Effect of common maintenance drugs on the risk and severity of COVID-19 in elderly patients

Kin Wah Fung; Seo H Baik; Fitsum Baye; Zhaonian Zheng; Vojtech Huser; Clement J McDonald

doi:10.1371/journal.pone.0266922

. 2022 Apr 18;17(4):e0266922. doi: 10.1371/journal.pone.0266922

Effect of common maintenance drugs on the risk and severity of COVID-19 in elderly patients

Kin Wah Fung ^1,^*, Seo H Baik ¹, Fitsum Baye ¹, Zhaonian Zheng ¹, Vojtech Huser ¹, Clement J McDonald ¹

Editor: Masaki Mogi²

PMCID: PMC9015134 PMID: 35436293

Abstract

Background

Maintenance drugs are used to treat chronic conditions. Several classes of maintenance drugs have attracted attention because of their potential to affect susceptibility to and severity of COVID-19.

Methods

Using claims data on 20% random sample of Part D Medicare enrollees from April to December 2020, we identified patients diagnosed with COVID-19. Using a nested case-control design, non-COVID-19 controls were identified by 1:5 matching on age, race, sex, dual-eligibility status, and geographical region. We identified usage of angiotensin-converting enzyme inhibitors (ACEI), angiotensin-receptor blockers (ARB), statins, warfarin, direct factor Xa inhibitors, P2Y12 inhibitors, famotidine and hydroxychloroquine based on Medicare prescription claims data. Using extended Cox regression models with time-varying propensity score adjustment we examined the independent effect of each study drug on contracting COVID-19. For severity of COVID-19, we performed extended Cox regressions on all COVID-19 patients, using COVID-19-related hospitalization and all-cause mortality as outcomes. Covariates included gender, age, race, geographic region, low-income indicator, and co-morbidities. To compensate for indication bias related to the use of hydroxychloroquine for the prophylaxis or treatment of COVID-19, we censored patients who only started on hydroxychloroquine in 2020.

Results

Up to December 2020, our sample contained 374,229 Medicare patients over 65 who were diagnosed with COVID-19. Among the COVID-19 patients, 278,912 (74.6%) were on at least one study drug. The three most common study drugs among COVID-19 patients were statins 187,374 (50.1%), ACEI 97,843 (26.2%) and ARB 83,290 (22.3%). For all three outcomes (diagnosis, hospitalization and death), current users of ACEI, ARB, statins, warfarin, direct factor Xa inhibitors and P2Y12 inhibitors were associated with reduced risks, compared to never users. Famotidine did not show consistent significant effects. Hydroxychloroquine did not show significant effects after censoring of recent starters.

Conclusion

Maintenance use of ACEI, ARB, warfarin, statins, direct factor Xa inhibitors and P2Y12 inhibitors was associated with reduction in risk of acquiring COVID-19 and dying from it.

1. Background and significance

Maintenance drugs are indicated for chronic conditions, taken indefinitely on regular, usually daily basis. Reports have attributed protective or aggravating effects of some maintenance drugs on the likelihood and/or severity of SARS-CoV-2 infection. These include angiotensin-converting enzyme inhibitors (ACEIs), angiotensin-receptor blockers (ARBs), statins, antithrombotic agents, hydroxychloroquine, and famotidine.

ACEI and ARB attracted early attention because they increase the expression of angiotensin-converting enzyme 2 (ACE2) [1, 2], the cellular doorway for SARS-CoV-2 [3, 4]. There are concerns that their use could increase the likelihood, or the severity, of a COVID-19 infection. However, most studies published to-date show no such increases [5–9], and some actually suggest that they might decrease COVID-19 severity [10, 11]. Due to the potential beneficial effect, several ongoing prospective trials are testing whether initiating ACEI or ARB treatment for patients after they are diagnosed with COVID-19 would confer any benefits [12–14].

Statins are among the commonest maintenance drugs in the elderly population. Attention was drawn to statins because of their potential effect on virus entry, replication or degradation, and their well-known anti-inflammatory properties [15, 16]. Several subsequent studies have shown the beneficial effects of statins in COVID-19 patients [17, 18].

Given the thrombotic propensity of COVID-19 [19–23], some think that antithrombotics could reduce the severity of COVID-19, and accordingly, anticoagulants are now recommended for hospitalized and high risk patients [24], though results of studies of maintenance antithrombotics have been mixed [25–29]. Famotidine has attracted attention because it was shown to have potential anti- SARS-CoV-2 activity based on structural homology modeling [30], and it improved COVID-19 outcomes in small studies [31–33]. Hydroxychloroquine was considered as treatment option for COVID-19 because of its immune modulation and anti-SARS-CoV-2 activity in vitro [34–36], and reported benefit in one clinical study [37].

Most of the early primary studies on the effect of these maintenance drugs have been small (tens or hundreds of COVID-19 patients who were also on the drugs) and would most likely lack statistical power to see important associations.

In the U.S., the Virtual Research Data Center (VRDC) [38] of the Centers for Medicare and Medicaid Services (CMS) carries de-identified data which include complete drug prescription, diagnoses, encounters as well as geographic, socio-demographic and vital status information for most (>93%) of the 65 and older U.S. residents. Over 80% of COVID-19 deaths occur in this age group, according to the U.S. Centers for Disease Control and Prevention (CDC). Therefore, this would be a propitious population in which to explore the association of maintenance drugs with COVID-19 risks.

In this study, we used the VRDC data and extended Cox regression analysis to examine associations between the above eight drug types and the occurrence of three outcomes: 1) COVID-19 infection, 2) COVID-19 hospitalization, and 3) death after a COVID19 diagnosis.

2. Materials and methods

2.1 Study population and case definition

VRDC provided us with a 20% random sample of all Medicare Part D enrollees. This set included 374,299 patients 65 or above, who had at least one record of the COVID-19 specific ICD10-CM diagnosis code of U07.1 between April 1 and December 31, 2020. We used both inpatient and outpatient claims and Medicare’s vital status to identify COVID-19 cases and the outcome events. We only counted COVID-19 cases occurring on or after April 1, 2020, when the specific code for COVID-19 first became available. We stopped accruing cases after December 31, 2020, because of potential incompleteness of data due to the time lag (at least 3–4 months) between data capture and availability through VRDC. This study was declared not human subject research by the Office of Human Research Protection at the National Institutes of Health and by the CMS’s Privacy Board.

2.2 Drugs, exposure definition and comorbidities

We identified all study drug preparations that were available in the U.S. market. For drug classes we identified the class members through Anatomical Therapeutic Chemical (ATC) classification codes and used generic drug names to find them in the CMS data. Our eight study drugs/drug classes were ACEI, ARB, statins, warfarin, direct factor Xa inhibitors, P2Y12 inhibitors, famotidine, and hydroxychloroquine (see S1 Table for full list of drugs and prescription frequencies). H2 blockers other than famotidine were not included because the potential beneficial effect was based on the chemical structure of famotidine and not the pharmacological class. Note that hydroxychloroquine was a class which also included two other aminoquinolines (chloroquine and primaquine), but hydroxychloroquine accounted for 99.7% of prescriptions.

We assumed that patients were on a given study drug during the window from the prescription dispensing date to 30 days after the end-of-supply day (we called this period the “current use period”). We added the 30-day buffer after the end-of-supply day because of the common behavior of drug stockpiling—patients maintaining a stock of drugs so that they will not run out immediately in case refill is interrupted. Drug use status was treated as a time-varying covariate in the Cox regression model. We defined a patient to be a current user if an outcome event fell within a current use period, former user if the event fell outside the current use period, and never user if they never had a prescription for that drug. Our primary analysis compared current users with never users as control. In a supplementary analysis, we used former users as control to see if that would give different results.

In the first few months of the pandemic, there were media reports of the potential beneficial or harmful effects of some of the study drugs on COVID-19. Publicity about the good effects of hydroxychloroquine was particularly rife, which could have led people to start taking it because of symptoms, or fear, of COVID -19. We looked at the trend of the usage of the study drugs, starting from January 2019, to identify abnormal patterns that could be related to COVID-19. To study the potential effect of COVID-19 affecting the use of drugs (“reverse causality”), we did separate analyses with special treatment of patients who were only recently started on a study drug (see section 2.3 below).

Medicare specifies the onset of 67 chronic conditions by algorithm and we followed the algorithm to define the occurrence and onset date of each condition [39]. To adjust for illness burden in our analysis we included 57 Medicare chronic conditions with >1% prevalence in the Master Beneficiary Summary File.

2.3 Statistical analysis and covariates

We excluded patients who were not enrolled in Parts A (hospital) and B (medical) to ensure complete capture of hospitalization and encounter diagnoses. We considered the effect of the study drugs on three outcomes: 1) the risk of acquiring a COVID-19 diagnosis, 2) the risk of COVID-19 hospitalization, and 3) the risk of death after being diagnosed with COVID-19. For the first outcome (diagnosis of COVID-19), we used a nested case-control analysis [40], where each index COVID-19 patient was matched to patients with no COVID-19 diagnosis up to the date COVID-19 was diagnosed in the index patient.

For the first outcome (COVID-19 diagnosis), each index case was matched to five controls on age in years, race, sex, dual-eligibility status and five regions of residence (Northeast, Midwest, South, West, and others) at the time of the diagnosis of COVID-19. All cases and control were followed from January 1, 2020, until COVID-19 diagnosis, death, disenrollment from Medicare Parts A/B/D or December 31, 2020, whichever came first.

For the analyses of the second and third outcomes (hospitalization and death), we included only patients diagnosed with COVID-19. For the hospitalization outcome, we followed all patients from COVID-19 diagnosis until COVID-19-related hospitalization, or the other censoring points (except COVID-19 diagnosis) as described for the first outcome. For the death outcome, we did the same, swapping hospitalization for death.

We approximated patient’s income level using the monthly indicators of dual-eligibility and low-income subsidy (LIS), which divided patients into three income groups: 1) dual-eligible: income ≤ 135% federal poverty line (FPL), 2) non-dual LIS: income > 135% and ≤ 150% FPL, and 3) non-dual no LIS: income > 150% FPL. To explore the effect of each study drug, we employed extended Cox regression analysis with days-on-study as time scale. We included age, gender, race, regions of residence, Medicare insurance type (Advantage or fee-for-service) and the degree of LIS as covariates. We also included binary flags for each of the 57 Medicare chronic conditions to control for the effect of co-morbidities. In order to protect against the immortal time bias and violation of proportional hazard assumption [41], we treated the use of each of the study drugs and almost all covariates as time-varying covariates. Only age, sex, race, and regions of residence were time-fixed, and their values were defined as of their values on January 1, 2020. Since the disease covariates were chronic diseases, we considered them as always present after their onset and always absent before then. The values of all time-varying covariates were reset at the time of each event in the Cox regression. We used Efron’s adjustment for tied events.

To mitigate the potential of COVID-19 influencing the use of drugs, we did a separate Cox regression in which patients who only started a study drug in 2020 (they had never been on the drug before January 1,2020) were censored when they first started the drug (“recent starter censoring”). We compared the results with and without such censoring.

In order to mitigate selection bias toward use of the study drugs, we developed time-varying propensity scores (PS) [42] separately for each study drug using logistic regressions. The PS was the likelihood of receiving a study drug, conditional on patient’s characteristics (demographics, socioeconomics, and presence of the chronic conditions). We iteratively estimated the PSs every month among the patients who remained in follow-up, considering all covariates that preceded the end of a given monthly cycle [43], and ran all Cox regression analyses with time-varying PSs as additional adjustments.

3. Results

3.1 Study population

In our 20% random sample of Part D enrollees, 374,299 Medicare beneficiaries aged 65 and above were diagnosed with COVID-19 between April 1 and December 31, 2020, among which 65,108 (17.4%) died (Table 1 and S1 Fig). Over the same period, CDC registered a national total of 19,852,636 COVID-19 cases and 350,510 deaths [44]. In the CDC statistics, even though patients ≥65 accounted for only 13.9% of cases, they accounted for 80.5% of mortalities. Therefore, our study population represents patients most at risk for COVID-19 death. Projecting from the CDC statistics, a 20% random sample of patients 65 and above would have 551,903 COVID-19 cases and 56,432 mortalities (Table 1). Therefore, our study population captured 67.8% of COVID-19 cases nationally. Our mortality number is 15% higher than the projected national count. Our death numbers were higher because Medicare data did not distinguish between causes of death. It is possible that some mortalities were not related to COVID-19. However, most of the mortalities occurred within a short time after the COVID-19 diagnosis (median 14 days, inter-quartile range 6–36 days). Compared with national statistics, the Medicare population was over-represented in females, whites and under-represented in Hispanics. Geographically, there was over-representation of the North-East region.

Table 1. Study population compared to U.S. national statistics, April to December 2020 (SRS—Simple random sample; CDC—Centers for Disease Control and Prevention).

	Medicare 20% SRS Age 65+ population (Apr-Dec 2020)		National statistics from CDC (Apr-Dec 2020)
	Covid-19 cases (%)	Mortality (%)	Covid-19 cases	Mortality
Total	374,299(100)	65,108(100)	551,903^*	56,432^*
Female	225,585(60.3)	36,093(55.0)	52.2%	45.7%
*Race*
White	284,707(76.1)	46,595(71.6)	50.0%	58.6%
Black	38,419(10.3)	8,702(13.4)	11.0%	13.6%
Hispanic	31,981(8.5)	6,401(9.8)	29.2%	18.9%
Asian	9,961(2.7)	2,032(3.1)	3.3%	4.0%
Other	9,231(2.5)	1,378(2.1)	6.6%	5.0%
*Geographic region*
Northeast	82,281(22.0)	16,292(25.0)	17.15%	23.45%
Midwest	90,921(24.3)	15,449(23.7)	21.76%	20.18%
South	139,567(37.3)	24,248(37.2)	38.95%	36.91%
West	60,830(16.3)	9,046(13.9)	21.71%	19.03%

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*The national case and mortality counts are extrapolations from the CDC data. They only include patients aged 65+, and reduced to 20% to be comparable with our 20% random sample.

3.2 Drug exposure and matching

Table 2 shows the breakdown of drug usage (all patients who had prescription for the drug during the follow up period, starting on January 1, 2020) among the COVID-19 patients and controls. Overall, 278,912 (74.6%) cases and 1,351,244 (72.4%) controls had prescriptions for at least one study drug. The three most common study drugs among COVID-19 patients were statins 187,374 (50.1%), ACEI 97,872 843 (26.2%) and ARB 83,290 (22.3%). For all study drugs except ACEI and ARB, the usage was significantly higher among COVID-19 patients than controls. The unmatched rate (less than five controls found) was 0.4%.

Table 2. Characteristics of Covid-19 patients and matched controls.

	Covid-19 patients (%)	Control (%)	(Control -Covid) Difference (95%CI)	PVALUE
Drug exposure
ACE Inhibitor	97,843(26.2)	517,078(27.7)	1.5(1.4,1.7)	0.000
ARB	83,290(22.3)	421,264(22.6)	0.3(0.2,0.4)	0.000
Statin	187,374(50.1)	915,226(49.0)	-1.1(-1.2,-0.9)	0.000
Warfarin	11,755(3.1)	47,251(2.5)	-0.6(-0.7,-0.6)	0.000
Direct Factor Xa Inhibitor	42,599(11.4)	161,365(8.6)	-2.7(-2.9,-2.6)	0.000
P2Y12 Inhibitor	40,199(10.7)	157,173(8.4)	-2.3(-2.4,-2.2)	0.000
Hydroxychloroquine	2,879(0.8)	11,846(0.6)	-0.1(-0.2,-0.1)	0.000
Famotidine	13,133(3.5)	40,984(2.2)	-1.3(-1.4,-1.3)	0.000
Any drug	278,912(74.6)	1,351,244(72.4)	-2.2(-2.3,-2.0)	0.000
No Rx	95,094(25.4)	515,332(27.6)	2.2(2.0,2.3)	0.000
Age
65–69	37,859(10.1)	189,150(10.1)	0.0(-0.1,0.1)	0.839
70–74	89,538(23.9)	447,650(24.0)	0.0(-0.1,0.2)	0.582
75–79	77,520(20.7)	387,477(20.8)	0.0(-0.1,0.2)	0.662
80–84	64,951(17.4)	324,637(17.4)	0.0(-0.1,0.2)	0.704
85 +	104,138(27.8)	517,662(27.7)	-0.1(-0.3,0.0)	0.168
Region
MIDWEST	90,840(24.3)	453,053(24.3)	-0.0(-0.2,0.1)	0.830
NORTHEAST	82,225(22.0)	410,408(22.0)	0.0(-0.1,0.1)	0.976
SOUTH	139,505(37.3)	696,758(37.3)	0.0(-0.1,0.2)	0.747
WEST	60,761(16.2)	303,161(16.2)	-0.0(-0.1,0.1)	0.946
OTHER	675(0.2)	3,196(0.2)	-0.0(-0.0,0.0)	0.213
Unmatched	293 (< 0.1)	-
Total	374,299 (100)	1,866,576 (100)
Number of patients with < 5 matches	1446 (0.4)

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As for abnormal patterns of drug usage related to COVID-19, we detected a sharp rise in the use of hydroxychloroquine in March and April 2020 (S2 Fig), which was not seen in other study drugs. FDA granted emergency use authorization for hydroxychloroquine for COVID-19 treatment on March 28, and the percentage of COVID-19 patients on hydroxychloroquine surged to over 0.8% compared to the baseline of 0.3% before the pandemic. There was a similar but smaller rise in non-COVID-19 patients, showing that some patients might be taking hydroxychloroquine for prophylaxis or suspicion of COVID-19. After FDA revoked the emergency use authorization on June 15, the usage of hydroxychloroquine began to drop, but remained slightly above the baseline before the pandemic.

3.3 Risk of being diagnosed with COVID-19

The total number of patients followed (cases and controls) was about 2.2 million, and around 350,000 ended up with the diagnosis of COVID-19 (Table 3). We did separate analyses with and without recent starter censoring for all study drugs. Only hydroxychloroquine exhibited significantly different results with censoring. We report the results with recent starter censoring for hydroxychloroquine as the main results in Table 3. The results for hydroxychloroquine without such censoring are shown for comparison.

Table 3. Effect of drug use on Covid-19 diagnosis, Covid-19 hospitalization and death (other non-drug covariates are listed in Table 4).

	Covid-19 diagnosis		Covid-19 hospitalization		Death
	No. of patients (events)	Hazard ratio (95% CI)	No. of patients (events)	Hazard ratio (95% CI)	No. of patients (events)	Hazard ratio (95% CI)
Drug use status: current vs. never
ACE Inhibitor	2,185,934 (354,342)	0.91(0.90,0.92)	358,392 (142,004)	0.98(0.97,1.00)	358,392 (61,778)	0.88(0.86,0.90)
ARB	2,185,934 (354,342)	0.92(0.91,0.92)	358,392 (142,004)	0.92(0.91,0.94)	358,392 (61,778)	0.85(0.83,0.87)
Statin	2,185,934 (354,342)	0.97(0.96,0.98)	358,392 (142,004)	0.95(0.94,0.96)	358,392 (61,778)	0.81(0.80,0.83)
Warfarin	2,185,934 (354,342)	0.88(0.86,0.91)	358,392 (142,004)	0.95(0.92,0.99)	358,392 (61,778)	0.82(0.78,0.87)
Direct Factor Xa Inhibitor	2,185,934 (354,342)	0.99(0.97,1.00)	358,392 (142,004)	0.89(0.88,0.91)	358,392 (61,778)	0.80(0.78,0.82)
P2Y12 Inhibitor	2,185,934 (354,342)	0.98(0.97,0.99)	358,392 (142,004)	0.96(0.95,0.98)	358,392 (61,778)	0.94(0.91,0.96)
Famotidine	2,185,934 (354,342)	1.12(1.10,1.15)	358,392 (142,004)	0.94(0.91,0.97)	358,392 (61,778)	1.00(0.96,1.04)
Hydroxychloroquine (New Users Censored)	2,185,934 (354,342)	0.95(0.91,1.00)	358,392 (142,004)	1.06(0.98,1.14)	358,392 (61,778)	1.08(0.95,1.24)
Hydroxychloroquine (No Censoring) ^*	2,186,365 (357,499)	1.63(1.58,1.68)	361,568 (143,728)	0.97(0.93,1.01)	361,568 (62,698)	1.06(0.99,1.14)
Drug use status: current vs. past
ACE Inhibitor	2,185,934 (354,342)	0.86(0.85,0.87)	358,392 (142,004)	0.95(0.93,0.97)	358,392 (61,778)	0.87(0.84,0.90)
ARB	2,185,934 (354,342)	0.93(0.91,0.94)	358,392 (142,004)	0.93(0.91,0.96)	358,392 (61,778)	0.84(0.82,0.87)
Statin	2,185,934 (354,342)	0.92(0.91,0.93)	358,392 (142,004)	0.93(0.92,0.95)	358,392 (61,778)	0.79(0.77,0.81)
Warfarin	2,185,934 (354,342)	0.84(0.81,0.87)	358,392 (142,004)	0.91(0.86,0.95)	358,392 (61,778)	0.83(0.77,0.90)
Direct Factor Xa Inhibitor	2,185,934 (354,342)	0.89(0.87,0.91)	358,392 (142,004)	0.88(0.85,0.90)	358,392 (61,778)	0.79(0.76,0.82)
P2Y12	2,185,934 (354,342)	1.01(0.99,1.03)	358,392 (142,004)	0.99(0.96,1.02)	358,392 (61,778)	0.90(0.87,0.94)
Famotidine	2,185,934 (354,342)	0.93(0.91,0.96)	358,392 (142,004)	0.95(0.92,0.99)	358,392 (61,778)	1.03(0.97,1.09)
Hydroxychloroquine (New Users Censored)	2,185,934 (354,342)	0.91(0.84,0.98)	358,392 (142,004)	0.97(0.86,1.09)	358,392 (61,778)	0.87(0.72,1.05)
Hydroxychloroquine (No Censoring) ^*	2,186,365 (357,499)	1.35(1.28,1.42)	361,568 (143,728)	1.12(1.03,1.22)	361,568 (62,698)	1.27(1.14,1.42)

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Bold: significant protective effect; Italic: significant harmful effect.

*not part of main analysis, shown here to illustrate the indication bias if recent starters of hydroxychloroquine were not censored.

Compared to patients who never used the drug, current users of ACEI, ARB, statin, warfarin, direct factor Xa inhibitors and P2Y12 inhibitors were associated with decreased risk of contracting COVID-19, ranging from 1% reduction (direct factor Xa inhibitors) to 12% reduction (warfarin). For famotidine, there was a 12% increase in risk. For hydroxychloroquine, with the proper censoring of recent starters, there was no effect on the risk of being diagnosed with COVID-19. However, without censoring, current users of hydroxychloroquine would appear to be associated with an increased risk of getting COVID-19 compared to never users (63% increase).

The results for the non-drug covariates are shown in Table 4. Compared with the youngest group of patients in our population (65–69), older age was associated with decreased risk of COVID-19 diagnosis. Female sex was associated with a 11% risk reduction compared to male. The risk of COVID-19 diagnosis was higher in all race groups compared to whites, 13% higher for Asians, 54% for Blacks and 85% for Hispanics. The comorbidities associated with the greatest increase in risk were dementia (132% increase), hypertension (125% increase) and schizophrenia (68% increase).

Table 4. Effect of non-drug covariates on Covid-19 diagnosis, Covid-19 hospitalization and death (drug-related covariates are listed in Table 3).

Co-variate	Reference	COVID-19 diagnosis	COVID hospitalization	Death
Co-variate	Reference	Hazard ratio (95% CI)
Demographics
70–74	65–69	0.98(0.97,0.99)	1.10(1.08,1.12)	1.12(1.07,1.16)
75–79	65–69	0.83(0.81,0.84)	1.40(1.36,1.43)	1.55(1.49,1.61)
80–84	65–69	0.75(0.74,0.76)	1.63(1.59,1.66)	2.03(1.95,2.12)
>85	65–69	0.83(0.81,0.85)	1.95(1.90,2.01)	3.33(3.19,3.48)
Female	Male	0.89(0.87,0.90)	0.80(0.79,0.82)	0.65(0.63,0.68)
Black	white	1.54(1.52,1.57)	0.99(0.97,1.01)	1.04(1.01,1.07)
Hispanic	white	1.85(1.82,1.88)	1.19(1.17,1.22)	1.31(1.27,1.35)
Asian	white	1.13(1.08,1.17)	0.99(0.94,1.04)	1.26(1.18,1.35)
Other	white	1.09(1.07,1.12)	1.07(1.04,1.11)	1.32(1.25,1.40)
Dual	Non-dual No LIS	0.45(0.44,0.45)	1.11(1.08,1.13)	0.64(0.62,0.66)
Non-dual LIS	Non-dual No LIS	0.82(0.80,0.83)	1.47(1.42,1.51)	3.06(2.95,3.17)
Midwest	Northeast	1.15(1.14,1.17)	1.43(1.40,1.46)	1.08(1.04,1.11)
South	Northeast	1.00(0.99,1.01)	1.25(1.23,1.27)	1.00(0.97,1.02)
West	Northeast	1.67(1.65,1.70)	1.23(1.20,1.26)	0.97(0.94,1.00)
Other	Northeast	0.83(0.76,0.91)	0.41(0.33,0.52)	0.54(0.42,0.71)
Comorbidities
ESRD	No	0.86(0.83,0.90)	0.75(0.72,0.78)	1.28(1.21,1.36)
AMI	No	0.70(0.69,0.71)	1.17(1.14,1.20)	1.39(1.35,1.44)
Atrial Fibrillation	No	0.30(0.30,0.31)	0.92(0.89,0.95)	1.24(1.18,1.30)
Cataract	No	1.08(1.07,1.09)	0.84(0.83,0.85)	0.80(0.78,0.82)
Chronic Kidney Disease	No	0.99(0.98,1.00)	1.34(1.32,1.35)	1.56(1.53,1.60)
COPD	No	0.97(0.96,0.98)	0.95(0.94,0.96)	1.04(1.02,1.07)
Heart Failure	No	0.96(0.95,0.96)	1.01(1.00,1.02)	1.13(1.10,1.15)
Diabetes	No	1.06(1.05,1.08)	1.18(1.16,1.19)	0.98(0.96,1.01)
Glaucoma	No	0.97(0.96,0.97)	1.00(0.99,1.02)	0.97(0.95,0.98)
Hip/Pelvic Fracture	No	1.47(1.45,1.50)	1.11(1.09,1.13)	1.16(1.13,1.19)
Ischemic Heart Disease	No	0.70(0.69,0.71)	0.90(0.88,0.92)	0.87(0.84,0.89)
Depression	No	1.04(1.03,1.05)	0.96(0.94,0.97)	0.96(0.94,0.98)
Alzheimer’S Disease Or Dementia	No	2.32(2.28,2.35)	1.32(1.29,1.34)	2.27(2.21,2.34)
Osteoporosis	No	0.85(0.84,0.86)	0.81(0.80,0.82)	0.85(0.84,0.87)
Rheumatoid Arthritis/Osteoarthritis	No	0.99(0.98,1.01)	0.84(0.83,0.86)	0.81(0.79,0.83)
Stroke/Transient Ischemic Attack	No	0.76(0.75,0.77)	1.00(0.98,1.01)	1.00(0.97,1.02)
Breast Cancer	No	1.01(0.99,1.02)	0.99(0.97,1.02)	0.97(0.94,1.01)
Colorectal Cancer	No	1.03(1.01,1.04)	0.95(0.93,0.98)	1.03(0.99,1.06)
Prostate Cancer	No	1.01(1.00,1.02)	0.96(0.94,0.99)	0.99(0.96,1.02)
Lung Cancer	No	0.98(0.96,1.00)	0.98(0.95,1.02)	1.53(1.46,1.59)
Endometrial Cancer	No	1.10(1.06,1.13)	1.06(1.01,1.10)	1.06(0.99,1.13)
Anemia	No	1.07(1.06,1.09)	0.94(0.93,0.96)	1.05(1.02,1.07)
Asthma	No	0.83(0.82,0.84)	0.87(0.86,0.88)	0.81(0.79,0.83)
Hyperlipidemia	No	0.57(0.55,0.59)	0.79(0.76,0.83)	0.58(0.54,0.62)
Hyperplasia	No	0.96(0.95,0.97)	0.87(0.85,0.89)	0.90(0.88,0.93)
Hypertension	No	2.25(2.17,2.33)	2.34(2.21,2.47)	0.95(0.88,1.03)
Hypothyroidism	No	1.00(0.99,1.01)	0.87(0.86,0.88)	0.91(0.90,0.93)
ADHD And Other Conduct Disorders	No	1.24(1.21,1.26)	0.99(0.96,1.01)	1.13(1.09,1.17)
Alcohol Use Disorders	No	1.14(1.12,1.16)	1.03(1.01,1.05)	1.00(0.97,1.03)
Anxiety Disorders	No	1.11(1.10,1.12)	0.98(0.97,1.00)	1.07(1.05,1.09)
Bipolar Disorder	No	1.13(1.11,1.14)	1.08(1.07,1.10)	1.05(1.03,1.08)
Traumatic Brain Injury	No	1.19(1.17,1.22)	1.07(1.04,1.10)	1.04(1.00,1.08)
Drug Use Disorder	No	0.91(0.90,0.93)	1.05(1.02,1.07)	0.98(0.94,1.01)
Intellectual Disabilities	No	0.79(0.77,0.81)	0.88(0.85,0.91)	0.91(0.86,0.96)
Learning Disabilities	No	1.08(1.05,1.12)	1.00(0.96,1.04)	1.06(1.00,1.13)
Other Developmental Delays	No	0.73(0.69,0.76)	0.84(0.79,0.90)	0.91(0.82,1.01)
Personality Disorders	No	1.03(1.01,1.04)	1.04(1.02,1.07)	0.98(0.95,1.01)
Schizophrenia	No	1.68(1.66,1.70)	1.04(1.02,1.05)	1.19(1.16,1.22)
Post-Traumatic Stress Disorder	No	0.85(0.82,0.87)	0.93(0.89,0.96)	0.89(0.83,0.95)
Cerebral Palsy	No	1.22(1.17,1.26)	0.90(0.85,0.95)	0.92(0.84,1.01)
Epilepsy	No	1.04(1.03,1.05)	0.93(0.91,0.95)	0.99(0.96,1.01)
Cystic Fibrosis	No	1.11(1.09,1.12)	0.98(0.96,1.00)	1.04(1.01,1.07)
Fibromyalgia, Chronic Pain And Fatigue	No	0.93(0.92,0.93)	0.89(0.88,0.90)	0.83(0.81,0.84)
Viral Hepatitis (General)	No	1.23(1.20,1.26)	1.04(1.01,1.07)	1.09(1.04,1.14)
Liver Disease Cirrhosis	No	0.99(0.98,1.00)	0.95(0.94,0.96)	1.03(1.01,1.05)
Hiv/Aids	No	1.10(1.05,1.15)	0.91(0.85,0.98)	0.92(0.83,1.02)
Leukemias And Lymphomas	No	0.98(0.96,0.99)	1.03(1.00,1.06)	1.24(1.19,1.29)
Migraine And Other Chronic Headache	No	0.78(0.77,0.79)	0.80(0.79,0.82)	0.74(0.71,0.76)
Mobility Impairments	No	0.90(0.89,0.91)	0.97(0.95,0.98)	0.97(0.95,1.00)
Multiple Sclerosis And Transverse Myelitis	No	1.11(1.07,1.14)	1.15(1.10,1.20)	1.05(0.98,1.13)
Obesity	No	0.96(0.95,0.97)	1.16(1.14,1.18)	0.90(0.88,0.92)
Overarching Opioid Use Disorder	No	0.93(0.91,0.94)	0.95(0.92,0.97)	0.94(0.90,0.98)
Peripheral Vascular Disease	No	1.08(1.07,1.09)	1.05(1.03,1.06)	1.05(1.03,1.08)
Spinal Cord Injury	No	1.14(1.12,1.16)	1.07(1.05,1.10)	1.09(1.06,1.13)
Tobacco Use Disorders	No	0.98(0.96,0.99)	1.07(1.05,1.09)	1.08(1.05,1.11)
Pressure Ulcers And Chronic Ulcers	No	1.22(1.20,1.23)	0.98(0.96,0.99)	1.41(1.38,1.44)
Deafness And Hearing Impairment	No	0.92(0.91,0.93)	0.94(0.93,0.95)	0.95(0.93,0.97)
Blindness And Visual Impairment	No	1.27(1.25,1.30)	1.03(1.01,1.06)	1.12(1.08,1.17)

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Bold: significant protective effect; Italic: significant harmful effect.

3.4 Risk of COVID-19 hospitalization

Overall, 142,004 (39.6%) COVID-19 patients were hospitalized for COVID-19. Current users of ACEI, ARB, statin, warfarin, direct factor Xa inhibitors, P2Y12 inhibitors and famotidine were associated with 2–11% decreased risk of COVID-19 hospitalization when compared with never users (Table 3). Hydroxychloroquine did not show any effect when recent starters were censored. The risk of COVID-19 hospitalization increased monotonically with age (Table 4). Compared to the 65–69 age group, the risk increased by 10%, 40%, 63% and 95% for groups 70–74, 75–79, 80–84 and ≥85 respectively. Female sex was associated with a 20% reduced risk compared to male. The risk of COVID-19 hospitalization for Hispanics was 19% higher than whites. Poorer patients were associated with higher risks of hospitalization with COVID-19. The three co-morbidities associated with largest increase in risk were hypertension (134%), chronic kidney disease (34%) and dementia (32%).

3.5 Risk of mortality

Overall, 61,778 (17.2%) COVID-19 patients died. Current users of ACEI, ARB, statin, warfarin, direct Xa inhibitors and P2Y12 inhibitors among COVID-19 patients were all associated with lower risks of mortality, from 6% less than never users (P2Y12 inhibitors) to 20% less (direct factor Xa inhibitors) (Table 3). Famotidine and hydroxychloroquine showed no significant effect. Mortality risk increased monotonically with age (Table 4). Compared to patients 65–69, all other age groups exhibited increased risk of death: 12%, 55%, 103% and 233% increased risk among 70–74, 75–79, 80–84 and ≥85 respectively. Female sex was associated with a 35% reduced risk of death compared to male. All race groups were associated with notably greater mortality risk than whites. Unlike analyses of other outcomes, the poorest patients (dual-eligible) were associated with 36% decreased risk of death, while the second poorest patients (non-dual LIS) had 206% increased risk of death compared to patients not dually eligible and not receiving LIS. The three co-morbidities associated with the greatest mortality risk were dementia (127%) chronic kidney disease (56%) and lung cancer (53%).

4. Discussion

The most important finding of our study is that after controlling for age, gender, race, socio-economic, geographic factors and co-morbidities, there was an associated decline in mortality risk of 12% or greater among current users for ACEIs, ARB, statins, and anticoagulants, and by a lesser amount (6%) among P2Y12 inhibitors users. To the best of our knowledge, ours is first single source study to show such beneficial associations, though it has been hinted at in meta-analyses [10, 11]. While more study is needed to identify the exact reasons for severity and mortality modification of the study drugs in COVID-19 patients, some possible mechanisms have been proposed. One predominant hypothesis is that SARS-CoV-2 down-regulates ACE2 expression, resulting in unabated angiotensin II activity that may be responsible for organ damage in COVID-19 [6]. ACEI and ARB reduce angiotensin II activity and so are protective [45]. Our findings provide strong support to the advice from professional societies and the WHO that ACEI and ARB be continued in COVID-19 patients [46, 47]. The benefits of antithrombotics can be explained by reduction in the thromboembolism observed in COVID-19 patients [21, 23, 48]. These results strongly support the current advice about the use of anti-clotting drugs in COVID-19 patients. The benefits of statins can be associated with their anti-viral and anti-inflammatory properties [15, 16]. In addition, our study gives justification for the need of clinical trials to initiate treatment with drugs that are potentially beneficial (such as ACEI, ARB and statins) in patients diagnosed with COVID-19.

The main strength of this study is the size of the study population and relative completeness of demographic, prescription, and co-morbidity data. The U.S. has the largest collection of COVID-19 cases in the world. The elderly represents the most at-risk population for severe COVID-19. Our study population covered over 370,000 COVID-19 cases and 65,000 mortalities in U.S. patients over 65. According to CDC statistics, over 80% of COVID-19 mortalities occurred in patients over 65. A large U.K. study based on NHS records registered 10,926 COVID-19 deaths, our study surpassed that number by almost six-fold [49]. In terms of study drug usage among COVID-19 patients, our numbers were also much higher. For instance, one of the biggest early original ACEI studies was from Italy, covering 1,502 ACEI users among COVID-19 patients [7]. We had 65 times that number. Recently published studies do have larger patient numbers. One such study supported by the American Heart Association’s Rapid Response Grant COVID-19 is by An et al. [50], with 4,652 patients on ACEI and 2,546 on ARB. However, this is still lower than the number of patients observed in our study, 97,843 on ACEI and 83,290 on ARB. Sample sizes of this magnitude are not seen in other studies, including meta-analyses [8, 10, 11]. Moreover, other studies tended to be restricted to institutional or regional populations. Our Medicare population includes most US elderly individuals. By one estimate 93% of all US adults over 65 are enrolled to Medicare [51]. Unlike meta-analysis that pools multiple, not necessarily comparable data sources, we used a single data source that has relatively complete and longitudinal medication information with well-documented data capture procedures.

The emergence of hydroxychloroquine as a potential “game-changer” in COVID-19 had been accompanied by the dramatic rise in its use, not only among COVID-19 patients but in the general population as well. Even after the emergency use authorization was revoked by FDA, the use of hydroxychloroquine remained somewhat higher than the baseline level before the pandemic. This shows that media claims, even those that are eventually shown to be unsubstantiated or disproved, may have lasting impact on the public psyche. In our observational study, the use of hydroxychloroquine is a classic example of indication bias—the indication for the exposure is directly related to the outcome being observed [52]. Since we have longitudinal prescription data (starting from 2019), we were able to compensate for this by censoring patients who were first started on hydroxychloroquine during the pandemic. Indeed, our results show that, without censoring, the use of hydroxychloroquine would have appeared to be associated with an increase in the risk of COVID-19 diagnosis and death, but the effects disappeared with proper censoring.

Regarding the risk of catching COVID-19, we found that ACEI, ARB, statins and antithrombotics were associated with a reduction in the risk of getting a COVID-19 diagnosis. One possible explanation is that if these drugs blunted the effects of COVID-19 infection, infected patients might have milder symptoms and would be more likely to not seek medical care and remain undiagnosed. The apparent reduction in risk of catching COVID-19 with advancing age could be related to the reduced level of social activity at extreme age, thus lowering the risk of exposure.

Apart from drug usage, we found the following risk factors for severe COVID-19—advanced age, male, non-white, and co-morbidities including chronic kidney diseases, dementia, hypertension, heart diseases, chronic obstructive pulmonary disease, chronic liver diseases and some malignancies. These findings concur with similar studies and are well-documented. In our study, the group with the lowest income (dual-eligible) was associated with a reduced risk of death. One possible explanation is that this group has better access to healthcare, since patients are eligible to both Medicaid and Medicare. However, this explanation only applies to the Medicaid and Medicare patients and not low income patients in general.

We recognize the following limitations. In our retrospective observation study, drug exposure was implied from prescription records and there was no verification that the drugs were dispensed or taken. Drug data from Medicare claims are incomplete for hospitals and nursing homes. As a result, the continuation of medications for inpatients could not be ascertained. For certain drugs (e.g., statins), it has been reported that rebound effects were associated with drug discontinuation which could potentially affect the outcomes of COVID-19 infection [53]. Some of the study drugs are often prescribed in combination with other drugs. For example, statins are often used together with anti-hypertensive medications. In our Medicare population, statins are taken together with ACEI (in 35% of patients on statins), ARB (28.6%), beta blockers (44.3%), calcium channel blockers (37.7%) and diuretics (39.8%). (S2 Table) This can be a potential confounder if not properly addressed. In our multiple variable analysis, the use of ACEI, ARB and statins are included in the same unified regression model as time-varying covariates. This ensures that the observed effects of each individual study drug are already adjusted for the co-prescription of other drugs included in the model [54]. It is true that the combined use of statins with other anti-hypertensive drugs (e.g., diuretics, beta blockers) has not been adjusted for. However, up to the start of the study, there was no strong evidence that diuretics, calcium channel blockers or beta blockers significantly affect the susceptibility to and severity of COVID-19. It is therefore unlikely that the observed effects of the study drugs on COVID-19 are caused by these other drugs. Furthermore, there could also be other residual confounding factors outside the scope of our regression models. We did not include COVID-19 patients before April because the COVID-19 specific diagnosis code was not in use. Compared with national data from CDC, we could be missing about one-third of COVID-19 cases. It is likely that milder cases would be missed since the patients did not seek medical care. This would also explain the relatively high hospitalization and mortality rate in our population compared to some other studies [55]. We used all-cause mortality because of the lack of an accurate cause of death in CMS data. Because of this, it is possible that some reduction of mortality is due to the general protective effect of the study drugs and not related to COVID-19 per se (e.g., statins and hypotensive agents can reduce cardiovascular mortality). However, since the mortality rate was 17% in our COVID-19 patients overall, 29% among those hospitalized, and death occurred within a median of 14 days of COVID-19 diagnosis, it is highly likely that COVID-19 was the main contributor to mortality. Another potential confounding factor is that some patients could be diagnosed with COVID-19 because of more frequent routine testing during an encounter for another reason (e.g., preparation for coronary bypass), which makes COVID-19 an incidental finding rather than the trigger event. However, overall, only 3.2% of our Medicare population were diagnosed with COVID-19, so the chance of a purely incidental COVID-19 diagnosis is small.

5. Conclusion

Analysis of more than 370,000 Medicare enrollees over 65 diagnosed with COVID-19 showed that the use of ACEI, ARB, statins, warfarin, direct factor Xa inhibitors and P2Y12 inhibitors was associated with a reduction in the risk of catching COVID-19 and developing severe disease. Hydroxychloroquine and famotidine were not associated with significant effects in these outcomes.

Supporting information

S1 Fig. Consort diagram.

(PDF)

Click here for additional data file.^{(183.6KB, pdf)}

S2 Fig. Trends of drug usage in 2019 and 2020.

(PDF)

Click here for additional data file.^{(81.6KB, pdf)}

S1 Table. Drug classes, clinical drugs and usage frequencies among all our study population.

(DOCX)

Click here for additional data file.^{(21.9KB, docx)}

S2 Table. The combination use of anti-hypertensive drugs with statins in Medicare patients for 2019–2020.

(DOCX)

Click here for additional data file.^{(15.5KB, docx)}

S3 Table. Data values for drug drug usage trend graphs in S2 Fig.

(XLSX)

Click here for additional data file.^{(75.1KB, xlsx)}

S4 Table. Detailed statistical data of the Cox regression models for death, hospitalization and acquiring COVID-19.

(XLSX)

Click here for additional data file.^{(56.1KB, xlsx)}

Acknowledgments

We would like to thank the diligent and helpful staff at the VRDC and Research Data Assistance Center (ResDAC), without them this study would not be possible.

Data Availability

The minimal data set relevant to this study is included in the Supporting information. All data in Supporting information can be used without restriction. As for raw data, CMS did not allow the authors to download or distribute any patient level data. The data stayed in their machine and the authors analyzed it with software they provide on their machine. The shared detailed statistical data should be sufficient for anyone to verify the study’s results. If researchers wish to access the raw data, they can contact the CMS Virtual Research Data Center. However, data access requires the payment of a fee. Note that the exact set of subjects may not be available even with access to raw data, because this study is based on a randomly selected 20% sample, and CMS will pull a new 20% sample for any new request. However, the results should be almost identical given the large sample size.

Funding Statement

This research was supported in part by the Intramural Research Program of the NIH, National Library of Medicine. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

References

1.Ferrario C.M., et al., Effect of angiotensin-converting enzyme inhibition and angiotensin II receptor blockers on cardiac angiotensin-converting enzyme 2. Circulation, 2005. 111(20): p. 2605–10. doi: 10.1161/CIRCULATIONAHA.104.510461 [DOI] [PubMed] [Google Scholar]
2.Soler M.J., et al., Pharmacologic modulation of ACE2 expression. Curr Hypertens Rep, 2008. 10(5): p. 410–4. doi: 10.1007/s11906-008-0076-0 [DOI] [PMC free article] [PubMed] [Google Scholar]
3.Li W., et al., Angiotensin-converting enzyme 2 is a functional receptor for the SARS coronavirus. Nature, 2003. 426(6965): p. 450–4. doi: 10.1038/nature02145 [DOI] [PMC free article] [PubMed] [Google Scholar]
4.Hoffmann M., et al., SARS-CoV-2 Cell Entry Depends on ACE2 and TMPRSS2 and Is Blocked by a Clinically Proven Protease Inhibitor. Cell, 2020. 181(2): p. 271–280 e8. doi: 10.1016/j.cell.2020.02.052 [DOI] [PMC free article] [PubMed] [Google Scholar]
5.Li J., et al., Association of Renin-Angiotensin System Inhibitors With Severity or Risk of Death in Patients With Hypertension Hospitalized for Coronavirus Disease 2019 (COVID-19) Infection in Wuhan, China. JAMA Cardiol, 2020. 5(7): p. 825–830. doi: 10.1001/jamacardio.2020.1624 [DOI] [PMC free article] [PubMed] [Google Scholar]
6.Vaduganathan M., et al., Renin-Angiotensin-Aldosterone System Inhibitors in Patients with Covid-19. N Engl J Med, 2020. 382(17): p. 1653–1659. doi: 10.1056/NEJMsr2005760 [DOI] [PMC free article] [PubMed] [Google Scholar]
7.Mancia G., et al., Renin-Angiotensin-Aldosterone System Blockers and the Risk of Covid-19. N Engl J Med, 2020. 382(25): p. 2431–2440. doi: 10.1056/NEJMoa2006923 [DOI] [PMC free article] [PubMed] [Google Scholar]
8.Fosbol E.L., et al., Association of Angiotensin-Converting Enzyme Inhibitor or Angiotensin Receptor Blocker Use With COVID-19 Diagnosis and Mortality. JAMA, 2020. 324(2): p. 168–177. doi: 10.1001/jama.2020.11301 [DOI] [PMC free article] [PubMed] [Google Scholar]
9.Reynolds H.R., et al., Renin-Angiotensin-Aldosterone System Inhibitors and Risk of Covid-19. N Engl J Med, 2020. 382(25): p. 2441–2448. doi: 10.1056/NEJMoa2008975 [DOI] [PMC free article] [PubMed] [Google Scholar]
10.Guo X., Zhu Y., and Hong Y., Decreased Mortality of COVID-19 With Renin-Angiotensin-Aldosterone System Inhibitors Therapy in Patients With Hypertension: A Meta-Analysis. Hypertension, 2020. 76(2): p. e13–e14. doi: 10.1161/HYPERTENSIONAHA.120.15572 [DOI] [PubMed] [Google Scholar]
11.Barochiner J. and Martinez R., Use of inhibitors of the renin-angiotensin system in hypertensive patients and COVID-19 severity: A systematic review and meta-analysis. J Clin Pharm Ther, 2020. doi: 10.1111/jcpt.13246 [DOI] [PMC free article] [PubMed] [Google Scholar]
12.Efficacy of Captopril in Covid-19 Patients With Severe Acute Respiratory Syndrome (SARS) CoV-2 Pneumonia (CAPTOCOVID) [clinical trial]. https://clinicaltrials.gov/ct2/show/NCT04355429.
13.Angiotensin Converting Enzyme Inhibitors in Treatment of Covid 19 [clinical trial]. https://clinicaltrials.gov/ct2/show/NCT04345406.
14.Valsartan for Prevention of Acute Respiratory Distress Syndrome in Hospitalized Patients With SARS-COV-2 (COVID-19) Infection Disease [clinical trial]. https://clinicaltrials.gov/ct2/show/NCT04335786.
15.Rodrigues-Diez R.R., et al., Statins: Could an old friend help in the fight against COVID-19? Br J Pharmacol, 2020. 177(21): p. 4873–4886. doi: 10.1111/bph.15166 [DOI] [PMC free article] [PubMed] [Google Scholar]
16.Pawlos A., et al., COVID-19: Direct and Indirect Mechanisms of Statins. Int J Mol Sci, 2021. 22(8). doi: 10.3390/ijms22084177 [DOI] [PMC free article] [PubMed] [Google Scholar]
17.Permana H., et al., In-hospital use of statins is associated with a reduced risk of mortality in coronavirus-2019 (COVID-19): systematic review and meta-analysis. Pharmacol Rep, 2021. 73(3): p. 769–780. doi: 10.1007/s43440-021-00233-3 [DOI] [PMC free article] [PubMed] [Google Scholar]
18.Pal R., et al., Statin use and clinical outcomes in patients with COVID-19: An updated systematic review and meta-analysis. Postgrad Med J, 2021. doi: 10.1136/postgradmedj-2020-139172 [DOI] [PubMed] [Google Scholar]
19.Tang N., et al., Abnormal coagulation parameters are associated with poor prognosis in patients with novel coronavirus pneumonia. J Thromb Haemost, 2020. 18(4): p. 844–847. doi: 10.1111/jth.14768 [DOI] [PMC free article] [PubMed] [Google Scholar]
20.Bikdeli B., et al., COVID-19 and Thrombotic or Thromboembolic Disease: Implications for Prevention, Antithrombotic Therapy, and Follow-Up: JACC State-of-the-Art Review. J Am Coll Cardiol, 2020. 75(23): p. 2950–2973. doi: 10.1016/j.jacc.2020.04.031 [DOI] [PMC free article] [PubMed] [Google Scholar]
21.Zhang Y., et al., Coagulopathy and Antiphospholipid Antibodies in Patients with Covid-19. N Engl J Med, 2020. 382(17): p. e38. doi: 10.1056/NEJMc2007575 [DOI] [PMC free article] [PubMed] [Google Scholar]
22.Cui S., et al., Prevalence of venous thromboembolism in patients with severe novel coronavirus pneumonia. J Thromb Haemost, 2020. 18(6): p. 1421–1424. doi: 10.1111/jth.14830 [DOI] [PMC free article] [PubMed] [Google Scholar]
23.Klok F.A., et al., Confirmation of the high cumulative incidence of thrombotic complications in critically ill ICU patients with COVID-19: An updated analysis. Thromb Res, 2020. 191: p. 148–150. doi: 10.1016/j.thromres.2020.04.041 [DOI] [PMC free article] [PubMed] [Google Scholar]
24.Rico-Mesa J.S., et al., The Role of Anticoagulation in COVID-19-Induced Hypercoagulability. Curr Cardiol Rep, 2020. 22(7): p. 53. doi: 10.1007/s11886-020-01328-8 [DOI] [PMC free article] [PubMed] [Google Scholar]
25.Tremblay D., et al., Impact of anticoagulation prior to COVID-19 infection: a propensity score-matched cohort study. Blood, 2020. 136(1): p. 144–147. doi: 10.1182/blood.2020006941 [DOI] [PMC free article] [PubMed] [Google Scholar]
26.Russo V., et al., Clinical impact of pre-admission antithrombotic therapy in hospitalized patients with COVID-19: A multicenter observational study. Pharmacol Res, 2020. 159: p. 104965. doi: 10.1016/j.phrs.2020.104965 [DOI] [PMC free article] [PubMed] [Google Scholar]
27.Sivaloganathan H., Ladikou E.E., and Chevassut T., COVID-19 mortality in patients on anticoagulants and antiplatelet agents. Br J Haematol, 2020. doi: 10.1111/bjh.16968 [DOI] [PMC free article] [PubMed] [Google Scholar]
28.Inama G., et al., Coronavirus disease 2019 infection in patients with recent cardiac surgery: does chronic anticoagulant therapy have a protective effect? J Cardiovasc Med (Hagerstown), 2020. 21(10): p. 765–771. doi: 10.2459/JCM.0000000000001066 [DOI] [PubMed] [Google Scholar]
29.Rossi R., et al., Protective role of chronic treatment with direct oral anticoagulants in elderly patients affected by interstitial pneumonia in COVID-19 era. Eur J Intern Med, 2020. 77: p. 158–160. doi: 10.1016/j.ejim.2020.06.006 [DOI] [PMC free article] [PubMed] [Google Scholar]
30.Wu C., et al., Analysis of therapeutic targets for SARS-CoV-2 and discovery of potential drugs by computational methods. Acta Pharm Sin B, 2020. 10(5): p. 766–788. doi: 10.1016/j.apsb.2020.02.008 [DOI] [PMC free article] [PubMed] [Google Scholar]
31.Bourinbaiar A.S. and Fruhstorfer E.C., The effect of histamine type 2 receptor antagonists on human immunodeficiency virus (HIV) replication: identification of a new class of antiviral agents. Life Sci, 1996. 59(23): p. PL 365–70. doi: 10.1016/s0024-3205(96)00553-x [DOI] [PubMed] [Google Scholar]
32.Janowitz T., et al., Famotidine use and quantitative symptom tracking for COVID-19 in non-hospitalised patients: a case series. Gut, 2020. 69(9): p. 1592–1597. doi: 10.1136/gutjnl-2020-321852 [DOI] [PMC free article] [PubMed] [Google Scholar]
33.Freedberg D.E., et al., Famotidine Use Is Associated With Improved Clinical Outcomes in Hospitalized COVID-19 Patients: A Propensity Score Matched Retrospective Cohort Study. Gastroenterology, 2020. 159(3): p. 1129–1131 e3. doi: 10.1053/j.gastro.2020.05.053 [DOI] [PMC free article] [PubMed] [Google Scholar]
34.Keyaerts E., et al., In vitro inhibition of severe acute respiratory syndrome coronavirus by chloroquine. Biochem Biophys Res Commun, 2004. 323(1): p. 264–8. doi: 10.1016/j.bbrc.2004.08.085 [DOI] [PMC free article] [PubMed] [Google Scholar]
35.Vincent M.J., et al., Chloroquine is a potent inhibitor of SARS coronavirus infection and spread. Virol J, 2005. 2: p. 69. doi: 10.1186/1743-422X-2-69 [DOI] [PMC free article] [PubMed] [Google Scholar]
36.Sinha N. and Balayla G., Hydroxychloroquine and COVID-19. Postgrad Med J, 2020. 96(1139): p. 550–555. doi: 10.1136/postgradmedj-2020-137785 [DOI] [PMC free article] [PubMed] [Google Scholar]
37.Arshad S., et al., Treatment with hydroxychloroquine, azithromycin, and combination in patients hospitalized with COVID-19. Int J Infect Dis, 2020. 97: p. 396–403. doi: 10.1016/j.ijid.2020.06.099 [DOI] [PMC free article] [PubMed] [Google Scholar]
38.Research Data Assistance Center (ResDAC). CMS Virtual Research Data Center (VRDC). https://www.resdac.org/cms-virtual-research-data-center-vrdc.
39.The Chronic Condition Warehouse. Chronic Conditions Data Warehouse: CCW Chronic Condition Algorithms. https://www.ccwdata.org/web/guest/condition-categories.
40.Ernster V.L., Nested case-control studies. Prev Med, 1994. 23(5): p. 587–90. doi: 10.1006/pmed.1994.1093 [DOI] [PubMed] [Google Scholar]
41.Azzato E.M., et al., Prevalent cases in observational studies of cancer survival: do they bias hazard ratio estimates? Br J Cancer, 2009. 100(11): p. 1806–11. doi: 10.1038/sj.bjc.6605062 [DOI] [PMC free article] [PubMed] [Google Scholar]
42.Rosenbaum P.R. and Rubin D.B., The Central Role of the Propensity Score in Observational Studies for Causal Effects. Biometrika, 1983. 70: p. 41–55. [Google Scholar]
43.Wyss R., et al., Use of Time-Dependent Propensity Scores to Adjust Hazard Ratio Estimates in Cohort Studies with Differential Depletion of Susceptibles. Epidemiology, 2020. 31(1): p. 82–89. doi: 10.1097/EDE.0000000000001107 [DOI] [PubMed] [Google Scholar]
44.Centers for Disease Control and Prevention. COVID-19 Response. COVID-19 Case Surveillance Public Data Access, Summary, and Limitations. 2020; https://data.cdc.gov/Case-Surveillance/COVID-19-Case-Surveillance-Public-Use-Data/vbim-akqf.
45.Elshafei A., et al., RAAS, ACE2 and COVID-19; a mechanistic review. Saudi J Biol Sci, 2021. 28(11): p. 6465–6470. doi: 10.1016/j.sjbs.2021.07.003 [DOI] [PMC free article] [PubMed] [Google Scholar]
46.Bozkurt B., Kovacs R., and Harrington B., Joint HFSA/ACC/AHA Statement Addresses Concerns Re: Using RAAS Antagonists in COVID-19. J Card Fail, 2020. 26(5): p. 370. doi: 10.1016/j.cardfail.2020.04.013 [DOI] [PMC free article] [PubMed] [Google Scholar]
47.World Health Organization. COVID-19 and the use of angiotensin-converting enzyme inhibitors and receptor blockers—Scientific Brief. 2020; https://www.who.int/news-room/commentaries/detail/covid-19-and-the-use-of-angiotensin-converting-enzyme-inhibitors-and-receptor-blockers.
48.Fontelo P., et al., A review of thromboembolic events in hospitalized COVID-19 patients. Thromb J, 2021. 19(1): p. 47. doi: 10.1186/s12959-021-00298-3 [DOI] [PMC free article] [PubMed] [Google Scholar]
49.Williamson E.J., et al., Factors associated with COVID-19-related death using OpenSAFELY. Nature, 2020. 584(7821): p. 430–436. doi: 10.1038/s41586-020-2521-4 [DOI] [PMC free article] [PubMed] [Google Scholar]
50.An J., et al., COVID-19 morbidity and mortality associated with angiotensin-converting enzyme inhibitors or angiotensin receptor blockers use among 14,129 patients with hypertension from a US integrated healthcare system. Int J Cardiol Hypertens, 2021. 9: p. 100088. doi: 10.1016/j.ijchy.2021.100088 [DOI] [PMC free article] [PubMed] [Google Scholar]
51.Administration on Aging—U.S. Department of Health and Human Services. 2018 Profile of Older Americans. https://acl.gov/aging-and-disability-in-america/data-and-research/profile-older-americans.
52.Csizmadi I., Collet J.-P., and Boivin J.-F., Bias and Confounding in Pharmacoepidemiology, in Pharmacoepidemiology. 2006. p. 791–809. [Google Scholar]
53.Cubeddu L.X. and Seamon M.J., Statin withdrawal: clinical implications and molecular mechanisms. Pharmacotherapy, 2006. 26(9): p. 1288–96. doi: 10.1592/phco.26.9.1288 [DOI] [PubMed] [Google Scholar]
54.Pourhoseingholi M.A., Baghestani A.R., and Vahedi M., How to control confounding effects by statistical analysis. Gastroenterol Hepatol Bed Bench, 2012. 5(2): p. 79–83. [PMC free article] [PubMed] [Google Scholar]
55.Verity R., et al., Estimates of the severity of coronavirus disease 2019: a model-based analysis. Lancet Infect Dis, 2020. 20(6): p. 669–677. doi: 10.1016/S1473-3099(20)30243-7 [DOI] [PMC free article] [PubMed] [Google Scholar]

PLoS One. doi: 10.1371/journal.pone.0266922.r001

Decision Letter 0

Masaki Mogi

Transfer Alert

This paper was transferred from another journal. As a result, its full editorial history (including decision letters, peer reviews and author responses) may not be present.

19 Oct 2021

PONE-D-21-31663Effect of common maintenance drugs on the risk and severity of COVID-19 in elderly patientsPLOS ONE

Dear Dr. Fung,

Thank you for submitting your manuscript to PLOS ONE. After careful consideration, we feel that it has merit but does not fully meet PLOS ONE’s publication criteria as it currently stands. Therefore, we invite you to submit a revised version of the manuscript that addresses the points raised during the review process.

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Major revisions are needed in the present form. See the Reviewers' comments carefully and respond them appropriately.

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Masaki Mogi

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PLOS ONE

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Reviewer #1: Partly

Reviewer #2: Yes

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Reviewer #1: Yes

Reviewer #2: Yes

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Reviewer #1: No

Reviewer #2: Yes

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Reviewer #1: Yes

Reviewer #2: Yes

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5. Review Comments to the Author

Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters)

Reviewer #1: This is nested case-control study derived from the Medicare claims database, to evaluate the association between various medications and the incidence and severity of COVID. In a 20% random sample of the database, COVID-19 cases were matched 1:5 with non-COVID controls. Analyses adjusted for various factors including comorbidities, geographical region, and insurance status. The authors accounted for indication bias for hydroxychloroquine, by censoring recent initiators of the medication. The authors found that several medications were associated with reduced risk of getting COVID and of severe COVID (hospitalization, death).

Overall this is a very interesting study and the authors have done a nice job presenting the findings. The very large dataset provides sufficient power to both evaluate and account for a large number of variables.

I have several comments:

Throughout the manuscript – but especially in the abstract and the Discussion, the authors need to be very careful to avoid implying causation from the observed associations.

Abstract Conclusion- “…was found to be protective against…” needs to be tempered to describe the association. i.e. “was associated with…”

Similarly, in the Discussion, instead of saying “risk declined by 15% or greater” – should say it was “associated with” a lower risk; and instead of “beneficial effects”, would say “beneficial associations”.

The choice of which medications to evaluate is not well explained. It does not make sense to me to include clopidogrel but not other agents of the same class. If including clopidogrel, you need to include the other P2Y12 agents: ticagrelor, prasugrel, (and ticlopidine – though less commonly used) – similar to how you look at ACEi’s and ARBs as classes. What is the rationale for only using clopidogrel?

Similarly, why only famotidine? Is the anti-viral activity unique to the medication and not the others within this class? If so, this should be stated and referenced, and you need to exclude subjects on the other H2 blockers. If not, should include all H2 blockers as a class.

Apixaban should be included as an anticoagulant as well.

In any observational study, some residual confounding may persist – and this limitation needs to be stated.

In addition, patients who are not on any prescription medications at all may differ from those on medications in important ways besides just health status – even though there are claims present to cause them to be included in the dataset, a subset may have less healthcare access, which could influence the associations seen. It would be of interest to see a sensitivity analysis excluding those who are not on any medications.

Methods -

The authors state “All cases and control were followed from January 1, 2020 until COVID-19 diagnosis, death, disenrollment from Medicare Parts A/B/D or December 31, 2020, whichever came first” – however cases must have been followed beyond their COVID diagnosis, or else outcomes such as death would not be known…Please specify that this is just the follow-up for the first outcome, risk of acquiring COVID.

Are you capturing all the COVID diagnoses? With a 40% hospitalization rate, it would seem that a lot of COVID cases are not actually being captured. Also, mild cases are probably often undiagnosed and thus not captured. You need a discussion of this in the Limitations section.

Table 3 and Table 4 are quite interesting – just a suggestions, but if color is allowed, it would be nice to use that instead of bold vs italic, to make the significant findings a bit easier to notice.

The Discussion section could benefit from a more thorough description and citing of prior literature.

Reviewer #2: This is an interesting and important paper. Its focus is on whether drugs that are often taken by older patients might reduce the occurrence of outcomes of SARS-CoV-2 infection. It is a direct examination of whether these drugs can be considered “repurposed” for COVID-19 care. They show that RAS inhibitors and several anticoagulant preparations can be helpful.

The strengths of the study are its very large size, its use of a single database and its very good statistical methods (time varying propensity scoring). It usefully shows that taking hydroxychloroquine was not helpful, a finding that should lay to rest any lingering hopes that it is. The study also shows that famotidine, a drug hyped as having potential benefits, was not beneficial. The study’s inevitable limitation is that it cannot ascertain how well its subjects were adhering to their prescribed medications in the days and weeks immediately before the diagnosis of SARS-CoV-2 infection was made.

The study has one gobsmackingly glaring omission. The authors have not included statins among the drugs they studied. Statins are taken considerably more frequently by older persons (>65 years of age) than any of the other drugs they studied. There are approximately 50 individual published reports of statin effects in COVID-19 patients and at least 14 meta-analyses of these reports. Twelve studies report inpatient statin treatment is always associated with reduced COVID-19 mortality.

Why did the authors not study statins? They must explain why they excluded statins from their study. If possible, they could re-examine their database to determine whether statin treatment was associated with any COVID-19 outcomes.

The authors must bear in mind that outpatient documentation of statin treatment is unable to document whether statin treatment was continued after hospital admission. There is a very real risk of a rebound effect following statin withdrawal (see Cubeddu LX et al. Pharmacotherapy 2006; 26:1288-96). Whether this effect follows inpatient withdrawal of any of the drugs included in the authors’ study is unknown; the evidence for withdrawal effects for discontinued ACE inhibitors and ARBs is mixed. However, this effect must be considered a possibility and must be discussed by the authors.

The authors should acknowledge that many of the drugs they studied are taken in combination preparations and the drugs with which they are combined might have their own effects on COVID-19 outcomes. For example, ARBs are often taken in combination with calcium channel blockers and CCBs by themselves are said to improve COVID-19 outcomes. A sensitivity analysis that focuses only on the effects of losartan (an ARB taken by 65% of all study subjects) and other ARBs alone might help settle this question.

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PLoS One. 2022 Apr 18;17(4):e0266922. doi: 10.1371/journal.pone.0266922.r002

Author response to Decision Letter 0

8 Dec 2021

PONE-D-21-31663

Effect of common maintenance drugs on the risk and severity of COVID-19 in elderly patients

Dear PLOS ONE editors,

The authors would like to thank the reviewers for their detailed review and thoughtful comments and suggestions. The following is our specific response to the comments.

I have several comments:

Throughout the manuscript – but especially in the abstract and the Discussion, the authors need to be very careful to avoid implying causation from the observed associations.

Abstract Conclusion- “…was found to be protective against…” needs to be tempered to describe the association. i.e. “was associated with…”

Response: The verbiage throughout the manuscript has been changed as suggested.

Response: Thanks for the suggestion. We have modified the list of drugs as suggested. Clopidogrel is expanded to P2Y12 inhibitors, which include clopidogrel, prasugrel and ticagrelor, there are no patients on ticlopidine in our population.

Response: the suggested beneficial effect of famotidine is based on structural homology analysis [Wu et al] and does not apply to other H2 blockers. This has been clarified in the manuscript.

Apixaban should be included as an anticoagulant as well.

Response: thanks for the suggestion. Apixiban is now included in direct factor Xa inhibitors.

In any observational study, some residual confounding may persist – and this limitation needs to be stated.

Response: residual confounding factors is added as a limitation

Response: we have done the suggested analysis. Overall, among our COVID-19 patients and controls, only 2.5% did not have any prescription at all for the period 2019-2020. Because of the small percentage, we do not think that a separate subset analysis is warranted.

Methods -

Response: clarification added to Methods

Response: Compared with national data from CDC, we could be missing about one-third of COVID-19 cases. It is likely that milder cases would be missed since the patients did not seek medical care. This would also explain the higher COVID-19 hospitalization and mortality rate in our study compared to other studies. This is now added as a limitation

Response: results are color-coded as suggested

The Discussion section could benefit from a more thorough description and citing of prior literature.

Response: more references are added

Response: implying drug exposure from prescription records is added as limitation

Response: Thanks for the suggestion. Statins have been added to the list of study drugs. Statins are indeed associated with beneficial effects. References are added regarding the effects of statins and the possible mechanisms.

Response: this is added as limitation, specifically mentioning the possible rebound effect mentioned by Cubeddu et al.

Response: at the time of the study, we focused on the 8 maintenance drugs/drug classes that received the most attention in relation to COVID-19. There could be other drugs (e.g., calcium channel blockers) that could potentially affect the outcome of COVID-19 infection. But generally, the evidence associated with those drugs tends to be weaker and they were not included in our study. It is true that some of those drugs are frequently taken in combination with some of the study drugs. The possible confounding due to drug combination is added as a limitation.

Attachment

Submitted filename: Response to reviewers comments PLOS ONE.docx

Click here for additional data file.^{(18.4KB, docx)}

PLoS One. doi: 10.1371/journal.pone.0266922.r003

Decision Letter 1

Masaki Mogi

3 Jan 2022

PONE-D-21-31663R1Effect of common maintenance drugs on the risk and severity of COVID-19 in elderly patientsPLOS ONE

Dear Dr. Fung,

==============================

Major revisions are still necessary in the present form of the manuscript.See the comments from the two Reviewers carefully and respond them appropriately.

==============================

Please submit your revised manuscript by Feb 17 2022 11:59PM. If you will need more time than this to complete your revisions, please reply to this message or contact the journal office at plosone@plos.org. When you're ready to submit your revision, log on to https://www.editorialmanager.com/pone/ and select the 'Submissions Needing Revision' folder to locate your manuscript file.

Please include the following items when submitting your revised manuscript:

A rebuttal letter that responds to each point raised by the academic editor and reviewer(s). You should upload this letter as a separate file labeled 'Response to Reviewers'.
A marked-up copy of your manuscript that highlights changes made to the original version. You should upload this as a separate file labeled 'Revised Manuscript with Track Changes'.
An unmarked version of your revised paper without tracked changes. You should upload this as a separate file labeled 'Manuscript'.

We look forward to receiving your revised manuscript.

Kind regards,

Masaki Mogi

Academic Editor

PLOS ONE

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Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

1. If the authors have adequately addressed your comments raised in a previous round of review and you feel that this manuscript is now acceptable for publication, you may indicate that here to bypass the “Comments to the Author” section, enter your conflict of interest statement in the “Confidential to Editor” section, and submit your "Accept" recommendation.

Reviewer #1: (No Response)

Reviewer #2: All comments have been addressed

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2. Is the manuscript technically sound, and do the data support the conclusions?

Reviewer #1: Partly

Reviewer #2: Yes

**********

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

Reviewer #1: No

Reviewer #2: Yes

**********

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

Reviewer #1: Yes

Reviewer #2: Yes

**********

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

Reviewer #1: Yes

Reviewer #2: Yes

**********

6. Review Comments to the Author

Reviewer #1: The manuscript is improved, but some concerns remain:

An additional consideration is that very few individuals use a statin alone, in the absence of ACEI/ARBs (as well as anti-platelet medications). This number may be less than 7% of subjects. One must address the co-occurrence of these medications with care. Previous recent studies of medication use among patients with COVID-19 have shown that over 80% of patients on statins also take anti-hypertensive medication. These co-occurrences could be a substantial source of bias if not carefully addressed. I don’t see that this was addressed at all in statistical analyses or study design.

Intro

P14 – the authors state that almost all of the primary studies of these maintenance drugs are small. This is no longer true, especially for ACEi/ARB and statins. There are now studies with thousands and thousands of patients. (For instance, studies from the American Heart Association COVID-19 study teams, among others.)

Cause of death not specified – so unclear whether statins, ACE/ARB etc are reducing CVD mortality or COVID mortality. The fact that they often occurred a median of 14 days after a diagnosis could also reflect, in part, increased testing among any hospitalized patient during this time period (i.e. prior to any cardiac procedure or surgery.)

Limited to Medicare population – more access to healthcare, might not apply to other populations.

P21, last 2 paragraphs – grammar needs some attention (i.e. “older age”, not “older patients”, is associated with reduced risk; “current use of”, not “current uses of” hydroxychloroquine). Recommend “female sex” was “associated with” a reduced risk, not “Female experienced a 11% risk reduction” [sic]; and “older age”, not “older patients” were associated with decreased risk. Similar changes on p25 and p26 are recommended.

Tables 3 & 4 – Please list in the footnote for each Table what covariates these analyses are adjusted for.

The Discussion of why these classes of medications (especially ACEI/ARBS and statins) may be associated with benefits is grossly oversimplified.

Reviewer #2: Please see my comments to the editor and to the authors. I am uncertain about whether the authors have made their data available to others. See item 4 above.

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7. PLOS authors have the option to publish the peer review history of their article (what does this mean?). If published, this will include your full peer review and any attached files.

If you choose “no”, your identity will remain anonymous but your review may still be made public.

Do you want your identity to be public for this peer review? For information about this choice, including consent withdrawal, please see our Privacy Policy.

Reviewer #1: No

Reviewer #2: No

Attachment

Submitted filename: Fung.PONE.resubmission.comments.docx

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PLoS One. 2022 Apr 18;17(4):e0266922. doi: 10.1371/journal.pone.0266922.r004

Author response to Decision Letter 1

16 Feb 2022

PONE-D-21-31663

Effect of common maintenance drugs on the risk and severity of COVID-19 in elderly patients

Dear PLOS ONE editors,

The authors would like to thank the reviewers for their thoughtful comments and suggestions. The following is our specific response.

Reviewer #1: The manuscript is improved, but some concerns remain:

Response: It is true that statins are often co-prescribed with other maintenance drugs, especially anti-hypertensives. In our Medicare population, statins are taken together with ACEI (in 35% of patients on statins), ARB (28.6%), beta blockers (44.3%) and diuretics (39.8%) (see newly added supplementary table 2). This can be a potential confounder if not properly addressed. In our multiple regression analysis, the use of ACEI, ARB and statins are included in the same unified regression model as time-varying covariates. This ensures that the observed effects of each individual study drug are already adjusted for the co-prescription of other drugs included in the model. A new reference [reference 54] is added to explain the statistical control of confounding effects. It is true that the combined use of statins with other anti-hypertensive (e.g., diuretics, beta blockers) has not been adjusted for. However, up to the start of the study, there is no strong evidence that diuretics, calcium channel blockers or beta blockers significantly affect the susceptibility to and severity of COVID-19. It is therefore unlikely that the observed effects of the study drugs on COVID-19 are caused by these other drugs.

Intro

Response: That sentence in the introduction has been modified to “Most of the early primary studies…have been small…”. In the Discussion, it is now stated that: “Recently published studies do have larger patient numbers. One such study supported by the American Heart Association’s Rapid Response Grant COVID-19 is by An et al. [new reference 50 added] with 4,652 patients on ACEI and 2,546 on ARB. However, this is still lower than the number of patients observed in our study, 97,843 on ACEI and 83,290 on ARB.”

Response: This point is well taken. To elaborate on this issue, the following is added to Discussion: “We used all-cause mortality because of the lack of an accurate cause of death in CMS data. Because of this, it is possible that some reduction of mortality is due to the general protective effect of the study drugs and not related to COVID-19 per se (e.g., statins and hypotensive agents can reduce cardiovascular mortality). However, since the mortality rate was 17% in our COVID-19 patients overall, 29% among those hospitalized, and death occurred within a median of 14 days of COVID-19 diagnosis, it is highly likely that COVID-19 was the main contributor to mortality. Another potential confounding factor is that some patients could be diagnosed with COVID-19 because of more frequent routine testing during an encounter for another reason (e.g., preparation for coronary bypass), which makes COVID-19 an incidental finding rather than the trigger event. However, overall, only 3.2% of our Medicare population were diagnosed with COVID-19, so the chance of a purely incidental COVID-19 diagnosis is small.”

Limited to Medicare population – more access to healthcare, might not apply to other populations.

Response: The sentence is now qualified. “In our study, the group with the lowest income (dual-eligible) was associated with a reduced risk of death. One possible explanation is that this group has better access to healthcare, since patients are eligible to both Medicaid and Medicare. However, this explanation only applies to the Medicaid and Medicare patients and not low income patients in general.”

Response: text modified as suggested, thanks.

Tables 3 & 4 – Please list in the footnote for each Table what covariates these analyses are adjusted for.

Response: A note is added to the title of each table to refer to the other table for covariates that are adjusted for.

The Discussion of why these classes of medications (especially ACEI/ARBS and statins) may be associated with benefits is grossly oversimplified.

Response: Detailed discussion of the possible pharmacologic mechanisms for the benefits of the study drugs is beyond the scope of this paper. We can only highlight the most predominant hypotheses. Newer references [references 45, 48] are added for the possible pharmacologic effects of ACEI/ARB and antithrombotics.

Reviewer #2: Please see my comments to the editor and to the authors. I am uncertain about whether the authors have made their data available to others. See item 4 above.

Response: We’ve submitted new data and modified the Data availability statement as follows: “Concerning data availability, the minimal data set is included in the Supporting information. All data in Supporting information can be used without restriction. This includes the precise values used to build the drug usage trend graphs (S3 Table), and the detailed statistical data obtained in the three Cox regressions (S4 Table), from which the hazard ratios can be derived. As for raw data, CMS does not let us download (or distribute) any patient level data. The data stay on their machine, and we analyze it with software they provide on their machine. The shared detailed statistical data, that we are allowed to take out of their machine, should be sufficient for anyone to verify our results. If researchers wish to access the raw data, they can contact the CMS Virtual Research Data Center. However, data access requires the payment of a fee. Note that the exact set of subjects may not be available even with access to raw data, because our study is based on a randomly selected 20% sample, and CMS will pull a new 20% sample for any new request. However, the results should be almost identical to ours, given the large sample size.”

Attachment

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PLoS One. doi: 10.1371/journal.pone.0266922.r005

Decision Letter 2

Masaki Mogi

30 Mar 2022

Effect of common maintenance drugs on the risk and severity of COVID-19 in elderly patients

PONE-D-21-31663R2

Dear Dr. Fung,

We’re pleased to inform you that your manuscript has been judged scientifically suitable for publication and will be formally accepted for publication once it meets all outstanding technical requirements.

Within one week, you’ll receive an e-mail detailing the required amendments. When these have been addressed, you’ll receive a formal acceptance letter and your manuscript will be scheduled for publication.

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Kind regards,

Masaki Mogi

Academic Editor

PLOS ONE

Additional Editor Comments (optional):

Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

Reviewer #1: All comments have been addressed

Reviewer #2: (No Response)

**********

2. Is the manuscript technically sound, and do the data support the conclusions?

Reviewer #1: Yes

Reviewer #2: Yes

**********

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

Reviewer #1: Yes

Reviewer #2: Yes

**********

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

Reviewer #1: Yes

Reviewer #2: Yes

**********

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

Reviewer #1: Yes

Reviewer #2: Yes

**********

6. Review Comments to the Author

Reviewer #1: I have no further comments for the authors; they have, overall, addressed the issues I have brought up.

Reviewer #2: The difference between the conflicting findings of outpatient documented statin treatment and uniform findings that inpatient treatment reduces COVID-19 severity and mortality is critically important. Documentation of statin treatment based only on out patient information does not take into account the effects of statin withdrawal after hospital admission. Moreover, if inpatients are treated with statins, treatment might be withdrawn if they are transferred to ICUs, although intravenously administered statins are licensed if not widely available.7 Whenever statins are withdrawn, their beneficial effects on the host response can be rapidly lost.8 For example, cardiovascular investigators who studied patients hospitalized with acute myocardial infarction 15–20 years ago found that those who had been treated with statins as outpatients and whose statins were continued after hospital admission had lower mortality rates than those who had never received statins.9 The same benefit was seen in those who were started on statin treatment after hospitals admission. However, those who had been treated with statins as outpatients but whose treatment was withdrawn after hospital is underway. In the absence of clinical trials, physicians may have to rely on the findings of observational studies alone.

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Reviewer #1: No

Reviewer #2: No

PLoS One. doi: 10.1371/journal.pone.0266922.r006

Acceptance letter

Masaki Mogi

8 Apr 2022

PONE-D-21-31663R2

Effect of common maintenance drugs on the risk and severity of COVID-19 in elderly patients

Dear Dr. Fung:

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

If your institution or institutions have a press office, please let them know about your upcoming paper now to help maximize its impact. If they'll be preparing press materials, please inform our press team within the next 48 hours. Your manuscript will remain under strict press embargo until 2 pm Eastern Time on the date of publication. For more information please contact onepress@plos.org.

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Thank you for submitting your work to PLOS ONE and supporting open access.

Kind regards,

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on behalf of

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Associated Data

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

Supplementary Materials

S1 Fig. Consort diagram.

(PDF)

Click here for additional data file.^{(183.6KB, pdf)}

S2 Fig. Trends of drug usage in 2019 and 2020.

(PDF)

Click here for additional data file.^{(81.6KB, pdf)}

S1 Table. Drug classes, clinical drugs and usage frequencies among all our study population.

(DOCX)

Click here for additional data file.^{(21.9KB, docx)}

S2 Table. The combination use of anti-hypertensive drugs with statins in Medicare patients for 2019–2020.

(DOCX)

Click here for additional data file.^{(15.5KB, docx)}

S3 Table. Data values for drug drug usage trend graphs in S2 Fig.

(XLSX)

Click here for additional data file.^{(75.1KB, xlsx)}

S4 Table. Detailed statistical data of the Cox regression models for death, hospitalization and acquiring COVID-19.

(XLSX)

Click here for additional data file.^{(56.1KB, xlsx)}

Attachment

Submitted filename: Response to reviewers comments PLOS ONE.docx

Click here for additional data file.^{(18.4KB, docx)}

Attachment

Submitted filename: Fung.PONE.resubmission.comments.docx

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Attachment

Submitted filename: Second response to reviewers comments PLOS ONE 0216.docx

Click here for additional data file.^{(20.1KB, docx)}

Data Availability Statement

[pone.0266922.ref001] 1.Ferrario C.M., et al., Effect of angiotensin-converting enzyme inhibition and angiotensin II receptor blockers on cardiac angiotensin-converting enzyme 2. Circulation, 2005. 111(20): p. 2605–10. doi: 10.1161/CIRCULATIONAHA.104.510461 [DOI] [PubMed] [Google Scholar]

[pone.0266922.ref002] 2.Soler M.J., et al., Pharmacologic modulation of ACE2 expression. Curr Hypertens Rep, 2008. 10(5): p. 410–4. doi: 10.1007/s11906-008-0076-0 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0266922.ref003] 3.Li W., et al., Angiotensin-converting enzyme 2 is a functional receptor for the SARS coronavirus. Nature, 2003. 426(6965): p. 450–4. doi: 10.1038/nature02145 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0266922.ref004] 4.Hoffmann M., et al., SARS-CoV-2 Cell Entry Depends on ACE2 and TMPRSS2 and Is Blocked by a Clinically Proven Protease Inhibitor. Cell, 2020. 181(2): p. 271–280 e8. doi: 10.1016/j.cell.2020.02.052 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0266922.ref005] 5.Li J., et al., Association of Renin-Angiotensin System Inhibitors With Severity or Risk of Death in Patients With Hypertension Hospitalized for Coronavirus Disease 2019 (COVID-19) Infection in Wuhan, China. JAMA Cardiol, 2020. 5(7): p. 825–830. doi: 10.1001/jamacardio.2020.1624 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0266922.ref006] 6.Vaduganathan M., et al., Renin-Angiotensin-Aldosterone System Inhibitors in Patients with Covid-19. N Engl J Med, 2020. 382(17): p. 1653–1659. doi: 10.1056/NEJMsr2005760 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0266922.ref007] 7.Mancia G., et al., Renin-Angiotensin-Aldosterone System Blockers and the Risk of Covid-19. N Engl J Med, 2020. 382(25): p. 2431–2440. doi: 10.1056/NEJMoa2006923 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0266922.ref008] 8.Fosbol E.L., et al., Association of Angiotensin-Converting Enzyme Inhibitor or Angiotensin Receptor Blocker Use With COVID-19 Diagnosis and Mortality. JAMA, 2020. 324(2): p. 168–177. doi: 10.1001/jama.2020.11301 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0266922.ref009] 9.Reynolds H.R., et al., Renin-Angiotensin-Aldosterone System Inhibitors and Risk of Covid-19. N Engl J Med, 2020. 382(25): p. 2441–2448. doi: 10.1056/NEJMoa2008975 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0266922.ref010] 10.Guo X., Zhu Y., and Hong Y., Decreased Mortality of COVID-19 With Renin-Angiotensin-Aldosterone System Inhibitors Therapy in Patients With Hypertension: A Meta-Analysis. Hypertension, 2020. 76(2): p. e13–e14. doi: 10.1161/HYPERTENSIONAHA.120.15572 [DOI] [PubMed] [Google Scholar]

[pone.0266922.ref011] 11.Barochiner J. and Martinez R., Use of inhibitors of the renin-angiotensin system in hypertensive patients and COVID-19 severity: A systematic review and meta-analysis. J Clin Pharm Ther, 2020. doi: 10.1111/jcpt.13246 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0266922.ref012] 12.Efficacy of Captopril in Covid-19 Patients With Severe Acute Respiratory Syndrome (SARS) CoV-2 Pneumonia (CAPTOCOVID) [clinical trial]. https://clinicaltrials.gov/ct2/show/NCT04355429.

[pone.0266922.ref013] 13.Angiotensin Converting Enzyme Inhibitors in Treatment of Covid 19 [clinical trial]. https://clinicaltrials.gov/ct2/show/NCT04345406.

[pone.0266922.ref014] 14.Valsartan for Prevention of Acute Respiratory Distress Syndrome in Hospitalized Patients With SARS-COV-2 (COVID-19) Infection Disease [clinical trial]. https://clinicaltrials.gov/ct2/show/NCT04335786.

[pone.0266922.ref015] 15.Rodrigues-Diez R.R., et al., Statins: Could an old friend help in the fight against COVID-19? Br J Pharmacol, 2020. 177(21): p. 4873–4886. doi: 10.1111/bph.15166 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0266922.ref016] 16.Pawlos A., et al., COVID-19: Direct and Indirect Mechanisms of Statins. Int J Mol Sci, 2021. 22(8). doi: 10.3390/ijms22084177 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0266922.ref017] 17.Permana H., et al., In-hospital use of statins is associated with a reduced risk of mortality in coronavirus-2019 (COVID-19): systematic review and meta-analysis. Pharmacol Rep, 2021. 73(3): p. 769–780. doi: 10.1007/s43440-021-00233-3 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0266922.ref018] 18.Pal R., et al., Statin use and clinical outcomes in patients with COVID-19: An updated systematic review and meta-analysis. Postgrad Med J, 2021. doi: 10.1136/postgradmedj-2020-139172 [DOI] [PubMed] [Google Scholar]

[pone.0266922.ref019] 19.Tang N., et al., Abnormal coagulation parameters are associated with poor prognosis in patients with novel coronavirus pneumonia. J Thromb Haemost, 2020. 18(4): p. 844–847. doi: 10.1111/jth.14768 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0266922.ref020] 20.Bikdeli B., et al., COVID-19 and Thrombotic or Thromboembolic Disease: Implications for Prevention, Antithrombotic Therapy, and Follow-Up: JACC State-of-the-Art Review. J Am Coll Cardiol, 2020. 75(23): p. 2950–2973. doi: 10.1016/j.jacc.2020.04.031 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0266922.ref021] 21.Zhang Y., et al., Coagulopathy and Antiphospholipid Antibodies in Patients with Covid-19. N Engl J Med, 2020. 382(17): p. e38. doi: 10.1056/NEJMc2007575 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0266922.ref022] 22.Cui S., et al., Prevalence of venous thromboembolism in patients with severe novel coronavirus pneumonia. J Thromb Haemost, 2020. 18(6): p. 1421–1424. doi: 10.1111/jth.14830 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0266922.ref023] 23.Klok F.A., et al., Confirmation of the high cumulative incidence of thrombotic complications in critically ill ICU patients with COVID-19: An updated analysis. Thromb Res, 2020. 191: p. 148–150. doi: 10.1016/j.thromres.2020.04.041 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0266922.ref024] 24.Rico-Mesa J.S., et al., The Role of Anticoagulation in COVID-19-Induced Hypercoagulability. Curr Cardiol Rep, 2020. 22(7): p. 53. doi: 10.1007/s11886-020-01328-8 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0266922.ref025] 25.Tremblay D., et al., Impact of anticoagulation prior to COVID-19 infection: a propensity score-matched cohort study. Blood, 2020. 136(1): p. 144–147. doi: 10.1182/blood.2020006941 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0266922.ref026] 26.Russo V., et al., Clinical impact of pre-admission antithrombotic therapy in hospitalized patients with COVID-19: A multicenter observational study. Pharmacol Res, 2020. 159: p. 104965. doi: 10.1016/j.phrs.2020.104965 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0266922.ref027] 27.Sivaloganathan H., Ladikou E.E., and Chevassut T., COVID-19 mortality in patients on anticoagulants and antiplatelet agents. Br J Haematol, 2020. doi: 10.1111/bjh.16968 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0266922.ref028] 28.Inama G., et al., Coronavirus disease 2019 infection in patients with recent cardiac surgery: does chronic anticoagulant therapy have a protective effect? J Cardiovasc Med (Hagerstown), 2020. 21(10): p. 765–771. doi: 10.2459/JCM.0000000000001066 [DOI] [PubMed] [Google Scholar]

[pone.0266922.ref029] 29.Rossi R., et al., Protective role of chronic treatment with direct oral anticoagulants in elderly patients affected by interstitial pneumonia in COVID-19 era. Eur J Intern Med, 2020. 77: p. 158–160. doi: 10.1016/j.ejim.2020.06.006 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0266922.ref030] 30.Wu C., et al., Analysis of therapeutic targets for SARS-CoV-2 and discovery of potential drugs by computational methods. Acta Pharm Sin B, 2020. 10(5): p. 766–788. doi: 10.1016/j.apsb.2020.02.008 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0266922.ref031] 31.Bourinbaiar A.S. and Fruhstorfer E.C., The effect of histamine type 2 receptor antagonists on human immunodeficiency virus (HIV) replication: identification of a new class of antiviral agents. Life Sci, 1996. 59(23): p. PL 365–70. doi: 10.1016/s0024-3205(96)00553-x [DOI] [PubMed] [Google Scholar]

[pone.0266922.ref032] 32.Janowitz T., et al., Famotidine use and quantitative symptom tracking for COVID-19 in non-hospitalised patients: a case series. Gut, 2020. 69(9): p. 1592–1597. doi: 10.1136/gutjnl-2020-321852 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0266922.ref033] 33.Freedberg D.E., et al., Famotidine Use Is Associated With Improved Clinical Outcomes in Hospitalized COVID-19 Patients: A Propensity Score Matched Retrospective Cohort Study. Gastroenterology, 2020. 159(3): p. 1129–1131 e3. doi: 10.1053/j.gastro.2020.05.053 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0266922.ref034] 34.Keyaerts E., et al., In vitro inhibition of severe acute respiratory syndrome coronavirus by chloroquine. Biochem Biophys Res Commun, 2004. 323(1): p. 264–8. doi: 10.1016/j.bbrc.2004.08.085 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0266922.ref035] 35.Vincent M.J., et al., Chloroquine is a potent inhibitor of SARS coronavirus infection and spread. Virol J, 2005. 2: p. 69. doi: 10.1186/1743-422X-2-69 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0266922.ref036] 36.Sinha N. and Balayla G., Hydroxychloroquine and COVID-19. Postgrad Med J, 2020. 96(1139): p. 550–555. doi: 10.1136/postgradmedj-2020-137785 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0266922.ref037] 37.Arshad S., et al., Treatment with hydroxychloroquine, azithromycin, and combination in patients hospitalized with COVID-19. Int J Infect Dis, 2020. 97: p. 396–403. doi: 10.1016/j.ijid.2020.06.099 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0266922.ref038] 38.Research Data Assistance Center (ResDAC). CMS Virtual Research Data Center (VRDC). https://www.resdac.org/cms-virtual-research-data-center-vrdc.

[pone.0266922.ref039] 39.The Chronic Condition Warehouse. Chronic Conditions Data Warehouse: CCW Chronic Condition Algorithms. https://www.ccwdata.org/web/guest/condition-categories.

[pone.0266922.ref040] 40.Ernster V.L., Nested case-control studies. Prev Med, 1994. 23(5): p. 587–90. doi: 10.1006/pmed.1994.1093 [DOI] [PubMed] [Google Scholar]

[pone.0266922.ref041] 41.Azzato E.M., et al., Prevalent cases in observational studies of cancer survival: do they bias hazard ratio estimates? Br J Cancer, 2009. 100(11): p. 1806–11. doi: 10.1038/sj.bjc.6605062 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0266922.ref042] 42.Rosenbaum P.R. and Rubin D.B., The Central Role of the Propensity Score in Observational Studies for Causal Effects. Biometrika, 1983. 70: p. 41–55. [Google Scholar]

[pone.0266922.ref043] 43.Wyss R., et al., Use of Time-Dependent Propensity Scores to Adjust Hazard Ratio Estimates in Cohort Studies with Differential Depletion of Susceptibles. Epidemiology, 2020. 31(1): p. 82–89. doi: 10.1097/EDE.0000000000001107 [DOI] [PubMed] [Google Scholar]

[pone.0266922.ref044] 44.Centers for Disease Control and Prevention. COVID-19 Response. COVID-19 Case Surveillance Public Data Access, Summary, and Limitations. 2020; https://data.cdc.gov/Case-Surveillance/COVID-19-Case-Surveillance-Public-Use-Data/vbim-akqf.

[pone.0266922.ref045] 45.Elshafei A., et al., RAAS, ACE2 and COVID-19; a mechanistic review. Saudi J Biol Sci, 2021. 28(11): p. 6465–6470. doi: 10.1016/j.sjbs.2021.07.003 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0266922.ref046] 46.Bozkurt B., Kovacs R., and Harrington B., Joint HFSA/ACC/AHA Statement Addresses Concerns Re: Using RAAS Antagonists in COVID-19. J Card Fail, 2020. 26(5): p. 370. doi: 10.1016/j.cardfail.2020.04.013 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0266922.ref047] 47.World Health Organization. COVID-19 and the use of angiotensin-converting enzyme inhibitors and receptor blockers—Scientific Brief. 2020; https://www.who.int/news-room/commentaries/detail/covid-19-and-the-use-of-angiotensin-converting-enzyme-inhibitors-and-receptor-blockers.

[pone.0266922.ref048] 48.Fontelo P., et al., A review of thromboembolic events in hospitalized COVID-19 patients. Thromb J, 2021. 19(1): p. 47. doi: 10.1186/s12959-021-00298-3 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0266922.ref049] 49.Williamson E.J., et al., Factors associated with COVID-19-related death using OpenSAFELY. Nature, 2020. 584(7821): p. 430–436. doi: 10.1038/s41586-020-2521-4 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0266922.ref050] 50.An J., et al., COVID-19 morbidity and mortality associated with angiotensin-converting enzyme inhibitors or angiotensin receptor blockers use among 14,129 patients with hypertension from a US integrated healthcare system. Int J Cardiol Hypertens, 2021. 9: p. 100088. doi: 10.1016/j.ijchy.2021.100088 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0266922.ref051] 51.Administration on Aging—U.S. Department of Health and Human Services. 2018 Profile of Older Americans. https://acl.gov/aging-and-disability-in-america/data-and-research/profile-older-americans.

[pone.0266922.ref052] 52.Csizmadi I., Collet J.-P., and Boivin J.-F., Bias and Confounding in Pharmacoepidemiology, in Pharmacoepidemiology. 2006. p. 791–809. [Google Scholar]

[pone.0266922.ref053] 53.Cubeddu L.X. and Seamon M.J., Statin withdrawal: clinical implications and molecular mechanisms. Pharmacotherapy, 2006. 26(9): p. 1288–96. doi: 10.1592/phco.26.9.1288 [DOI] [PubMed] [Google Scholar]

[pone.0266922.ref054] 54.Pourhoseingholi M.A., Baghestani A.R., and Vahedi M., How to control confounding effects by statistical analysis. Gastroenterol Hepatol Bed Bench, 2012. 5(2): p. 79–83. [PMC free article] [PubMed] [Google Scholar]

[pone.0266922.ref055] 55.Verity R., et al., Estimates of the severity of coronavirus disease 2019: a model-based analysis. Lancet Infect Dis, 2020. 20(6): p. 669–677. doi: 10.1016/S1473-3099(20)30243-7 [DOI] [PMC free article] [PubMed] [Google Scholar]

PERMALINK

Effect of common maintenance drugs on the risk and severity of COVID-19 in elderly patients

Kin Wah Fung

Seo H Baik

Fitsum Baye

Zhaonian Zheng

Vojtech Huser

Clement J McDonald

Roles

Abstract

Background

Methods

Results

Conclusion

1. Background and significance

2. Materials and methods

2.1 Study population and case definition

2.2 Drugs, exposure definition and comorbidities

2.3 Statistical analysis and covariates

3. Results

3.1 Study population

Table 1. Study population compared to U.S. national statistics, April to December 2020 (SRS—Simple random sample; CDC—Centers for Disease Control and Prevention).

3.2 Drug exposure and matching

Table 2. Characteristics of Covid-19 patients and matched controls.

3.3 Risk of being diagnosed with COVID-19

Table 3. Effect of drug use on Covid-19 diagnosis, Covid-19 hospitalization and death (other non-drug covariates are listed in Table 4).

Table 4. Effect of non-drug covariates on Covid-19 diagnosis, Covid-19 hospitalization and death (drug-related covariates are listed in Table 3).

3.4 Risk of COVID-19 hospitalization

3.5 Risk of mortality

4. Discussion

5. Conclusion

Supporting information

Acknowledgments

Data Availability

Funding Statement

References

Decision Letter 0

Masaki Mogi

Roles

Transfer Alert

Author response to Decision Letter 0

Decision Letter 1

Masaki Mogi

Roles

Author response to Decision Letter 1

Decision Letter 2

Masaki Mogi

Roles

Acceptance letter

Masaki Mogi

Roles

Associated Data

Supplementary Materials

Data Availability Statement

ACTIONS

PERMALINK

RESOURCES

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