Severe mental illness diagnosis in English general hospitals 2006-2017: A registry linkage study.

BACKGROUND: The higher mortality rates in people with severe mental illness (SMI) may be partly due to inadequate integration of physical and mental healthcare. Accurate recording of SMI during hospital admissions has the potential to facilitate integrated care including tailoring of treatment to account for comorbidities. We therefore aimed to investigate the sensitivity of SMI recording within general hospitals, changes in diagnostic accuracy over time, and factors associated with accurate recording. METHODS AND
FINDINGS: We undertook a cohort study of 13,786 adults with SMI diagnosed during 2006-2017, using data from a large secondary mental healthcare database as reference standard, linked to English national records for 45,706 emergency hospital admissions. We examined general hospital record sensitivity across patients' subsequent hospital records, for each subsequent emergency admission, and at different levels of diagnostic precision. We analyzed time trends during the study period and used logistic regression to examine sociodemographic and clinical factors associated with psychiatric recording accuracy, with multiple imputation for missing data. Sensitivity for recording of SMI as any mental health diagnosis was 76.7% (95% CI 76.0-77.4). Category-level sensitivity (e.g., proportion of individuals with schizophrenia spectrum disorders (F20-29) who received any F20-29 diagnosis in hospital records) was 56.4% (95% CI 55.4-57.4) for schizophrenia spectrum disorder and 49.7% (95% CI 48.1-51.3) for bipolar affective disorder. Sensitivity for SMI recording in emergency admissions increased from 47.8% (95% CI 43.1-52.5) in 2006 to 75.4% (95% CI 68.3-81.4) in 2017 (ptrend < 0.001). Minority ethnicity, being married, and having better mental and physical health were associated with less accurate diagnostic recording. The main limitation of our study is the potential for misclassification of diagnosis in the reference-standard mental healthcare data.
CONCLUSIONS: Our findings suggest that there have been improvements in recording of SMI diagnoses, but concerning under-recording, especially in minority ethnic groups, persists. Training in culturally sensitive diagnosis, expansion of liaison psychiatry input in general hospitals, and improved data sharing between physical and mental health services may be required to reduce inequalities in diagnostic practice.

Introduction

Severe mental illnesses (SMI), defined as schizophrenia spectrum and bipolar affective disorders, have lifetime prevalence of around 0.5% and 1% respectively [1, 2], and are associated with several physical comorbidities that increase the risk of general hospital admission [3, 4]. Life expectancy is 10–15 years lower for people with SMI [5], and this health gap is increasing over time [6], with SMI contributing to 3.5% of worldwide years lost to disability [7]. The 2019 Lancet Commission on Physical Health of People with Mental Illness identified the need for multidisciplinary approaches to multimorbidity in people with mental illness [8]. An important aspect of such healthcare integration is detection of psychiatric conditions in healthcare settings to allow continuity of previous care such as medication and tailoring of inpatient and discharge plans to account for comorbidity [9]. However, the separation of physical and mental healthcare in the United Kingdom, with different hospital settings using discrete electronic health records (EHRs), may inhibit this.

There has been limited research into recognition of mental health conditions within secondary physical healthcare settings. One study investigated SMI recording in primary care [10], and another investigated recognition specifically of deliberate self-poisoning within one UK general hospital’s EHRs [11]; both were conducted over 20 years ago. A further study examined recording of alcohol disorders in primary and secondary care [12]. Other studies have investigated accuracy of psychiatric, rather than general hospital, records, in which recognition of mental illness is likely to be higher [13]. No studies have investigated time-trend changes or factors associated with systematic differences in SMI recording. Understanding such changes may inform future approaches to improving illness recording and elucidate potential biases in hospital records, which are being increasingly used for case-ascertainment in epidemiological studies [14].

We therefore aimed to evaluate the accuracy of SMI recording during English general hospital admissions of a person with preexisting SMI who is admitted to a general hospital for any health condition, using a large secondary mental healthcare database to identify people with SMI, and linked national general hospital data to assess diagnostic recording. Our specific objectives were to calculate the sensitivity of SMI diagnosis in general hospital records, evaluate time-trend changes of SMI diagnosis recording in general hospital records from 2006 to 2017, and examine association of sociodemographic and clinical factors with accuracy of psychiatric recording in general hospital records.

Methods

Study design and participants

We undertook a cohort study, using data from the South London and Maudsley National Health Service (NHS) Foundation Trust (SLaM), one of Europe’s largest secondary mental healthcare trusts providing support to around 1.36 million people living in 4 ethnically diverse communities in South London, UK (Croydon, Lambeth, Lewisham, and Southwark). We used the “Clinical Record Interactive Search” (CRIS) data extraction tool, which enables construction of databases suitable for research by identifying, retrieving, and linking a pseudonymized version of SLaM patient records for over 450,000 individuals [15]. CRIS uses natural language processing (NLP) algorithms developed on General Architecture for Text Engineering (GATE) software [16] to extract information from unstructured fields of the clinical record. SLaM data were linked using deterministic matching procedures to NHS Digital’s Hospital Episode Statistics data source to identify admissions to any English hospital. Oxfordshire Research Ethics Committee C (18/SC/0372) approved these resources for secondary analysis. The terms of the ethical approval do not require consent to be provided, but all participants have the right to opt out of data use at any time. This study is reported as per the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) guideline (S1 STROBE Checklist) [17].

These data were selected to generate “reference-standard” SMI diagnoses because they included information from the predominant diagnostic and treatment service for mental illnesses in the catchment area. Patients were diagnosed after assessment by mental health staff, e.g., a psychiatrist, nurse, or psychologist, according to the International Statistical Classification of Diseases and Related Health Problems, Tenth Revision (ICD-10) [18], which is the predominant diagnostic framework in the UK.

We included study participants who (1) had clinical contact with SLaM mental health services between 1 January 2006 and 31 March 2017 while aged 18 or over, (2) were diagnosed at any time with schizophrenia (ICD-10 code F20), schizotypal disorder (F21), delusional disorder (F22), schizoaffective disorder (F25), other nonorganic psychotic disorder (F28), unspecified nonorganic psychosis (F29), manic episode (F30), or bipolar affective disorder (F31), including those described in clinical coding or records as “probable” or “in remission,” and (3) were admitted to an English NHS general (nonpsychiatric) hospital after the first diagnosis of SMI in CRIS. Participants were identified from diagnoses in structured EHR diagnostic fields or unstructured text. Participants were assigned to only 1 diagnostic category following the “hierarchy” of psychiatric diagnoses [19] (e.g., an individual diagnosed sequentially with schizophrenia [F20] and a manic episode [F30] would be assigned to F20 group), as in previous studies [20]. We did this to avoid double-counting patients who may have been diagnosed at different times within the reference-standard database as having a schizophrenia-like disorder and bipolar disorder; we judged that this most likely represented evolving clinical opinion rather than coexistence of the 2 disorders, which is clinically unlikely [21].

Outcomes

We obtained outcome data from Hospital Episode Statistics (HES) records, which include clinical diagnoses according to ICD-10 criteria from admissions to any hospital within England [22]. The HES database is primarily designed to allocate payment to hospitals for the care they provide, and its secondary uses are for research and health service planning. The index date for our study was the first SMI diagnosis within SLaM, and we obtained data from HES on all subsequent admissions of included patients to general (i.e., nonpsychiatric) hospitals, including dates of admission and discharge, admission method (emergency, i.e., unplanned admission, or elective, such as admission for renal dialysis, wound dressing, chemotherapy, or elective surgery), and up to 20 primary and secondary recorded diagnoses. Our outcome of interest was recording of psychiatric illness in any of the 20 recorded primary or secondary diagnoses.

Diagnoses recorded in HES include those clinically identified by hospital staff during admissions, those derived from preexisting clinical records from secondary mental health trusts or previous hospital medical records, or those obtained following communication with primary care. Separate EHRs are used in different hospital trusts, meaning that diagnoses are not automatically entered in general hospital records from secondary mental healthcare or primary care records. Some EHRs may automatically populate diagnosis fields with previously recorded chronic conditions, although there are no data available to determine the extent of this practice.

Covariates

We obtained age, sex, ethnicity (White, Mixed, Asian or British Asian, Black or Black British, and other) and marital status (single, married or cohabiting, divorced or separated, and widowed) from SLaM records data. We used the Index of Multiple Deprivation (IMD) [23] to rate neighborhood-level socioeconomic deprivation. Clinical presentation was derived using the Health of the Nation Outcome Scale (HoNOS), which is a clinician-rated measure routinely applied in UK mental health services to assessed patients with good validity and adequate reliability [24]. HoNOS comprises of 12 subscales, each rated on a 5-point Likert scale with higher values indicating more severe problems; we dichotomized these into 0–1, indicating no or minor problems, and 2–4, indicating more severe problems. As we aimed to assess association of mental illness severity with diagnostic recording, we combined the subscales reflecting mental health symptoms (agitation, self-injury, drug/alcohol use, cognitive impairment, delusions/hallucinations and depressed mood) into an ordinal scale indicating 0 symptoms, 1 current mental health symptom, 2 current mental health symptoms, and 3+ current mental health symptoms, as in previous studies [25]. We also used the physical illness and activity of daily living (ADL) impairment subscales as covariates. All covariates were derived from time closest to first general hospital admission.

Analysis

Our prospective analysis plan is in S1 Text. We described sociodemographic characteristics according to whether SMI had ever been recorded in hospital records using chi-squared tests for categorical data and independent t tests for continuous data. We described the primary diagnoses for admissions.

Sensitivity of general hospital SMI diagnosis. We examined sensitivity of psychiatric diagnosis recording at patient-level (proportion of people with SLaM SMI diagnosis who ever had a mental illness recorded in their complete general hospital records as a primary or secondary diagnosis) and emergency admission-level (proportion who have a mental illness recorded in primary or secondary diagnoses during each emergency admission). We chose to examine emergency admissions as nonemergency admissions are usually recurrent brief admissions, which we considered were unlikely to warrant a full diagnostic assessment [26]. In response to peer-reviewer comments, we additionally calculated (1) patient-level sensitivity according to the number of previous hospital admissions (0, 1–5, ≥6) and (2) admission-level sensitivity according to the primary diagnosis recorded for each hospital admission (grouped into ICD-10 categories). We report our sensitivity calculations at different levels of diagnostic accuracy. Our primary analyses calculated sensitivity at the level of any psychiatric diagnosis (proportion of individuals with SLaM diagnosis of any F20-31 disorder who receive any psychiatric diagnosis [F00-99] in HES). We also examined category-specific sensitivity (e.g., proportion of individuals with schizophrenia spectrum disorders [F20-29] who received any F20-29 diagnosis in HES) and disorder-specific diagnosis (e.g. proportion of individuals with schizophrenia diagnosis [F20] who specifically received an F20 diagnosis in HES). We calculated 95% confidence intervals for sensitivity using the Clopper–Pearson method [27].

Time-trend changes from 2006 to 2017. We calculated sensitivity for each participant’s first emergency hospital admission following SMI diagnosis stratified by year of admission and used Cochran–Armitage test to examine sensitivity changes over time [28].

Association of sociodemographic and clinical factors with psychiatric diagnosis being unrecorded. We calculated the association of sociodemographic and clinical characteristics with unrecorded diagnosis (no psychiatric diagnosis ever being recorded at a patient level) using multivariable logistic regression. Included variables were age, sex, ethnicity, marital status, IMD, number of mental illness symptoms, physical illness, ADL impairment, and number of hospital admissions (log-transformed because of the skewed distribution). To explore the impact of missing data, we conducted 2 non-prespecified sensitivity analyses. We first repeated the analysis without HoNOS variables because these data were missing for 21% of the cohort. We also used multiple imputation with chained equations to create 20 imputed datasets using STATA’s mi package by replacing missing values using a model constructed from all available covariates and outcome variables; our imputation used predictive mean matching for continuous data and logistic regression for categorical data. We then conducted logistic regression on each imputed dataset before combining coefficients using Rubin’s rules [29]. The fraction of missing information after imputation was 0.33, so 20 imputed datasets is likely to give replicable estimates of standard error [30].

All analyses were undertaken using STATA SE version 15 (Stata Corp. https://www.stata.com/).

Results

We identified 28,832 individuals with F20-31 diagnosis who were seen by SLaM between 1 January 2006 and 31 March 2017 (Fig 1). We excluded 2,410 because of diagnosis of acute and transient psychotic disorder (F23) or induced delusional disorders (F24). A further 12,636 individuals were excluded as they had no hospital admissions in the study period. Our final sample included 13,786 individuals who had 45,706 emergency admissions (Fig 1). Primary ICD-10 diagnoses are described in S1 Table; mental disorder was the primary diagnosis in 7.9% of emergency hospital admissions.

Fig 1

Cohort of individuals with SMI who were also admitted into general hospitals.

CRIS, Clinical Record Interactive Search; F20, schizophrenia; F21, schizotypal disorder; F22, delusional disorder; F23, acute and transient psychotic disorder; F24, induced delusional disorders; F25, schizoaffective disorder; F28, other nonorganic psychotic disorder; F29, unspecified nonorganic psychosis; F30, manic episode; F31, bipolar affective disorder; HES, Hospital Episode Statistics; ICD-10, International Statistical Classification of Diseases and Related Health Problems, Tenth Revision; SMI, severe mental illness.

The mean age was 46.9 years, 51% of participants were female, and the majority were single (64.7%) (Table 1). Most were White ethnicity (59.5%), and the largest ethnic minority group was Black African/Caribbean (27.7%). Three-quarters (10,574, 76.7%) of participants had a schizophrenia spectrum disorder, and 3,212 (23.3%) had bipolar disorder. At the time closest to first general hospital admission, many participants displayed no (24.8%) or one (25.1%) significant mental health symptoms; full information on prevalence of clinical problems in individual HoNOS domains is in S2 Table. The median number of hospital admissions was 3 admissions (interquartile range [IQR] 1, 5) with median duration of follow-up being 7.19 years [IQR 0.4, 10]; 3,047 people died during follow-up.

Table 1

Sociodemographic and clinical characteristics of all participants, according to whether psychiatric diagnosis ever made in subsequent general hospital records.

Sociodemographic and Clinical Characteristics		All Patients(n = 13,786)		Psychiatric Diagnosis Recorded (n = 10,574)		No Psychiatric Diagnosis Recorded (n = 3,212)		Significance Test
		N	%	N	%	N	%
Agea	Mean (SD)	46.9 (17.3)		47.7 (17.5)		44.4 (16.7)		t = 9.5, p < 0.001
	18–24	1,279	9.3	933	8.8	346	10.8	χ2 = 92.5, p < 0.001
	25–34	2,669	19.4	1,957	18.5	712	22.2
	35–44	3,108	22.5	2,310	21.9	798	24.8
	45–54	2,368	17.2	1,823	17.2	545	17.0
	55–64	1,968	14.3	1,586	15.0	382	11.9
	65+	2,394	17.4	1,965	18.6	429	13.4
	Missing	0		0		0
Sex	Female	7,036	51.0	5,352	50.6	1,684	52.4	χ2 = 3.2, p = 0.072
	Missing	0		0		0
Ethnicity	White	7,896	59.5	6,308	61.8	1,588	51.9	χ2 = 99.2, p < 0.001
	Mixed	326	2.5	244	2.4	82	2.7
	Asian	677	5.1	505	4.9	172	5.6
	Black African/ Caribbean	3,673	27.7	2,659	26.0	1,014	33.1
	Other	703	5.3	498	4.9	205	6.7
	Missing	511		360		151
Marital Statusb	Single	8,217	64.7	6,362	64.9	1,855	64.1	χ2 = 38.6, p < 0.001
	Married	2,076	16.4	1,512	15.4	564	19.5
	Divorced	1,621	12.8	1,281	13.1	340	11.7
	Widowed	780	6.1	644	6.6	136	4.7
	Missing	1,092		775		317
Mean deprivation score (SD)b		30.0 (1.1)		30.0 (1.1)		30.1 (1.0)		t = −0.7, p = 0.467
	Missing	467		389		78
Mental Health Symptoms(from HoNOS subscales)c	No Symptoms	3,427	31.2	2,465	29.4	962	37.2	χ2 = 93.1, p < 0.001
	1 Symptom	3,458	31.5	2,611	31.1	847	32.8
	2 Symptoms	2,314	21.1	1,841	22.0	473	18.3
	3+ Symptoms	1,771	16.1	1,467	17.5	304	11.8
	Missingd	2,816		2,190		626
	Number of symptoms (IQR)	1 (0, 2)		1 (0, 2)		1 (0, 2)
Number of HES admissions (IQR) Range of admissions		3 (1, 5)1–1,413		3 (2, 6)1–951		1 (1, 5)1–1,413

aAt time of first CRIS admission.

bNearest to first general hospital admission.

cHoNOS domains closest to general hospital admission.

dHoNOS domain with most missing data.

CRIS, Clinical Record Interactive Search; HES, Hospital Episode Statistics; HoNOS, Health of the Nation Outcome Scale; IQR, interquartile range; SD, standard deviation.

Sensitivity of general hospital SMI diagnosis

We found that 10,574 of 13,786 people with SMI had any psychiatric illness recorded during their subsequent general hospital admissions, meaning that sensitivity at the level of each individual patient’s complete hospital records was 76.7% (95% CI 76.0–77.4) (Table 2). Patient-level sensitivity was 57.3% (55.8–58.8) for patients with only 1 admission, 80.7% (79.7–81.6) for those with 2–5 admissions, 92.8% (91.5, 93.9) for those with 6–10 admissions, and 96.7 (95.7, 97.6) for those with 11+ admissions.

Table 2

Sensitivity of general hospital diagnoses of SMI 2006–17, at the level of each individual patient’s whole-hospital records.

Psychiatric Diagnosis	HESDiagnosis	Number of True Positives	Sensitivity %(95% CI)
	Any F code	10,574/13,786	76.7 (76.0–77.4)
F20-29	% with any F	7,796/10,061	77.5 (76.7–78.3)
	% with F20-29	5,672/10,061	56.4 (55.4–57.4)
F30-31	% with any F	2,778/3,725	74.6 (73.2–76.0)
	% with F30-31	1,852/3,725	49.7 (48.1–51.3)

F20-29 Schizophrenia, schizotypal and delusional disorders (excluding acute and transient psychotic disorders; and induced delusional disorder); F30-31 Manic episodes and bipolar affective disorder.

CI, confidence interval; HES, Hospital Episode Statistics; SMI = severe mental illness.

Sensitivity is lower when examining more specific diagnosis. Sensitivity was 56.4% (55.4–57.4) for category-specific ICD-10 diagnosis for schizophrenia spectrum disorder (F20-29) and 49.7% (48.1–51.3) for bipolar affective disorders (F30-31). Full results for disorder-specific recording can be found in S3 Table. Patient-level sensitivity for schizophrenia (F20) was 45.1% (44.5–45.7) and 40.4% (39.5–41.3) for bipolar affective disorder (F31). Disorder-specific sensitivity for other rarer conditions such as schizoaffective or schizotypal disorder was lower.

For admission-level recordings, 32,033 of 45,706 emergency hospital admissions had any psychiatric diagnosis recorded meaning that sensitivity was 70.1% (69.7–70.5) (Table 3). Recording of any psychiatric diagnosis was 71.1% (70.7–71.6) for schizophrenia spectrum disorders and 67.1% (66.2–67.9) for bipolar affective disorders. Category-specific admission-level sensitivity was 45.5% (45.0–46.1) for admissions of people with schizophrenia spectrum disorder (F20-29) and 39.6% (38.7–40.5) for bipolar affective disorders. Admission-level sensitivity according to primary diagnosis varied from 33.1% (31.3–34.9) for admissions related to pregnancy, childbirth, and the puerperium (ICD-10 codes O00-O99) to 79.7% (78.4, 81.0) for poisoning-related admissions (T36-65) and 81.0% (79.0, 82.9) for endocrine, nutritional, and metabolic diseases (E00-E90) (see S1 Table for full results).

Table 3

Sensitivity of general hospital diagnoses of SMI 2006–17, at the level of emergency hospital admissions only.

Psychiatric Diagnosis	HESDiagnosis	Number of True Positives	Sensitivity %(95% CI)
	Any F code	32,033/45,706	70.1 (69.7–70.5)
F20-29	% with any F	24,143/33,941	71.1 (70.7–71.6)
	% with F20-29	15,453/33,941	45.5 (45.0–46.1)
F30-31	% with any F	7,890/11,765	67.1 (66.2–67.9)
	% with F30-31	4,660/11,765	39.6 (38.7–40.5)

F20-29 Schizophrenia, schizotypal, and delusional disorders (excluding acute and transient psychotic disorders; and induced delusional disorder); F30-31 Manic episodes and bipolar affective disorder.

CI = confidence interval; HES = Hospital Episode Statistics; SMI = severe mental illness.

Time-trend changes from 2006 to 2017

Sensitivity of recording for any psychiatric diagnosis increased from 47.8% (43.1–52.5) for emergency admissions during 2006 to 75.4% (68.3–81.4) for admissions during 2017 (ptrend< 0.001 [χ2 = 326, 1 df]), although much of this change was observed between 2009 and 2012 (Fig 2; full data in S4 Table).

Fig 2

Time-trend of sensitivity for schizophrenia spectrum disorders and bipolar affective disorders diagnosis in general hospitals.

Points represent the proportion of people’s first emergency hospital admissions following severe mental illness diagnosis in which a mental illness is recorded. Error bars show 95% confidence interval, and linear trend line shows change over time.

Association of sociodemographic and clinical factors with psychiatric diagnosis being unrecorded

In unadjusted analyses, age, ethnicity, marital status, mental and physical symptoms, functional impairment, and number of hospital admissions were associated with diagnostic recording (Table 4). In mutually adjusted multivariable analysis, those from Black African/Caribbean backgrounds were more likely (odds ratio [OR] = 1.38 [95% CI 1.24, 1.55; p < 0.001]) to have no psychiatric diagnosis ever recorded compared with those from White ethnic backgrounds. Marital status was also associated with diagnostic accuracy; single people were less likely (OR = 0.78 [95% CI 0.63–0.92; p < 0.001]) to have no psychiatric disorder recorded compared with married individuals, as were divorced (OR = 0.76 [95% CI 0.63–0.92; p = 0.004]) or widowed people (OR = 0.77 [95% CI 0.60–1.00; p = 0.046]). More mental health symptoms were associated with greater diagnostic accuracy, with 2 (OR = 0.71 [95% CI 0.62–0.83; p < 0.001]) or 3 plus symptoms (OR = 0.61 [95% CI 0.52–0.73; p < 0.001]) being associated with lower risk of no psychiatric diagnosis being recorded. Difficulties with activities of daily living (OR = 0.71; 95% CI 0.74–0.95; p < 0.001) and physical illness (OR = 0.84; 95% CI 0.74–0.95; p = 0.004) were also associated with lower risk of unrecorded diagnosis. Results were consistent in sensitivity analyses without inclusion of HoNOS symptoms (S5 Table) and with multiple imputation for missing covariates (S6 Table).

Table 4

Association of clinical and sociodemographic characteristics with psychiatric diagnosis of people with severe mental illness not being recorded in general hospital records: Univariate and multivariable logistic regression.

Sociodemographic andClinical Characteristics			Unadjusted Analysis(n = 13,786)		Mutually Adjusted Multivariable Analysis (n = 10,189)
			Odds Ratio (95% CI)	p value	Odds Ratio (95% CI)	p value
Age (per 10-year increment)			0.89 (0.87–0.91)	<0.001	0.98 (0.94–1.01)	0.180
Sex	Female (Ref)		1		1
	Male		0.93 (0.86–1.01)	0.072	0.90 (0.82–1.00)	0.054
Ethnicity	White (Ref)		1		1
	Asian		1.35 (1.13–1.62)	0.001	1.10 (0.87–1.38)	0.424
	Black African/Caribbean		1.51 (1.38–1.66)	<0.001	1.38 (1.24–1.55)	<0.001
	Mixed		1.33 (1.03–1.72)	0.027	1.26 (0.93–1.71)	0.136
	Other		1.64 (1.38–1.94)	<0.001	1.25 (1.00–1.55)	0.047
Marital Status	Married (Ref)		1		1
	Single		0.78 (0.70–0.87)	<0.001	0.72 (0.62–0.82)	< 0.001
	Divorced		0.71 (0.61–0.83)	<0.001	0.76 (0.63–0.92)	0.004
	Widowed		0.57 (0.46–0.70)	<0.001	0.77 (0.60–1.00)	0.046
Deprivation Score (per 10-unit increase)			1.01 (0.98–1.05)	0.467	1.01 (0.96–1.06)	0.824
Clinical symptoms and function (Health of the Nation Outcome Scale) domains	Mental Health Subscale	No Symptoms (Ref)	1		1
		1 Symptom	0.83 (0.75–0.93)	0.001	0.90 (0.79–1.01)	0.081
		2 Symptoms	0.66 (0.58–0.75)	<0.001	0.71 (0.62–0.83)	<0.001
		3+ Symptoms	0.53 (0.46–0.61)	<0.001	0.61 (0.52–0.73)	<0.001
	Problem with Physical Illness		0.55 (0.50–0.60)	<0.001	0.84 (0.74–0.95)	0.004
	Problem with Daily Living		0.54 (0.49–0.60)	<0.001	0.71 (0.62–0.80)	<0.001

Multivariable analysis adjusted for age, sex, ethnicity, marital status, deprivation score, clinical symptoms and function and log number of hospital admissions.

Discussion

In this study, we examined accuracy of general hospital records for people with SMI admitted to general hospitals, finding that at least some form of psychiatric diagnosis was recorded in 70.1% of individual emergency hospital admissions and in 76.7% of patients’ complete hospital discharge records. Our findings suggest that accuracy of SMI recording in general hospitals has improved over time with sensitivity for any psychiatric diagnosis in those experiencing emergency hospital admissions increasing from 47.8% in 2006 to 75.4% in 2017. Unrecorded psychiatric diagnosis was more likely in people with milder symptoms or higher ADL scores, married individuals, and ethnic minority groups.

Although there has been improvement in recording of SMI within the general hospitals investigated, our findings show there are nearly 25% of individuals with established SMI diagnosis who have never had a preexisting SMI recorded throughout their, on average, 3 subsequent general hospital admissions. Moreover, recording is lower for category-specific sensitivity. Thus, the proportion of individuals with schizophrenia spectrum disorders who received a diagnosis within that same ICD-10 F20-29 category was 56.4%, compared with 77.5% for recording of any psychiatric diagnosis, and for bipolar affective disorders, these figures were 49.7% and 74.6%, respectively. The lower sensitivity for more specific diagnoses may reflect uncertainty about diagnostic labels, including whether these are accurate and used or communicated appropriately [31]; there is the potential for disagreement between clinicians and for different diagnostic practices in different settings. The relatively low sensitivity at a category-level is potentially problematic, when considering the importance of specific diagnosis for treatment outcomes and continuity of care, such as the different symptom clusters, illness severity, and pharmacological treatment indicated for schizophrenia compared with, for example, an anxiety disorder [32].

We acknowledge, however, that it is not necessarily the role of a physician or surgeon in a physical healthcare setting to diagnose a specific psychiatric condition, and instead, recognition of the presence of a mental illness may suffice if it leads to a referral to more specialist mental healthcare service such as liaison psychiatry. Other holistic measures to promote the wellbeing of a general hospital inpatient with SMI may also be instituted after identification of the presence of a psychiatric disorder. These may include consultation with family to identify whether psychiatric symptoms are new or preexisting, discussion with community mental health teams, or provision of a side room offered so that sleep could be prioritized.

We are not aware of any previous studies that have examined sensitivity of psychiatric diagnosis recording in English general hospitals, so cannot compare our findings directly with others. Our findings are lower than the 88% and 96.3% coding accuracy reported for general practice EHR [10, 33] and deliberate self-poisoning admissions in UK hospital [34]. However, general practice records are likely to include lifelong diagnostic records, rather than be a snapshot of a clinical episode such as hospital admissions, so may be more likely to include a diagnosis received in another service sector. Self-poisoning is likely to have been the cause of presentation to hospital, and so recording of this is expected to be higher. Sensitivity in our study was higher than the figure of 52% reported for recognition of alcohol disorders by hospital staff [12].

Increased recording of diagnosis since the introduction of payment by results in 2006/2007 from 49.5% to 81.3% in 2017 is likely due to a range of different reasons from changes to policy approaches such as the “Five-Year Forwards” plan for whole-person centered care [34], financial incentives [35], improvements in coding practices [35], and expansion of liaison psychiatric services [36], whereby psychiatric diagnoses are more readily available (whether assigned anew or confirmed from previous records) from mental health specialists.

The association between clinical symptoms and unrecorded psychiatric diagnosis showed a dose–response gradient. It is likely that increasing symptom severity reflects clinical complexity meaning that difficulties are more prominent, and diagnosis is therefore more likely to be recorded. Similarly, symptom severity might reflect more frequent or longer hospital admissions during which clinicians have more opportunity to investigate clinical records. These individuals might be better known to clinical staff as they have more regularly updated HES records. People with milder SMI are less likely to have acute psychiatric healthcare needs, so lower accuracy in milder cases may be of less concern.

Unrecorded diagnosis was more common in individuals from Black African/Caribbean or other ethnic minority backgrounds. There are several potential reasons for this, relating to patient or service-level factors [37]. Patient-level factors include language barriers, lack of information disclosure, or distrust of clinicians from different backgrounds, and service-level factors include clinician bias, stigma, or lack of cultural awareness [38]. Considering poor health outcomes for people from minority ethnic groups with SMI, including increased cardiovascular risk factors [39], reduced support post discharge [40], and increased rates of compulsory admissions [41], this is particularly concerning. Recording of SMI in general hospitals might be an early opportunity for support as this is a setting for key treatment decisions and an opportunity for enhanced continuity of care [9, 42]. We did not find association of diagnostic accuracy with Asian or mixed backgrounds, possibly because of the smaller sample size giving less statistical power or reflecting other factors such as socioeconomic status.

Counter to our expectation based on analyses of other mental disorders [43, 44], being married was associated with higher likelihood of diagnosis being unrecorded. This may be explained by marital status being a marker of SMI severity and chronicity, whereby symptoms are higher in those who are single as compared with those in relationships. However, adjustment for symptom severity did not attenuate this association. Alternatively, it may be that support from a partner makes mental health symptoms less apparent as clinicians might assume that someone is receiving support from home or feel less inclined to ask about symptoms of mental illness. Similarly, it might be that partners are less reluctant to disclose information in regard to SMI diagnosis because of increased stigma associated with such conditions.

Sensitivity appeared to vary according to the primary reason for admission so that admissions for poisoning (which may have been precipitated by mental health symptoms) or those for endocrine and metabolic conditions (which may result from psychotropic adverse effects) had higher diagnostic recording. The lower recording for pregnancy and childbirth-related is of concern and warrants further future investigation, though it may reflect coding practice differences.

Important strengths of this study include its large sample size, representativeness of people with SMI diagnosis, and data availability over a decade enabling analysis of changes over time. The main limitation is that EHRs are not primarily collected for research purposes, meaning that using SLaM records as the “reference standard” might be problematic because the amount of clinical contact before diagnosis, and the diagnostic process may have varied, so some of our cohort may have been misdiagnosed. However, SLaM provides specialist diagnostic services, and we grouped patients according to the most recent recorded diagnoses from either structured quantitative health outcomes or rich unstructured clinical records. This approach also allowed construction of a large cohort that was representative of people with clinically diagnosed SMI, which would not have been feasible had we interviewed all participants with a standardized clinical assessment. Although there is wider debate about the validity of psychiatric diagnostic constructs [45], SMI categories are considered stable and persistent over time [46]. Furthermore, our primary analysis considered sensitivity at the level of all psychiatric diagnoses, for example, whether an individual with schizophrenia had any mental illness recorded, as we acknowledged the potential limitations of the reference standard.

Our cohort’s derivation from secondary mental health services may have meant that participants had more pronounced symptoms and care-seeking behaviors, which would likely result in overestimation of sensitivity, but we included individuals with SMI in remission and included a range of clinical severity. Although HES records cover all English hospitals, most admissions will have been in South London hospitals. Diagnostic practice in rural settings where clinical populations and resources may differ. Finally, use of general hospital discharge diagnoses may miss nuances of diagnostic practice in general hospital in which the SMI may have been recognized during the admission but not recorded on final discharge, and our observational study design meant that we were unable to examine whether diagnostic recording affected clinical outcomes.

Future studies should examine whether diagnostic recording affects outcomes, such as length of stay or readmission rate, and has any negative effects such as stigma or diagnostic overshadowing of physical illness. This may require more detailed scrutiny of individuals’ case records and could be analyzed as part of future evaluations of liaison/consultation psychiatry services. It is also important to consider other relevant metrics of diagnostic accuracy, such as positive predictive value or specificity, which would require reference-standard data representative of people without psychiatric illness. Future research should also aim to elucidate the mechanisms for association between ethnicity, marital status, and symptom severity and diagnostic recording in general hospitals and evaluate effective approaches to improving diagnostic practice.

Our findings have important clinical, research, and policy implications. Researchers wishing to use hospital EHRs such as HES to ascertain cases of schizophrenia-like illnesses and bipolar disorder should be aware that around one quarter of known cases are likely to be unrecorded and there is potential for systematic bias whereby ethnic minorities, married people, and people with milder symptoms will be missed. Although our findings suggest that sensitivity is improving over time, there were around 30% of admissions in which people with established SMI did not have any psychiatric diagnosis recorded, suggesting that more needs to be done by policymakers to bridge the gap for “whole-person centered” care. Hospital settings should endeavor to improve diagnostic recording, particularly for high-risk groups. This may include training staff in culturally sensitive diagnosis for ethnic minority populations, expansion of mental healthcare input in general hospitals and collaborative working with these liaison psychiatry services, and proactive contact with primary care and mental health services to elicit information about past psychiatric history. Better data sharing between physical and mental health services such as through harmonized clinical records could improve accuracy of mental illness in physical healthcare, and physical illness in mental healthcare services, to move towards truly integrated healthcare for people with mental illness.

STROBE, Strengthening the Reporting of Observational Studies in Epidemiology.

(DOCX)

Click here for additional data file.

Prospective analysis plan.

(DOCX)

Click here for additional data file.

Primary diagnosis (ICD-10 code) for emergency hospital admissions of people with severe mental illness.

ICD-10, International Statistical Classification of Diseases and Related Health Problems, Tenth Revision.

(DOCX)

Click here for additional data file.

Clinical characteristics of people with severe mental illness admitted to a general hospital, according to whether mental illness was recorded in their hospital records.

(DOCX)

Click here for additional data file.

Disorder-specific sensitivity of general hospital emergency admission records for people with severe mental illness.

(DOCX)

Click here for additional data file.

Recording of mental illness in people with severe mental illness admitted to general hospitals: By year of first emergency hospital admission.

(DOCX)

Click here for additional data file.

Association of sociodemographic characteristics with recording of mental illness in general hospital records: Multivariable regression without Health of Nation Outcome Scale clinical characteristics.

(DOCX)

Click here for additional data file.

Association of clinical and sociodemographic characteristics with psychiatric diagnosis of people with severe mental illness not being recorded in general hospital records: Multivariable regression with multiple imputation for missing variables.

(DOCX)

Click here for additional data file.

11 Mar 2020

Dear Dr Mansour,

Thank you for submitting your manuscript entitled "Severe mental illness diagnosis in English general hospitals 2006-2017: sensitivity, time-trends, and predictors of diagnostic accuracy" for consideration by PLOS Medicine.

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2 May 2020

Dear Dr. Mansour,

Thank you very much for submitting your manuscript "Severe mental illness diagnosis in English general hospitals 2006-2017: sensitivity, time-trends, and predictors of diagnostic accuracy" (PMEDICINE-D-20-00812R1) for consideration at PLOS Medicine.

Your paper was evaluated by a senior editor and discussed among all the editors here. It was also discussed with an academic editor with relevant expertise, and sent to independent reviewers, including a statistical reviewer. The reviews are appended at the bottom of this email and any accompanying reviewer attachments can be seen via the link below:

[LINK]

In light of these reviews, I am afraid that we will not be able to accept the manuscript for publication in the journal in its current form, but we would like to consider a revised version that addresses the reviewers' and editors' comments. Obviously we cannot make any decision about publication until we have seen the revised manuscript and your response, and we plan to seek re-review by one or more of the reviewers.

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Requests from the editors:

*We'd suggest revising the title according to PLOS Medicine's style, this should include the study design in the subtitle (ie, after a colon) - eg here ": registry linkage study" (or similar).

*Please restructure your abstract using the PLOS Medicine headings (Background, Methods and Findings, Conclusions). The writeup for each subsection should be complete sentences (rather than sentence fragments).

*At this stage, we ask that you include a short, non-technical Author Summary of your research to make findings accessible to a wide audience that includes both scientists and non-scientists. The Author Summary should immediately follow the Abstract in your revised manuscript. This text is subject to editorial change and should be distinct from the scientific abstract. Please see our author guidelines for more information: https://journals.plos.org/plosmedicine/s/revising-your-manuscript#loc-author-summary

*We'd suggest ensuring that the study is reported according to the STROBE guideline (for observational studies), and include the completed STROBE checklist as Supporting Information. 1 Please add the following statement, or similar, to the Methods: "This study is reported as per the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) guideline (SChecklist)." The STROBE guideline can be found here: http://www.equator-network.org/reporting-guidelines/strobe/. When completing the checklist, please use section and paragraph numbers, rather than page numbers.

*Did your study have a prospective protocol or analysis plan? Please state this (either way) early in the Methods section.

a) If a prospective analysis plan (from your funding proposal, IRB or other ethics committee submission, study protocol, or other planning document written before analyzing the data) was used in designing the study, please include the relevant prospectively written document with your revised manuscript as a Supporting Information file to be published alongside your study, and cite it in the Methods section. A legend for this file should be included at the end of your manuscript.

b) If no such document exists, please make sure that the Methods section transparently describes when analyses were planned, and when/why any data-driven changes to analyses took place.

c) In either case, changes in the analysis-- including those made in response to peer review comments-- should be identified as such in the Methods section of the paper, with rationale.

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Comments from the reviewers:

Reviewer #1: Thanks for the opportunity to review your manuscript. My role is as a statistical reviewer, and so my comments and queries focus on the data and the analysis in this work (and the presentation of these). This manuscript aims to estimate the sensitivity of diagnosis for serious mental illness during general hospital admissions (i.e. not to specialist mental health units), and whether this varies according to patient characteristics.

I think this is a well-written and presented manuscript. The sensitivity analyses are appropriate and anticipated my initial thoughts about the missing data issues we usually encounter from routinely-collected data.

The analyses looking at how patient-level factors are associated with missed diagnosis are estimated using logistic regression. The description of the odds ratios, i.e. 'those from Black African/Caribbean backgrounds were 1.38 … times more likely' is incorrect, as an odds ratio will be a biased estimator of a rate ratio where the outcome (missed recording of the mental health diagnosis) is very common (e.g. ~50% overall). Either the analyses should be updated to estimate a rate ratio (i.e. log-binomial model or similar) or the wording should be adjusted when describing the odds ratios throughout the manuscript.

I found it slightly confusing to go from the outcome being sensitivity in the first part of the manuscript but the regression analyses then invert the outcome to be missed diagnoses.

Specific comments:

P4, Paragraph 1. There are details of the source database and the linkage method used, is there any information available on the quality assurance procedures and the estimate false positive rate of record matching?

P6, Paragraph 2. Is the 'chi square test for trend' the Cochrane-Armitage test?

P6, Paragraph 3. What was the fraction of missing information found after imputation, and is the number of imputed datasets appropriate for the observed FMI?

Which version of Stata was used?

What type of model was used for each of the variables in the MI with chained equations? i.e. for categorical variables was this logistic or discriminant analysis?

P7, Paragraph 4. What was the association between number of hospital admissions, and how would this be interpreted from the regression model/Table 4 given it's log-transformed? (also p25 appendix 5)

P7, Paragraph 5. There is an overall increase in trend but Figure 2 reveals that most of the change was from 2009 to 2012, rather than a linear increase over time.

P11, Paragraph 3. Is there a way with the datasets to zoom in specific hospital admissions where an accurate mental health diagnosis would equivocally be needed? This is more of a curious question than something that needs addressing the manuscript.

P22, Appendix 2. One of the few drawbacks to a large dataset like this is that Chi-square tests effectively just signal a large sample size - I would suggest including a measure of effect size (RR/OR) with corresponding CIs as well as a p-value and summary data.

P22, Appendix 4. Is the CI for the sensitivity (here and elsewhere in the MS) from a normal-approximation method or something else?

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Reviewer #2: Thanks for the opportunity to peer review this well-conducted study.

It addresses an important topic - the disparities in mortality and morbidity, largely due to preventable medical conditions, that are experienced by people with severe mental illnesses, and the role of health service integration and diagnostic practice as mechanisms for this health inequality.

The study uses electronic medical records to link specialist mental health service users' clinical and sociodemographic profiles with their episodes of care in emergency medical facilities, and specifically focuses on the accuracy (sensitivity) of psychiatric diagnoses that are recorded in emergency medical care.

Overall, this study makes good use of available data to provide examination of a key aspect of care: the methods, measures, and approach to analysis are clearly reported, the ability to adjust for the effects of a range of sociodemographic factors is particularly informative (and the findings and implications for minority ethnic groups with SMI are rightly highlighted), as is the exploration of time trends in diagnostic accuracy over the ten year study period.

My main concern relates to the extent to which this measurable aspect of care - explicit psychiatric diagnosis recorded during the general medical /emergency episode - may be associated with differential clinical outcome. It clearly is an accessible indicator of integrated care. But a little more consideration of the impacts of this being noted or omitted would be helpful. The authors stress the importance of 'specific [psychiatric] diagnosis for treatment outcomes and continuity of care'.. and note that this may facilitate '..a referral to more specialist mental healthcare service such as liaison psychiatry'.. and that it will enable '..continuity of previous care such as medication, and tailoring of inpatient and discharge plans to account for comorbidity'.

These are important principles of care, but, could some more practical/ concrete inference or example be made? Could it be useful to consider the types of presenting condition in the emergency setting to clarify relationships between diagnosis and care? e.g. Appendix 1 - indicates common primary diagnoses to be injury and poisoning, - where mental ill-health may be directly related to event or risk.. or endocrine/metabolic disease where anti-psychotic medication may be especially relevant.. or maybe a broader statement that for many of the presenting problems, psychotropic medications may be directly relevant to management decisions such as prescribing.

I have some minor suggestions for the introduction section.

The initial quantification of SMI prevalence ('..affect[s] around 3% of the population..') uses lifetime prevalence derived from a single, albeit high quality, general population study in Finland; it could be useful to clarify the statement - at least by noting this rather high estimate is for lifetime prevalence, and the authors might consider alternatively noting the point or period estimates available from systematic review/s.

The authors note limited studies concerning mental disorder recognition in medical settings, but may wish to modify their statement about the paucity of research in this area, noting that Mitchell et al (2012) have a conducted systematic review of alcohol misuse identification (2012), and they might also allude to the larger body of work (some of which they later cite) on psychosis recognition in primary care.

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Reviewer #3: This analysis aimed to examine the recording of SMI in secondary physical healthcare settings (general hospitals) compared to that recorded in secondary mental healthcare in (1) sensitivity, (2) time trends, (3) factors associated with accurate coding. The paper is nicely structured and written.

In the rationale, the authors describe that understanding coding of psychiatric disorders in general hospitals will facilitate our understanding of the healthcare received for physical needs in people with SMI, i.e. from HES can we identify people with SMI who are in hospital for any reason, and how accurate the diagnostic labels are. I agree this is important, as physical health problems appear to be a main driver of the mortality and morbidity gap.

Unfortunately the analysis carried out is sub-optimal to address this aim.

The main problem is that the primary diagnosis in HES relates to the reason for admission. "The primary diagnosis is the main reason the patient is receiving care in hospital, while the secondary diagnoses are relevant co-morbidities and external causes if these have been identified." - this is taken from a HES metadata guide (available here https://digital.nhs.uk/data-and-information/publications/statistical/hospital-admitted-patient-care-activity/2018-19)

Accordingly, someone with SMI being admitted for a respiratory condition, for example, would have a primary code from ICD-10 chapter J, and may have a secondary code about their psychiatric diagnosis from chapter F (if it was recorded!). By focusing only on the primary code, you have primarily selected on psychiatry related admissions, and not on admissions with a primary indication for physical health problems. The HONOS data by recording status in Table 1 and Appendix 6 appear to indicate this. Because your rationale is based on physical health problems, I do not think this was your intention. If it was, then please reframe the paper accordingly (i.e. you wanted to look at psychiatric codes for psychiatric problems admitted through emergency admissions in general hospitals).

My main recommendation is to re-analyse the data using the secondary codes in HES, and then consider the effect of the primary versus secondary codes, perhaps through sensitivity analyses. You could further investigate the issues of coding with reason for admission with the use of consultant speciality codes.

Although this means reconstructing and re-analysing the whole dataset, the value of your findings the research community is such that this is well worth doing. If this is not done, then I think there are only very limited information to be gained as your HES analytic sample is overly selected.

The below relates to a revised analysis.

Query the terminology of 'gold-standard' used to describe the diagnosis applied in the SLaM data, perhaps 'reference standard' is more appropriate? What is the 'right' label and how it is arrived at (and who decides) in this context is appropriate to be elaborated on, however briefly, and there is a multitude of literature on this.

Could the objectives be made more clear in that they relate to the general hospitals, e.g. … association of sociodemographic and clinical factors with [more] accurate psychiatric recording [in general hospital records]

Methods

How much influence did the hierarchical classification have? How did you operationalise this, for example if someone has 2 diagnoses in SLaM, but you found a diagnosis of bipolar disorder in HES and they were assigned as having schizophrenia in your algorithm, was this counted as missed code? You find that sensitivity for F31 is lower than for F20, how much of this is due to the method? I may be missing something but I don't see why this classification is necessary if some people have more than one SMI, please either justify, or do not apply the hierarchy. It would be helpful if you provided the number with more than one diagnosis code in SLaM.

Further elaboration on HES, for example its function and purpose, who enters the data, how the diagnoses are arrived at, the purpose and structure of diagnostic data contained within it (e.g. primary versus secondary), and any changes to the recording of these data over the studies time period are needed.

You could move the rationale behind focusing on emergency admissions from the sensitivity analysis to the main methods section on HES. Please reference the evidence for rationale.

How did the covariates of age, sex, ethnicity and marital status get into the SLaM records? E.g. via an admissions interview, or self-reported questionnaire, or from relatives, or a mixture? Are there any indications of its accuracy?

Please note which address was used to generate IMD, e.g. first, last contact in the dataset? It isn't usual to analyse an IMD mean score; usually IMD is categorised into quintiles or deciles (quartiles for very small datasets).

Please consider how the use of multiple admissions data can be analysed (see point in Results and Discussion below)

Results and discussion

In figure 1, please add the final number of people with SMI with a discharged admission (i.e. analysed number). It is noted that 13,786 people were admitted, but please clarify in the figure that when the 25,941 discharged admissions were discarded that none of your sample were lost?

Page 7 under Sensitivity of general hospital SMI diagnosis: "We found that 10,574 of 13,786 people with SMI *were diagnosed with any psychiatric illness* during their…" I don't think this is what you meant, but the terminology is also used in the discussion, and in the Interpretation section of the Abstract. The distinction of being 'diagnosed' with SMI in hospital versus 'a pre-existing SMI being noted' could be more clear and consistent. They may both be true, in which case this needs commenting on. As such, it becomes important to understand and comment further on *how* existing diagnoses make it into HES related to patient / informant disclosure, or linkage to other systems? Is there any literature to bring in here? How these diagnoses are recorded is central to your analysis, so consider introducing some of what you write across page 10/11 into the introduction.

Related to this is the important point you make about a previous admission being a source of information for a current admission. Did you not consider adding this to your analysis plan? Even though you don't have true 'incident' admission, you might be able to pick this up as a signal. If your aim is to help researchers understand SMI recording in HES better, then pointing them to this potential source of under-reporting in more distant versus recent more admissions might be important.

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Any attachments provided with reviews can be seen via the following link:

[LINK]

22 May 2020

Submitted filename: Mansour May 2020 Response to reviewers.docx

Click here for additional data file.

9 Jul 2020

Dear Dr. Mansour,

Thank you very much for re-submitting your manuscript "Accuracy of severe mental illness diagnosis in English general hospitals 2006-2017: a registry linkage study" (PMEDICINE-D-20-00812R2) for review by PLOS Medicine.

I have discussed the paper with my colleagues and it was also seen again by two reviewers. I am pleased to say that provided the remaining editorial and production issues are dealt with we are planning to accept the paper for publication in the journal.

The remaining issues that need to be addressed are listed at the end of this email. Any accompanying reviewer attachments can be seen via the link below. Please take these into account before resubmitting your manuscript:

[LINK]

Our publications team (plosmedicine@plos.org) will be in touch shortly about the production requirements for your paper, and the link and deadline for resubmission. DO NOT RESUBMIT BEFORE YOU'VE RECEIVED THE PRODUCTION REQUIREMENTS.

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Please also check the guidelines for revised papers at http://journals.plos.org/plosmedicine/s/revising-your-manuscript for any that apply to your paper. If you haven't already, we ask that you provide a short, non-technical Author Summary of your research to make findings accessible to a wide audience that includes both scientists and non-scientists. The Author Summary should immediately follow the Abstract in your revised manuscript. This text is subject to editorial change and should be distinct from the scientific abstract.

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We ask every co-author listed on the manuscript to fill in a contributing author statement. If any of the co-authors have not filled in the statement, we will remind them to do so when the paper is revised. If all statements are not completed in a timely fashion this could hold up the re-review process. Should there be a problem getting one of your co-authors to fill in a statement we will be in contact. YOU MUST NOT ADD OR REMOVE AUTHORS UNLESS YOU HAVE ALERTED THE EDITOR HANDLING THE MANUSCRIPT TO THE CHANGE AND THEY SPECIFICALLY HAVE AGREED TO IT.

Please ensure that the paper adheres to the PLOS Data Availability Policy (see http://journals.plos.org/plosmedicine/s/data-availability), which requires that all data underlying the study's findings be provided in a repository or as Supporting Information. For data residing with a third party, authors are required to provide instructions with contact information for obtaining the data. PLOS journals do not allow statements supported by "data not shown" or "unpublished results." For such statements, authors must provide supporting data or cite public sources that include it.

If you have any questions in the meantime, please contact me (cmoyer@plos.org) or the journal staff at plosmedicine@plos.org.

We look forward to receiving the revised manuscript by Jul 16 2020 11:59PM.

Sincerely,

Caitlin Moyer, Ph.D.

Associate Editor

PLOS Medicine

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Requests from Editors:

1.Data Availability statement: You have noted that “Due to the nature of the data, open access is not possible. However, data is available subject to the standard conditions of the CRIS at Maudsley database. Please contact the CRIS administrator for details.” Please provide contact information (email address and/or web link) for the CRIS administrator.

2.Abstract: Methods and Findings: As noted in response to reviewer 3, consider replacing the term “gold standard” with “reference standard”

3.Abstract: Conclusions: We suggest revising the first sentence to “Our findings suggest that there have been improvements in recording of SMI diagnoses, but concerning under-recording,

especially in minority ethnic groups, persists.” or similar.

4. Author Summary: Why was this study done?: Please revise to:

--People with severe mental illness (SMI) have increased mortality and morbidity, largely due to preventable medical conditions, and these disparities have the potential to be ameliorated through better healthcare integration.

5. Author Summary: What do these findings mean?: Please revise to:

--A limitation of our study is that our use of electronic health records for the reference-standard means that some people with SMI may have been misclassified.

6. Introduction: Final paragraph, bottom of page 4: Please describe the specific objectives of your study in paragraph form, rather than presenting as a numbered list.

7.Methods: Please specify the nature of participant consent, including whether informed consent was written or oral.

8.Methods: Page 8: Please remove the word “the” in the following sentence: “The We then conducted logistic regression on each imputed dataset before combining coefficients using Rubin’s rules.”

9.Results: top of page 10: We suggest revising this sentence for clarify, to: “Marital status was also associated with diagnostic accuracy; single people were less likely (OR = 0.78 (95% CI 0.63, 0.92; p < 0.001)) to have no psychiatric disorder recorded compared to married individuals, as were divorced or widowed people.” Please include the OR, 95% CIs and p values for diagnostic accuracy for individuals who are divorced or widowed.

10.Discussion: first paragraph: We suggest revising the following sentence to “Our findings suggest that accuracy of SMI recording in general hospitals has improved over time with sensitivity for any psychiatric diagnosis in those experiencing emergency hospital admissions increasing from 47.8% in 2006 to 75.4% in 2017.” or similar

11.Discussion: first paragraph: We suggest clarifying this sentence by revising to: “Unrecorded psychiatric diagnosis was more likely in people with milder symptoms or higher ADL scores, married individuals, and ethnic minority groups.” or similar

12.Discussion: Please remove sub headings from the Discussion section (e.g.“strengths and limitations”)

13.Discussion: Page 13: Please spell out the abbreviation for PPV.

14.Discussion: Top of page 14: We suggest revising to: “Whilst our findings suggest that sensitivity is improving over time, there were around 30% of admissions where people with established SMI did not have any psychiatric diagnosis recorded, suggesting that more needs to be done by policymakers to bridge the gap for ‘whole-person centered’ care”

15. Discussion: Page 14: We suggest revising to: “Better data sharing between physical and mental health services such as through harmonised clinical records could improve accuracy of mental illness in physical healthcare, and physical illness in mental healthcare services, to move towards truly integrated healthcare for people with mental illness.”

16. Declaration of interests, Ethical Approval, and Data Sharing: Please remove these sections from the manuscript and make sure that the relevant information is included in the “Competing Interests” , Ethics Statement, and Data Availability sections of the manuscript submission form.

17. Reference list: Please use the "Vancouver" style for reference formatting, and see our website for other reference guidelines https://journals.plos.org/plosmedicine/s/submission-guidelines#loc-references

18. Figure 1: Please include a descriptive legend. Please spell out abbreviations for SMI, CRIS, ICD-10, HES. Please include the different F codes in the legend, or indicate where they can be found.

19.Figure 2: Please include a legend describing the figure, including an explanation of the trend line and what is represented by the error bars.

20. Table 1: Please include 95% CIs in addition to p values.

21. Table 2 and Table 3: Please include a descriptive legend. Please define abbreviations for SMI, HES, CI. Please describe the range of F code diagnosis for each category.

22. Table 4: Please indicate in the legend, the factors that are adjusted for in the adjusted analyses.

23. S2 Table: Please update the “significance test” column with what is being described (please include the test statistic, 95% CIs and p values). Please indicate if the values for other columns indicate n and percent.

24. S5 Table and S6 Table: Please also include the unadjusted results. In the legend, please indicate which factors are adjusted for in the analyses.

25. In-text citations throughout the manuscript: Instead of superscript notation, please place in-text citations in brackets before punctuation, like this [1].

Comments from Reviewers:

Reviewer #1: Thanks for the revised manuscript and detailed responses to my comments. I am happy with the revisions made and from the statistical side I don't think any further changes need to be made.

94% succesfully matching overall is reasonable for admission data with deterministic matching and I'm comfortable with the data quality in this context. The presenteation of the logged admission data is more easily understood now. The 'missed diagnosis' part of the analysis is clearer now and I agree that it does not need to be changed further. Supp table 1 is interesting it's reassuring that MH diagnosis are accurately being captured for in mental and behavioural disorders admissions. I don't think this needs further comment but hopefully this can be a useful resource for other researchers interested in metnal health in other conditions (the low rate in preganancy/childbirth is worrying!).

Reviewer 3 has made some valuable comments on this manuscript and it sounds like they have familiarity with this particular dataset. Your responses look reasonable to me and I'm interested in seeing R3's take on these.

Reviewer #3: I am satisfied that the authors have addressed my concerns. This paper is a useful and careful contribution to the literature.

Any attachments provided with reviews can be seen via the following link:

[LINK]

21 Jul 2020

Dear Mr Mansour,

On behalf of my colleagues and the academic editor, Dr. Stephanie Prady, I am delighted to inform you that your manuscript entitled "Accuracy of severe mental illness diagnosis in English general hospitals 2006-2017: a registry linkage study" (PMEDICINE-D-20-00812R3) has been accepted for publication in PLOS Medicine.

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