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58 June 2026, Vol. 116, No. 5
RESEARCH
Since 5 May 2023, the COVID-19 pandemic has no longer been
classified as a global health emergency. The development and
distribution of COVID-19 vaccines provided an essential tool in
reducing risk of severe disease, [1,2] and supported the lifting of
pandemic restrictions.[3]
Although COVID-19 is no longer classified as a global health
emergency, understanding the safety profile of COVID-19 vaccines
remains crucial for informing future vaccination strategies and
guiding responses to potential pandemics, particularly in the context
of the continued rise of vaccine misinformation and denialism. It
therefore remains important to evaluate the prevalence and severity
of adverse events associated with both COVID-19 vaccination and
SARS-CoV-2 infection to better inform the risk-benefit profile of
COVID-19 vaccination across different age groups.
Several studies have demonstrated that the vaccines are generally
safe and well tolerated, with most adverse events being mild and
transient.[4] However, there are reports of several rare severe adverse
events, such as anaphylaxis, myocarditis post BNT162b2 vaccination,
and thrombosis with thrombocytopenia syndrome (TTS) post
vaccination with viral vaccine platforms such as AD26.COV2 and
Chadox vaccination.[1,5-7] SARS-CoV-2 infection has been linked to
a wide range of serious complications, including respiratory failure,
This open-access article is distributed under
Creative Commons licence CC-BY-NC 4.0.
Analysis of adverse events following COVID-19
vaccination and infection: A retrospective, comparative
cohort study using a claims database from Discovery
Health, a managed care organisation in South Africa
K Bernhardt,1 BCom Hons (Actuarial Science), FASSA; S Collie,1 BSc Hons (Actuarial Science), FASSA
; D W C Jacobs,1 MB BCh,
FC Paed(SA); P Mekgwe,1 RN, CDI (AHIMA); G Gray,2 MB BCh, FC Paed(SA)
; J Peter,3 MB BCh, PhD
; L-G Bekker,4 MB BCh, PhD
1 Discovery Health, Johannesburg, South Africa
2 South African Medical Research Council, Cape Town, South Africa
3 Department of Medicine, Faculty of Health Sciences, University of Cape Town, South Africa
4 Desmond Tutu HIV Centre, University of Cape Town, South Africa
Corresponding author: D W C Jacobs (DaveJ@discovery.co.za)
Background. Adverse events following COVID-19 vaccination have been studied extensively in recent years. However, there remains a
paucity of data directly comparing adverse events among COVID-19 vaccinees and individuals with SARS-CoV-2 infection within the
insured population in South Africa (SA). Moreover, the breadth of conditions assessed in this study exceeds that of most existing research,
providing a unique perspective on potential immune-mediated outcomes. This study therefore contributes to a more comprehensive
understanding of the risk-benefit profile of COVID-19 vaccination across different age groups.
Objective. To evaluate the rate of adverse events occurring in COVID-19 vaccinees compared with individuals who have had SARS-CoV-2
infection.
Methods. We conducted a retrospective cohort study, matching vaccinated individuals and those who have had a SARS-CoV-2 infection
with comparable unexposed counterparts. Incident risk rates for 99 possible immune-mediated adverse events were compared between
populations over a 42-day observation period to estimate relative risk ratios and confidence intervals. We used data from Discovery Health,
a large managed care organisation in SA.
Results. A total of 3 112 918 individuals aged ≥12 years who received a COVID-19 vaccination were included in the study, with an average
of 76% successfully matched to a suitable comparator based on their risk profile. Additionally, 443 220 individuals with documented
SARS-CoV-2 infection were analysed, with an average of 99.7% matched to an appropriate comparator. For recipients of the BNT162b2
vaccine, aged 12 - 17 years, we found an increased risk of lymphadenopathy and vertigo, compared with an increased risk of appendicitis,
arrhythmia, encephalomyelitis, lymphadenopathy, myocarditis, seizure, syncope, type 1 diabetes and vertigo post SARS-CoV-2 infection.
For those aged ≥18 years, we found no increased risk for any conditions post BNT162b2 vaccination. Additionally, no conditions
post AD26.COV2.S vaccination had an increased risk for any age group. Post documented SARS-CoV-2 infection for persons in age
groups 18 - 39, 40 - 59 and ≥60 years, we found an increased risk of acute kidney injury, anaemia, appendicitis, arrhythmia, axonal and
neuronal neuropathy, cerebrovascular accident, deep-vein thrombosis, encephalomyelitis, endometriosis, eosinophilic oesophagitis,
fibrosing alveolitis, glomerulonephritis, inflammatory bowel disease, intracranial haemorrhage, lymphadenopathy, myocardial infarction,
myocarditis, myositis, pericarditis, pulmonary embolism, rheumatic fever, seizure, syncope, thrombocytopenia, type 1 diabetes, urticaria,
vertigo, multiple sclerosis, cholangitis and/or pancreatitis. Notably, not all conditions presented with an increased risk in each age group.
Conclusions. Across all age subgroups analysed, the risks associated with SARS-CoV-2 infection exceeded the increased risks following
COVID-19 vaccination.
Keywords: COVID-19, COVID-19 adverse events, vaccine safety, SARS-CoV-2 infection
S Afr Med J 2026;116(5):e3941. https://doi.org/10.7196/SAMJ.2026.v116i5.3941
59 June 2026, Vol. 116, No. 5
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thromboembolic events, multisystem inflammatory syndrome, long-
term sequelae often referred to as ‘long-COVID’ [8,9] and death. [10,11]
In addition, case reports and case series connecting both COVID-19
vaccination and SARS-CoV-2 infection to emergent autoimmune
diseases have been published.[12-15]
Objective
To contribute to the understanding of the comparative risks associated
with COVID-19 vaccination and SARS-CoV-2 infection by analysing
a broad range of immune-mediated conditions using administrative
data from Discovery Health, a large South African (SA) managed care
service provider.
Methods
Real-world safety studies among large populations are more likely
to provide sufficient volumes to power risk-ratio estimates of rare
adverse events among the vaccinated population than clinical trials,
which typically have lower volumes. A comparison with unvaccinated
controls provides greater certainty that identified adverse events are
vaccine-induced, as it provides a risk relative to the population’s
baseline risk over a consistent timeframe. Registries of voluntary
vaccine safety adverse events, such as the Vaccine Adverse Events
Reporting System by the Centers for Disease Control and Prevention
and the US Food and Drug Administration, and the reporting system
managed by the SA Health Products Regulatory Agency, lack a
relevant temporal comparison of underlying adverse events in the
unvaccinated general population.
This study conducts two independent analyses, comparing adverse
events in vaccinated individuals and SARS-CoV-2 infected individuals
(the study population) with those in their respective unvaccinated or
uninfected ‘clinical twins’ (the comparators). The outcomes of these
two analyses are subsequently compared to assess the relative impact
of adverse events following COVID-19 vaccination v. infection,
which constitutes the primary objective of the study.
Database
The study was conducted using data from Discovery Health, a large
SA managed care service organisation. This database is limited to
the insured population, who are therefore likely to differ from the
general SA population in terms of socioeconomic status, access to
healthcare and health-seeking behaviour. Consequently, the results
should be interpreted within the context of an insured population.
Nevertheless, the database provides a valuable and reliable source
for assessing real-world vaccine safety at scale, and has been used in
numerous published studies regarding SARS-CoV-2 infection and
the effectiveness of vaccinations.[16,17]
The Discovery Health database records extensive details on all
submitted claims, including World Health Organization (WHO)
ICD-10 diagnosis and National Pharmaceutical Product Index
(NAPPI) health product codes. WHO ICD-10 codes were used to
identify diagnoses of the conditions analysed, while NAPPI codes
were used to track vaccinations. Additionally, vaccination data were
supplemented with information from the SA National Department
of Health (NDoH). SARS-CoV-2 infections were identified using a
combination of claims data (WHO ICD-10 codes) and pathology
result data, which included antigen, antibody and polymerase chain
reaction (PCR) test results.
Conditions
For the period 17 February 2021 - 30 June 2022, we assessed the risk
of 99 conditions post vaccination (Ad26COV2S and BNT162b2)
and infection among Discovery Health clients aged ≥12 years who
had at least 12 months of medical insurance cover on Discovery
Health. The list of conditions analysed includes a compilation of
possible immune-related conditions published by the American
Autoimmune Related Diseases Association, and conditions of
interest identified in other studies of adverse events associated
with COVID-19 vaccination. [1,18,19] Conditions were grouped
into larger condition cohorts, to ensure sufficient volume while
maintaining clinical appropriateness. Conditions were identified
in the administrative data according to the WHO ICD-10 code
billed. Nonspecific conditions, conditions where an appropriate
set of WHO ICD-10 codes could not be mapped, and conditions
pertaining to children aged <12 years were not analysed ( Appendix
Table S1 and Table S2).
Participants and setting
The two study populations were identified as those individuals who
had had a COVID-19 vaccination (the ‘vaccination analysis’) or a
SARS-CoV-2 infection (the ‘infection analysis’) within the study
period, and had at least 12 months of medical insurance cover on
Discovery Health within SA. The COVID-19 vaccinations considered
in the vaccination analysis were Ad26.COV2.S (Janssen vaccine) and
BNT162b2 (Pfizer-BioNTech vaccine). Both doses of Ad26.COV2.S
were included, as well as the first three doses of BNT162b2. Adverse
events were analysed following each dose, with results evaluated
separately by vaccine type, thus minimising the need to account for
the varying number of doses in each regimen.
For the infection analysis, adverse events were assessed across
SA ’s first four waves of SARS-CoV-2 infection, driven by the D614G,
Beta, Delta and Omicron variants. SARS-CoV-2 infections were
identified using pathology results and WHO ICD-10 coding (U07.1),
which indicates a confirmed positive test. Additionally, registrations
and claims under Discovery Health’s WHO outbreak benefit were
considered in the identification process, as certain benefits under
this arrangement were available only to members with confirmed
infection.
In the vaccination analysis, individuals with a prior SARS-CoV-2
infection (as defined above) were excluded, while in the infection
analysis, individuals were removed from the study population if
they had had a vaccination within the last 6 months. This approach
was taken to ensure sufficient sample size in both study populations
while removing the potential for prior events, such as infections or
vaccinations, invalidating the results of the study. In the infection
analysis, it was assumed that long-term adverse events from the
vaccine were highly unlikely, thus minimising any potential impact
on the validity of results in the infection analysis.
It is important to note that many SARS-CoV-2 infections during
the pandemic were not diagnosed, primarily because individuals
with asymptomatic or mild infection did not seek testing. In addition,
some infections may have been missed due to false-negative test
results. Consequently, these undetected infections are not reflected
in our dataset.
Sample size
A total of 3 112 918 vaccination entries were included in the study
across the two vaccine types and the four age categories. The majority
of vaccinations (89%) were BNT162b2, with 51% representing the
first dose, as shown in Tables 1 and 2. It is important to note that
the vaccination types were analysed separately, given the different
regimen structure. In the infection analysis, 29 453 individuals aged
12 - 17, 189 857 aged 18 - 39, 163 380 aged 40 - 59, and 60 530 aged
≥60 years were infected with SARS-CoV-2 before the removal of
those with a history of the condition.
60 June 2026, Vol. 116, No. 5
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Outcomes
We assessed the rate of new onset of 99 adverse conditions in the
study populations over a 42-day period, starting from the date
of vaccination and infection (the ‘trigger date’) for the respective
studies. The date of the positive SARS-CoV-2 test, identified using
pathology results and WHO ICD-10 coding (U07.1), was used as
a proxy for the date of infection. A ‘censor date’ was also calculated
for each matched pair, defined as the date of the earliest occurrence
of any of the following events for either the study population or the
comparator individual:
• death
• withdrawal from the medical scheme.
For the vaccination analysis:
• SARS-CoV-2 infection for either individual
• COVID-19 vaccination for the comparator.
For the infection analysis:
• COVID-19 vaccination for either individual
• SARS-CoV-2 infection for the comparator.
The ‘observation period’ is defined as the period between the trigger
date and the censor date, and serves as the basis for calculating the
rate of each condition.
The primary outcome measure used in the study was the relative
risk (RR) ratio between the incidence of the condition in the study
population and the respective comparator group. In the formula
below, s denotes ‘study population’ , c denotes ‘comparators’ and i
denotes the ith condition:
𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅 𝑟𝑟𝑟𝑟𝑅𝑅𝑅𝑅𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟 𝑟𝑟𝑟𝑟𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑟𝑟𝑟𝑟𝑖𝑖𝑖𝑖 =
𝐷𝐷𝐷𝐷𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑅𝑅𝑅𝑅𝑟𝑟𝑟𝑟𝑠𝑠𝑠𝑠,𝑖𝑖𝑖𝑖
𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝐸𝐸𝐸𝐸𝑟𝑟𝑟𝑟𝑅𝑅𝑅𝑅𝑠𝑠𝑠𝑠,𝑖𝑖𝑖𝑖
�
𝐷𝐷𝐷𝐷𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑅𝑅𝑅𝑅𝑟𝑟𝑟𝑟𝑐𝑐𝑐𝑐,𝑖𝑖𝑖𝑖
𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝐸𝐸𝐸𝐸𝑟𝑟𝑟𝑟𝑅𝑅𝑅𝑅𝑐𝑐𝑐𝑐,𝑖𝑖𝑖𝑖
�
A diagnosis was inferred from the administrative data when a
participant had a claim linked to one of the respective WHO ICD-
10 codes for each condition ( Appendix Table S1 ). Exposure was
measured as the number of days between the ‘trigger date’ and the
‘censor date’ .
A risk difference per 100 000 lives was also calculated using a
similar formula to the one above, with the exception of subtracting
the rates from each other rather than dividing. The rate difference
was then scaled to reflect a per 100 000 lives measure.
To obtain confidence intervals (CIs) around the outcomes in both
the study and comparator population, as well as the relative measures
between the populations, we used bootstrapping with replacement,
iterating 500 times. This approach enabled the calculation of CIs
without assuming any specific statistical distribution. In addition, CIs
around the RR ratio between the study and comparator population
was calculated.
The 95% CI was obtained for each of the respective measures
analysed (i.e. the risk ratio, number of adverse events and risk
difference per 100 000 lives), by assessing the 2.5th percentile and
97.5th percentile across bootstraps. The bootstrapping methodology
was applied independently between the vaccination and infection
analyses.
Study design
In each bootstrap iteration, individuals in our study population were
randomly matched to a clinical twin, based on the specified matching
criteria below. Once a clinical twin was selected, it was ineligible for
further selection within that bootstrap.
Each of the rates were compared with those of matched comparators
across the following age subgroups: 12 - 17, 18 - 39, 40 - 59 and ≥60
years. The results were analysed by the various age subgroups to
ensure a more appropriate comparison between the vaccination and
infection analyses at the end.
The ‘clinical twin’ approach was used to determine a suitable set
of comparators for each of the study populations, ensuring that
the comparators had a similar risk composition compared with the
study population. Comparators were selected based on the following
matching criteria among participants:
• Age: clinical twins were matched within 1 year of age for individuals
aged 18 - 64 years; participants aged 12 - 17 years were matched
within the same age group, as were those aged ≥65 years.
• Sex: male or female.
• Location: hospital service area in SA.
• Number of chronic conditions: 0, 1, ≥2.
• Number of prior influenza vaccinations in the period 5 years
prior to COVID-19 vaccination or SARS-CoV-2 infection: 0 , 1,
≥2. People who had an influenza vaccination in the years leading
up to and during the COVID-19 pandemic were likely to be more
Table 1. Volume of vaccinations (before matching) by vaccine type and age category
Age group, years BNT162b2 vaccination volume Ad26.COV2.S vaccination volume Tota l
12 - 17 130 102 - 130 102
18 - 39 831 772 158 713 990 485
40 - 59 962 553 148 754 1 111 307
≥60 856 514 24 510 881 024
Total 2 780 941 331 977 3 112 918
Table 2. Vaccination distribution by dose number (before matching)
Age group, years
BNT162b2 vaccination volumes, % Ad26.COV2.S vaccination volumes, %
Dose 1 Dose 2 Dose 3 Dose 1 Dose 2
12 - 17 71.7 28.2 0.1 - -
18 - 39 51.8 43.3 4.9 81.5 18.5
40 - 59 47.6 43.2 9.2 76.0 24.0
≥60 41.7 39.2 19.1 68.0 32.0
Total 48.2 41.3 10.5 78.1 21.9
61 June 2026, Vol. 116, No. 5
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aware of promoting their general health
and wellbeing and to adhere to preventive
measures.
• Medical scheme product option: looking
at out-of-hospital benefits, we divided 24
plans available through both Discovery
Health Medical Scheme and 18 of the
Discovery Health administered schemes
into 5 groups to account for variation in
submission of out-of-hospital claims and
associated WHO ICD-10 codes.
• Additionally, comparators were not
matched if they had had a prior SARS-
CoV-2 infection or if they had had
a COVID-19 vaccination in the past
6-month period.
The study aimed to determine the rate of
new onset for each condition within the
analysis, i.e. individuals with a prior history of
a specific condition would be excluded from
the analysis for that condition. Consequently,
the study population would need to vary for
each of the 99 conditions, with bootstrapping
applied independently for each condition
across the two analyses. However, owing to the
large population sizes and the computational
resources available, the bootstrapping could
not be performed on all the populations.
A two-stage process was therefore
introduced. The first stage was set up to
identify conditions of interest that might
present with an increase in risk post
vaccination or infection. In the first stage,
for each of the 99 conditions, individuals
with a prior history of the condition were
removed from the study population after
bootstrapping was performed. Adverse events
of interest were identified if the incident RR
ratio exceeded 0.95 (95% CI), and there were
at least 10 total incidents across both the study
and comparator populations, on average,
across the 500 bootstraps. In the second
stage, for each of the conditions of interest
identified, individuals with a prior history
of the condition were removed from the
study population before bootstrapping was
performed. Therefore, a new study population
was created for each of the conditions, with
population inclusion and exclusion criteria
applied before bootstrapping.
In the second stage, the conditions
associated with an increased risk following
vaccination or infection were identified.
Ethical approval
This study used retrospective, anonymised
administrative claims data held by
Discovery Health. As no identifiable
patient information was accessible to
the researchers, formal ethics committee
approval was not sought.
Results
We analysed 130 102, 990 485, 1 111 307 and
881 024 individuals aged 12 - 17, 18 - 39, 40 -
59 and ≥60 years, respectively, who received
a COVID-19 vaccine dose. On average, 99%,
87%, 77% and 59% of participants in these age
groups were matched, respectively (Table 3).
Between 1 March 2020 and 30 June
2022, 29 453, 189 857, 163 380 and 60 530
individuals aged 12 - 17, 18 - 39, 40 - 59 and
≥60 years, respectively, who were infected
with SARS-CoV-2, were analysed. On
average, 99.9%, 99.6%, 99.7% and 99.8% of
participants in these age groups met the
inclusion criteria and were matched (Table 3).
No increased risk, compared with
comparators, was identified in the study for
any condition post AD26.COV2 vaccine.
In individuals aged 12 - 17 years, we
observed an increased risk difference
per 100 000 person years (RD) for
lymphadenopathy (148, 95% CI 12 - 288)
and for vertigo (114, 95% CI 16 - 212)
following BNT162b2 vaccination. The
relative risk (RR) of these conditions within
42 days of vaccination was 1.6 (95% CI 1 -
2.4) for lymphadenopathy and 1.9 (95% CI
1.1 - 3.2) for vertigo (Table 4 and Fig. 1). For
those aged ≥18 years, we found no increased
risk for any conditions relative to matched
comparators post BNT162b2 vaccination.
Across all age subgroups, 31 unique
adverse event conditions following SARS-
CoV-2 infection were identified. For those
aged 12 - 17 years, we found an increased
RD of appendicitis (1 801, 95% CI 1 286 -
2 395), arrhythmia (365, 95% CI 125 - 623),
encephalomyelitis (281, 95% CI 93 - 468),
lymphadenopathy (348, 95% CI 69 - 714),
myocarditis (332, 95% CI 155 - 526), seizure
(1 692, 95% CI 1 101 - 2 253), syncope (772,
95% CI 413 - 1178), type 1 diabetes (306,
95% CI 124 - 528) and vertigo (335, 95% CI
95 - 616) (Table 3 and Fig. 1).
For those aged 18 - 39 years, we found an
increased RD of acute kidney injury (497,
95% CI 391 - 605), anaemia (506, 95% CI
330 - 672), appendicitis (564, 95% CI 410 -
715), arrhythmia (378, 95% CI 256 - 495),
axonal and neuronal neuropathy (42, 95% CI
Fig. 1: Risk difference per 100 000 lives for 12- to 17-year-olds
Appendicitis
Arrhythmia
Encephalomyelitis
Lymphadenopathy
Myocarditis
Seizure
Syncope
Vertigo
Type 1 diabetes
COVID-19 Vaccine
500 1 000 1 500 2 000 2 500 3 000
Fig. 1. Risk difference per 100 000 lives for 12 - 17-year-olds.
62 June 2026, Vol. 116, No. 5
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10 - 81), cerebrovascular accident (151, 95%
CI 76 - 229), deep-vein thrombosis (163,
95% CI 82 - 243), encephalomyelitis (99,
95% CI 46 - 153), endometriosis (137, 95%
CI 16 - 258), fibrosing alveolitis (57, 95%
CI 25 - 96), glomerulonephritis (306, 95%
CI 190 - 426), inflammatory bowel disease
(66, 95% CI 5 - 131), myocardial infarction
(173, 95% CI 106 - 238), myocarditis (450,
95% CI 355 - 540), myositis (126, 95% CI
23 - 220), pericarditis (139, 95% CI 78 -
198), pulmonary embolism (1 313, 95% CI
1 146 - 1 483), rheumatic fever (126, 95%
CI 71 - 182), seizure (896, 95% CI 713 -
1 089), syncope (329, 95% CI 201 - 456),
thrombocytopenia (144, 95% CI 79 - 208),
type 1 diabetes (356, 95% CI 256 - 467),
urticaria (157, 95% CI 21 - 279), multiple
sclerosis (31, 95% CI 0 - 66) and cholangitis
(49, 95% CI 15 - 86) (Table 3 and Fig. 2).
For those aged 40 - 59 years, we found an
increased RD of acute kidney injury (4 659,
95% CI 4 335 - 4 986), anaemia (893, 95%
CI 651 - 1 125), appendicitis (289, 95% CI
161 - 414), arrhythmia (1 228, 95% CI 1 025 -
1 436), axonal and neuronal neuropathy
(173, 95% CI 99 - 248), cerebrovascular
accident (743, 95% CI 586 - 901), deep-
vein thrombosis (395, 95% CI 256 - 526),
encephalomyelitis (180, 95% CI 106 - 259),
eosinophilic oesophagitis (77, 95% CI 6 -
146), fibrosing alveolitis (198, 95% CI 130 -
272), glomerulonephritis (449, 95% CI 301 -
606), inflammatory bowel disease (119, 95%
Table 3. Data characteristics: COVID-19 vaccine and SARS-CoV-2 population (post matching)
Characteristic
BNT162b2 (n=2 036 333) AD26.COV2 (n=329 104) SARS-CoV-2 (n=441 935)
Vaccinated Control Vaccinated Control Vaccinated Control
Age, years, median (IQR) 44 (32 - 59) 43 (32 - 59) 40 (32 - 49) 40 (32 - 48) 40 (30 - 52) 39 (30 - 51)
Age group, years, n (%)
<18 128 546 (0.06) 128 546 (0.06) 0 0 29 415 (0.07) 29 415 (0.07)
18 - 39 704 431 (0.35) 738 259 (0.36) 157 513 (0.48) 162 963 (0.5) 189 166 (0.43) 194 854 (0.44)
40 - 49 390 170 (0.19) 379 779 (0.19) 95 147 (0.29) 93 174 (0.28) 96 577 (0.22) 95 140 (0.22)
50 - 59 316 740 (0.16) 323 211 (0.16) 52 128 (0.16) 50 319 (0.15) 66 358 (0.15) 64 568 (0.15)
60 - 69 269 246 (0.13) 242 133 (0.12) 17 642 (0.05) 14 313 (0.04) 33 430 (0.08) 30 641 (0.07)
70 - 79 165 411 (0.08) 158 969 (0.08) 5 425 (0.02) 5 750 (0.02) 18 454 (0.04) 19 211 (0.04)
≥80 61 789 (0.03) 65 435 (0.03) 1 249 (0) 2 586 (0.01) 8 535 (0.02) 8 107 (0.02)
Sex, n (%)
Female 1 089 593 (0.54) 1 089 593 (0.54) 202 396 (0.61) 202 396 (0.61) 242 558 (0.55) 242 558 (0.55)
Male 946 740 (0.46) 946 740 (0.46) 126 708 (0.39) 126 708 (0.39) 199 377 (0.45) 199 377 (0.45)
Chronic count, n (%)
0 1 257 527 (0.62) 1 257 527 (0.62) 225 391 (0.68) 225 391 (0.68) 265 202 (0.6) 265 202 (0.6)
1 355 196 (0.17) 355 196 (0.17) 60 204 (0.18) 60 204 (0.18) 84 949 (0.19) 84 949 (0.19)
≥2 423 610 (0.21) 423 610 (0.21) 43 509 (0.13) 43 509 (0.13) 91 785 (0.21) 91 785 (0.21)
Flu vaccinated, years n (%)
0 1 776 220 (0.87) 1 776 220 (0.87) 283 092 (0.86) 283 092 (0.86) 386 553 (0.87) 386 553 (0.87)
1 141 019 (0.07) 141 019 (0.07) 30 728 (0.09) 30 728 (0.09) 33 960 (0.08) 33 960 (0.08)
≥2 119 093 (0.06) 119 093 (0.06) 15 284 (0.05) 15 284 (0.05) 21 423 (0.05) 21 423 (0.05)
IQR = interquartile range; flu = influenza.
Fig. 2: Risk difference per 100 000 lives for 18- to 39-year-olds
COVID-19 Vaccine
Acute kidney injury
Anaemia
Appendicitis
Arrhythmia
Axonal and neuronal neuropathy
Cerebrovascular accident
Deep-vein thrombosis
Encephalomyelitis
Endometriosis
Fibrosing alveolitis
Glomerulonephritis
In/f_lammatory bowel disease
Myocardial infarction
Myocarditis
Myositis
Pericarditis
Pulmonary embolism
Rheumatic fever
Seizure
Syncope
Thrombocytopenia
Type 1 diabetes
Urticaria
Multiple sclerosis
Cholangitis
500 1 000 1 500 2 000
Fig. 2. Risk difference per 100 000 lives for 18 - 39-year-olds.
63 June 2026, Vol. 116, No. 5
RESEARCH
CI 32 - 209), intracranial hemorrhage (159,
95% CI 84 - 239), myocardial infarction (658,
95% CI 496 - 811), myocarditis (1003, 95%
CI 842 - 1 165), pericarditis (140, 95% CI
87 - 205), pulmonary embolism (2 723, 95%
CI 2 459 - 2 982), rheumatic fever (242, 95%
CI 167 - 338), seizure (1570, 95% CI 1 317 -
1 797), syncope (1077, 95% CI 885 - 1 262),
thrombocytopenia (145, 95% CI 75 - 227),
type 1 diabetes (986, 95% CI 822 - 1 164),
vertigo (469, 95% CI 268 - 666), cholangitis
(62, 95% CI 19 - 111) and pancreatitis (40,
95% CI 6 - 80) (Appendix Table S3 and Fig. 3).
For those aged ≥60 years, we found an
increased RD of acute kidney injury (17 299,
95% CI 16 185 - 18 595), anaemia (3 258, 95%
CI 2 642 - 3 942), appendicitis (251, 95% CI
90 - 414), arrhythmia (5 913, 95% CI 5 005 -
6 759), axonal and neuronal neuropathy
(224, 95% CI 89 - 376), cerebrovascular
accident (5 273, 95% CI 4 593 - 6 006),
deep-vein thrombosis (1 554, 95% CI 1 167 -
1 982), encephalomyelitis (321, 95% CI 144 -
485), eosinophilic oesophagitis (183, 95%
CI 37 - 335), fibrosing alveolitis (570, 95%
CI 361 - 784), glomerulonephritis (1 304,
95% CI 892 - 1 749), inflammatory bowel
disease (168, 95% CI 18 - 343), intracranial
haemorrhage (983, 95% CI 677 - 1 297),
lymphadenopathy (297, 95% CI 68 - 567),
myocardial infarction (3 653, 95% CI
3 029 - 4 273), myocarditis (745, 95% CI
536 - 1 003), pericarditis (132, 95% CI 26 -
252), pulmonary embolism (6 625, 95%
CI 5 942 - 7 344), rheumatic fever (977,
95% CI 725 - 1 266), seizure (5 691, 95%
CI 5 067 - 6 480), syncope (4 260, 95% CI
3 583 - 4 956), thrombocytopenia (517, 95%
CI 308 - 724), type 1 diabetes (1 681, 95%
CI 1 235 - 2 089), vertigo (552, 95% CI 88 -
1 058) and cholangitis (148, 95% CI 36 - 286)
(Appendix Table S3 and Fig. 4).
After identifying conditions with an
increased risk following vaccination or
infection, the RDs for these conditions
were compared between the vaccination
and infection analyses. The RDs for adverse
events (identified as having an increased risk)
were consistently larger across conditions
following SARS-CoV-2 infection (infection
analysis) compared with vaccination
(vaccination analysis) (Fig. 1).
Discussion
Transient common lymphadenopathy is a
relatively common feature following any
vaccination.[20-22] Among individuals aged
12 - 17 years, lymphadenopathy was noted
as a post-vaccination event following a
dose of BNT162b2. Lymphadenopathy was
also noted as a post-infection event for
individuals aged 12 - 17, as well as ≥60 years.
Previous studies have also documented
transient lymphadenopathy following
COVID-19 vaccination. [1,23,24] For those
aged 12 - 17, the RD for lymphadenopathy
was greater post infection than post
vaccination.
In our analysis, we observed an elevated
risk of vertigo in individuals aged 12 - 17
years following BNT162b2 vaccination.
Contrary to our findings, a review of
the literature did not reveal an increased
risk of vertigo following COVID-19
vaccination in this age group. Notably,
vertigo has been documented as an adverse
event following COVID-19 vaccination
in adults, as indicated by case reports
and studies. [25,26] Furthermore, transient
audiological and vestibular symptoms
following SARS-CoV-2 infection and
COVID-19 vaccination in children aged
5 - 11 years have been reported. [27]
Fig. 3: Risk difference per 100 000 lives for 40- to 59-year-olds
COVID-19 Vaccine
Acute kidney injury
Anaemia
Appendicitis
Arrhythmia
Axonal and neuronal neuropathy
Cerebrovascular accident
Deep-vein thrombosis
Encephalomyelitis
Eosinophilic oesophagitis
Fibrosing alveolitis
Glomerulonephritis
In/f_lammatory bowel disease
Intracranial haemorrhage
Myocardial infarction
Myocarditis
Pericarditis
Pulmonary embolism
Rheumatic fever
Seizure
Syncope
Thrombocytopenia
Vertigo
Type 1 diabetes
Cholangitis
Pancreatitis
1 000 2 000 3 000 4 000 5 000 6 000
Fig. 3. Risk difference per 100 000 lives for 40 - 59-year-olds.
Table 4. Adverse events within 42 days post vaccination with BNT162b2 dose 1, 2 or 3 in 12 - 17-year-olds (N=128 546)
BNT162b2
Average vaccine
population exposure,
person-half-years
Average clinical twin
exposure, person-
half-years
Adverse events for study
population, per 100 000
person-years (95% CI)
Adverse events
for clinical twin
population, per 100 000
person-years (95% CI)
Risk ratio
(95% CI)
Lymphadenopathy 23 662 23 664 414 (296 - 529) 265 (182 - 355) 1.6 (1 - 2.4)
Vertigo 25 487 25 488 251 (165 - 345) 137 (78 - 200) 1.9 (1.1 - 3.2)
64 June 2026, Vol. 116, No. 5
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In a real-world study of vaccine adverse events
using administrative healthcare data, onset
risk of 25 medical conditions was assessed
following COVID-19 vaccination and
documented SARS-CoV-2 infection. [1] We
assessed 24 of these conditions in our study.
Post vaccination, onset of lymphadenopathy
was a consistent finding. Barda et al .[1]
additionally reported a significant increased
risk for myocarditis, herpes zoster and
appendicitis, whereas we additionally
reported a significant increased risk for
vertigo following vaccination.
Furthermore, Straus et al .[28] found an
increased risk of myocarditis following
an mRNA-1273 COVID-19 vaccination
for younger males. Additionally, Husby
et al .[29] found increased risk of myocarditis
or myopericarditis following an mRNA-
1273 vaccination, driven by a significant
increased risk among individuals aged 12 -
39 years. Husby et al.[29] also considered the
incidence of myocarditis or myopericarditis
following the BNT162b2 vaccination,
and found an increased risk only among
female participants. Although our study did
segment the population by age, it was not
segmented by sex, which, following a review
of the published literature, is probably an
important variable to accurately understand
the risk of myocarditis following COVID-19
vaccination.
No adverse medical events were identified
following Ad26.COV2.S vaccination,
which may be due to the lower volumes
of vaccination relative to BNT162b2 in SA
and in the study population, resulting in
insufficient volumes to power estimates of
adverse events.
Post-infection adverse events were
prominently seen in older age groups:
individuals aged ≥60 had an increased risk of
25 unique conditions, while those <18 years
had an increased risk for nine conditions.
Notably, the following seven conditions were
observed as a post-infection event across
all age groups: appendicitis, arrhythmia,
encephalomyelitis, myocarditis, seizure,
syncope and type 1 diabetes. People in older
age groups were observed to have a higher
RR of adverse events, for a given condition,
following documented SARS-CoV-2
infection, with a high risk of onset of acute
kidney injury and myocarditis, in particular.
Following SARS-CoV-2 infection, we
found results consistent with Barda et al .[1]
in at least one age group, for 18 of the
24 conditions analysed, following a SARS-
CoV-2 infection. Notably, Barda et al .[1]
reported a significant increased risk for
lymphopenia and neutropenia following
a documented SARS-CoV-2 infection,
findings that we did not observe in our
study. Conversely, our study identified a
significant increased risk of appendicitis,
lymphadenopathy, seizures and vertigo
following a SARS-CoV-2 infection, which
were not highlighted as significant in Barda
et al.’s[1] findings. However, these associations
are not entirely novel; prior studies have
reported increased risks of appendicitis and
vertigo following SARS-CoV-2 infection. [30,31]
Additionally, several case reports and meta-
analyses have linked lymphadenopathy and
seizures to SARS-CoV-2 infection.[32,33]
Study limitations
As an analytical retrospective cohort study,
this research identifies associations rather
than proves causal relationships between
vaccination, infection and subsequent
adverse events. Even with comprehensive
matching to control for confounding,
the potential influence of unmeasured
factors cannot be entirely excluded. Since
exposure to vaccination or infection was
not randomised, differences in health status,
healthcare-seeking behaviour, or diagnostic
practices may have influenced the results.
While this study analysed extensive data
and a large variety of conditions using the
WHO’s ICD-10 codes from administrative
records, the data records lack clinical
validation.
As detailed in the study, certain cohorts,
such as the AD26.COV2 vaccine analysis,
were relatively small. This limitation may
have resulted in rare conditions not being
identified as having an increased risk,
because the sample size might not have been
sufficient to detect less common adverse
events.
Due to the differing regimen sizes, a
direct comparison between vaccine types
was not feasible. The study’s objective was
not to assess the effectiveness of any specific
vaccine regimen, but rather to evaluate
the relative difference in adverse events
experienced following vaccination v. SARS-
CoV-2 infection.
Vaccine administration dates were known,
ensuring that the 42-day observation period
was accurate. However, infection dates were
inferred from the date of the positive SARS-
CoV-2 test, which may occur a few days
after actual infection. While this introduces
some deviation in the observation periods,
Fig. 4: Risk difference per 100 000 lives for 60+ year olds
Fig. 4: Risk difference per 100 000 lives for 60+ year olds
Acute kidney injury
Anaemia
Appendicitis
Arrhythmia
Axonal and neuronal neuropathy
Cerebrovascular accident
Deep-vein thrombosis
Encephalomyelitis
Eosinophilic oesophagitis
Fibrosing alveolitis
Glomerulonephritis
In/f_lammatory bowel disease
Intracranial haemorrhage
Lymphadenopathy
Myocardial infarction
Myocarditis
Pericarditis
Pulmonary embolism
Rheumatic fever
Seizure
Syncope
Thrombocytopenia
Vertigo
Type 1 diabetes
Cholangitis
4 000 8 000 12 000 16 000 20 000
COVID-19 Vaccine
Fig. 4. Risk difference per 100 000 lives for ≥60-year-olds.
65 June 2026, Vol. 116, No. 5
RESEARCH
the impact is likely minimal owing to the length of the observation
window relative to any potential delay in testing.
Although the study population is diverse in race and socioeconomic
status, results may not be generalisable to the full uninsured SA
population. Furthermore, the results may not be applicable to
populations outside of SA owing to differences in healthcare systems,
access to care and underlying population characteristics
Conclusion
In this large study of insured South Africans aged ≥12 years, the
RD of adverse events following natural SARS-CoV-2 infection was
substantially larger than that following COVID-19 vaccination across
all age groups (Figs 1 - 4).
Data availability. Anonymised datasets analysed during the current study
are not publicly available. However, they are available upon reasonable
request, following ethical approval.
Declaration. None.
Acknowledgements. The authors acknowledge the broader Discovery
Health teams whose ongoing work in developing, supporting and
maintaining the underlying data systems enabled this study.
Author contributions. KB performed the data analysis and drafted the
manuscript. SC contributed to study oversight, data interpretation and
manuscript drafting. DWCJ led the clinical interpretation and ICD-10
code mapping, with support from PM. L-GB, GG and JP contributed
medical review, interpretation of findings and manuscript revision. All
authors reviewed and approved the final manuscript.
Funding. None.
Conflicts of interest. None.
AI contribution. Artificial intelligence (AI) tools were used solely to assist
with grammar and to improve sentence structure for clarity.
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Received 28 July 2025; accepted 12 November 2025.