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INCREASED RISK OF HERPES ZOSTER IN ADULTS ≥50 YEARS OLD DIAGNOSED WITH COVID-19
IN THE UNITED STATES

Amit Bhavsar,
Amit Bhavsar
GSK
,
Wavre
,
Belgium
Correspondence: Amit Bhavsar, MBBS, MHA, GSK, Avenue Fleming 20, 1300 Wavre,
Belgium (amit.b.bhavsar@gsk.com).
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Germain Lonnet,
Germain Lonnet
Business & Decision Life Sciences
,
Brussels, Belgium, c/o GSK, Wavre
,
Belgium
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Chengbin Wang,
Chengbin Wang
GSK
,
Rockville, Maryland
,
USA
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Konstantina Chatzikonstantinidou,
Konstantina Chatzikonstantinidou
Aixial, an Alten Company
,
Brussels, Belgium c/o GSK, Wavre
,
Belgium
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Raunak Parikh,
Raunak Parikh
GSK
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Wavre
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Belgium
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Yves Brabant,
Yves Brabant
GSK
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Wavre
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Nathalie Servotte
GSK
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Meng Shi,
Meng Shi
GSK
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Robyn Widenmaier,
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Emmanuel Aris
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Open Forum Infectious Diseases, Volume 9, Issue 5, May 2022, ofac118,
https://doi.org/10.1093/ofid/ofac118
Published:
09 March 2022
Article history
Received:
18 January 2022
Editorial decision:
26 February 2022
Accepted:
07 March 2022
Published:
09 March 2022
Corrected and typeset:
05 April 2022

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CITE

Amit Bhavsar and others, Increased Risk of Herpes Zoster in Adults ≥50 Years
Old Diagnosed With COVID-19 in the United States, Open Forum Infectious
Diseases, Volume 9, Issue 5, May 2022, ofac118,
https://doi.org/10.1093/ofid/ofac118

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ABSTRACT

Background

Case reports have described herpes zoster (HZ) in patients with coronavirus
disease 2019 (COVID-19). However, this constitutes low-quality evidence for an
association. We therefore performed a retrospective cohort study to assess the
risk of developing HZ following a COVID-19 diagnosis.

Methods

We compared the HZ incidence in ≥50-year-olds diagnosed with COVID-19 vs those
never diagnosed with COVID-19. We used data from the US MarketScan Commercial
Claims and Encounters and Medicare Supplemental (3/2020–2/2021) and Optum
Clinformatics Data Mart (3–12/2020) databases. Individuals with COVID-19 were
exact-matched 1:4 to those without COVID-19 by age, sex, presence of HZ risk
factors, and health care cost level. Adjusted incidence rate ratios (aIRRs) were
estimated by Poisson regression.

Results

A total of 394 677 individuals ≥50 years old with COVID-19 were matched with 1
577 346 individuals without COVID-19. Mean follow-up time after COVID-19
diagnosis and baseline characteristics were balanced between cohorts.
Individuals diagnosed with COVID-19 had a 15% higher HZ risk than those without
COVID-19 (aIRR, 1.15; 95% CI, 1.07–1.24; P < .001). The increased HZ risk was
more pronounced (21%) following COVID-19 hospitalization (aIRR, 1.21; 95% CI,
1.03–1.41; P = .02).

Conclusions

We found that COVID-19 diagnosis in ≥50-year-olds was associated with a
significantly increased risk of developing HZ, highlighting the relevance of
maintaining HZ vaccination.

Open in new tabDownload slide
coronavirus, COVID-19, herpes zoster, SARS-CoV-2, shingles
Topic:
* herpes zoster disease
* human herpesvirus 3
* diagnosis
* covid-19

Issue Section:
Major Article

Herpes zoster (HZ), also known as shingles, is caused by reactivation of latent
varicella zoster virus (VZV) and is characterized by a painful, vesicular,
dermatomal rash [1, 2]. Risk factors for HZ include older age—with a sharp
increase in incidence seen after 50 years of age—and immunosuppression (eg, in
transplant recipients, persons with malignancies, or those on immunosuppressive
medications) [2–5]. The elevated HZ risk in these populations is thought to be a
consequence of a decline in VZV-specific cell-mediated immunity below a
threshold required to maintain latency of the virus [2, 4].

Since the start of the coronavirus disease 2019 (COVID-19) pandemic [6], several
case reports and case series have been published describing HZ cases in COVID-19
patients, often occurring within 1 week of COVID-19 diagnosis or COVID-19
hospitalization [7]. A descriptive analysis of data from Brazil’s Ministry of
Health showed a 35% increase in HZ diagnoses between March and August 2020
compared with the same periods in 2017–2019 [8]. Similarly, a doubling in HZ
infections was seen in an outpatient clinic in Turkey in May and June 2020
compared with the same period in 2019 [9]. It was previously hypothesized that
infection with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)
could lead to VZV reactivation as a result of SARS-CoV-2-induced T-cell immune
dysfunction [7, 10–14]. While it is biologically plausible that SARS-CoV-2
infection triggers HZ, there is currently no strong epidemiological evidence
available that assesses HZ risk in COVID-19 patients. We therefore performed a
retrospective cohort study based on administrative health claims data to assess
if individuals who had been diagnosed with COVID-19 were more likely to develop
HZ than those never diagnosed with COVID-19. We focused on ≥50-year-olds because
they are at increased risk of both HZ and severe COVID-19 [2, 3, 15, 16].

METHODS

STUDY DESIGN AND DATA SOURCE

We performed a retrospective cohort study using data from the Truven MarketScan
Commercial Claims and Encounters (CCAE) and Medicare Supplemental (MS) databases
(from March 13, 2020, to February 28, 2021) and the Optum Clinformatics Data
Mart database (from March 13 to December 31, 2020). The MarketScan CCAE and MS
databases contain inpatient and outpatient claims as well as outpatient
prescription drug claims for >39.7 million people (annually) from >120 large
employers and >40 health plans in the United States [17]. We supplemented these
data with the MarketScan CCAE and MS early view data sets, in which data are
available within 45 days of the end of the service month. The Optum
Clinformatics Data Mart database contains information on medical claims,
prescription drugs, and outpatient laboratory tests from >87 million individuals
(total) in the United States insured with a commercial health plan or Medicare
Advantage plan. No overlap is expected between the MarketScan and Optum
databases.

Both databases are pseudonymized and compliant with the Health Insurance
Portability and Accountability Act. The study did therefore not need ethics
committee approval.

OUTCOMES

As our primary outcome, we compared the HZ incidence in ≥50-year-olds who had
been diagnosed with COVID-19 with that in ≥50-year-olds never diagnosed with
COVID-19. We also assessed the HZ incidence in ≥50-year-olds following different
time intervals since COVID-19 diagnosis and following COVID-19 hospitalization.
Finally, we evaluated the HZ incidence in different age groups of adults ≥18
years old with vs without COVID-19 diagnosis (Supplementary Data).

STUDY POPULATION AND DEFINITIONS

For the primary analysis, 2 cohorts were defined: a COVID-19_50+ cohort, with
individuals who had a first-time COVID-19 event during the study, and an
exact-matched non-COVID-19_50+ cohort, with individuals who had no COVID-19
event and no clinically–epidemiologically diagnosed, probable, or suspected
COVID-19 event at any time during or before the study. A COVID-19 event was
defined by the occurrence of an inpatient or outpatient claim with a COVID-19
diagnosis based on International Classification of Diseases, 10th revision
(ICD-10), codes in which SARS-CoV-2 was identified; the ICD-10 code for
clinically–epidemiologically diagnosed, probable, or suspected COVID-19 was not
considered to identify COVID-19 cases for inclusion in the COVID-19_50+ cohort
(Supplementary Data). The index date for individuals in the COVID-19_50+ cohort
was the date of the first COVID-19 event during the study. The index date for
individuals in the non-COVID-19_50+ cohort was the index date of the
corresponding matched individual in the COVID-19_50+ cohort.

Both cohorts included individuals ≥50 years old on March 13, 2020, registered in
the above-mentioned MarketScan or Optum databases, with at least 365 days of
continuous follow-up until the index date (allowing gaps of maximum 7 days). To
be eligible, individuals could have no history of HZ (based on ICD-9 and ICD-10
codes) (Supplementary Data) and no history of vaccination against COVID-19 or HZ
(based on National Drug Codes and Current Procedural Terminology codes)
(Supplementary Data) before or on the index date.

Matching was performed separately in the MarketScan and Optum databases, based
on age stratum (50–59, 60–64, 65–74, 75–84, and ≥85 years), sex, presence of at
least 1 immunocompromising condition or other risk factor for HZ (Table 1)
(Supplementary Data), and health care cost level within 183 days before March
13, 2020 (<30th percentile of the cost distribution of the COVID-19_50+ cohort,
30th–<70th percentile, and ≥70th percentile). We matched each
COVID-19_50+ individual with up to 4 non-COVID-19_50+ individuals, selected
randomly within each combination of matching variables. We had originally
planned to only select individuals with a health care claim within 15 days of
the index date of the corresponding COVID-19_50+ person. However, this
requirement was dropped because it created selection bias by favoring
individuals with many visits (ie, possibly unhealthier) as controls.

Table 1.

Immunocompromising Conditions and Risk Factors Included in the Matching
Algorithm

Immunocompromising Conditions . HIV or AIDS (excluding asymptomatic HIV)
Hematologic malignancy
Other intrinsic immune conditions
Solid malignancya
Organ transplanta
Rheumatologic or inflammatory conditionsb
Individuals who received chemotherapy, immunosuppressive medications (any
duration), or systemic corticosteroids for ≥14 d Risk Factors Rheumatoid
arthritis
Inflammatory bowel disease
Chronic obstructive pulmonary disease
Asthma
Chronic kidney disease
Diabetes with or without complication
Depression

See the Supplementary Data for information on how these were identified.

If receiving chemotherapeutic or immune-modulating agents.

If receiving chemotherapeutic or immune-modulating agents or systemic
corticosteroids.

Open in new tab
Table 1.

Immunocompromising Conditions and Risk Factors Included in the Matching
Algorithm

See the Supplementary Data for information on how these were identified.

If receiving chemotherapeutic or immune-modulating agents.

If receiving chemotherapeutic or immune-modulating agents or systemic
corticosteroids.

Open in new tab

To evaluate the HZ risk in ≥50-year-olds who had been hospitalized with
COVID-19, a subset of the COVID-19_50+ cohort was used that included individuals
with a COVID-19-associated inpatient claim within 21 days of the first COVID-19
diagnosis (as it was previously shown that for most COVID-19 patients, the time
between first symptom and hospitalization was <21 days [18, 19]). Their matches
from the non-COVID-19_50+ cohort were used as controls.

Individuals were followed for the occurrence of HZ from the day after the index
date until the end of continuous enrollment (ie, insurance coverage interrupted
for ≥7 consecutive days), HZ diagnosis, HZ or COVID-19 vaccination, death, or
study end, whichever came first. An HZ event was defined by the occurrence of
either an inpatient claim with an HZ diagnosis (identified by ICD-10 codes)
(Supplementary Data) or 2 outpatient claims with HZ diagnoses no more than 30
days apart or 1 outpatient claim with an HZ diagnosis and a pharmacy claim for
antiviral treatment (Supplementary Data) within 7 days before or after the HZ
diagnosis claim.

SENSITIVITY ANALYSES

To assess the robustness of the study design, we performed a sensitivity
analysis using fractures as control exposure because we expected there would be
no association between fractures and HZ. Fractures were defined by a claim with
an arm, leg, hand, or foot fracture diagnosis, identified by ICD-10 codes
(Supplementary Data). We avoided fractures most commonly associated with
osteoporosis (eg, hip and vertebral fractures) to limit possible confounding. A
fracture cohort and an exact-matched nonfracture cohort of ≥50-year-olds were
defined using similar criteria as the non-COVID-19_50+ cohort and using the same
matching variables. Individuals in the fracture cohort had a fracture event
during the study period but not during the 183 days before March 13, 2020, while
individuals in the nonfracture cohort had no fracture events during the study or
the preceding 183 days. The index date was defined as for the COVID-19 analysis
but using the date of the first fracture diagnosis during the study.

To assess the impact of the COVID-19 case definition, we performed a sensitivity
analysis with a more specific definition of a COVID-19 event, identified either
as 1 inpatient claim with a COVID-19 diagnosis or at least 2 outpatient claims
with a COVID-19 diagnosis no more than 30 days apart.

STATISTICAL METHODS

A feasibility assessment indicated that the available data would provide enough
power to detect an increased risk in the COVID-19_50+ cohort vs the
non-COVID-19_50+ cohort (Supplementary Data).

We descriptively analyzed baseline characteristics (such as age, sex, health
care cost, immunocompromising conditions, and other risk factors for HZ) for the
different cohorts using frequencies and proportions for categorical variables
and mean and SD for continuous variables. The standardized mean difference (SMD)
was calculated to evaluate whether baseline characteristics were balanced
between matched cohorts; variables with an SMD >0.2 were considered for
inclusion as covariates in the Poisson regression model.

Crude HZ incidence rates in ≥50-year-olds with vs without COVID-19 (and in those
with COVID-19 hospitalization vs without COVID-19) were calculated by dividing
the number of observed HZ cases by the total number of person-years in each
cohort. The adjusted incidence rate ratio (aIRR) was estimated based on HZ
incidence rates modeled by Poisson regression, with age stratum (≥65 years vs
50–64 years), sex, database (MarketScan or Optum), and other possible
confounders as covariates and time in years as offset. Only variables with a
statistically significant effect were kept in the model (by backward selection),
except the effect of COVID-19, which was always kept.

Similar models were considered to calculate the aIRRs for different time
intervals after the index date (1–30, 31–90, 91–183, and >183 days)
in ≥50-year-olds for the different age groups and for the sensitivity analyses.

Missing data were not imputed. Statistical analyses were performed using SAS
software, version 9.04.01 (SAS Institute Inc., Cary, NC, USA).

RESULTS

PARTICIPANTS

A total of 1 449 224 individuals in the MarketScan and Optum databases had a
COVID-19 diagnosis during the study, of whom 642 696 were ≥50 years old. Of
these, 394 677 met inclusion criteria and were part of the COVID-19_50+ cohort.
The non-COVID-19_50+ cohort included 1 577 346 matched individuals (Figure 1).
The mean length of follow-up after the index date (SD) was similar in both
cohorts: 98.85 (80.99) days in the COVID-19_50+ and 104.63 (81.94) days in the
non-COVID-19_50+ cohort. Forty-one percent of COVID-19 diagnoses occurred in
November and December 2020. Baseline characteristics were balanced between the 2
cohorts (SMD < 0.2) (Table 2), except for costs claimed for reimbursement during
1 year before the index date, which were higher in the COVID-19_50+ cohort (SMD
= 0.22 for log[costs during 1 year before index + 1]) (Table 2).

Table 2.

Baseline Characteristics for the COVID-19_50+ and Matched
Non-COVID-19_50+ Cohorts

Characteristic . COVID-19_50+
(n = 394 677) . Non-COVID-19_50+
(n = 1 577 346) . SMD . Database, No. (%) 0.00 MarketScan 157 061 (39.79) 627
122 (39.76) Optum 237 616 (60.21) 950 224 (60.24) Mean age ± SD,
y 64.84 ± 11.64 64.86 ± 11.47 0.00 Age group, No. (%) 0.00 50–64 y 232 157
(58.82) 927 422 (58.8) ≥65 y 162 520 (41.18) 649 924 (41.2) Sex, No.
(%) 0.00 Female 212 805 (53.92) 850 491 (53.92) Male 181 872 (46.08) 726
855 (46.08) ≥1 immunocompromised condition or risk factor before index date,
No. (%) 251 109 (63.62) 979 735 (62.11) 0.03 ≥1 immunocompromised condition
before index date, No. (%) 80 817 (20.48) 306 290 (19.42) 0.03 ≥1 risk factor
before index date, No. (%) 229 365 (58.11) 881 909 (55.91) 0.04 Risk factors
before index date, No. (%) Rheumatoid arthritis 12 575 (3.19) 47 370
(3.00) 0.01 Inflammatory bowel disease 5012 (1.27) 22 195 (1.41) 0.01 Chronic
obstructive pulmonary disease 66 487 (16.85) 251 080 (15.92) 0.03 Asthma 34 362
(8.71) 128 442 (8.14) 0.02 Chronic kidney disease 47 068 (11.93) 166 153
(10.53) 0.05 Depressiona 74 176 (18.79) 238 085 (15.09) 0.10 Diabetes 149 344
(37.84) 523 254 (33.17) 0.10 Mean costs during 1 y before index ± SD, US$ 64
187.27 ± 235 350.6 42 201.82 ± 158 474.78 0.12 Mean log(costs during 1 y before
index + 1) ± SD 8.99 ± 2.29 8.37 ± 2.88 0.22

Abbreviations: COVID-19, coronavirus disease 2019; COVID-19_50+, cohort of
individuals ≥50 years old with a first-time COVID-19 diagnosis during the study
period; non-COVID-19_50+, cohort of individuals ≥50 years old with no history of
COVID-19, clinically–epidemiologically diagnosed COVID-19, probable COVID-19, or
suspected COVID-19 at any time, matched to individuals in the
COVID-19_50+ cohort; SMD, standardized mean difference.

Within 1 year before the index date.

Open in new tab
Table 2.

Baseline Characteristics for the COVID-19_50+ and Matched
Non-COVID-19_50+ Cohorts

Within 1 year before the index date.

Open in new tab
Figure 1.
Open in new tabDownload slide

Disposition of individuals in the COVID-19_50+ and non-COVID-19_50+ cohorts.
aEligible for matching were those with ≥1 day of follow-up after March 13, 2020,
and ≥365 days until March 13, 2020, no history of HZ, HZ vaccination, or
COVID-19 vaccination until March 13, 2020, and no estimated negative total cost
(in inpatient, outpatient, and pharmacy claims) during the 183 days before March
13, 2020. Further exclusions based on history of HZ, HZ vaccination, or COVID-19
vaccination until the index date were only done at the time of matching (when
the index date was determined). bFour matches were identified for each
individual with COVID-19, but not all had follow-up time beyond the index date;
the latter were therefore not part of the matched non-COVID-19_50+ cohort.
Abbreviations: COVID-19, coronavirus disease 2019; COVID-19_50+, cohort of
individuals ≥50 years old with a first-time COVID-19 diagnosis during the study
period; non-COVID-19_50+, cohort of individuals ≥50 years old with no history of
COVID-19, clinically–epidemiologically diagnosed COVID-19, probable COVID-19, or
suspected COVID-19 at any time, matched to individuals in the
COVID-19_50+ cohort; HZ, herpes zoster; n, number of individuals remaining at
the indicated step.

A total of 78 050 (19.78%) patients from the COVID-19_50+ cohort were
hospitalized with COVID-19 and were included in the analysis of HZ risk after
COVID-19 hospitalization. Baseline characteristics for the hospitalized cohort
and their matches are shown in Supplementary Table 1. The costs claimed for
reimbursement during 1 year before the index date were higher among patients
hospitalized with COVID-19 (SMD = 0.29 for log[costs during 1 year before
index + 1]), as was the occurrence of diabetes any time before the index date
(SMD = 0.23). The other baseline characteristics were balanced between cohorts.

RISK OF HZ IN INDIVIDUALS WITH VS WITHOUT COVID-19

The crude HZ incidence rates per 1000 person-years were 8.16 (95% CI, 7.63–8.72)
in ≥50-year-olds diagnosed with COVID-19 and 6.81 (95% CI, 6.57–7.05) in their
matches without COVID-19 (Table 3). Poisson regression (adjusted for age, sex,
and log[costs during 1 year before index + 1]) showed that ≥50-year-olds
diagnosed with COVID-19 had a 15% higher risk of developing HZ than those
without COVID-19 (aIRR, 1.15; 95% CI, 1.07–1.24; P < .001) (Figure 2). Our model
also showed an increased risk of HZ (independent of COVID-19) in women vs men,
in persons aged ≥65 years vs 50–64 years, and in those with higher health care
costs (Supplementary Table 2). The increased risk of HZ in ≥50-year-olds with
COVID-19 was observed during the first 6 months after COVID-19 diagnosis, with
statistically significant aIRR estimates during days 1–30 and days 91–183 and a
nonsignificant aIRR during days 31–90. No increased HZ risk was seen in COVID-19
patients after day 183 (Table 3, Figure 2). When analyzing the HZ risk by age
group, an increased risk of developing HZ was observed following COVID-19
diagnosis among 50–64-year-olds and ≥65-year-olds, although the increased risk
in the latter group was not statistically significant (Table 3, Figure 2).

Table 3.

HZ incidence Rates in Individuals Diagnosed With COVID-19 and Those Never
Diagnosed With COVID-19

. COVID-19 Cohorts . Non-COVID-19 Cohorts . Analysis No. of Individuals Days at
Risk No. of HZ Cases Crude IR per 1000 PY (95% CI) No. of Individuals Days at
Risk No. of HZ Cases Crude IR per 1000 PY (95% CI) Overall, ≥50 y 394 677 39 012
531 872 8.16 (7.63–8.72) 1 577 346 165 043 695 3077 6.81
(6.57–7.05) Hospitalized, ≥50 y 78 050 7 104 711 197 10.13 (8.77–11.64) 312
055 35 838 002 779 7.94 (7.39–8.51) Days 1–30,a ≥50 y 394 677 10 541
107 248 8.59 (7.56–9.73) 1 577 346 42 802 543 793 6.77 (6.31–7.25) Days 31–90,a
≥50 y 303 760 13 707 247 304 8.10 (7.22–9.06) 1 257 148 57 814 728 1105 6.98
(6.58–7.40) Days 91–183,a ≥50 y 165 483 10 847 688 263 8.86 (7.82–9.99) 710
816 47 150 431 858 6.65 (6.21–7.10) Days >183,a ≥50 y 70 986 3 916 489 57 5.32
(4.03–6.88) 312 049 17 275 993 321 6.79 (6.07–7.57) 50–64 y 232 157 24 430
573 480 7.18 (6.55–7.85) 927 422 97 072 577 1549 5.83 (5.54–6.13) ≥65 y 162
520 14 581 958 392 9.82 (8.87–10.84) 649 924 67 971 118 1528 8.21 (7.81–8.63)

Abbreviations: COVID-19, coronavirus disease 2019; COVID-19 cohorts, cohorts of
individuals of the indicated ages with a first-time COVID-19 diagnosis during
the study period and, for “hospitalized,” with a COVID-19-associated inpatient
claim within 21 days of the first COVID-19 diagnosis; HZ, herpes zoster; IR,
incidence rate; non-COVID-19 cohorts, cohorts of individuals of the indicated
ages with no history of COVID-19, clinically–epidemiologically diagnosed
COVID-19, probable COVID-19, or suspected COVID-19 at any time, matched to
individuals in the corresponding COVID-19 cohorts; PY, person-years.

Time after the index date.

Open in new tab
Table 3.

HZ incidence Rates in Individuals Diagnosed With COVID-19 and Those Never
Diagnosed With COVID-19

Time after the index date.

Open in new tab
Figure 2.
Open in new tabDownload slide

Relative risk of HZ in individuals diagnosed with COVID-19 vs those never
diagnosed with COVID-19 (or with vs without fractures). Bold formatting
indicates statistical significance. All analyses were on individuals ≥50 years
old diagnosed with COVID-19 vs those never diagnosed with COVID-19, unless when
otherwise stated. aAdjusted for sex, age category, and log(cost during 1 year
before index + 1). For the sensitivity analysis on fractures, the standardized
mean difference for log(cost during 1 year before index + 1) between the
fracture and nonfracture cohorts was just below 0.20, but the variable was still
included in the model for consistency with the main analyses. bAdjusted for sex
and log(cost during 1 year before index + 1). Diabetes before the index date and
diabetes before March 13, 2020, were considered for inclusion in the model
because of an observed imbalance but were discarded due to nonsignificant
effects. cAdjusted for sex, log(cost during 1 year before index + 1), and
interaction between cohort and age category. Analysis was produced based on the
population ≥18 years old. Abbreviations: COVID-19, coronavirus disease 2019;
days, days after the index date; HZ, herpes zoster.

When we used a more specific case definition for COVID-19 diagnosis (which
resulted in a cohort of 177 503 individuals with COVID-19 and a cohort of 709
527 matched controls), the aIRR of the HZ incidence in ≥50-year-olds with vs
without COVID-19 was 1.24 (95% CI, 1.12–1.37; P < .001) (Figure 2, sensitivity
analysis). Of note, with this case definition, the proportion of individuals
with COVID-19 who were hospitalized was higher (43.97%) than with the case
definition for the main analysis (19.78%).

The crude HZ incidence in ≥50-year-old patients who had been hospitalized with
COVID-19 was 10.13 (95% CI, 8.77–11.64) per 1000 person-years, compared with
7.94 (7.39–8.51) per 1000 person-years in their matches (Table 3). Patients
hospitalized with COVID-19 had a 21% higher risk of developing HZ than those
never diagnosed with COVID-19 (aIRR, 1.21; 95% CI, 1.03–1.41; P = .02) (Figure
2).

SENSITIVITY ANALYSIS OF HZ RISK IN INDIVIDUALS WITH VS WITHOUT FRACTURES

For the sensitivity analysis using fractures as exposure, 123 141
individuals age ≥50 years with arm, leg, hand, or foot fractures were matched to
492 270 individuals without fractures with ≥1 day of follow-up. The mean (SD)
follow-up time after the index date was similar in both cohorts (139.28 ± 85.87
days vs 143.46 ± 85.42 days), and baseline characteristics were balanced between
cohorts, except log(costs during 1 year before index + 1; SMD = 0.20)
(Supplementary Table 3). No statistically significant difference in HZ incidence
was observed between ≥50-year-olds with and without fractures: the crude
incidence rates were 8.28 (95% CI, 7.48–9.15) and 7.21 (95% CI, 6.83–7.59) per
1000 person-years, respectively, and the aIRR was 1.10 (95% CI, 0.98–1.23;
P = .11) (Figure 2).

DISCUSSION

Previous case reports, case series, and descriptive analyses have suggested a
possible association between COVID-19 and HZ [7–9, 20–23]. However, as these
types of studies provide low-grade evidence for an association, it has not
previously been possible to determine whether patients with COVID-19 have a
higher risk of developing HZ. To our knowledge, our study is the first large,
retrospective cohort study designed to investigate the hypothesis that COVID-19
could increase the risk of HZ. We found that during the first year of the
COVID-19 pandemic, ≥50-year-old individuals with a first-time COVID-19 diagnosis
had a significantly higher risk of developing HZ than those never diagnosed with
COVID-19. Maintaining latency of VZV after initial infection requires sufficient
levels of VZV-specific T-cell immunity, and declines in cell-mediated immunity
(eg, in older people due to immunosenescence or under immunosuppressing
conditions) can trigger VZV reactivation and lead to HZ [2, 4]. As SARS-CoV-2
infection can result in T-cell immune dysfunction, it was previously
hypothesized that this could trigger latent VZV reactivation [7, 20]. Several
studies have shown that a large proportion of COVID-19 patients present with
lymphopenia [10–12, 24], with significantly lower counts of total lymphocytes,
CD4+ T cells, CD8+ T cells, B cells, and natural killer cells in COVID-19
patients compared with healthy controls [12]. Some studies have also suggested
that SARS-CoV-2 infection may impair CD4+ helper and regulatory T-cell function
and cause hyperactivation of CD8+ T cells, followed by their exhaustion [10,
14]. Lymphopenia has been shown to be more pronounced in severe COVID-19 cases
[10–12, 24]. In line with this, we found a greater increase in the HZ risk when
selecting for potentially more severe cases of COVID-19: the risk of developing
HZ was 15% higher in individuals diagnosed with COVID-19, 24% higher when using
a more specific case definition (requiring either 1 inpatient or 2 outpatient
claims, hence selecting for a higher proportion of hospitalized patients), and
21% higher in patients hospitalized with COVID-19.

In the published case reports and case series, more than half of the described
HZ cases occurred within 1 week after COVID-19 diagnosis or hospitalization, but
some cases were also reported after 8–10 weeks [7]. This is consistent with the
results of the present study, in which an increased risk was observed up to 6
months after COVID-19 diagnosis. No increased risk of developing HZ was seen
beyond 6 months after COVID-19 diagnosis in our study, which may indicate a
recovery of cell-mediated immunity. However, caution is warranted when
interpreting these results as the number of individuals with a follow-up time >6
months was lower than for the other assessed time intervals.

In our analyses by age, we found an increased HZ risk in 50–64-year-olds and
in ≥65-year-olds. The latter was not statistically significant, but this may be
due to the smaller sample size of this age group, not compensated by the
relatively high HZ incidence due to older age; our study was not powered to
assess the association between COVID-19 and HZ in the different age subgroups.

Our study has several strengths. We used data from 2 large US databases and
matched persons with and without COVID-19 by various known HZ risk factors.
Moreover, to calculate the IRRs, we used a Poisson regression model to adjust
any variables that showed an imbalance between the COVID-19 and non-COVID-19
cohorts despite matching, thereby further controlling for possible confounding.
Our model identified older age and female sex as independent risk factors for
HZ, confirming its validity, as age and sex are known HZ risk factors [2, 4].
The sensitivity analysis on individuals with vs without fractures indicated no
significantly different HZ incidence between these 2 groups, suggesting that the
effect of COVID-19 on HZ was due to COVID-19 rather than other factors. Although
HZ cases were not laboratory-confirmed, our definition to identify HZ cases was
more specific than that often used in other studies: 1 inpatient claim or 2
outpatient claims within 30 days or 1 outpatient claim and a pharmacy claim (in
our study) compared with a single claim with HZ diagnosis (in other studies [25,
26]). The latter was shown to have a positive predictive value (PPV) of 85%–100%
[27], meaning that our definition also had a very high PPV.

The limitations of our study include those inherent to retrospective research
based on claims data. The 2 databases do not contain information from
individuals insured through Medicaid or Medicare (other than Medicare
Advantage). As the rates of these public insurance plans are high in certain
economically disadvantaged and racial/ethnic groups, the results of our study
may not be generalizable. Even though our study design controlled for possible
confounding, there may have been other factors that contributed to (or
diminished) the observed HZ risk. For instance, the MarketScan and Optum
databases contain no information on race and ethnicity. As COVID-19 has
disproportionately affected the African American population [28, 29], the
COVID-19 cohort in our study may include proportionately more African Americans
than the non-COVID-19 cohort. This may have led to an underestimation of the
effect of COVID-19 on HZ, given that Black individuals have a lower risk of HZ
than White individuals [4, 30]. Some COVID-19 and HZ cases may have been missed,
although for HZ, no differential rate of missed cases is expected between
cohorts. If COVID-19 cases were missed (either because they were asymptomatic or
mild and therefore not tested—which may have been especially the case in the
early months of the pandemic when testing capacity was limited—or because their
tests were not recorded in the database), individuals with COVID-19 might have
been assigned to the non-COVID-19 cohort, which could have influenced the
estimated IRRs. We could also not determine the sensitivity and specificity of
the COVID-19 diagnoses. However, we did not include claims with the ICD-10 code
for clinically–epidemiologically diagnosed, probable, or suspected COVID-19
(virus not identified) in the COVID-19 cohort, and we believe that the codes
used to identify COVID-19 patients were more specific to laboratory-confirmed
COVID-19. While we excluded HZ- and COVID-19-vaccinated persons, it is possible
that not all vaccinations were recorded in the database. Missed COVID-19
vaccinations would likely not have impacted the results, as the study mostly
covered the period before COVID-19 mass vaccination.

In conclusion, our results indicate that ≥50-year-olds diagnosed with COVID-19
have a significantly higher risk of developing HZ, suggesting that SARS-CoV-2
infection may trigger reactivation of latent VZV. Health care professionals
should consider that COVID-19 may be a risk factor for HZ. As HZ is a
vaccine-preventable disease, maintaining recommended HZ vaccination
in ≥50-year-olds may help reduce the HZ burden during the pandemic.

SUPPLEMENTARY DATA

Supplementary materials are available at Open Forum Infectious Diseases online.
Consisting of data provided by the authors to benefit the reader, the posted
materials are not copyedited and are the sole responsibility of the authors, so
questions or comments should be addressed to the corresponding author.

ACKNOWLEDGMENTS

The authors are grateful to Jasur Danier for his contributions to the study
design. They also thank Modis for medical writing support (provided by Natalie
Denef), graphic design (provided by Ioana Cristina Ilea), and manuscript
coordination (provided by Julie Mellery), on behalf of GSK.

Financial support. This work was supported by GlaxoSmithKline Biologicals S.A.
GlaxoSmithKline Biologicals S.A. was involved in all stages of the study conduct
and analysis and covered all costs associated with the development and
publishing of this manuscript.

Potential conflicts of interest. A.B., C.W., R.P., Y.B., N.S., M.S., R.W., and
E.A. are employees of the GSK group of companies. A.B., C.W., R.P., R.W., and
E.A. hold shares in the GSK group of companies as part of their employee
remuneration. G.L. is employed by Business & Decision Life Sciences, and K.C. is
employed by Aixial, an Alten Company, both working on behalf of the GSK group of
companies. All authors declare no other financial or nonfinancial relationships
and activities. All authors have submitted the ICMJE Form for Disclosure of
Potential Conflicts of Interest. Conflicts that the editors consider relevant to
the content of the manuscript have been disclosed.

Author contributions. A.B., G.L., C.W., K.C., R.P., N.S., M.S., R.W., and E.A.
were involved in the conception or design of the study. G.L., C.W., K.C., Y.B.,
N.S., and E.A. participated in the collection or generation of the study data.
A.B., G.L., C.W., K.C., R.P., Y.B., N.S., and E.A. performed the study. A.B.,
G.L., C.W., K.C., Y.B., N.S., M.S., and R.W. contributed to the study with
materials or analysis tools. All authors were involved in the analyses or
interpretation of the data, reviewed and revised the manuscript, and approved
the final manuscript as submitted. All authors had full access to all study data
and take responsibility for the integrity of the data and the accuracy of the
data analysis.

Prior presentations. The results of this study were presented at the 17th
European Union Geriatric Medicine Society (EuGMS) Congress, 11–13 October 2021,
Athens, Greece, and virtual; and at the 2021 Canadian Immunization Conference,
8–9 December 2021, virtual.

Data availability. Study documents can be requested for further research from
www.clinicalstudydatarequest.com.

Patient consent. This study was conducted in accordance with the ethical
principles derived from the Declaration of Helsinki and the Council for
International Organizations of Medical Sciences (CIOMS) International Ethical
Guidelines, the Food and Drug Administration Code of Federal Regulations Title
21 (21 CFR), and all other applicable regulations and local laws. The study was
reviewed by GSK’s Clinical Health Economics Regulatory Modeling and Epidemiology
review board. As GSK owns a license to analyze the MarketScan and Optum
databases and these databases are both pseudonymized and fully compliant with
the Health Insurance Portability and Accountability Act, no additional ethics
committee approval was needed for this research. Patient consent was not needed
for this type of study.

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© The Author(s) 2022. Published by Oxford University Press on behalf of
Infectious Diseases Society of America.
This is an Open Access article distributed under the terms of the Creative
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provided the original work is not altered or transformed in any way, and that
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