Can routine assessment of older people’s mental health lead to improved outcomes: A regression discontinuity analysis

Kalpita Baird; Ailish Byrne; Sarah Cockayne; Rachel Cunningham-Burley; Caroline Fairhurst; Joy Adamson; Wesley Vernon; David J Torgerson; on behalf of the REFORM trial

doi:10.1371/journal.pone.0300651

. 2024 Mar 19;19(3):e0300651. doi: 10.1371/journal.pone.0300651

Can routine assessment of older people’s mental health lead to improved outcomes: A regression discontinuity analysis

Kalpita Baird ^1,^*, Ailish Byrne ¹, Sarah Cockayne ¹, Rachel Cunningham-Burley ¹, Caroline Fairhurst ¹, Joy Adamson ¹, Wesley Vernon ², David J Torgerson ¹; on behalf of the REFORM trial^¶

Editor: Vanessa Carels³

PMCID: PMC10950230 PMID: 38502676

Abstract

Objective

To assess whether case finding for depression among people aged 65 and above improves mental health.

Design

Opportunistic evaluation using a regression discontinuity analysis with data from a randomised controlled trial.

Setting

The REFORM trial, a falls prevention study that recruited patients from NHS podiatry clinics.

Participants

1010 community-dwelling adults over the age of 65 with at least one risk factor for falling (recent previous fall or fear of falling).

Intervention

Letter sent to patient’s General Practitioner if they scored 10 points or above on the 15-item Geriatric Depression Scale (GDS-15) informing them of the patient’s risk of depression.

Main outcome measure

GDS-15 score six months after initial completion of GDS-15.

Results

895 (88.6%) of the 1010 participants randomised into REFORM had a valid baseline and six-month GDS-15 score and were included in this study. The mean GDS-15 baseline score was 3.5 (SD 3.0, median 3.0, range 0–15); 639 (71.4%) scored 0–4, 204 (22.8%) scored 5–9 indicating mild depression, and 52 (5.8%) scored 10 or higher indicating severe depression. At six months follow-up, those scoring 10 points or higher at baseline had, on average, a reduction of 1.08 points on the GDS-15 scale (95% confidence interval -1.83 to -0.33, p = 0.005) compared to those scoring less than 10, using the simplest linear regression model.

Conclusion

Case finding of depression in podiatry patients based on a GDS-15 score of 10 or more followed by a letter to their General Practitioner significantly reduced depression severity. Whether this applies to all older patients in primary care is unknown. Further research is required to confirm these findings. Regression discontinuity analyses could be prespecified and embedded within other existing research studies.

Introduction

Depression is the most prevalent mental health disorder amongst the older population [1]. It is estimated that one in four adults over the age of 65 years suffer from depression [2] but approximately 85% of those suffering do not receive help through the National Health Service (NHS) to manage their condition [3]. This may partially reflect patients’ reluctance to disclose depressive symptoms to healthcare providers–potentially linked to a fear of the stigma surrounding mental health disorders [3]. An additional concern is evidence that General Practitioners (GPs) and those in first line contact with this population are faced with difficulties in identifying depressed patients [4, 5], in part due to the symptoms of depression amongst older adults varying considerably from those displayed by younger adults [6]. This lack of recognition likely contributes to a significant number of depressed patients failing to receive the help they need from healthcare services.

Whilst the United States Preventative Task Force recommends screening for depression in the general adult population [7], routine screening is not currently recommended in the United Kingdom (UK). Rather practitioners are advised to ‘be alert’ to possible depression (particularly in people with a history of depression or a chronic physical health problem) [8]. Furthermore, a large cluster randomised trial evaluating the screening for depression in primary care patients consulting with osteoarthritis found no evidence of a benefit [9].

The benefits of case finding for depression within primary care settings is widely debated [10–12]. There is limited evidence to suggest that patients screened for depression had better outcomes than patients who were not, when the same treatment resources were available to both groups [10]. Whilst screening could potentially identify people who do not recognise that they are experience depressive symptoms, leading to earlier detection, currently there is no strong evidence for routine case finding of depression among the older population [13]. In this paper we report a natural experiment using a regression discontinuity analysis to assess whether case finding of depression in older adults reduces depressive symptoms six months later.

Materials and methods

This study was an unexpected evaluation resulting from the REFORM randomised controlled trial (RCT) for the prevention of falls. REFORM was a multicentre, two-armed RCT that recruited 1010 participants aged 65 and over with a risk factor for falling (recent previous fall or fear of falling) to assess the effectiveness of a podiatry intervention for the prevention of falls in older people [14]. Regulatory approval for the study was obtained from the East of England–Cambridge East Research Ethics Committee (REC) on 9 November 2011 (REC reference number 11/EE/0379). Galway REC approved the study on 26 April 2013 (REC reference number C.A 886). The University of York, Department of Health Sciences Research Governance Committee approved the study on 2 August 2011. Research management and governance approval was obtained for each trust. Participation in the study was voluntary, and all participants gave written informed consent before taking part in the REFORM study. Patients were recruited from podiatry clinic lists and baseline data were collected from them prior to randomisation. Baseline data were collected between November 2012 and November 2014. The primary outcome was the incidence rate of falls over 12 months; however, secondary outcomes included the proportion of participants presenting with depression at six and 12 months post-randomisation. Within the REFORM trial, depression was classified through the use of the 15-item Geriatric Depression Scale (GDS-15) [15] measured at baseline, and again at six and 12 months post-randomisation. The GDS-15 (see S1 Fig) is a popular tool specifically targeted towards detecting depression amongst the older population and is a shortened version of the original 30-item scale [16, 17]. The scale includes items assessing how the responder has felt over the last week, including ‘Are you basically satisfied with your life?’ and ‘Do you feel full of energy?’. A total score of 0–4 is considered normal, 5–9 indicates mild depression, and 10–15 indicates severe depression. The validity of the GDS-15 to identify depression within older cohorts, both in its original 30-item and shortened versions, within the UK and cross-culturally, has been displayed consistently across the literature [18–22]. The GDS-15 displays superior or equal levels of specificity, sensitivity, and appropriateness as several other popular depression scales, including the Beck Depression Inventory, Centre for Epidemiologic Studies Depression Scale, Zung Self-rating Depression Scale, and the Hamilton Rating Scale for Depression [23–27].

Participants in the REFORM trial completed and returned postal questionnaires to the York Trials Unit to collect participant-reported outcomes, including the GDS-15, at baseline, six and 12 months post-randomisation. On return of the questionnaire, a participant’s GDS-15 score was calculated by hand by a trial coordinator. If a participant scored 10 or more, their consent form was reviewed to confirm whether they had given permission for the study team to contact their GP if any concerns were raised about their health during the course of the study. For those who agreed, an ethically approved letter was sent by second-class post to the participant’s GP, detailing the participant’s GDS-15 score, the date the scale was completed, and information about how to interpret the score. Based on this information, the letter recommended that a consultation with the participant be arranged as soon as practically possible to discuss possible treatment options for their depression that may be available, should the participant choose to take up any help. The trial manager’s contact details and web link to the study protocol were provided in case the GP had any queries. This happened at each timepoint, except if the GP had been previously notified about a high GDS-15 score for a participant at an earlier trial assessment time point, they were not contacted again, even if the GDS-15 score was 10 or above on a subsequent assessment. We did not follow up with the patients or GPs regarding what, if any, action or treatment was undertaken as a result of the GP receiving our letter, as this was not related to any objectives of the main REFORM trial.

Study design and statistical methods–Regression discontinuity design

A regression discontinuity design (RDD) is where a score or threshold is given to a continuous assignment variable and used to determine whether an individual is offered an intervention or not [28, 29]. The continuous assignment variable used for this RDD was the participant’s score at baseline on the GDS-15, where the threshold to receive the intervention (GP contacted) was a score of 10 (or more). The RDD considers the change in score six months later. No other interventions were applied on the basis of the participant’s baseline GDS-15 score.

Two versions of RDD exist: ‘sharp’ and ‘fuzzy’. Sharp RDD is deterministic in nature, meaning that all who meet the required threshold or score on the continuous assignment variable are offered the intervention (and those who do not meet the threshold are not); whereas fuzzy RDD is probabilistic, meaning that those who meet the required threshold or score are more likely to be offered the intervention than those who do not [30]. This study used a sharp RDD as all bar one participant who met the required threshold received the intervention (51/52, 98.1%). For the remaining participant, a letter was not sent to their GP as they did not consent to this. Given this only applies to one participant, this participant is included in all statistical analyses assuming that they did receive the intervention.

A limitation of the RDD is a concern around manipulation of the continuous assignment variable e.g. completion of the measure in such a way as to ensure the cut-point is met or just missed, depending on the context [31]. This is of limited concern here as the GDS-15 is a patient-reported outcome measure and participants were not aware, prior to its completion, that their scoring over a particular threshold on the GDS-15 would trigger a notification letter to their GP with recommendations for a follow-up visit. Manipulation of the assignment variable would therefore be unlikely. However, this was assessed visually using a histogram to check for clustering around the cut-point, which might indicate deliberate attempts to reach, or narrowly avoid, the threshold.

In order for the RDD to be valid, certain assumptions must be met [28, 30]. One assumption is that there is continuity in other variables between those who scored slightly above and those who scored slightly below the threshold [30]. This assumption would indicate that participants around the threshold are mainly similar in most other aspects except for the score on the assignment variable and, if met, would allow the estimation of causal effects of the intervention. Continuity amongst other variables around the cut-point was assessed using the methods described by Sood et al. 2014 [32]. The observed characteristics of participants that scored eight or nine and those that scored 10 or 11 (i.e. those falling just either side of the threshold) were compared, using a t-test or chi-squared test, as appropriate, at the 5% significance level.

To conduct the RDD, we considered a parametric approach [31], which uses every observation in the sample. Different functional forms of the regression model were estimated, increasing in complexity: linear, quadratic, cubic, all with and without interaction terms of the GDS-15 at baseline with treatment, and with and without adjusting for further covariates (participant’s age, gender, REFORM trial allocation, and number of prescribed medications at baseline) to improve precision.

The simplest form, the linear regression model without an interaction, is:

y_{i} = α_{0} + α_{1} {A b o v e}_{i} + α_{2} {B a s e l i n e G D S}_{i} + ε_{i}

(1)

Where:

y_i is participant i’s score on the GDS-15 at six months
α₀ is a constant
ε_i is a random error term
Above_i is an indicator variable that is equal to one if participant i’s score on the GDS-15 at baseline is 10 or more, and zero otherwise
BaselineGDS_i is participant i’s score on the GDS-15 at baseline centred at the cut-point of 10.
α₁, α₂ are the regression coefficients.

In this case, α₁ is our estimate of the impact of using the GDS-15 to detect severe depression at baseline on depression severity six-months later.

Final model selection was based on minimising the Akaike’s Information Criterion (AIC) and on the results of an F-test. Whilst AIC can demonstrate whether one model fits the data better than another, it does not give an indication of how well the model fits the data overall. For this reason, the F-test approach as described by Lee and Lemieux [31] was also conducted.

Public patient involvement

There was no public or patient involvement for this analysis; however, the REFORM trial as a whole had a patient reference group, comprising of four people who were representative of trial participants, who gave comments on patient-facing material including the questionnaires containing the GDS-15 instrument.

Results

In total, 895 (88.6%) of the 1010 participants randomised into REFORM had a valid baseline and six-month GDS-15 score and were included in this RDD analysis. Among these 895 participants, the mean GDS-15 baseline score was 3.5 (SD 3.0, median 3.0, range 0–15); 639 (71.4%) scored between 0 and 4, 204 (22.8%) between 5 and 9, and 52 (5.8%) between 10 and 15, indicating severe depression. These 52 participants were recruited from 43 GP surgeries (average 1.2 per surgery, range 1 to 3). At baseline the mean GDS score among those scoring <10 was 3.0 (SD 2.3, median 3) and among those scoring 10 or more was 11.5 (SD 1.4, median 11).

At six months, the overall mean GDS-15 score was 3.7 (SD 3.1, median 3.0, range 0–15); 618 (69.1%) scored between 0 and 4, 221 (24.7%) between 5 and 9, and 56 (6.3%) between 10 and 15. At 6 months the mean GDS score among those scoring <10 was 3.4 (SD 2.8, median 3) and among those scoring 10 or more was 9.4 (SD 2.6, median 9). Of the 52 participants with a score of 10 or more at baseline, 31 (59.6%) were no longer severely depressed at six months, of which 30 were still mildly depressed but one dropped to within the ‘normal’ range.

The correlation between the GDS-15 score at baseline and at six months was 0.76 (95% confidence interval (CI), 0.73 to 0.79)

Manipulation of the continuous assignment variable

Visual inspection of a histogram of the baseline GDS-15 scores for the 895 participants with available data showed a positively skewed distribution and no clear evidence of manipulation around the cut-point of 10 (Fig 1).

Baseline characteristics and testing continuity

Baseline characteristics of the participants by GDS-15 score, are presented in Table 1. There are some notable differences between the two groups. For example, the percentages of those with arthritis and those on four or more medications are higher in the group that scored 10 or more on the GDS-15 than those who scored less than 10.

Table 1. Participant baseline characteristics stratified by GDS-15 score.

		Baseline GDS-15 Score
Characteristic		< 10 (Not Severely Depressed) N = 843	≥ 10 (Severely Depressed) N = 52	Total N = 895
Age (years), mean (SD)		77.7 (7.0)	76.0 (7.3)	77.6 (7.1)
Gender, n (%)	Male	328 (38.9)	25 (48.1)	353 (39.4)
	Female	515 (61.1)	27 (51.9)	542 (60.6)
Ethnicity, n (%)	White	836 (99.2)	51 (98.1)	887 (99.1)
	Other	3 (0.3)	1 (1.9)	4 (0.4)
REFORM Treatment	Usual Care	435 (51.6)	26 (50.0)	461 (51.5)
Group, n (%)	Intervention	408 (48.4)	26 (50.0)	434 (48.5)
Comorbidity ^† , n (%)	Arthritis	477 (56.6)	40 (76.9)	517 (57.8)
	Depression	61 (7.2)	21 (40.4)	82 (9.2%)
	Diabetes	268 (31.8)	26 (50.0)	294 (32.8)
	Dizziness/Vertigo	147 (17.4)	22 (42.3)	169 (18.9)
	Meniere’s Disease	25 (3.0)	5 (9.6)	30 (3.4)
	Numbness in feet	132 (15.7)	15 (28.8)	147 (16.4)
	Osteoporosis	124 (14.7)	7 (13.5)	131 (14.6)
Body Mass Index, mean (SD)		27.6 (5.1)	30.4 (8.0)	27.8 (5.4)
≥4 Medications, n (%)		496 (58.8)	45 (86.5)	541 (60.4)
I tend to bounce back	Not true at all	9 (1.1)	2 (3.8)	11 (1.2)
after illness or	Rarely true	11 (1.3)	8 (15.4)	19 (2.1)
hardship, n(%)	Sometimes true	115 (3.6)	19 (36.5)	134 (15.0)
	Often true	248 (29.4)	17 (32.7)	265 (29.6)
	True nearly all of the time	456 (54.1)	5 (9.6)	461 (51.5)
I am able to adapt to	Not true at all	10 (1.2)	2 (3.8)	12 (1.3)
change, n (%)	Rarely true	20 (2.4)	5 (9.6)	25 (2.8)
	Sometimes true	162 (19.2)	22 (42.3)	184 (20.6)
	Often true	255 (30.2)	20 (38.5)	275 (30.7)
	True nearly all of the time	393 (46.6)	2 (3.8)	395 (44.1)
EQ-5D-5L score, mean (SD)		0.7 (0.2)	0.4 (0.3)	0.7 (0.2)
Post-16 Education, n (%)		485 (57.5)	26 (50.0)	511 (57.1)
Degree or Equivalent, n (%)		318 (37.7)	15 (28.8)	333 (37.2)
Living	Live alone	361 (42.8)	26 (50.0)	387 (43.2)
Arrangements ^† , n (%)	Live with a partner	433 (51.4)	24 (46.2)	457 (51.1)
	Live with a friend/relative	44 (5.2)	1 (1.9)	45 (5.0)
	Sheltered accommodation	25 (3.0)	3 (5.8)	27 (3.1)

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^† Not mutually exclusive, participants asked to cross all that apply.

Continuity was evaluated by comparing participants who scored slightly below the threshold with those who scored slightly above it (Table 2). The majority of characteristics were balanced and continuous around the continuous assignment variable cut point. There are a few exceptions such as the difference between groups in the number of participants taking four or more prescribed medications at baseline and EQ-5D-5L score. However, the validity of the regression discontinuity design still holds as we might expect one or two significant results just by chance given the number of comparisons tested.

Table 2. Comparison of characteristics to test balance and continuity between participants who scored slightly above and below the continuous assignment variable threshold.

		Baseline GDS-15 Score
Characteristic		8–9 N = 44	10–11 N = 27	P-value
Age (years), mean (SD)		78.2 (7.7)	77.4 (7.8)	0.66
Gender ^† , n (%)	Male	21 (47.7)	11 (40.7)	0.57
	Female	23 (52.3)	16 (59.3)	0.57
Ethnicity ^† , n (%)	White	44 (100.0)	27 (100.0)	N/A
REFORM Treatment	Usual Care	18 (40.9)	12 (44.4)	0.77
Group ^† , n (%)	Intervention	26 (59.1)	15 (55.6)	0.77
Comorbidity ^† , n (%)	Arthritis	26 (59.1)	20 (74.1)	0.20
	Depression	10 (22.7)	11 (40.7)	0.11
	Diabetes	14 (31.8)	12 (44.4)	0.28
	Dizziness/Vertigo	16 (36.4)	12 (44.4)	0.50
	Meniere’s Disease	2 (4.6)	2 (7.4)	0.61
	Numbness in feet	8 (18.2)	9 (33.3)	0.15
	Osteoporosis	9 (20.5)	6 (22.2)	0.86
Body Mass Index, mean (SD)		29.9 (5.9)	29.7 (8.7)	0.91
≥4 Medications ^† , n (%)		30 (68.2)	24 (88.9)	0.05
I tend to bounce back	Not true at all	0.0 (0.0)	0.0 (0.0)	0.33
after illness or	Rarely true	3 (6.8)	5 (18.5)
hardship ^† , n(%)	Sometimes true	15 (34.1)	9 (33.3)
	Often true	17 (38.6)	10 (37.0)
	True nearly all of the time	8 (18.2)	2 (7.4)
I am able to adapt to	Not true at all	2 (4.6)	0 (0.0)	0.12
change ^† , n (%)	Rarely true	3 (6.8)	4 (14.8)
	Sometimes true	21 (47.7)	9 (33.3)
	Often true	11 (25.0)	12 (44.4)
	True nearly all of the time	7 (15.9)	1 (3.7)
EQ-5D-5L score, mean (SD)		0.6 (0.2)	0.4 (0.3)	0.03
Post-16 Education ^† , n (%)		22 (50.0)	13 (48.1)	0.87
Degree or Equivalent ^† , n (%)		14 (31.8)	10 (37.0)	0.57
Living	Live alone	20 (45.5)	16 (59.3)	0.26
Arrangements ^† , n (%)	Live with a partner	21 (47.7)	10 (37.0)	0.38
	Live with a friend/relative	3 (6.8)	0 (0.0)	0.17
	Sheltered accommodation	2 (4.6)	1 (3.7)	0.86

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† Chi-squared test used; t-test used for all other comparisons. Highlighted rows indicate statistical differences at the 5% significance level.

Regression discontinuity analysis results

Several functional forms of the regression model were estimated, starting with the simplest linear regression model, which adjusted only for the treatment (GP contacted) and GDS-15 score at baseline and increasing in complexity.

The linear regression model with no interaction term, and cubic regression model with an interaction term, produced the lowest AIC value (with and without covariates) (Table 3).

Table 3. Different functional forms which were estimated, alongside the treatment estimate and AIC values.

Functional form	Treatment estimate	SE, 95% CI	p-value	AIC
No covariates
Linear	-1.08	0.38 (-1.83, -0.33)	0.005	3785.7
Linear interaction	-0.84	0.46 (-1.74, 0.08)	0.072	3786.8
Quadratic	-0.67	0.59 (-1.83, 0.48)	0.255	3786.8
Quadratic interaction	-0.52	0.63 (-1.76, 0.72)	0.411	3790.1
Cubic	-0.58	0.60 (-1.75, 0.60)	0.334	3788.1
Cubic interaction	1.08	0.91 (-0.71, 2.87)	0.237	3785.5
With covariates
Linear	-1.02	0.38 (-1.77, -0.27)	0.008	3772.2
Linear interaction	-0.83	0.47 (-1.75, 0.08)	0.074	3773.7
Quadratic	-0.71	0.59 (-1.87, 0.45)	0.230	3773.7
Quadratic interaction	-0.54	0.64 (-1.80, 0.70)	0.389	3777.0
Cubic	-0.63	0.60 (-1.81, 0.55)	0.294	3775.2
Cubic interaction	0.99	0.92 (-0.80, 2.79)	0.277	3772.8

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Again, starting with the simplest linear regression model, an F-test was calculated and was not found to be statistically significant (p = 0.40). This suggests that the simple linear regression model adequately depicts the relationship between the outcome and rating variables and therefore can serve as an appropriate choice for the RDD estimation model as opposed to a more complex model.

Fig 2 shows a scatter plot of participants’ GDS-15 score at six months against their GDS-15 score at baseline with the fitted regression line from the selected model. There is a noticeable drop in the regression line at the cut-point, with those scoring 10 or higher on the GDS-15 at baseline scoring less on the GDS-15 at six months than expected, with a clear discontinuity present. The regression coefficient of interest is -1.08 (95% CI -1.83 to -0.33, p = 0.005), which indicates that participants who scored 10 or more and whose GP was subsequently contacted had a just over one-point reduction in depression severity six months later.

The robustness of the linear regression model and sensitivity of the treatment estimate was assessed by dropping the outermost 1% of the data and fitting the linear regression model again. When discarding the outermost 1%, the treatment estimate becomes -1.23 (SE 0.43, 95% CI -2.09 to -0.38), an absolute change of 0.15.

Discussion

Depression among older people is a large source of morbidity in the community. There has been no large RCT to test the effectiveness of primary care case finding among the general population of older people in the UK. In this study we undertook a regression discontinuity analysis of people aged 65 years and above who were registered with local NHS podiatry services and had at least one risk factor for falling. As part of the REFORM trial we gave participants the GDS-15 to complete at baseline and six months. At baseline, 29% of participants had GDS-15 scores indicating they were at least mildly depressed, with 6% exhibiting symptoms of severe depression (score of 10 or more). These figures are in line with national findings that around a quarter of older people suffer from depression [2]. For ethical reasons we contacted the GPs of all participants who scored 10 points or above on the GDS-15 at enrolment to the REFORM trial. Following completion of the study, we realised that these data were appropriate to conduct a retrospective regression discontinuity analysis, and observed that, after six months, this intervention resulted in a significant improvement in the GDS-15 score, suggesting that a form of routine screening using the GDS-15 might be effective at reducing the severity of depression in older people.

These findings differ from those of a recent cluster RCT in the US to determine the effect of case-finding for depression, which was unable to demonstrate any benefit [33]. Thirteen primary care medical clinics were randomised to intervention and control. As in our study, the GDS-15 was used to measure depressive symptoms and, for patients in the intervention group, their physician was informed if they scored over a certain threshold. The threshold was set at six, which was lower than our threshold, and different interventions were recommended to be offered to people scoring 6–10 and those scoring 11–15. There was no statistically significant difference in mean GDS-15 score at the two year follow-up.

Strengths and limitations

There are a number of limitations to this study. First, all of our participants had podiatry needs and were at high risk of falling and so we cannot be sure our results will generalise to all people aged over 65 living in the community. Another limitation is that we are unsure exactly what, if any, intervention GPs gave to patients that led to an improvement in care, or what the time interval was between baseline and intervention, as these data were not collected. We assumed that GPs followed best professional practice, which may have included a consultation with the participant to discuss their symptoms and subsequent management of their depression with medication and/or psychological therapy, but in any future study it would be important to get a detailed description of what treatments were offered to (and accepted by) patients and how long this took.

Further there are limitations to the analysis design. The RDD is a quasi-experimental design and, in lacking random assignment as in an RCT, is not as powerful a design to investigate the effectiveness of an intervention. This was an opportunistic analysis, embedded within an RCT; therefore the sample size was constrained and not calculated specifically for this analysis. Generally, to answer the same research question, an RDD requires a much larger sample size than an RCT, at least three to five times more participants [34, 35]. Also, there is the possibility that the main trial REFORM intervention could also influence the GDS outcome here; however, in theory, within each group of the RDD (those who score <10 and those who score 10+), there will be an equal distribution of intervention and control participants for the main trial through randomisation. Therefore, any impact of the REFORM intervention will be equal across the RDD groups and so we can still investigate whether the GP intervention has an impact over and above any effect of the REFORM intervention. We adjusted for REFORM trial allocation in the analysis models to account for this. There was an approximately 1.1 point GDS-15 score reduction at the cut-point and whether this is clinically meaningful is uncertain; however, previous studies have utilised a similar (1.2 point) change in the GDS-15 as a clinically important difference [36]. The parametric approach to a regression discontinuity design requires the selection of a model that best fits the data. Different techniques to select the final model may give different results. Here, when we considered the lowest AIC, there was little difference between the linear regression and cubic regression with an interaction; however, these gave treatment effects that were qualitatively different, similar in magnitude but in the opposite direction. When the final model is specified, one sensitivity check that is suggested includes repeating the analysis with the outermost 1%, 5% and 10% data points excluded. However given the small sample size here and that only 6% of participants scored above the cut-point, we were restricted on the robustness checks that could be carried out. The treatment estimate when the outermost 1% of the data are dropped increased in magnitude, which may cast further doubt that the functional form (the simplest linear regression model) is correctly specified.

On the other hand, a strength of the regression discontinuity design is the way in which a treatment effect can be visualised. The discontinuity or drop in the regression line makes it clear if a treatment effect is or is not present. In addition, it allows the intervention to be provided to all those who most require it and thus, in some cases, provides a more ethically justifiable alternative to an RCT; some patients who would receive the intervention in an RDD would be denied it in an RCT due to random allocation.

Another strength of the study is the generalisability to a wide number of GP surgeries. The 52 participants with a GDS-15 baseline score of 10 or more were recruited from 43 GP surgeries, which suggests that the effect, if true, is not driven by the management strategies of a ‘few’ GPs and is likely to be representative of the impact of typical GP care.

In summary, a serendipitous evaluation suggests that routine case finding for depression with the GDS-15 may improve the mental wellbeing of older people. Given the promise indicated from such a simple intervention, the feasibility, acceptability, effectiveness, and cost-effectiveness of a well-designed case-finding approach is worthy of further exploration. In addition, our exemplar demonstrates how a regression discontinuity analysis can be conducted retrospectively or prospectively embedded in other studies to answer a further research question, or at least to generate plausible future research hypotheses.

Supporting information

S1 Fig. Geriatric depression scale (15-items).

(DOCX)

pone.0300651.s001.docx^{(14.8KB, docx)}

Acknowledgments

We thank the REFORM trial participants for their participation in the REFORM trial and providing outcome data.

^Members of the ‘REFORM trial’ author group: Sarah Cockayne, Caroline Fairhurst, Joy Adamson, David Torgerson, Sara Rodgers, Arabella Scantlebury, Belen Corbacho Martin, Catherine Hewitt, Kate Hicks, Zoe Richardson, Judith Watson (York Trials Unit, Department of Health Sciences, University of York, York, UK), Wesley Vernon (University of Huddersfield, Huddersfield, UK), Lorraine Green, Anne Maree Keenan, Anthony C Redmond (Leeds Institute of Rheumatic and Musculoskeletal Medicine, Faculty of Medicine and Health, University of Leeds, Leeds, UK, and National Institute for Health Research (NIHR) Leeds Musculoskeletal Biomedical Research Unit, Leeds Teaching Hospitals NHS Trust, Leeds, UK), Robin Hull (Podiatry Department, Harrogate and District NHS Foundation Trust, Harrogate, UK), Sarah E Lamb (Oxford NIHR Biomedical Research Unit, Oxford, UK), Caroline McIntosh (Discipline of Podiatric Medicine, National University of Ireland Galway, Galway, Ireland), and Hylton B Menz (School of Allied Health, College of Science, Health and Engineering, La Trobe University, Melbourne, VIC, Australia).

Sarah Cockayne is the lead author of the REFORM trial group (sarah.cockayne@york.ac.uk).

Data Availability

"There are ethical restrictions on sharing a de-identified data set; consent to share anonymous data from the study was not obtained from participants. Data are available upon reasonable request. Data requests, with statistical analysis plans, can be sent to the Department of Health Sciences (DOHS), University of York [dohs-stats@york.ac.uk] or to the corresponding author, and will be considered on a case-by-case basis. All data requests will be managed in accordance with York Trials Unit, University of York, standard operating procedures."

Funding Statement

The author(s)s received no specific funding for this work.

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PLoS One. doi: 10.1371/journal.pone.0300651.r001

Decision Letter 0

Alexandra Schaefer

27 Mar 2023

PONE-D-23-04252

Can routine assessment of older people’s mental health lead to improved outcomes: A regression discontinuity analysis

PLOS ONE

Dear Dr. Baird,

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"The author(s)s received no specific funding for this work. However, the original REFORM trial was funded by the National Institute for Health Research (NIHR) Health Technology Assessment (HTA) Programme (Programme grant number 09/77/01). "

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"I have read the journal's policy and the authors of this manuscript have the following competing interests: SC, CF, JA, WV, and DT received funding from the National Institute for Health Research (NIHR) Health Technology Assessment (HTA) paid to their employer, University of York, for the REFORM trial. All other authors declare no competing interests."

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Additional Editor Comments:

The reviewers feel that the manuscript is well-written, but needs to undergo minor revisions. For example, they suggest that you state your hypothesis and/or research questions in the introduction section and provide a more comprehensive discussion of the importance/benefit of screening for depression in primary care settings. Please find the detailed comments appended below.

[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: Yes

Reviewer #2: Yes

Reviewer #3: Yes

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

Reviewer #1: Yes

Reviewer #2: Yes

Reviewer #3: Yes

**********

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

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Reviewer #1: Yes

Reviewer #2: No

Reviewer #3: No

**********

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

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Reviewer #1: Yes

Reviewer #2: Yes

Reviewer #3: 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: Important note: This review pertains only to ‘statistical aspects’ of the study and so ‘clinical aspects’ [like medical importance, relevance of the study, ‘clinical significance and implication(s)’ of the whole study, etc.] are to be evaluated [should be assessed] separately/independently. Further please note that any ‘statistical review’ is generally done under the assumption that (such) study specific methodological [as well as execution] issues are perfectly taken care of by the investigator(s). This review is not an exception to that and so does not cover clinical aspects {however, seldom comments are made only if those issues are intimately / scientifically related & intermingle with ‘statistical aspects’ of the study}. Agreed that ‘statistical methods’ are used as just tools here, however, they are vital part of methodology [and so should be given due importance]. I look at the manuscript in/with statistical view point, other reviewer(s) look(s) at it with different angle so that in totality the review is very comprehensive. However, there should be efforts from authors side to improve (may be by taking clues from reviewer’s comments). Therefore, please do not limit the revision only (with respect) to comments made here.

COMMENTS: Although this manuscript is well drafted [and the study is excellent with respect to most of the aspects], I have few observations/concerns (different opinion) which are given below:

Firstly, {although on one hand I must congratulate and appreciate authors having used this design} it may please be noted that the RD (regression discontinuity) design lacks the random assignment feature. In addition to this, there are several issues* that disallow a status equal to the randomized experiment and the RD design has to be classified as a quasi-experimental design.

Few of the issues described in literature are:

- it is a new design and not yet as clear as the randomized experiment.

- the RD design heavily depends on correctly modeling the functional form of the relationship between the assignment variable and the outcome variable. Therefore, in order to obtain unbiased estimates, more sophisticated statistical analyses are required than is usual in experimental settings.

- the RD design has less power than the randomized experiment. This is mainly due to multicollinearity between the assignment and treatment variable. The loss of power is greater the more the cutoff deviates from the mean of the assignment variable. When nonlinear effects (e.g., interaction and quadratic effects) are present, multicollinearity increases as well as reduces the power of the design. In order to increase the power to 0.80, the RD design needs up to 3 times as many participants as a randomized experiment

Though sometimes the argument(s) made that “from a methodological point of view, inferences which are drawn from a well-implemented RD design are comparable in internal validity to conclusions from randomized experiments” it is not very convincing. It is often said that, in principle the RD design is as strong in internal validity as its randomized experimental alternatives, however, in practice, the validity of the RD design depends directly on how well the analyst can model the true pre-post relationship, certainly a nontrivial statistical problem.

The real allure of the RD Design is that it allows us to assign the treatment or program to those who most need or deserve it. Thus, the real attractiveness of the design is ethical – we don’t have to deny the program or treatment to participants who might need it as we do in randomized studies.

With this background information I have, few questions are raised in mind [though they may appear to be silly on prima-facie, you need to clarify these things because mind you that this is a scientific/academic document and so all details should be clearly/correctly communicated (do not take readers’ for granted)].

• As per line 26 [Design] data are from a randomised controlled trial. My question is {though in lines 142-143 it is stated that adjustment for covariates included that for participant’s REFORM trial allocation}, participant’s those who are randomly allocated to intervention arm of REFORM trial are already influenced/affected by that intervention [those who are randomly allocated to control arm (even if active control or placebo) of REFORM trial are already influenced/affected differently] then will it likely to affect your outcome.

• There may be substantial period between ‘baseline’ reading time and your intervention. What about that?

Section ‘Study Design and Statistical Methods’ in lines 106 onwards is fairly well drafted but authors may think including above (and/or other such) points. I agree with the ‘Limitations’ part highlighted/enumerated in lines 260 onwards [but not so much with ‘Strengths’ part of the section].

Except these minor points, the article is acceptable. However, mind you that as pointed out in ‘important note’ above “This review pertains only to ‘statistical aspects’ of the study and so ‘clinical aspects’ should be assessed separately/independently.

Reviewer #2: The study is well structured and described in the manuscript, and I think it brings important and insightful contributions regarding ways to improve mental health assistance to the older population. However, I made some suggestions and recommendations detailed in the manuscript's comment boxes, attached to this review. Basically, I suggest the authors (1) to state their hypothesis and/or research questions in the introduction; (2) to justify why they chose to run the sharp version of RDD; (3) to describe other instruments and questionnaires used along with the GDS-15 to collect data, especially those described in the results; (4) to revise Table 1, there are many points to pay attention. I recommend running statistical analyses to check for significant differences between groups in the results in Table 1. There is no information in methods about the instruments displayed in Table 1, so the reader can't interpret appropriately the results. I recommend you remove this data from the table or describe the instruments in the methods and how we interpret them. The sum of the cases for gender in Table 1 needs to be revised, it does not match. Please, specify what BMI means; (5) In Table 2 you need to highlight the cells of "during the past 4 weeks, have you worried about a fall" to indicate there was a significant difference between groups; (6) The results must be interpreted taking into account the sociodemographic background of the sample, and I think that authors should go deeper in this matter in the discussion

Reviewer #3: I found the paper to be very interesting. The authors adequately described and analysed their findings. While the effect of the intervention (1.1-point reduction) may not be clinically significant, the authors have provided appropriate context and reported on the uncertainty of it in the discussion section. The paper is somewhat concise, it is well written. I do have a few suggestions for the authors:

(Introduction) It would be helpful to provide a more comprehensive discussion on the importance/benefits of screening for depression in primary care settings.

(Methods/Results) It is not clear when the data were collected, which could be clarified in the methods or results section.

(Methods) It would be informative to include data from the second follow-up assessment (12 months) to evaluate the long-term effects of the intervention.

(Results) It would be helpful to report means and medians of GDS-15 scores in both groups (<10/10+) at baseline and 6 months to provide readers with a better understanding of the true effect of the intervention.

(Results) Including information about depression treatment, if available, would be useful. If such information is not available, the authors should acknowledge this as a limitation of their study.

**********

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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: Yes: Heloisa Goncalves Ferreira

Reviewer #3: No

**********

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Attachment

Submitted filename: renamed_7798e.docx

pone.0300651.s002.docx^{(17.8KB, docx)}

Attachment

Submitted filename: PONE-D-23-04252_reviewer.docx

pone.0300651.s003.docx^{(223KB, docx)}

PLoS One. 2024 Mar 19;19(3):e0300651. doi: 10.1371/journal.pone.0300651.r002

Author response to Decision Letter 0

20 Oct 2023

Dear Sir/Madam,

We previously submitted our manuscript titled “Can routine assessment of older people’s mental health lead to improved outcomes: A regression discontinuity analysis” [PONE-D-23-04252]. We thank the Academic Editor and reviewers for their comments and feedback and have addressed each of the points raised in turn below, as well as in the manuscript.

Response to Reviewers

Rebuttal Letter

Comments raised by the Academic Editor

Formatting Requirements

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

Thank you, we have made sure the formatting of the manuscript complies with these templates.

Role of Funder Statement

Our previous financial disclosure statement read as follows: “The author(s) received no specific funding for this work. However, the original REFORM trial was funded by the National Institute for Health Research (NIHR) Health Technology Assessment (HTA) Programme (Programme grant number 09/77/01).”

To this should be added the following: “The funders had no role in study design, data collection, and analysis, decision to publish, or preparation of the manuscript.”

Competing Interests Statement

Our previous Competing Interests statement read as follows: “I have read the journals’ policy and the authors of this manuscript have the following competing interests: SC, CF, JA, WV and DT received funding from the National Institute for Health Research (NIHR) Health Technology Assessment (HTA) paid to their employer, University of York, for the REFORM trial. All other authors declare no competing interests.”

This has now been amended to: “I have read the journal’s policy and the authors of this manuscript have the following competing interests: SC, CF, JA, WV and DT received funding from the National Institute for Health and Care Research (NIHR, previously just called the National Institute for Health Research) Health Technology Assessment (HTA) paid to their employer, University of York, for the REFORM trial. All other authors declare no competing interests. This does not alter our adherence to PLOS ONE policies on sharing data and materials.”

Data Availability Statement

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 this study there are ethical restrictions on sharing a de-identified data set. Consent to share anonymous data from the study was not obtained from participants. It is therefore not possible to openly share the data with other researchers via a public repository, and data are only available upon request. Data requests are to be sent to dohs-stats@york.ac.uk.

Acknowledgements

One of the noted authors is a group or consortium “REFORM trial’. In addition to naming the author group, please list the individual authors and affiliations within this group in the acknowledgements section of your manuscript. Please also indicate clearly a lead author for this group along with a contact email address.

Thank you, we have added the extra authors from the REFORM trial group (who are not named authors of this manuscript) to the Acknowledgements and indicated who the lead author for this group is.

Ethics Statement

Please include your full ethics statement in the ‘methods’ section of your manuscript file. In your statement, please include the full name of the IRB or ethics committee who approved or waived your study, as well as whether or not you obtained informed written or verbal consent. If consent was waived for your study, please include this information in your statement as well.

We have added the following statement to the ‘methods’ section of the manuscript:

Regulatory approval for the study was obtained from the East of England – Cambridge East Research Ethics Committee (REC) on 9 November 2011 (REC reference number 11/EE/0379). Galway REC approved the study on 26 April 2013 (REC reference number C.A 886). The University of York, Department of Health Sciences Research Governance Committee approved the study on 2 August 2011. Research management and governance approval was obtained for each trust. Participation in the study was voluntary, and all participants gave written informed consent before taking part in the REFORM study.

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We have indicated captions for this Supporting Information file at the end of the manuscript and updated the in-text citation from Appendix A to S1 Fig.

References

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.

We have noticed that in our previous manuscript the following reference contained a broken link. This link has now been updated in our manuscript.

Age, U., Hidden in plain sight: The unmet mental health needs of older people. Age UK, London, available at: www. ageuk. org. uk/brandpartnerglobal/wiganboroughvpp/hidden_in_plain_sight_older_ peoples_mental_health. pdf (accessed 19 February 2019), 2016.

We have noticed that since our earlier submission the guidelines in the following reference have been updated and have updated the reference in our manuscript to reflect the most recent guidelines.

Excellence, N.I.f.C., Depression in adults: Recognition and management. Clinical guideline [CG90]. 2009, NICE London.

We have also included new references in our introduction in response to the feedback from the reviewers. These are as follows:

10. Thombs, B.D., et al., Rethinking recommendations for screening for depression in primary care. Canadian Medical Association Journal, 2012. 184(4): p. 413-418.

11. Kessler, D., D. Sharp, and G. Lewis, Screening for depression in primary care. British Journal of General Practice, 2005. 55(518): p. 659-660.

12. Thombs, B.D. and R.C. Ziegelstein, Does depression screening improve depression outcomes in primary care? BMJ : British Medical Journal, 2014. 348: p. g1253.

Final comments raised by the Academic Editor

The reviewers feel that the manuscript is well-written but needs to undergo minor revisions. For example, they suggest that you state your hypothesis and/or research question in the introduction section and provide a more comprehensive discussion of the importance/benefit of screening for depression in primary care settings.

Thank you, we have responded to each of the reviewer’s comments in turn below.

Comments raised by Reviewer #1

This review pertains only to ‘statistical aspects’ of the study and so ‘clinical aspects’ [like medical importance, relevance of the study, clinical significance and implication(s) of the whole study, etc] are to evaluated [should be assessed] separately/independently.

Further please note that any ‘statistical review’ is generally done under the assumption that (such) study specific methodological [as well as execution] issues are perfectly taken care of by the investigator(s). This review is not an exception to that and so does not cover clinical aspects [however, seldom comments are made only if those issues are intimately/scientifically related & intermingle with ‘statistical aspects’ of the study]

Agreed that ‘statistical methods’ are used as just tools here, however, they are vital part of methodology [and so should be given due importance]. I look at the manuscript in/with statistical view point, other reviewer(s) look(s) at it with different angle so that in totality the review if very comprehensive. However, there should be efforts from the authors side to improve (maybe by taking clues from reviewer’s comments). Therefore, please do not limit the revision only (with respect) to comments made here.

Although this manuscript is well drafted [and the study is excellent with respect to most of the aspects], I have a few observations/concerns (different opinion) which are given below:

Firstly, (although on one hand I must congratulate and appreciate authors having used this design) it may please be noted that the RD (regression discontinuity) design lacks the random assignment feature. In addition to this, there are several issues that disallow a status equal to the randomised experiment and the RD design has to be classified as a quasi-experimental design.

Few of the issues described in literature are:

• It is a new design and not yet as clear as the randomized experiment

• The RD design heavily depends on correctly modeling the functional form of the relationship between the assignment variable and the outcome variable. Therefore, in order to obtain unbiased estimates, more sophisticated statistical analyses are required than is usual in experimental settings.

• The RD design has less power than the randomized experiment. This is mainly due to multicollinearity between the assignment and treatment variable. The loss of power is greater the more the cutoff deviates from the mean of the assignment variable. When nonlinear effects (e.g. interaction and quadratic effects) are present, multicollinearity increases as well as reduces the power of the design. In order to increase the power to 0.80, the RD design needs up to 3 times as many participants as a randomised experiment

Though sometimes the argument(s) made that “from a methodological point of view, inferences which are drawn from a well-implemented RD design are comparable in internal validity to conclusions from randomised experiments” it is not very convincing. It is often said that, in principle the RD design is as strong in internal validity as its randomised experimental alternatives, however, in practice, the validity of the RDD depends directly on how well the analyst can model the true pre-post relationship, certainly a non-trivial statistical problem.

We acknowledge these limitations of the RDD relative to an RCT and have added the following to the Discussion:

“The RDD is a quasi-experimental design and, in lacking random assignment as in an RCT, is not as powerful a design to investigate the effectiveness of an intervention.”

We already acknowledged that the RDD relies on specifying the correct functional form for the data, and that a greater sample size is required than for an RCT.

The real allure of the RDD is that it allows us to assign the treatment or program to those who most need or deserve it. Thus, the real attractiveness of the design is ethical - we don’t have to deny the program or treatment to participants who might need it as we do in randomized studies.

Thank you, we agree this is a strength of the RDD over an RCT and have added the following to the Discussion:

“In addition, it allows the intervention to be provided to all those who most require it and thus, in some cases, provides a more ethically justifiable alternative to an RCT; some patients who would receive the intervention in an RDD would be denied it in an RCT due to random allocation.”

As per line 26 [Design] data are from a randomised controlled trial. My question is {though in lines 142-143 it is stated that adjustment for covariates included that for participant’s REFORM trial allocation}, participant’s those who are randomly allocated to intervention arm of REFORM trial are already influenced/affected by that intervention [those who are randomly allocated to control arm (even if active control or placebo) of REFORM trial are already influenced/affected differently] then will it likely to affect your outcome.

We agree but, in theory, within each group of the RDD (those who score <10 and those who score 10+), there will be an equal distribution of intervention and control participants for the main trial. Therefore, any impact of the main trial intervention will be equal across the RDD groups and so we can still investigate whether the GP intervention has an impact over and above any effect of the REFORM intervention. As the researcher points out, we did specifically include main trial allocation as a covariate in the analysis models to adjust for this effect.

We have added this point to the Discussion.

There may be substantial period between ‘baseline’ reading time and your intervention. What about that?

We acknowledge this is a limitation and have added to the Discussion:

“Another limitation is that we are unsure exactly what, if any, intervention GPs gave to patients that led to an improvement in care, or what the time interval was between baseline and intervention, as these data were not collected. We assumed that GPs followed best professional practice, which may have included a consultation with the participant to discuss their symptoms and subsequent management of their depression with medication and/or psychological therapy, but in any future study it would be important to get a detailed description of what treatments were offered to (and accepted by) patients and how long this took. ”

Section ‘Study Design and Statistical Methods’ in lines 106 onwards is fairly well drafted but authors may think including above (and/or other such) points. I agree with the ‘limitations’ part highlighted/enumerated in lines 260 onwards [ but not so much the ‘Strengths’ part of the section’]

Please see responses above.

Except these minor points, the article is acceptable. However, mind you that as pointed out above ‘This review pertains only to statistical aspects’ of the study and so clinical aspects should be assessed separately/independently.

We thank the reviewer for their helpful comments.

Comments raised by Reviewer #2

The study is well structured and described in the manuscript, and I think it brings important and insightful contributions regarding ways to improve mental health assistance to the older population. However, I made some suggestions and recommendations detailed in the manuscript’s comment boxed, attached to this review. Basically, I suggest the authors:

To state their hypothesis and/or research questions in the introduction

Our research question was whether case finding of depression in older adults reduces depressive symptoms six months later. This is stated at the end of the introduction:

“In this paper we report a natural experiment using a regression discontinuity analysis to assess whether case finding of depression in older adults reduces depressive symptoms six months later.”

To justify why they chose to run the sharp version of the RDD

We ran the sharp version of the RDD as all participants bar one received the intervention. The choice of the sharp version of the RDD reflects that the intervention was deterministic, rather than probabilistic in nature. We did not feel it was necessary to run a fuzzy version of the RDD just as one patient did not receive the intervention.

To describe other instruments and questionnaires used along with the GDS-15 to collect data, especially those described in the results

Upon reflection, we have decided to streamline Tables 1 and 2, by removing data on the Short Falls Efficacy Scale, Frenchay Activities Index, referral to a falls clinic, data on falls in the last six months, worries over having a fall over the last four weeks, broken bones in the last 12 months, current use of an insole and orthotic and modifications to shoes as these outcomes were more pertinent to the main REFORM trial.

To revise Table 1, there are many points to pay attention. I recommend running statistical analyses to check for significant differences between groups in the results in Table 1.

We visually checked for notable differences between the groups presented in Table 1 and have commented on these. We have used statistical tests to check for significant differences between groups around the cutpoint in Table 2 as this is a recommended part of an RDD analysis.

B. There is no information in the methods about the instruments displayed in Table 1, so the reader can’t interpret appropriately the results. I recommend you remove this data from the table or describe the instruments in the methods and how we interpret them.

See previous comment and response

C. The sum of the cases for gender in Table 1 needs to be revised, it does not match.

This has been revised now.

D. Please specify what BMI means

Changed to Body Mass Index

In Table 2 you need to highlight the cells of ‘during the past 4 weeks, have you worried about a fall” to indicate there was a significant difference between groups

The p-value for this variable is 0.09 and therefore is not significant at the 5% level and remains unhighlighted in Table 2. We have reiterated this in the footnote to Table 2.

The results must be interpreted taking into account the sociodemographic background of the sample and I think that authors should go deeper in this matter in the discussion

In the original REFORM trial very little sociodemographic data were collected. Whilst it would have been possible to have used postcode to calculate indices of deprivation, we no longer hold this data, as the standard operating procedures at the point of when the trial was set up, allowed for storage of personal data for five years. This time point has now passed and data relating to postcode have therefore been destroyed.

All other data collected from/about the participants is summarised in the Tables provided.

Comments raised by Reviewer #3

I found the paper to be very interesting. The authors adequately described and analysed their findings. Whilst the effect of the intervention (-1.1 point reduction) may not be clinically significant , the authors have provided appropriate context and reported on the uncertainty of it in the discussion section. The paper is somewhat concise, it is well written. I do have a few suggestions for the authors:

(Introduction) It would be helpful to provide a more comprehensive discussion on the importance/benefits of screening for depression in primary care settings

Literature suggests that screening for depression in primary care settings is a highly debatable issue, with limited evidence on the importance and/or benefits of screening for depression in primary care settings. This has been amended and in our introduction, we now say:

The benefits of case finding for depression within primary care settings is widely debated. There is limited evidence to suggest that patients screened for depression had better outcomes than patients who were not, when the same treatment resources were available to both groups. Whilst screening could potentially identify people who do not recognise that they are experiencing depressive symptoms, leading to earlier detection, currently there is no strong evidence for routine case finding of depression among the older population. In this paper we report a natural experiment using a regression discontinuity analysis to assess whether case finding of depression in older adults reduces depressive symptoms six months later.

(Methods/results) it is not clear when the data were collected, which could be clarified in the methods or results section

Thank you, we have clarified that baseline data were collected between November 2012 and November 2014.

(Methods) it would be informative to include data from the second follow-up assessment (12-months) to evaluate the long-term effects of the intervention

After consideration, we have not included the 12 month GDS scores as these data would be difficult to interpret. This is because we repeated the intervention at 6 months. Therefore, for any participant who scored over the cutpoint at 6 months who had scored below it at baseline, their GP was contacted. Therefore, a proportion of the original ‘control’ group at baseline would have received the intervention 6 months later, which would impact the long-term effect of the original intervention.

(Results) it would be helpful to report means and medians of GDS-15 scores in both groups (<10/10+) at baseline and 6 months to provide readers with a better understanding of the true effect of the intervention.

Thank you, we have added these statistics to the Results section of the manuscript.

(Results) including information about depression treatment, if available, would be useful. If such information is not available, the authors should acknowledge this as a limitation of their study.

Data on the action/treatment undertaken in response to the GP receiving our letter were not collected in the REFORM trial as this was not an objective or outcome of the main trial. We already acknowledge this as a limitation in the Discussion, but have made this explicit in the Methods section now:

“We did not follow up with the patients or GPs regarding what, if any, action or treatment was undertaken as a result of the GP receiving our letter, as this was not related to any objectives of the main REFORM trial.”

In the Discussion we now say:

We resubmit our manuscript (within the six months of the decision date) having addressed these comments and very much look forward to receiving your feedback.

Kind regards,

Kalpita Baird

Attachment

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PLoS One. doi: 10.1371/journal.pone.0300651.r003

Decision Letter 1

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1 Mar 2024

Can routine assessment of older people’s mental health lead to improved outcomes: A regression discontinuity analysis

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PLoS One. doi: 10.1371/journal.pone.0300651.r004

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8 Mar 2024

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S1 Fig. Geriatric depression scale (15-items).

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Data Availability Statement

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PERMALINK

Can routine assessment of older people’s mental health lead to improved outcomes: A regression discontinuity analysis

Kalpita Baird

Ailish Byrne

Sarah Cockayne

Rachel Cunningham-Burley

Caroline Fairhurst

Joy Adamson

Wesley Vernon

David J Torgerson

Roles

Abstract

Objective

Design

Setting

Participants

Intervention

Main outcome measure

Results

Conclusion

Introduction

Materials and methods

Study design and statistical methods–Regression discontinuity design

Public patient involvement

Results

Manipulation of the continuous assignment variable

Fig 1. Histogram of GDS-15 scores during baseline data collection.

Baseline characteristics and testing continuity

Table 1. Participant baseline characteristics stratified by GDS-15 score.

Table 2. Comparison of characteristics to test balance and continuity between participants who scored slightly above and below the continuous assignment variable threshold.

Regression discontinuity analysis results

Table 3. Different functional forms which were estimated, alongside the treatment estimate and AIC values.

Fig 2. Scatter plots of participant’s GDS-15 score at six-months against their GDS-15 score at baseline, with the fitted values of the selected linear regression model.

Discussion

Strengths and limitations

Supporting information

Acknowledgments

Data Availability

Funding Statement

References

Decision Letter 0

Alexandra Schaefer

Roles

Author response to Decision Letter 0

Decision Letter 1

Vanessa Carels

Roles

Acceptance letter

Vanessa Carels

Roles

Associated Data

Supplementary Materials

Data Availability Statement

ACTIONS

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RESOURCES

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