Skip to main content

Exploring the impact of mental health conditions on vaccine uptake in high-income countries: a systematic review



Vaccination is an essential public health intervention to reduce morbidity and mortality from infectious diseases. Despite being at higher at risk of infectious diseases, health inequalities towards vaccine uptake in people with mental health issues have not been systematically appraised.


We searched 7 databases from 1994 to 26/03/2021. We included all studies with a relative measure of effect comparing a group with a mental health issue to a control group. All studies covering any mental health issue were eligible with no constraints to study population, vaccine type or region, provided in a high-income country for comparability of health care systems. The study outcomes were synthesised by study population, mental health issue and type of vaccine.


From 4,069 titles, 23 eligible studies from 12 different countries were identified, focusing on adults (n = 13) or children (n = 4) with mental health issues, siblings of children with mental health issues (n = 2), and mothers with mental health issue and vaccine uptake in their children (n = 6). Most studies focused on depression (n = 12), autism, anxiety, or alcoholism (n = 4 respectively). Many studies were at high risk of selection bias.


Mental health issues were associated with considerably lower vaccine uptake in some contexts such as substance use disorder, but findings were heterogeneous overall and by age, mental health issue or types of vaccine. Only individuals with mental health issues and physical comorbidities had consistently higher uptake in comparison to other adults.

Mental health should be considered as a health inequality for vaccine uptake but more context specific research is needed focusing more on specific mental health issues and subgroups of the population to understand who misses vaccination and why.

Peer Review reports


Mental health issues do not only lower well-being and quality of life directly [1] but also affect physical health and life expectancy [2, 3]. These health inequalities apply to many people as mental health issues are very common: A global meta-analysis by Steel et al. [4] showed that 17.6% of the global population have met diagnostic criteria of a common mental disorder within the last 12 months and around 30% of the population have experienced a mental disorder at least once in their lives.

This disparity of physical health between people with mental health issues and without might be partly due to inequalities in healthcare access and utilization. People with severe mental illness (major depressive disorder, schizophrenia, bipolar disorder) suffer from greater health disparity due to inequalities in health care access and utilization [5], and people with common mental disorders experience problems with accessing primary care services [6].

Although many studies focused on non-communicable diseases and unhealthy lifestyle in people with mental health issues [5], these individuals also have a higher risk for infectious diseases which is probably driven by a combination of many environmental and social risk factors, but also a potentially higher genetic susceptibility [7]. This makes vaccine uptake a crucial public health intervention for people with mental health problems.

Previous reviews have not investigated health inequalities concerning mental health issues and vaccine uptake in a systematic way [8] or have focused on the uptake of influenza or pneumococcal vaccine only [9, 10]. Consequently, this systematic review is the first study systematically exploring the impact of mental health issues on the choice and access to vaccination services– covering several types of vaccination, different types and definitions of mental health issues, and across different subgroup of the population, such adults at different ages in community or care home settings, or parents making decisions about the vaccination for their children. We focused on high-income settings where routine-access to vaccination services is usually available.


Our systematic review followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines [11]. A review protocol was developed a priori and registered with the International prospective register of systematic reviews (PROSPERO) on the 26th of March 2021 (registration number CRD42021245322). The full protocol can be found in the supplementary material.


The studies of interest in this systematic review were those that included any type of population who has access to a routinely delivered vaccination programme in a high-income country, as defined by the World Bank [12], of any age, or population subgroup. This also includes groups such as refugees and homeless people in cases with a vaccination programme for those groups in place.

Exposure of interest

We included studies covering a broad range of mental health issues, such as diagnoses covered in the 10th edition of the International Classification of Diseases (ICD-10) (e.g., major depressive disorder, autism spectrum disorder, etc.) or symptom-based reports (e.g., items from the SF-12 [13] or PROMIS Global-10 [14], etc.) and severe psychological distress.

Although we included studies with alcohol dependence or abuse defined as such by the study authors, we did not include studies reporting smoking and drinking behaviours per se because definitions of abnormal substance use are highly influenced by context and social norms. Furthermore, we did not include studies covering potential risk factors for mental health issues such as family stress and including life-circumstances such as prison detention which as usually closely linked to mental health issues.

For our analysis, we grouped the mental health issues into depressive disorders, anxiety (including mixed anxiety and depression), substance use disorder and alcohol abuse, autism spectrum disorders and severe mental illness including bipolar disorder, schizophrenia and other forms of psychoses.

Control group

We included a control without mental health issue or the general population. In case–control studies, a vaccinated versus a non-vaccinated group should be compared with mental health as a covariate in the statistical model.

Outcomes of interest

We included all studies covering the uptake and timing of a recommended vaccine for an individual or a relative with a mental health issue in comparison to a control group.

Study eligibility

Every type of observational study published in a peer-reviewed journal was eligible for inclusion, e.g., cohort studies, cross-sectional studies, case–control studies. Conference abstracts and PhD theses were also eligible if they contained enough information which was the only deviation from the PROSPERO protocol. We made this decision during the search and screening process as several of these studies added value to the broad range of study populations and mental health conditions and seemed of comparable study quality to other studies published in journals.

Any type of relative measure of effect or enough information to calculate a relative measure of effect was sufficient for inclusion. There were no constraints to language of the studies.

Information sources and search strategy

We searched Embase, MEDLINE and PsycINFO via Ovid, CINAHL via EBESCO, the Cochrane Library, Scopus and Open grey for studies published from 1994 up to the 26/03/2021. The study inclusion was limited to 1994 and later, as the publication of the Diagnostic and Statistical Manual of Mental Disorders IV (DSM-IV) in 1994 led to a significant change in how mental disorders were defined and diagnosed [15].

The search terms were based on terms covering all mental health issues in the 10th edition of the International Classification of Diseases (ICD-10) and other systematic reviews of mental health issues [10, 16]. The search terms for vaccine uptake were based on search terms used in Jain et al. [17] expanded to cover all vaccines given routinely in high income countries. Terms for preventive health and childcare visits were also included to detect vaccination as part of a preventive care appointment.

The search terms for every database can be found in the supplementary material. Backwards citation tracking was conducted for all included papers and encountered systematic reviews on the topic.

Data acquisition and extraction

Duplicates were removed using a R-script written by AS ( which uses the revtools R-package [18]. The script removes duplicates in a first step using string matching of the digital object identifiers. In a second step, the titles of the papers are compared using a string distance algorithm and a fuzzy string-matching algorithm. AS tested her deduplication algorithm including different sensitivity parameters of the string distance algorithm and the fuzzy string algorithm against a conventional deduplication algorithm as used in the free software Mendeley.

Every study’s title and abstract were screened by two authors independently (OOA & AS). In case of a disagreement, the study was included into a full-text screening which was conducted by the same two reviewers blinded to each other’s decision. Any difference was resolved through discussion by OOA and AS. The data extraction was conducted by AS using a standardized table. A list of extracted items can be found in the supplementary material. If a study had more than one estimate, we extracted the estimate adjusted for the largest number of confounders and the crude estimate. If the estimates were for different types of mental health issues or different types of vaccination, all estimates were extracted.

The studies were grouped by age (adults over 65, general adult population, mothers and their children, and children), by different mental health issue and by type of vaccine.

Risk of bias assessment

A tool for assessing the risk of bias was developed based on the ROBINS-I [19] tool and was adapted according to the topic of the review. Detailed decision criteria for each type of bias can be found in the supplement (see Table S.1). The risk of bias assessment was conducted by AS and HM in parallel and blinded to each other’s rating for five studies. As there were no disagreement for the piloted studies, AS continued with the rating of the remaining studies.

Additionally, a funnel plot was used to graphically explore potential including reporting bias [20, 21].

Data analysis

Crude effect sizes in form of odds ratios and confidence intervals were calculated for all studies which did not provide any adjusted measure of effect based on their proportions of vaccinated individuals in the exposed and control groups. For this purpose, the sample sizes of Howard et al. [22] and Lawrence et al. [23] had to be approximated as it was unclear whether the grouping into mental health issues was mutually exclusive. Studies could present effect estimates for different vaccines and different mental health issues. In order to make different studies better comparable, we averaged the uptake over 3 years for each type of vaccine and mental health condition for the study by Browne et al. [24]. For the Howard et al. [22] paper, we derived the confidence intervals from the given p-values.

All results were analysed and presented by the underlying study population grouped by age group (e.g., older adults, adults, children, and mother and children), the underlying mental health issue and type of vaccine. We also explored potentially different outcomes for different definitions of being vaccinated, e.g., vaccinated in the last year or ever vaccinated. If a study had more than one effect estimate for different vaccines or mental health conditions, all of them were presented separately. In studies adjusting for different confounders, we presented the effect estimate adjusting for the maximum number of confounders.

Heterogeneity, clinical and statistical, was deemed too high to reasonably apply meta-analytic methods. A grade approach was used to present the certainty of the evidence.


Study characteristics

A total of 4,069 titles were identified of which 64 were included in the full-text screening resulting in 23 eligible studies published between 2000 and 2021 (see Fig. 1). One of the 23 included studies [25] was identified through backwards citation tracking.

Fig. 1
figure 1

Results from the literature search and screening

About half of the eligible studies (n = 12) were cross-sectional, and 11 of the studies were cohort studies. Studies covered a range of population groups with 12 studies looking at adults over 50 and older, one at the general adult population, four studies at children with mental health issues and six studies exploring the impact of mental health issues in mothers on vaccine uptake in their children. There were 14 studies conducted in the United States, four in the United Kingdom, one single multi-national study in 10 different European countries, and one study each in Australia, Canada, and Denmark. Many studies included more than one mental health issue as exposure and allowed their subjects to fall under more than one category. Depression was the most common mental health issue explored (n = 12), followed by autism spectrum disorder (n = 4), anxiety (n = 4) and substance use disorder including alcoholism (n = 4), two studies using general mental health scores, two studies on psychological distress, two studies on dementia, and one study each on eating disorders, psychosis, learning disability, post-traumatic stress disorder. The studies focused on vaccines either given at older age or during childhood: 12 studies looked at influenza vaccine uptake, 4 at pneumococcal vaccine, 10 at different childhood immunizations and one study at tetanus vaccine. The definition and time period of being vaccinated varied between studies as national vaccination schedules differed.

An overview of all included studies and more details about the study population can be found in the in Table 1.

Table 1 Study overview

Risk of bias assessment

According to Table 2, most studies showed a high risk of selection bias. This was due to selection of subgroups of populations who were already actively engaging with the health care system or covered by a certain health care insurance in the United States.

Table 2 Risk of bias assessment

Potential confounding bias may have resulted from a lack of adjustment in some studies which were not designed for linking mental health with vaccine uptake but were part of surveillance.

Some of the studies from the United States did not provide sufficient information on whether a vaccine was covered by the respective health insurance or looked at Medicare use only which could still require co-payment for some vaccines [25].

A few studies were adjusting for factors closely linked to health care seeking behaviour such as health insurance and health care utilisation which might have resulted in collider bias in those studies. This means that it was adjusted for something which is a consequence of both outcome and exposure, which might result in a distorted association [36]. Whilst most studies on electronic health records provided relatively reliable information on vaccine uptake, those were more likely to suffer from moderate biases towards the diagnosis of health care issues in primary care. The majority of cross-sectional studies assessed all participants for mental health issues using questionnaires but collected data about vaccine receipt by participant self-recall and were consequently potentially biased by differential misclassification of vaccination status.

A graphical bias exploration using a funnel plot (see supplementary Figures S.5, S.6, S.7) indicated some asymmetry. We interpreted this finding as being attributable to the high heterogeneity between the studies caused by very different study populations, different ways of assessing mental health and vaccine receipt.

Vaccine uptake in different study populations

Overall, a wide variety of different subgroups of the population was covered in the studies. These ranged from elderly adults, general adult population, children and mothers with their children. All effect estimates can be found in the supplement material ordered by the different subgroups (see supplementary Figures S.1, S.2, S.3, S.4).


11 studies explored the uptake of influenza and pneumococcal vaccine in populations over 65 years, two studies covered the same vaccines in adults between 50 and 65 years and one explored the uptake of tetanus vaccine in the general population over 18 years. More detailed results broken down by mental health issue and type of vaccine can be found in Fig. 2.

Fig. 2
figure 2

Odds of vaccine uptake in the group with mental health issues in comparison to a control group by age, type of vaccine, and type of mental health issue. MHI: Mental health issue. SUD: Substance use disorder. PTSD: post-traumatic stress disorder. DTP: diphtheria, tetanus, pertussis. *Study which only presented a crude odds ratio without any adjustment for potential confounders

Overall, there were no consistent findings regarding the association between mental health issues and vaccine uptake. In adults over 65, two studies including mental health issues in general or psychological distress found a consistent lower vaccine uptake for all different types of vaccines in comparison to adults with no reported mental health issue [28, 37].

Two other studies showed some evidence for lower Influenza vaccine uptake in community-dwelling individuals with dementia [26, 27]. In addition to this result, Shah et al. [27] differentiated between influenza vaccine uptake in a community setting and in a care home setting, with dementia being a predictor of marginally higher uptake in a care home setting alongside other chronic comorbidities. These were the only two studies for this age group not conducted in the US but in several European countries.

The results for studies in adults between 50 and 65 were consistent with each other, the cohort study in adults with depression controlling for the presence of other chronic diseases [38], and a cross-sectional study in veterans with from anxiety, depression, PTSD, SUD or other severe mental illness showed a higher uptake of Influenza vaccine uptake in comparison to the control group [24].

A single cross-sectional study found a lower uptake of tetanus vaccine within the recommended 10-year time frame for adults with from autism spectrum disorder based on self-rating or external diagnosis [39].

Children with mental health issues

Overall, there were four studies looking at mental health issues in children themselves and how they impacted their vaccine uptake. A study in children attending a school for learning abilities in the U.K. showed a non-significant trend for slightly lower uptake of most vaccines and more missed vaccines in comparison to children who did not attend those schools [33].

Three studies looked at children with autism spectrum disorder [34, 35, 40]. There was no difference between the exposed children and the control group for children aged 6 or older. Only [35] found a statistically significant lower uptake in children at the age 5 with autism for all different types of vaccines. For more details see supplementary Figure S1.

Siblings of children with autism spectrum disorder

Two studies looked at younger siblings of children with autism in Canada and in the United States [34, 35]. Zerbo et al. [35] found inconclusive results for children older than 6 years. For siblings younger than 6 years, both studies showed consistent results of a higher risk of delaying a vaccine or not being fully vaccinated at different ages. For more details see supplementary Figure S.2.

Children of mothers with mental health issues

Six studies covered the impact of maternal mental health on the vaccine uptake in their children [2932, 41, 42]. For children at the age of five, there were consistent results for a lower odds of receiving a vaccine if they had a mother affected by a mental health issue or with a lower score of mental well-being [29, 31]. This was both in a U.S. and a U.K. setting. However, Gilbert et al. [29] did not find conclusive results for the impact of mental well-being among low-income mothers in delaying their children’s vaccination.

Studies covering the vaccine uptake at the age of two did not show a clear trend of higher or lower uptake. Particularly studies on depression showed opposite results [30, 31, 42]. It should be noted though, that these studies were conducted in different health care setting such as the U.S., Denmark and the U.K.

For younger children under two, a single study from Australia showed a higher risk of delaying vaccines for children of mothers with mental health issues but no higher risk for partially completed vaccination schedules [32]. Minkovitz et al. [42] did not show a statistically significant lower or higher uptake in children at the age one, independent of whether the depression was diagnosed 2–4 months postpartum or later in childhood 30–33 months postpartum. For more details see supplementary Figure S.3.

Effect of different mental health issues

Expecting different mechanisms of how a mental health issue could impact and influence vaccine uptake in different subgroups, we also explored results grouped by different mental health issues.

Despite the heterogeneity across different studies, several studies found substantial health inequalities for individuals with mental health issues. Osam et al. [31] found a substantially lower vaccine uptake in children of mothers with substance use disorder, similarly adults over 65 with substance use disorder showed a significantly lower influenza uptake in comparison to healthy controls [24, 37]. Some studies found a drastically lower vaccine uptake in people with depression or their children [26, 31].


Depressive disorders and symptoms were the most studied mental health issues across all studies. All studies with depression (or maternal depression, as applicable) as exposure can be found in supplementary Figure S.4.

Definitions of ‘being depressed’ varied between studies from assessing symptoms only, using structured tools, health records, different times of occurrence and depression mixed with anxiety. For maternal depression, studies also differentiated between different onsets of the symptoms. Except two studies of middle-aged adults, there was neither a consistent trend within an age group nor between different definitions for depression nor vaccines.


Siblings of children with autism spectrum disorder showed a lower uptake of different vaccines at younger age as well as higher odds for having an incomplete or delayed vaccination status [34, 35]. This was the case for all different types of vaccines. Two studies found a lower uptake in individuals with autism, Zerbo et al. [35] in children under the age of 6 and Nicolaidis et al. [39] in adults. Both studies were based in a U.S. health care setting.

Other mental health issues

More details on the impact of mental health issues can be found in the figures in the supplementary material (Figures S.1, S.2, S.3, S.4).

Timing of diagnosing a mental health issue

Two cohort studies explored the effect of time of the occurrence of the mental health issue in relation to the vaccine receipt. Minkovitz et al. [42] found a lower uptake for all types of childhood vaccines if the mother experienced depressive symptoms between 2 and 4 months after birth but not for depressive symptoms between 30 to 33 months after birth. Lyngsøe et al. [30] did not find any difference between mothers having experienced depression in the past and currently experiencing depression. However, most studies did not provide any information about the timing between the occurrence of a mental health issues and when the individuals received their vaccine.

Different types of vaccine

During childhood, there was no consistent trend in differences of uptake noticeable for different types of vaccines. This also applied to different vaccines for adults of all ages.

Timeliness of the vaccine

Only four studies explored the effect of mental health issues on vaccine delay or being partially vaccinated [29, 33, 34]. Turner et al. [32] found higher odds of delaying vaccines for children of mothers with mental health issues but not for eventual coverage later. In contrast, Gilbert et al. [29] found that women with lower self-rated mental health status were less likely to have children with up-to-date vaccinations but no difference in delaying vaccination. One study indicated higher odds of being only partially vaccinated for children with learning disability [33] and another study showed both, a lower uptake and a higher proportion of delayed vaccines in younger siblings of children with autism [34].


This is the first review to our knowledge which systematically explored the link between a range of mental health issues and all vaccines recommended across the life course. We identified 23 papers with very different underlying study populations as well as different definitions of the same mental health issue and vaccine receipt.

The results of the studies differed depending on age of the study population, setting, geographical region, and the mental health issue involved. This finding aligns with a previous review by Lord et al. [10], indicating that more context specific research within certain populations is needed. In adults we saw inconsistent results, only for adults between 50 and 65 years there was a consistent trend of having higher odds of getting vaccinated in the group with mental health issues than without. There was no consistent result for children with autism but a trend for missing some vaccines in children with learning abilities. In contrast, younger siblings of children with autism showed consistently lower vaccine uptake across studies. For children of mothers with mental health issues, there was only a trend of being less vaccinated at the age of 5 across different maternal mental health issues. There were no consistent trends across ages for different mental health issues. Nevertheless, some studies found substantial health inequalities in vaccine uptake, e.g. for individuals with substance use disorder or depression [24, 26, 37, 43]. Inconsistent results across studies do not necessarily imply that there is no true health inequality but could be impacted by very different study designs, case definitions, sampling methods, and differences in health care systems.

Overall, the study populations were drawn from very different subpopulations. Our bias assessment indicated that many studies were at risk of selection bias by focusing on a population which already accessed health care services in a certain way. Additionally, many differences can probably be linked to systematic or structural factors that may shape vaccination behaviours such as access to health care, health insurance, or place of residence [44, 45]. This might be especially relevant to studies conducted in the United States where many different types of insurances offer vaccination under varying conditions and sometimes require co-payment [25]. Unfortunately, only some of the U.S. studies presented data on the insurance or Medicare status but did not provide information about whether vaccination were financially covered for individuals. Additionally, personal attitudes and beliefs [4648], and institutional trust [49] are other factors which might have impacted the individual’s vaccination uptake. Depending on age and other living circumstances, some people might rely more on other people in order to get vaccinated such as children or people with dementia.

A higher vaccine uptake in adults between 50 and 65 years could be due to physical health conditions which made them eligible for influenza and pneumococcal vaccine in the first place [24, 38] and then led to higher ascertainment of mental health issues due to regular health care visits. This theory could be supported by the finding by Shah et al. [27] who only found a higher vaccine uptake in dementia patients living in care homes with more frequent access to health care than community-dwelling dementia patients. The consistent trend of lower vaccine uptake in younger siblings of children with autism [34, 35], [34, 35] could be explained by the parent’s fear that a vaccine might have caused autism in the older sibling following a popular belief after the later disregarded publication by Wakefield [50]. A lower vaccine coverage in children of mothers with mental health issues mainly at the age of 5 might be impacted by barriers to accessing services as there is less structure for the children’s health reviews which usually go along with routine vaccination in the first year of life (e.g., [51]).

Some health care seeking behaviours seem to be influenced by mental health issues, however existing studies in several high-income countries showed inconsistent results, for instance higher health care utilization in patients with anxiety [52] and depression [53], in contrast to lower uptake of other preventive care services in patients with depression [54] or severe mental illness [5]. A U.K. study by Milan & Dáu [55] showed that mothers with traumatic experiences and PTSD had a lower uptake of the COVID-19 vaccine and reported a bigger distrust in institutions than mothers without any mental health issues. Another study by Renbarger et al. [56] illustrated that women with SUD can feel judged and scrutinised by health care professionals and consequently, try to avoid primary care services. Similar experiences could apply to participants of some of the included studies. However, even within groups of mental health issues, heterogeneity in findings remained.

The quality of the included studies varied and many of them were subject of potential selection bias, focusing on populations already actively engaging with the health care system. This might also lead to a misclassification of the exposure as some people might not be diagnosed with their mental health issue due to access or might perceive a diagnosis as stigmatising. Hence, the general impact of mental health on vaccine uptake might be underestimated as more vulnerable groups were not represented or people with mental health issues might be misclassified into the control group.

Overall, this systematic review used a comprehensive search strategy in multiple databases including grey literature, and minimised human error through screening all included studies by two authors and using a comprehensive framework to assess potential bias in all studies. We aimed to address the gap in knowledge of the association of vaccine uptake across a wide range of mental health issues with no constraints to age, subgroup of the population, type of vaccination, or language of the study. Another strength was as an extensive bias assessment of the included studies which identified various patterns of bias across studies which helped to carefully set their results into context.

There are some limitations to our review. Although including studies in all languages, we only conducted our search in English language data bases. We also did not conduct any hand searching of suitable references in the included articles.

Many studies applied different ways of defining a complete vaccination status and documenting vaccine receipt which made the outcome more difficult to compare. Although we included a broad definition of mental health issues and examined all vaccine types in all age groups, there was not enough data from the studies to meta-analyse the results and draw an overall conclusion. Further, defining and diagnosis mental health issues is changing and subject to temporal, cultural and contextual trends [57]. The very different study populations, different health care systems with different access barriers and different ways of defining and ascertaining mental health made any comparison of results across studies extremely difficult. Those differences in health care systems may have also impacted access to health care and vaccination services. This might particularly important as the review was dominated by studies from the US where health insurance has even bigger impact on access to health care in comparison to other high income countries [58]. Furthermore, the temporality between mental health issue and vaccine uptake was often not considered in the existing studies. It remains unclear whether there more recent mental health issues or chronic conditions might be more impactful than a history of mental health issues.

Many included studies had a high risk of selection bias or ascertainment bias. All studies covered only individuals which already accessed the health care services because of their mental health issues, consequently the impact of mental health on vaccination uptake might be underestimated missing those who did not seek help, e.g., due to access barriers or perceived stigma. This is very important as help-seeking behaviour for mental health issues is often related to similar factors impacting vaccine uptake, such as sociodemographic status, gender, ethnicity, age, comorbidities and perceived need of the health care service [59]. This makes the interpretation of the results more complex. Some studies which based the vaccination status on recall only might also suffer from differential misclassification bias. Potential confounding was also additional issue for many studies as they were initially designed for surveillance purposes only. Other studies tended to over-adjust for potential factors on the causal pathway – including access to health care system or other indicators for health care utilisation.

Overall, we found inconsistent findings in this review which indicates that the receipt of vaccination is likely to be shaped by multiple factors including access to health care, vaccine hesitancy, the nature of the mental health issue, the contact with the health care providers amongst others. Generally, there is not much existing evidence on a potential link between mental health issues and vaccination uptake, for both individuals and mothers and their children. However, some studies indicated potentially big health inequalities for individuals with substance use disorder or depression. The burden of mental health issues has remarkably increased in the last decades [4] together with vaccine hesitancy in high-income countries to non-negligible proportions [44] which makes it important to understand whether there is a link between mental health and vaccine hesitancy and/or issues accessing vaccination services in order to prevent lower vaccine-coverage.

There is more research needed on different patterns of vaccine uptake, a better differentiation between mental health issues, their timing in relation to the vaccination, and potential disorder specific issues. In addition, there is a need for clinically evaluated mental health outcomes as the reliability of self-reported diagnoses and symptoms may not accurately classify people. Some groups of people with mental health issues seem to be especially neglected in research despite their higher risk of health issues, for instance people with severe mental illness [5]. The temporality of mental health issues in relation to vaccine uptake was often not considered in existing studies but might be useful to identify individuals at higher risk for missing vaccination. Additionally, a better understanding of mechanisms through which mental health issues might affect vaccine uptake is needed. Our review should motivate future research to investigate how mental health issues may impact individuals access primary health care and vaccination services and to shift research on vaccine uptake in people with mental health issues towards more context and disorder sensitive designs. This can help to better asses potential health inequalities and develop more targeted public health interventions if needed.

Availability of data and materials

Data availability is not applicable to this article as no new data were created or analysed in this study. The extraction table can be requested from the corresponding author (AS). The automated deduplication code can be accessed via GitHub (see link in the methods).


  1. Connell J, Brazier J, O’Cathain A, Lloyd-Jones M, Paisley S. Quality of life of people with mental health problems: a synthesis of qualitative research. Health Qual Life Outcomes. 2012;10(1):138.

    Article  Google Scholar 

  2. Hjorthøj C, Stürup AE, McGrath JJ, Nordentoft M. Years of potential life lost and life expectancy in schizophrenia: a systematic review and meta-analysis. The Lancet Psychiatry. 2017;4(4):295–301.

    Article  Google Scholar 

  3. Walker ER, McGee RE, Druss BG. Mortality in mental disorders and global disease burden implications. JAMA Psychiat. 2015;72(4):334.

    Article  Google Scholar 

  4. Steel Z, Marnane C, Iranpour C, Chey T, Jackson JW, Patel V, et al. The global prevalence of common mental disorders: a systematic review and meta-analysis 1980–2013. Int J Epidemiol. 2014;43(2):476–93.

    Article  Google Scholar 

  5. De Hert M, Correll CU, Bobes J, Cetkovich-Bakmas M, Cohen DAN, Asai I, et al. Physical illness in patients with severe mental disorders. I. prevalence, impact of medications and disparities in health care. World Psychiatry. 2011;10(1):52–77.

    Article  Google Scholar 

  6. Levinson Miller C, Druss BG, Dombrowski EA, Rosenheck RA. Barriers to primary medical care among patients at a community mental health center. Psychiatr Serv. 2003;54(8):1158–60.

    Article  Google Scholar 

  7. Nudel R, Wang Y, Appadurai V, Schork AJ, Buil A, Agerbo E, et al. A large-scale genomic investigation of susceptibility to infection and its association with mental disorders in the Danish population. Transl Psychiatry. 2019;9(1):283.

    Article  Google Scholar 

  8. Happell B, Scott D, Platania-Phung C. Provision of Preventive Services for Cancer and Infectious Diseases Among Individuals with Serious Mental Illness. Arch Psychiatr Nurs. 2012;26:192–201.

    Article  Google Scholar 

  9. Lorenz RA, Norris MM, Norton LC, Westrick SC. Factors associated with influenza vaccination decisions among patients with mental illness. Int J Psychiatry Med. 2013;46(1):1–13.

    Article  Google Scholar 

  10. Lord O, Malone D, Mitchell AJ. Receipt of preventive medical care and medical screening for patients with mental illness: a comparative analysis. Gen Hosp Psychiatry. 2010;32(5):519–43.

    Article  Google Scholar 

  11. Page MJ, McKenzie JE, Bossuyt PM, Boutron I, Hoffmann TC, Mulrow CD, et al. The PRISMA 2020 statement: an updated guideline for reporting systematic reviews. BMJ. 2021;29: n71.

    Article  Google Scholar 

  12. The World Bank. High income [Internet]. World Bank Open Data. 2021 [cited 2021 Oct 26]. Available from:

  13. Jenkinson C, Layte R, Jenkinson D, Lawrence K, Petersen S, Paice C, et al. A shorter form health survey: can the SF-12 replicate results from the SF-36 in longitudinal studies? J Public Health. 1997;19(2):179–86.

    Article  CAS  Google Scholar 

  14. Cella D, Riley W, Stone A, Rothrock N, Reeve B, Yount S, et al. The Patient-Reported Outcomes Measurement Information System (PROMIS) developed and tested its first wave of adult self-reported health outcome item banks: 2005–2008. J Clin Epidemiol. 2010;63(11):1179–94.

    Article  Google Scholar 

  15. Malhi GS. DSM-5: Ordering disorder? Aust N Z J Psychiatry. 2013;47(1):7–9.

    Article  Google Scholar 

  16. Carreira H, Williams R, Strongman H, Bhaskaran K. Identification of mental health and quality of life outcomes in primary care databases in the UK: A systematic review. BMJ Open. 2019;9(7).

  17. Jain A, Van HAJ, Boccia D, Thomas SL. Lower vaccine uptake amongst older individuals living alone : a systematic review and meta-analysis of social determinants of vaccine uptake. Vaccine. 2017;35(18):2315–28.

  18. Westgate MJ. revtools: an R package to support article screening for evidence synthesis. Res Synth Methods. 2019;10(4):606–14.

    Article  Google Scholar 

  19. Sterne JA, Hernán MA, Reeves BC, Savović J, Berkman ND, Viswanathan M, et al. ROBINS-I: a tool for assessing risk of bias in non-randomised studies of interventions. BMJ. 2016;12: i4919.

    Article  Google Scholar 

  20. Egger M, Smith GD, Schneider M, Minder C. Bias in meta-analysis detected by a simple, graphical test. BMJ. 1997;315(7109):629–34.

    Article  CAS  Google Scholar 

  21. Sterne JAC, Sutton AJ, Ioannidis JPA, Terrin N, Jones DR, Lau J, et al. Recommendations for examining and interpreting funnel plot asymmetry in meta-analyses of randomised controlled trials. BMJ. 2011;343:d4002.

    Article  Google Scholar 

  22. Howard DH, Sentell T, Gazmararian JA. Impact of health literacy on socioeconomic and racial differences in health in an elderly population. J Gen Intern Med. 2006;21(8):857–61.

    Article  Google Scholar 

  23. Lawrence T, Zubatsky M, Meyer D. The association between mental health diagnoses and influenza vaccine receipt among older primary care patients. Psychol Health Med. 2020;25(9):1083–93.

    Article  Google Scholar 

  24. Browne KC, Hoerster KD, Piegari R, Fortney JC, Nelson KN, Post EP, et al. Clinical care quality among veterans health administration patients with mental illness following medical home implementation. Psychiatr Serv (Washington, DC). 2019;70(9):816–23.

    Article  Google Scholar 

  25. U.S. Department of Health & Human Services. Get Vaccinated [Internet]. 2021 [cited 2022 Oct 20]. Available from:

  26. Mangtani P, Breeze E, Kovats S, Ng ESW, Roberts JA, Fletcher A. Inequalities in influenza vaccine uptake among people aged over 74 years in Britain. Prev Med. 2005;41(2):545–53.

    Article  Google Scholar 

  27. Shah SM, Carey IM, Harris T, DeWilde S, Cook DG. The impact of dementia on influenza vaccination uptake in community and care home residents. Age Ageing. 2012;41(1):64–9.

    Article  Google Scholar 

  28. Thorpe JM, Kalinowski CT, Patterson ME, Sleath BL. Psychological distress as a barrier to preventive care in community-dwelling elderly in the United States. Med Care. 2006;44(2):187–91.

    Article  Google Scholar 

  29. Gilbert RM, Mersky JP, Lee CTP. Prevalence and correlates of vaccine attitudes and behaviors in a cohort of low-income mothers. Prev Med Rep. 2021;21:101292.

    Article  Google Scholar 

  30. Lyngsoe BK, Vestergaard CH, Rytter D, Vestergaard M, Munk-Olsen T, Bech BH. Attendance of routine childcare visits in primary care for children of mothers with depression: a nationwide population-based cohort study. Br J Gen Pract. 2018;68(667):e97-104.

    Article  Google Scholar 

  31. Osam CS, Pierce M, Hope H, Ashcroft DM, Abel KM. The influence of maternal mental illness on vaccination uptake in children: a UK population-based cohort study. Eur J Epidemiol. 2020;35(9):879–89.

    Article  Google Scholar 

  32. Turner C, Boyle F, O’Rourke P. Mothers’ health post-partum and their patterns of seeking vaccination for their infants. Int J Nurs Pract. 2003;9(2):120–6.

    Article  Google Scholar 

  33. Tuffrey C, Finlay F. Immunisation status amongst children attending special schools. Ambul Child Health. 2001;7(3–4):213–7.

    Google Scholar 

  34. Kuwaik GA, Roberts W, Zwaigenbaum L, Bryson S, Smith IM, Szatmari P, et al. Immunization uptake in younger siblings of children with autism spectrum disorder. Asaria Dempsey, Doja, Dorell, Farrington, Fombonne, Glanz, Gust, Halperin, Harrington, Hilton, Hollingshead, Kondro, Kuwaik, Lord, Lord, Madsen, McWha, Nagaraj, Offit, Oliveira, Ozonoff, Poling, Roggendorf, Rosenberg, Taylor, Taylor, Taylor, Wakefield, Z D, editor. Autism. 2014;18(2):148–55.

  35. Zerbo O, Modaressi S, Goddard K, Lewis E, Fireman BH, Daley MF, et al. Vaccination patterns in children after autism spectrum disorder diagnosis and in their younger siblings. JAMA Pediatr. 2018;172(5):469–75.

    Article  Google Scholar 

  36. Catalogue of bias collaboration, Lee H, Aronson J, Nunan D. Collider bias. Catalogue of bias. 2019.

  37. Druss BG, Rosenheck RA, Desai MM, Perlin JB. Quality of preventive medical care for patients with mental disorders. Med Care. 2002;40(2):129–36.

    Article  Google Scholar 

  38. Xiang X. Adoption and maintenance of health behaviors among middle-aged and older adults: The role of chronic disease diagnosis and depression. Dissertation Abstracts International Section A: Humanities and Social Sciences. 2016;77(4-A(E)):No-Specified.

  39. Nicolaidis C, Raymaker D, McDonald K, Dern S, Boisclair WC, Ashkenazy E, et al. Comparison of healthcare experiences in autistic and non-autistic adults: a cross-sectional online survey facilitated by an academic-community partnership. J Gen Intern Med. 2013;28(6):761–9.

    Article  Google Scholar 

  40. Angkustsiri K, Li DD, Hansen RL. Early immunization rates among children with typical development and autism spectrum disorders. In Sacramento, California; 2012. p. 134.

  41. Lyman LM. Associations between maternal depression symptoms, socioeconomic factors and vaccine receipt by very young children. Dissertation Abstracts International: Section B: The Sciences and Engineering. 2009;69(12-B):7449.

  42. Minkovitz CS, Strobino D, Scharfstein D, Hou W, Miller T, Mistry KB, et al. Maternal depressive symptoms and children’s receipt of health care in the first 3 years of life. Pediatrics. 2005;115(2):306–14.

    Article  Google Scholar 

  43. Pierce M, Hope HF, Kolade A, Gellatly J, Osam CS, Perchard R, et al. Effects of parental mental illness on children’s physical health: Systematic review and meta-analysis. Br J Psychiatry. 2020;217(1):354–63.

    Article  Google Scholar 

  44. De FA, Simas C, Karafillakis E, Paterson P, Larson HJ. Mapping global trends in vaccine confidence and investigating barriers to vaccine uptake : a large-scale retrospective temporal modelling study. Lancet. 2020;396(10255):898–908.

  45. Bryden GM, Browne M, Rockloff M, Unsworth C. The privilege paradox : Geographic areas with highest socio-economic advantage have the lowest rates of vaccination. Vaccine. 2019;37(32):4525–32.

  46. Forster AS, Rockliffe L, Chorley AJ, LA Marlow V, Bedford H, Smith SG, et al. SSM -population health review article a qualitative systematic review of factors in fl uencing parents ’ vaccination decision-making in the United Kingdom. SSM - Popul Health. 2016;2:603–12.

    Google Scholar 

  47. Bish A, Yardley L, Nicoll A, Michie S. Factors associated with uptake of vaccination against pandemic influenza : a systematic review  Vaccine. 2011;29:6472–84.

  48. Smith LE, Amlôt R, Weinman J, Yiend J, Rubin GJ. A systematic review of factors affecting vaccine uptake in young children. Vaccine. 2017;35(45):6059–69.

    Article  Google Scholar 

  49. Larson HJ, Jarrett C, Eckersberger E, Smith DMD, Paterson P. Understanding vaccine hesitancy around vaccines and vaccination from a global perspective: a systematic review of published literature, 2007–2012. Vaccine. 2014;32(19):2150–9.

    Article  Google Scholar 

  50. Smeeth L, Cook C, Fombonne E, Heavey L, Rodrigues LC, Smith PG, et al. MMR vaccination and pervasive developmental disorders: a case-control study. Lancet. 2004;364(9438):963–9.

    Article  Google Scholar 

  51. NHS. Your baby’s health and development reviews [Internet]. Baby’s development. 2020 [cited 2021 Oct 28]. Available from:

  52. Horenstein A, Heimberg RG. Anxiety disorders and healthcare utilization: a systematic review. Clin Psychol Rev. 2020;81:101894.

    Article  Google Scholar 

  53. Tusa N, Koponen H, Kautiainen H, Korniloff K, Raatikainen I, Elfving P, et al. The profiles of health care utilization among a non-depressed population and patients with depressive symptoms with and without clinical depression. Scand J Prim Health Care. 2019;37(3):312–8.

    Article  Google Scholar 

  54. Egede LE, Grubaugh AL, Ellis C. The effect of major depression on preventive care and quality of life among adults with diabetes. Gen Hosp Psychiatry. 2010;32(6):563–9.

    Article  Google Scholar 

  55. Milan S, Dáu ALBT. The role of trauma in mothers’ covid-19 vaccine beliefs and intentions. J Pediatr Psychol. 2021;46(5):526–35.

    Article  Google Scholar 

  56. Renbarger KM, Shieh C, Moorman M, Latham-Mintus K, Draucker C. Health care encounters of pregnant and postpartum women with substance use disorders. West J Nurs Res. 2020;42(8):612–28.

    Article  Google Scholar 

  57. Kendler KS, Zachar P, Craver C. What kinds of things are psychiatric disorders? Psychol Med. 2011;41(6):1143–50.

    Article  CAS  Google Scholar 

  58. Vladeck B. Universal health insurance in the United States: reflections on the past, the present, and the future. Am J Public Health. 2003;93(1):16–9.

    Article  Google Scholar 

  59. Roberts T, Miguel Esponda G, Krupchanka D, Shidhaye R, Patel V, Rathod S. Factors associated with health service utilisation for common mental disorders: a systematic review. BMC Psychiatry. 2018;18(1):1–19.

    Article  CAS  Google Scholar 

Download references


Not applicable.


This study is funded by the National Institute for Health Research (NIHR) Health Protection Research Unit in Vaccines and Immunisation (NIHR200929), a partnership between UK Health Security Agency and the London School of Hygiene and Tropical Medicine. The views expressed are those of the author(s) and not necessarily those of the NIHR, UK Health Security Agency or the Department of Health and Social Care. AS, HIM, SMJ and OOA are funded by the National Institute for Health Research (NIHR) Health Protection Research Unit in Vaccines and Immunisation. CWG is funded by a Wellcome Intermediate Clinical Fellowship 201440/Z/16/Z. DO is supported by the NIHR University College London Hospital Biomedical Research Centre. DO is also supported by NIHR Applied Research Collaborative (ARC) North Thames.

Author information

Authors and Affiliations



The study was conceptualized by AS, HM and CWG. The study protocol was designed by AS, HM, CWG, HC, SMJ and DO. AS and OOA conducted the abstract and full-text screening. The data extraction was undertaken by AS. The bias assessment was conceptualized and conducted by AS and HM. The manuscript was written by AS, and edited by HM, CWG, SMJ, DO and HC. All authors gave approval to the final version to be published. HM and CWG contributed equally to the paper and are joint last authors. AS is the guarantor.

Corresponding author

Correspondence to Anne M. Suffel.

Ethics declarations

Ethics approval and consent to participate

Not applicable.

Consent for publication

Not applicable.

Competing interests

There are no conflicts of interest.

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.


Additional file 2: Table S. 1

Risk of biasassessment tool 

Additional file 3: Figure S.1

Vaccine uptake in children with mental healthissues. Figure S.2 Vaccine uptake in siblingsof children with autism spectrum disorder. Figure S.3 Vaccine uptake in children of mothers withmental health issues. Figure S.4  Vaccine uptake in individuals with depression. Figure S.5  Funnel plot for allincluded studies. Figure S.6 Funnel plot for all studies covering adults. FigureS.7 Funnel plot for all individuals with depression

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 The Creative Commons Public Domain Dedication waiver ( 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

Suffel, A.M., Ojo-Aromokudu, O., Carreira, H. et al. Exploring the impact of mental health conditions on vaccine uptake in high-income countries: a systematic review. BMC Psychiatry 23, 15 (2023).

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: