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. 2021 Oct 14;18(10):e1003820. doi: 10.1371/journal.pmed.1003820

HMG-CoA reductase inhibitors and COVID-19 mortality in Stockholm, Sweden: A registry-based cohort study

Rita Bergqvist 1,*,#, Viktor H Ahlqvist 1,#, Michael Lundberg 1,2, Maria-Pia Hergens 1,3, Johan Sundström 4,5, Max Bell 6,7, Cecilia Magnusson 1,2
Editor: Weiping Jia8
PMCID: PMC8516243  PMID: 34648516

Abstract

Background

The relationship between statin treatment and Coronavirus Disease 2019 (COVID-19) mortality has been discussed due to the pleiotropic effects of statins on coagulation and immune mechanisms. However, available observational studies are hampered by study design flaws, resulting in substantial heterogeneity and ambiguities. Here, we aim to determine the relationship between statin treatment and COVID-19 mortality.

Methods and findings

This cohort study included all Stockholm residents aged 45 or older (N = 963,876), followed up from 1 March 2020 until 11 November 2020. The exposure was statin treatment initiated before the COVID-19-pandemic, defined as recorded statin dispensation in the Swedish Prescribed Drug Register between 1 March 2019 and 29 February 2020. COVID-19-specific mortality was ascertained from the Swedish Cause of Death Registry. Hazard ratios (HRs) were calculated using multivariable Cox regression models. We further performed a target trial emulation restricted to initiators of statins.

In the cohort (51.6% female), 169,642 individuals (17.6%) were statin users. Statin users were older (71.0 versus 58.0 years), more likely to be male (53.3% versus 46.7%), more often diagnosed with comorbidities (for example, ischemic heart disease 23.3% versus 1.6%), more frequently on anticoagulant and antihypertensive treatments, less likely to have a university-level education (34.5% versus 45.4%), and more likely to have a low disposable income (20.6% versus 25.2%), but less likely to reside in crowded housing (6.1% versus 10.3%).

A total of 2,545 individuals died from COVID-19 during follow-up, including 765 (0.5%) of the statin users and 1,780 (0.2%) of the nonusers. Statin treatment was associated with a lowered COVID-19 mortality (adjusted HR, 0.88; 95% CI, 0.79 to 0.97, P = 0.01), and this association did not vary appreciably across age groups, sexes, or COVID-19 risk groups. The confounder adjusted HR for statin treatment initiators was 0.78 (95% CI, 0.59 to 1.05, P = 0.10) in the emulated target trial. Limitations of this study include the observational design, reliance on dispensation data, and the inability to study specific drug regimens.

Conclusions

Statin treatment had a modest negative association with COVID-19 mortality. While this finding needs confirmation from randomized clinical trials, it supports the continued use of statin treatment for medical prevention according to current recommendations also during the COVID-19 pandemic.


In this cohort study, Rita Bergqvist and colleagues investigate the relationship between statin treatment and COVID-19 mortality.

Author summary

Why was this study done?

  • Lipid-lowering HMG-CoA reductase inhibitors (statins) may influence Coronavirus Disease 2019 (COVID-19) mortality via their pleiotropic effects on coagulation and the immune system.

  • Previous studies on this topic have been inconsistent, so we performed a population-based cohort study to investigate the relationship between statins and COVID-19 mortality.

What did the researchers do and find?

  • Using data from Swedish health registers, a cohort of 963,876 residents of Stockholm, Sweden, were followed from 1 March 2020 until 11 November 2020.

  • Prescription dispensation data were matched to healthcare data and the Swedish Cause of Death Registry and analyzed using multivariable cause-specific survival analysis.

  • Statin treatment was associated with a moderately lower risk of COVID-19 mortality (hazard ratio (HR), 0.88; 95% CI, 0.79 to 0.97), after accounting for a series of preexisting health conditions and other factors. The association was corroborated by sensitivity analyses and did not vary substantially across risk groups.

What do these findings mean?

  • These findings suggest that statin treatment may have a modest preventive therapeutic effect on COVID-19 mortality, although randomized trials are needed to determine the causality of the observed association.

  • In summary, the findings support the continued use of statins for conditions such as cardiovascular disease and hyperlipidemia, in line with current recommendations, during the COVID-19 pandemic.

Introduction

The Coronavirus Disease 2019 (COVID-19) pandemic continues to evolve. Vaccines are underway, and possible treatments are being tested in trials and clinics. The understanding of the pathophysiology of the disease continues to grow along with the knowledge of risk factors for severe COVID-19 disease and death. Hyperinflammation and hypercoagulability have been identified as central to the development of severe COVID-19 disease and complications [14]. Hence, drugs that modulate the host immune response and inhibit thrombosis and vascular dysfunction have received widespread attention. Special attention has been given to low-risk and low-cost treatments that can be easily administered across different settings [515].

Lipid-lowering HMG-CoA reductase inhibitors (statins), which are used in vast groups of patients for the prevention and treatment of cardiovascular diseases, are one of the pharmaceutical classes that have been proposed as possible preventive and/or adjuvant treatment of severe COVID-19. In brief, statins inhibit the enzyme HMG-CoA reductase central to the endogenous synthesis of cholesterol. This inhibition results in decreased levels of harmful LDL cholesterol in blood plasma. Statins are also known to have pleiotropic effects, including immune modulation, decreased inflammation in blood vessels, improved endothelial function, decreased thrombocyte aggregation, decreased risk of thrombosis, increased fibrinolysis, and stabilization of atherosclerotic plaques [1618].

Several observational studies have investigated statins and report, with some discrepancies, an association between statin use and decreased COVID-19 mortality [1927]. However, many previous studies have failed to avoid the potential pitfalls of observational studies, like those commonly described under the emulated trial framework [28]. In addition, most previous studies have had small sample sizes, owing to the fact that they have been based on early pandemic cases, and often included only hospitalized populations. As such, a recently published meta-analysis showed substantial heterogeneity (i2 90%) between previous studies, likely due to methodological discrepancies [29]. For example, certain studies of statins and COVID-19 mortality have suggested a substantial excessive risk (odds ratio (OR) 6.21), while others have suggested implausible benefits (OR 0.46) [29].

Pending ongoing clinical trials [3038], we present a large population-based observational study using Swedish health register data, with the aim of determining the relationship between statin treatment and COVID-19 mortality, scrutinize if this relationship is robust to target trial emulation of statin initiation and examine whether such a relationship is consistent across age groups, sexes, and COVID-19 risk groups.

Methods

Study design and data sources

We designed a total population study, covering all Stockholm residents meeting the study criteria (see below), based on routinely collected data from the following registries: the longitudinal integrated database for health insurance and labor market studies (LISA) [39] for information on education and income, the VAL databases of Region Stockholm containing information from both hospitals and outpatient clinics on preexisting conditions according to the International Classification of Diseases (ICD), as well as data from the Swedish Prescribed Drug Register [40] on collected prescriptions coded by anatomical therapeutic classification (ATC) codes. Data on COVID-19 related deaths were collected from the Swedish Cause of Death Registry [41]. Registries and records were linked through the personal identity number assigned to each Swedish resident [42]. The study has been approved by the Regional Ethical Review Board, Stockholm (2021–00810). This study is reported as per the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) guideline (S1 STROBE Checklist).

Study population

The study included all individuals aged 45 or older, residing in Stockholm county in 2019 as well as 1 March 2020, as recorded in the Total Population Registry. We chose to only study those older than 45 to effectively exclude >99% of pregnant women, as statins are contraindicated during pregnancy. Furthermore, the lower age limit enhances the stability of our covariate control, as there had been few COVID-19 deaths among young people during the study period. Furthermore, statins are contraindicated in active liver disease, individuals diagnosed with liver disease (ICD-10: K70 to K77) up to 5 years prior to the start of the study (i.e., 1 Jan 2015 or later) were hence excluded (n = 2,103). In total, 963,876 individuals were retained for analysis.

Exposure(s)

The primary exposure was defined as one or more collected prescriptions of any type of statin (ATC code C10AA), between 1 March 2019 and 29 February 2020 (allowing for both possible intermediate- and short-term pharmacological effects). We restricted exposure collection to the period before the outbreak of the pandemic to avoid possible bias originating from altered healthcare seeking and drug collecting behaviors during the outbreak.

Follow-up/outcomes

Individuals were followed until death from COVID-19, other deaths, or 11 November 2020, whichever occurred first. The primary outcome, death from COVID-19, was established using the Swedish Cause of Death Registry, which is based on the death certificate filled in by physicians. This certification is based on a clinical evaluation supported by, for example, radiology, microbiological analysis, and/or assessment of symptoms. For the main analysis, all deaths for which COVID-19 was registered as the main cause or a contributing cause were included.

Statistical analysis

Using measures of central tendency and dispersion, we descriptively presented our sample by statin exposure. To quantify the relationship between statin treatment and COVID-19-specific mortality, we employed cause-specific Cox regression, using days since 1 March 2020 as the underlying timescale.

We present unadjusted and adjusted hazard ratios (HRs) and their 95% confidence intervals, adjusting for sex (categorical), age (continuous), country of birth (categorical), the highest level of obtained education (categorical), annual disposable income in 2018 (quartiles), residential area (categorical), household crowding (m2 per person) (deciles), nursing home (yes/no), exposure to angiotensin-converting enzyme (ACE) inhibitors (yes/no), angiotensin receptor blockers (yes/no), and anticoagulants (yes/no) as well as a broad range of conditions (yes/no); atrial fibrillation, cancer, cerebrovascular disorders, chronic kidney disease, chronic lower respiratory disease, dementia (inc. Alzheimer disease), type 1 and type 2 diabetes, dyslipidemia, heart failure, hypertension, ischemic heart disease, obesity, other neurological conditions, peripheral vascular disease, pure hypercholesterolemia, renal failure stage 3 to 5, stroke and transient ischemic attack (TIA), and vascular disease (see S1 Table for ICD-10/ATC codes).

We evaluated the proportional hazard assumption by testing the slope of the Martingale residuals as well as through visual inspection of log of minus log plots, finding limited evidence of a violation of the proportional hazard assumption. As the fraction of residents with missing covariate information was small (6.2%), all analyses were performed using the so-called missing indicator approach, where residents with missing covariate information are treated as a distinct category in each covariate. We repeated our main analysis using complete case, to examine a possible difference compared to the missing indicator approach.

Subgroup analyses were performed for age groups, sexes, different statin indications as well as COVID-19 risk groups (identified by authorities and/or in previous studies on risk factors [43]), and evaluated for heterogeneity using the Cochran’s Q statistic. Computations were performed using SAS version 9.4. P values <0.05 were considered statistically significant.

The rapid development of the COVID-19 pandemic prevented us from preregistering our statistical analysis plan, as we prioritized a rapid execution of our study.

Sensitivity analyses

We performed several sensitivity analyses to scrutinize our results: We (1) restricted our main analysis to statin initiators (emulating a target trial with an intention-to-treat analysis [44]); (2) repeated the main analysis with COVID-19 as the main underlying cause of death as the outcome measure; (3) performed analysis of all-cause mortality without COVID-19 (positive outcome analysis [45]); and (4) performed our main analysis using the Fine-Gray method for competing risks [46], treating non-COVID deaths as competing events.

Results

Sample characteristics

A total of 963,876 residents aged 45 or older were included in our main analysis, out of which 496,865 individuals (51.6%) were female (Table 1). Among the residents, 169,642 individuals (17.6%) had collected at least one statin prescription during the year preceding the pandemic.

Table 1. Selected characteristics of the studied cohort.

Characteristic Statin users
(N = 169,642)
Nonusers
(N = 794,234)
Sex, No. (%)
Female 73,683 (43.4) 423,182 (53.3)
Age
Years, Median (IQR) 71.0 (64–78) 58.0 (49–67)
45–69 years, No. (%) 75,801 (44.7) 609,427 (76.7)
70–79 years, No. (%) 61,829 (36.5) 122,806 (15.5)
≥80 years, No. (%) 32,012 (18.9) 62,001 (7.8)
Comorbidities/preexisting conditions, No. (%)
Type 1 diabetes 6,578 (3.9) 4,481 (0.6)
Type 2 diabetes 55,260 (32.6) 37,721 (4.8)
Obesity 15,793 (9.3) 31,418 (4)
Dyslipidemia 77,670 (45.8) 23,205 (2.9)
Pure hypercholesterolemia 3,507 (2.1) 16,524 (2.1)
Dementia (inc. Alzheimer disease) 5,480 (3.2) 12,933 (1.6)
Hypertension 125,978 (74.3) 196,100 (24.7)
Heart failure 16,030 (9.5) 16,956 (2.1)
Vascular disease 13,997 (8.3) 19,789 (2.5)
Cerebrovascular disorders 20,442 (12.1) 12,593 (1.6)
Stroke and TIA 6,741 (4) 31,903 (4)
Chronic lower respiratory disease 14,296 (8.4) 27,848 (3.5)
Ischemic heart disease 39,518 (23.3) 12,599 (1.6)
Peripheral vascular disease 4,776 (2.8) 1,657 (0.2)
Renal failure stage 3–5 2,957 (1.7) 14,025 (1.8)
Atrial fibrillation 23,296 (13.7) 33,965 (4.3)
Chronic kidney disease 13,152 (7.8) 13,501 (1.7)
Cancer 27,232 (16.1) 71,283 (9)
Other neurological conditions 8,666 (5.1) 20,750 (2.6)
Any of the above conditions 51,959 (30.6) 71,734 (9)
Other medications, No. (%)
ACE inhibitors 4,524 (2.7) 8,953 (1.1)
Angiotensin receptor blockers 6,074 (3.6) 12,642 (1.6)
Anticoagulants 8,436 (5) 16,362 (2.1)
Socioeconomic status, No. (%)
Education
Primary education 36,602 (21.6) 104,740 (13.2)
Secondary education 69,759 (41.1) 301,743 (38)
Tertiary education 58,505 (34.5) 360,418 (45.4)
Unknown 4,776 (2.8) 27,333 (3.4)
Disposable income quartile
Q1 49,102 (28.9) 185,789 (23.4)
Q2 46,922 (27.7) 187,751 (23.6)
Q3 35,868 (21.1) 199,199 (25.1)
Q4 34,921 (20.6) 200,039 (25.2)
Unknown 2,829 (1.7) 21,456 (2.7)
Residing in a nursing home, No. (%)
Yes 3,473 (2.1) 12,090 (1.5)
Country of birth, No. (%)
Sweden 120,959 (71.3) 570,543 (71.8)
Household crowding (m2 per person), No. (%)
Decile 1 10,267 (6.1) 82,114 (10.3)
Decile 2 10,026 (5.9) 81,649 (10.3)
Decile 3 12,472 (7.4) 80,133 (10.1)
Decile 4 14,231 (8.4) 75,963 (9.6)
Decile 5 17,673 (10.4) 76,617 (9.7)
Decile 6 17,975 (10.6) 74,845 (9.4)
Decile 7 19,082 (11.3) 73,227 (9.2)
Decile 8 20,883 (12.3) 72,939 (9.2)
Decile 9 20,695 (12.2) 70,777 (8.9)
Decile 10 21,149 (12.5) 71,347 (9)
Unknown 5,189 (3.1) 34,623 (4.4)

ACE, angiotensin-converting enzyme; IQR, interquartile range; TIA, transient ischemic attack.

Statin users were older (median age of 71.0 years, compared to 58.0 years for nonusers) and more likely to be male (53.3% versus 46.7%). They had more often been diagnosed with comorbidities (ischemic heart disease 23.3% versus 1.6%, heart failure 9.45% versus 2.13%, hypertension 74.3% versus 24.7%, type 2 diabetes 32.6% versus 4.75%) and were more frequently on anticoagulant and antihypertensive treatments (ACE inhibitors 2.7% versus 1.1%; angiotensin receptor blockers 3.6% versus 1.6% and anticoagulants 5.0% versus 2.1%) compared to nonusers. A smaller share of statin users had university-level education (34.5% versus 45.4%), and statin users had a lower disposable income (Q1: 28.9% versus 23.4%, Q4: 20.6% versus 25.2%) but less crowded housing (decile 1: 6.1% versus 10.3%). There were no major differences between users and nonusers regarding country of origin or residential area.

Main analysis of COVID-19 mortality

A total of 2,545 individuals died from COVID-19 during follow-up, including 765 (0.5%) of the statin users and 1,780 (0.2%) of the nonusers (unadjusted HR, 2.02; 95% CI, 1.85 to 2.20) (Fig 1). However, when adjusting for confounders, statin treatment was associated with a moderately lower risk of COVID-19 mortality (HR, 0.88; 95% CI, 0.79 to 0.97). This association was similar across age groups (P = 0.821) and sexes (P = 0.657) and COVID-19 risk groups/indications for statin treatment (P = 0.727).

Fig 1. HRs of death from COVID-19 in relation to dispensations of statins overall and the HRs of death from COVID-19 in relation to dispensations of statins within strata of age, sex, and within COVID-19 risk groups.

Fig 1

chr, chronic; CI, confidence interval; COVID-19, Coronavirus Disease 2019; HR, hazard ratio; TIA, transient ischemic attack.

Sensitivity analyses

In our emulated target trial (statin initiators only), the negative association between statin treatment and COVID-19 mortality remained but was not statistically significant (HR, 0.78; 95% CI, 0.59 to 1.05) (S1 Text). Restricting our analysis to those with COVID-19 as the main underlying cause of death did not alter our findings (S1 Text). Our positive outcome analysis was consistent with a protective effect of statins (as expected), although the estimated protective effect was greater than that of meta-analysis of randomized controlled trials examining statins for primary prevention in high-risk populations [47] (S1 Text). The fact that we were able to replicate the known association between statin treatment and reduced (overall) mortality gives further support for the validity of the results from our COVID-19 analysis. There was no difference between our main analysis and a complete case replication of our analysis (HR, 0.87; 95% CI, 0.79 to 0.97, N = 903,782). There was no difference between our main analysis and when treating non-COVID deaths as competing events using the Fine–Gray method (adjusted HR, 0.88; 95% CI, 0.80 to 0.98) (S2 Table).

Discussion

Interpretation of results

In this register-based cohort study of 963,876 Stockholm residents aged 45 or older, statin treatment was associated with a moderately lower risk of COVID-19 mortality. Adjusted HRs were largely consistent across age groups, sexes, as well as across different COVID-19 risk groups and indications for statin treatment. This result was corroborated by our sensitivity analysis explicitly emulating a target trial among initiators of statin treatment.

Several cohort studies of hospitalized COVID-19 patients have reported negative associations between statin use and mortality [2027], in line with our findings. These studies have been summarized in meta-analyses indicating an overall reduction of unfavorable COVID-19 outcomes (including death, intensive care unit (ICU) admission, and mechanical ventilation) of about 30% in statin users [48,49]. The meta-analyses also demonstrate substantial heterogeneity between studies (i2 90%) [29], and a recent large-scale study on severe COVID-19 and all-cause mortality in COVID-19–positive Danish citizens showed no association between statin use and either outcome [19].

Existing studies have been limited in their appreciation of the complexity of causal inference when using observational data and may thus be prone to the critical pitfalls of observational studies of clinical interventions. Such pitfalls have, for example, been clearly demonstrated in works on statins and cancer [28] and, indeed, in studies of COVID-19 [50]. These include failure to differentiate between new and prevalent users (leading to survival bias), using post-baseline information to establish exposure (leading to immortal time bias), introducing collider bias by sampling only hospitalized patients and/or conditioning on positive PCR test results or adjusting for potential mediators (such as disease severity).

Strengths and limitations

Our study has several strengths. Most importantly, we applied a rigorous methodological approach to avoid the potential biases mentioned above, resulting in a study with high internal validity. To avoid conditioning on PCR testing, which may introduce collider bias (as previously observed in connection with COVID-19 research [50]), the outcome measure was based on data from The Swedish Cause of Death Register. This register contains all deaths determined by physicians to be caused (in full or in part) by COVID-19, based on testing or other diagnostics. Moreover, mortality carries a smaller risk of selection bias than outcome measures such as ICU admission or severity of disease.

Furthermore, we were able to control for a large set of important confounders using data collected in a standardized manner. We also avoid confounding that may arise from altered behavior during the COVID-19 pandemic. Specifically, the end of the period of exposure was set to 1 March 2020—the start of the pandemic in Stockholm—since the collection of prescriptions during a pandemic could be a proxy for certain behaviors affecting the risk of exposure to the virus (for example, the frequenting of public spaces). While we believe that this is a major strength of our study, such time restrictions imply that we include statin dispensations, which, in theory, could have been collected long before a COVID-19 infection. Indeed, individuals who collect a sole prescription in early 2019 will be classified as exposed. However, this is in line with the intention-to-treat principle. As such, our estimate may represent a conservative estimate of the causal effect of statins on COVID-19 mortality; our estimate can be interpreted as that obtained from an observational intention-to-treat analysis in the presence of noncompliance, which we believe is the clinically relevant causal contrast. Another important aspect is the differentiation in recommendations for different age groups during spring and summer; people aged 70 or older were recommended to isolate, which could render analyses based on dispensation during the spring and summer unrecoverable from such selection mechanisms.

Lastly, in context of other evidence on COVID-19, the duration of follow-up is long and the number of exposed cases high. Our study includes the total population of Stockholm County, whereas most previous studies were hospital-based. Hence, the results are likely to have higher external validity.

However, the study also has several important limitations. Firstly, as in any observational study, residual confounding cannot be ruled out. For example, we have not adjusted for smoking or body mass index (BMI), but only diagnosed obesity. Both smoking and high BMI are strongly linked to statin use [51], and high BMI has been identified as a risk factor for COVID-19 death [52]. Although we would expect this to result in an underestimation of a potential protective effect, we cannot dismiss the possibility that our findings may be explained by confounding.

Secondly, as in most pharmacoepidemiological studies, we have assumed that drug dispensation captures drug use correctly. This assumption is easily challenged since not all patients filling prescriptions adhere to treatment. This misclassification is, however, unlikely to explain our findings. Any nondifferential misclassification will generally dilute associations, and we judge a possible differential misclassification to bias our findings toward a positive association if adherence is particularly low in severely ill patients (including in COVID-19) or patients that have low health literacy and fewer means of avoiding the infection.

Lastly, we have not been able to study possible effects of dosage, type, or brand of statins. Specifically, despite our study being, the hereby largest study of statins and COVID-19 mortality, we had an insufficient sample size to perform such granular analysis. As such, our estimate should be interpreted as the mean of possibly heterogeneous effects.

Further aspects should be considered when interpreting our findings. This study does not concern the possible effect of statin treatment for treatment of severe COVID-19, but its possible preventive effect. Furthermore, although our duration of follow-up is longer than comparable studies, future studies could shed light on the long-term effects of statins on COVID-19 mortality and possible heterogeneity in effects over subsequent COVID-19 waves. Although we emulated a target trial with an intention-to-treat analysis, the study does not provide an analysis of the risk of adverse events in potential target groups. Safety evaluations are outside the scope of the current study as they require extensive clinical and qualitative safety data, and such evaluations should address several aspects of the overall concept of statin safety. Nonetheless, this has been addressed in previous clinical trials, but not in connection with this new potential indication (COVID-19 prevention). Although statins are considered to have a favorable safety profile [53] compared to many other drug classes discussed in connection with COVID-19, a clinical risk assessment must always be made. Finally, although our study has greater generalizability due to the general population design (as compared to hospital-based studies), its results should be interpreted in light of the Swedish COVID-19 strategy, which may have implications for the generalizability of our findings.

Conclusions

The current pandemic constitutes a unique situation in that policy and treatment decisions are being continuously made on the basis of limited knowledge. In the absence of results from randomized clinical trials [3038], our study can provide some guidance. Specifically, it gives further support for continuing statin treatment for conditions such as cardiovascular disease and hyperlipidemia, in line with current recommendations [5457], during the COVID-19 pandemic.

Supporting information

S1 STROBE Checklist. Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) checklist.

(DOCX)

S1 Table. Covariate definition according to ICD-10/ATC code and period of data collection.

(DOCX)

S2 Table. Sensitivity analysis using the Fine–Gray subdistribution hazard model.

(DOCX)

S1 Text. Extended details on sensitivity analyses.

(DOCX)

Abbreviations

ACE

angiotensin-converting enzyme

ATC

anatomical therapeutic classification

BMI

body mass index

COVID-19

Coronavirus Disease 2019

HR

hazard ratio

ICD

International Classification of Diseases

ICU

intensive care unit

LISA

longitudinal integrated database for health insurance and labor market studies

OR

odds ratio

TIA

transient ischemic attack

Data Availability

Swedish privacy law prohibits us from making register data publicly available. The data supporting our findings were used under license and ethical approval for the current study. Readers interested in obtaining microdata or replicating our study may seek similar approvals and inquire through Statistics Sweden. For further advice see: https://www.scb.se/en/services/guidance-for-researchers-and-universities/, or contact Statistics Sweden at: mikrodata@scb.se.

Funding Statement

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

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Decision Letter 0

Callam Davidson

24 May 2021

Dear Dr Bergqvist,

Thank you for submitting your manuscript entitled "HMG-CoA reductase inhibitor therapy and its relationship to COVID-19 mortality in the general population of Stockholm" for consideration by PLOS Medicine.

Your manuscript has now been evaluated by the PLOS Medicine editorial staff as well as by an academic editor with relevant expertise and I am writing to let you know that we would like to send your submission out for external peer review.

However, before we can send your manuscript to reviewers, we need you to complete your submission by providing the metadata that is required for full assessment. To this end, please login to Editorial Manager where you will find the paper in the 'Submissions Needing Revisions' folder on your homepage. Please click 'Revise Submission' from the Action Links and complete all additional questions in the submission questionnaire.

Please re-submit your manuscript within two working days, i.e. by May 26 2021 11:59PM.

Login to Editorial Manager here: https://www.editorialmanager.com/pmedicine

Once your full submission is complete, your paper will undergo a series of checks in preparation for peer review. Once your manuscript has passed all checks it will be sent out for review.

Feel free to email us at plosmedicine@plos.org if you have any queries relating to your submission.

Kind regards,

Callam Davidson

Associate Editor

PLOS Medicine

Decision Letter 1

Callam Davidson

28 Jun 2021

Dear Dr. Bergqvist,

Thank you very much for submitting your manuscript "HMG-CoA reductase inhibitor therapy and its relationship to COVID-19 mortality in the general population of Stockholm" (PMEDICINE-D-21-02232R1) for consideration at PLOS Medicine.

Your paper was evaluated by an associate 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 invite you to submit a revised version that addresses the reviewers' and editors' comments fully. You will appreciate that we cannot make a decision about publication until we have seen the revised manuscript and your response, and we expect to seek re-review by one or more of the reviewers.

In revising the manuscript for further consideration, your revisions should address the specific points made by each reviewer and the editors. 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. In your rebuttal letter you should indicate your response to the reviewers' and editors' comments, the changes you have made in the manuscript, and include either an excerpt of the revised text or the location (eg: page and line number) where each change can be found. Please submit a clean version of the paper as the main article file; a version with changes marked should be uploaded as a marked up manuscript.

In addition, we request that you upload any figures associated with your paper as individual TIF or EPS files with 300dpi resolution at resubmission; please read our figure guidelines for more information on our requirements: http://journals.plos.org/plosmedicine/s/figures. While revising your submission, please upload your figure files to the PACE digital diagnostic tool, https://pacev2.apexcovantage.com/. PACE helps ensure that figures meet PLOS requirements. To use PACE, you must first register as a user. Then, login and navigate to the UPLOAD tab, where you will find detailed instructions on how to use the tool. If you encounter any issues or have any questions when using PACE, please email us at PLOSMedicine@plos.org.

We hope to receive your revised manuscript by Jul 19 2021 11:59PM. Please email us (plosmedicine@plos.org) if you have any questions or concerns.

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We ask every co-author listed on the manuscript to fill in a contributing author statement, making sure to declare all competing interests. 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. If new competing interests are declared later in the revision process, this may also hold up the submission. 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. You can see our competing interests policy here: http://journals.plos.org/plosmedicine/s/competing-interests.

Please use the following link to submit the revised manuscript:

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Your article can be found in the "Submissions Needing Revision" folder.

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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.

Please let me know if you have any questions, and we look forward to receiving your revised manuscript.

Sincerely,

Callam Davidson,

Associate Editor

PLOS Medicine

plosmedicine.org

-----------------------------------------------------------

Requests from the editors:

Please update the title of the manuscript to match the style used by PLOS Medicine. “Prescriptions for HMG-CoA reductase inhibitors and COVID-19 mortality in Stockholm, Sweden: a registry-based cohort study”, or similar, would be suitable.

Please confirm whether ‘Swedish secrecy law’ (in your response to the data availability question in the submission form) should be updated to ‘Swedish privacy law’.

Please structure the ‘Abstract’ using three sections: ‘Background’, ‘Methods and Findings’, and ‘Conclusions’. The ‘Objectives’ section in the current abstract can be rephrased as the final line of the ‘Background’ (“The objective of this study was to determine the relationship…” etc.). The ‘Methods’ and ‘Results’ sections can be combined, and the final sentence of the ‘Methods and Findings’ section ought to summarise 2-3 of the study’s main limitations (please begin this sentence “Limitations of this study include…”).

Please present your conclusions in the past tense (e.g., “there was a modest negative association between statin treatment and COVID-19 mortality”).

Please state summary demographic information in the abstract and include relevant p-values.

Please provide an ‘Author Summary’ section as outlined here: https://journals.plos.org/plosmedicine/s/revising-your-manuscript

Please remove the ‘Conflicts of interest’ and ‘Role of the funding source’ from the title page of the manuscript. If published, the relevant information will be included as metadata based on your responses to the submission forms.

Please update lines 191-192 to read “However, when adjusting for confounders, statin treatment was associated with a moderately lower risk of COVID-19 mortality (HR, 0.88; 95% CI, 0.79-0.97)”. Please similarly adjust other relevant statements throughout the manuscript (e.g. lines 221-222).

If a protocol and/or statistical analysis plan were prepared in advance of this study, please could these be provided as supplementary files with the next revision.

Please refer to any attached protocol document in the Methods section (main text) and highlight any analyses that were not prespecified.

Please make sure references do not contain any bold/italicized text and use “et al.” after listing the first six authors of a paper.

In the reference list, please add “[preprint]” to all preprints, e.g., reference 26.

Thank you for providing a completed STROBE checklist. In the checklist, please refer to individual items by section (e.g., "Methods") and paragraph number, not by line or page numbers as these generally change in the event of publication.

Comments from the reviewers:

Reviewer #1: This is a well-executed observational study investigating the hypothesis that statins improve the COVID-related mortality. The extent of the study as well as the rigorous approach are wellcomed.

Some of the limitations are well-explained. For example, the type of statin used was not explored, which is a missed opportunity seen the differences between the statines. Moreover, it is not explained why this sub-analysis was not done.

Also, the exposure was defined as one or more collected prescriptions: if only one prescription? Also, information about the continuation till "outcome" seems missing: what if in the last days-weeks, no statines were anymore given?

Although one could ask these and other questions, I still think this is a valuable study worth to be published, adding to the "pleiotropic" effects of statines.

Reviewer #2: The authors have done study statin in COVID-19. My comments are

1. Should be stated that chronic statin user before got COVID-19 affect the mortality.

2. There was little data regarding how long did statin give protection. And this was retrospective cohort study, there was bias that could not be controlled. This issue should be added as the limitation of the study.

3. Was there any that regarding the safety of statin users?

4. Different type of statin use may give different effect --> should also be discussed

5. In this study was interesting, dementia, obesity and diabetes was higher in statin group. This issue should be discussed DOI: 10.1016/j.amjms.2020.10.026

6. The routine use of anticoagulant higher in statin group may give a confounder effect

7. How about the use of diabetes drugs? some give benefit https://doi.org/10.1007/s40200-021-00777-4

8. In table 1 should give p value to know if both group was same or differ in characteristics

9.

Reviewer #3: My Review:

The authors present a large, Stockholm, Sweden population-based observational study of statin use and COVID-19 mortality. The study population included all residents of Stockholm over 45 yrs of age without liver disease. The exposure was any collection of a statin prescription from March 2019 through Feb 2020; outcome was death from COVID-19. They found that although unadjusted analysis revealed higher mortality for statin users (a sicker population), after extensive adjustment for confounders, statin use was associated with a reduction in COVID-19 mortality. Sensitivity analyses revealed this association to hold true for non-COVID deaths; for COVID-19 as the main cause of death; and after restricting analyses to statin users who initiated their statin use in the year prior to COVID.

I commend the authors for this interesting study. The large sample size allows for a thorough adjustment for covariates/potential confounders.

Comments:

1. Abstract - HR is listed as 0.88 - the authors need to specify that this is the adjusted HR.

2. Fig 1 should present the coefficients from the model which adjusts for confounders.

The main analysis should be presented in the main figure.

Similarity across age groups, sexes, risk groups, etc. should be tested using interaction terms in the main model. The current Fig 1 can be used as a sensitivity analysis

3. The use of the term 'emulated target trial' is very unclear. Either explain this thoroughly, with references, in the methods, or drop the term. Please explain simply what this analysis is and what it shows.

4. Positive control analysis- the authors state that"

"Our positive outcome analysis (benchmark) was consistent with a protective effect of statins (as expected), although the estimated protective effect was greater than that of the benchmark."

This statement is unclear- Please change to something like:

"In a similar model, the estimated protective effect of statins for non-COVID deaths was greater than for COVID deaths. "

Please state this plainly. Please present the model, in the appendix.

These results needs to be highlighted in the abstract and discussion. It suggests confounding, in that statin users are generally healthier than non-statin users, and this explains the observed protective effect in COVID.

5. There is significant potential for misclassification here - What if only 1 or 2 statin prescriptions were obtained, in early 2019 - this would not be likely to influence outcomes. Why start so early on? I would recommend that the criteria be that a prescription was picked up in the 3 months prior to COVID; or at the very least, perform a sensitivity analysis for those with at least 1 obtained from ~Jan 2020 on? The authors state this could cause additional biases, however the small risk of that does not seem to outweigh the large likelihood of the misclassification with the current methodology.

Reviewer #4: This is an interesting study on the assoication between statin therapy and COVID-19 mortality using the population data from Stockholm. Although well written, there are a few major issues needing attention.

1) The key findings are in Figure 1. We can see the effect of statin changed the direction from increasing the risk (OR 2.02) to protective (OR 0.88) after adjustment of all the confounders. The effect of 0.88 with a p-value of 0.01 is marginal and close to borderline significance. While this still might be possible after adjustment, we really have to scrutinise the statistical analyses to make sure we leave nothing to chance. Also in Figure 1, after adjustment, we can see that over 80 yrs old, IHD, hypertension and heart failure all have protective effect (reduce the risk) of Covid death. Why? This is quite difficult to comprehend and interpret.

2) Competing risk. As the outcome of survival analysis (Cox model) is Covid death rather than all-cause mortality, the competing risk from other deaths needs to be addressed in the analysis. Therefore, survival analysis taking into account of competing risk should be performed as main analysis. As the current result is close to borderline significance and also changes the direction of effect after adjustment, the competing risk issue becomes even more important to make sure the results are robust and reliable.

3) For adjusted confounders, why use age of 45 as a cut-off for inclusion? Is there ethnicity information available?

4) In results in the abstract and also in the main text, it says "A total of 2 545 individuals died from COVID-19 during follow-up, including 765 (0.5%) statin users and 1780 (0.2%) non-users". However, where are these 0.5% and 0.2% coming from? I can't reproduce these percentages using any figures in Table 1. Can authors please clarity this?

5) In Table 1, for age, median and IQR should be presented in the form of xx (yy-zz) instead.

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

[LINK]

Decision Letter 2

Callam Davidson

5 Aug 2021

Dear Dr. Bergqvist,

Thank you very much for submitting your revised manuscript "HMG-CoA reductase inhibitors and COVID-19 mortality in Stockholm, Sweden: a registry-based cohort study" (PMEDICINE-D-21-02232R2) for consideration at PLOS Medicine.

Your paper was seen again by the reviewers. The reviews are appended at the bottom of this email and any accompanying reviewer attachments can be seen via the link below:

[LINK]

As you will see, one of the reviewers still has some comments that require your attention. 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.

In revising the manuscript for further consideration, your revisions should address the specific points made by each reviewer and the editors. 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. In your rebuttal letter you should indicate your response to the reviewers' and editors' comments, the changes you have made in the manuscript, and include either an excerpt of the revised text or the location (eg: page and line number) where each change can be found. Please submit a clean version of the paper as the main article file; a version with changes marked should be uploaded as a marked up manuscript.

In addition, we request that you upload any figures associated with your paper as individual TIF or EPS files with 300dpi resolution at resubmission; please read our figure guidelines for more information on our requirements: http://journals.plos.org/plosmedicine/s/figures. While revising your submission, please upload your figure files to the PACE digital diagnostic tool, https://pacev2.apexcovantage.com/. PACE helps ensure that figures meet PLOS requirements. To use PACE, you must first register as a user. Then, login and navigate to the UPLOAD tab, where you will find detailed instructions on how to use the tool. If you encounter any issues or have any questions when using PACE, please email us at PLOSMedicine@plos.org.

We hope to receive your revised manuscript by Aug 26 2021 11:59PM. Please email us (plosmedicine@plos.org) if you have any questions or concerns.

***Please note while forming your response, if your article is accepted, you may have the opportunity to make the peer review history publicly available. The record will include editor decision letters (with reviews) and your responses to reviewer comments. If eligible, we will contact you to opt in or out.***

We ask every co-author listed on the manuscript to fill in a contributing author statement, making sure to declare all competing interests. 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. If new competing interests are declared later in the revision process, this may also hold up the submission. 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. You can see our competing interests policy here: http://journals.plos.org/plosmedicine/s/competing-interests.

Please use the following link to submit the revised manuscript:

https://www.editorialmanager.com/pmedicine/

Your article can be found in the "Submissions Needing Revision" folder.

To enhance the reproducibility of your results, we recommend that you deposit your laboratory protocols in protocols.io, where a protocol can be assigned its own identifier (DOI) such that it can be cited independently in the future. Additionally, PLOS ONE offers an option to publish peer-reviewed clinical study protocols. Read more information on sharing protocols at https://plos.org/protocols?utm_medium=editorial-email&utm_source=authorletters&utm_campaign=protocols

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.

We look forward to receiving your revised manuscript, please let me know if you have any questions.

Sincerely,

Callam Davidson,

Associate Editor

PLOS Medicine

plosmedicine.org

-----------------------------------------------------------

Requests from the editors:

Please update the language of your Author Summary as follows:

• Bullet point 1: Lipid-lowering HMG-CoA reductase inhibitors (statins) may influence COVID-19 mortality via their pleiotropic effects on coagulation and the immune system.

• Bullet point 2: Previous studies on this topic have been inconsistent, so we performed a population-based cohort study to investigate the relationship between statins and COVID-19 mortality.

• Bullet point 3: Using data from Swedish health registers, a cohort of 963876 residents of Stockholm, Sweden, were followed from the 1st of March 2020 until the 11th of November 2020.

Please consider changing the word ‘importantly’ in bullet point 5 of your Author Summary (line 79) to ‘substantially’ or similar. Alternatively, you could say ‘The association was corroborated by sensitivity analyses and was relatively consistent across risk groups’.

For the final bullet point of your Author Summary (line 84), please replace the word ‘total’ with ‘conclusion’ or ‘summary’.

Line 117: Please change ‘set in the rich Swedish health registers’ to ‘using Swedish health register data’, or similar.

Line 117: Please change ‘to determine’ to ‘of determining’.

Line 139: Remove the erroneous hyphen in the word ‘pregnant’.

Line 142: Remove the erroneous hyphen in the word ‘contraindicated’.

Citations should be in square brackets, and preceding punctuation. Please update throughout.

Comments from the reviewers:

Reviewer #2: The authors have responded very well to reviewers' comments

Reviewer #3: The authors have, in my opinion, provided a rigorous and complete rebuttal to my requests and to those of the other reviewers. Although I do not agree with 100% of their answers, I believe they have provided sufficient justification in those few grey areas where some nuanced differences of opinion remain.

I have no further comments at this time.

Reviewer #4: Many thanks authors for their effort to improve the manuscript. Most of my comments were satisfactorily addressed by the authors. However, there is still one remaining issue - the competing risk issue. I am not at all convinced/satisfied with the authors' response on competing risk. To simply censor the other deaths is inadequate and will not solve the competing risk problem in the cause-specific Cox models. To address the competing risk in the Covid death survival analysis, models such as Fine-Gray method are needed as there are potentially quite a few non-Covid deaths in the cohorts which present as competing risk. While it's appreciated that authors gave some references for the arguement, but the vast majority of the stats literature supports the use of the Fine-Gray method (for example) for cause specific survival analysis to effectively and adequately address the competing risk issue. By the way, what is the all-cause mortality in the cohorts? and what's the number of other deaths? what's the proporiton of Covid deaths vs other deaths? So far this competing risk issue hasn't been appropriately addrressed in the paper which could lead to the results and conclusions being subject to scrutiny.

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

[LINK]

Decision Letter 3

Callam Davidson

16 Sep 2021

Dear Dr. Bergqvist,

Thank you very much for re-submitting your manuscript "HMG-CoA reductase inhibitors and COVID-19 mortality in Stockholm, Sweden: a registry-based cohort study" (PMEDICINE-D-21-02232R3) for review by PLOS Medicine.

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

***Please note while forming your response, if your article is accepted, you may have the opportunity to make the peer review history publicly available. The record will include editor decision letters (with reviews) and your responses to reviewer comments. If eligible, we will contact you to opt in or out.***

In revising the manuscript for further consideration here, please ensure you address the specific points made by each reviewer and the editors. In your rebuttal letter you should indicate your response to the reviewers' and editors' comments and the changes you have made in the manuscript. Please submit a clean version of the paper as the main article file. A version with changes marked must also be uploaded as a marked up manuscript file.

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.

We hope to receive your revised manuscript within 1 week. Please email us (plosmedicine@plos.org) if you have any questions or concerns.

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.

To enhance the reproducibility of your results, we recommend that you deposit your laboratory protocols in protocols.io, where a protocol can be assigned its own identifier (DOI) such that it can be cited independently in the future. Additionally, PLOS ONE offers an option to publish peer-reviewed clinical study protocols. Read more information on sharing protocols at https://plos.org/protocols?utm_medium=editorial-email&utm_source=authorletters&utm_campaign=protocols

Please review your reference list to ensure that it is complete and correct. If you have cited papers that have been retracted, please include the rationale for doing so in the manuscript text, or remove these references and replace them with relevant current references. Any changes to the reference list should be mentioned in the rebuttal letter that accompanies your revised manuscript.

Please note, when your manuscript is accepted, an uncorrected proof of your manuscript will be published online ahead of the final version, unless you've already opted out via the online submission form. If, for any reason, you do not want an earlier version of your manuscript published online or are unsure if you have already indicated as such, please let the journal staff know immediately at plosmedicine@plos.org.

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

We look forward to receiving the revised manuscript by Sep 23 2021 11:59PM.   

Sincerely,

Callam Davidson,

Associate Editor 

PLOS Medicine

plosmedicine.org

------------------------------------------------------------

Requests from Editors:

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 (S1 Checklist)."

Line 206: Please include ‘years’ when stating median age of your sample

Table 1: Please define the abbreviations IQR, TIA, and ACE in the footnotes

Figure 1: TIA should be defined as transient ischemic attack (rather than ischemic attack, transient).

Lines 293, 295, 296, 514, 523, 527: Please remove the hyphens from words where they should not appear on these lines.

Comments from Reviewers:

Reviewer #4: Many thanks authors for their great effort to improve the manuscript. The authors have addressed my concerns comprehensively. I am satisfied with the response and revision. No further issues needing attention.

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

[LINK]

Decision Letter 4

Callam Davidson

20 Sep 2021

Dear Dr Bergqvist, 

On behalf of my colleagues and the Academic Editor, Dr Weiping Jia, I am pleased to inform you that we have agreed to publish your manuscript "HMG-CoA reductase inhibitors and COVID-19 mortality in Stockholm, Sweden: a registry-based cohort study" (PMEDICINE-D-21-02232R4) in PLOS Medicine.

Before your manuscript can be formally accepted you will need to complete some formatting changes, which you will receive in a follow up email. Please be aware that it may take several days for you to receive this email; during this time no action is required by you. Once you have received these formatting requests, please note that your manuscript will not be scheduled for publication until you have made the required changes.

When making the formatting changes, please also make the following update:

* In your data availability statement (part of the submission form), please also include the contact email address (mikrodata@scb.se) as well as the URL you have already provided.

In the meantime, please log into Editorial Manager at http://www.editorialmanager.com/pmedicine/, click the "Update My Information" link at the top of the page, and update your user information to ensure an efficient production process. 

PRESS

We frequently collaborate with press offices. If your institution or institutions have a press office, please notify them about your upcoming paper at this point, to enable them to help maximise its impact. If the press office is planning to promote your findings, we would be grateful if they could coordinate with medicinepress@plos.org. If you have not yet opted out of the early version process, we ask that you notify us immediately of any press plans so that we may do so on your behalf.

We also ask that you take this opportunity to read our Embargo Policy regarding the discussion, promotion and media coverage of work that is yet to be published by PLOS. As your manuscript is not yet published, it is bound by the conditions of our Embargo Policy. Please be aware that this policy is in place both to ensure that any press coverage of your article is fully substantiated and to provide a direct link between such coverage and the published work. For full details of our Embargo Policy, please visit http://www.plos.org/about/media-inquiries/embargo-policy/.

To enhance the reproducibility of your results, we recommend that you deposit your laboratory protocols in protocols.io, where a protocol can be assigned its own identifier (DOI) such that it can be cited independently in the future. Additionally, PLOS ONE offers an option to publish peer-reviewed clinical study protocols. Read more information on sharing protocols at https://plos.org/protocols?utm_medium=editorial-email&utm_source=authorletters&utm_campaign=protocols

Thank you again for submitting to PLOS Medicine. We look forward to publishing your paper. 

Sincerely, 

Callam Davidson 

Associate Editor 

PLOS Medicine

Associated Data

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

    Supplementary Materials

    S1 STROBE Checklist. Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) checklist.

    (DOCX)

    S1 Table. Covariate definition according to ICD-10/ATC code and period of data collection.

    (DOCX)

    S2 Table. Sensitivity analysis using the Fine–Gray subdistribution hazard model.

    (DOCX)

    S1 Text. Extended details on sensitivity analyses.

    (DOCX)

    Attachment

    Submitted filename: Rebuttal - Point by Point.docx

    Attachment

    Submitted filename: V2 Rebuttal - Point by Point.docx

    Attachment

    Submitted filename: Rebuttal - Point by Point.docx

    Data Availability Statement

    Swedish privacy law prohibits us from making register data publicly available. The data supporting our findings were used under license and ethical approval for the current study. Readers interested in obtaining microdata or replicating our study may seek similar approvals and inquire through Statistics Sweden. For further advice see: https://www.scb.se/en/services/guidance-for-researchers-and-universities/, or contact Statistics Sweden at: mikrodata@scb.se.


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