How do multi-morbidity and polypharmacy affect general practice attendance and referral rates? A retrospective analysis of consultations.

BACKGROUND: As prevalence of multimorbidity and polypharmacy rise, health care systems must respond to these challenges. Data is needed from general practice regarding the impact of age, number of chronic illnesses and medications on specific metrics of healthcare utilisation.
METHODS: This was a retrospective study of general practices in a university-affiliated education and research network, consisting of 72 practices. Records from a random sample of 100 patients aged 50 years and over who attended each participating practice in the previous two years were analysed. Through manual record searching, data were collected on patient demographics, number of chronic illnesses and medications, numbers of attendances to the general practitioner (GP), practice nurse, home visits and referrals to a hospital doctor. Attendance and referral rates were expressed per person-years for each demographic variable and the ratio of attendance to referral rate was also calculated.
RESULTS: Of the 72 practices invited to participate, 68 (94%) accepted, providing complete data on a total of 6603 patients' records and 89,667 consultations with the GP or practice nurse; 50.1% of patients had been referred to hospital in the previous two years. The attendance rate to general practice was 4.94 per person per year and the referral rate to the hospital was 0.6 per person per year, giving a ratio of over eight attendances for every referral. Increasing age, number of chronic illnesses and number of medications were associated with increased attendance rates to the GP and practice nurse and home visits but did not significantly increase the ratio of attendance to referral rate. DISCUSSION: As age, morbidity and number of medications rise, so too do all types of consultations in general practice. However, the rate of referral remains relatively stable. General practice must be supported to provide person centred care to an ageing population with rising rates of multi-morbidity and polypharmacy.

Background

Increasing age, multi-morbidity and polypharmacy present important challenges to health care systems and general practice has a front-line role in their management [1, 2]. However, general practice in Britain has been described as being ‘in crisis’ for several years due to extremely serious challenges with recruitment and workload [3, 4]. In England, a recent funding strategy will support Primary Care Networks, whereby practices work collaboratively within a locality with a population health agenda, and it remains to be seen if this can successfully alleviate the pressure on this key component of the health service [5]. Studies of databases generated from millions of general practitioner (GP) consultations have shown significant increases in consultation rates and subsequent workload as well as increased complexity [6, 7]. This rise in general practice healthcare utilisation has not been matched by a corresponding growth in the workforce capacity [8], and research findings with GP participants warned about both patient and doctor safety in this context [9]. The burden of workload and symptoms of burnout among GPs have been reported in Ireland [10] and other European countries [11], with one study reporting an association between GP burnout and the prevalence of multi-morbidity in the practice [12].

Multi-morbidity, defined as having at least two chronic diseases [13], is present in over one quarter of adults registered at general practices (and is present in a much higher proportion of those attending general practice) and is associated with higher attendance at general practice and hospital [14]. Multi-morbidity is strongly associated with increasing age and with populations in western societies becoming older, the prevalence of multi-morbidity is rising and this trend can be expected to continue [15, 16]. Similarly, polypharmacy, which is defined as taking five or more regular medications [17], is associated with chronic illness and multi-morbidity [18], older age [19] and increased health care utilisation [20]. While specialists provide disease specific management and prescribe system specific medications, GPs co-ordinate the care, medication prescribing and follow up of their patients. In many systems, such as Ireland and Britain, GPs act as gatekeepers to the hospital system, assessing and managing untriaged and undifferentiated presentations and initiating referrals to the hospital system. Much of the work involved in managing polypharmacy and multi-morbidity takes place in general practice [8]. It is very important for healthcare planning that this work is reported in a clear way that illustrates the amount and type of activity that is conducted as well as the rate of referral to hospital doctors.

While several largescale studies have reported on health care utilization in general practice and referral rates to hospital doctors, there has been criticism of the methods used. Researchers have reported that dependency on certain coding systems may have missed out on un-coded illness and that the inclusion criteria for chronic illness was, in some instances, too narrow in its scope and thus failed to include all chronic conditions [14]. Therefore, the manual searching of patient records to investigate practice network or national databases is considered to be preferable as they overcome the limitations of coding and facilitate a deeper insight into the notes to determine patient factors and how they impacted management [21]. Conversely, this method may not account for attendances to emergency departments that did not involve a GP referral, but many of these visits may be recorded in the notes when the relevant hospital discharge letter is filed. Furthermore, the reporting in other studies has failed to distinguish between nurse and GP consultations as well as in-practice consultations and home visits [22], the latter being an important but time-consuming task for GPs [23]. The role of the practice nurse does not involve making referrals to hospitals [24], and it is, therefore, important to capture data on who exactly in the practice the patient attended when calculating referral rates. Precise data on the work taking place in general practice is important to know to understand the role of general practice in the health service. This study aimed to provide comprehensive data on healthcare utilisation in general practice and referrals to hospital. Specific objectives were: to determine the impact of age, chronic illness and number of regularly prescribed medications on consultation rates with the GP, practice nurse and on home visits and referral rates to hospital.

Materials and methods

Study population

This study was a retrospective analysis of consultations and was granted full ethical approval by the Irish College of General Practitioners Research Ethics Committee (ICGP, 09/05/2015). As per the ethics application, all data were fully anonymised before leaving the practice and before being accessed for analysis. Individual informed consent was not deemed necessary by the ethics committee. The study took place over a two-year period in general practices associated with the University of Limerick Education and Research Network for General Practice (ULEARN-GP) [25]. All 72 practices in the network with a student on placement in 2015/16 were invited to participate. At the time of the study, the network covered three of Ireland’s four health regions, and was representative of the national demographic in terms of size, personnel, urban-rural mix, age and socio-economic profiles [25, 26]. Participating practices were asked to use practice software to extract a random sample of 100 patients aged 50 years and older that had attended the practice at least once in the previous two years. This study was part of a larger investigation of processes of care and communication between general practice and hospitals [27]. Senior medical students on placement in the practices in conjunction with their GP supervisors were trained by a faculty team on how to select the sample, and how to search their medical records for the relevant data.

Data collection

After appropriate training, data were extracted, anonymised, coded and entered onto a Microsoft Excel document by each student for each patient selected in the sample. Students were taught by faculty how to use practice software to extract a list of patients over 50 years of age who had attended the practice in the previous two years. They were also shown how to use a randomisation function on the software to extract 100 patients for inclusion in the study. For each patient record, entries for a two-year period extending from 1st September 2013 to 31st August 2015 were analysed. Medical records were searched for the presence of chronic illness through disease coding, free text entry or documentation in expert reports from hospital or consultation records. The number of chronic illnesses and number of regularly prescribed medications was recorded. Chronic illness was defined as a long-term medical condition that cannot be completely cured by medicines; a list of chronic illnesses compiled by the clinicians on the research team and based on a list utilised by a national longitudinal study [28] was provided for the students (S1 Table). Demographic data was collected on each patient, including gender, age, and eligibility for a General Medical Services (GMS) card. This card is given on a means tested basis to individuals and families with lower incomes and at the time of the study approximately 43% of the population were eligible for a GMS card [29]. The income thresholds are higher for those aged over 70.

Health care utilisation data recorded included: number of visits to the GP, number of practice nurse visits, number of home visits, number of referrals to hospital doctors, including Emergency Department, specialist outpatients and injury assessment units. Referrals for radiology and other diagnostic procedures that did not involve a consultation with a hospital doctor were recorded separately.

Data analysis

Data were coded onto Microsoft Excel spreadsheets in each practice and only completely anonymised data was submitted by the practices to the research team for analysis. Only data that had complete demographic details were included in the analysis. Data was described using counts and percentages for categorical variables; mean (standard deviation) for normally distributed numeric variables; and median (interquartile range) for skewed distributions. Pearson’s correlation coefficient was used to measure the strength of the association between numeric variables. Attendance and referral rates were presented by calculating the rate per person-year with 95% confidence intervals for each demographic variable separately. The ratio of attendance to the GP and hospital referral was calculated. A chi-square test was used to test the association between categorical variables. A significance level of 5% was used for all tests. The strength of the association was measured using Cramer’s V with a value of < 0.2 considered weak, 0.2 to 0.6 considered moderate and >0.6 considered a strong association [30]. Statistical analysis was conducted using IBM SPSS version 26.

Results

Practice and patient characteristics and referral rates

Sixty-eight (94%) of the 72 practices that were invited agreed to participate in the study, yielding a total of 6800 patients’ records to be evaluated. Of these, 197 records (3%) were excluded as insufficient demographic data was recorded. Data over the two-year study period was available for analysis in 6603 records (13,206 person-years). Over half the patients (57%) were eligible for a medical card and eligibility increased with age (88% of those aged 70 and over). Approximately half (52%) were female, and the median age was 63 (IQR 56–72) years.

All of the practices were mixed public-private, were computerised and had a practice nurse. Table 1 compares the profile of participating practices to the national profile in 2015 [26]. Most of the study practices (93%) had a co-operative system of out-of-hours cover, similar to the national profile (92%). Participating practices had higher percentages involved in postgraduate GP training (43% v 22%) and rural location (37% v 21%). In terms of practice size, 68% had between 500 and 1999 patients; 16% were single-handed practices, 31% had two GPs, 24% had three GPs and 29% had four or more GPs.

Table 1

Comparison of 2015 national profile to practice profile.

	National, 2015 [26]	ULEARN-GP participating practices, 2015
Number of practices	462	68
Practice type
Mixed GMS and private	89%	100%
Private only	11%	0%
GMS list size
<500	18%	16%
500–1999	75%	68%
>2000	7%	16%
Practice location
Rural	21%	37%
Urban	42%	28%
Mixed	37%	35%
Premises
Purpose-built	54%	35%
Adapted/ other	46%	65%
Practice operation
Computerisation	94%	100%
Out of hours
Internal rota	1%	0
External rota	6%	8%
Co-operative	93%	92%
Practice staff
Single-handed GP	18%	16%
Practice nurse	82%	100%
Education
Involved in post-graduate training	22%	43%

The median number of chronic conditions was 1 (IQR 0–2) and the median number of medications was 3 (IQR 1–7). Age was positively correlated with the number of chronic diseases (r = 0.37, p<0.001) and the number of medications (r = 0.46, p<0.001). The number of chronic illnesses and the number of medications were strongly positively correlated (r = 0.67, p<0.001). The prevalence of multi-morbidity was 38% and the prevalence of polypharmacy was 39%. In the previous two years, 3310 (50%) had been referred to hospital at least once. Likelihood of referral increased with age eligibility for a GMS card, number of chronic diseases and number of prescribed medications (Table 2). The strongest associations with being referred were with number of chronic diseases and number of prescribed medications (Cramer’s V > 0.2, Table 2) and the highest proportion of those with any referral was for patients with five or more prescribed medications (66%).

Table 2

Characteristics of patients by referral status.

Characteristics	No referral to hospital n = 3310 (50%)	At least one referral to hospital n = 3293 (50%)	p-value (Cramer’s V)
Gender			0.02 (0.03)
Female	1659 (48.7%)	1746 (51.3%)
Male	1603 (51.7%)	1495 (48.3%)
Age group			<0.001 (0.13)
50–59	1428 (57.0%)	1078 (32.7%)
60–69	1006 (50.5%)	985 (49.5%)
70–79	570 (41.9%)	791 (58.1%)
≥ 80	306 (41.1%)	439 (58.9%)
GMS eligibility		`	<0.001 (0.16)
Eligible	1645 (43.4%)	2146 (56.6%)
Non-eligible	1665 (59.3%)	1145 (40.7%)
Number of chronic illnesses			<0.001 (0.23)
None	1555 (63.8%)	882 (36.2%)
One	843 (50.1%)	841 (49.9%)
Two or more	912 (36.7%)	1571 (63.3%
Number of regularly prescribed medications			<0.001 (0.29)
None	1087 (72.7%)	408 (27.3%)
1–4	1347 (52.6%)	1216 (47.4%)
Five or more	876 (34.4%)	1669 (65.6%)

Consultation data and referral rates

A total of 89,667 practice consultations were recorded over two years for the 6603 patients. Of these, 1253 (1.4%) were home visits by the GP and 23,110 (26%) were attendances to the practice nurse. There were 65,304 attendances to the GP in 13,206 person-years which gives a rate of 4.94 per person-year (95% confidence interval 4.91 to 4.98) i.e. a patient aged 50 years or over, on average attended the GP five times per year. There were 7,859 hospital referrals, giving a referral rate of 0.60 per person-year (95% confidence interval 0.58 to 0.61) i.e. a patient over 50 on average was referred less than once a year to a hospital. The consultation rate of 4.94 to the GP is over eight times the referral rate of 0.60 in these patients aged 50 or over. Table 3 summarises number of attendances and referrals by age group, gender, GMS eligibility, number of chronic conditions and prescribed medications. Females attended the GP more than males, had over double the rate of home visits and had a higher referral rate. The ratio of GP attendance to referral rate was, however, similar for both males and females. Patients eligible for a GMS card had higher rates of GP and nurse attendance, home visits and hospital referral rates which may reflect their older age profile.

Table 3

Attendances and referrals by gender, GMS eligibility, age group, number of chronic illnesses and prescribed medications.

	GP attendances per person-year (95% CI)	Home visits per person-year (95% CI)	Nurse attendances per person-year (95% CI)	Hospital referrals per person-year (95% CI)	Ratio of GP attendances/ referral
Gender
Male (n = 3098)	4.58 (4.53, 4.64)	0.06 (0.05, 0.07)	1.78 (1.75, 1.82)	0.53 (0.51, 0.55)	8.6
Female (n = 3405)	5.30 (5.24, 5.35)	0.13 (0.12, 0.14)	1.72 (1.69, 1.75)	0.64 (0.62, 0.66)	8.3
GMS eligibility
Eligible (n = 3791)	6.57 (6.52, 6.63)	0.15 (0.14, 0.15)	2.45 (2.42, 2.49)	0.74 (0.72, 0.76)	8.9
Non-eligible (n = 2810)	2.74 (2.70, 2.79)	0.03 (0.02, 0.03)	0.81 (0.78, 0.83)	0.40 (0.38, 0.41)	6.9
Age group
50–59 (n = 2506)	3.59 (3.54, 3.64)	0.01 (0.01, 0.02)	1.00 (0.97, 1.03)	0.45 (0.43, 0.47)	8.0
60–69 (n = 1991)	4.55 (4.48, 4.61)	0.04 (0.03, 0.04)	1.56 (1.52, 1.60)	0.59 (0.57, 0.62)	7.7
70–79 (n = 1361)	6.44 (6.34, 6.54)	0.11 (0.10, 0.12)	2.49 (2.44, 2.55)	0.74 (0.71, 0.78)	8.7
≥ 80 (n = 745)	7.85 (7.70, 7.99)	0.50 (0.46, 0.53)	3.41 (3.32, 3.50)	0.82 (0.78, 0.87)	9.6
Number of chronic illnesses
None (n = 2437)	2.70 (2.65, 2.75)	0.03 (0.02, 0.03)	0.73 (0.71, 0.75)	0.37 (0.35, 0.38)	7.3
One (n = 1684)	4.52 (4.45, 4.59)	0.07 (0.06, 0.08)	1.38 (1.34, 1.42)	0.54 (0.52, 0.57)	8.4
Two or more (n = 2482)	7.44 (7.36, 7.52)	0.18 (0.17, 0.19)	3.00 (2.95, 3.05)	0.86 (0.83, 0.88)	8.7
Number of prescribed medications
None (n = 1495)	1.74 (1.69, 1.78)	0.01 (0.01, 0.02)	0.42 (0.40, 0.45)	0.21 (0.20, 0.23)	8.3
1–4 (n = 2563)	4.00 (3.95, 4.06)	0.03 (0.03, 0.04)	1.28 (1.25, 1.32)	0.49 (0.47, 0.51)	8.2
Five or more (n = 2545)	7.77 (7.70, 7.85)	0.21 (0.20, 0.22)	3.00 (2.95, 3.05)	0.93 (0.90, 0.95)	8.4

Table 3 and Fig 1 illustrate that with each 10-year increase in age, the rates of attendance to the GP and practice nurse increase. The ratio of GP attendances to hospital referrals was 8.0 for patients aged 50–59 and 9.6 for patients aged 80 years and over, indicating slightly more GP attendances per referral as age increases.

Fig 1

Attendances and referrals by age group.

Fig 2 categorises the number chronic illnesses from none through to six or more. The attendance rates to the nurse and GP rise with each additional chronic illness but the ratio of GP attendance to referral rate remains relatively stable, e.g. 8.4 for patients with one chronic illness and 8.7 for patients with six or more chronic illnesses.

Fig 2

Attendances and referrals by number of chronic illnesses.

The number of regularly prescribed medications is associated with increased GP and nurse attendances and Fig 3 illustrates a sharp increase in both attendance rates with five or more medications. The ratio of GP attendance to hospital referral remains relatively stable at 8.3 in a patient prescribed no medication and 8.4 for a patient prescribed five or more medications.

Fig 3

Attendances and referrals by number of prescribed medications.

Discussion

Summary of main findings

This paper has captured one of the unique capabilities of general practice–its ability to deal with increasing age and medical complexity of patients without relying on more referrals to hospital specialists. The study was set in 68 general practices across the Republic of Ireland and involved an analysis of the medical records of 6800 patients aged 50 years and over, yielding data on a total of 89,667 practice consultations. It included over 20,000 practice nurse visits and over 1,000 home visits by GPs. The rates of GP attendance and referral to hospital were reported per person-year and were 5 and 0.6 respectively. This meant that for one referral to the hospital there were eight attendances to the GP, excluding consultations that took place in patients’ homes and practice nurse consultations.

Approximately one half of the study population was referred to hospital doctors at least once in the two-year time period. The strongest associations with any referral were with number of chronic diseases and number of prescribed medications. Increasing age, number of chronic illnesses and number of medications was associated with increased attendances to the GP and practice nurse as well as the number of home visits. However, the ratio of attendance to the GP and referral to hospital remained relatively stable between 6.9 and 9.6. These figures indicate that much of the management of older patients, patients with considerable multimorbidity or polypharmacy are managed in general practice and that much of the workload associated with additional morbidity and medication is conducted by the general practice team.

Comparison to the literature

The GP consultation rate of five per person per year was very similar to that reported in Britain as were the higher attendance rates with increasing age and among those with higher numbers of chronic illnesses [2]. The relatively stable ratio of attendances to hospital referrals reported in our study is similar to previous research on the impact multimorbidity on healthcare utilisation conducted in Ireland [31]. As these studies reported all primary care consultations, including practice nurse and GP visits, as a single entity we cannot make direct comparisons on attendances other than the shared conclusion that as morbidity increased so too did attendance to general practice in each study [22, 31].

An international study across 16 countries found that as the number of chronic illnesses increased so too did the use of primary and secondary health care utilisation [32]. The methodology used a self-report survey and so cannot be directly compared to our study. It also took place in countries with different type of health care system structures with varying degrees of gatekeeping, meaning that GPs would not necessarily decide on the secondary care pathway for patients. Similar to our study, the population was aged 50 years and older, most likely chosen because of their increased susceptibility to chronic disease [33]. Analysis of a large clinical practice research database in England reported higher health care utilisation in general practice, higher number of prescription medications and higher hospitalisations with multi-morbidity [14]. To our knowledge, no study to date has reported the association between polypharmacy, health care utilisation in primary care, and referral to hospital.

The study findings suggest that much of the multi-morbidity is managed in general practice. GPs provide person centred care, through highly developed doctor-patient relationships and continuity of care and GPs are known to provide individually tailored management plans whereby they collaborate with patients to agree on self-management and pharmacological options [34, 35]. However, serious threats to this model exist [36], including the capacity of general practice to continue to absorb the workload involved. These consultations take more time than single complaint presentations [37] and administrative time for medication reviews [34] and these factors must be considered in funding models for general practice. Managing polypharmacy in the context of providing holistic care to each individual when the available guidelines are focussed on single diseases is a challenge for GPs [1, 37]. Most guidelines recommend appropriate management for individual diseases but do not reflect how the presence of other chronic illnesses might affect prescribing decisions. Guidelines that are patient-centred and that are based on chronic disease clusters would be more useful for GPs [38]. It is important also to recognise the value of clinical judgement [39], as the social context, disease presentation and pattern will be different for each patient.

Strengths and limitations

The study involved detailed analysis of patient records, extracting data from letters from hospital consultants, radiology reports and free text entries, in addition to disease coding, thereby providing a detailed picture of the chronic illness status of the study population. This approach of using multiple sources has been considered advantageous rather than relying on adherence to coding by the GPs only. However, as stated, the approach may miss self-presentations to the hospital, especially to emergency departments, and such presentations were not included in this study. The high participation rate (94%) of a representative sample of GP practices contributes to external validity. The breakdown of consultations in general practice into attendance at the GP, attendance at the nurse and home visits gives a detailed picture of the workflow in this setting. Out of hours consultations were not included as there was no consistency across practices on how this information was recorded and stored. Further, information was recorded on reason for referral so that patients referred for radiology and other diagnostics could be differentiated from those referred for assessment by a hospital consultant or team.

Our focus was on older adults with at least one attendance to the practice in the previous two years. We aimed to include active patients in the practice and minimise those who had potentially moved away or changed practices with no recent record of attendance. Our approach, however, may overestimate healthcare utilisation by excluding those with no recent record of attending. An additional limitation was that each practice had a separate data collector. Even though these were all trained for consistency, it is possible this could have led to inter-rater variability in terms of data collection process. Irish general practice software systems are not generally used by other healthcare professionals working in primary care such as physiotherapists, dieticians and healthcare assistants; this study involves GPs and practice nurses only. Further, this study used a definition of multi-morbidity of two or more chronic conditions; those chronic conditions were based on a pre-defined list that was provided for the students and set by the clinicians on the research team, and conditions not on the list were not included in the dataset. Finally, during data collection the number of chronic conditions and medications were recorded for each case but not the name of the chronic condition or medication, and, consequently, the analysis cannot identify which chronic conditions or medications were associated with attendance.

Implications for future research and practice

The authors believe that future research, analysing patient records, should investigate the impact of specific clusters of chronic illness, especially mental illness, on health care utilisation patterns in general practice. Training for GPs focussed on coding chronic illness as well as developments in software to make coding easier or invisible, would improve the accuracy and reach of health services research in general practice. The study was conducted prior to the Covid 19 pandemic and, no doubt, telemedicine, including video consults, telephone consults and consults using other technology to send audio-visual files, will be much more frequent into the future. GPs with the support of software developers should be able to record this workflow in a way that is user-friendly, minimally time consuming and easily extractable for future research. Finally, the workload conducted by reception staff, including phone-calls, triage, advice and information giving must be captured and presented in order to more completely illustrate the entire burden of work complex multimorbidity presents for general practice.

Conclusion

This large-scale study of individual patient records has provided a detailed and precise picture of the quantity and type of work taking place in general practice. We have shown that approximately one half of patients aged 50 years and over who attended the GP in the previous two years were referred to hospital. As age, morbidity and medication numbers rise, so too do all types of consultations in general practice but the ratio of GP attendance to referral rate remains relatively stable, indicating that GPs are managing these patients without increased referral rate per consultation. The implications of this finding are extremely important, as it demonstrates that general practice is bearing most of the burden of increased morbidity and complexity, thereby absorbing excess workload and saving hospital outpatients appointments, emergency department presentations and hospital admissions. Consequently, there are benefits for healthcare economics as well as for the lives of patients who can be managed in the community. General practice must be supported to develop its capacity to provide person centred, individually-tailored care to an ageing population with rising rates of multi-morbidity and polypharmacy.

List of chronic conditions.

(PDF)

Click here for additional data file.

25 Jun 2021

PONE-D-21-15158

How do multi-morbidity and polypharmacy affect general practice attendance and referral rates? A retrospective analysis of consultations.

PLOS ONE

Dear Dr. O'Regan,

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 Aug 09 2021 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:  http://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,

Vijayaprakash Suppiah, PhD

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

and

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

2.  Please provide additional details regarding participant consent. In the ethics statement in the Methods and online submission information, please ensure that you have specified (a) whether consent was informed and (b) what type you obtained (for instance, written or verbal, and if verbal, how it was documented and witnessed). If your study included minors, state whether you obtained consent from parents or guardians. If the need for consent was waived by the ethics committee, please include this information.

If you are reporting a retrospective study of medical records or archived samples, please ensure that you have discussed whether all data were fully anonymized before you accessed them and/or whether the IRB or ethics committee waived the requirement for informed consent. If patients provided informed written consent to have data from their medical records used in research, please include this information.

3. 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 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 identifying or sensitive patient information) 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. Please see http://www.bmj.com/content/340/bmj.c181.long for guidelines on how to de-identify and prepare clinical data for publication. 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.

[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

**********

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

Reviewer #1: Yes

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: Yes

**********

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: Dear Editor,

thank you for asking me to review this paper submission. Comments:

1) Abstract: Methods not clearly described. How practices were in the Research Network (72 practices were invited to participate but were there lots more practices who were not included? If so, how were the 72 selected?). If 68 practices selected, each contributing 100 patients, why isnt the sample 6800 patients? Were findings 'adjusted' or 'unadjusted', e.g. for age, gender, deprivation; it would be helpful to know in the Abstract if this is a univariate or multivariate analysis.

2) Introduction: Reference 5 is cited as evidence of 'the recent funding strategy' for primary care. However, it's worth clarifying that much of this funding in England will be allocated to Primary Care Networks (practices working collaboratively within a locality with a population health agenda) rather than being allocated to individual practices in the traditional way.

3) Introduction: Reference 14 is misquoted. The authors state: 'Multi-morbidity, defined as having at least two chronic diseases (13), is present in over one quarter of adults attending general practice...(14)'. In fact, multimorbidity is present in over a quarter of those REGISTERED at general practices, but account for a much higher proportion of those ATTENDING the practice.

4) Introduction: the authors state: 'Therefore, the manual searching of patient records to investigate practice network or national databases is considered to be preferable as they overcome the limitations of coding...'. This provides a strong justification for this study. Studies of coded data are likely to miss cases of multimorbidity (MM). However, the converse should be acknowledged - that a study purely of primary care data will miss emergency presentations to secondary care, which bypass primary care (although these should be recorded retrospectively in primary care case-notes).

5) Methods: as per the Abstract, it's not clear if the Network of GP practices consisted of more than 72 practices. Nor why <100 cases were recruited in each practice

6) Methods: the authors used Cramer’s V to measure strength of association. This is not a commonly used test. How does it differ from Pearson's r value? My reading is that this study has used univariate parametric correlation coefficients, but I may be mistaken. It is important that the readers know how the data was analysed (univariate presumably, parametric or non-parametric associations?)

7) Results: we need a clear definition of MM. Which MM's were included and which were not? Without that list, it's not possible to make sense of the finding: 'The prevalence of multi-morbidity was 38%...'

8) Results, Figure 1. The authors state: 'Fig 1 illustrates that with each 10-year increase in age, the rates of attendance to the GP and practice nurse increase.' But they haven't demonstrated this, at least not without further Stats analysis. What if the mean attendance rate confidence intervals overlapped for each 10-year attendance rate value? Without CI's or a test of difference, the authors cannot conclude that there is a difference in these rates. The same applies to the data shown in Figures 2&3. This is an essential revision required before publication.

9) Discussion: The authors do have a key finding that is of importance: 'However, the ratio of attendance to the GP and referral to hospital remained relatively stable between 6.9 and 9.6. These figures indicate that much of the management

of older patients, patients with considerable multimorbidity or polypharmacy are managed in general practice.' This is important and needs emphasis.

10) Discussion, Strengths and Limitations: the authors state: ' This approach of using multiple sources has been considered advantageous rather than relying on adherence to coding by the GPs'. I suspect the authors don't have the all-important data which would provide a strong justification for their approach. Namely, how much of the data they found were buried away in un-coded data? That would justify manual data searching as in this study, and as opposed to electronic data searching of coded (no freetext) anonymised databases such as CPRD.

11) Discussion, Strengths and Limitations: there are many limitations which are not mentioned. A key limitation is how MM was classified (what was and what was not an MM). Why weren't Health Care Assistant consultations included or other healthcare providers within primary care (such as dietitians, midwives, physios etc, if present)? Also, as stated above, what about patients accessing hospital care by bypassing their GP, e.g. emergency admissions - was this data retrospectively captured through inclusion of hospital discharge summaries (this isnt clear in the Methods).

only'

Reviewer #2: The study objectively shows what many GPs feel on a daily routine: the workload is increasing, the patient cases are becoming more and more complex. Thus, GPs need new tools to be able to treat patients as efficiently and professionally as possible. In my view, the most important result of their study is the constant rate of referrals to hospital. The protective effect for the clinics should not be underestimated. The results of the study are comprehensible and answer the research question. Essentially, I still miss a more detailed description of the data collected:

What were the characteristics of the participating GP practices? Number of doctors, age, number of patients, etc.

Is there information on the most common diseases or medication groups that led to the definition of multimorbidity and polypharmacy?

In which specialist departments were the patients referred?

Are there diseases that led to particularly frequent referrals - where did outpatient care in GP practices reach its medical limits?

Perhaps additional tables could be used to describe the cohort studied even better and thus make it more comparable for other international 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: Yes: Mark Ashworth

Reviewer #2: Yes: Markus Bleckwenn

[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.

19 Aug 2021

19.08.2021

Dear editor and reviewers,

We are very grateful for your time in reading and reviewing this manuscript. We believe that the suggestions received are valuable and we have responded to each of them in detail below. For each response, the corresponding alteration in the manuscript in quoted, where appropriate.

We would like to point out that this was a retrospective analysis of patient records. Each one of the 72 practices affiliated with the Medical School that had a medical student on placement was invited to participate and 68 of them accepted. Of the 6800 records a small percentage were discounted for not having adequate demographic data.

We had full ethical approval for the study. No anonymised data left the practice at any stage and no approval exists for publishing the dataset.

Statistical queries, including confidence intervals and use of Cramer’s V test, have been addressed.

Finally, the research team has a prior publication from this dataset, which has been referenced, and which gives more detail on the destination of the referrals, including hospital specialty.

We hope that the comments have been addressed to the satisfaction of the reviewers.

Best wishes,

Dr Andrew O’Regan, GP and senior lecturer in general practice, on behalf of the research team

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. Please provide additional details regarding participant consent. In the ethics statement in the Methods and online submission information, please ensure that you have specified (a) whether consent was informed and (b) what type you obtained (for instance, written or verbal, and if verbal, how it was documented and witnessed). If your study included minors, state whether you obtained consent from parents or guardians. If the need for consent was waived by the ethics committee, please include this information.

If you are reporting a retrospective study of medical records or archived samples, please ensure that you have discussed whether all data were fully anonymized before you accessed them and/or whether the IRB or ethics committee waived the requirement for informed consent. If patients provided informed written consent to have data from their medical records used in research, please include this information.

Response of the authors: In the subsection ‘Study Population’ of ‘Materials and Methods’, the authors have clarified as follows:

“This study was a retrospective analysis of consultations and was granted full ethical approval by the Irish College of General Practitioners Research Ethics Committee (ICGP, 09/05/2015). As per the ethics application, all data were fully anonymised before leaving the practice and before being accessed for analysis. Individual informed consent was not deemed necessary by the ethics committee.”

3. 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 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 identifying or sensitive patient information) 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.

Response of the authors: sharing of this dataset is restricted by the Irish College of General Practitioners research ethics committee. Access to the dataset is available upon reasonable request to the Irish College of General Practitioners Research Ethics Committee (research@icgp.ie).

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. Please see http://www.bmj.com/content/340/bmj.c181.long for guidelines on how to de-identify and prepare clinical data for publication. 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.

Response of the authors: same as point a) above.

[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

________________________________________

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

Reviewer #1: Yes

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: Yes

________________________________________

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: Dear Editor,

thank you for asking me to review this paper submission. Comments:

1) Abstract: Methods not clearly described. How practices were in the Research Network (72 practices were invited to participate but were there lots more practices who were not included? If so, how were the 72 selected?). If 68 practices selected, each contributing 100 patients, why isnt the sample 6800 patients? Were findings 'adjusted' or 'unadjusted', e.g. for age, gender, deprivation; it would be helpful to know in the Abstract if this is a univariate or multivariate analysis.

Response of the authors: We have clarified the practice research network query in the abstract and in the main manuscript as follows:

Abstract

“This was a retrospective study of general practices in a university-affiliated education and research network, consisting of 72 practices.”

Main manuscript

“This study was a retrospective analysis of consultations that took place over a two-year period in general practices associated with the University of Limerick Education and Research Network for General Practice (ULEARN-GP) (25). All 72 practices in the network with a student on placement in 2015/16 were invited to participate.”

We have clarified that 100 patients were selected at random from each practice, resulting in a sample of 6800 patients. A few practices did not provide basic demographic data (age, GMS status) on all 100 patients and these patients were excluded (n =197, 3% of sample). With the word count restriction in the Abstract we have clarified this point in the methods section.

Bivariate associations are given in Table 2 and referral rates are provided by each demographic variable separately (age, gender, etc.). We have clarified this in the Abstract and Methods.

2) Introduction: Reference 5 is cited as evidence of 'the recent funding strategy' for primary care. However, it's worth clarifying that much of this funding in England will be allocated to Primary Care Networks (practices working collaboratively within a locality with a population health agenda) rather than being allocated to individual practices in the traditional way.

Response of the authors: the authors thank the reviewer for pointing out this important distinction. We have clarified in the text as follows:

“In England, a recent funding strategy will support Primary Care Networks, whereby practices work collaboratively within a locality with a population health agenda, and it remains to be seen if this can successfully alleviate the pressure on this key component of the health service.”

3) Introduction: Reference 14 is misquoted. The authors state: 'Multi-morbidity, defined as having at least two chronic diseases (13), is present in over one quarter of adults attending general practice...(14)'. In fact, multimorbidity is present in over a quarter of those REGISTERED at general practices, but account for a much higher proportion of those ATTENDING the practice.

Response of the authors: the authors again thank the reviewer for pointing out this important distinction. We have clarified in the text as follows:

“Multi-morbidity, defined as having at least two chronic diseases (13), is present in over one quarter of adults registered at general practices (and is present in a much higher proportion of those attending general practice) and is associated with higher attendance at general practice and hospital (14).”

4) Introduction: the authors state: 'Therefore, the manual searching of patient records to investigate practice network or national databases is considered to be preferable as they overcome the limitations of coding...'. This provides a strong justification for this study. Studies of coded data are likely to miss cases of multimorbidity (MM). However, the converse should be acknowledged - that a study purely of primary care data will miss emergency presentations to secondary care, which bypass primary care (although these should be recorded retrospectively in primary care case-notes).

Response of the authors: we have acknowledged this point in the introduction:

“Conversely, this method may not account for attendances to emergency departments that did not involve a GP referral, but many of these visits may be recorded in the notes when the relevant hospital discharge letter is filed.”

5) Methods: as per the Abstract, it's not clear if the Network of GP practices consisted of more than 72 practices. Nor why <100 cases were recruited in each practice

Response of the authors: we have clarified that all 72 affiliated practices with a student on clinical placement in 2015/16 were invited. We only included cases that had complete demographic data. We have clarified this point in the methods section and again in the results section.

Methods: “Only data that had complete demographic details were included in the analysis.”

Results: “197 records (3%) were excluded as insufficient demographic data was recorded.”

6) Methods: the authors used Cramer’s V to measure strength of association. This is not a commonly used test. How does it differ from Pearson's r value? My reading is that this study has used univariate parametric correlation coefficients, but I may be mistaken. It is important that the readers know how the data was analysed (univariate presumably, parametric or non-parametric associations?)

Cramer’s V is analagous to Pearson’s r but used for categorical variables (all variables in Table 1) instead of the continuous variables needed for Pearson’s r. Cramer’s V is a measure of the strength of the association between two categorical variables, independent of sample size, and values range from 0 to 1. We have now provided a reference for it (Cohen, J. 1988. Statistical Power Analysis for the Behavioral Sciences, 2nd Edition. Routledge).

7) Results: we need a clear definition of MM. Which MM's were included and which were not? Without that list, it's not possible to make sense of the finding: 'The prevalence of multi-morbidity was 38%...'

Response of the authors: we defined multi-morbidity in the introduction as “having at least two chronic illnesses.” [Reference: Le Reste JY, Nabbe P, Manceau B, Lygidakis C, Doerr C, Lingner H, et al.]

Also added to the Methods section: “A list of chronic illnesses, which were defined as a long-term medical condition that cannot be completely cured by medicines, was provided for the students (see supplementary table 1)”. This list is provided now as a supplementary file.

8) Results, Figure 1. The authors state: 'Fig 1 illustrates that with each 10-year increase in age, the rates of attendance to the GP and practice nurse increase.' But they haven't demonstrated this, at least not without further Stats analysis. What if the mean attendance rate confidence intervals overlapped for each 10-year attendance rate value? Without CI's or a test of difference, the authors cannot conclude that there is a difference in these rates. The same applies to the data shown in Figures 2&3. This is an essential revision required before publication.

Response of the authors: We thank the reviewer for this important point. We have now added confidence intervals to all the estimates in Table 2. The confidence intervals are narrow and not overlapping and support the conclusions above.

9) Discussion: The authors do have a key finding that is of importance: 'However, the ratio of attendance to the GP and referral to hospital remained relatively stable between 6.9 and 9.6. These figures indicate that much of the management

of older patients, patients with considerable multimorbidity or polypharmacy are managed in general practice.' This is important and needs emphasis.

Response of the authors: The following has been added under the Conclusions subheading: The implications of this finding are extremely important, as it demonstrates that general practice is bearing most of the burden of increased morbidity and complexity, thereby absorbing excess workload and saving hospital outpatients appointments, emergency department presentations and hospital admissions. Consequently, there are benefits for healthcare economics as well as for the lives of patients who can be managed in the community.

10) Discussion, Strengths and Limitations: the authors state: ' This approach of using multiple sources has been considered advantageous rather than relying on adherence to coding by the GPs'. I suspect the authors don't have the all-important data which would provide a strong justification for their approach. Namely, how much of the data they found were buried away in un-coded data? That would justify manual data searching as in this study, and as opposed to electronic data searching of coded (no freetext) anonymised databases such as CPRD.

Response of the authors: the reviewer is correct on both counts; the proportion of the data found through file-searching of un-coded data would be very helpful and, unfortunately, we did not include the location of recorded information in our protocol.

11) Discussion, Strengths and Limitations: there are many limitations which are not mentioned. A key limitation is how MM was classified (what was and what was not an MM). Why weren't Health Care Assistant consultations included or other healthcare providers within primary care (such as dietitians, midwives, physios etc, if present)? Also, as stated above, what about patients accessing hospital care by bypassing their GP, e.g. emergency admissions - was this data retrospectively captured through inclusion of hospital discharge summaries (this isnt clear in the Methods).

only'

Response of the authors: the authors have added the following sentences to the limitations section to address the points above:

“…the approach may miss self-presentations to the hospital, especially to emergency departments, and such presentations were not included in this study.”

“Irish general practice software systems are not generally used by other healthcare professionals working in primary care such as physiotherapists, dieticians and healthcare assistants; this study involves GPs and practice nurses only. Finally, this study used a definition of multi-morbidity of two or more chronic conditions, based on a pre-defined list that was provided for the students and set by the clinicians involved in the research team, and conditions not on the list were not included in the dataset.”

Reviewer #2: The study objectively shows what many GPs feel on a daily routine: the workload is increasing, the patient cases are becoming more and more complex. Thus, GPs need new tools to be able to treat patients as efficiently and professionally as possible. In my view, the most important result of their study is the constant rate of referrals to hospital. The protective effect for the clinics should not be underestimated. The results of the study are comprehensible and answer the research question. Essentially, I still miss a more detailed description of the data collected:

Response of the authors: we thank the reviewer for positive feedback and for acknowledging the importance of the results in the context of the role of general practice in healthcare systems. Some of the detail is reported in a previous publication emanating from this dataset (Dinsdale, 2021). We will address the specific feedback below.

What were the characteristics of the participating GP practices? Number of doctors, age, number of patients, etc.

Response of the authors: A new table and the following paragraph have been inserted.

All of the practices were mixed public-private, were computerised and had a practice nurse. Table 1 compares the profile of participating practices to the national profile in 2015 (26). Most of the study practices (93%) had a co-operative system of out-of-hours cover, similar to the national profile (92%). Participating practices had higher percentages involved in postgraduate GP training (43% v 22%) and rural location (37% v 21%). In terms of practice size, 68% had between 500 and 1999 patients; 16% were single-handed practices, 31% had two GPs, 24% had three GPs and 29% had four or more GPs.

Is there information on the most common diseases or medication groups that led to the definition of multimorbidity and polypharmacy?

Only a count of the number of chronic conditions and number of medications was recorded.

In which specialist departments were the patients referred?

Response of the authors: The specialties most frequently referred to were internal medicine (29.7% of all referrals), emergency department (11.8%), general surgery (10.1%), orthopaedic surgery (8.0%) and medical assessment unit (6.8%). This has been previously reported in Dinsdale E, Hannigan A, O’Connor R, O’Doherty J, Glynn L, Casey M, Hayes P, Kelly D, Cullen W, O’Regan A. Communication between primary and secondary care: deficits and danger. Family practice. 2020 Feb;37(1):63-8.

Are there diseases that led to particularly frequent referrals - where did outpatient care in GP practices reach its medical limits?

Response of the authors: our data cannot tell us which chronic illnesses led to the most referrals. We have reported the frequency of referral to each hospital department previously.

(Dinsdale E, Hannigan A, O’Connor R, O’Doherty J, Glynn L, Casey M, Hayes P, Kelly D, Cullen W, O’Regan A. Communication between primary and secondary care: deficits and danger. Family practice. 2020 Feb;37(1):63-8.)

Perhaps additional tables could be used to describe the cohort studied even better and thus make it more comparable for other international data!?.

Response of the authors: A new table and the following paragraph have been inserted.

All of the participating practices were mixed public-private, were computerised and had a practice nurse. Table 1 compares the profile of participating practices to the national profile in 2015. Most of the study practices (93%) had a co-operative system of out-of-hours cover, similar to the national profile (92%). Participating practices had higher percentages involved in postgraduate GP training (43% v 22%) and rural location (37% v 21%). In terms of practice size, 75% had between 500 and 1999 patients; 16% were single-handed practices, 31% had two GPs, 24% had three GPs and 29% had four or more GPs.

Note to the reviewers – this study was a retrospective analysis of practice patients and did not distinguish between consultations conducted by various GPs at each practice. We have not, therefore, included gender and age of the GP-tutors as it was not necessarily their own particular consultations (and referrals) that were included.

________________________________________

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: Yes: Mark Ashworth

Reviewer #2: Yes: Markus Bleckwenn

Submitted filename: Response_to_Reviewers.docx

Click here for additional data file.

28 Dec 2021

12 Jan 2022

12/01/2021

Dear editor and reviewers,

The authors are very grateful for the thorough reading and subsequent comments received throughout the review process. The edits from this round of feedback have been helpful, and the paper has been strengthened by them. Please find our point-by-point responses below.

Best wishes,

Dr Andrew O’Regan,

General Practitioner and Senior Lecturer in General Practice

University of Limerick School of Medicine

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: Thank you for asking me to review the revised version of this paper. The revisions have been substantial and greatly improved the standard of this piece of research. I have minor comments only:

Response of the authors: thank you for this acknowledgement and the comments that follow, all of which are helpful.

1) Abstract: this paper is in large part about hospital referral rates for patients with multimorbidity. More precision is required in the Results section of the Abstract which simply says: 'Half of patients had been referred to hospital in the previous two years'. The exact % figure should be given to one decimal place.

Response of the authors: the results section of the abstract now reads: “50.1% of patients had been referred to hospital in the previous two years.”

2) Abstract: a key strength of this work is the use of manual record searching rather than searching of an electronic database. This feature should be added to the Abstract e.g. 'manual record searching'.

Response of the authors: the following clause has been added to the methods section of the abstract: “Through manual record searching…”

3) pg22: minor typo. The sentence says 'aged eligibility' and should read 'age eligibility'. Also, pg 28: 'Analysis of a large clinical practice research database in England reported higher health care utilisation in general practice, higher prescription medications...', should read '...higher number of prescription medications', or similar.

Response of the authors: the typos on page 22 and again on page 28 have been corrected. Thank you for spotting.

4) A further Limitation to be added is that the authors have not identified which LTCs or medications are associated with increased GP/Practice Nurse/Home Visit attendance rates. So we dont know in clinical terms what the drivers are for increased attendance rates (is it mental health conditions, or diabetes, or hypertension, etc etc, all commonly managed in primary care)?

Response of the authors: We agree that this is an important limitation and have added the following sentence to the limitations section: “Finally, during data collection the number of chronic conditions and medications were recorded for each case but not the name of the chronic condition or medication, and, consequently, the analysis cannot identify which chronic conditions or medications were associated with attendance.”

Submitted filename: Response to Reviewers.docx

Click here for additional data file.

17 Jan 2022

How do multi-morbidity and polypharmacy affect general practice attendance and referral rates? A retrospective analysis of consultations.

PONE-D-21-15158R2

Dear Dr. O'Regan,

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 for payment will follow shortly after the formal acceptance. To ensure an efficient process, please log into Editorial Manager at http://www.editorialmanager.com/pone/, click the 'Update My Information' link at the top of the page, and double check that your user information is up-to-date. If you have any billing related questions, 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,

Vijayaprakash Suppiah, PhD

Academic Editor

PLOS ONE

24 Jan 2022

PONE-D-21-15158R2

How do multi-morbidity and polypharmacy affect general practice attendance and referral rates? A retrospective analysis of consultations

Dear Dr. O'Regan:

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

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 plosone@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. Vijayaprakash Suppiah

Academic Editor

PLOS ONE