Dementia risk reduction potential among faith communities: Insights into modifiable risk factors in the English population

Dementia risk reduction potential among faith communities: Insights into modifiable risk factors in the English population | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Dementia risk reduction potential among faith communities: Insights into modifiable risk factors in the English population Sanda Umar Ismail, Heather Brown, Faraz Ahmed, Carol Holland This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7290936/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 11 You are reading this latest preprint version Abstract Background: Dementia prevention is a global public health priority. However, limited research has examined modifiable dementia risk factors (MRFs) within faith communities, despite their significant presence in the population of England. Faith communities often exhibit distinct social structures and lifestyle behaviours, such as strong social cohesion and shared health norms, that may shape both the exposure to and impact of MRFs. This study aimed to explore the distribution and relative contribution of MRFs among faith and nonfaith communities in England to inform contextually tailored prevention strategies. Methods: We conducted a cross-sectional analysis using data from Wave 5 (2010–2011) of the English Longitudinal Study of Ageing (ELSA). Population attributable fractions (PAFs) for dementia were calculated for 14 MRFs using relative risks derived from the Lancet Commission on Dementia Prevention and prevalence estimates for the English population. Weighted PAFs were computed for the general ELSA population (n= 8812), faith (n= 7364) and nonfaith communities (n= 1448), with analyses adjusted for overlapping risk factors. Results: The overall weighted PAF was 35.3% in the general ELSA sample, 34.3% in the faith community, and 46.1% in the nonfaith community. Within faith communities, the highest PAF contributors were depression (9.2%), social isolation (4.5%), hearing loss (4.1%), and visual impairment (3.1%). For nonfaith communities, these were depression (11.4%), social isolation (7.5%), high low-density lipoprotein cholesterol (5.4%) and low education (4.6%). Interpretation: Faith communities in England exhibited differences in the relative contribution of modifiable dementia risk factors compared with nonfaith communities. These findings may help inform the development of contextually tailored prevention strategies, although further longitudinal and interventional studies are required to confirm these patterns and assess causality. A deeper understanding within specific faith groups could refine these interventions. Importantly, the findings also highlight how culturally informed, community-centred approaches can enhance dementia prevention in secular populations with similar social structures—such as exercise groups or meditation circles—informing inclusive public health strategies across diverse community settings. Dementia faith communities modifiable risk factors population attributable fraction public health interventions Introduction Dementia and modifiable risk factors: a public health priority Dementia remains a significant global public health challenge. While evidence from high-income countries indicates a decline in age-specific incidence rates, largely attributed to improvements in education, cardiovascular health, and lifestyle modifications [ 1 ], the absolute prevalence continues to rise due to population ageing. In England and Wales, dementia has become the leading cause of death [ 2 ]. In addition to its profound health implications, dementia imposes substantial socioeconomic burdens, with projected costs in England alone expected to exceed £80 billion by 2040 [ 3 ]. In the absence of a cure, dementia prevention has emerged as a critical public health priority, emphasising the identification and mitigation of modifiable risk factors (MRFs) across the life course [ 4 ]. Reports from the Lancet Commission highlight 14 key MRFs, including less education in early life (before 45 years of age), depression, smoking, physical inactivity, untreated hearing loss, diabetes, hypertension during middle life (45–64 years of age), social isolation, air pollution and untreated vision impairment in late life (65 years of age and above), which are collectively estimated to account for 45% of all dementia cases worldwide [ 5 , 6 ]. These findings highlight significant opportunities for intervention, positioning risk factor control and promoting healthy lifestyles at the forefront of dementia prevention strategies [ 7 ]. It is therefore an opportune moment to begin delineating which modifiable risk factors are most salient for specific population subgroups. Population-specific risk profiles and targeted prevention The distribution of dementia risk factors is not uniform across all segments of society. Different populations and communities can exhibit distinct risk factor profiles, leading to variations in dementia risk. For example, studies that have analysed the population distribution of modifiable dementia risk factors among ethnic communities have revealed disparities in these distributions in comparison to the general English population [ 8 – 10 ]. Compared with White populations, Mukadam et al.[ 8 ] used a large and representative dataset of English electronic health records to identify higher prevalence rates and stronger impacts of modifiable risk factors, such as diabetes in South Asians and hypertension in Black populations. Similarly, Chen et al.[ 10 ] reported persistent disparities in population attributable fractions (PAFs) for dementia, with lower-income groups showing higher cumulative modifiable risk due to factors such as social isolation, low education, and smoking. These findings highlight the need to examine specific risk factor burdens present in different communities, as these findings will inform tailored prevention strategies. However, while previous research on the distribution of MRFs of dementia across specific subsections of populations has focused on ethnicity [ 8 , 9 ] and socioeconomic status [ 10 ], another critical yet understudied subgroup is faith communities. Faith communities as a distinct population segment Faith communities include individuals who identify with and actively participate in organised religious groups such as churches, mosques, temples, and other congregations [ 11 ]. While this analysis combines various religious affiliations into a single 'faith community' category for statistical power, we acknowledge that this approach may mask significant differences between specific religious traditions. This limitation is addressed further in the Discussion. In England, a significant proportion of the population identifies with a faith community. According to the recent census, approximately 57% of people report a religious affiliation, with nearly 30% of those aged 65 and older identifying as Christian [ 12 ]. Religious identity entails shared norms, beliefs, and social structures that influence health behaviours and outcomes. These effects are not derived from spirituality itself but from modifiable behavioural and social pathways within faith communities. For example, faith-based norms often discourage smoking or excessive alcohol use, contributing to healthier lifestyle choices [ 13 , 14 ]. Religious gatherings also promote social interaction, cognitive stimulation, and emotional support—all factors known to reduce the risk of dementia [ 15 – 17 ]. Faith-based settings can also serve as powerful venues for public health intervention. They offer accessible infrastructure, trusted leadership, and established routines that can support culturally tailored interventions. A UK study demonstrated that dementia-friendly interventions implemented in church congregations significantly improved awareness and comfort among members [ 18 ]. Similarly, faith-based health programmes have successfully improved cardiovascular health behaviours [ 19 ]. However, challenges exist. Cultural and language barriers, combined with stigma around cognitive decline, may impede effective communication and engagement in health programmes in minority faith communities [ 20 ]. These issues can delay diagnosis and hinder the delivery of preventive care. Despite their potential, faith communities remain significantly underrepresented in dementia prevention research. Most existing studies fail to disaggregate data by religious affiliation or involvement, limiting our understanding of how faith-based social environments may shape dementia risk profile. While evidence suggests that religious involvement may influence key health behaviours and intermediate risk factors [ 21 ], the extent to which MRFs are distributed differently in faith versus nonfaith populations remains unclear. This lack of disaggregated data represents a critical gap, where MRFs could be targeted more effectively if we knew what the differentiation is for these groups. To address this gap, the present study examines the distribution and relative contribution of 14 MRFs of dementia among faith and nonfaith communities within the English Longitudinal Study of Ageing (ELSA). By identifying distinct risk profiles within these groups, this research contributes to a more nuanced understanding of dementia risk and supports the development of culturally responsive, community-based prevention strategies that harness the unique structures of faith-based settings. Beyond faith communities, these findings also provide broader insights into how culturally informed and community-centred approaches can increase dementia prevention efforts within diverse secular populations, particularly those sharing similar social structures and practices with faith communities, such as exercise groups, meditation circles, or community-based clubs. This expanded perspective can inform inclusive public health policies and tailor interventions applicable across various community settings. Methods We followed the STROBE guidelines [ 22 ] in reporting this study, with a registered protocol: https://doi.org/10.17605/OSF.IO/R8WTU . Study design and participants We utilised data from the ELSA, a nationally representative longitudinal survey of individuals aged 50 years and older in England. ELSA collects biennial data on various aspects of participants’ lives, including lifestyle, economic status, health, and social care, with the survey initially launched in 1998. The questionnaire items used to assess religious affiliation and the 13 modifiable dementia risk factors analysed in this study were part of the standard ELSA Wave 5 survey instruments. Full details of the questionnaire, including wording of the items, are publicly available from the UK Data Service [ 23 ]. For our analysis, we focused on data from Wave 5 (June 2010–July 2011), as it was the first wave to include information on faith (religious) community affiliation. The participants were asked to identify their religious affiliation from categories such as no religion, Christian, Buddhist, Hindu, Jewish, Muslim, Sikh, and other non-Christian. Owing to the small number of respondents in some non-Christian groups, we consolidated the responses into two broad categories: nonfaith community (no religion) and faith community (Christian and non-Christian). We included only participants with valid measures of faith affiliation and excluded individuals with dementia (based on self-reported diagnoses), as this condition could influence the prevalence of certain risk factors, such as depression and social isolation. The conventional definitions of the life course approach in dementia prevention research consider early life as < 45 years and midlife as 45–64 years [ 6 ], but given that the ELSA does not include younger adults, we consider early life to be < 50 years and midlife to be 50–64 years. Definition and prevalence of dementia risk factors We selected our set of modifiable risk factors based on the latest data from the Lancet Commission [ 6 ]. Prevalence data were available within the ELSA Wave 5 dataset for 13 of the 14 risk factors; the exception was traumatic brain injury (TBI), for which the ELSA did not collect direct information. We therefore estimated the prevalence of TBI using external data on head injury admissions from a national hospital statistics report. Specifically, we assumed that the prevalence of prior serious head injury in our cohort was comparable to the annual incidence of hospitalised head injury in England reported by Headway UK [ 24 ]. This estimation approach is consistent with previous research that incorporates external data when certain risk factor information is missing in the study dataset [ 25 , 26 ]. Table 1 outlines the operational definitions of each risk factor used in this study in comparison to those used by Livingston et al. [ 6 ]. Table 1 Definitions of risk factors (Livingston et al., 2024 vs. this study) Risk factors Definition used by Livingston et al. (2024) Definition in this study Early life Early life (age < 45 years) Early life (< 50 years) – assessed retrospectively in ages ≥ 50 Low education 7–10 years of education, primary school, continuation school, folk high school Self-reported highest academic level obtained is less than secondary school Midlife Midlife (45–64 years) Midlife (age 50–64 years) Depression The Hospital Anxiety and Depression Scale-Depression (HADS-D). A score of ≥ 8 indicates depression. 8-item Center for Epidemiologic Studies Depression scale (CESD-8). A score of 3 or more indicates depression [ 33 ]. Traumatic brain injury Have you ever been hospitalised for a head injury? Head injury from a hospital episode statistics system report (NHS Health and Social Care Information Centre [ 24 ]. High LDL cholesterol LDL cholesterol ≥ 130 mg/dL LDL cholesterol ≥ 3.36 mmol/l. Physical inactivity Self-report questionnaire: How has your physical activity in leisure time been during the last year? How has your physical activity in leisure time been during the last year? Self-reported work and activity frequency, categorising individuals as sedentary, low, moderate, or highly active. Physically inactive refers to those categorised as sedentary or low activity. Hearing loss Hearing threshold level ≥ 25 dB measured with a combination of questionnaire, otoscopy, and pure-tone audiometry. Merging two questions that asked respondents about how good their hearing was (excellent, very good, good, fair or poor) and a positive response of a ‘yes’ to the question about whether they find it difficult to follow a conversation if there is background noise (such as TV, radio or children playing) [ 47 ]. Smoking Current smoker daily Self-reported measures of current smokers - Do you currently smoke cigarettes? Hypertension Systolic blood pressure ≥ 140 or diastolic blood pressure ≥ 90 Self-report of a diagnosis of high blood pressure (mean systolic blood pressure ≥ 140 mm Hg or mean diastolic blood pressure ≥ 90 mm Hg. Obesity BMI ≥ 30 BMI ≥ 30 Diabetes Do you have, or have you ever had diabetes? Or nonfasting blood glucose ≥ 11.1 mmol/l at HUNT2. Blood glycated haemoglobin level ≥ 48 mmol/mol (6.5%) [ 48 ]. Excessive alcohol ≥ 21 units (= 168 g) per week Self-reported alcohol consumption over the past week of over 14 units per week[ 49 ] . Late-life age Late-life age (> 65 years) Late-life age (age > = 65 years) Social isolation Who do you live with? Those who answer no in all categories live alone. Not married/not cohabiting Air pollution Those who live in a municipality with a mean level of particle pollution ≥ 1.5 in 2016 are exposed. Fuels used in household for heating or other purpose - coal/smokeless fuel. Visual impairment Would you describe your impairment as slight, moderate, or severe or no impairment? Self-reports about how good their eyesight was (excellent, very good, good, fair or poor). Those who rated their eyesight as fair or poor were regarded as having visual impairment. [Insert Table 1 Here ] Statistical analysis We used the standard methodology outlined by Livingston et al.[ 6 , 27 ] to calculate communality and PAFs, quantifying each risk factor’s contribution to overall dementia risk. The PAF is a method for estimating the proportion of a health problem in a population that could be prevented if a specific risk factor was eliminated. In brief, the PAF for each risk factor was calculated based on its prevalence and the relative risk of dementia associated with that factor. A high PAF value does not suggest a particular risk factor is more prevalent in a certain community, but it does suggest that its prevalence, combined with the significance of that risk factor for the health outcome, is higher in that community. Prevalence was calculated for the total ELSA sample and the faith and nonfaith subgroups. To assess the shared variance among the 13 risk factors, we performed a principal component analysis (PCA) via tetrachoric correlations, and we excluded TBI from this PCA because of the lack of individual-level data for that factor (TBI was instead assigned the average communality of the other risk factors—part of the standard procedure for calculating communality). The overall PAF was then computed, considering cumulative risk reduction and adjusting for overlap among factors by weighting each PAF by a factor derived from its communality (i.e., the variance explained in the PCA). Finally, we derived individual “weighted” PAF values from the adjusted overall PAF (see the Supplementary Material for details of this approach). To enable direct comparison with the Lancet Commission findings, we applied the same relative risks reported by Livingston et al. [ 6 ] for each risk factor, despite slight differences in our factor definitions, so that our PAF estimates would be consistent and comparable with those of prior studies. Group differences in risk factor prevalence (faith vs. nonfaith) were evaluated via chi-square tests. Multiple imputation was employed to address missing data. We used the fully conditional specification (FCS) method for imputation [ 28 ], which is suitable for datasets with mixed continuous and categorical variables. Five imputed datasets were generated to account for uncertainty due to missing data, following best practices [ 29 ]. The imputation model included all the variables used in the analysis, and we assumed that the data were missing at random (MAR) conditional on the observed covariates. The variables with missing values were automatically selected by the software for prediction to optimise the imputation accuracy. After imputation, we exported the five completed datasets to R (version 4.2.3). Each imputed dataset was analysed separately in R. We calculated tetrachoric correlations for each dataset (after removing the indicator variable for imputation number) and then pooled the correlation matrices by averaging across the five imputations. This resulted in a final tetrachoric correlation matrix that reflects variability across imputations. The PCA and PAF calculations described above were then applied to this pooled correlation matrix to produce our reported results. Results Participants and descriptive data Our sample size initially comprised 10274 participants. We excluded 223 individuals with dementia and 1314 without a valid faith affiliation response, yielding a final analytic sample of 8737 participants. Table 2 summarises the sociodemographic characteristics of the participants and the prevalence of the 14 modifiable dementia risk factors in the general ELSA sample and by faith group. Most participants (62.1%, n = 899) within the nonfaith community were aged 50–64 years, and women were overrepresented in the faith community (58.6%, n = 4318). Almost all the participants were White in both the nonfaith (98.5%, n = 1427) and faith (97.1%, n = 7154) communities. Table 2 Sociodemographic characteristics and prevalence of the 14 modifiable risk factors for dementia in the general ELSA sample, as well as in nonfaith and faith communities. Characteristics and risk factors General ELSA community (n = 8812) Nonfaith community (n = 1448) Faith community (n = 7364) Sociodemographic characteristics, n (%) Age, 50–64 years 4360 (49.5) 899 (62.1) 3461 (47.0) Age, 65 + years 4452 (50.5) 519 (37.9) 3903 (53.0) Sex, female 4946 (56.1) 628 (43.4) 4318 (58.6) Ethnicity, White 8581 (97.4) 1427 (98.5) 7154 (97.1) Modifiable risk factors, prevalence % (95 CI) Early life Low education 24.5 (23.6–25.4) 28.4 (26.1–30.7) 25.9 (24.9–26.9) Midlife Depression 53.3 (52.2–54.3) 47.7 (15.1–50.2) 74.7 (73.7–75.7) High LDL cholesterol 51.5 (50.5–52.6) 69.3 (67.0–71.7) 45.7 (44.6–46.8) Hearing loss 32.4 (31.5–33.4) 32.3 (29.9–34.7) 67.4 (66.4–68.5) Obesity 33.1 (32.1–34.1) 22.8 (20.6–25.0) 28.9 (27.9–30.0) Smoking 26.3 (25.4–27.3) 30.3 (28.0–32.7) 15.0 (14.2–15.8) Alcohol 34.0 (33.0–35.0) 40.7 (38.1–43.2) 60 (58.9–61.2) Hypertension 26.9 (26.0–27.9) 34.5 (32.1–37.0) 34.6 (33.5–35.6) Diabetes 7.0 (6.9–8.0) 5.0 (3.9–6.1) 7.9 (7.3–8.5) Physical inactivity 21.9 (21.0–22.7) 14.4 (12.6–16.2) 28.0 (27.0–29.1) Traumatic brain injury 0.10 (0.03–0.17) 0.10 (0.03–0.17) 0.10 (0.03–0.17) Late life Social isolation 34.3 (33.3–35.3) 52.2 (49.6–54.8) 49.7 (48.6–50.9) Visual impairment 3.0 (2.7–3.4) 16.1 (14.2–18.0) 37.9 (36.8–39.0) Air pollution 5.2 (4.7–5.6) 10.5 (8.9–12.1) 35.4 (34.3–36.5) [Insert Table 2 Here] The prevalence of several dementia risk factors differed significantly between the faith and nonfaith communities. Specifically, the faith community presented a significantly greater prevalence of depression, hearing loss, obesity, excessive alcohol consumption, diabetes, physical inactivity, visual impairment, and exposure to air pollution than did the nonfaith community (all p < 0.05). Conversely, high LDL cholesterol and current smoking were more prevalent in the nonfaith community than in the faith community (p 0.05). Dementia risk factor contributions and weighted PAFs Although the Population Attributable Fraction (PAF) is influenced by the prevalence of a risk factor in the population (see Tables 2 and 4 ), it is also critically dependent on the strength of its association with the outcome, typically measured by the relative risk (RR). A risk factor may be highly prevalent, but if its RR is low, the PAF may still be modest. Conversely, a less prevalent risk factor may have a relatively high PAF if its RR is substantial. The weighted PAF reflects the relative contribution of each risk factor to the overall burden of dementia, taking into account both its prevalence and RR, as well as the co-occurrence with other contributing risk factors. This means that a risk factor may appear important due to high prevalence but may not contribute significantly to the overall risk if it overlaps considerably with other factors or has a weaker association with the outcome. The weighted PAF for the ELSA community was 35.3%, which is the proportion of dementia cases that could be prevented if we eliminated all 14 risk factors. The top four contributing factors were depression (10.4%), social isolation (4.5%), high LDL cholesterol (3.6%) and low education (3.4%) (Table 3 ). Table 3 Relative risk, communality and PAF of 14 modifiable risk factors for the general ELSA community and the Danish sample by Livingston et al. [ 24 ]. Risk factors RR for dementia (95% CI) Unweighted PAF Weighted PAF This study Livingston et al. 2024 This study Livingston et al. 2024 Early life Low education 1.6 (1.3, 2.0) 12.8% 12.2% 3.4% 4.5% Midlife Depression 2.2 (1.7, 3.0) 39.0% 8.3% 10.4% 3.0% High LDL cholesterol 1.3 (1.3, 1.4) 13.4% 18.7% 3.6% 6.9% Hearing loss 1.4 (1.0, 1.9) 11.5% 19.1% 3.1% 7.0% Obesity 1.3 (1.0, 1.7) 9.0% 3.8% 2.4% 1.4% Smoking 1.3 (1.2, 1.4) 7.3% 6.3% 1.9% 2.3% Excessive alcohol 1.2 (1.0, 1.5) 6.4% 2.6% 1.7% 1.0% Hypertension 1.2 (1.1, 1.4) 5.1% 5.9% 1.4% 2.2% Diabetes 1.7 (1.6, 1.8) 4.7% 6.4% 1.2% 2.3% Physical inactivity 1.2 (1.2, 1.3) 4.2% 6.4% 1.1% 2.4% Traumatic brain injury 1.7 (1.4, 1.9) 1.0% 7.8% 0.02% 2.9% Late life Social isolation 1.6 (1.3, 1.8) 17.1% 12.6% 4.5% 4.6% Air pollution 1.1 (1.1, 1.1) 0.5% 7.0% 0.1% 2.6% Visual impairment 1.5 (1.4, 1.6) 1.5% 6.0% 0.4% 2.2% Over PAF for all risk factors - - - 35.3 45.3 RR = Relative risk; LDL = low-density lipoprotein [Insert Table 3 Here] Key differences can be seen in the relative impact of MRFs for dementia when comparing the weighted PAFs between this study and Livingston et al. [ 6 ]. The overall weighted PAF in this study was notably lower than that in Livingston et al.’s Danish sample (45.3%), suggesting a potentially smaller proportion of preventable dementia cases in the ELSA cohort under the exposure and risk distributions. Depression exhibited the greatest divergence, with a weighted PAF of 10.4% in this study versus 3.0% in Livingston et al. [ 6 ], indicating a potentially greater influence of midlife mental health in the UK context. Conversely, risk factors such as hearing loss (3.1% vs. 7.0%), high LDL cholesterol (3.6% vs. 6.9%), and air pollution (0.1% vs. 2.6%) had substantially lower weighted contributions in this study. Social isolation and low education had relatively similar weighted PAFs across both studies. Further analysis explored PAFs stratified by faith community affiliation (Table 4 ). In the nonfaith community, the overall weighted PAF for all 14 modifiable risk factors was 46.1%, meaning that if all these risk factors were controlled or eliminated, approximately 46.1% of dementia cases in this group would be preventable. The four risk factors contributing the most to this PAF in the nonfaith group were depression, social isolation, high LDL cholesterol, and low education (these were also the top four contributors in the overall ELSA sample). In the faith community, the total PAF for all risk factors was lower, at 34.3%. The leading contributors in the faith group were depression, social isolation, hearing loss, and visual impairment. Notably, depression was the single largest contributor to dementia risk in both faith and nonfaith communities, highlighting its importance across the board. The overall PAF of the faith community was significantly lower by 11.8 percentage points (95% CI 9.0%, 4.6%, p < 0.001) than that of the nonfaith community. This gap indicates that the 14 MRFs account for a smaller proportion of dementia cases among those with religious affiliation. Several specific risk factors drove this difference. Table 4 Comparison of the PAFs of the 14 modifiable risk factors between nonfaith and faith communities. Risk factors Nonfaith community (n = 1488) Faith community (n = 7364) Weighted PAF difference (95% CI) PAF Weighted PAF PAF Weighted PAF Early life Low education 14.6% 4.6% 13.4% 2.6% 2.0% (0.9%, 3.1%)** Midlife Depression 36.4% 11.4% 47.3% 9.2% 2.2% (0.4%, 4.0%)* High LDL cholesterol 17.2% 5.4% 12.1% 2.3% 3.1% (1.9%, 4.3%)** Hearing loss 11.4% 3.6% 21.2% 4.1% -0.5% (-1.6%, 0.6%) Obesity 6.4% 2.0% 8.0% 1.6% 0.4% (-0.4%, 1.2%) Smoking 8.3% 2.6% 4.3% 0.8% 1.8% (1.0%, 2.6%)** Alcohol 7.5% 2.4% 10.7% 2.1% 0.3% (-0.6%, 1.2%) Hypertension 6.5% 2.0% 6.5% 1.3% 0.7% (-0.1%, 1.5%) Diabetes 3.4% 1.1% 5.2% 1.0% 0.1% (-0.5%, 0.7%) Physical inactivity 2.8% 0.9% 5.3% 1.0% -0.01% (-0.6%, 0.4%) Traumatic brain injury 0.1% 0.02% 0.1% 0.02% 0.00% (-0.01%, 0.01%) Late life Social isolation 23.9% 7.5% 23.0% 4.5% 3% (1.6%, 4.4%)** Air pollution 1.0% 0.3% 3.4% 0.7% -0.4% (-0.7%, -0.1%)* Visual impairment 7.5% 2.3% 15.9% 3.1% -0.8% (-1.7%, 0.1%) Overall PAF 80.6% 46.1% 87.0% 34.3% 11.8% (9.0%, 14.6%)** **p < 0.001; *p < 0.05; LDL = low-density lipoprotein For example, the weighted PAF for low education in the nonfaith group was 4.6%, whereas it was 2.6% in the faith group, a difference of 2.0% (95% CI 0.9%– 3.1%, p < 0.001). Similarly, the nonfaith community had higher weighted PAFs than did the faith community for depression (difference was 2.2%, 95% CI 0.4%, 4.0%), high LDL cholesterol (3.1%, 95% CI 1.9%, 4.3%), smoking (1.8%, 95% CI 1.0%, 2.6%), and social isolation (3.0%, 95% CI 1.6%, 4.4%), indicating a greater contribution to dementia risk in the nonfaith community. In contrast, air pollution had a slightly greater impact on dementia risk in the faith community (weighted PAF 0.7% in faith vs. 0.3% in nonfaith community, difference 0.4%, 95% CI -0.7%, -0.1%). The PAFs for the other risk factors did not differ significantly between the groups. Discussion This study is the first to examine the distribution and contribution of MRFs within faith communities in England, drawing from a nationally representative older adult cohort. By comparing the ELSA data to previously published estimates and stratifying the results by religious affiliation, we contribute a novel and context-specific understanding of dementia prevention potential in this underexplored population segment. Overall PAF and comparison with previous studies The overall weighted PAF for the 14 modifiable risk factors was 35.3% in this study, compared to 45.3% in the study by Livingston et al.[ 6 ] using a Danish sample. Notable differences were observed in the relative contributions of specific risk factors. For example, depression emerged as the largest contributor in this study, whereas other factors, such as hearing loss, high LDL cholesterol, and air pollution, accounted for smaller proportions of the attributable risk than did the Danish cohort. The observed differences in weighted PAFs between our analysis and those of Livingston et al.[ 6 ] underscore the importance of context-specific estimates in dementia prevention research. Therefore, discrepancies in the contribution of individual risk factors likely reflect differences in population structure, health behaviours, environmental exposures, and data capture methods between the UK and Danish samples. Nevertheless, the higher contribution of depression to the overall PAF in our sample draws attention to its critical role in dementia risk. Depression remains substantially underdiagnosed and undertreated among older adults in the UK. Studies have shown that fewer than one in five older adults with depressive symptoms receive adequate psychological or pharmacological treatment [ 30 , 31 ], highlighting a systemic gap in geriatric mental health care. Moreover, there is a persistence of depression prevalence over time in England, despite increased antidepressant usage [ 32 ]. These findings reinforce the need for locally informed dementia prevention strategies that prioritise the most impactful risk factors within specific populations. Relying on international benchmarks without adjusting for context may lead to misdirected efforts or an underestimation of the role that certain factors play within national or regional settings. The overall weighted PAF for the 14 MRFs in our ELSA sample was lower than the 49% estimated by Chen et al. [ 10 ] using the same ELSA wave. This discrepancy likely reflects methodological differences. We included two additional MRFs—LDL cholesterol and visual impairment—and used a lower CESD-8 cut-off score for depression (≥ 3 vs. ≥4), which was grounded in validation studies among older adults [ 33 ]. These decisions were methodologically justified and aimed to maximise sensitivity while preserving comparability. Furthermore, our analytic sample was restricted to individuals with valid faith affiliation data, which may have influenced the demographic composition and overall risk burden. This restriction excluded approximately 13% of the original ELSA sample for that wave (1314 individuals), which may have biased prevalence estimates if participants with systematically different risk profiles were excluded. Taken together, these differences warrant cautious interpretation but underscore the value of methodological transparency in estimating attributable dementia risk. Differences in risk factor prevalence and PAFs between faith and non-faith communities The significantly greater prevalence of depression in the faith community (74.7%) than in the nonfaith community (47.7%) is particularly notable. This disparity highlights the dual influence of religious engagement on mental health outcomes. While religious involvement can offer structured routines, community support, and spiritual coping mechanisms that mitigate stress and bolster psychological well-being [ 34 , 35 ], certain religious contexts may inadvertently exacerbate depressive symptoms through feelings of guilt, spiritual inadequacy, or fear of divine punishment [ 36 , 37 ]. Moreover, stigma related to mental health issues within faith communities can discourage individuals from seeking professional help, potentially exacerbating untreated depression [ 38 ]. Alternatively, people with mental health conditions may specifically seek religious involvement and support to help them cope, so the religious community may include more than average numbers of people with such difficulties. Some religious organisations may even offer specific support to such groups. Additionally, religious discrimination further compounds mental health risks - individuals who experience religious discrimination have twice the risk of common mental disorders, including depression [ 39 ]. Despite the higher prevalence of depression among the faith community relative to the nonfaith group, the faith community had a significantly lower weighted PAF (34.3%) than the nonfaith community (46.1%). Weighted PAFs are shaped not only by the prevalence of a risk factor but by its patterns of co-occurrence with other risk factors in the population. The relatively lower PAF for depression among the faith community suggests that the 14 MRFs account for a smaller share of dementia risk within faith-affiliated individuals. Nevertheless, across both subgroups, nonfaith and faith communities, depression consistently emerged as the leading modifiable risk factor (higher PAF) for dementia. In the nonfaith group, depression accounted for 11.4% of the overall dementia risk, and it accounted for 9.2% in the faith group. This pattern mirrors global findings from the Lancet Commission[ 6 , 27 ] and UK-specific studies such as Chen et al. [ 10 ], which also identifies depression as among the top contributors to dementia risk. These converging results highlight depression not only as a clinically important risk factor but also as a whole-population priority for dementia prevention strategies. Social isolation emerged consistently as the second highest PAF for both nonfaith and faith communities. In our analysis, social isolation accounted for a notable 7.5% within the nonfaith community, whereas it accounted for 4.5% within faith communities. The substantial contribution of social isolation to dementia risk highlights the essential role of sustained social connections in cognitive health. Research has consistently demonstrated that robust social networks and frequent social interactions stimulate cognitive function, promote emotional resilience, and reduce stress—all mechanisms linked to lower dementia risk [ 15 , 40 ]. While faith communities are often presumed to have protective social structures due to communal activities [ 34 , 41 ], our findings indicate that social isolation remains a significant challenge within these groups. While faith communities provide one model of social infrastructure, equivalent benefits can be achieved through inclusive, nonreligious community initiatives. Also, given the greater PAF for social isolation in nonfaith communities, it is important to explore secular approaches to social support and cognitive engagement. Community-based interventions, such as befriending schemes, local volunteering programmes, hobby clubs, or intergenerational mentorship, can offer similar protective benefits by fostering regular social interaction and emotional connection. The prevalence of hearing loss was significantly greater in the faith community (67.4%) than in the nonfaith community (32.3%), and so was its PAF. Hearing loss was identified as the third highest PAF within faith communities, indicating a distinct vulnerability within this population group compared with the nonfaith community. This significantly higher PAF emphasises a critical yet under-addressed risk factor for dementia within this community. Hearing impairment contributes to social withdrawal, communication difficulties, and cognitive strain, all of which accelerate cognitive decline and increase the risk of dementia[ 6 ]. Within faith communities, hearing loss could impede participation in communal worship and social activities, thereby diminishing the cognitive and emotional benefits traditionally associated with active faith engagement [ 11 ]. Hearing loss prevention and management interventions within faith-based settings, such as regular hearing screenings, the provision of assistive hearing devices, and the promotion of auditory-friendly environments during communal events, may represent a promising focus for dementia prevention efforts in faith-based settings. Visual impairment was more prevalent among faith-affiliated participants (37.9%) than among nonfaith individuals (16.1%). It also ranked as the fourth highest contributor to dementia risk within the faith community, in contrast with its lower ranking within the nonfaith population. This higher prevalence and associated PAF for visual impairment among faith-affiliated individuals is noteworthy and highlights another unique risk within religious populations. Visual impairment restricts daily functional activities, social interactions, and participation in cognitively stimulating tasks, all of which contribute to cognitive decline [ 6 ]. Within religious settings, impaired vision can severely limit participation in faith-based activities, such as reading sacred texts or words to hymns and songs, engaging with symbolic rituals, or navigating community spaces, potentially exacerbating feelings of isolation and psychological distress. Thus, visual impairment may disproportionately undermine the protective social and cognitive aspects traditionally attributed to religious participation [ 16 ]. The significant contribution of visual impairment to the overall risk of dementia amidst the other modifiable risk factors within faith communities suggests potential opportunities for targeted preventative measures, such as incorporating regular vision screenings in faith community-based health initiatives and facilitating accessible adaptations (e.g., large-print religious texts, adequate lighting, and inclusive spatial layouts in worship areas). Addressing visual impairment not only preserves cognitive function but also enhances overall participation in community and spiritual life, amplifying protective benefits against dementia. Study limitations Our study has several important limitations. We utilised cross-sectional data from Wave 5 (2010–2011) of the ELSA. Although foundational for dementia prevention modelling, these data predate several recent population-level changes, including shifts in mental health awareness, health behaviours, and religious participation in England. As such, the results may not fully reflect current prevalence patterns, although they remain instructive for understanding structural and cultural influences on risk. Faith affiliation was measured only at one time point and was grouped broadly into religious vs. nonreligious categories. This simplification, while necessary for statistical power, likely masks interfaith heterogeneity and may underestimate nuanced behavioural or cultural influences. For example, educational inequities may disproportionately impact some minority faith communities, as evidenced by higher rates of limited formal education among certain groups, such as Muslims, but higher educational attainment among Hindus and Jews in the UK [ 42 ]. Additionally, Leung and Stanner [ 43 ] offer valuable insights into the relationship between traditional dietary habits and chronic disease risk among ethnic and minority religious groups in the UK. They reported that among South Asians, the frequent use of ghee, fried, and sugary desserts, which are often tied to religious customs, increases LDL cholesterol and obesity risk. Moreover, some religious communities may face unique health disparities, such as cultural stigmas or systemic discrimination [ 39 ], which can shape their dementia risk factor profile. To address this, future research should disaggregate faith community affiliations to understand the distinct risks within individual faith traditions. This will facilitate the design of culturally sensitive public health strategies that engage specific religious groups and tailor interventions to the specific needs of these communities. We used secondary data from the ELSA and thus were constrained by the available data. Notably, we relied on external prevalence estimates for TBI, using hospital administrative data rather than data directly from ELSA participants. This approach may lead to potential inaccuracies in estimating the true incidence of TBI within our sample and its subgroups. We also assumed, via our data imputation method, that the data were missing at random; however, this assumption may not hold true, potentially introducing selection bias if the missing data were systematic rather than random. Restricting our analytic sample to respondents with complete religious affiliation data introduced potential selection bias. These individuals may systematically differ from those with missing data in ways relevant to both dementia risk and faith engagement. Our PAF calculations assume that relative risks from the Lancet Commission, which are derived from pooled international studies [ 6 ], apply equally to our study population, which may not be accurate for all subgroups. This methodological constraint means that our PAF estimates should be interpreted as indicative of potential prevention impact rather than precise predictions of achievable risk reduction. Implications for dementia prevention Despite these limitations, our study contributes essential insights into dementia risk factors among faith communities in England and highlights critical areas for targeted public health interventions. Given that more than half of the English population identifies with a faith community, religious institutions can serve as crucial platforms for targeted health interventions that could be culturally contextualised. Faith-based organisations (FBOs) play a unique role in disseminating health education, particularly in underserved communities, and can act as hubs for integrating broader public health strategies [ 44 ]. Moreover, partnerships between public health agencies and religious institutions have successfully reached vulnerable populations and can provide early screening and management of modifiable dementia risk factors such as depression, hearing loss and visual impairment [ 45 ]. However, efforts to engage faith communities in these interventions must also address issues of medical mistrust, which may stem from historical marginalisation, cultural, or prior negative healthcare experiences. Building trust through culturally competent communication, a sustained community presence, and partnerships with trusted faith leaders is crucial to the success of any health initiative in these settings. Embedding programmes promoting lifestyle modifications, such as fostering improved social connections, into faith communities can amplify their reach and cultural relevance [ 46 ] This study’s novel integration of faith community affiliation into dementia risk profile analysis reinforces the potential importance of cultural, social, and health determinants in shaping dementia risk profiles. These findings may help inform the development of contextually tailored prevention strategies, although further longitudinal and interventional studies are required to confirm these patterns and assess causality. Our findings highlight that faith communities in England exhibited differences in the relative contribution of modifiable dementia risk factors compared with nonfaith communities, possibly shaped by structural, behavioural, and psychosocial factors. Recognising these variations is critical to ensuring that dementia prevention efforts are inclusive, equitable, and effective across diverse communities. However, more granular analyses will facilitate the design of public health strategies that engage specific faith communities and tailor interventions to their unique social, cultural, and health-related contexts. While these findings emphasise which MRFs need to be prioritised among faith communities, they also have broader implications beyond these groups. Specifically, our research highlights how culturally sensitive and community-oriented preventive interventions can be effective in secular settings with comparable social structures, such as exercise clubs or meditation groups. The nuanced understanding derived from these results underscores the importance of tailored public health approaches that leverage community structures and practices, both religious and secular, to increase dementia prevention efforts. Declarations Human Ethics and Consent to Participate declarations ELSA Wave 5 received ethical approval from the Berkshire Research Ethics Committee. All participants provided written informed consent to participate in the study at the time of data collection. The research was conducted in accordance with the ethical principles outlined in the Declaration of Helsinki. The secondary analysis of these data for the present study was approved by the Faculty of Health and Medicine Research Ethics Committee of Lancaster University (Ref: FHMREC22080). Consent to publish declaration Not applicable. Clinical trial number Not applicable. Availability of data and materials The datasets analysed during the current study are available from the UK Data Service (https://ukdataservice.ac.uk/) for researchers who meet the criteria for access to ELSA data. Derived data supporting the findings of this study are available from the corresponding author on reasonable request. Competing interests No conflict of interest. Funding Sanda Umar Ismail received support from the NIHR Applied Research Collaboration North West Coast and Alzheimer’s Society and is funded through a Post-Doctoral Fellowship. Heather Brown is funded by NIHR Applied Research Collaboration North West Coast. Carol Holland received funding from the North West Coast and the BBSRC/MRC for the Cognitive Frailty Interdisciplinary Network; grant number: BB/W018322/1 The views expressed are those of the authors and not necessarily those of the funders, NHS or Department of Health and Social Care. Authors' contributions S.U. Ismail planned the study, performed all statistical analyses, and wrote the paper. C. Holland was responsible for securing the funding, helped plan the study, supervised the data analysis, and revised the manuscript. H. Brown and F. Ahmed helped to plan the study, supervised the data analysis, and revised the manuscript. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7290936","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":502132365,"identity":"5e37a306-10ec-4a82-bcd3-a866b9d7cffc","order_by":0,"name":"Sanda Umar Ismail","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABCUlEQVRIie2RMUvDQBTH33GQLC90feWq+QqBLoJivoqH0CkFd496EkiW07lS8LOkHFwWP0DFxSI4ORkQQRQjnYSmcXS433DLux///+MBeDz/Eqbbh0aDMNe/B9Sj4NAsNwr2KxswWck/KrHm+etKHSDcry+bM3WcpuHVI7wrkDd6u5JUrBCZI2QLmYu5O5UG64QZB3LRkZIAK3gWEHIhtYg0P0GaAEQa5G1nMZY32RdhMFzmH5G+SDF+Bva5Q4GKaTEtCJHahpG2zFAA/Cels5htf06vCQllcYiuluZuAnbkaNy1flyWT032NttPa2sfUJ2nYenY+kUd7c2rjhi+tW3/IT0ej8ezi2+/6U+zdLZo9wAAAABJRU5ErkJggg==","orcid":"","institution":"University of Liverpool","correspondingAuthor":true,"prefix":"","firstName":"Sanda","middleName":"Umar","lastName":"Ismail","suffix":""},{"id":502132366,"identity":"9e5d2d71-266c-4d79-bb35-7b46db7763a0","order_by":1,"name":"Heather Brown","email":"","orcid":"","institution":"Lancaster University","correspondingAuthor":false,"prefix":"","firstName":"Heather","middleName":"","lastName":"Brown","suffix":""},{"id":502132367,"identity":"8a24de1a-5e99-4c52-a1e7-00b712c6632a","order_by":2,"name":"Faraz Ahmed","email":"","orcid":"","institution":"Lancaster University","correspondingAuthor":false,"prefix":"","firstName":"Faraz","middleName":"","lastName":"Ahmed","suffix":""},{"id":502132368,"identity":"a07131f6-dbc9-495b-95e4-6c019be6006e","order_by":3,"name":"Carol Holland","email":"","orcid":"","institution":"Lancaster University","correspondingAuthor":false,"prefix":"","firstName":"Carol","middleName":"","lastName":"Holland","suffix":""}],"badges":[],"createdAt":"2025-08-04 12:08:16","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7290936/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7290936/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":90082450,"identity":"1c3162d3-aa67-43e2-9265-ad1edb36d666","added_by":"auto","created_at":"2025-08-28 09:21:43","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1340747,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7290936/v1/14006175-c0f3-4e51-b613-7819ca81ecc9.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"\u003cp\u003eDementia risk reduction potential among faith communities: Insights into modifiable risk factors in the English population\u003c/p\u003e","fulltext":[{"header":"Introduction","content":"\u003cdiv id=\"Sec2\" class=\"Section2\"\u003e\u003ch2\u003eDementia and modifiable risk factors: a public health priority\u003c/h2\u003e\u003cp\u003eDementia remains a significant global public health challenge. While evidence from high-income countries indicates a decline in age-specific incidence rates, largely attributed to improvements in education, cardiovascular health, and lifestyle modifications [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e], the absolute prevalence continues to rise due to population ageing. In England and Wales, dementia has become the leading cause of death [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. In addition to its profound health implications, dementia imposes substantial socioeconomic burdens, with projected costs in England alone expected to exceed \u0026pound;80\u0026nbsp;billion by 2040 [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eIn the absence of a cure, dementia prevention has emerged as a critical public health priority, emphasising the identification and mitigation of modifiable risk factors (MRFs) across the life course [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Reports from the Lancet Commission highlight 14 key MRFs, including less education in early life (before 45 years of age), depression, smoking, physical inactivity, untreated hearing loss, diabetes, hypertension during middle life (45\u0026ndash;64 years of age), social isolation, air pollution and untreated vision impairment in late life (65 years of age and above), which are collectively estimated to account for 45% of all dementia cases worldwide [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. These findings highlight significant opportunities for intervention, positioning risk factor control and promoting healthy lifestyles at the forefront of dementia prevention strategies [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. It is therefore an opportune moment to begin delineating which modifiable risk factors are most salient for specific population subgroups.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003ePopulation-specific risk profiles and targeted prevention\u003c/h2\u003e\u003cp\u003eThe distribution of dementia risk factors is not uniform across all segments of society. Different populations and communities can exhibit distinct risk factor profiles, leading to variations in dementia risk. For example, studies that have analysed the population distribution of modifiable dementia risk factors among ethnic communities have revealed disparities in these distributions in comparison to the general English population [\u003cspan additionalcitationids=\"CR9\" citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Compared with White populations, Mukadam et al.[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e] used a large and representative dataset of English electronic health records to identify higher prevalence rates and stronger impacts of modifiable risk factors, such as diabetes in South Asians and hypertension in Black populations. Similarly, Chen et al.[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e] reported persistent disparities in population attributable fractions (PAFs) for dementia, with lower-income groups showing higher cumulative modifiable risk due to factors such as social isolation, low education, and smoking.\u003c/p\u003e\u003cp\u003eThese findings highlight the need to examine specific risk factor burdens present in different communities, as these findings will inform tailored prevention strategies. However, while previous research on the distribution of MRFs of dementia across specific subsections of populations has focused on ethnicity [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e] and socioeconomic status [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e], another critical yet understudied subgroup is faith communities.\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eFaith communities as a distinct population segment\u003c/h3\u003e\n\u003cp\u003eFaith communities include individuals who identify with and actively participate in organised religious groups such as churches, mosques, temples, and other congregations [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. While this analysis combines various religious affiliations into a single 'faith community' category for statistical power, we acknowledge that this approach may mask significant differences between specific religious traditions. This limitation is addressed further in the Discussion. In England, a significant proportion of the population identifies with a faith community. According to the recent census, approximately 57% of people report a religious affiliation, with nearly 30% of those aged 65 and older identifying as Christian [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Religious identity entails shared norms, beliefs, and social structures that influence health behaviours and outcomes. These effects are not derived from spirituality itself but from modifiable behavioural and social pathways within faith communities. For example, faith-based norms often discourage smoking or excessive alcohol use, contributing to healthier lifestyle choices [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Religious gatherings also promote social interaction, cognitive stimulation, and emotional support\u0026mdash;all factors known to reduce the risk of dementia [\u003cspan additionalcitationids=\"CR16\" citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eFaith-based settings can also serve as powerful venues for public health intervention. They offer accessible infrastructure, trusted leadership, and established routines that can support culturally tailored interventions. A UK study demonstrated that dementia-friendly interventions implemented in church congregations significantly improved awareness and comfort among members [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. Similarly, faith-based health programmes have successfully improved cardiovascular health behaviours [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. However, challenges exist. Cultural and language barriers, combined with stigma around cognitive decline, may impede effective communication and engagement in health programmes in minority faith communities [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. These issues can delay diagnosis and hinder the delivery of preventive care.\u003c/p\u003e\u003cp\u003eDespite their potential, faith communities remain significantly underrepresented in dementia prevention research. Most existing studies fail to disaggregate data by religious affiliation or involvement, limiting our understanding of how faith-based social environments may shape dementia risk profile. While evidence suggests that religious involvement may influence key health behaviours and intermediate risk factors [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e], the extent to which MRFs are distributed differently in faith versus nonfaith populations remains unclear. This lack of disaggregated data represents a critical gap, where MRFs could be targeted more effectively if we knew what the differentiation is for these groups.\u003c/p\u003e\u003cp\u003eTo address this gap, the present study examines the distribution and relative contribution of 14 MRFs of dementia among faith and nonfaith communities within the English Longitudinal Study of Ageing (ELSA). By identifying distinct risk profiles within these groups, this research contributes to a more nuanced understanding of dementia risk and supports the development of culturally responsive, community-based prevention strategies that harness the unique structures of faith-based settings. Beyond faith communities, these findings also provide broader insights into how culturally informed and community-centred approaches can increase dementia prevention efforts within diverse secular populations, particularly those sharing similar social structures and practices with faith communities, such as exercise groups, meditation circles, or community-based clubs. This expanded perspective can inform inclusive public health policies and tailor interventions applicable across various community settings.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003eWe followed the STROBE guidelines [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e] in reporting this study, with a registered protocol: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.17605/OSF.IO/R8WTU\u003c/span\u003e\u003cspan address=\"10.17605/OSF.IO/R8WTU\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/p\u003e\n\u003ch3\u003eStudy design and participants\u003c/h3\u003e\n\u003cp\u003eWe utilised data from the ELSA, a nationally representative longitudinal survey of individuals aged 50 years and older in England. ELSA collects biennial data on various aspects of participants\u0026rsquo; lives, including lifestyle, economic status, health, and social care, with the survey initially launched in 1998. The questionnaire items used to assess religious affiliation and the 13 modifiable dementia risk factors analysed in this study were part of the standard ELSA Wave 5 survey instruments. Full details of the questionnaire, including wording of the items, are publicly available from the UK Data Service [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eFor our analysis, we focused on data from Wave 5 (June 2010\u0026ndash;July 2011), as it was the first wave to include information on faith (religious) community affiliation. The participants were asked to identify their religious affiliation from categories such as no religion, Christian, Buddhist, Hindu, Jewish, Muslim, Sikh, and other non-Christian. Owing to the small number of respondents in some non-Christian groups, we consolidated the responses into two broad categories: nonfaith community (no religion) and faith community (Christian and non-Christian). We included only participants with valid measures of faith affiliation and excluded individuals with dementia (based on self-reported diagnoses), as this condition could influence the prevalence of certain risk factors, such as depression and social isolation.\u003c/p\u003e\u003cp\u003eThe conventional definitions of the life course approach in dementia prevention research consider early life as \u0026lt;\u0026thinsp;45 years and midlife as 45\u0026ndash;64 years [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e], but given that the ELSA does not include younger adults, we consider early life to be \u0026lt;\u0026thinsp;50 years and midlife to be 50\u0026ndash;64 years.\u003c/p\u003e\n\u003ch3\u003eDefinition and prevalence of dementia risk factors\u003c/h3\u003e\n\u003cp\u003eWe selected our set of modifiable risk factors based on the latest data from the Lancet Commission [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Prevalence data were available within the ELSA Wave 5 dataset for 13 of the 14 risk factors; the exception was traumatic brain injury (TBI), for which the ELSA did not collect direct information. We therefore estimated the prevalence of TBI using external data on head injury admissions from a national hospital statistics report. Specifically, we assumed that the prevalence of prior serious head injury in our cohort was comparable to the annual incidence of hospitalised head injury in England reported by Headway UK [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. This estimation approach is consistent with previous research that incorporates external data when certain risk factor information is missing in the study dataset [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e outlines the operational definitions of each risk factor used in this study in comparison to those used by Livingston et al. [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e].\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eDefinitions of risk factors (Livingston et al., 2024 vs. this study)\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"3\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRisk factors\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eDefinition used by Livingston et al. (2024)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eDefinition in this study\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEarly life\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003eEarly life (age\u0026thinsp;\u0026lt;\u0026thinsp;45 years)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003eEarly life (\u0026lt;\u0026thinsp;50 years) \u0026ndash; assessed retrospectively in ages\u0026thinsp;\u0026ge;\u0026thinsp;50\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLow education\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e7\u0026ndash;10 years of education, primary school, continuation school, folk high school\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eSelf-reported highest academic level obtained is less than secondary school\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMidlife\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003eMidlife (45\u0026ndash;64 years)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003eMidlife (age 50\u0026ndash;64 years)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDepression\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eThe Hospital Anxiety and Depression Scale-Depression (HADS-D). A score of \u0026ge;\u0026thinsp;8 indicates depression.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e8-item Center for Epidemiologic Studies Depression scale (CESD-8). A score of 3 or more indicates depression [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e].\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTraumatic brain injury\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eHave you ever been hospitalised for a head\u003c/p\u003e\u003cp\u003einjury?\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eHead injury from a hospital episode statistics system report (NHS Health and Social Care Information Centre [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e].\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHigh LDL cholesterol\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eLDL cholesterol\u0026thinsp;\u0026ge;\u0026thinsp;130 mg/dL\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eLDL cholesterol\u0026thinsp;\u0026ge;\u0026thinsp;3.36 mmol/l.\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePhysical inactivity\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSelf-report questionnaire: How has your physical activity in leisure time been during the last year? How has your physical activity in leisure time been during the last year?\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eSelf-reported work and activity frequency, categorising individuals as sedentary, low, moderate, or highly active. Physically inactive refers to those categorised as sedentary or low activity.\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHearing loss\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eHearing threshold level\u0026thinsp;\u0026ge;\u0026thinsp;25 dB measured with a combination of questionnaire, otoscopy, and pure-tone audiometry.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eMerging two questions that asked respondents about how good their hearing was (excellent, very good, good, fair or poor) and a positive response of a \u0026lsquo;yes\u0026rsquo; to the question about whether they find it difficult to follow a conversation if there is background noise (such as TV, radio or children playing) [\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e].\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSmoking\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCurrent smoker daily\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eSelf-reported measures of current smokers - Do you currently smoke cigarettes?\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHypertension\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSystolic blood pressure\u0026thinsp;\u0026ge;\u0026thinsp;140 or diastolic blood pressure\u0026thinsp;\u0026ge;\u0026thinsp;90\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eSelf-report of a diagnosis of high blood pressure (mean systolic blood pressure\u0026thinsp;\u0026ge;\u0026thinsp;140 mm Hg or mean diastolic blood pressure\u0026thinsp;\u0026ge;\u0026thinsp;90 mm Hg.\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eObesity\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eBMI\u0026thinsp;\u0026ge;\u0026thinsp;30\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eBMI\u0026thinsp;\u0026ge;\u0026thinsp;30\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDiabetes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eDo you have, or have you ever had diabetes? Or nonfasting blood glucose\u0026thinsp;\u0026ge;\u0026thinsp;11.1 mmol/l at HUNT2.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eBlood glycated haemoglobin level\u0026thinsp;\u0026ge;\u0026thinsp;48 mmol/mol (6.5%) [\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e].\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eExcessive alcohol\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026ge;\u0026thinsp;21 units (=\u0026thinsp;168 g) per week\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eSelf-reported alcohol consumption over the past week of over 14 units per week[\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e] .\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLate-life age\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003eLate-life age (\u0026gt;\u0026thinsp;65 years)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003eLate-life age (age\u0026thinsp;\u0026gt;\u0026thinsp;=\u0026thinsp;65 years)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSocial isolation\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eWho do you live with? Those who answer no in all categories live alone.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eNot married/not cohabiting\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAir pollution\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eThose who live in a municipality with a mean level of particle pollution\u0026thinsp;\u0026ge;\u0026thinsp;1.5 in 2016 are exposed.\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eFuels used in household for heating or other purpose - coal/smokeless fuel.\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVisual impairment\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eWould you describe your impairment as slight, moderate, or severe or no impairment?\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eSelf-reports about how good their eyesight was (excellent, very good, good, fair or poor). Those who rated their eyesight as fair or poor were regarded as having visual impairment.\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e[Insert Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e Here ]\u003c/p\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003eStatistical analysis\u003c/h2\u003e\u003cp\u003eWe used the standard methodology outlined by Livingston et al.[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e] to calculate communality and PAFs, quantifying each risk factor\u0026rsquo;s contribution to overall dementia risk. The PAF is a method for estimating the proportion of a health problem in a population that could be prevented if a specific risk factor was eliminated. In brief, the PAF for each risk factor was calculated based on its prevalence and the relative risk of dementia associated with that factor. A high PAF value does not suggest a particular risk factor is more prevalent in a certain community, but it does suggest that its prevalence, combined with the significance of that risk factor for the health outcome, is higher in that community. Prevalence was calculated for the total ELSA sample and the faith and nonfaith subgroups. To assess the shared variance among the 13 risk factors, we performed a principal component analysis (PCA) via tetrachoric correlations, and we excluded TBI from this PCA because of the lack of individual-level data for that factor (TBI was instead assigned the average communality of the other risk factors\u0026mdash;part of the standard procedure for calculating communality).\u003c/p\u003e\u003cp\u003eThe overall PAF was then computed, considering cumulative risk reduction and adjusting for overlap among factors by weighting each PAF by a factor derived from its communality (i.e., the variance explained in the PCA). Finally, we derived individual \u0026ldquo;weighted\u0026rdquo; PAF values from the adjusted overall PAF (see the Supplementary Material for details of this approach). To enable direct comparison with the Lancet Commission findings, we applied the same relative risks reported by Livingston et al. [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e] for each risk factor, despite slight differences in our factor definitions, so that our PAF estimates would be consistent and comparable with those of prior studies. Group differences in risk factor prevalence (faith vs. nonfaith) were evaluated via chi-square tests.\u003c/p\u003e\u003cp\u003eMultiple imputation was employed to address missing data. We used the fully conditional specification (FCS) method for imputation [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e], which is suitable for datasets with mixed continuous and categorical variables. Five imputed datasets were generated to account for uncertainty due to missing data, following best practices [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. The imputation model included all the variables used in the analysis, and we assumed that the data were missing at random (MAR) conditional on the observed covariates. The variables with missing values were automatically selected by the software for prediction to optimise the imputation accuracy.\u003c/p\u003e\u003cp\u003eAfter imputation, we exported the five completed datasets to R (version 4.2.3). Each imputed dataset was analysed separately in R. We calculated tetrachoric correlations for each dataset (after removing the indicator variable for imputation number) and then pooled the correlation matrices by averaging across the five imputations. This resulted in a final tetrachoric correlation matrix that reflects variability across imputations. The PCA and PAF calculations described above were then applied to this pooled correlation matrix to produce our reported results.\u003c/p\u003e\u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\u003ch2\u003eParticipants and descriptive data\u003c/h2\u003e\u003cp\u003e Our sample size initially comprised 10274 participants. We excluded 223 individuals with dementia and 1314 without a valid faith affiliation response, yielding a final analytic sample of 8737 participants. Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e summarises the sociodemographic characteristics of the participants and the prevalence of the 14 modifiable dementia risk factors in the general ELSA sample and by faith group. Most participants (62.1%, n\u0026thinsp;=\u0026thinsp;899) within the nonfaith community were aged 50\u0026ndash;64 years, and women were overrepresented in the faith community (58.6%, n\u0026thinsp;=\u0026thinsp;4318). Almost all the participants were White in both the nonfaith (98.5%, n\u0026thinsp;=\u0026thinsp;1427) and faith (97.1%, n\u0026thinsp;=\u0026thinsp;7154) communities.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eSociodemographic characteristics and prevalence of the 14 modifiable risk factors for dementia in the general ELSA sample, as well as in nonfaith and faith communities.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCharacteristics and risk factors\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eGeneral ELSA community (n\u0026thinsp;=\u0026thinsp;8812)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eNonfaith community (n\u0026thinsp;=\u0026thinsp;1448)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eFaith community (n\u0026thinsp;=\u0026thinsp;7364)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e\u003cp\u003eSociodemographic characteristics, n (%)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAge, 50\u0026ndash;64 years\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e4360 (49.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e899 (62.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e3461 (47.0)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAge, 65\u0026thinsp;+\u0026thinsp;years\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e4452 (50.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e519 (37.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e3903 (53.0)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSex, female\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e4946 (56.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e628 (43.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e4318 (58.6)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEthnicity, White\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e8581 (97.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1427 (98.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e7154 (97.1)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e\u003cp\u003e\u003cb\u003eModifiable risk factors, prevalence % (95 CI)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e\u003cp\u003e\u003cb\u003eEarly life\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLow education\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e24.5 (23.6\u0026ndash;25.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e28.4 (26.1\u0026ndash;30.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e25.9 (24.9\u0026ndash;26.9)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eMidlife\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDepression\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e53.3 (52.2\u0026ndash;54.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e47.7 (15.1\u0026ndash;50.2)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e74.7 (73.7\u0026ndash;75.7)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHigh LDL cholesterol\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e51.5 (50.5\u0026ndash;52.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e69.3 (67.0\u0026ndash;71.7)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e45.7 (44.6\u0026ndash;46.8)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHearing loss\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e32.4 (31.5\u0026ndash;33.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e32.3 (29.9\u0026ndash;34.7)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e67.4 (66.4\u0026ndash;68.5)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eObesity\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e33.1 (32.1\u0026ndash;34.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e22.8 (20.6\u0026ndash;25.0)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e28.9 (27.9\u0026ndash;30.0)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSmoking\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e26.3 (25.4\u0026ndash;27.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e30.3 (28.0\u0026ndash;32.7)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e15.0 (14.2\u0026ndash;15.8)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAlcohol\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e34.0 (33.0\u0026ndash;35.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e40.7 (38.1\u0026ndash;43.2)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e60 (58.9\u0026ndash;61.2)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHypertension\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e26.9 (26.0\u0026ndash;27.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e34.5 (32.1\u0026ndash;37.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e34.6 (33.5\u0026ndash;35.6)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDiabetes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e7.0 (6.9\u0026ndash;8.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e5.0 (3.9\u0026ndash;6.1)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e7.9 (7.3\u0026ndash;8.5)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePhysical inactivity\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e21.9 (21.0\u0026ndash;22.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e14.4 (12.6\u0026ndash;16.2)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e28.0 (27.0\u0026ndash;29.1)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTraumatic brain injury\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.10 (0.03\u0026ndash;0.17)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.10 (0.03\u0026ndash;0.17)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.10 (0.03\u0026ndash;0.17)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e\u003cp\u003e\u003cb\u003eLate life\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSocial isolation\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e34.3 (33.3\u0026ndash;35.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e52.2 (49.6\u0026ndash;54.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e49.7 (48.6\u0026ndash;50.9)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVisual impairment\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3.0 (2.7\u0026ndash;3.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e16.1 (14.2\u0026ndash;18.0)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e37.9 (36.8\u0026ndash;39.0)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAir pollution\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e5.2 (4.7\u0026ndash;5.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e10.5 (8.9\u0026ndash;12.1)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e35.4 (34.3\u0026ndash;36.5)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e[Insert Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e Here]\u003c/p\u003e\u003cp\u003eThe prevalence of several dementia risk factors differed significantly between the faith and nonfaith communities. Specifically, the faith community presented a significantly greater prevalence of depression, hearing loss, obesity, excessive alcohol consumption, diabetes, physical inactivity, visual impairment, and exposure to air pollution than did the nonfaith community (all p\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Conversely, high LDL cholesterol and current smoking were more prevalent in the nonfaith community than in the faith community (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05). There were no significant group differences in the prevalence of low education, hypertension, or TBI (p\u0026thinsp;\u0026gt;\u0026thinsp;0.05).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003eDementia risk factor contributions and weighted PAFs\u003c/h2\u003e\u003cp\u003eAlthough the Population Attributable Fraction (PAF) is influenced by the prevalence of a risk factor in the population (see Tables\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e and \u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e), it is also critically dependent on the strength of its association with the outcome, typically measured by the relative risk (RR). A risk factor may be highly prevalent, but if its RR is low, the PAF may still be modest. Conversely, a less prevalent risk factor may have a relatively high PAF if its RR is substantial. The weighted PAF reflects the relative contribution of each risk factor to the overall burden of dementia, taking into account both its prevalence and RR, as well as the co-occurrence with other contributing risk factors. This means that a risk factor may appear important due to high prevalence but may not contribute significantly to the overall risk if it overlaps considerably with other factors or has a weaker association with the outcome. The weighted PAF for the ELSA community was 35.3%, which is the proportion of dementia cases that could be prevented if we eliminated all 14 risk factors. The top four contributing factors were depression (10.4%), social isolation (4.5%), high LDL cholesterol (3.6%) and low education (3.4%) (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eRelative risk, communality and PAF of 14 modifiable risk factors for the general ELSA community and the Danish sample by Livingston et al. [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e].\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"6\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eRisk factors\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eRR for dementia\u003c/p\u003e\u003cp\u003e(95% CI)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u003cp\u003eUnweighted PAF\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u003cp\u003eWeighted PAF\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eThis study\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eLivingston et al. 2024\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eThis study\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eLivingston et al. 2024\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eEarly life\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLow education\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.6 (1.3, 2.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e12.8%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e12.2%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e3.4%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e4.5%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eMidlife\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDepression\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2.2 (1.7, 3.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e39.0%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e8.3%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e10.4%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e3.0%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHigh LDL cholesterol\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.3 (1.3, 1.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e13.4%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e18.7%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e3.6%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e6.9%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHearing loss\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.4 (1.0, 1.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e11.5%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e19.1%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e3.1%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e7.0%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eObesity\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.3 (1.0, 1.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e9.0%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e3.8%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e2.4%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1.4%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSmoking\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.3 (1.2, 1.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e7.3%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e6.3%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.9%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e2.3%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eExcessive alcohol\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.2 (1.0, 1.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e6.4%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.6%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.7%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1.0%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHypertension\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.2 (1.1, 1.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e5.1%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e5.9%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.4%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e2.2%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDiabetes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.7 (1.6, 1.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4.7%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e6.4%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.2%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e2.3%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePhysical inactivity\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.2 (1.2, 1.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4.2%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e6.4%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.1%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e2.4%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTraumatic brain injury\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.7 (1.4, 1.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.0%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e7.8%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.02%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e2.9%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eLate life\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSocial isolation\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.6 (1.3, 1.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e17.1%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e12.6%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e4.5%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e4.6%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAir pollution\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.1 (1.1, 1.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.5%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e7.0%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.1%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e2.6%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVisual impairment\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.5 (1.4, 1.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.5%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e6.0%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.4%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e2.2%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOver PAF for all risk factors\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e35.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e45.3\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"6\"\u003eRR\u0026thinsp;=\u0026thinsp;Relative risk; LDL\u0026thinsp;=\u0026thinsp;low-density lipoprotein\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e[Insert Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e Here]\u003c/p\u003e\u003cp\u003eKey differences can be seen in the relative impact of MRFs for dementia when comparing the weighted PAFs between this study and Livingston et al. [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. The overall weighted PAF in this study was notably lower than that in Livingston et al.\u0026rsquo;s Danish sample (45.3%), suggesting a potentially smaller proportion of preventable dementia cases in the ELSA cohort under the exposure and risk distributions. Depression exhibited the greatest divergence, with a weighted PAF of 10.4% in this study versus 3.0% in Livingston et al. [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e], indicating a potentially greater influence of midlife mental health in the UK context. Conversely, risk factors such as hearing loss (3.1% vs. 7.0%), high LDL cholesterol (3.6% vs. 6.9%), and air pollution (0.1% vs. 2.6%) had substantially lower weighted contributions in this study. Social isolation and low education had relatively similar weighted PAFs across both studies.\u003c/p\u003e\u003cp\u003eFurther analysis explored PAFs stratified by faith community affiliation (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). In the nonfaith community, the overall weighted PAF for all 14 modifiable risk factors was 46.1%, meaning that if all these risk factors were controlled or eliminated, approximately 46.1% of dementia cases in this group would be preventable. The four risk factors contributing the most to this PAF in the nonfaith group were depression, social isolation, high LDL cholesterol, and low education (these were also the top four contributors in the overall ELSA sample). In the faith community, the total PAF for all risk factors was lower, at 34.3%. The leading contributors in the faith group were depression, social isolation, hearing loss, and visual impairment. Notably, depression was the single largest contributor to dementia risk in both faith and nonfaith communities, highlighting its importance across the board.\u003c/p\u003e\u003cp\u003eThe overall PAF of the faith community was significantly lower by 11.8 percentage points (95% CI 9.0%, 4.6%, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) than that of the nonfaith community. This gap indicates that the 14 MRFs account for a smaller proportion of dementia cases among those with religious affiliation. Several specific risk factors drove this difference.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eComparison of the PAFs of the 14 modifiable risk factors between nonfaith and faith communities.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"6\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eRisk factors\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003eNonfaith community\u003c/p\u003e\u003cp\u003e(n\u0026thinsp;=\u0026thinsp;1488)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003eFaith community\u003c/p\u003e\u003cp\u003e(n\u0026thinsp;=\u0026thinsp;7364)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eWeighted PAF difference (95% CI)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePAF\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eWeighted PAF\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003ePAF\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eWeighted PAF\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003e\u003cem\u003eEarly life\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLow education\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e14.6%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4.6%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e13.4%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2.6%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e2.0% (0.9%, 3.1%)**\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eMidlife\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDepression\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e36.4%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e11.4%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e47.3%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e9.2%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e2.2% (0.4%, 4.0%)*\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHigh LDL cholesterol\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e17.2%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e5.4%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e12.1%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2.3%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e3.1% (1.9%, 4.3%)**\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHearing loss\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e11.4%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3.6%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e21.2%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e4.1%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-0.5% (-1.6%, 0.6%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eObesity\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e6.4%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2.0%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e8.0%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.6%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.4% (-0.4%, 1.2%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSmoking\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e8.3%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2.6%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e4.3%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.8%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.8% (1.0%, 2.6%)**\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAlcohol\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e7.5%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2.4%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e10.7%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2.1%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.3% (-0.6%, 1.2%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHypertension\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e6.5%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2.0%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e6.5%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.3%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.7% (-0.1%, 1.5%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDiabetes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3.4%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.1%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e5.2%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.0%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.1% (-0.5%, 0.7%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePhysical inactivity\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2.8%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.9%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e5.3%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.0%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-0.01% (-0.6%, 0.4%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTraumatic brain injury\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.1%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.02%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.1%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.02%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.00% (-0.01%, 0.01%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"6\" nameend=\"c6\" namest=\"c1\"\u003e\u003cp\u003e\u003cb\u003eLate life\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSocial isolation\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e23.9%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e7.5%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e23.0%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e4.5%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e3% (1.6%, 4.4%)**\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAir pollution\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.0%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.3%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e3.4%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.7%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-0.4% (-0.7%, -0.1%)*\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVisual impairment\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e7.5%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2.3%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e15.9%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e3.1%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-0.8% (-1.7%, 0.1%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOverall PAF\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e80.6%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e46.1%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e87.0%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e34.3%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e11.8% (9.0%, 14.6%)**\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"6\"\u003e**p\u0026thinsp;\u0026lt;\u0026thinsp;0.001; *p\u0026thinsp;\u0026lt;\u0026thinsp;0.05; LDL\u0026thinsp;=\u0026thinsp;low-density lipoprotein\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eFor example, the weighted PAF for low education in the nonfaith group was 4.6%, whereas it was 2.6% in the faith group, a difference of 2.0% (95% CI 0.9%\u0026ndash; 3.1%, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Similarly, the nonfaith community had higher weighted PAFs than did the faith community for depression (difference was 2.2%, 95% CI 0.4%, 4.0%), high LDL cholesterol (3.1%, 95% CI 1.9%, 4.3%), smoking (1.8%, 95% CI 1.0%, 2.6%), and social isolation (3.0%, 95% CI 1.6%, 4.4%), indicating a greater contribution to dementia risk in the nonfaith community. In contrast, air pollution had a slightly greater impact on dementia risk in the faith community (weighted PAF 0.7% in faith vs. 0.3% in nonfaith community, difference 0.4%, 95% CI -0.7%, -0.1%). The PAFs for the other risk factors did not differ significantly between the groups.\u003c/p\u003e\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study is the first to examine the distribution and contribution of MRFs within faith communities in England, drawing from a nationally representative older adult cohort. By comparing the ELSA data to previously published estimates and stratifying the results by religious affiliation, we contribute a novel and context-specific understanding of dementia prevention potential in this underexplored population segment.\u003c/p\u003e\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\u003ch2\u003eOverall PAF and comparison with previous studies\u003c/h2\u003e\u003cp\u003eThe overall weighted PAF for the 14 modifiable risk factors was 35.3% in this study, compared to 45.3% in the study by Livingston et al.[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e] using a Danish sample. Notable differences were observed in the relative contributions of specific risk factors. For example, depression emerged as the largest contributor in this study, whereas other factors, such as hearing loss, high LDL cholesterol, and air pollution, accounted for smaller proportions of the attributable risk than did the Danish cohort. The observed differences in weighted PAFs between our analysis and those of Livingston et al.[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e] underscore the importance of context-specific estimates in dementia prevention research. Therefore, discrepancies in the contribution of individual risk factors likely reflect differences in population structure, health behaviours, environmental exposures, and data capture methods between the UK and Danish samples.\u003c/p\u003e\u003cp\u003eNevertheless, the higher contribution of depression to the overall PAF in our sample draws attention to its critical role in dementia risk. Depression remains substantially underdiagnosed and undertreated among older adults in the UK. Studies have shown that fewer than one in five older adults with depressive symptoms receive adequate psychological or pharmacological treatment [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e], highlighting a systemic gap in geriatric mental health care. Moreover, there is a persistence of depression prevalence over time in England, despite increased antidepressant usage [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. These findings reinforce the need for locally informed dementia prevention strategies that prioritise the most impactful risk factors within specific populations. Relying on international benchmarks without adjusting for context may lead to misdirected efforts or an underestimation of the role that certain factors play within national or regional settings.\u003c/p\u003e\u003cp\u003eThe overall weighted PAF for the 14 MRFs in our ELSA sample was lower than the 49% estimated by Chen et al. [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e] using the same ELSA wave. This discrepancy likely reflects methodological differences. We included two additional MRFs\u0026mdash;LDL cholesterol and visual impairment\u0026mdash;and used a lower CESD-8 cut-off score for depression (\u0026ge;\u0026thinsp;3 vs. \u0026ge;4), which was grounded in validation studies among older adults [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. These decisions were methodologically justified and aimed to maximise sensitivity while preserving comparability. Furthermore, our analytic sample was restricted to individuals with valid faith affiliation data, which may have influenced the demographic composition and overall risk burden. This restriction excluded approximately 13% of the original ELSA sample for that wave (1314 individuals), which may have biased prevalence estimates if participants with systematically different risk profiles were excluded. Taken together, these differences warrant cautious interpretation but underscore the value of methodological transparency in estimating attributable dementia risk.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\u003ch2\u003eDifferences in risk factor prevalence and PAFs between faith and non-faith communities\u003c/h2\u003e\u003cp\u003eThe significantly greater prevalence of depression in the faith community (74.7%) than in the nonfaith community (47.7%) is particularly notable. This disparity highlights the dual influence of religious engagement on mental health outcomes. While religious involvement can offer structured routines, community support, and spiritual coping mechanisms that mitigate stress and bolster psychological well-being [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e], certain religious contexts may inadvertently exacerbate depressive symptoms through feelings of guilt, spiritual inadequacy, or fear of divine punishment [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. Moreover, stigma related to mental health issues within faith communities can discourage individuals from seeking professional help, potentially exacerbating untreated depression [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. Alternatively, people with mental health conditions may specifically seek religious involvement and support to help them cope, so the religious community may include more than average numbers of people with such difficulties. Some religious organisations may even offer specific support to such groups. Additionally, religious discrimination further compounds mental health risks - individuals who experience religious discrimination have twice the risk of common mental disorders, including depression [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eDespite the higher prevalence of depression among the faith community relative to the nonfaith group, the faith community had a significantly lower weighted PAF (34.3%) than the nonfaith community (46.1%). Weighted PAFs are shaped not only by the prevalence of a risk factor but by its patterns of co-occurrence with other risk factors in the population. The relatively lower PAF for depression among the faith community suggests that the 14 MRFs account for a smaller share of dementia risk within faith-affiliated individuals. Nevertheless, across both subgroups, nonfaith and faith communities, depression consistently emerged as the leading modifiable risk factor (higher PAF) for dementia. In the nonfaith group, depression accounted for 11.4% of the overall dementia risk, and it accounted for 9.2% in the faith group. This pattern mirrors global findings from the Lancet Commission[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e] and UK-specific studies such as Chen et al. [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e], which also identifies depression as among the top contributors to dementia risk. These converging results highlight depression not only as a clinically important risk factor but also as a whole-population priority for dementia prevention strategies.\u003c/p\u003e\u003cp\u003eSocial isolation emerged consistently as the second highest PAF for both nonfaith and faith communities. In our analysis, social isolation accounted for a notable 7.5% within the nonfaith community, whereas it accounted for 4.5% within faith communities. The substantial contribution of social isolation to dementia risk highlights the essential role of sustained social connections in cognitive health. Research has consistently demonstrated that robust social networks and frequent social interactions stimulate cognitive function, promote emotional resilience, and reduce stress\u0026mdash;all mechanisms linked to lower dementia risk [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]. While faith communities are often presumed to have protective social structures due to communal activities [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e], our findings indicate that social isolation remains a significant challenge within these groups. While faith communities provide one model of social infrastructure, equivalent benefits can be achieved through inclusive, nonreligious community initiatives.\u003c/p\u003e\u003cp\u003eAlso, given the greater PAF for social isolation in nonfaith communities, it is important to explore secular approaches to social support and cognitive engagement. Community-based interventions, such as befriending schemes, local volunteering programmes, hobby clubs, or intergenerational mentorship, can offer similar protective benefits by fostering regular social interaction and emotional connection.\u003c/p\u003e\u003cp\u003eThe prevalence of hearing loss was significantly greater in the faith community (67.4%) than in the nonfaith community (32.3%), and so was its PAF. Hearing loss was identified as the third highest PAF within faith communities, indicating a distinct vulnerability within this population group compared with the nonfaith community. This significantly higher PAF emphasises a critical yet under-addressed risk factor for dementia within this community. Hearing impairment contributes to social withdrawal, communication difficulties, and cognitive strain, all of which accelerate cognitive decline and increase the risk of dementia[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Within faith communities, hearing loss could impede participation in communal worship and social activities, thereby diminishing the cognitive and emotional benefits traditionally associated with active faith engagement [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Hearing loss prevention and management interventions within faith-based settings, such as regular hearing screenings, the provision of assistive hearing devices, and the promotion of auditory-friendly environments during communal events, may represent a promising focus for dementia prevention efforts in faith-based settings.\u003c/p\u003e\u003cp\u003eVisual impairment was more prevalent among faith-affiliated participants (37.9%) than among nonfaith individuals (16.1%). It also ranked as the fourth highest contributor to dementia risk within the faith community, in contrast with its lower ranking within the nonfaith population. This higher prevalence and associated PAF for visual impairment among faith-affiliated individuals is noteworthy and highlights another unique risk within religious populations. Visual impairment restricts daily functional activities, social interactions, and participation in cognitively stimulating tasks, all of which contribute to cognitive decline [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Within religious settings, impaired vision can severely limit participation in faith-based activities, such as reading sacred texts or words to hymns and songs, engaging with symbolic rituals, or navigating community spaces, potentially exacerbating feelings of isolation and psychological distress. Thus, visual impairment may disproportionately undermine the protective social and cognitive aspects traditionally attributed to religious participation [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eThe significant contribution of visual impairment to the overall risk of dementia amidst the other modifiable risk factors within faith communities suggests potential opportunities for targeted preventative measures, such as incorporating regular vision screenings in faith community-based health initiatives and facilitating accessible adaptations (e.g., large-print religious texts, adequate lighting, and inclusive spatial layouts in worship areas). Addressing visual impairment not only preserves cognitive function but also enhances overall participation in community and spiritual life, amplifying protective benefits against dementia.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\u003ch2\u003eStudy limitations\u003c/h2\u003e\u003cp\u003eOur study has several important limitations. We utilised cross-sectional data from Wave 5 (2010\u0026ndash;2011) of the ELSA. Although foundational for dementia prevention modelling, these data predate several recent population-level changes, including shifts in mental health awareness, health behaviours, and religious participation in England. As such, the results may not fully reflect current prevalence patterns, although they remain instructive for understanding structural and cultural influences on risk.\u003c/p\u003e\u003cp\u003eFaith affiliation was measured only at one time point and was grouped broadly into religious vs. nonreligious categories. This simplification, while necessary for statistical power, likely masks interfaith heterogeneity and may underestimate nuanced behavioural or cultural influences. For example, educational inequities may disproportionately impact some minority faith communities, as evidenced by higher rates of limited formal education among certain groups, such as Muslims, but higher educational attainment among Hindus and Jews in the UK [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]. Additionally, Leung and Stanner [\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e] offer valuable insights into the relationship between traditional dietary habits and chronic disease risk among ethnic and minority religious groups in the UK. They reported that among South Asians, the frequent use of ghee, fried, and sugary desserts, which are often tied to religious customs, increases LDL cholesterol and obesity risk.\u003c/p\u003e\u003cp\u003eMoreover, some religious communities may face unique health disparities, such as cultural stigmas or systemic discrimination [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e], which can shape their dementia risk factor profile. To address this, future research should disaggregate faith community affiliations to understand the distinct risks within individual faith traditions. This will facilitate the design of culturally sensitive public health strategies that engage specific religious groups and tailor interventions to the specific needs of these communities.\u003c/p\u003e\u003cp\u003eWe used secondary data from the ELSA and thus were constrained by the available data. Notably, we relied on external prevalence estimates for TBI, using hospital administrative data rather than data directly from ELSA participants. This approach may lead to potential inaccuracies in estimating the true incidence of TBI within our sample and its subgroups. We also assumed, via our data imputation method, that the data were missing at random; however, this assumption may not hold true, potentially introducing selection bias if the missing data were systematic rather than random.\u003c/p\u003e\u003cp\u003eRestricting our analytic sample to respondents with complete religious affiliation data introduced potential selection bias. These individuals may systematically differ from those with missing data in ways relevant to both dementia risk and faith engagement.\u003c/p\u003e\u003cp\u003eOur PAF calculations assume that relative risks from the Lancet Commission, which are derived from pooled international studies [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e], apply equally to our study population, which may not be accurate for all subgroups. This methodological constraint means that our PAF estimates should be interpreted as indicative of potential prevention impact rather than precise predictions of achievable risk reduction.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e\u003ch2\u003eImplications for dementia prevention\u003c/h2\u003e\u003cp\u003eDespite these limitations, our study contributes essential insights into dementia risk factors among faith communities in England and highlights critical areas for targeted public health interventions. Given that more than half of the English population identifies with a faith community, religious institutions can serve as crucial platforms for targeted health interventions that could be culturally contextualised. Faith-based organisations (FBOs) play a unique role in disseminating health education, particularly in underserved communities, and can act as hubs for integrating broader public health strategies [\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eMoreover, partnerships between public health agencies and religious institutions have successfully reached vulnerable populations and can provide early screening and management of modifiable dementia risk factors such as depression, hearing loss and visual impairment [\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e]. However, efforts to engage faith communities in these interventions must also address issues of medical mistrust, which may stem from historical marginalisation, cultural, or prior negative healthcare experiences. Building trust through culturally competent communication, a sustained community presence, and partnerships with trusted faith leaders is crucial to the success of any health initiative in these settings. Embedding programmes promoting lifestyle modifications, such as fostering improved social connections, into faith communities can amplify their reach and cultural relevance [\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e]\u003c/p\u003e\u003cp\u003eThis study\u0026rsquo;s novel integration of faith community affiliation into dementia risk profile analysis reinforces the potential importance of cultural, social, and health determinants in shaping dementia risk profiles. These findings may help inform the development of contextually tailored prevention strategies, although further longitudinal and interventional studies are required to confirm these patterns and assess causality. Our findings highlight that faith communities in England exhibited differences in the relative contribution of modifiable dementia risk factors compared with nonfaith communities, possibly shaped by structural, behavioural, and psychosocial factors. Recognising these variations is critical to ensuring that dementia prevention efforts are inclusive, equitable, and effective across diverse communities. However, more granular analyses will facilitate the design of public health strategies that engage specific faith communities and tailor interventions to their unique social, cultural, and health-related contexts. While these findings emphasise which MRFs need to be prioritised among faith communities, they also have broader implications beyond these groups. Specifically, our research highlights how culturally sensitive and community-oriented preventive interventions can be effective in secular settings with comparable social structures, such as exercise clubs or meditation groups. The nuanced understanding derived from these results underscores the importance of tailored public health approaches that leverage community structures and practices, both religious and secular, to increase dementia prevention efforts.\u003c/p\u003e\u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eHuman Ethics and Consent to Participate declarations\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eELSA Wave 5 received ethical approval from the Berkshire Research Ethics Committee. All participants provided written informed consent to participate in the study at the time of data collection. The research was conducted in accordance with the ethical principles outlined in the Declaration of Helsinki. The secondary analysis of these data for the present study was approved by the Faculty of Health and Medicine Research Ethics Committee of Lancaster University (Ref: FHMREC22080).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to publish declaration\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eClinical trial number\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets analysed during the current study are available from the UK Data Service (https://ukdataservice.ac.uk/) for researchers who meet the criteria for access to ELSA data. Derived data supporting the findings of this study are available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNo conflict of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSanda Umar Ismail received support from the NIHR Applied Research Collaboration North West Coast and Alzheimer\u0026rsquo;s Society and is funded through a Post-Doctoral Fellowship. Heather Brown is funded by NIHR Applied Research Collaboration North West Coast.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eCarol Holland received funding from the North West Coast and the BBSRC/MRC for the Cognitive Frailty Interdisciplinary Network; grant number: BB/W018322/1\u003c/p\u003e\n\u003cp\u003eThe views expressed are those of the authors and not necessarily those of the funders, NHS or Department of Health and Social Care.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026apos; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eS.U. Ismail planned the study, performed all statistical analyses, and wrote the paper.\u003c/p\u003e\n\u003cp\u003eC. Holland was responsible for securing the funding, helped plan the study, supervised the data analysis, and revised the manuscript.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eH. Brown and F. Ahmed helped to plan the study, supervised the data analysis, and revised the manuscript.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable \u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eMatthews FE, Stephan BCM, Robinson L, et al. A two decade dementia incidence comparison from the Cognitive Function and Ageing Studies I and II. Nat Commun. 2016;7:11398.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eOffice for National Statistics [ONS]. 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Accessed 28 Jul 2025.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-public-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"pubh","sideBox":"Learn more about [BMC Public Health](http://bmcpublichealth.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/pubh/default.aspx","title":"BMC Public Health","twitterHandle":"@BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Dementia, faith communities, modifiable risk factors, population attributable fraction, public health interventions","lastPublishedDoi":"10.21203/rs.3.rs-7290936/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7290936/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground: \u003c/strong\u003eDementia prevention is a global public health priority. However, limited research has examined modifiable dementia risk factors (MRFs) within faith communities, despite their significant presence in the population of England. Faith communities often exhibit distinct social structures and lifestyle behaviours, such as strong social cohesion and shared health norms, that may shape both the exposure to and impact of MRFs. This study aimed to explore the distribution and relative contribution of MRFs among faith and nonfaith communities in England to inform contextually tailored prevention strategies.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods: \u003c/strong\u003eWe conducted a cross-sectional analysis using data from Wave 5 (2010–2011) of the English Longitudinal Study of Ageing (ELSA). Population attributable fractions (PAFs) for dementia were calculated for 14 MRFs using relative risks derived from the Lancet Commission on Dementia Prevention and prevalence estimates for the English population. Weighted PAFs were computed for the general ELSA population (n= 8812), faith (n= 7364) and nonfaith communities (n= 1448), with analyses adjusted for overlapping risk factors.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults: \u003c/strong\u003eThe overall weighted PAF was 35.3% in the general ELSA sample, 34.3% in the faith community, and 46.1% in the nonfaith community. Within faith communities, the highest PAF contributors were depression (9.2%), social isolation (4.5%), hearing loss (4.1%), and visual impairment (3.1%). For nonfaith communities, these were depression (11.4%), social isolation (7.5%), high low-density lipoprotein cholesterol (5.4%) and low education (4.6%).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eInterpretation: \u003c/strong\u003eFaith communities in England exhibited differences in the relative contribution of modifiable dementia risk factors compared with nonfaith communities. These findings may help inform the development of contextually tailored prevention strategies, although further longitudinal and interventional studies are required to confirm these patterns and assess causality. A deeper understanding within specific faith groups could refine these interventions. Importantly, the findings also highlight how culturally informed, community-centred approaches can enhance dementia prevention in secular populations with similar social structures—such as exercise groups or meditation circles—informing inclusive public health strategies across diverse community settings.\u003c/p\u003e","manuscriptTitle":"Dementia risk reduction potential among faith communities: Insights into modifiable risk factors in the English population","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-08-28 09:13:38","doi":"10.21203/rs.3.rs-7290936/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-10-21T12:52:16+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-09-29T13:10:14+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-09-26T15:09:54+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"123302924461675542672422421856148233376","date":"2025-09-25T03:39:54+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"295164163671516276239537712704559850275","date":"2025-09-16T08:43:33+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"226152379863643350479358880340875846958","date":"2025-09-12T07:09:23+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-09-11T18:28:21+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-09-09T17:39:20+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-08-13T17:22:31+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-08-12T15:06:01+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Public Health","date":"2025-08-12T15:02:36+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-public-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"pubh","sideBox":"Learn more about [BMC Public Health](http://bmcpublichealth.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/pubh/default.aspx","title":"BMC Public Health","twitterHandle":"@BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"88d18ac2-cf2b-467b-9659-f860e18479b9","owner":[],"postedDate":"August 28th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2025-11-04T13:08:28+00:00","versionOfRecord":[],"versionCreatedAt":"2025-08-28 09:13:38","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7290936","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7290936","identity":"rs-7290936","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}