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\begin{document}

\title{Disproportionate drug allergy labelling among middle-aged patients and
barriers to excipient allergy testing from the Hong Kong Comprehensive
Excipient and Drug Allergy Registry (CEDAR)}



\author[1]{Chiang Valerie}%
\author[2]{Andy Ka Chun Kan}%
\author[2]{Saha Chinmoy}%
\author[1]{Elaine Au Y L}%
\author[2]{Philip Li}%
\affil[1]{Queen Mary's Hospital Department of Pathology and Department of Microbiology}%
\affil[2]{The University of Hong Kong Li Ka Shing Faculty of Medicine}%


\vspace{-1em}



  
  \date{May 17, 2024}


\begingroup
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\selectlanguage{english}
\begin{abstract}
Background: Longitudinal whole-population studies can explore new
dimensions in drug allergy research. Excipient allergy testing remains a
barrier to allergy evaluation in countries without legislations
mandating ingredient disclosure in registered drugs. The Comprehensive
Excipient and Drug Allergy Registry (CEDAR) was established to
investigate Hong Kong's drug allergy landscape and potential role of
excipient registries. Methods: Drug allergy data from over 7,337,778
individuals between 2016-2021 was analyzed. Excipient lists were
gathered from all formulations of the top 50 reported drug allergies and
checked for the presence of polyethylene glycol (PEG). Results: The
absolute-prevalence and -incidence of reported drug allergy was 5.61\%
and 277/100,000 population in 2021, respectively. Annual incidence of
newly reported drug allergy was stable between 2016-2019, until a
significant drop in 2020 (-16.3\%, p=0.037). The most common implicated
drugs were anti-infectives (245,832 {[}44.5\%{]}), NSAIDs (106,843
{[}19.3\%{]}), and nervous system drugs (45,802 {[}8.3\%{]}). There was
significant higher incidence among individuals aged \textgreater{}40,
contributing to the majority of newly reported allergies (377,004,
68.2\%). Beta-lactams and nervous system drugs were the most common
reported culprits of anaphylaxis and Stevens-Johnson syndrome. CEDAR was
unable to confirm presence or absence of PEG in any of the top reported
culprits due to insufficient excipient information. Conclusion: We
report the detailed drug allergy landscape of the Hong Kong population.
Excipient registries are ineffective in countries without mandatory
excipient listings . Contrary to traditional doagma, we identified a
disproportionately higher incident drug allergy among middle-aged and
older patients. Findings need to be confirmed by region-specific big
data studies.%
\end{abstract}%



\sloppy


\textbf{Title:}

\textbf{Disproportionate drug allergy labelling among middle-aged
patients and barriers to excipient allergy testing from the Hong Kong
Comprehensive Excipient and Drug Allergy Registry (CEDAR)}

\textbf{Short running title:}

\textbf{Hong Kong Comprehensive Excipient and Drug Allergy Registry
(CEDAR)}

\textbf{Authors:}

Valerie Chiang\textsuperscript{1}, Andy K C Kan\textsuperscript{2},
Chinmoy Saha\textsuperscript{2}, Elaine Y L Au\textsuperscript{1},
Philip H Li\textsuperscript{2}

\textsuperscript{1} Division of Clinical Immunology, Department of
Pathology, Queen Mary Hospital, Hong Kong

\textsuperscript{2} Division of Rheumatology and Clinical Immunology,
Department of Medicine, Queen Mary Hospital, The University of Hong
Kong, Hong Kong

\textbf{Corresponding Author:}

Dr Philip H Li, Department of Medicine, Queen Mary Hospital, The
University of Hong Kong, 102 Pokfulam Road, Hong Kong (liphilip@hku.hk,
Tel: +852 2255-5995).

\textbf{Acknowledgments:}

Dr Joyce Ka Yin Chan, and her team from the Information Technology and
Health Informatics Division, Hospital Authority Head Office, assisted
with data extraction and analysis.

\textbf{Funding:}

This work was supported by the Health and Medical Research Fund (Food
and Health Bureau) - COVID1903011.

\textbf{Author contributions:}

VC, AK, CS, EA, and PL researched the data. VC, AK, and PL analyzed and
interpreted the data. VC and AK performed statistical analyses. VC
drafted the manuscript. PL supervised the project, critically reviewed
and edited the manuscript. All authors reviewed and contributed to the
final version of the manuscript.

\textbf{Word count: 250} (abstract), \textbf{2837} (main text)

\textbf{Conflict of Interest:}

The authors have no conflicts of interest in relation to this work.



\textbf{Abstract}

\emph{Background}

Longitudinal whole-population studies can explore new dimensions in drug
allergy research. Excipient allergy testing remains a barrier to allergy
evaluation in countries without legislations mandating ingredient
disclosure in registered drugs. The Comprehensive Excipient and Drug
Allergy Registry (CEDAR) was established to investigate Hong Kong's drug
allergy landscape and potential role of excipient registries.

\emph{Methods}

Drug allergy data from over 7,337,778 individuals between 2016-2021 was
analyzed. Excipient lists were gathered from all formulations of the top
50 reported drug allergies and checked for the presence of polyethylene
glycol (PEG).

\emph{Results}

The absolute-prevalence and -incidence of reported drug allergy was
5.61\% and 277/100,000 population in 2021, respectively. Annual
incidence of newly reported drug allergy was stable between 2016-2019,
until a significant drop in 2020 (-16.3\%, p=0.037). The most common
implicated drugs were anti-infectives (245,832 {[}44.5\%{]}), NSAIDs
(106,843 {[}19.3\%{]}), and nervous system drugs (45,802 {[}8.3\%{]}).
There was significant higher incidence among individuals aged
\textgreater{}40, contributing to the majority of newly reported
allergies (377,004, 68.2\%). Beta-lactams and nervous system drugs were
the most common reported culprits of anaphylaxis and Stevens-Johnson
syndrome. CEDAR was unable to confirm presence or absence of PEG in any
of the top reported culprits due to insufficient excipient information.

\emph{Conclusion}

We report the detailed drug allergy landscape of the Hong Kong
population. Excipient registries are ineffective in countries without
mandatory excipient listings . Contrary to traditional doagma, we
identified a disproportionately higher incident drug allergy among
middle-aged and older patients. Findings need to be confirmed by
region-specific big data studies.

\textbf{Keywords:} Age Distribution; Big Data; Drug Allergy;
Epidemiology; Excipients



\textbf{Background}

Population-based studies have demonstrated the potential of use of `big
data' in clinical and drug allergy research.\textsuperscript{1-4} The
availability of longitudinal datasets can explore new dimensions of drug
allergy research which would not have been possible with only
traditional single-centre or cross-sectional studies. For example, we
previously reported one of the largest drug allergy epidemiological
studies by taking advantage of Hong Kong's unified electronic healthcare
record system with data from more than 95\% (7.1 million) of the
population.\textsuperscript{1} Based on population-wide data, we were
able to accurately report the near-absolute prevalence and annual
incidence of reported drug and beta-lactam allergies. However, at the
time, we were only able to provide a condensed one-year snapshot on the
landscape of drug/beta-lactam allergy and did not further analyse the
granularity of individual patient data; such as specific-drug allergy
culprits, individual patients' age of reported drug allergy or duration
of reported drug allergy. More detailed analyses are also important to
identify country- or population-specific characteristics, which may
vastly differ from Western cohorts, to further inform future drug
allergy interventions and research.\textsuperscript{5,6}

Another important limitation to drug allergy evaluation and research is
the ability to accurately identify specific culprit allergens. For
example, it is well established that up to 90\% of all reported
penicillin `allergy' are incorrect after allergy evaluation, likely due
to initial misdiagnoses or loss of sensitisation over
time.\textsuperscript{7-10}Alternatively, mislabelled drug allergy can
also be due to the possibility of missing hidden allergens found within
drugs or vaccines such as in the case of excipient
allergies.\textsuperscript{11,12}Although an uncommon cause of drug
allergy, excipient allergy can almost never be excluded in countries
lacking in pharmaceutical legislations to mandate complete ingredient
disclosure in registered medications. This can and has led to cases of
missed- and mis-diagnoses with potentially lethal
consequences.\textsuperscript{13-15} The devastating impact of such
failures in pharmaceutical legislations have been exemplified during
global COVID-19 vaccination campaigns. Despite genuine vaccine or
excipient allergy being exceedingly rare, the indirect consequences from
the fear of potential excipient allergy and inability to identify
potential excipients among culprit drug formulations greatly impeded
vaccine uptake and fuelled vaccine hesistancy.\textsuperscript{16-18}

To overcome these issues, the Comprehensive Excipient and Drug Allergy
Registry (CEDAR) was established in Hong Kong -- allowing the use of big
data to investigate reported drug allergies in detail, and to help
establish an excipient registry to facilitate comprehensive drug allergy
evaluations. Utilising CEDAR, we investigated the 5-year trend of
detailed drug allergy epidemiology and the potential role of an
excipient registry to aid drug allergy evaluation. In particular, we
selected polyethylene glycol (PEG), one of the most widely implicated
excipients since the launch of mRNA COVID-19 vaccines, as an example for
the potential use of excipient registries.

\textbf{Methods}

\emph{Collection of anonymised patient data}

Anonymised patient data from the Hospital Authority was collected by the
Information Technology and Health Informatics Division, Hospital
Authority Head Office. Informed consent was waived (because all data
were anonymous and collected retrospectively) and data extraction was
approved by the institutional review board of the University of Hong
Kong and Hospital Authority Hong Kong West Cluster. Hong Kong is a
unique entity where the Hospital Authority is the sole publicly funded
healthcare system for the entire territory. It possesses facilities in
seven regions (Hong Kong East, Hong Kong West, Kowloon Central, Kowloon
East, Kowloon West, New Territories East, and New Territories West),
comprising 43 hospitals, 49 specialist outpatient clinics, and 73
general outpatient clinics; it provides approximately 90\% of all
in-patient services.\textsuperscript{19} All clinical services utilise a
unified `Electronic Patient Records' system, providing medical records
for over 7.3 million individuals. This system allows complete and
uniform drug allergy data to be gathered and analysed. A snapshot of
cross-sectional data from all available patient records was retrieved as
of 23\textsuperscript{rd} July, 2021. Physician-reported drug allergy,
age at the time of reported drug allergy and names of reported culprits
were collected. Incidence of newly reported drug allergy, as well as all
cases of anaphylaxis and Stevens-Johnson syndrome from
1\textsuperscript{st} January, 2016 to 31\textsuperscript{st} December,
2020 were reviewed. Drugs were categorised according to the British
National Formulary Drug Classes, except for non-steroidal
anti-inflammatory drugs (NSAIDs), which were separated into its own
category.\textsuperscript{20} Drugs which were entered as `free text'
into the electronic system were checked and converted back to structured
drug items when possible. Those `free text' reported drug allergy which
could not converted or were incomplete were excluded from analysis.
Drugs which did not fit into any drug classes were categorised as
`Others' and listed in Supplementary Text 1.

\emph{Construction of excipient allergy registry -- using PEG as an
example}

An excipient registry was constructed based on the top 50 reported drug
allergy culprits as retrieved from CEDAR. In view of the recent
relevance for mRNA vaccines, PEG was selected as the pilot registry. An
excipient registry was created by compiling the excipient lists
pertaining to all available formulations of each of the drugs of the top
50 most implicated drug allergies. All excipient lists from drug product
inserts, and if unavailable, through contacting individual
pharmaceutical companies - as there is no pharmaceutical legislation to
mandate listing of excipients of registered drugs in Hong Kong.
Depending on the status of PEG, formulations were then categorised as
either `contains PEG', `does not contain PEG', or `unknown'.

\emph{Statistical Analysis}

All statistics were analysed using IBM SPSS Statistics 27.0 (IBM Corp.,
Armonk, NY, USA). Prevalence of drug allergy was calculated. Values were
presented as numbers (percentages) as appropriate. Age distributions of
drug allergy patients and individuals without drug allergy were compared
using the binomial test. Trends of incidence of reported drug allergy
were analysed using Spearman's correlation. Two-sided p-values
\textless{}0.05 were considered statistically significant.

\textbf{Results}

\emph{From over 7.3 million individuals, the absolute-prevalence of
reported drug allergy in Hong Kong was 5.61\%}

Anonymised data was collected from the electronic health records of
7,337,778 unique individuals. Among them, 411,885 had at least one
physician-reported drug allergy in 2021. The near-absolute prevalence of
physician-reported drug allergy in Hong Kong was therefore 5.61\%. A
total of 552,897 drug allergy labels were shared amongst the 441,885
unique individuals; 346,969 (62.8\%) of the reported drug allergy were
found among female individuals. Among individuals with reported drug
allergy, 28,467 (6.91\%) had more than 2 concomitant reported drug
allergies.

\emph{Stable incidence of newly reported drug allergy between 2016-2019
with a significant drop in 2020}

In 2016, 25,810 new physician-reported drug allergy labels were created
with an incidence of 352 per 100,000 population. The annual incidence
remained relatively stable from 2016 to 2017 (352 per 100,000), 2018
(337 per 100,000) and 2019 (331 per 100,000) (p=0.200). There was no
significant change in incidence rate until a significant decrease in
2020 to 277 per 100,000 (p=0.037). The annual incidence and relative
differences of physician-reported drug allergy are shown in Figure 1.

\emph{Most common reported drug allergies were to beta-lactams, making
up 26.0\% of all labels}

Out of the 552,897 reported drug allergy labels, the most common
implicated drugs were anti-infectives (245,832 {[}44.5\%{]}) and NSAIDs
(106,843 {[}19.3\%{]}), followed by nervous system drugs (45,802
{[}8.3\%{]}) and cardiovascular system drugs (27,977 {[}5.1\%{]})
(Figure 2). Among the 245,832 reported anti-infective allergy labels,
143,925 (58.5\%) were towards beta-lactams (Table 1). Among
beta-lactams, penicillins accounted for the vast majority (119,274
{[}82.9\%{]}) of reported culprits. Amoxicillin-clavulanate was the most
commonly reported (30,396 {[}21.1\%{]}), followed by
phenoxymethylpenicillin (penicillin V) (25,678 {[}17.8\%{]}),
amoxicillin (21,806 {[}15.2\%{]}), ampicillin (16,335 {[}11.3\%{]}), and
cloxacillin (9,689 {[}6.7\%{]}).

\emph{Most reported drug allergy among indviduals aged 50-59 years, with
disproportionately higher incidence of newly reported drug allergy above
40}

The age distribution of reported drug allergy in comparison to
non-allergic individuals (i.e. those without any reported drug allergy),
is shown in Figure 3. Most drug allergies were reported among
individuals in the 50-59 year age group (106,249, 19.2\%); and more than
half of all drug allergies (377,004, 68.2\%) were reported among
individuals at or above the age of 40. There were disproportionately
fewer individuals with reported drug allergy below 40, compared to the
age distribution of non-allergic individuals (4.3\% vs 14.7\%,
p\textless{}0.001 for 0-9 years; 4.1\% vs 9.3\%, p\textless{}0.001 for
10-19 years; 9.2\% vs 13.5\%, p\textless{}0.001 for 20-29 years; 14.2\%
vs 15.3\%, p\textless{}0.001 for 30-39 years). On the contrary, there
were disproportionately more reported drug allergies among individuals
ages above 40 compared to non-allergic individuals (16.7\% vs 15.5\%,
p\textless{}0.001 for 40-49 years; 19.2\% vs 13.9\%, p\textless{}0.001
for 50-59 years; 15.0\% vs 8.6\%, p\textless{}0.001 for 60-69 years;
10.9\% vs 6.0\%, p\textless{}0.001 for 70-79 years; 5.4\% vs 2.7\%,
p\textless{}0.001 for 80-89 years; 1.0\% vs 0.5\%, p\textless{}0.001 for
90 years or above).

\emph{Beta-lactams and nervous system drugs were the most common
reported culprits of drug-induced anaphylaxis and Stevens-Johnson
syndrome}

Between 2016 and 2020, there were a total of 1,325 reported cases of
drug-induced anaphylaxis (0.32\% of drug allergy) and 1,706 cases
(0.41\% of drug allergy) of Stevens-Johnson syndrome in Hong Kong.
Breakdown of reported drug culprits are shown in Table 2 and Table 3,
respectively. Anti-infectives (544 {[}41.1\%{]}) were the most common
culprits of drug-induced anaphylaxis (in particular, beta-lactams: 402
{[}30.3\%{]}), followed by musculoskeletal system drugs (158
{[}11.9\%{]}) and nervous system drugs (157 {[}11.8\%{]}).
Anti-infectives (735 {[}43.1\%{]}) were also the most common culprits of
Stevens-Johnson syndrome (in particular, beta-lactams 348 {[}20.4\%{]}),
followed by nervous system drugs (390 {[}22.9\%{]}) and musculoskeletal
system drugs (348 {[}20.4\%{]}).

\emph{Unable to confirm presence or absence of PEG in any of the top 50
reported culprits of drug allergy}

The top 50 reported drug allergy culprits are listed in Supplementary
Text 2, which accounted for 72.5\% (400,700/552,897) of all reported
drug allergy (Figure 4). In total, there were 2,070 unique drug
formulations of these 50 drugs available in Hong Kong. All 2,070
formulations were examined (either by extracting from product inserts or
contacting individual pharmaceutical companies) for presence or absence
of PEG. Complete excipient information was only available for 36.1\%
(748/2,070) formulations. Out of these, 124 (16.6\%) were confirmed to
contain PEG while the remaining 83.4\% were confirmed to not contain
PEG. Of the remaining 1,322 (63.9\%) formulations, excipient information
was not available and we were unable to confirm presence or absence of
PEG despite attempts at contacting pharmaceutical companies. After
re-mapping all 748 formulations with available excipient information
back into the original 50 drugs, we were unable to confirm the presence
or absence of PEG in any single one of these drugs registered in Hong
Kong (i.e. there were formulations with unknown excipient information
for each of the top 50 allergy culprits).

\textbf{Discussion}

Using our population-wide data, CEDAR was able to generate a registry of
comprising of more than 7,337,778 individuals. To our knowledge, this is
the largest population-wide drug epidemiology study to date; allowing
detailed characterisation of the absolute-prevalence and -incidence,
individual drug culprits, as well as the age of incident drug allergies
of our entire population.

Our study is also the first to investigate the longitudinal incidence of
drug allergy labelling on a population-wide basis. CEDAR revealed that
the annual incidence of reported drug allergy Hong Kong remained
relatively stable over the 5-year study period (331-352 per 100,000
population), until a significant 16.3\% drop in 2020. This was likely
due to the COVID-19 pandemic resulting in a population-wide reduction in
medical visits, drug prescriptions, or allergy
reporting.\textsuperscript{21} It would be interesting to see if the
incidence of drug allergy will rebound back to pre-COVID figures
following relaxation of social distancing restrictions. Our study also
confirmed that about 1 in 18 (5.61\%) of Hong Kong's population have
physician-reported drug allergies, which differs quite substantially
with other regions (especially in Western cohorts), as well as our own
previous hospital-based studies.\textsuperscript{8,22-26} Although drug
allergy prevalence varies across time and region (likely due to
combination of differing prescribing practices and biological
differences) we postulate discrepancies may also arise due to previous
type I errors from inadequate sampling, which would be minimised from
this population-wide study. This needs to be confirmed by collaborative
inter-regional and -ethnic big data studies in the future.

Interestingly, our unified physician-reported drug allergy system
revealed that most drug allergies were reported among middle-aged
individuals, with disproportionately higher incidence among those above
40. This is contrary to the traditional dogma reflected from the
previous experience from Western cohorts, suggesting the majority of
individuals acquire drug allergy labels during childhood and carry them
into adulthood.\textsuperscript{27} We postulate this stark difference
may be due to either genuine inter-population differences, or a paradigm
shift that has remained unnoticed due to the paucity of prior systematic
or large population-based drug allergy studies. This emphasises the
importance of region-specific big data studies, which can discover
unique features and trends of drug allergy labelling among different
populations. Furthermore, identifying target subpopulations would inform
the best strategy for allergy education and proactive delabelling at a
population level. For example, there are significantly fewer adult
allergists than paediatric allergists in Hong Kong (1:2,800,000 vs
1:540,000 per population), which is disproportionate to the incidence
and burden of drug allergy in our locality.\textsuperscript{28} Hence,
we advocate for more active drug allergy delabelling initiatives among
adults, further resources and positions for training of adult
allergists, as well as strengthening the ability of frontline healthcare
professionals to discern symptoms of genuine allergy -- especially among
adult patients.

This study also confirms the severity of a penicillin-dominated drug
allergy landscape in Hong Kong. Within anti-infectives, beta-lactams
constituted the majority of all reported drug allergy, with penicillins
contributing to almost 82.9\% of all beta-lactam labels. Penicillins
were among the most commonly reported culprits of drug-induced
anaphylaxis and Steven-Johnson syndrome. However, our previous studies
revealed that only 10-13.8\% of reported penicillin allergy are found to
be correct after evaluation.\textsuperscript{1,10} Incorrect labels lead
to unnecessary penicillin avoidance, posing immense challenges in
antimicrobial stewardship. This is especially relevant in Hong Kong,
where there has been an upsurge of various multi-drug resistant
organisms.\textsuperscript{29,30} Penicilin allergy labels have also
shown to affect geriatric and immunocompromised patients, associated
with a multitude of adverse clinical outcomes, including increased
healthcare costs, more frequent and longer hospital stays, and even
death.\textsuperscript{22,31-33} Despite the severe lack of allergists
in the Asia-Pacific region, previous multi-disciplinary initiatives have
shown much promise in tackling drug
allergies.\textsuperscript{10,34,35}For example, a nurse-led penicillin
allergy delabelling initiative demonstrated superior outcomes compared
to traditional allergist evaluation in Hong Kong and similar strategies
employing trained pharmacists are underway.\textsuperscript{10} Further
research on wider applications of similar multidisciplinary drug allergy
initiatives is warranted.

Given the direct and indirect consequences of genuine or perceived
excipient allergy -- from rare cases of excipient-associated anaphylaxis
to inappropriate fear of excipient allergy leading to vaccine hesitancy
-- the ability to accurately disprove excipient allergy is of paramount
importance.\textsuperscript{13-17} However, diagnosis of excipient
allergy is near impossible when excipient information is not available.
This is a common problem for many regions where no pharmaceutical
legislations exist to mandate excipient disclosure in registered
medications. Despite our best efforts to construct a excipient registry,
we were unable to obtain excipient information from the vast majority of
drug formulations. Using PEG as an example, we were able to confirm its
presence or absence among only 724 of 2,070 formulations investigated,
only representing a mere 36\% included in the registry. This made it
impossible to exclude or confirm presence of PEG in all formulations of
any of the top 50 reported drug allergy culprits in Hong Kong. Lack of
transparent drug information impedes in comprehensive evaluations in
drug allergy, and undermines the safety and public confidence of
population-wide vaccination programmes. Despite past efforts to advocate
for regulartory improvements, ingredient disclosure remains to be a
persistent issue in our locality and legislative changes have yet to
materialise.\textsuperscript{16,17}

Given its observational nature, this study has several limitations.
First, only limited patient data was available, and more detailed
clinical data such as comorbidities, medication use or hospitalisation
records were not available for further subgroup analysis. Second, we did
not include reported drug allergies which were entered as `free text'
into the patient's electronic health record that could not be manually
converted into structured item (often due to incomplete information).
Third, drug allergy labels in Hong Kong were physician-reported, and
albeit more preferred than patient-reported adverse reactions, may still
be inaccurate and do not reflect only genuine and confirmed drug
allergy. This highlights the importance of dedicated and interventional
studies in the future.

In conclusion, population-specific drug allergy research is needed to
inform the directions of appropriate public health initiatives in an
evidence-based manner. The use of big data can unlock new dimensions of
drug allergy research - such as drug allergy incidence and comparative
age distribution of reported drug allergies. Using our population-based
data, we were able to report the longitudinal incidence of reported drug
allergy, specific allergy culprits as well as identifying
disproportionate drug allergy labelling among middle-aged patients.
Future inter-regional and -ethnic big data studies will be required to
confirm external validity of our findings to other populations. Lastly,
excipient registries remain ineffective and proper allergy evaluation
cannot be conducted in countries where excipient information are
unavailable. International and collaborative efforts should be united to
advocate for universal recommendations toward ingredient disclosure for
all registered medications.

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12. Li PH, Wagner A, Thomas I, Watts TJ, Rutkowski R, Rutkowski K.
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13. Li PH, Yeung HHF, Lau CS, Au EYL. Excipient allergy and importance
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Pract.}2020;8(6):2122-2123.

14. Caballero ML, Krantz MS, Quirce S, Phillips EJ, Stone CA, Jr. Hidden
Dangers: Recognizing Excipients as Potential Causes of Drug and Vaccine
Hypersensitivity Reactions. \emph{J Allergy Clin Immunol
Pract.}2021;9(8):2968-2982.

15. Caballero ML, Quirce S. Immediate Hypersensitivity Reactions Caused
by Drug Excipients: A Literature Review. \emph{J Investig Allergol Clin
Immunol.} 2020;30(2):86-100.

16. Chiang V, Leung ASY, Au EYL, et al. Consensus Statements on the
Approach to COVID-19 Vaccine Allergy Safety in Hong Kong. \emph{Front
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17. Chiang V, Leung ASY, Au EYL, et al. Updated consensus statements on
COVID-19 Vaccine Allergy Safety in Hong Kong. \emph{Asia Pac
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18. Kan AKC, Wong TTH, Chiang V, Lau CS, Li PH. Chronic Spontaneous
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22. Li PH, Chung HY, Lau CS. Epidemiology and outcomes of geriatric and
non-geriatric patients with drug allergy labels in Hong Kong. \emph{Hong
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23. Yuson C, Shakib S, Smith W. Prevalence of drug allergy in South
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24. Kvedariene V, Sitkauskiene B, Tamasauskiene L, et al. Prevalence of
self-reported drug hypersensitivity reactions among Lithuanian children
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25. Thong BY, Tan TC. Epidemiology and risk factors for drug
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26. Macy E, Poon KYT. Self-reported antibiotic allergy incidence and
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e771-777.

27. Stone CA, Jr., Trubiano J, Coleman DT, Rukasin CRF, Phillips EJ. The
challenge of de-labeling penicillin allergy.
\emph{Allergy.}2020;75(2):273-288.

28. Lee TH, Leung TF, Wong G, et al. The unmet provision of allergy
services in Hong Kong impairs capability for allergy
prevention-implications for the Asia Pacific region. \emph{Asian Pac J
Allergy Immunol.} 2019;37(1):1-8.

29. Cheng VC, Wong SC, Ho PL, Yuen KY. Strategic measures for the
control of surging antimicrobial resistance in Hong Kong and mainland of
China. \emph{Emerg Microbes Infect.} 2015;4(2):e8.

30. Li PH, Cheng VC, Yip T, Yap DY, Lui SL, Lo WK. Epidemiology and
Clinical Characteristics of Acinetobacter Peritoneal Dialysis-Related
Peritonitis in Hong Kong-With a Perspective on Multi-Drug and Carbapenem
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31. Chan SCW, Yeung WWY, Wong JCY, et al. Prevalence and Impact of
Reported Drug Allergies among Rheumatology Patients. \emph{Diagnostics
(Basel).} 2020;10(11).

32. West RM, Smith CJ, Pavitt SH, et al. `Warning: allergic to
penicillin': association between penicillin allergy status in 2.3
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prescribing and health outcomes. \emph{J Antimicrob
Chemother.}2019;74(7):2075-2082.

33. Mattingly TJ, 2nd, Fulton A, Lumish RA, et al. The Cost of
Self-Reported Penicillin Allergy: A Systematic Review. \emph{J Allergy
Clin Immunol Pract.} 2018;6(5):1649-1654.e1644.

34. Chiang V, Saha C, Yim J, et al. The Role of the Allergist in
Coronavirus Disease 2019 Vaccine Allergy Safety: A Pilot Study on a
''Hub-and-Spoke'' Model for Population-Wide Allergy Service. \emph{Ann
Allergy Asthma Immunol.} 2022;129(3):308-312.e301.

35. Chiang V, To KKW, Hung IFN, et al. COVID-19 Vaccine Allergy Safety
Track (VAS-Track) pathway: real-world outcomes on vaccination rates and
antibody protection. \emph{Asian Pac J Allergy Immunol.} 2023.

\textbf{Figure legends}

\textbf{Figure 1: Annual incidence of new drug allergy labels from 2016
to 2020}\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image1/image1}

\end{center}
\end{figure}



\textbf{Figure 2: Distribution of all 552,897 drug allergy labels
according to drug category}\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image2/image2}

\end{center}
\end{figure}

+Others: Please refer to Supplementary Text 1

\textbf{Figure 3: Age distribution of individuals with reported drug
allergy in comparison to non-allergic individuals}\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image3/image3}

\end{center}
\end{figure}

*p\textless{}0.05, **p\textless{}0.01, ***p\textless{}0.001.

\textbf{Figure 4: Breakdown of the formulations and excipient
information available of the top 50 reported drug allergies in Hong
Kong}\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image4/image4}

\end{center}
\end{figure}

PEG, polyethylene glycol.

\textbf{Table 1: Distribution of reported allergy toward
anti-infectives}\selectlanguage{english}
\begin{longtable}[]{@{}ll@{}}
\toprule
\begin{minipage}[b]{0.47\columnwidth}\raggedright\strut
Total number of drug allergy labels\strut
\end{minipage} & \begin{minipage}[b]{0.47\columnwidth}\raggedright\strut
245,832\strut
\end{minipage}\tabularnewline
\midrule
\endhead
\begin{minipage}[t]{0.47\columnwidth}\raggedright\strut
\textbf{Drug class/name}\strut
\end{minipage} & \begin{minipage}[t]{0.47\columnwidth}\raggedright\strut
\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.48\columnwidth}\raggedright\strut
Beta-lactams, n (\%)\strut
\end{minipage} & \begin{minipage}[t]{0.48\columnwidth}\raggedright\strut
143,925 (58.5)\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.48\columnwidth}\raggedright\strut
Tetracyclines, n (\%)\strut
\end{minipage} & \begin{minipage}[t]{0.48\columnwidth}\raggedright\strut
22,711 (9.24)\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.48\columnwidth}\raggedright\strut
Anti-fungals, n (\%)\strut
\end{minipage} & \begin{minipage}[t]{0.48\columnwidth}\raggedright\strut
17,797 (7.2)\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.48\columnwidth}\raggedright\strut
Macrolides, n (\%)\strut
\end{minipage} & \begin{minipage}[t]{0.48\columnwidth}\raggedright\strut
17,301 (7.0)\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.48\columnwidth}\raggedright\strut
Quinolones, n (\%)\strut
\end{minipage} & \begin{minipage}[t]{0.48\columnwidth}\raggedright\strut
16,844 (6.9)\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.48\columnwidth}\raggedright\strut
Nitroimidazole, n (\%)\strut
\end{minipage} & \begin{minipage}[t]{0.48\columnwidth}\raggedright\strut
5,975 (2.4)\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.48\columnwidth}\raggedright\strut
Amphenicols, n (\%)\strut
\end{minipage} & \begin{minipage}[t]{0.48\columnwidth}\raggedright\strut
4,670 (1.9)\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.48\columnwidth}\raggedright\strut
Aminoglycosides, n (\%)\strut
\end{minipage} & \begin{minipage}[t]{0.48\columnwidth}\raggedright\strut
3,803 (1.5)\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.48\columnwidth}\raggedright\strut
Glycopeptides, n (\%)\strut
\end{minipage} & \begin{minipage}[t]{0.48\columnwidth}\raggedright\strut
2,591 (1.1)\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.48\columnwidth}\raggedright\strut
Other anti-infectives, n (\%)\strut
\end{minipage} & \begin{minipage}[t]{0.48\columnwidth}\raggedright\strut
2,529 (1.0)\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.48\columnwidth}\raggedright\strut
Anti-virals, n (\%)\strut
\end{minipage} & \begin{minipage}[t]{0.48\columnwidth}\raggedright\strut
2,107 (0.9)\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.48\columnwidth}\raggedright\strut
Anti-mycobacterial, n (\%)\strut
\end{minipage} & \begin{minipage}[t]{0.48\columnwidth}\raggedright\strut
1,928 (0.8)\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.48\columnwidth}\raggedright\strut
Lincosamides, n (\%)\strut
\end{minipage} & \begin{minipage}[t]{0.48\columnwidth}\raggedright\strut
1,751 (0.7)\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.48\columnwidth}\raggedright\strut
Anti-protozoals, n (\%)\strut
\end{minipage} & \begin{minipage}[t]{0.48\columnwidth}\raggedright\strut
1,287 (0.5)\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.48\columnwidth}\raggedright\strut
Sulphonamides, n (\%)\strut
\end{minipage} & \begin{minipage}[t]{0.48\columnwidth}\raggedright\strut
531 (0.3)\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.48\columnwidth}\raggedright\strut
Anti-helminths, n (\%)\strut
\end{minipage} & \begin{minipage}[t]{0.48\columnwidth}\raggedright\strut
82 (0.0)\strut
\end{minipage}\tabularnewline
\bottomrule
\end{longtable}

\textbf{Table 2: Reported culprits of drug-induced anaphylaxis according
to drug category}\selectlanguage{english}
\begin{longtable}[]{@{}ll@{}}
\toprule
\begin{minipage}[b]{0.47\columnwidth}\raggedright\strut
Total number of patients with reported drug-induced anaphylaxis\strut
\end{minipage} & \begin{minipage}[b]{0.47\columnwidth}\raggedright\strut
1325\strut
\end{minipage}\tabularnewline
\midrule
\endhead
\begin{minipage}[t]{0.47\columnwidth}\raggedright\strut
\textbf{Drug class/name}\strut
\end{minipage} & \begin{minipage}[t]{0.47\columnwidth}\raggedright\strut
\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.48\columnwidth}\raggedright\strut
Anti-infectives, n (\%)\strut
\end{minipage} & \begin{minipage}[t]{0.48\columnwidth}\raggedright\strut
544 (41.1)\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.47\columnwidth}\raggedright\strut
Beta-lactams, n (\%)\strut
\end{minipage} & \begin{minipage}[t]{0.47\columnwidth}\raggedright\strut
402 (30.3)\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.47\columnwidth}\raggedright\strut
Quinolones, n (\%)\strut
\end{minipage} & \begin{minipage}[t]{0.47\columnwidth}\raggedright\strut
30 (2.3)\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.47\columnwidth}\raggedright\strut
Macrolides, n (\%)\strut
\end{minipage} & \begin{minipage}[t]{0.47\columnwidth}\raggedright\strut
25 (1.9)\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.47\columnwidth}\raggedright\strut
Aminoglycosides, n (\%)\strut
\end{minipage} & \begin{minipage}[t]{0.47\columnwidth}\raggedright\strut
24 (1.8)\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.47\columnwidth}\raggedright\strut
Glycopeptides, n (\%)\strut
\end{minipage} & \begin{minipage}[t]{0.47\columnwidth}\raggedright\strut
19 (1.4)\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.47\columnwidth}\raggedright\strut
Anti-protozoals, n (\%)\strut
\end{minipage} & \begin{minipage}[t]{0.47\columnwidth}\raggedright\strut
13 (1.0)\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.47\columnwidth}\raggedright\strut
Sulphonamides, n (\%)\strut
\end{minipage} & \begin{minipage}[t]{0.47\columnwidth}\raggedright\strut
9 (0.7)\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.47\columnwidth}\raggedright\strut
Tetracyclines, n (\%)\strut
\end{minipage} & \begin{minipage}[t]{0.47\columnwidth}\raggedright\strut
9 (0.7)\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.47\columnwidth}\raggedright\strut
Anti-fungals, n (\%)\strut
\end{minipage} & \begin{minipage}[t]{0.47\columnwidth}\raggedright\strut
4 (0.3)\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.47\columnwidth}\raggedright\strut
Lincosamides, n (\%)\strut
\end{minipage} & \begin{minipage}[t]{0.47\columnwidth}\raggedright\strut
3 (0.2)\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.47\columnwidth}\raggedright\strut
Anti-virals, n (\%)\strut
\end{minipage} & \begin{minipage}[t]{0.47\columnwidth}\raggedright\strut
2 (0.2)\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.47\columnwidth}\raggedright\strut
Anti-mycobacterials, n (\%)\strut
\end{minipage} & \begin{minipage}[t]{0.47\columnwidth}\raggedright\strut
1 (0.1)\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.47\columnwidth}\raggedright\strut
Other anti-infectives, n (\%)\strut
\end{minipage} & \begin{minipage}[t]{0.47\columnwidth}\raggedright\strut
3 (0.2)\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.48\columnwidth}\raggedright\strut
Musculoskeletal System, n (\%)\strut
\end{minipage} & \begin{minipage}[t]{0.48\columnwidth}\raggedright\strut
158 (11.9)\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.48\columnwidth}\raggedright\strut
Nervous System, n (\%)\strut
\end{minipage} & \begin{minipage}[t]{0.48\columnwidth}\raggedright\strut
157 (11.8)\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.48\columnwidth}\raggedright\strut
Anaesthesia, n (\%)\strut
\end{minipage} & \begin{minipage}[t]{0.48\columnwidth}\raggedright\strut
155 (11.7)\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.48\columnwidth}\raggedright\strut
Immune System and Malignant Diseases, n (\%)\strut
\end{minipage} & \begin{minipage}[t]{0.48\columnwidth}\raggedright\strut
99 (7.5)\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.48\columnwidth}\raggedright\strut
Gastrointestinal System, n (\%)\strut
\end{minipage} & \begin{minipage}[t]{0.48\columnwidth}\raggedright\strut
46 (3.5)\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.48\columnwidth}\raggedright\strut
Respiratory System, n (\%)\strut
\end{minipage} & \begin{minipage}[t]{0.48\columnwidth}\raggedright\strut
28 (2.1)\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.48\columnwidth}\raggedright\strut
Blood and Nutrition, n (\%)\strut
\end{minipage} & \begin{minipage}[t]{0.48\columnwidth}\raggedright\strut
25 (1.9)\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.48\columnwidth}\raggedright\strut
Skin, n (\%)\strut
\end{minipage} & \begin{minipage}[t]{0.48\columnwidth}\raggedright\strut
19 (1.4)\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.48\columnwidth}\raggedright\strut
Cardiovascular system, n (\%)\strut
\end{minipage} & \begin{minipage}[t]{0.48\columnwidth}\raggedright\strut
19 (1.4)\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.48\columnwidth}\raggedright\strut
Eye, n (\%)\strut
\end{minipage} & \begin{minipage}[t]{0.48\columnwidth}\raggedright\strut
12 (0.9)\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.48\columnwidth}\raggedright\strut
Endocrine System, n (\%)\strut
\end{minipage} & \begin{minipage}[t]{0.48\columnwidth}\raggedright\strut
10 (0.8)\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.48\columnwidth}\raggedright\strut
Ear, Nose and Throat, n (\%)\strut
\end{minipage} & \begin{minipage}[t]{0.48\columnwidth}\raggedright\strut
6 (0.5)\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.48\columnwidth}\raggedright\strut
Vaccines, n (\%)\strut
\end{minipage} & \begin{minipage}[t]{0.48\columnwidth}\raggedright\strut
4 (0.3)\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.48\columnwidth}\raggedright\strut
Genito-urinary system, n (\%)\strut
\end{minipage} & \begin{minipage}[t]{0.48\columnwidth}\raggedright\strut
1 (0.1)\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.48\columnwidth}\raggedright\strut
Emergency treatment of poisoning, n (\%)\strut
\end{minipage} & \begin{minipage}[t]{0.48\columnwidth}\raggedright\strut
1 (0.1)\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.48\columnwidth}\raggedright\strut
Unknown drug culprit, n (\%)\strut
\end{minipage} & \begin{minipage}[t]{0.48\columnwidth}\raggedright\strut
40 (3.0)\strut
\end{minipage}\tabularnewline
\bottomrule
\end{longtable}

\textbf{Table 3: Reported culprits of Stevens-Johnson syndrome according
to drug category}\selectlanguage{english}
\begin{longtable}[]{@{}ll@{}}
\toprule
\begin{minipage}[b]{0.47\columnwidth}\raggedright\strut
\textbf{Total number of patients with reported Stevens-Johnson
syndrome}\strut
\end{minipage} & \begin{minipage}[b]{0.47\columnwidth}\raggedright\strut
\textbf{1706}\strut
\end{minipage}\tabularnewline
\midrule
\endhead
\begin{minipage}[t]{0.47\columnwidth}\raggedright\strut
\textbf{Drug class/name}\strut
\end{minipage} & \begin{minipage}[t]{0.47\columnwidth}\raggedright\strut
\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.48\columnwidth}\raggedright\strut
Anti-infectives, n (\%)\strut
\end{minipage} & \begin{minipage}[t]{0.48\columnwidth}\raggedright\strut
735 (43.1)\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.47\columnwidth}\raggedright\strut
Beta-lactams, n (\%)\strut
\end{minipage} & \begin{minipage}[t]{0.47\columnwidth}\raggedright\strut
348 (20.4)\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.47\columnwidth}\raggedright\strut
Sulphonamides, n (\%)\strut
\end{minipage} & \begin{minipage}[t]{0.47\columnwidth}\raggedright\strut
81 (4.7)\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.47\columnwidth}\raggedright\strut
Tetracyclines, n (\%)\strut
\end{minipage} & \begin{minipage}[t]{0.47\columnwidth}\raggedright\strut
70 (4.1)\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.47\columnwidth}\raggedright\strut
Quinolones, n (\%)\strut
\end{minipage} & \begin{minipage}[t]{0.47\columnwidth}\raggedright\strut
64 (3.8)\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.47\columnwidth}\raggedright\strut
Macrolides, n (\%)\strut
\end{minipage} & \begin{minipage}[t]{0.47\columnwidth}\raggedright\strut
50 (2.9)\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.47\columnwidth}\raggedright\strut
Anti-mycobacterials, n (\%)\strut
\end{minipage} & \begin{minipage}[t]{0.47\columnwidth}\raggedright\strut
31 (1.8)\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.47\columnwidth}\raggedright\strut
Anti-protozoals, n (\%)\strut
\end{minipage} & \begin{minipage}[t]{0.47\columnwidth}\raggedright\strut
22 (1.3)\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.47\columnwidth}\raggedright\strut
Aminoglycosides, n (\%)\strut
\end{minipage} & \begin{minipage}[t]{0.47\columnwidth}\raggedright\strut
21 (1.2)\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.47\columnwidth}\raggedright\strut
Anti-virals, n (\%)\strut
\end{minipage} & \begin{minipage}[t]{0.47\columnwidth}\raggedright\strut
14 (0.8)\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.47\columnwidth}\raggedright\strut
Glycopeptides, n (\%)\strut
\end{minipage} & \begin{minipage}[t]{0.47\columnwidth}\raggedright\strut
12 (0.7)\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.47\columnwidth}\raggedright\strut
Anti-fungals, n (\%)\strut
\end{minipage} & \begin{minipage}[t]{0.47\columnwidth}\raggedright\strut
8 (0.5)\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.47\columnwidth}\raggedright\strut
Lincosamides, n (\%)\strut
\end{minipage} & \begin{minipage}[t]{0.47\columnwidth}\raggedright\strut
7 (0.4)\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.47\columnwidth}\raggedright\strut
Other anti-infectives, n (\%)\strut
\end{minipage} & \begin{minipage}[t]{0.47\columnwidth}\raggedright\strut
7 (0.4)\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.48\columnwidth}\raggedright\strut
Nervous System, n (\%)\strut
\end{minipage} & \begin{minipage}[t]{0.48\columnwidth}\raggedright\strut
390 (22.9)\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.48\columnwidth}\raggedright\strut
Musculoskeletal System, n (\%)\strut
\end{minipage} & \begin{minipage}[t]{0.48\columnwidth}\raggedright\strut
348 (20.4)\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.48\columnwidth}\raggedright\strut
Cardiovascular system, n (\%)\strut
\end{minipage} & \begin{minipage}[t]{0.48\columnwidth}\raggedright\strut
55 (3.2)\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.48\columnwidth}\raggedright\strut
Gastrointestinal System, n (\%)\strut
\end{minipage} & \begin{minipage}[t]{0.48\columnwidth}\raggedright\strut
39 (2.3)\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.48\columnwidth}\raggedright\strut
Respiratory System, n (\%)\strut
\end{minipage} & \begin{minipage}[t]{0.48\columnwidth}\raggedright\strut
33 (1.9)\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.48\columnwidth}\raggedright\strut
Anaesthesia, n (\%)\strut
\end{minipage} & \begin{minipage}[t]{0.48\columnwidth}\raggedright\strut
24 (1.4)\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.48\columnwidth}\raggedright\strut
Immune System and Malignant Diseases, n (\%)\strut
\end{minipage} & \begin{minipage}[t]{0.48\columnwidth}\raggedright\strut
16 (0.9)\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.48\columnwidth}\raggedright\strut
Ear, Nose and Throat, n (\%)\strut
\end{minipage} & \begin{minipage}[t]{0.48\columnwidth}\raggedright\strut
8 (0.5)\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.48\columnwidth}\raggedright\strut
Eye, n (\%)\strut
\end{minipage} & \begin{minipage}[t]{0.48\columnwidth}\raggedright\strut
7 (0.4)\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.48\columnwidth}\raggedright\strut
Skin, n (\%)\strut
\end{minipage} & \begin{minipage}[t]{0.48\columnwidth}\raggedright\strut
5 (0.3)\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.48\columnwidth}\raggedright\strut
Blood and Nutrition, n (\%)\strut
\end{minipage} & \begin{minipage}[t]{0.48\columnwidth}\raggedright\strut
5 (0.3)\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.48\columnwidth}\raggedright\strut
Genito-urinary system, n (\%)\strut
\end{minipage} & \begin{minipage}[t]{0.48\columnwidth}\raggedright\strut
4 (0.2)\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.48\columnwidth}\raggedright\strut
Vaccines, n (\%)\strut
\end{minipage} & \begin{minipage}[t]{0.48\columnwidth}\raggedright\strut
2 (0.1)\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.48\columnwidth}\raggedright\strut
Endocrine System, n (\%)\strut
\end{minipage} & \begin{minipage}[t]{0.48\columnwidth}\raggedright\strut
2 (0.1)\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.48\columnwidth}\raggedright\strut
Unknown Aetiology, n (\%)\strut
\end{minipage} & \begin{minipage}[t]{0.48\columnwidth}\raggedright\strut
33 (1.9)\strut
\end{minipage}\tabularnewline
\bottomrule
\end{longtable}

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