Skip to main content
Download PDF
  • Research article
  • Open access
  • Published:

The association of herpes zoster and influenza vaccinations with the risk of developing dementia: a population-based cohort study within the UK Clinical Practice Research Datalink

BMC Public Health volume 23, Article number: 1903 (2023) Cite this article

Abstract

Background

Dementia affects ability to remember, think, or make decisions that interfere with doing everyday activities. There is no cure, therefore any prevention or delay of the onset is of importance. This study aims to investigate the association between zoster and influenza vaccinations and the risk of developing dementia.

Methods

We conducted a retrospective population-based cohort study using electronic health records from 1469 general practices contributing to the Clinical Practice Research Datalink (CPRD) Aurum database with linked hospital episode statistics (HES) and Office for National Statistics (ONS) mortality records. We built two 'matched cohorts': zoster vaccine (854,745 exposed individuals) matched with 8.8 million comparators without a history of zoster vaccination, and influenza vaccine (742,487 exposed individuals) matched with 7.12 million comparators without a history of vaccination as another comparator group. The cohorts were then followed to assess the association of exposure (vaccine) with outcome (dementia diagnosis).

Results

Zoster vaccination was associated with a lower risk of dementia diagnosis (adjusted hazard ratio (HR) 0.78 with 95% CI: 0.77–0.79), Alzheimer’s diagnosis (adjusted HR 0.91 with 95% CI: 0.89–0.92 and other types of dementia (adjusted HR 0.71 with 95% CI: 0.69–0.72). Influenza vaccination also was associated with a slightly reduced hazard of dementia risk (adjusted HR 0.96 with 95% CI: 0.94–0.97).

Conclusion

Both zoster vaccine for prevention of shingles / herpes zoster and influenza vaccine to prevent influenza were associated with diminished risk of dementia, with the zoster association appearing more pronounced.

Peer Review reports

Background

Globally, it has been estimated that the number of people with dementia would increase from 57.4 million cases in 2019 to 152.8 (130.8–175.9) million cases in 2050 [1]. Dementia affects 850,000 people in the UK. It has been estimated to rise to one million by 2025. The National Health Service costs for its management are also expected to double by 2050 [2]. In addition to lifestyle and genetic factors, epidemiological studies have linked infection by various types of virus with dementia [3,4,5,6,7]. In recent years, several studies have investigated the effect of herpes zoster (HZ also known as shingles) on dementia risk [8,9,10,11,12,13,14], and in 2021, we reported a reduction between zoster vaccination and dementia risk using data from the UK Biobank study (OR: 0.81 with 95% CI: 0.66 to 0.99) [12]. Also in 2021, a study analysed medical health record data in a large Veterans Health Affairs (VHA) cohort and replicated their analysis in another cohort comprisingMarketScan® commercial and Medicare claims. The authors reported a significant association with lower dementia risk in both cohorts (VHA HR = 0.69; 95% CI: 0.67–0.72; MarketScan HR = 0.65; 95% CI: 0.57–0.74) [14]. Another recent study used data from The Behavioral Risk Factor Surveillance System (BRFSS) to evaluate the relationship between HZ vaccination and cognitive impairment. They also reported that herpes zoster vaccination reduces the risk of dementia [15]. In 2022, a meta-analysis and systematic review of seven studies with 1,857,134 participants from population-based observational studies reported overall pooled results indicating that several common types of vaccine, including those against HZ and influenza were associated with lower dementia risk (HR = 0.65, 95% CI: 0.60–0.71, P-value overall effect < 0.001; I2 = 91.8%, P-value heterogeneity < 0.001) [16]. Questions remain as to whether residual confounding is affecting all of these observational studies and also as to whether there is a specific effect of vaccinations against herpes zoster or a non-specific immune stimulatory effect of multiple vaccinations. It is against this background that we sought to explore the separate effects of both zoster and flu vaccination in a large nationally representative dataset with time dependent adjustment for potential confounders including that of prescribed anti-viral medications.

In the UK, shingles vaccination has been offered routinely by the National Health Service (NHS) since 2013 for people aged 70–80 years. In this study, we aimed to investigate the association between zoster vaccination for shingles and dementia risk using data from the Clinical Practice Research Datalink (CPRD) Aurum database. As influenza (flu) vaccination is also common in the UK, we explored the association between influenza vaccination and dementia risk, which we considered to represent a comparator.

Methodology

Data sources

We conducted retrospective matched cohort studies using primary care electronic health records from the Clinical Practice Research Datalink (CPRD) Aurum database obtained under licence from the UK Medicines and Healthcare products Regulatory Agency (MHRA) [17, 18]. The CPRD contains anonymised consultation records and includes patient demographic information, symptoms, diagnoses, medication prescriptions, and vaccination data. In June 2021, there were 1360 general practices using the EMIS clinical platform that were based in England and contributing data to CPRD Aurum [19]. We restricted our cohorts to patients from these practices as data linkage is only available for English practices that participate in the CPRD linkage scheme, linking data for all eligible patients with a valid National Health Service (NHS) identifier. We utilised linkages between CPRD Aurum and external data sources. Inpatient Hospital Episode Statistics (HES) were used to augment capture of diagnoses of dementia (as both an outcome and an exclusion factor) and comorbidities. Data from the Office for National Statistics (ONS) were used to ascertain specific causes of death according to the 10th revision of the International Classification of Diseases (ICD-10). Individual patients are entitled to opt out of the linkage scheme.

Study cohorts

Cohort members were aged 70 years and over, were registered in a practice contributing to CPRD Aurum, and were administered their first zoster vaccination between 1st January 2013 and 31st October 2020 (identified using the product codes listed in Additional file 1 and the clinical Read/SNOMED/EMIS codes listed in Additional file 2). We did not include the Shingrix vaccine because it was not in use in the contributing CPRD practices during our study period (the end of the study period was 31st October 2020). Henceforth, we define a patient’s index date as the date of the first recorded evidence that a relevant vaccination occurred. We included patients who had been registered with their general practice for at least one year and had no recorded history of dementia at that time. Follow-up ended on the first to occur of dementia diagnosis (in primary or secondary care); death; migration from general practice; the end of data collection for the practice; the end of the study period (31st October 2020).

Using incidence density sampling, a representative comparison cohort was created whereby each patient with an incident vaccination was matched with up to 10 comparators without a history of zoster vaccination to create a ‘matched set’. Requiring the comparators to have been unaffected by any form of dementia prior to the index date of the 'case', we matched the patients on age, gender, and registered general practice. The same registration and practice contribution criteria were applied when sampling patients for the comparison cohort. The process of matching was as follows. Each vaccinated patient was matched on sex, age (within 3 years), and general practice with up to 10 registered comparators on the date of vaccination. The comparators were required to have no history of the vaccination in question. Matching was undertaken using the nearest neighbour approach (via the minimisation of the Mahalanobis distance [20]). In the event of ties, random selection was used. The distribution of the number of comparators in the matched sets can be found in Additional file 3. To update vaccine status, patients who featured as comparators (without a history of the relevant vaccination type) could subsequently go on to become a case if they received the vaccine at a later date. At this point, their follow-up time as a comparator would be terminated. The influenza vaccination cohort was delineated in similar way to zoster vaccination cohort. Influenza vaccine identified using the product codes listed in Additional file 4 and the clinical Read/SNOMED/EMIS codes listed in Additional file 5. In this study, repeat vaccinations are not incorporated. Once a patient has a relevant vaccine in their record, they are defined as a ‘case’ and their exposure status is described using a dichotomous variable: “Ever exposed to zoster vaccine? = YES”.

Dementia diagnoses

Diagnoses of dementia were identified in the primary care records using the Read/SNOMED/EMIS codes listed in Additional file 6, and in the HES and ONS mortality records using the International Classification of Diseases 10th Revision (ICD-10) codes listed in Additional file 7. We also looked separately at diagnoses of Alzheimer’s disease (AD) and other forms of dementia (an identifier is included in the code lists). Dementia diagnosis dates were used as both the study outcome and for exclusions from the study cohort if the diagnosis was recorded prior to vaccination or matching (for the comparison group).

Statistical analyses

All data processing and statistical analyses were conducted using Stata V.16 (StataCorp, College Station, Texas, USA). To compare the prevalence of comorbidities at baseline in the vaccinated patients and their matched comparators, we used conditional Poisson regression. For the specific forms of dementia (Alzheimer’s disease and 'other' dementia) and for the composite outcome ('any dementia'), we estimated the relative risk of onset using Cox regression models with stratification on the matched sets of exposed individuals and comparators. The proportional hazards assumption was formally assessed using the Grambsch–Therneau test [21] and graphical diagnostics were performed using scaled Schoenfeld residuals [22]. Stratified analyses were conducted according to gender and age-band.

By virtue of the matched cohort study design, the estimated hazard ratios (HRs) were naturally adjusted for the potentially confounding effects of age, gender, and practice-level factors. We also applied a series of adjustments on patient-level socio-economic status, ethnicity, indicators for the conditions included in the Charlson Comorbidity Index (CCI) (treated in separate models as baseline comorbidities and as time-dependent confounders) [23], and varicella-zoster antiviral status (again, treated as a baseline risk factor and as a time-dependent covariate in separate models). Read/SNOMED/EMIS codes used to identify the comorbidities included in the CCI can be found in Additional file 8. The corresponding ICD-10 codes can be found in Additional file 9. The codes for antiviral treatments can be found in Additional file 10.

Patient-level socioeconomic status was measured, according to patient postcodes, using English Indices of Multiple Deprivation (IMD) quintiles [24], a measure representing an area’s relative level of deprivation, ranked within England. Ethnicity was classified from primary care and Hospital Episode Statistics (HES) records as white/Asian/black/mixed/other. The relevant Read/SNOMED/EMIS code list can be found in Additional file 11. For patients with multiple records and conflicts, we defined ethnicity as (a) the category with most corresponding records; (b) according to the last available record when record numbers were tied across two or more categories; and (c) by randomly selecting between candidate ethnicities when record numbers were tied across two or more categories and multiple categories were entered in the last available record.

We fitted 3 different models.

  • Model 1: Adjusted for age, gender, and general practice factors (based on matched design).

  • Model 2: Further adjustments for IMD, ethnicity, and time-dependent indicators for the conditions in the Charlson Comorbidity Index.

  • Model 3: Further adjustments for IMD, ethnicity, time-dependent indicators for the conditions in the Charlson Comorbidity Index, and time-dependent antiviral status.

Sensitivity analyses

We used the E-value to demonstrate the minimum strength of association that an unmeasured confounder would need to have with both the exposure and the outcome to fully explain away a specific exposure–outcome association, conditional on the measured covariates. The E-value can be calculated for an observed risk ratio (denoted RR) by E-value = \(RR+\sqrt{RR\times RR-1}\)| If the original risk ratio is below 1, then the inverse is taken before applying the E-value formula. This formula can also be used for hazard ratio [25]. The lowest possible E-value is 1 (i.e. no unmeasured confounding is needed to explain away the observed association). The higher the E-value the stronger the confounder associations would have to be to explain away the effect [26]. Finally, we took a further step to assess the potential for residual unmeasured confounding of our exposure–outcome association measures, by conducting a set of comparator analyses with influenza vaccination as the exposure of interest. All aspects of the analyses were identical with the exception that the index dates (and subsequent delineation of the matched cohort) were defined by vaccination for influenza, rather than zoster vaccination.

We also carried out sensitivity analysis with left-censoring at 2 years after vaccination (or matching for the comparators) to assess the estimated HRs for a longer period after vaccination.

Patient and public involvement

There was no patient or public involvement in the study including development of the research question, selection of outcome measures, study design, conduct or dissemination of findings.

Results

Table 1 shows the characteristics for both the zoster and influenza vaccine cohorts. The total number of participants in the zoster vaccinated group was 854,745 and approximately 8.8 million participants with no record of zoster vaccination. The total number in the influenza vaccinated group was 742,487 and approximately 7.12 million participants with no record of influenza vaccination. The numbers of people developing dementia in zoster vaccinated and unvaccinated cohorts were 28,012 with a rate of 9.9 per 1000 person-year (95% CI 9.8–10.0) and 197,618 with a rate of 12.1 per 1000 person-year (95% CI 12.1–12.2), respectively. The numbers of people developing dementia in influenza vaccinated and unvaccinated cohorts were 21,931 with a rate of 17.2 per 1000 person-year (95%CI 17.0–17.4) and 120,297 with a rate of 15.8 (95%CI 15.7–15.9). Results can be found in Additional file 12. In both cohorts, the majority of participants are in the age group 70–74 and of white ethnicity (~ 80%). There was also a slightly higher number of females than males in both cohorts. Around a quarter of participants were from areas in the least deprived quintile. The distribution of IMD was similar between zoster and influenza vaccination cohorts. The highest prevalence ratio of co-morbidity in both zoster and influenza cohort – comparison of the prevalence between 'vaccinated group' VS 'comparator (non-vaccinated) group’—was diabetes with chronic complications (1.12 and 1.19). Similar results were seen with anti-viral treatments (1.06 for the zoster cohort and 1.10 for the influenza cohort). The median follow-up times in the zoster vaccine exposed individuals and the unvaccinated comparator group were 3.07 yrs (IQR: 1.73, 5.04) and 3.01 yrs (IQR: 1.57, 4.99). The median follow-up times in the influenza vaccine exposed individuals and the unvaccinated comparator group were 1.08 yrs (IQR: 0.98, 2.07) and 1.08 yrs (IQR: 0.97, 2.07). Table 2 showed the hazard ratio of developing dementia and subtypes with zoster vaccination as an exposure. Results from all models suggested zoster vaccination was significantly associated with a reduced risk of developing dementia. The fully adjusted model (model 3: adjusted for IMD, ethnicity, time-dependent indicators for the conditions in the Charlson Comorbidity Index, and time-dependent antiviral status) produced a hazard ratio of 0.78 (with 95% CI: 0.77–0.79) and for other types of dementia (HR: 0.71; 95% CI: 0.69–0.72). The reduced HR was less pronounced in AD (HR: 0.91; 95% CI: 0.89–0.92). The results from the stratified analysis by gender suggested similar patterns as seen in non-stratified analysis. However, the HRs for males were slightly higher than those for females. The HRs for all types of dementia decreased with increasing age group.

Table 1 General characteristics of vaccinated and unvaccinated groups based on vaccination type
Full size table
Table 2 Hazard ratio of developing dementia according to zoster vaccination
Full size table

In the influenza vaccination cohort (Table 3), the results from the fully adjusted model (model 3) also suggested a slightly reduced hazard ratio (HR) risk of AD and other types of dementia (HR 0.96 with 95% of 0.94–0.97 and HR 0.89 with 95% CI of 0.87–0.90, respectively) but not with AD (HR 1.10 with 95% CI of 1.07–1.12). Results similar to those reported in zoster were seen with gender. In age subgroup analyses, results were inconsistent as reported in zoster vaccination. Moreover, non-significant HRs were observed in age group 70–74, 75–79 and 90 + .

Table 3 Hazard ratio of developing dementia according to influenza vaccination
Full size table

In the zoster cohort, the smallest E-value was seen in AD (1.44 with E-value closet to 1 equal to 1.38). Similar patterns were also applied to stratified analyses by gender and age group (Table 2). In the influenza cohort, a smaller E-value was found (1.26 with E-value closet to 1 of 1.20) (Table 3).

Table 4 showed results of HRs of developing dementia according to zoster and influenza vaccination from the sensitivity analysis for the left censoring at 2 years of follow-up. HRs for all models showed similar pattern although slightly attenuated.

Table 4 Hazard ratio of developing dementia according to zoster and influenza vaccination—sensitivity analysis with left-censoring at 2 years of follow-up
Full size table

Discussion

In this study, we investigated the effect of zoster vaccination on dementia risk in a large UK population-based data, the CPRD. We reported an inverse association between zoster vaccination and dementia outcome in our fully adjusted model (HR 0.78, 95% CI: 0.77–0.79). For Alzheimer’s disease, the effect size is smaller (HR 0.91, 95% CI: 0.89–0.92) however this result is likely only significant because of the very large sample size involved. The magnitude is negligible. To investigate if the result seen was exclusive to HZ vaccination, we also explored the effect of influenza vaccine and dementia/Alzheimer’s disease. We found a slight decreased hazard risk with HR of 0.96 (95% CI: 0.94–0.97) for dementia and HR of 1.10 (95% CI: 1.07–1.12) for Alzheimer’s disease.

A number of population-based studies have suggested a link between herpes zoster infection or vaccine against shingles and dementia [6,7,8,9, 27]. There are, however, other population based-studies that did not show any association between zoster infection and dementia risk [28, 29].

To prevent viral HZ, HZ vaccine stimulates the body’s immune system such that when VZV reactivates, the immune system rapidly recognises it as harmful and will trigger a response to limit the infection.

Our findings are in keeping with other population-based studies. These studies were included in a recent meta-analysis study which was published in 2022 [16]. The study included 17 high quality population-based studies with 1,857,134 participants. The studies investigated various types of vaccine including vaccines targeting HZ. The results suggested a significant decreased risk of dementia by 31% in their subgroup analysis of HZ-vaccinated compared to non-vaccinated subjects. The author reported significant heterogeneity of the included studies; nevertheless, all studies showed a protective effect of HZ vaccination (HR 0.69, 95% CI: 0.67–0.72, P < 0.001). Our study reported a slightly smaller effect of risk reduction. Another study investigated the association of shingles vaccination with incident dementia in Wales between 2013 and 2020 using retrospectively collected national health data. The study showed adjusted hazard ratio: 0.72; 95% CI: 0.69–0.75). The authors suggested selection bias could account for the reduced risk [30]. In contrast, the study using CPRD (both Gold and Aurum dataset) with a population aged ≥ 50 years during 1988–2018 suggested no association between shingles vaccination and dementia (OR 0.95 with 95%CI 0.92–0.98). The fact that shingles vaccination is available in the UK for people aged 70 onwards, by inclusion of people aged 50 could reduce the overall estimated risk. The authors also suggested frailty may also play a role as the study had limited access to data to enable assessment of the frailty of the participants [31]. This was also the case for our study.

Our study derives strength from using a very large UK population-based dataset, CPRD Aurum. The CPRD collects fully coded patient electronic health records from GP practices. The CPRD data are available for research with an approved project. Our analyses accounted for confounding factors that other studies did not adjust for such as time-dependent viral medication and CCI. We explored our fitted model with fixed covariates and time-dependent covariates. The latter approach was recommended based on the assumption that factors may change over the period of time that the subject is observed [32]. The observed association between outcome and time-dependent covariates may be biased if they are not appropriately modelled [33]. In our study, anti-viral treatments and co-morbidity were the two factors that could potentially change over time, therefore fitting the model with these two time-dependent variables could reduce any bias in our findings. Model 1 (fitted with confounding factors including gender, age, and practice-level factors), model 2 (with further adjustments for IMD, ethnicity, and time-dependent indicators for the conditions in the Charlson Comorbidity Index-CCI) and model 3 (with further adjustments for IMD, ethnicity, time-dependent indicators for the conditions in the Charlson Comorbidity Index, and time-dependent antiviral status) results showed similar effects. In this study, we applied an index with an adjustment on indicators for each of the conditions to derive a CCI. Most of the studies in the literature used a combination of selected illnesses. CCI provides a more overview of health status. We also investigated the prevalence ratios of co-morbidity, which were similar between the two cohorts indicating that the potential for selection bias was low.

To enable comparison between zoster and influenza vaccination, we restricted both cohorts for eligibility from year 2013 onwards when zoster vaccination was widely available from the National Health Services. We investigated if a common vaccine such as influenza, when used over the same time period, had a similar effect to zoster vaccine, thereby testing if any vaccine, regardless of its type, showed similar effects. The analysis of influenza vaccination was carried out as a “comparator group” which aimed to help identify and resolve whether there was possible residual confounding [34]. Our results showed that influenza vaccination was also associated with reduction of dementia risk: however the effect was very small (HR 0.96 with 95% C.I 0.94–0.97). Influenza vaccine is routinely offered by the UK National Health Service to adult aged 50 and over and anyone with specific underlying health problems (https://www.nhs.uk/conditions/vaccinations/flu-influenza-vaccine/). Our findings were in-keeping with findings from recent meta-analysis studies. In January 2022, a meta-analysis of influenza vaccination by Veronese et al. reported a significant reduction associated with influenza vaccination and dementia risk (RR = 0.97; 95% CI: 0.94–1.00; I2 = 99%). The authors included 5 studies with 292,157 older people free from dementia at baseline. Each study adjusted for various confounding factors, however none of these studies adjusted for anti-viral treatment and co-morbidity adjustment was different between studies. The authors discussed potential bias introduced by not including the influenza vaccination status during follow-up as a covariate, except in one study [35]. Publication bias was also identified as a source of bias in their meta-analysis. In May 2022, another meta-analysis study investigated various types of vaccines and dementia risk. They reported significant risk reduction in all vaccinations including rabies (HR = 0.43), tetanus & diphtheria & pertussis (HR = 0.69), herpes zoster (HR = 0.69), influenza (HR = 0.74), hepatitis A (HR = 0.78), typhoid (HR = 0.80), and hepatitis B (HR = 0.82) vaccinations [16]. However, the study found that any individuals who received between 1 to 3 influenza vaccinations during the follow-up period had no reduced risk of dementia compared to unvaccinated individuals. Only individuals with at least 4 annual influenza vaccinations had a 49% reduced hazard risk of dementia (HR = 0.51, 95% CI: 0.32–0.80, P = 0.003). The authors addressed unmeasured or residual confounding as a potential bias of the analyses in the original studies. Despite the slightly decreased HR seen in our study, the benefit of influenza vaccine in preventing influenza itself is paramount, particularly in the elderly population.

We also performed an E-value analysis. This particular analysis is proposed as a sensitivity analysis in observational studies to assess the robustness of an association which has potential unmeasured confounders which could potentially bias the results. A large E-value implies that considerable unmeasured confounding would be needed to explain an effect estimate. A small E-value implies little unmeasured confounding would be needed to explain an effect estimate [26]. The E-value results for zoster vaccination using the HR from model 3 produced a value of 1.89 with a lower limit of the confidence interval of 1.85. This indicates that our finding for zoster vaccination of a reduced HR of 0.78 (95%CI 0.77–0.79) is therefore unlikely to be accounted for by unmeasured confounding factors. The result seen for the E-value for the influenza vaccine result (E-value 1.26 with E-value closet to 1 of 1.20), however, suggests that residual confounding could account for the magnitude of the result (0.96 with 95%CI 0.94–0.97)).

To explore if vaccination could directly influence dementia development over longer time periods after vaccination, we carried out sensitivity analysis with left-censoring at 2 years after vaccination (or matching for the comparators). Although slightly attenuated, the pattern of results remains the same.

Our results support the suggestion that there is a specific effect of vaccinations against Herpes zoster rather than a non-specific immune stimulatory effect of other types of vaccinations, given that the influenza vaccination did not show a large effect.

The limitations of our study included potential variation in the definition and use of medical codes between practices [36]. However, we included only practices that were classed as ‘research standard’. In addition, the records of anti-viral treatment could be incomplete due to missing information on over-the-counter medications. Furthermore, selection bias cannot be ruled out. For example, there is a possibility that people who had received vaccinations were more likely to visit a physician than those in the non-vaccinated group, potentially indirectly increasing the chance of dementia detection. We have adjusted for co-morbidity but as for any epidemiological study there remains potential for bias and confounding. For studies of the effect of vaccination, this may pertain in particular to the possibility that the group that takes up vaccination are inherently healthier [37]. In reality, the only way to fully address this in future studies is to undertake a randomised control trial.

Conclusion

In conclusion, our study found a significant reduced hazard of developing dementia in individuals who had had HZ vaccination as compared to non-vaccinated individuals.

Availability of data and materials

The data that support the findings of this study are from CPRD (https://cprd.com/) which were used under license for the current study, and so are not publicly available. Researchers would need to apply directly to CPRD, have a protocol approved and receive data directly from CPRD.

Abbreviations

CPRD:

The Clinical Practice Research Datalink

HES:

Hospital episode statistics

ONS:

Office for National Statistics

HR:

Hazard ratio

95% CI:

95% Confident interval

HZ:

Herpes zoster

VHA:

Veterans Health Affairs

BRFSS:

The Behavioral Risk Factor Surveillance System

MHRA:

Medicines and Healthcare products Regulatory Agency

NHS:

National Health Service

ICD-10:

International Classification of Diseases

SNOMED:

Systematized Nomenclature of Medicine

EMIS:

Egton Medical Information Systems

CCI:

Charlson Comorbidity Index

IMD:

Indices of Multiple Deprivation

IQR:

Interquartile Range

AD:

Alzheimer’s disease

References

  1. GBD 2019 Dementia Forecasting Collaborators. Estimation of the global prevalence of dementia in 2019 and forecasted prevalence in 2050: an analysis for the Global Burden of Disease Study 2019. Lancet Public Health. 2022;7(2):e105–e125. https://doi.org/10.1016/S2468-2667(21)00249-8.

  2. Lancet T. Dementia in the UK: preparing the NHS for new treatments. Lancet (London, England). 2018;391(10127):1237.

    Article  Google Scholar 

  3. Choi HG, Soh JS, Lim JS, Sim SY, Lee SW. Association between dementia and hepatitis B and C virus infection. Medicine. 2021;100(29):e26476.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  4. Chu CS, Tsai SJ, Cheng CM, Su TP, Chen TJ, Bai YM, Liang CS, Chen MH. Dengue and dementia risk: a nationwide longitudinal study. J Infect. 2021;83(5):601–6.

    Article  PubMed  Google Scholar 

  5. Lam JO, Hou CE, Hojilla JC, Anderson AN, Gilsanz P, Alexeeff SE, Levine-Hall T, Hood N, Lee C, Satre DD, et al. Comparison of dementia risk after age 50 between individuals with and without HIV infection. AIDS (London, England). 2021;35(5):821–8.

    Article  CAS  PubMed  Google Scholar 

  6. Tzeng NS, Chung CH, Lin FH, Chiang CP, Yeh CB, Huang SY, Lu RB, Chang HA, Kao YC, Yeh HW, et al. Anti-herpetic medications and reduced risk of dementia in patients with herpes simplex virus infections-a nationwide, population-based cohort study in Taiwan. Neurotherapeutics. 2018;15(2):417–29.

    Article  PubMed  PubMed Central  Google Scholar 

  7. Young-Xu Y, Powell EI, Zwain GM, Yazdi MT, Gui J, Shiner B. Symptomatic herpes simplex virus infection and risk of dementia in US veterans: a cohort study. Neurotherapeutics. 2021;18(4):2458–67.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  8. Bae S, Yun SC, Kim MC, Yoon W, Lim JS, Lee SO, Choi SH, Kim YS, Woo JH, Kim SY, et al. Association of herpes zoster with dementia and effect of antiviral therapy on dementia: a population-based cohort study. Eur Arch Psychiatry Clin Neurosci. 2021;271(5):987–97.

    Article  PubMed  Google Scholar 

  9. Chen VC, Wu SI, Huang KY, Yang YH, Kuo TY, Liang HY, Huang KL, Gossop M. Herpes zoster and dementia: a nationwide population-based cohort study. J Clin Psychiatry. 2018;79(1):16m11312.

    Article  PubMed  Google Scholar 

  10. Choi HG, Park BJ, Lim JS, Sim SY, Jung YJ, Lee SW. Herpes zoster does not increase the risk of neurodegenerative dementia: a case-control study. Am J Alzheimers Dis Other Demen. 2021;36:15333175211006504.

    Article  PubMed  Google Scholar 

  11. Lopatko Lindman K, Hemmingsson ES, Weidung B, Brännström J, Josefsson M, Olsson J, Elgh F, Nordström P, Lövheim H. Herpesvirus infections, antiviral treatment, and the risk of dementia-a registry-based cohort study in Sweden. Alzheimers Dement (New York, N Y). 2021;7(1):e12119.

    Article  Google Scholar 

  12. Lophatananon A, Mekli K, Cant R, Burns A, Dobson C, Itzhaki R, Muir K. Shingles, Zostavax vaccination and risk of developing dementia: a nested case-control study-results from the UK Biobank cohort. BMJ Open. 2021;11(10):e045871.

    Article  PubMed  PubMed Central  Google Scholar 

  13. Tsai MC, Cheng WL, Sheu JJ, Huang CC, Shia BC, Kao LT, Lin HC. Increased risk of dementia following herpes zoster ophthalmicus. PLoS One. 2017;12(11):e0188490.

    Article  PubMed  PubMed Central  Google Scholar 

  14. Scherrer JF, Salas J, Wiemken TL, Hoft DF, Jacobs C, Morley JE. Impact of herpes zoster vaccination on incident dementia: a retrospective study in two patient cohorts. PLoS One. 2021;16(11):e0257405.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  15. Lehrer S, Rheinstein PH. Herpes zoster vaccination reduces risk of dementia. In vivo (Athens, Greece). 2021;35(6):3271–5.

    CAS  PubMed  Google Scholar 

  16. Wu X, Yang H, He S, Xia T, Chen D, Zhou Y, Liu J, Liu M, Sun Z. Adult vaccination as a protective factor for dementia: a meta-analysis and systematic review of population-based observational studies. Front Immunol. 2022;13:872542.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  17. Wolf A, Dedman D, Campbell J, Booth H, Lunn D, Chapman J, Myles P. Data resource profile: clinical practice research datalink (CPRD) Aurum. Int J Epidemiol. 2019;48(6):1740–1740g.

    Article  PubMed  PubMed Central  Google Scholar 

  18. Padmanabhan S, Carty L, Cameron E, Ghosh RE, Williams R, Strongman H. Approach to record linkage of primary care data from clinical practice research datalink to other health-related patient data: overview and implications. Eur J Epidemiol. 2019;34(1):91–9.

    Article  PubMed  Google Scholar 

  19. CPRD. Release Notes: CPRD Aurum June 2021. 2021.

    Google Scholar 

  20. Rubin DB. Bias reduction using Mahalanobis-metric matching. Biometrics. 1980;36(2):293–8.

    Article  Google Scholar 

  21. Grambsch PM, Therneau TM. Proportional hazards tests and diagnostics based on weighted residuals. Biometrika. 1994;81(3):515–26.

    Article  Google Scholar 

  22. Schoenfeld D. Partial residuals for the proportional hazards regression model. Biometrika. 1982;69(1):239–41.

    Article  Google Scholar 

  23. Charlson ME, Pompei P, Ales KL, MacKenzie CR. A new method of classifying prognostic comorbidity in longitudinal studies: development and validation. J Chronic Dis. 1987;40(5):373–83.

    Article  CAS  PubMed  Google Scholar 

  24. Ministry of Housing Communities & Local Government: English indices of deprivation 2015. In.: National Statistics. 2015.

  25. Mathur MB, Ding P, Riddell CA, VanderWeele TJ. Web Site and R package for computing E-values. Epidemiology. 2018;29(5):e45–7.

    Article  PubMed  PubMed Central  Google Scholar 

  26. VanderWeele TJ, Ding P. Sensitivity analysis in observational research: introducing the E-Value. Ann Intern Med. 2017;167(4):268–74.

    Article  PubMed  Google Scholar 

  27. Lopatko Lindman K, Hemmingsson E-S, Weidung B, Brännström J, Josefsson M, Olsson J, Elgh F, Nordström P, Lövheim H. Herpesvirus infections, antiviral treatment, and the risk of dementia—a registry-based cohort study in Sweden. Alzheimer’s Dementia. 2021;7(1):e12119.

    Article  PubMed  PubMed Central  Google Scholar 

  28. Johannesdottir Schmidt SA, Veres K, Sørensen HT, Obel N, Henderson VW. Incident herpes zoster and risk of dementia: a population-based Danish cohort study. Neurology. 2022;99(7):e660-668.

    Article  PubMed  Google Scholar 

  29. Warren-Gash C, Williamson E, Shiekh SI, Borjas-Howard J, Pearce N, Breuer JM, Smeeth L. No evidence that herpes zoster is associated with increased risk of dementia diagnosis. Ann Clin Transl Neurol. 2022;9(3):363–74.

    Article  PubMed  PubMed Central  Google Scholar 

  30. Schnier C, Janbek J, Lathe R, Haas J. Reduced dementia incidence after varicella zoster vaccination in Wales 2013–2020. Alzheimer’s Dementia (New York, N Y). 2022;8(1):e12293.

    Article  PubMed  Google Scholar 

  31. Douros A, Ante Z, Suissa S, Brassard P. Common vaccines and the risk of incident dementia: a population-based cohort study. J Infect Dis. 2022;227(11):1227–36.

  32. Fisher LD, Lin DY. Time-dependent covariates in the cox proportional-hazards regression model. Annu Rev Public Health. 1999;20(1):145–57.

    Article  CAS  PubMed  Google Scholar 

  33. Ngwa JS, Cabral HJ, Cheng DM, Pencina MJ, Gagnon DR, LaValley MP, Cupples LA. A comparison of time dependent Cox regression, pooled logistic regression and cross sectional pooling with simulations and an application to the Framingham Heart Study. BMC Med Res Methodol. 2016;16(1):148.

    Article  PubMed  PubMed Central  Google Scholar 

  34. Lipsitch M, Tchetgen Tchetgen E, Cohen T. Negative controls: a tool for detecting confounding and bias in observational studies. Epidemiology. 2010;21(3):383–8.

    Article  PubMed  PubMed Central  Google Scholar 

  35. Veronese N, Demurtas J, Smith L, Michel JP, Barbagallo M, Bolzetta F, Noale M, Maggi S. Influenza vaccination reduces dementia risk: a systematic review and meta-analysis. Ageing Res Rev. 2022;73:101534.

    Article  CAS  PubMed  Google Scholar 

  36. Tai TW, Anandarajah S, Dhoul N, de Lusignan S. Variation in clinical coding lists in UK general practice: a barrier to consistent data entry? Inform Prim Care. 2007;15(3):143–50.

    PubMed  Google Scholar 

  37. Simonsen L, Taylor RJ, Viboud C, Miller MA, Jackson LA. Mortality benefits of influenza vaccination in elderly people: an ongoing controversy. Lancet Infect Dis. 2007;7(10):658–66.

    Article  PubMed  Google Scholar 

Download references

Acknowledgements

This study was conducted using CPRD Aurum data obtained under licence from the UK Medicines and Healthcare products Regulatory Agency. The data are provided by patients and collected by the NHS as part of their care and support. The Office for National Statistics is the provider of the ONS data contained within the CPRD data. Hospital Episode Data and the ONS data, ©2022, are reused with the permission of NHS Digital. All rights reserved. The study was approved by the CPRD's Independent Scientific Advisory Committee (protocol 20_194). The views expressed are those of the authors and not necessarily those of the MHRA, the National Institute for Health and Care Research (NIHR), the NHS or the Department of Health and Social Care (DHSC).

Funding

We would like to thank the Advantage Foundation for providing funding to carry out this study. Data access for this study was funded by the University of Manchester. MC and DMA are funded by the National Institute of Health and Care Research (NIHR) Greater Manchester Patient Safety Translational Research Centre (PSTRC-2016–003). The funding bodies played no role in the design of the study and collection, analysis, and interpretation of data and in writing the manuscript.

Author information

Author notes

  1. Darren M. Ashcroft and Kenneth R. Muir are joint senior authors.

  2. Artitaya Lophatananon and Matthew Carr are joint first authors.

Authors and Affiliations

  1. Division of Population Health, Health Services Research and Primary Care, School of Health Sciences, Faculty of Biology, Medicine and Health, The University of Manchester, Manchester, M13 9PT, UK

    Artitaya Lophatananon, Brian Mcmillan & Kenneth R. Muir

  2. Centre for Pharmacoepidemiology and Drug Safety, Division of Pharmacy & Optometry, School of Health Sciences, Faculty of Biology, Medicine and Health, The University of Manchester, M13 9PT, Manchester, UK

    Matthew Carr, Curtis Dobson & Darren M. Ashcroft

  3. NIHR Greater Manchester Patient Safety Translational Research Centre (PSTRC), School of Health Sciences, Faculty of Biology, Medicine and Health, The University of Manchester, Manchester, M13 9PT, UK

    Matthew Carr & Darren M. Ashcroft

  4. The Oxford Institute of Population Ageing, University of Oxford, Oxford, OX2 6PR, UK

    Ruth Itzhaki

  5. Division of Informatics, Imaging & Data Sciences, School of Health Sciences, Faculty of Biology, Medicine and Health, The University of Manchester, Manchester, M13 9PT, UK

    Rosa Parisi

Authors
  1. Artitaya Lophatananon
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Matthew Carr
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Brian Mcmillan
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Curtis Dobson
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Ruth Itzhaki
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Rosa Parisi
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Darren M. Ashcroft
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Kenneth R. Muir
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

AL and MC were responsible for the data management, the statistical analysis and drafted the manuscript. BM, DMA and MC identified and checked all the medication codes. KRM and DMA conceived and designed the study. AL, MC, DMA, CD, RI and KRM contributed to the data presentation and interpretation. All authors read and approved the final manuscript.

Corresponding author

Correspondence to Kenneth R. Muir.

Ethics declarations

Ethics approval and consent to participate

This study was approved by the CPRD Independent Scientific Advisory Committee (ISAC), and so is covered by their ethics approval (East Midlands—Derby Research Ethics Committee, REC reference 21/EM/0265). All practices have consented to participate in the CPRD linkage scheme. Since patients cannot be identified from data a GP practice sends to CPRD, the patient’s consent to share data with CPRD is not required. This study adhered to the Declaration of Helsinki.

Consent for publication

Not applicable.

Competing interests

All authors declare that they have no competing interests.

Additional information

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary Information

Additional file 1.

Product codes for herpes zoster vaccine in Aurum database. The file listed product codes for herpes zoster vaccine used in Aurum database.

Additional file 2.

 Medical codes for herpes zoster vaccination in Aurum database. The file listed medical codes for herpes zoster vaccination used in Aurum database.

Additional file 3.

 The distribution of the number of matched comparators – by matched set. The file depicted number and percentage of matched comparators.

Additional file 4.

 Product codes for influenza vaccine in Aurum database. The file listed product codes for influenza vaccine used in Aurum database.

Additional file 5.

 Medical codes for influenza vaccination in Aurum database.  The file listed medical codes for influenza vaccination used in Aurum database.

Additional file 6.

 Medical codes for dementia in Aurum database. The file listed medical codes for dementia outcome in Aurum database.

Additional file 7.

 International Classification of Diseases 10th Revision (ICD-10) codes for dementia. The file listed ICD-10 codes for dementia outcome.

Additional file 8.

 Medical codes for Charlson Comorbidity Index in Aurum database. The file listed medical codes for Charlson Comorbidity Index used in Aurum database.

Additional file 9.

 International Classification of Diseases codes for Charlson Comorbidity Index in Aurum database. The file listed ICD-10 codes for Charlson Comorbidity Index used in Aurum database.

Additional file 10.

 Product codes for anti-viral medications in Aurum database. The file listed product codes for anti-viral medications in Aurum database.

Additional file 11.

 Codes for ethnicity in Aurum database.  The file listed codes for ethnicity.

Additional file 12.

 The numbers and rates of people developing dementia in zoster vaccinated and unvaccinated cohorts. The file depicted numbers and rates per 1000 year of people developing dementia in zoster vaccinated and unvaccinated cohorts.

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Lophatananon, A., Carr, M., Mcmillan, B. et al. The association of herpes zoster and influenza vaccinations with the risk of developing dementia: a population-based cohort study within the UK Clinical Practice Research Datalink. BMC Public Health 23, 1903 (2023). https://doi.org/10.1186/s12889-023-16768-4

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1186/s12889-023-16768-4

Keywords

BMC Public Health

ISSN: 1471-2458

Contact us