda Casjens

Roles

Associated Data

Supplementary Materials

Data Availability Statement

ACTIONS

PERMALINK

RESOURCES

Similar articles

Cited by other articles

Links to NCBI Databases