Abstract
Background: People with disabilities on average experience health care barriers, poorer health and higher mortality.
Objectives: This study aims to review and synthesise life expectancy (LE) and years of life lost (YLL) comparing people with disabilities to those without in low and middle-income countries (LMICs).
Method: A systematic review was conducted across six databases. Longitudinal studies with a comparator group that measured LE in or YLL between people with and without disabilities in LMICs were eligible for inclusion. Two reviewers independently assessed study eligibility, extracted data and assessed the risk of bias. Meta-analyses were undertaken using R 4.3.3. The study assessed heterogeneity with I2 and publication bias with a funnel plot. Sub-group and meta-regression analyses were performed, and the risk of bias was evaluated.
Results: Twelve full-text articles were included in this meta-analysis. The pooled mean LE was lower in people with disabilities (57.98 years; 95% confidence intervals [CI]: 53.40–62.95) compared with people without disabilities (70.86 years; 95% CI: 64.06–78.38). The overall weighted years of YLL in people with disabilities was 15.84 years (95% CI: 11.1–22.61). There was no significant difference in YLL between men (16.33 years; 95% CI: 11.49–23.21) and women (13.7 years; 95% CI: 8.45–22.22).
Conclusion: The average LE in people with disabilities was substantially lower compared to those without disabilities in LMICs. This inequity highlights that health systems and public health efforts are failing to meet the needs of people with disabilities and must be improved to become more inclusive.
Contribution: The study emphasises the need for inclusive policies and robust research in the health system to address health disparities.
Keywords: disability; life expectancy; years of life lost; LMICs; meta-analysis.
Introduction
Globally, there are over 1.3 billion (16%) people with disabilities (World Health Organization [WHO] 2022), with 80% residing in low- and middle-income countries (LMICs) (WHO 2022). This number is anticipated to increase with further population growth and ageing (WHO 2011). People with disabilities face a range of exclusions and adverse conditions. With respect to health, on average, they experience poorer health and higher mortality on account of a number of different pathways (Kuper & Phyllis Heydt 2019; WHO 2011, 2015). Firstly, they are on average poorer and often reside in economically impoverished settings (WHO 2011, 2022), contributing to poorer health and functioning (Centers for Disease Control and Prevention 2008). Secondly, they have a higher prevalence of comorbidities and secondary conditions resulting from their impairment, such as diabetes, stroke and pressure sores (Kuper & Phyllis Heydt 2019; WHO 2022). Thirdly, people with disabilities are more susceptible to behavioural health risks (WHO 2011), such as physical inactivity (Hollis et al. 2020), smoking (Armour et al. 2007) and obesity (Maïano et al. 2016). Fourthly, people with disabilities commonly face barriers when seeking care, including unfavourable attitudes from health care professionals (Adugna et al. 2020), high cost of medical care (Dagnachew, Meshesha & Mekonen 2021) and inaccessible facilities (Pinto et al. 2021). Consequently, they often lack adequate access to both general and disability-specific health care and rehabilitation services (Kuper & Phyllis Heydt 2019; WHO 2022), despite their greater health care needs (Kuper & Phyllis Heydt 2019). As a result of these factors, people with disabilities face an elevated risk of morbidity and mortality compared to their non-disabled peers (Lauer & McCallion 2015; Park et al. 2017).
There is growing evidence of a shorter life expectancy (LE) for people with disabilities, including in LMICs, which may be around 10–20 years (Da Roza et al. 2023; Egüez-Guevara & Andrade 2015; Keeler et al. 2010; Ma et al. 2019; Rotenberg, Smythe & Kuper 2023; Ruffieux et al. 2023; Zhan et al. 2023). This gap appears to vary based on impairment type, for instance, being particularly high in people with mental illness (28.4 years) (Fekadu et al. 2015), functional impairments (16–20 years) (Bahk, Kang & Khang 2019) and physical impairments (12.7–17.1 years). There is also variation across LMICs (Da Roza et al. 2023; Ma et al. 2019; Moreno et al. 2019; Scalfari et al. 2013), for instance, a review showed that LE gaps for bipolar disorder were greater in Africa (29 years) than in Asia (12 years) (Chan et al. 2022), and overall LMICs exhibit higher LE gaps by disability status (26.1 years) compared to upper-middle-income countries (14.6 years) (Rotenberg et al. 2023). However, the LE gap for people with disabilities has not yet been systematically reviewed in LMICs, although LE serves as a key indicator of health status, outcomes and quality of life (Britain 2001), and a proxy for health equity (Rotenberg et al. 2023). Assessing LE disparities between disabled and non-disabled individuals is important to raise public awareness of health inequities and help policymakers craft effective strategies to address health care needs and prevent avoidable mortality (Chan et al. 2023; Issifou & Pewitt 2022). Consequently, this systematic review and meta-analysis were undertaken to compare LE among people with and without disabilities in LMICs and estimate average years of life lost (YLL).
Methods
Protocol and registration
We searched several databases (e.g., Cochrane Library, Joanna Briggs Institute [JBI] Library and DARE database) to prevent duplications for systematic reviews and meta-analyses on the subject being studied. The study protocol was registered with PROSPERO – Prospective Register of Systematic Reviews and Meta-analysis (CRD42024499640) – and followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) reporting guidelines (Online Appendix, Table 1-A1).
Searching strategies
We used the Population, Intervention, Comparator, Outcome, Timing, and Study design (PICOT/S) framework to clarify the research parameters (Table 1).
| TABLE 1: PICOT/S framework for systematic review and meta-analysis on the link between life expectancy and disability in low and middle-income countries. |
We systematically searched six electronic databases: Medline, Embase, Global Health, Scopus, Web of Science and Cochrane Library for studies on 03 March 2023. Google and Google Scholar search engines assessed for grey literature and additional sources. In addition, the reference tracing of included studies was conducted and more eligible articles were obtained. The initial search was carried out in the Medline database using the search strategy string (Online Appendix, Table 2-A1). The search was conducted using keywords, vocabulary words and MeSH (medical subject headings) terms related to disability, LE and LMICs (classified by the World Bank Group) (Hamadeh et al. 2022). Boolean operators (‘OR’, ‘AND’ and ‘NOT’) as well as truncations (*) were applied both individually and collectively.
Eligibility criteria for study inclusion
We included published articles between 01 January 2005 and 03 October 2023, benchmarking the World Health Assembly Resolution on Universal Health Coverage in 2005 and the growing momentum advocating for the adoption of the United Nations Convention on the Rights of Persons with Disabilities (UNCRPD) in 2006 (Guide 2014; Latko et al. 2011). Eligible studies had to fulfil the following criteria: (1) quantitative observational (cross-sectional, case-control, cohort) or interventional (trial) studies; (2) report and/or compare data on the mean or standard error of LE (at birth or later) between people with and without disabilities of all ages; (3) undertaken in LMICs (Hamadeh et al. 2022); (4) published in English; and (5) disability assessed using the Washington Group module and/or other reliable and/or validated measures of disability (Hanass-Hancock et al. 2023). The review excluded records with no full text, editorials, review studies or qualitative research. Studies without a clear measure of LE were excluded.
Study outcome and explanatory variables
The primary outcome of this review was the LE of people with and without disabilities, measured as the average age at death, or YLL, measured as the mean difference in LE between people with disabilities and those without disabilities or the general population. Other measures of LE, such as subjective LE or disability-free/healthy LE, were not eligible. The LE and YLL were recorded by sex (male and female), where available.
Study screening and data extraction strategy
After retrieving all records from the databases, we exported records to the bibliographic software, Endnote Version 20 reference manager, to remove the duplicate studies. Then, the remaining studies were double-screened (DDA and HK) using the Rayyan app based on title and abstract against criteria to identify possibly eligible studies. Full-text studies were evaluated to decide the inclusion of articles in the analysis. The disagreements in study screening were resolved through a consensus-based discussion.
The Joanna Briggs Institute (JBI) data extraction tool was applied to systematically extract and organise data, ensuring consistency and accuracy. All required data were independently extracted by two authors (DDA and HK) and recorded in a Microsoft Excel spreadsheet. The data extraction protocol includes the first author’s name, publication year, study settings/country, study design, sample size, methods of analysis used, sex of respondents, age at which LE was estimated and a measure of LE (e.g. mean LE, YLL, a measure of effect where available (e.g., 95% confidence interval [CI], p-value), methods of calculating LE and disability type.
Methods of assessing the outcomes
This systematic review and meta-analysis reported LE of people with disabilities and people without disabilities or the general population. If multiple reports of LE at different set ages were available, the mean LE for the longest duration of follow-up and at the youngest set age were selected. For studies with multiple LE or YLL estimates for different impairment types, we used one estimate per study after calculating the weighted average based on the number of participants for each impairment type. For studies that provided CIs instead of standard deviations, we converted the CIs to standard deviations for the meta-analysis. If studies did not report standard deviations for LE/YLL estimates, we employed multiple imputation methods using pooled effect sizes from other studies included in the meta-analysis (Furukawa et al. 2006).
Quality assessment of included studies
The quality of the included studies was evaluated using the JBI critical appraisal checklist (Online Appendix, Table 3-A1) (Barker et al. 2024). The JBI checklist contains 11 parameters that were listed from ‘participant ascertainment’ to ‘the appropriateness of the statistical analysis’. Two reviewers independently (DDA and HK) assessed the quality of the included studies. Disagreements among reviewers were resolved through discussion. Finally, studies with an overall quality appraisal score of ≥ 5 were included in the review.
Data analysis and presentation
We extracted the data and exported it to R 4.3.3 statistical software (The R Project for Statistical Computing, Auckland, North Island, New Zealand) for further analysis. A random-effects model (Restricted Maximum Likelihood Methods) was employed in this meta-analysis to obtain pooled effect estimates, summarised as average LE for people with and without disabilities or the general population, and the YLL, with 95% CIs (Borenstein et al. 2010). The heterogeneity between the included articles was computed and checked using Cochrane Q test statistic (chi-square), I2 index and p-values. The heterogeneity was classified as low (25%), moderate (50%) or high (75%) based on the results of the I2 test (Higgins et al. 2003). Meta-regression and sub-group analyses (considering sex, age group and disability type) were performed respectively, using a random-effect model to investigate the sources of heterogeneity. A sensitivity analysis was conducted to assess the impact of a single study on the overall estimation of meta-analysis.
Univariable and multi-variable meta-regression analyses were performed to identify how much each study characteristic contributed to the heterogeneity in estimating the pooled YLL estimates. The multivariable meta-regression analysis included all potential moderators in the final model. This analysis aimed to measure the extent to which all moderators included in the final model explained the observed true heterogeneity (R2) and the remaining unexplained or residual heterogeneity (I2) and to assess if the model adequately explained the observed variability (Qm). Forest plots were computed to visualise the presence of heterogeneity among studies. A meta-cumulative analysis was conducted to examine the pattern of effects and the significance of cumulative effects over the publication years. The publication bias was assessed objectively using Egger’s regression and Begg’s test (Begg & Mazumdar 1994; Egger et al. 1997), and subjectively by observing the funnel plot. A p-value of less than or equal to 0.05 was considered statistically significant.
Review findings
Search results
Out of the initial 8476 articles retrieved on LE and disability status, 2400 records were excluded because of duplications, leaving 6089 articles for title and abstract screening. Then, 56 studies were selected for full-text screening; 44 studies were excluded (Online Appendix, Table 4-A1) leaving 12 articles that fulfilled the eligibility criteria and were included in the analysis (Figure 1).
Characteristics of the included studies
This study extracted and analysed data from the 12 eligible studies. Sample sizes of the included studies ranged from 510 (Ran et al. 2020) to 1 359 812 (Zhan et al. 2023) participants. In terms of regional distribution, eight studies were conducted in Asia (six from China, one each from India and Mongolia), two in South America (both from Brazil) and two in Africa (Ethiopia and South Africa). All studies employed a longitudinal observational study design and were published between the years 2015 and 2023. In the included studies, participants were identified from death registers, health care records, health insurance databases, disability registers and study cohorts. Most included studies (n = 11) used the International Classification of Diseases (ICD) criteria for diagnosing or measuring disability. The follow-up duration varied from 2 to 24 years. In the included studies, various impairment types were evaluated, including psychosocial (n = 7), cognitive (n = 2), neurological disorders (n = 2) and multiple disabilities (n = 1) (Online Appendix, Table 5-A1). Approximately nine studies reported the average LE and the LE gaps concurrently (Table 2).
| TABLE 2: Characteristics of included studies. |
In terms of LE estimation methods, five studies used life table methods, three employed Chiang’s method and the remainder used other approaches (Table 3). The ages at which LE was derived varied: about six studies used age at birth, while the other studies used different set ages (e.g. 20, 60 years). The mean LE differed between people with disabilities and the reference population (e.g. total population, individuals without disabilities), ranging from 47.27 to 89.34 years and from 55.6 to 80.2 years, respectively. The YLL (or LE gaps) also varied between 2.19 and 28.4 years.
Quality appraisal
The quality scores of the included studies ranged from 7 to 11 (maximum 11 points – showing lowest possible risk of bias). Thus, all included studies were of medium to high quality, and none were rated as low (Online Appendix, Table 3-A1).
Meta-analysis
The mean life expectancy in people with and without disabilities
Nine studies reported LE in people with disabilities and eight in people without disabilities. The analysis estimated the average LE of people with disabilities to be 57.98 (95% CI: 53.4–62.95) years. There was significant variability in the data (I2 = 100%, p < 0.001), indicating a high level of heterogeneity; as a result, a random effect model was employed. For people without disabilities, the average LE was 70.86 (95% CI: 64.06–78.39), again with a high heterogeneity among studies (I2 = 100%, p < 0.001) (Figure 2). People with disabilities experienced a substantially lower mean LE compared to those without disabilities (-13.29 years; 95% CI: -21.58 to -5.0; p = 0.002) (Online Appendix, Figure 1-A1).
 |
FIGURE 2: Forest plot showing the life expectancy among people with and without disabilities. |
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Mean years of life lost
Eight studies presented YLL, and for the remaining four, this estimate was calculated as the mean difference in LE between people with and without disabilities. The weighted average YLL was 15.84 years (95% CI: 11.1–22.61; I2 = 99.8%, p < 0.001), encompassing a predictive interval from 3.83 to 65.61 years (Figure 3). It appeared that YLL was higher for men with disabilities (16.33; 95% CI: 11.49–23.21 years) compared with women with disabilities (13.70; 95% CI: 8.45–22.22) although this difference was not statistically significant (t = 392; p = 0.69) (Online Appendix, Figure 2-A1).
 |
FIGURE 3: Forest plot showing the years of life lost in people with disabilities. |
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Publication bias and sensitivity analysis for mean years of life lost: The funnel plot displayed an asymmetrical distribution graphically, but the results of the Egger regression test (p = 0.79) and the Begg and Mazumdar test (p = 1.0) did not reach statistical significance, indicating the absence of small study effects. The trim-and-fill analysis identified and included two additional studies to address any potential oversights after examining the funnel plot and resulted in a pooled YLL estimate of 14.51 (95% CI: 10.5–20.03), with an overlap of CIs indicating a lack of statistically significant difference (Online Appendix, Figure 3-A1). A leave-one-out meta-analysis using the REML (restricted maximum likelihood) method was conducted to assess the influence of each study on the overall results. No indicated outliers confirmed robust and consistent results without single-study effects.
Weighted mean differences in years of life lost by gender: There was a higher level of heterogeneity in the meta-analysis when assessing the mean difference in YLL between disabled male and female participants (I2 = 96.8%, p < 0.001). With a random-effects model applied, the mean difference of YLL in male versus female participants was estimated to be 0.42 (95% CI: -0.56 to 1.40) (p = 0.40), showing a lack of statistically significant difference in YLL by disability status between male and female participants (Figure 4).
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FIGURE 4: Forest plot showing the mean difference of years of life lost between men and women with disabilities. |
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Sub-group analysis in the weighted mean of years of life lost: The sub-group analyses were performed for different factors to identify the variation in YLL across included studies. Only the analysis stratified by WHO region revealed a statistically significant variation, as there were greater mean YLL observed in the region of the Americas at 26.06 years (95% CI:18.48–36.7) compared to the Western Pacific region at 17.2 years (95% CI:14.48–20.46) (z = 7.91, p = 0.048). Other variables, such as publication year, number of disabilities, risk of bias, study settings, disability type and follow-up duration, did not show significant heterogeneity in the pooled YLL between people with disabilities and the general population in the sub-group analysis (Online Appendix, Table 6-A1).
Meta-regression of the weighted mean of years of life lost: In the univariable meta-regression analysis, we fitted the characteristics including publication year (Qm = 0.54, p = 0.46), WHO region (Qm = 2.24, p = 0.52), length of study follow-up (Qm = 0.35, p = 0.55), risk of bias (Qm = 0.87, p = 0.35), study settings (Qm = 0.97, p = 0.32), disability type (Qm = 0.019, p = 0.99) and number of people with disabilities (Qm = 0.74, p = 0.39), none of which showed statistically significant association with mean YLL. Conversely, two characteristics showed significant associations with the weighted mean of YLL estimates in people with disabilities: method of LE estimation (Qm = 34.74; p < 0.0001) and source of data (Qm = 57.69, p < 0.0001) (Online Appendix, Table 7-A1). After multivariable adjustment of the above characteristics, the meta-regression, which accounted for all potential moderators, explained 76.03% (Qm = 42.06; p < 0.0001) of the variability in YLL (Online Appendix, Table 8-A1).
Discussion
Summary of key review findings
This systematic review and meta-analysis analysed 12 studies that provided quantitative data on LE and YLL among people with and without disabilities in LMICs. The findings revealed that across 9 studies, the average LE was lower in people with disabilities (57.98 years) compared to people without disabilities (70.86 years), and across all 12 studies, the YLL was approximately 16 years. There was no clear difference in LE between men and women with disabilities. Overall, there was no evidence of publication bias influencing the link between disability and YLL, and the individual studies showed a low risk of bias. The review also highlighted that no single study disproportionately influenced the collective estimation derived from the meta-analysis, suggesting a fair and equitable contribution from each study. However, sub-group analysis by data sources revealed significant differences in LE estimates, particularly larger effect size estimates from household surveys.
Comparability with existing studies
Previous studies show that people with disabilities have a higher mortality rate and consequently are more likely to die at earlier ages compared to those without disabilities (Kuper et al. 2024; Smythe & Kuper 2024), aligning with our review. This gap arises through multiple pathways including greater poverty and marginalisation of people with disabilities and consequently poorer social determinants of health, higher risk of secondary health conditions or life-limiting conditions (Garcia-Arguello et al. 2017) and treatment side effects (e.g., metabolic syndrome caused by antipsychotics) (De Hert et al. 2012; Leung et al. 2012) or the presence of life-limiting conditions, stigma and lower health care utilisation and treatment adherence (Clement et al. 2015; Corrigan, Druss & Perlick 2014; Kuper & Phyllis Heydt 2019; WHO 2022). This stark LE gap highlights the critical need to develop disability-inclusive health system.
The LE gap in people with disabilities was also comparable with meta-analyses of YLL among people with psychosocial impairments (Chan et al. 2023; Hjorthøj et al. 2017) and across different countries globally (Laursen et al. 2013; Pan et al. 2020; Ren et al. 2023). Our findings also converge with global evidence, indicating a 10–20 years LE gap for people with disabilities, irrespective of location or type of impairment (Kuper et al. 2024). Yet, YLL in this study was higher than those meta-analyses conducted globally on bipolar disorder (Chan et al. 2022; Jayatilleke et al. 2017) and lower than findings from other studies (Laursen 2011; Laursen et al. 2013, 2016; Ren et al. 2023; Rotenberg et al. 2023; Weye et al. 2020). The differences between these studies may be because of variations in their study populations (e.g., disability type), regional differences (e.g., disparities in healthcare access, policy, socio-economic factors and lifestyle), temporal changes (e.g., improved health access over time) or differences in definition/inclusion of disability in the studies (Laursen 2011; Laursen et al. 2013).
There was no difference in YLL between men and women with disabilities in this study, in contrast to the findings of previous meta-analyses (Chan et al. 2022, 2023; Hjorthøj et al. 2017) and other primary studies (Erlangsen et al. 2017; Jayatilleke et al. 2017; Kessing, Vradi & Andersen 2015; Laursen 2011; Moreno-Küstner et al. 2021; Ren et al. 2023). This difference could be attributed to multiple factors. Firstly, equitable health care access and improved social support systems may have reduced gender disparities seen in other contexts, where women often face greater barriers (Chan et al. 2023; WHO 2011). Secondly, differences in disability type and comorbidity profiles in our reviews may have minimised variability between genders, as some conditions affect men and women similarly (Thakral, Lacroix & Molton 2019). Thirdly, prior studies highlighting gender differences often emphasised regions or populations with distinct risk factors, such as higher male mortality from risk-taking behaviours or poorer health-seeking behaviours (Erlangsen et al. 2017; Jayatilleke et al. 2017). Fourthly, methodological differences, such as smaller sample sizes in earlier studies, may have exaggerated disparities (Hjorthøj et al. 2017; Laursen 2011). Finally, historical contexts reflected in meta-analyses and older studies may no longer align with current trends because of evolving social and health care landscapes (Moreno-Küstner et al. 2021; Shi 2015).
Strengths and limitations of the study
This is the first systematic review of the link between disability and LE in LMICs. Our approach was characterised by rigorous methodologies, including the pre-registration of protocols, adherence to PRISMA guidelines for systematic reviews, extensive searches conducted across multiple databases and the implementation of dual assessments at every stage of the review process. While our search strategy did not include studies from grey literature, there was little evidence that publication bias influenced the summary of this meta-analysis. The overall quality of the included studies was high, and the risk of bias did not significantly affect the association between disability and LE/YLL.
However, this meta-analysis is not without limitations. Our literature search for English language publications may have caused us to miss important studies. All the studies, except for one, focussed on people with psychiatric or neurological conditions which could limit the generalisability of the findings (e.g., psychotropic medication may have impacts on LE). Over half of the studies were from China, and data were missing from two WHO regions, suggesting that the studies in our review might not fully represent all LMICs, potentially limiting the generalisability of our findings. We could not carry out stratified analyses by disability type and across countries because of few eligible studies, overlooking significant variations between different subpopulations of people with disabilities. Moreover, all but one of the included studies used biomedical measures of disability based on ICD criteria despite the importance of measuring functioning including physical, sensory and psychosocial functioning. We did not consider the severity of disability and so could not assess the presence of a dose-response relationship with LE. Most of the included studies relied on health system records not initially intended for research and may have missed important factors. Furthermore, a few studies faced challenges in finding an appropriate comparison group, whether among individuals with disabilities or the general population. There was high heterogeneity in the meta-analysis for LE and YLL, likely because of limited reporting of characteristics, as meta-regression could not fully account for variability, suggesting other unknown factors.
Implications for research, policy and practice
Our study highlights that individuals with disabilities experience a marked reduction in LE. This underscores the pressing need for policy reforms aimed at providing inclusive health care services and addressing social determinants of health, particularly in LMICs. Therefore, it is imperative for the global disability community to advocate for affordable and inclusive health care systems that can improve the well-being, quality of life and societal integration of people with disabilities.
There are also implications for research. There is a need for more consistent promotion of the bio-psycho-social model of disability view with assessment of disability through functional differences, and production of disaggregated data by disability type, as underlined in the UNCRPD declaration. Overall, we only identified 12 studies on this important topic, showing that more evidence is needed, including on the gap between male and female individuals with disabilities.
Conclusion
Disability substantially reduced LE in LMICs. This inequity in YLL shows that health systems are failing to provide inclusive services, and therefore, interventions are needed to promote disability-inclusive health care aligned with global health targets. Moreover, this disparity in the shorter LE of people with disabilities highlighted the need for targeted policy interventions and focussed research efforts across disability types, sex and global regions. By addressing these disparities through inclusive policies and robust research, it is possible to improve health outcomes and extend the LE of people with disabilities, thereby promoting equity and social justice.
Acknowledgements
The authors would like to express their gratitude to Russell Burke and Kathleen Perris (Assistant Librarian – Info Services) for their support with this systematic review and meta-analysis. They are also grateful to Mr. Abebaw Gedif for his assistance with R statistical software during the analysis.
Competing interests
The author D.D.A. reported that he received funding from the Younger Family Fund under the Missing Billion Project in Uganda which may be affected by the research reported in the enclosed publication. He has disclosed those interests fully and has implemented an approved plan for managing any potential conflicts arising from their involvement. The terms of these funding arrangements have been reviewed and approved by the affiliated university in accordance with its policy on objectivity in research.
Authors’ contributions
D.D.A., H.K. and F.B.M. conceived the study. D.D.A. designed a search strategy, executed the search, and drafted and revised the article. D.D.A. and H.K. screened the titles and full texts of the studies and also handled data extraction and risk of bias assessment. H.K., F.B.M. and M.Y.M. critically reviewed the article. All authors contributed intellectually, reviewed critically and approved the final article prior to submission.
Funding information
The author D.D.A. was funded for his PhD by the Younger Family Fund under the Missing Billion Project in Uganda. However, the funders did not influence the study design, data analysis, interpretation or writing of the article.
Data availability
The protocol for this systematic review is available in PROSPERO under the number CRD42024499640. The data sets generated and analysed during the current review can be obtained from the corresponding author D.D.A. upon reasonable request.
Disclaimer
The views and opinions expressed in this article are those of the authors and are the product of professional research. The article does not necessarily reflect the official policy or position of any affiliated institution, funder, agency or that of the publisher. The authors are responsible for this article’s results, findings and content.
References
Adugna, M.B., Nabbouh, F., Shehata, S. & Ghahari, S., 2020, ‘Barriers and facilitators to healthcare access for children with disabilities in low and middle income sub-Saharan African countries: A scoping review’, BMC Health Services Research 20, 1–11. https://doi.org/10.1186/s12913-019-4822-6
Andrade, F.C.D., Corona, L.P. & de Oliveira Duarte, Y.A., 2019. ‘Educational differences in cognitive life expectancy among older adults in Brazil’, Journal of the American Geriatrics Society 67(6), 1218–1225.
Armour, B.S., Campbell, V.A., Crews, J.E., Richard, R.A., Malarcher, A. & Maurice, E., 2007, ‘Peer reviewed: State-level prevalence of cigarette smoking and treatment advice, by disability status, United States, 2004’, Preventing Chronic Disease 4, A86.
Bahk, J., Kang, H.-Y. & Khang, Y.-H., 2019, ‘The life expectancy gap between registered disabled and non-disabled people in Korea from 2004 to 2017’, International Journal of Environmental Research and Public Health 16(14), 2593. https://doi.org/10.3390/ijerph16142593
Barker, T.H., Habibi, N., Aromataris, E., Stone, J.C., Leonardi-Bee, J., Sears, K. et al., 2024, ‘The revised JBI critical appraisal tool for the assessment of risk of bias for quasi-experimental studies’, JBI Evidence Synthesis 22(3), 378–388. https://doi.org/10.11124/JBIES-23-00268
Banerjee, T.K., Dutta, S., Das, S., Ghosal, M., Ray, B.K., Biswas, A. et al., 2017, ‘Epidemiology of dementia and its burden in the city of Kolkata, India’, International Journal of Geriatric Psychiatry 32(6), 605–614.
Begg, C.B. & Mazumdar, M., 1994, ‘Operating characteristics of a rank correlation test for publication bias’, Biometrics 50(4), 1088–1101. https://doi.org/10.2307/2533446
Borenstein, M., Hedges, L.V., Higgins, J.P. & Rothstein, H.R., 2010, ‘A basic introduction to fixed-effect and random-effects models for meta-analysis’, Research Synthesis Methods 1(2), 97–111. https://doi.org/10.1002/jrsm.12
Britain, D.O.H.G., 2001, NHS Plan: Technical supplement on target setting for health improvement, Department of Health, London.
Centers for Disease Control and Prevention, 2008, ‘Racial/ethnic disparities in self-rated health status among adults with and without disabilities – United States, 2004–2006’, MMWR. Morbidity and Mortality Weekly Report 57, 1069–1073.
Chan, J.K.N., Correll, C.U., Wong, C.S.M., Chu, R.S.T., Fung, V.S.C., Wong, G.H.S. et al., 2023, ‘Life expectancy and years of potential life lost in people with mental disorders: A systematic review and meta-analysis’, EClinicalMedicine 65, 102294. https://doi.org/10.1016/j.eclinm.2023.102294
Chan, J.K.N., Tong, C.H.Y., Wong, C.S.M., Chen, E.Y.H. & Chang, W.C., 2022, ‘Life expectancy and years of potential life lost in bipolar disorder: Systematic review and meta-analysis’, The British Journal of Psychiatry 221(3), 567–576. https://doi.org/10.1192/bjp.2022.19
Clement, S., Schauman, O., Graham, T., Maggioni, F., Evans-Lacko, S., Bezborodovs, N. et al., 2015, ‘What is the impact of mental health-related stigma on help-seeking? A systematic review of quantitative and qualitative studies’, Psychological Medicine 45(1), 11–27. https://doi.org/10.1017/S0033291714000129
Corrigan, P.W., Druss, B.G. & Perlick, D.A., 2014, ‘The impact of mental illness stigma on seeking and participating in mental health care’, Psychological Science in the Public Interest 15(2), 37–70. https://doi.org/10.1177/1529100614531398
Da Roza, D.L., De Rezende, M.G., Barros, R.E.M., De Azevedo-Marques, J.M., Santos, J.L.F., Morais, L.C.C. et al., 2023, ‘Excess mortality in a cohort of Brazilian patients with a median follow-up of 11 years after the first psychiatric hospital admission’, Social Psychiatry and Psychiatric Epidemiology 58, 319–330. https://doi.org/10.1007/s00127-022-02304-z
Dagnachew, N., Meshesha, S.G. & Mekonen, Z.T., 2021, ‘A qualitative exploration of barriers in accessing community pharmacy services for persons with disability in Addis Ababa, Ethiopia: A cross sectional phenomenological study’, BMC Health Services Research 21, 1–8. https://doi.org/10.1186/s12913-021-06488-z
De Hert, M., Detraux, J., Van Winkel, R., Yu, W. & Correll, C.U., 2012, ‘Metabolic and cardiovascular adverse effects associated with antipsychotic drugs’, Nature Reviews Endocrinology 8, 114–126. https://doi.org/10.1038/nrendo.2011.156
Egger, M., Smith, G.D., Schneider, M. & Minder, C., 1997, ‘Bias in meta-analysis detected by a simple, graphical test’, BMJ 315, 629–634. https://doi.org/10.1136/bmj.315.7109.629
Egüez-Guevara, P. & Andrade, F.C.D., 2015, ‘Gender differences in life expectancy with and without disability among older adults in Ecuador’, Archives of Gerontology and Geriatrics 61(3), 472–479. https://doi.org/10.1016/j.archger.2015.08.012
Erlangsen, A., Andersen, P.K., Toender, A., Laursen, T.M., Nordentoft, M. & Canudas-Romo, V., 2017, ‘Cause-specific life-years lost in people with mental disorders: A nationwide, register-based cohort study’, Lancet Psychiatry 4(12), 937–945. https://doi.org/10.1016/S2215-0366(17)30429-7
Fekadu, A., Medhin, G., Kebede, D., Alem, A., Cleare, A.J., Prince, M. et al., 2015, ‘Excess mortality in severe mental illness: 10-year population-based cohort study in rural Ethiopia’, The British Journal of Psychiatry 206(4), 289–296. https://doi.org/10.1192/bjp.bp.114.149112
Furukawa, T.A., Barbui, C., Cipriani, A., Brambilla, P. & Watanabe, N., 2006, ‘Imputing missing standard deviations in meta-analyses can provide accurate results’, Journal of Clinical Epidemiology 59(1), 7–10. https://doi.org/10.1016/j.jclinepi.2005.06.006
Garcia-Arguello, L.Y., O’Horo, J.C., Farrell, A., Blakney, R., Sohail, M.R., Evans, C.T. et al., 2017, ‘Infections in the spinal cord-injured population: A systematic review’, Spinal Cord 55, 526–534. https://doi.org/10.1038/sc.2016.173
Guide, T., 2014, The convention on the rights of persons with disabilities, United Nations of Human Rights, New York.
Hamadeh, N., Rompaey, C.V., Metreau, E. & Eapen, S.G., 2022, New World Bank country classifications by income level: 2022–2023, viewed 11 February 2023, from https://blogs.worldbank.org/opendata/new-world-bank-country-classifications-income-level-2022-2023.
Hanass-Hancock, J., Kamalakannan, S., Murthy, G., Palmer, M., Pinilla-Roncancio, M., Velarde, M.R. et al., 2023, ‘What cut-off (s) to use with the Washington Group short set of questions?’, Disability and Health Journal 16(4), 101499. https://doi.org/10.1016/j.dhjo.2023.101499
Higgins, J.P., Thompson, S.G., Deeks, J.J. & Altman, D.G., 2003, ‘Measuring inconsistency in meta-analyses’, BMJ 327, 557–560. https://doi.org/10.1136/bmj.327.7414.557
Hjorthøj, C., Stürup, A.E., Mcgrath, J.J. & Nordentoft, M., 2017, ‘Years of potential life lost and life expectancy in schizophrenia: A systematic review and meta-analysis’, The Lancet Psychiatry 4(4), 295–301. https://doi.org/10.1016/S2215-0366(17)30078-0
Hollis, N.D., Zhang, Q.C., Cyrus, A.C., Courtney-Long, E., Watson, K. & Carroll, D.D., 2020, ‘Physical activity types among US adults with mobility disability, Behavioral Risk Factor Surveillance System, 2017’, Disability and Health Journal 13(3), 100888. https://doi.org/10.1016/j.dhjo.2020.100888
Issifou, I. & Pewitt, J., 2022, On the importance of monitoring inequality in life expectancy, United Nations Department of Economic and Social Affairs (UN DESA), New York.
Jayatilleke, N., Hayes, R.D., Dutta, R., Shetty, H., Hotopf, M., Chang, C.-K. et al., 2017, ‘Contributions of specific causes of death to lost life expectancy in severe mental illness’, European Psychiatry 43, 109–115. https://doi.org/10.1016/j.eurpsy.2017.02.487
Keeler, E., Guralnik, J.M., Tian, H., Wallace, R.B. & Reuben, D.B., 2010, ‘The impact of functional status on life expectancy in older persons’, Journals of Gerontology Series A: Biomedical Sciences and Medical Sciences 65A(7), 727–733. https://doi.org/10.1093/gerona/glq029
Kessing, L.V., Vradi, E. & Andersen, P.K., 2015, ‘Life expectancy in bipolar disorder’, Bipolar Disorders 17(5), 543–548. https://doi.org/10.1111/bdi.12296
Kuper, H. & Heydt, P., 2019, The Missing Billion: Access to health services for 1 billion people with disabilities, viewed 25 February 2024, from https://www.lshtm.ac.uk/research/centres/international-centre-evidence-disability/missing-billion.
Kuper, H., Rotenberg, S., Banks, L.M. & Smythe, T., 2024, ‘The association between disability and mortality: A mixed-methods study’, The Lancet Public Health 9(5), e306–e315. https://doi.org/10.1016/S2468-2667(24)00054-9
Latko, B., Temporão, J.G., Frenk, J., Evans, T.G., Chen, L.C., Pablos-Mendez, A. et al., 2011, ‘The growing movement for universal health coverage’, The Lancet 377(9784), 2161–2163. https://doi.org/10.1016/S0140-6736(10)62006-5
Lauer, E. & McCallion, P., 2015, ‘Mortality of people with intellectual and developmental disabilities from select US state disability service systems and medical claims data’, Journal of Applied Research in Intellectual Disabilities 28(5), 394–405. https://doi.org/10.1111/jar.12191
Laursen, T.M., 2011, ‘Life expectancy among persons with schizophrenia or bipolar affective disorder’, Schizophrenia Research 131(1–3), 101–104. https://doi.org/10.1016/j.schres.2011.06.008
Laursen, T.M., Musliner, K.L., Benros, M.E., Vestergaard, M. & Munk-Olsen, T., 2016, ‘Mortality and life expectancy in persons with severe unipolar depression’, Journal of Affective Disorders 193, 203–207. https://doi.org/10.1016/j.jad.2015.12.067
Laursen, T.M., Wahlbeck, K., Hällgren, J., Westman, J., Ösby, U., Alinaghizadeh, H. et al., 2013, ‘Life expectancy and death by diseases of the circulatory system in patients with bipolar disorder or schizophrenia in the Nordic countries’, PLoS One 8(6), e67133. https://doi.org/10.1371/journal.pone.0067133
Leung, J.Y., Barr, A.M., Procyshyn, R.M., Honer, W.G. & Pang, C.C., 2012, ‘Cardiovascular side-effects of antipsychotic drugs: The role of the autonomic nervous system’, Pharmacology & Therapeutics 135(2), 113–122. https://doi.org/10.1016/j.pharmthera.2012.04.003
Liu, X., Wang, D., Fan, R., Wang, R., Xiang, H., Yang, X. et al., 2022, ‘Life expectancy and potential years of life lost for schizophrenia in western China’, Psychiatry research 308, 114330.
Luo, Z., Lv, H., Chen, Y., Xu, X., Liu, K., Li, X. et al., 2021, ‘Years of life lost due to premature death and their trends in people with selected neurological disorders in Shanghai, China, 1995–2018: A population-based study’, Frontiers in neurology 12, 625042.
Ma, Y., Qian, Y., Sun, Z., Niu, M., Yu, D., Li, H. et al., 2019, ‘Death characteristics of neurological diseases in inner Mongolia between 2008 and 2015’, Open Journal of Preventive Medicine 9(3), 21–28. https://doi.org/10.4236/ojpm.2019.93003
Maïano, C., Hue, O., Morin, A.J. & Moullec, G., 2016, ‘Prevalence of overweight and obesity among children and adolescents with intellectual disabilities: A systematic review and meta-analysis’, Obesity Reviews 17(7), 599–611. https://doi.org/10.1111/obr.12408
Moreno-Küstner, B., Guzman-Parra, J., Pardo, Y., Sanchidrián, Y., Díaz-Ruiz, S. & Mayoral-Cleries, F., 2021, ‘Excess mortality in patients with schizophrenia spectrum disorders in Malaga (Spain): A cohort study’, Epidemiology and Psychiatric Sciences 30, e11. https://doi.org/10.1017/S2045796020001146
Moreno, X., Lera, L., Moreno, F. & Albala, C., 2019, ‘Life expectancy with and without cognitive impairment among Chilean older adults: Results of the National Survey of Health (2003, 2009 and 2016)’, BMC Geriatrics 19, 1–6. https://doi.org/10.1186/s12877-019-1387-5
Page, M.J., McKenzie, J.E., Bossuyt, P.M., Boutron, I., Hoffmann, T.C., Mulrow, C.D. et al., 2021, ‘The PRISMA 2020 statement: An updated guideline for reporting systematic reviews’, Systematic Reviews 10, 89. https://doi.org/10.1186/s13643-021-01626-4
Pan, Y.-J., Yeh, L.-L., Chan, H.-Y. & Chang, C.-K., 2020, ‘Excess mortality and shortened life expectancy in people with major mental illnesses in Taiwan’, Epidemiology and Psychiatric Sciences 29, e156. https://doi.org/10.1017/S2045796020000694
Park, J.M., Oh, U., Roh, B.-R. & Moon, Y., 2017, ‘Disparities in mortality by disability: An 11-year follow-up study of 1 million individuals’, International Journal of Public Health 62, 989–996. https://doi.org/10.1007/s00038-017-0966-5
Pinto, A., Köptcke, L.S., David, R. & Kuper, H., 2021, ‘A national accessibility audit of primary health care facilities in Brazil-are people with disabilities being denied their right to health?’, International Journal of Environmental Research and Public Health 18(6), 2953. https://doi.org/10.3390/ijerph18062953
Ran, M.S., Xiao, Y., Fazel, S., Lee, Y., Luo, W., Hu, S.H. et al., 2020, ‘Mortality and suicide in schizophrenia: 21-year follow-up in rural China’, BJPsych Open 6(6), e121. https://doi.org/10.1192/bjo.2020.106
Ren, J., Duan, Y., Wang, J., Sun, Y., Wang, M., Geng, Z. et al., 2023, ‘Mortality and excess life-years lost in patients with schizophrenia under community care: A 5-year follow-up cohort study’, Brazilian Journal of Psychiatry 45(3), 216–225. https://doi.org/10.47626/1516-4446-2022-2918
Rotenberg, S.H., Smythe, T. & Kuper, H., 2023, ‘Left behind: Modelling the life expectancy disparities amongst people with disabilities in low and middle-income countries’, medRxiv, 2023.07. 12.23292565.
Ruffieux, Y., Wettstein, A., Maartens, G., Folb, N., Mesa-Vieira, C., Didden, C. et al., 2023, ‘Life years lost associated with mental illness: A cohort study of beneficiaries of a South African medical insurance scheme’, Journal of affective disorders 340, 204–212. https://doi.org/10.1016/j.jad.2023.08.013
Scalfari, A., Knappertz, V., Cutter, G., Goodin, D.S., Ashton, R. & Ebers, G.C., 2013, ‘Mortality in patients with multiple sclerosis’, Neurology 81(2), 184–192. https://doi.org/10.1212/WNL.0b013e31829a3388
Shi, X.S.J., Wheeler, K.K., Stallones, L., Ameratunga, S., Shakespeare, T., Smith, G.A. et al., 2015, ‘Unintentional injuries in children with disabilities: A systematic review and meta-analysis’, Injury Epidemiology 2, 21. https://doi.org/10.1186/s40621-015-0053-4
Smythe, T. & Kuper, H., 2024, ‘The association between disability and all-cause mortality in low-income and middle-income countries: A systematic review and meta-analysis’, The Lancet Global Health 12(5), e756–e770. https://doi.org/10.1016/S2214-109X(24)00042-1
Thakral, M., Lacroix, A.Z. & Molton, I.R., 2019, ‘Sex/gender disparities in health outcomes of individuals with long-term disabling conditions’, Rehabilitation Psychology 64(4), 221–228. https://doi.org/10.1037/rep0000248
Weye, N., Momen, N.C., Christensen, M.K., Iburg, K.M., Dalsgaard, S., Laursen, T.M. et al., 2020, ‘Association of specific mental disorders with premature mortality in the Danish population using alternative measurement methods’, JAMA Network Open 3(6), e206646. https://doi.org/10.1001/jamanetworkopen.2020.6646
World Health Organization (WHO), 2011, World report on disability 2011, World Health Organization, Geneva.
World Health Organization (WHO), 2015, World report on ageing and health, World Health Organization, Geneva.
World Health Organization (WHO), 2022, Global report on health equity for persons with disabilities, World Health Organization, Geneva.
Wu, J., Wang, Y., Wang, L., Wu, H., Li, J. & Zhang, L., 2023, ‘Trends and burden in mental disorder death in China from 2009 to 2019: A nationwide longitudinal study’, Frontiers in Psychiatry 14, 1169502.
Zhan, P., Li, D., Zhang, X. & Bai, X. 2023, ‘The average life expectancy of persons with disabilities in China’, SSM-Population Health 24, 101526. https://doi.org/10.1016/j.ssmph.2023.101526
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