Fatal opioid overdoses during and shortly after hospital admissions in England: A case-crossover study.

BACKGROUND: Hospital patients who use illicit opioids such as heroin may use drugs during an admission or leave the hospital in order to use drugs. There have been reports of patients found dead from drug poisoning on the hospital premises or shortly after leaving the hospital. This study examines whether hospital admission and discharge are associated with increased risk of opioid-related death. METHODS AND
FINDINGS: We conducted a case-crossover study of opioid-related deaths in England. Our study included 13,609 deaths between January 1, 2010 and December 31, 2019 among individuals aged 18 to 64. For each death, we sampled 5 control days from the period 730 to 28 days before death. We used data from the national Hospital Episode Statistics database to determine the time proximity of deaths and control days to hospital admissions. We estimated the association between hospital admission and opioid-related death using conditional logistic regression, with a reference category of time neither admitted to the hospital nor within 14 days of discharge. A total of 236/13,609 deaths (1.7%) occurred following drug use while admitted to the hospital. The risk during hospital admissions was similar or lower than periods neither admitted to the hospital nor recently discharged, with odds ratios 1.03 (95% CI 0.87 to 1.21; p = 0.75) for the first 14 days of an admission and 0.41 (95% CI 0.30 to 0.56; p < 0.001) for days 15 onwards. 1,088/13,609 deaths (8.0%) occurred in the 14 days after discharge. The risk of opioid-related death increased in this period, with odds ratios of 4.39 (95% CI 3.75 to 5.14; p < 0.001) on days 1 to 2 after discharge and 2.09 (95% CI 1.92 to 2.28; p < 0.001) on days 3 to 14. 11,629/13,609 deaths (85.5%) did not occur close to a hospital admission, and the remaining 656/13,609 deaths (4.8%) occurred in hospital following admission due to drug poisoning. Risk was greater for patients discharged from psychiatric admissions, those who left the hospital against medical advice, and those leaving the hospital after admissions of 7 days or more. The main limitation of the method is that it does not control for time-varying health or drug use within individuals; therefore, hospital admissions coinciding with high-risk periods may in part explain the results.
CONCLUSIONS: Discharge from the hospital is associated with an acute increase in the risk of opioid-related death, and 1 in 14 opioid-related deaths in England happens in the 2 weeks after the hospital discharge. This supports interventions that prevent early discharge and improve linkage with community drug treatment and harm reduction services.

Introduction

People who use illicit opioids such as heroin sometimes report unpleasant experiences when admitted to the hospital for medical treatment. In some cases, hospital staff are suspicious when patients describe their symptoms, believing they are “drug seeking” [1,2]. In other cases, staff are concerned about the safety of giving opioid-based medicines to patients who may be taking opioids from other sources. Sometimes, staff are too busy to verify a patient’s usual dose of methadone or buprenorphine or do not have sufficient knowledge or training about opioid dependence [3,4]. These factors can lead to inadequate pain control or delayed or insufficient opioid substitution [4]. Patients have also said that some staff are judgmental about illicit drug use and therefore hide the fact that they use drugs [5].

Opioid withdrawal can lead patients to leave the hospital to buy drugs. Some bring a supply into the hospital to keep them going, and some arrange for dealers to visit them while they are staying on a ward [3]. Surveys of hospital patients who use illicit opioids suggest that in-hospital use is common [5,6]. Using drugs in the hospital is associated with high-risk practices, including using alone in a toilet cubicle, rushing the procedure, taking a bigger dose to reduce the need for top-ups, and not having the usual equipment such a new needle and syringe, a tourniquet, and sterile water [5].

There have been newspaper reports of hospital patients taking heroin and being found dead in a hospital toilet, car park, or another public place close to the hospital [7,8]. However, we do not know how many times this has happened or if hospital admissions increase the risk of opioid-related death. The period after discharge from the hospital may also be risky, because opioid tolerance may be reduced, and patients may be unwell and more susceptible to a drug overdose. This study examines whether the risk of opioid-related death is increased during hospital admission and in the period after discharge. We expected these periods to be associated with increased risk of opioid-related death.

Methods

Ethical approval

This study was approved by the Public Health England Research Ethics and Governance Group (PHE REGG), ref R&D412, on October 26, 2020. Data were anonymised before analysis, and personal identifiers such as name, address, or NHS number were not available to the research team.

This is a case-crossover study estimating the risk of opioid-related deaths associated with admission to the hospital. Case-crossover studies measure acute “triggering” effects of transient exposures [9]. They make within-subject comparisons in the exposure status when an event occurred (in this study, when someone died after using opioids) with the exposure status at other times. Case-crossover studies are one of a family of self-controlled study designs that only include participants who experienced an event. These designs focus on the timing of an event, in contrast to traditional epidemiological studies that focus on who experiences an event. This study is addressing the question “do hospital admission and discharge trigger opioid-related deaths?” We chose this design because it allows inclusion of a large proportion of cases and is statistically powerful, and it controls confounding more effectively than a cohort design that might compare people who use opioids and are admitted to the hospital with those not admitted.

Study participants

We studied opioid-related deaths among people in England aged 18 to 64 between January 1, 2010 and December 31, 2019 based on the date of death (rather than registration). We defined opioid-related deaths as those with an underlying cause of drug poisoning (using the UK Office for National Statistics definition of drug poisoning [10]: the International Classification of Diseases-10th Revision [ICD-10] codes X40-X44, X60-X64, X85, or Y10-14) and where an opioid is also specified (ICD-10 codes T40.0-T40.4 and T40.6) or if opioid dependence (ICD-10 F11) was the underlying cause of death. Coroners investigate drug-related deaths in England, including analysis of toxicology results. This means that the causes of death in this study have been validated to a greater degree than for most deaths. For simplicity, we refer to these deaths as “fatal opioid overdoses” in the title and author summary, although we use “opioid-related deaths” elsewhere to reflect the difficulty of attributing deaths to one specific drug.

Data were drawn from a database that includes mortality data from the UK Office for National Statistics and hospital records from the national Hospital Episode Statistics database, with probabilistic linkage between the 2 sources using the National Health Service (NHS) number, date of birth, sex, and home address [11]. This database does not include deaths if no linkage is found (most likely because the decedent was never admitted to the hospital). Using published mortality data [12], we estimated that 11.8% of deaths were excluded (Fig 1).

Fig 1

Derivation of study population.

Control days

For each case, we sampled 5 days at random from the period 730 to 28 days prior to death, limiting to the same day of the week as death (Fig 2). The reason for limiting to the same day of the week is that both drug-related deaths and hospital admissions vary by weekday, which may cause confounding. Fewer hospital admissions and discharges occur at the weekend [13], while deaths due to drug poisoning peak on Saturday [14]. We chose the period 730 to 28 days prior to death to avoid control days that are too close to death and may have correlated exposures, while also allowing reasonable exchangeability in the probability of hospital admission. We then observed the exposure status on the control days. In sensitivity analysis, we repeated the study with control days sampled from the periods 365 to 28 days before death and 1,095 to 28 days before death.

Fig 2

Illustration of patient timelines and selection of control windows for 5 participants.

Exposure status at time of opioid-related death is compared with 5 days sampled between 730 and 28 days before death.

Exposure status

Control days were classified as (A) currently admitted to the hospital (days 1 to 14 after admission); (B) currently admitted to hospital (15+ days after admission); (C) days 1 or 2 after discharge; (D) days 3 to 14 after discharge; and (E) neither hospitalised nor recently discharged (i.e., time spent in the community).

To understand additional differences in the risk of opioid-related death, we further classified admissions as psychiatric or nonpsychiatric; whether postdischarge risk periods followed discharge against medical advice or planned discharge; and whether postdischarge periods followed admissions of 1 day, 2 to 6 days, or 7+ days. We classified admissions as “psychiatric” if the patient was admitted to a specialist mental health provider or if the lead treatment specialty was recorded as “mental health.” We classified discharges as “against medical advice” if the doctor recorded the discharge method as “self-discharged or discharged by a relative or advocate” [15].

Some deaths occurred in hospital. If a patient was admitted due to drug poisoning (see Fig A in S1 Appendix for detail on how we classified these admissions), we moved the date of death to immediately prior to admission and censored the final admission. If a patient died on the day of discharge and hospital data showed that the patient died in the hospital, the death was assigned to exposure status A or B (currently in hospital), while if the hospital data showed that the patient was discharged alive, exposure status C was assigned (discharged in the past 1 to 2 days).

For 28 individuals, we found discordant data in which hospital discharge dates were recorded long after death dates. These discrepant dates are likely due to failed linkage rather than inaccuracies in hospital discharge or death dates, and we excluded these individuals from the study. A small number of individuals had admissions in the 3 days after death, for example, for organ donation, and these cases were retained.

Statistical analysis

We described the characteristics of deaths included in the study and then used conditional logistic regression (stratified by individual) to estimate the association between hospital admission and opioid-related death. The reference category was time neither admitted to hospital nor recently discharged (category E). We repeated the analysis with stratification by sex. We published a protocol before doing analysis [16]. In our protocol, the primary analysis was a self-controlled case series design and have included results using this approach in Fig D and Table B in S1 Appendix. We chose a case-crossover design instead because it allowed analysis of exposures with varying durations. Analysis was conducted using R version 4.0.3.

This study is reported as per the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) guideline [17] (see Table D in S1 Appendix).

Results

Description of cases

The study included 13,609 opioid-related deaths. A total of 9,765/13,609 decedents (71.8%) were male, 12,437/13,609 (91.4%) had “white” ethnicity, the median age at death was 42 (interquartile range [IQR] 35 to 49), and decedents predominantly lived in deprived neighbourhoods. Table 1 summarises the characteristics of cases.

Table 1

Characteristics of individuals included in the study.

Variable	Level	Number (%)
Total		13,609 (100.0)
Age at death	Median [IQR]	42 [35–49]
	Mean [SD]	41.9 [10.0]
Sex	Male	9,765 (71.8)
	Female	3,844 (28.2)
Ethnicity*	White British, White Irish, or Other White	12,437 (91.4)
	Asian or Asian British	193 (1.4)
	Other	145 (1.1)
	Black or Black British	133 (1.0)
	Mixed	99 (0.7)
	Unknown	602 (4.4)
Deprivation (IMD)	1: Most deprived	6,067 (44.6)
	2	3,370 (24.8)
	3	1,940 (14.3)
	4	1,328 (9.8)
	5: Least deprived	790 (5.8)
	Missing	114 (0.8)
Year of death	2010–2011	2,127 (15.6)
	2012–2013	2,442 (17.9)
	2014–2015	3,094 (22.7)
	2016–2017	3,323 (24.4)
	2018–2019	2,623 (19.3)
Geographical region	North West	2,550 (18.7)
	South East	1,894 (13.9)
	Yorkshire and The Humber	1,636 (12.0)
	South West	1,384 (10.2)
	West Midlands	1,356 (10.0)
	London	1,236 (9.1)
	East of England	1,184 (8.7)
	North East	1,112 (8.2)
	East Midlands	798 (5.9)
	Missing	459 (3.4)
Proximity in time to hospital admission	Died in hospital (admitted after opioid use)	656 (4.8)
	Died in hospital (admitted for other reasons)	236 (1.7)
	Died in the 14 days after discharge	1,088 (8.0)
	Not in hospital or within 2 weeks of discharge	11,629 (85.5)

* Ethnicity is derived from the hospital data. Where a participant had hospital admissions with different recorded ethnic categories, we used the most commonly recorded category or the most recent category where multiple categories had the same frequency.

IMD, Index of Multiple Deprivation, derived from the Lower Super Output Area of the patient’s home address; IQR, interquartile range.

Most opioid-related deaths (11,629/13,609, 85.5%) did not occur in the hospital or within 14 days of discharge. A total of 656/13,609 deaths (4.8%) occurred in hospital following admission due to drug poisoning. 236/13,609 deaths (1.7%) occurred during a hospital admission where the patient was admitted for a reason other than drug poisoning. The remaining 1,088/13,609 deaths (8.0%) occurred in the 14 days after discharge. Based on published data, we estimated that our study excluded 11.8% of deaths because the decedent was never admitted to the hospital (Fig 1). This suggests that in the population, 7.1% of opioid-related deaths occur in the 14 days after discharge.

Characteristics of hospital admissions in the 2 years prior to death

A total of 37,570 hospital admissions occurred in the 730 days prior to death, with a median of 1 (IQR 0 to 4) per individual. 3,742/37,570 (10.0%) of admissions ended in discharge against medical advice, and 3,418/37,570 (9.1%) were classified as psychiatric admissions. Table 2 summarises the characteristics of hospital admissions in the 2 years prior to death.

Table 2

Characteristics of hospital admissions in the 2 years prior to opioid-related deaths in England from January 1, 2010 to December 31, 2019.

Variable	Level	Number (%)
Total		37,570 (100.0)
Discharged AMA	Yes	3,742 (10.0)
	No	33,828 (90.0)
Length of admission (days)	1	15,026 (40.0)
	2–6	14,615 (38.9)
	7+	7,929 (21.1)
	Median [IQR]	2 [1–5]
	Mean [SD]	8.7 [38.2]
Psychiatric admission	No	34,152 (90.9)
	Yes	3,418 (9.1)
Drug poisoning	No	32,625 (86.8)
	Yes	4,945 (13.2)

AMA, discharge against medical advice; IQR, interquartile range.

Results of case-crossover analysis

Days 1 to 14 of hospital admissions had a similar risk of opioid-related death as periods in the community (conditional odds ratio of 1.03; 95% CI 0.87 to 1.21; p = 0.95). Days 15+ of hospital admission were associated with lower risk of opioid-related death (conditional odds ratio of 0.41; 95% CI 0.30 to 0.56; p < 0.001).

The risk of opioid-related death increased substantially after discharge, with a conditional odds ratio of 4.39 (95% CI 3.75 to 5.14; p < 0.001) in days 1 to 2 after discharge and 2.09 (95% CI 1.92 to 2.28; p < 0.001) in days 3 to 14. The risk was higher for people discharged after a psychiatric admission and for people who left the hospital against medical advice. Longer admissions were associated with greater risk of opioid-related death after discharge, and we observed this gradient in days 1 to 2 after discharge and days 3 to 14. Results of the case-crossover analysis are shown in Fig 3. Sex-stratified results suggested similar associations for men and women. Sensitivity analysis with control days sampled from 365 to 28 days before death showed slightly smaller associations between hospital discharge and opioid-related death, and sensitivity analysis with control days sampled from 1,095 to 28 days before death showed slightly larger associations. Full results for stratified analyses and sensitivity analyses are provided in Figs D and E and Table C in S1 Appendix.

Fig 3

Risk of opioid-related death according to time proximity to hospital admission (results of case-crossover analysis).

Discussion

In this nationwide study of opioid-related deaths in England over 10 years, we found that the risk of death is very high in the 2 weeks after hospital discharge, and this period accounts for 1 in 14 deaths. Patients who leave the hospital against medical advice and those leaving the hospital after a longer stay have higher risk. We also identified 236 cases where the data suggest that an opioid-related death occurred during a hospital admission. While each of these cases is a serious and potentially preventable incident, the overall results of our study suggest that the risk of opioid-related death during hospital admissions is similar or lower than during time spent in the community.

Comparison with other studies

We are aware of one other study that has investigated deaths due to drug poisoning in relation to hospital admissions [18,19]. This study used a cohort of people registered for drug treatment in Scotland and reported the rate of drug-related deaths according to time proximity to hospital admissions. The referent in this study was the mortality rate among people who were never admitted to the hospital, and the results may therefore reflect differences between people who were admitted to the hospital and those who were not. The associations are much greater than in our study; for example, the mortality rate in the 28 days after discharge is 15 times that of individuals who were never admitted. Even periods more than 1 year after discharge have 3 times the rate, suggesting that these differences are unlikely to relate to the hospital admission itself.

Research has shown that other life events are also associated with opioid-related deaths. In particular, studies in several countries have found a high risk of drug-related death immediately after the release from prison [20-23] and a protective effect of opioid agonist therapy during this period [24]. Another example is the first 2 weeks after cessation of community-based opioid agonist therapy [25]. These are times when opioid agonist therapy is interrupted, tolerance is changing, and people may get drugs from a different source or use drugs in different ways.

Strengths and limitations

By studying all opioid-related deaths in England, we were able to include people who have never been in drug treatment, a group that is often excluded from studies of this population. The design also meant that we were able to estimate the absolute number of opioid-related deaths that occurred during hospital admissions (236 over 10 years). It also provided power to observe the risk associated with different types and durations of hospital admission, which would be challenging even with an extremely large cohort study.

While the self-controlled methodology eliminates time-invariant confounders, the results may partially be explained by escalating drug use or deteriorating health over time. It is possible, for example, that someone in poor health or at times of more intense drug use would be more likely to be admitted to the hospital and more susceptible to death after using opioids. We tried to limit this type of confounding by selecting control days no more than 2 years before death. The density of hospital admissions does increase before death, but this increase is gradual (Fig C in S1 Appendix).

Our data did not include detailed information about drug use or enrollment in drug treatment services. We therefore could not confirm that participants were using drugs on the control days. People who used illicit opioids during the past decade in England have mostly been using drugs for many years. For example, in a cross-sectional survey of people who injected drugs in England in 2019, the median duration of drug use was 16 years, and only 6% of participants had injected for less than 2 years [26].

Our use of a national hospital dataset meant that we were able to include all hospital admissions in England, but it also meant that we had limited detail about individual patients. One limitation relates to “discharge against medical advice.” This was a binary variable in our analysis, when in reality there are a range of scenarios where discharge is negotiated between patients and hospital staff. A second limitation relates to our classification of hospital admissions ending in opioid-related death. If an admission ended in opioid-related death and the primary cause of admission was a common complication of opioid poisoning, such as cardiac arrest, then we assumed that the indication for admission was related to opioid poisoning. This is a sensitive approach, and, in some cases, the opioid poisoning may have happened while the patient was in the hospital. Therefore, it is possible that we have underestimated the association between hospital admissions (exposure periods A and B) and opioid-related death.

Although our definition of opioid-related death is widely used, its validity is not known. It can be difficult to determine the cause of death when someone dies suddenly and alone. It is possible that some participants in our study who died shortly after a hospital admission died for reasons related to the admission (such as an acute infection) rather than due to opioid poisoning. If the cause of death is unclear but the individual was known to use illicit drugs, a doctor may assume that the death was primarily due to drug use. This type of misclassification may partly explain the association between hospital discharge and opioid-related death.

Interpretation

We identified 3 reasons why discharge from the hospital may be associated with increased risk of opioid-related death. First, opioid tolerance could reduce during an admission. Animal models suggest that opioid tolerance has a half-life of 6 days [27], supporting reductions in tolerance after admissions of 2 to 6 days or 7+ days (the categories used in our analysis). We also saw increased risk after admissions of only 1 day, suggesting that other mechanisms are also important. Second, opioid agonist therapy may be interrupted or reduced either at admission or discharge (see Box 1). This may further contribute to reduced tolerance and increase the likelihood that patients will use riskier opioids such as injected heroin. Third, patients may have an acute illness that makes them more vulnerable to death after using opioids, particularly respiratory problems such as pneumonias and acute exacerbations of chronic obstructive pulmonary disease. These are common reasons for hospital admission in this population [28] and increase the risk associated with central nervous system depressants. Painful conditions may also be important because they are associated with increased use of illicit and prescribed opioids.

We observed a high risk soon after hospital discharge and estimate that 1 in 14 opioid-related deaths in the population occur during these 2 weeks. The safety of discharge may be improved with better linkage to community drug treatment services, and there is a need for research into interventions that can improve continuity of opioid agonist therapy between community and hospital settings. It is thought that half of people who die after using illicit opioids in England have never been in contact with drug treatment services [29]. Initiation of opioid agonist therapy in the hospital is therefore also important. A randomised trial showed that patients that started on buprenorphine in hospital and referred to a community drug service are more likely to continue with treatment and less likely to use illicit opioids, compared with those assigned to opioid detoxification [30]. Hospitals can also provide advice on reducing overdose risk, such as using small test doses and not using drugs alone, and tools to help reduce risk, such as basic life support training and take-home naloxone [31].

We found variation in the risk of opioid-related death in our detailed exposure periods. In particular, the period after discharge against medical advice was associated with opioid-related death. Discharge against medical advice may happen when a patient is experiencing pain or withdrawal and leaves the hospital to use illicit opioids. The finding that the risk of opioid-related death reduces after day 14 of a hospital admission should be treated with caution, because the subset of admissions that are longer than 14 days may be unusual. For example, these patients may have less severe drug dependence or better controlled pain and be less at risk of opioid-related death for these reasons.

In many countries, the average age of people who use illicit opioids is increasing, and the frequency of long-term conditions is also increasing [32]. People who use illicit opioids do not always seek timely healthcare, in part due to fear of stigma, opioid withdrawal in the hospital, and poor pain management [33]. Hospitals that enable patients to disclose illicit drug use without fear of discrimination will be a central element of accessible and high-quality hospital care for this population.

Conclusions

Discharge from the hospital is associated with an acute increase in the risk of opioid-related death, and 1 in 14 opioid-related deaths in England happens in the 2 weeks after the hospital discharge. This supports interventions that prevent early discharge and improve linkage with community drug treatment and harm reduction services.

Box 1. Interpretation by a client representative at a community drug and alcohol service

I know from personal experience and that of my peers that hospitals can be hostile, particularly when you are admitted in an emergency. Planned stays give you time to get prepared and make sure you have enough drugs to carry you through. When the stay is unplanned, you are reliant on the doctors giving you methadone or buprenorphine. They are not experts in this field and can be suspicious or at best conservative with their doses. Although some staff do their best to help, it is often made clear they suspect you are “drug seeking.” You have to beg to get the help you need. Many of my peers have left the hospital early in withdrawal and pain. They might be buying drugs from someone they do not know, perhaps unwell and with reduced tolerance, then using in an alley or public toilet. It is not surprising that so many people die due to drug overdoses in the days after leaving the hospital. Part of the solution is better communication between the local drug services and the hospital. This could help patients get the medication they need to stay in the hospital and help people arrange transport, accommodation, and timely opioid substitution when they leave.

Table A: Distribution of ICD-10 diagnoses in opioid-related deaths in England between January 1, 2010 and December 31, 2019. Table B: Results of alternative self-controlled methodologies. Values are conditional odds ratios of opioid-related deaths (95% CIs). Table C: Results of case-crossover analysis stratified by sex and calendar year of death. Values are conditional odds ratio of opioid-related death (95% CIs). Table D: STROBE Checklist. Fig A: Flowchart showing how the exposure status on the day of death was determined. Fig B: Distribution of age at death for 13,609 people who died due to fatal opioid overdose in England between January 1, 2010 and December 31, 2019. Deaths at ages under 18 or over 65 were excluded from the study. Fig C: Number of hospital admissions in the 730 days prior to death among 13,609 people who died due to opioid overdose in England between January 1, 2010 and December 31, 2019, by 10-day period. Fig D: Results of alternative self-controlled methodologies. The chart shows conditional odds ratios with 95% CIs. Fig E: Results of case-crossover analysis stratified by sex and calendar year of death. Values are conditional odds ratio of opioid-related death (95% CIs). ICD-10, International Classification of Diseases-10th Revision; STROBE, Strengthening the Reporting of Observational Studies in Epidemiology.

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15 Mar 2021

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Comments from the reviewers:

Reviewer #1: Thanks for the opportunity to review this paper, which uses large nationally representative datasets and a thoughtful approach to answer a critical public health question. I think these results should be published in a high impact open access journal, such as PLOS Medicine, but I think they could be presented in a clearer way, and I think additional limitations should be included in the discussion.

Note, I am no expert on case only designs, nor am I a statistician. I comment as an epidemiologist and as a clinician whose practice includes caring for hospitalised patients with a history of injecting drug use. Peer review by a statistician or an expert in case only methods would be valuable.

MAJOR POINTS

1. To my mind, the central issue here is to what extent the ICD-10 codes selected accurately identify deaths from opiate overdose, which is the focus of the paper.

It would be nice to see some discussion of the codes chosen in the main text of the manuscript - e.g. why include codes relating to Parkinson's medications or anti rheumatic drugs? It would also be nice if the authors could include a breakdown of the codes assigned for the patients in the study - e.g. if the vast majority were X42 or Y42, I would find that more persuasive.

An alternative explanation for the pattern of deaths observed would be that they relate directly to the hospital admission - e.g. partially treated infection, mental health crisis, venous thromboembolism - but that when a young person with a history of opiate dependence is found dead in the community, perhaps surrounded by injecting paraphernalia, doctors completing the death certificate assume that the cause of death is an overdose. This alternative explanation would be consistent with the higher mortality seen following discharges against medical advice and, possibly, with the high mortality seen following longer admissions (presumably these were associated with more severe underlying illness).

It would be nice to include a detailed discussion of this issue, as it is key to the conclusions we draw from the results. Do we know what mortality in the post discharge period looks like in people without a history of opiate dependence, particularly following admission with similar problems (bacteraemias, liver failure, schizophrenia, etc)?

2. It would be good to include greater justification of some of the analytical choices made - e.g. why only look at 5 control days? Why 14 days post discharge as the period of interest?

3. The issues regards data availability are problematic but, I suspect, outside the authors' control.

4. In the introduction, it would be good to include some justification for focusing on 'opioid related deaths' as opposed to 'opiate overdoses'. I note these terms are used slightly interchangeably in the manuscript. I would also like to see some statement about the frequency of these deaths in the population.

5. Inclusion in the study was conditional on having ever been admitted to hospital. It is good that the number of deaths excluded are quantified. It would be good to include some discussion of whether this may result in bias. I cannot immediately see that this would set up, e.g., collider bias. And I am not sure, anyway, whether that would be an issue in a case only analysis?

6. The authors have deviated significantly from the analysis planned in the published protocol. The discussion of the reasons for this in supplementary materials is good. However, this deviation from the published protocol should be mentioned more prominently in the main text of the manuscript. Will the analysis of the NPSAD data be presented separately?

7. There is good discussion about discrepant documentation of dates of discharge and death. However, these discordant dates do raise questions about the accuracy of the dates coded in the two databases - errors won't always be so obvious. Do we know anything about how accurate these data are? If so, some comment on this would be valuable, given accurate dates are key to the analysis presented.

8. A clear statement about whether any attempt was made to adjust for time varying confounders is needed.

9. I think 'Died in hospital following admission with an overdose' (or similar) (as in the tables) is clearer than 'occurred in hospital following drug use in the community' (as used in the text). It seems likely that many/most of the cohort were using drugs in the community.

10. Do we know anything about how accurately discharge against medical advice is coded in HES? Note, this variable is not always binary - sometimes clinicians try to accommodate patients' preferences for a shorter admission, where they would have preferred a longer one. Such instances won't be coded as 'discharge against medical advice'.

11. The paragraph at the end of page 9, which finishes on page 10, is not clearly written. An attempt is made to describe, in a single sentence, the reasons for hospital admission both overall, and in those who died following overdose in hospital. The sentence ends up being very long, with lots of semi colons. This should be rewritten.

12. The relative risk of death during days 1-14 of hospital admission in the abstract differs to the relative risk presented in the results section. I would give the figures for the relative risk of fatal overdose on days 15+ of hospital admission in the main text of the manuscript (results section).

13. I think the relative protection from overdose provided by a psychiatric admission, or by a medical admission of longer duration, is worth commenting on.

14. Paragraph at the end of page 12 - this is good. It might be worth explicitly discussing pain here. Many conditions that warrant admission to hospital are painful, and therefore the association between overdose and admission to hospital may be driven by increased opiate consumption (both illicit and prescribed) to manage associated pain. Pain could also be discussed in the relevant paragraph on page 13 (immediately after the 'Interpretation' header).

15. The protocol describes a planned analysis looking at risk of death during the 14 days prior to hospital admission ('period Z'). I assume this was not done because it is not possible - you cannot know whether people who die are in period Z, because people are not admitted to hospital post mortem. Some comment about this planned analysis would be valuable. The planned disaggregation of the overdose deaths into accidental and intentional, which I think is possible, would be good to see.

16. The limitations section in the protocol is better than the limitations section in the manuscript - much of the discussion from the protocol should be included in the manuscript.

17. The 'client representative' comment is excellent.

18. The figure on page 6 of supplementary materials is unreadable. I think this should be split into a number of separate figures. Note, the reference to this figure in the text on the previous page is missing a number.

MINOR POINTS

1. Only two authors seem to have made financial disclosures. Who paid for this work? If the other authors have no disclosures, that should be stated.

2. When the manuscript is resubmitted, please include line numbers, as this makes things easier for reviewers!

3. In Figure 2, it would be good to define the dotted lines in the legend. I would also say 'control day', rather than 'control period'.

4. To my mind, the opposite of acute is elective, and the opposite of psychiatric is non-psychiatric. I suggest 'non-psychiatric' would be a better term than 'acute', given there might have been some planned admissions.

5. In the first paragraph after the 'Interpretation' header, should it read 'more vulnerable to death following an opioid overdose'?

6. Is it worth discussing patient and peer BLS training, in addition to naloxone provision, as potential interventions to prevent death from overdose?

7. I agree with the final paragraph, but it seems an odd way to conclude the manuscript.

Dr Tom Yates

Imperial College London

Reviewer #2: Thanks for the opportunity to review your manuscript. My role is as a statistical reviewer - my comments and queries focus on the data and analysis presented. I have put general comments and questions first and followed these with queries specific to a section of the manuscript (with a Page reference).

This is clearly written and succinct manuscript that examines an interesting question. There is a protocol available and the analyses carried out match the description provided there. Tables and figures are clear and there is useful supplementary material available.

There is a planned sensitivity analysis using several different approaches to dealing with dependence of death with exposure period, and the effect of different control period. These results are robust to the different approaches to the death-exposure period issue. There effect estimate is shifted away from the null with a shorter period for the control windows. I don't think this is a problem - at the more reasonable period limitations it is similar to the main results, but I was interested in your interpretation as to the association between shorter periods from control windows and a more 'extreme' effect estimate.

P2. Findings - 1.7%, not clear what the denominator is (of total deaths, or total exposures)

The 'similar or lower' should be qualified to the different periods - a bit confusing otherwise.

P4. Study participants. Do the external cause and ICD-10 codes distinguish between iatrogenic opioid poisonings and others? Presumably medication error could lead to an adverse drug event (particularly in someone vulnerable e.g. with poor kidney or hepatic function).

Is the external cause data available specific to agents (e.g. prescription vs. non-prescription opioid forms)?

There is some discussion of this in the protocol but not in the main manuscript or supplementary appendix

P5. Was there any evidence for time-trends, i.e. overall changes in opioid deaths in hospitals? If there were this would presumably bias the results towards a positive association.

P6. Figure 2. Nice figure - my only quibble is that the diamond marker can overlap into a hospital admission period on the figure even where it occurs outside the period. Perhaps a line or other marker instead?

Reviewer #3: PLOS Medicine

Re. Manuscript Number: PMEDICINE-D-21-01225R1

Title: How prescription drug monitoring programs influence clinical practice: A mixed

methods systematic review and meta-analysis

Overall:

This well-written article by Lewer et al. assessed whether patients had a higher risk of opioid-related death after admission for inpatient treatment for causes other than opioid use. The analysis covered ten years of mortality data in the UK. The main findings were: 1) inpatients had no higher odds of an opioid-related death while admitted than people in the community; and 2) patients had higher odds of an opioid-related death after discharge (1-2 days and 3-14 days) when compared to the risk of dying while in the community. I commend the authors for addressing such an important topic. I do not have major concerns, as I believe that the rationale for the study was well described, the description of the methodology was straightforward and easy to understand, the results and conclusions answered the central question of interest stated in the study's objective. I do have minor suggestions:

- Introduction: add references for statements "Sometimes staff are too busy to verify a patient's usual dose of methadone or buprenorphine, or do not have sufficient knowledge or training about opioid dependence" and "Opioid withdrawal can lead patients to leave hospital to buy drugs. Some bring a supply into hospital to keep them going, and some arrange for dealers to visit them while they are staying on a ward."

- Methods, exposure status: Consider adding clear language stating that the sample included patients who had an opioid-related death but who were admitted for causes other than opioid use.

- Methods, statistical analysis: Consider adding more information about the approach used for the analysis, why it was selected as the best tool to analyze the data.

- Discussion: results by gender have some interesting findings (currently in the appendix); consider including this stratified result by gender in the results and discussion.

Reviewer #4: In this case-crossover analysis, the authors investigate whether the period during and just after hospitalization is a time of heightened risk for opioid-related death. They find that the risk of opioid-related death increased substantially after discharge, with the highest risk in days 1-2 after discharge. The risk was higher for people discharged after a psychiatric admission, people who left hospital against medical advice, and for those with longer admissions. These findings have good physiologic basis. The chosen approach (case-crossover) is an appropriate and robust way to answer the study question, which minimizes confounding by time-invariant confounders, since patients are compared to themselves. The authors appropriately note that their findings may be partially be explained by escalating drug use or deteriorating health over time. This is a well-written manuscript with interesting findings. I have just a few comments/suggestions:

Major issues:

Confounding by escalating drug use/deteriorating health over time: The authors appropriately note that their findings may be partially be explained by escalating drug use or deteriorating health over time. This is a strong limitation. I wonder if they could do a sensitivity analysis extending the control window back just 1 year instead of 2, and see if their results are similar (is this what was done in appendix 4? It's not totally clear to me. If so, would move these results regarding sensitivity analysis with varying time windows into the main manuscript). This might give some understanding of the degree to which this type of confounding could have influenced their results.

Possible selection bias: The authors state, "This database does not include deaths if no linkage is found (most likely because the decedent was never admitted to hospital)." This seems like it would tend to bias towards inclusion of individuals with a history of hospitalization, which could potentially increase the association between hospitalization and death, since individuals are also selected for inclusion by virtue of having experienced opioid-related death. If I am understanding the database correctly, this should be added as a limitation.

Minor:

Abstract: The abstract starts by stating that some hospital patients use illicit drugs while admitted, and there have been reports of patients found dead on hospital premises after using illicit drugs such as heroin. This led me to believe this was a study about use of opioids in hospitals, which is not a main focus of the analysis. I would instead start by stating why the time just after hospital discharge may be a time of heightened risk.

Shoshana J. Herzig, MD, MPH

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

[LINK]

20 May 2021

Submitted filename: response_to_reviews_v4.pdf

Click here for additional data file.

25 Jun 2021

Dear Dr. Lewer,

Thank you very much for re-submitting your manuscript "Fatal opioid overdoses during and shortly after hospital admissions in England: case-crossover study" (PMEDICINE-D-21-01225R2) for review by PLOS Medicine.

I have discussed the paper with my colleagues and the academic editor and it was also seen again by four 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.

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[LINK]

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Requests from Editors:

1) Thank you for providing your STROBE checklist. Please replace the page numbers with paragraph numbers per section (e.g. "Methods, paragraph 1"), since the page numbers of the final published paper may be different from the page numbers in the current manuscript.

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Comments from Reviewers:

Reviewer #1: I have not read the manuscript again in full, but have reviewed the authors' responses to my previous review. It is clear that they have put in substantial additional work and I learned from their detailed responses to my comments.

My main residual concern relates to the authors' response in point 13 - I am not fully persuaded. I write death certificates and liaise with coroners when on clinical duty. In hospitalised patients, where deaths are sudden and unwitnessed, it is sometimes hard to attribute a cause of death. Where someone is found dead in the community - with no access to regular patient observations, blood test results, etc - I imagine things are even less certain.

I am not convinced that toxicology, or a coroner reviewing what may be limited information, really helps here. Finding opiates in somebody who is opiate dependent does not tell us whether their death was a result of accidental poisoning (X42, etc), a result of the condition that recently put them into hospital, or a result of complications associated with that hospitalisation (venous thromoembolism, etc).

I would like to see greater acknowledgement of the inherent difficulty in attributing cause of death. If data are available on the proportion of these deaths that were witnessed, and the proportion of cases that underwent post mortem, that should be presented. I would like to see mention of misattribution of cause of death as a possible alternative explanation for the observed mortality patterns in the discussion.

Minor further point: In table 2, I struggle to see how the median can be 1, where only 40% of the observations are 1? This may be a rounding issue.

I continue to think that these results merit publication in a high impact journal, such as PLOS Medicine.

Dr Tom Yates

Imperial College London

Reviewer #2: Thanks for revised manuscript and comprehensive responses to my queries. From my perspective my original queries have been answered with the additional information and revisions to the manuscript.

The results with the subset of ICD-10 codes (T40.1) are similar to the main results, this is reassuring that there aren't issues around the specificity of the ICD-10 codes used to identify cases.

The extra information on the ICD-10 codes and time-stratified analyses are helpful additions. I don't think there's evidence that time-trend exists in this data that would cause a positive bias in the outcome-exposure relationships.

Good study and very nicely presented figures.

Reviewer #3: Overall:

The revisions to the manuscript addressed all the concerns and suggestions made in the first review round. My remaining suggestions are:

- The use of the word "proximity" initially I understood it as a space measure, which is not the focus of this paper. It may be worth adding an adjective to clarify the intended meaning, i.e., "time proximity."

- As the use of illicit, more potent, and fatal opioids (mainly fentanyl) increases in our populations, I wonder whether the estimates have a significant upward time trend (i.e., risk of death in the two weeks after hospital discharge was higher for patients admitted in 2018-2019 than for patients admitted in 2010-2011).

Reviewer #4: The addition of the sensitivity analyses varying the control window has strengthened the manuscript.

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

[LINK]

28 Jun 2021

Submitted filename: response_to_reviews_v5.pdf

Click here for additional data file.

5 Aug 2021

Dear Dr Lewer,

On behalf of my colleagues and the Academic Editor, Dr. Vikram Patel, I am pleased to inform you that we have agreed to publish your manuscript "Fatal opioid overdoses during and shortly after hospital admissions in England: case-crossover study" (PMEDICINE-D-21-01225R3) in PLOS Medicine.

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PLOS Medicine