Y. Cui1, W. Yang1, J. Shuai1, Y. Ma1, Y. Yan1
1. Department of Epidemiology and Medical Statistics, Xiangya school of public health, Central South university, Changsha, China
Corresponding Author: Yan Yan , Department of Epidemiology and Medical Statistics, Xiangya school of public health, Central South university, Changsha 410078, China. Tel: 86-18942514496; firstname.lastname@example.org
J Prev Alz Dis 2023;
Published online May 26, 2023, http://dx.doi.org/10.14283/jpad.2023.63
Background: Previous studies only focused on changes in the global age-specific incidence and mortality for Alzheimer’s disease and other dementias, failed to distinguish between cohort and period effects, and did not discuss risk factors separately.
Methods: In this study, Alzheimer’s disease disability-adjusted life years (DALYs) data to estimate the burden by gender, age, locations, and social-demographic status for 21 regions from 1990 to 2019. Additionally, trend analysis was performed using the age-period-cohort (APC) model and Join-point model.
Results: In most regions, indicators (incidence, mortality, and DALYs) increased steadily with socio-demographic index(SDI) increased. The age effects for Alzheimer’s disease and other dementias showed a significant increase from 40 to 95 years. The cohort effects rate ratios (RRs) had a rapid reduction attributed to smoking, high fasting plasma glucose, and high body mass index (BMI).
Conclusions: Countries in middle-low and low SDI regions have higher levels of risk factor exposure. As a result, rapid and effective government responses are necessary to control dementia risk factors and reduce the disease burden in these countries.
Key words: Alzheimer’s disease and other dementias, global burden, risk factors, age-period-cohort analysis, join-point.
Alzheimer’s disease and other dementias is considered to be one of the most common neurodegenerative diseases and common neurological disorders (1). In 2018, an estimated 50 million people worldwide had Alzheimer’s disease, and that number is expected to more than triple by 2050, driven by an aging population, resulting in 152 million people with Alzheimer’s disease (2). It is worth noting that dementia is the fifth leading cause of death worldwide (3). Some countries, including the US, UK, Sweden, the Netherlands and Canada, have reported unexpected declines in age-specific dementia rates (4-6). In contrast, China and Japan have seen an increase in Alzheimer’s disease incidence, while Nigeria has experienced relatively stable incidence (7, 8). Morbidity and mortality in Alzheimer’s disease varies significantly between countries. In China, due to increasing life expectancy and a growing elderly population, Alzheimer’s disease has become an important public health issue and has seriously affected the country, society and individuals (9). Recent research about China reported that among individuals over 65 years old, the prevalence of dementia is 5.60 % (3.50 to 7.60), and 9.5 million people in China have dementia (5.3 %) (10).The incidence of dementia over 65 years old is from 17.7 to 24.0 people per 1,000 people (11). In 2012, it was estimated that 5.2 million Americans over the age of 65 had Alzheimer’s disease, resulting in approximately $200 billion in healthcare-related costs. And a dramatic increase in the «oldest old» (i.e., 85 years of age) across all racial and ethnic groups is expected to lead to an increase in the prevalence of Alzheimer’s disease (12). Based on previous studies in Canada and Europe, the prevalence of dementia alone is estimated at 500,000 (13). Besides, Alzheimer’s disease is a very serious public health problem in low and middle-income countries. Sousa et al. said that dementia contributes most to disability in cities in Cuba, the Dominican Republic, Mexico, Peru, and India (14). With advances in health maintenance and increased longevity in developing countries, the dementia incidence will rise to a greater proportion in the coming years (15).
According to a recent study, smoking is not only associated with cardiovascular disease, chronic obstructive pulmonary disease, stroke, and cancer, but also with neurobiological and neurocognitive abnormalities of the brain. Epidemiological evidence indicates that smoking significantly increases the risk of Alzheimer’s disease and dementia (18, 19). Additionally, a higher BMI is associated with chronic diseases that increase the risk of dementia. Obesity is a feature of metabolic syndrome and has been identified as a risk factor for neurological disorders (20). However, the long -term trend of Alzheimer’s disease caused by smoking, High Fasting Plasma Glucose and High BMI is still unknown in different SDI quintile. As far as we know, there have been no related research to compare the age period cohort trends between different risk factors and socio-demographic index (SDI) regions. Besides, SDI was divided countries into five SDI quintiles, including High SDI, High-middle SDI, Middle SDI, Low-middle SDI, Low SDI (21). The GBD study divides neurological disorders into the following five specific types of data: Alzheimer’s disease and other dementias, Parkinson’s disease, Idiopathic epilepsy, Multiple sclerosis, Motor neuron disease. The main purpose of this study has not only to describe the impact of age, sex, geographic location and SDI associated with Alzheimer’s disease on global morbidity, mortality and disability-adjusted life years (DALYs) from 1990 to 2019, but also to explore Alzheimer’s disease DALYs attributable to risk factors based on data from the 2019 Global Burden of Disease (GBD) study using age-period-cohort (APC) model.
Materials and methods
The traditional indicators used for cancer surveillance and prevention and control are incidence and mortality. However, these indicators only measure the degree of harm caused by the disease, while the degree and duration of disability caused by the disease is not reflected. To address this limitation, DALYs is a comprehensive measure of population health that includes Years of Life Lost(YLL) and Years Lived with Disability (YLD) (22). By accounting for premature death and disability, DALY can provide a scientific and comprehensive assessment of disease burden across different diseases and regions. As a result, it has become an important indicator in the international cancer disease burden field in response to the needs of today’s biopsychosocial medical paradigm shift.
The GBD 2019 provided a comprehensive annual estimate of incidence, prevalence, death, and risk factors for 204 countries and territories globally, regionally, and nationally from 1990 to 2019 (23). In this study, data from the Institute for Health Metrics and Evaluation (IHME)website (https://www.healthdata.org/gbd/2019), which platform regularly publishes disease and injury incidence, prevalence, mortality, YLL, YLD and DALY indicators, with refinement to differentiate by country, year, sex, and age. For GBD 2019, we modeled the burden of nonfatal disease using DisMod-MR 2.1, which is a meta-regression-Bayesian modeling tool with three steps. We extracted estimates and their 95% uncertainty interval (UI) from GBD 2019 for the incidence, death, DALYs, as measures of Alzheimer’s disease and other dementias burden (ICD-10 codes:F00-F03). Furthermore, we used smoking, high fasting glucose, and high BMI as risk factors for dementia. For each of these risk factors, we set a theoretical minimum exposure level at which the risk of health outcomes was lowest (24, 25). Smoking was set at zero; high fasting glucose was greater than 4.5 and less than 5.4 mmol/L; and BMI was set at greater than 20 and less than 25 kg/m2. Population attributable fraction (PAF) is defined as the minimum exposure level of the theory of a certain population if the exposure of a certain risk factor will decrease in the proportion of diseases or deaths. Smoking, High fasting plasma glucose and High BMI attributable deaths were calculated by multiplying the PAF of the number of the deaths.
This study used a Join-point regression analysis model to assess global changes in morbidity, mortality and DALY rates for each SDI quintile based on Annual Percent Change (APC) and Average Annual Percent Change (AAPC). Finally, according to the Monte Carlo Permutation method was used to verify that the APC values of each trend segment and the total AAPC values were statistically significant (29). APC/AAPC>0 means the result increasing year by year, and APC/AAPC<0 means the values decreasing year by year. The above operations were implemented with the Join-point regression program version 18.104.22.168 provided by the National Cancer Institute (https://surveillance.cancer.gov/help/joinpoint/tech-help/citation).
We also demonstrated the burden of Alzheimer’s disease in all 21 regions from 1990 to 2019, flexibly modeling the association of morbidity, mortality, and DALY rates with SDI using restricted cubic splines. The above statistical descriptions and analyses all were performed using the R program (version 3.6.0, R Core Team).
Furthermore, since Mason first proposed the APC model in 1973 (26), the application of the APC model has been plagued by the problem of multi-collinearity. The APC model can be essentially considered as a multiple regression model with the following expressions (27):
Y = log(M) = μ + αagei + βperiodj + γcohortk + ε.
Where M represents the corresponding Alzheimer’s disease and other dementias mortality rate, and α, β, and γ denote the effect values of age, period, and cohort estimated by the APC model, respectively. Therefore, we calculated relative risk values (RR values) to help explain the independent effects of age, period, and cohort on Alzheimer’s disease and other dementias mortality. In this study, APC analysis was performed using Stata 12.0 software (StataCorp, College Station, TX, USA).
Overall Status of Alzheimer’s disease Burden in 1990 and 2019
Globally, the age standardized incidence rate(ASIR), age standardized death rate(ASDR), and age standardized DALY rates for Alzheimer’s disease showed an upward trend from 1990 to 2019. In 2019, the ASIR of Alzheimer’s disease was 95.0(107.9,81.6) per 100,000 persons, and the ASDR was 23.0(59.2,5.8) per 100,000 persons. The global DALY rate in 2019 was 3.5% higher than those of 1990s,(338.6 cases per 100,000 population [151.0,731.3]vs326.7 [143.3,731.0]) (Table 1).
Abbreviations: ASIR, age-standardized incidence rate; ASDR, age-standardized death rate; DALY, disability-adjusted life year; UI, uncertain interval; SDI, Socio-demographic Index.
In 2019, Alzheimer’s disease incidence rate for female was 332.4 million (155.0-688.3), withthe High SDI quintile contributed the most. Correspondingly, the age-standardized DALY rate was the lowest in the High SDI quintile (2.5%) and had the highest increase in Low SDI quintile (6.9%) (Figure1).
Results of Joinpoint Regression Analysis
The study presented significant changes in trend, with AAPC in incidence, death and DALY for each trend periods (Figure 2 and Supplemental Table 1). In the Low SDI quintile, the ASIR decreased from 1990 to 2019, with an overall AAPC of −0.1%. However, other SDI quantile areas showed upward trends. Join-point results showed that the death rate increased across all SDI quantile. Similarly, DALYs also showed a similar increasing trend with ASDR. In the Low-middle SDI quintile and Low SDI quintile, ASDR and DALY rates both showed an upward trend from 1990 to 2019, with an overall AAPC of 0.3% and 0.2%.
Alzheimer’s disease Burden and Sociodemographic Transition
There are different patterns of epidemiological between age-standardized incidence, death and DALY rates in the Global and other 21 regions during the period 1990-2019 were observed in Figure 3. At each SDI quantile, there was a large amount of heterogeneity between incidence, death and DALY rates. However, in some countries, particularly in South Asia, Central Sub-Saharan Africa, and Eastern Sub-Saharan Africa, Alzheimer’s death rates have not decreased despite improvements in SDI levels. Additionally, the incidence of Alzheimer’s disease has increased in some regions while decreasing in others. Finally, as shown in the figure, the estimated relationship between SDI and ASR is represented by a black line, and both show an upward trend overall.
Each colored line represents the temporal trend of the relationship for the specified region. Each point represents a specific year in that region.
Risk Factors Attributable to Alzheimer’s disease Burden
Globally, we plotted DALY attributable to the Smoking, High fasting plasma glucose of Alzheimer’s disease and other dementias by sex (Figure 4 and Supplemental Table 2). In 2019, the DALY rates of Alzheimer’s disease attributed to smoking were more than 3 times higher in male than female globally. On the other hand, the DALY rate for Alzheimer’s disease and other dementias attributed to high fasting plasma glucose was slightly higher in females than in males. Furthermore, the DALY rate for Alzheimer’s disease and other dementias attributed to obesity was 1.4 times higher in females than in males.Smoking in the male is the main risk for DALYs in East Asia. In the following regions where there is also a part of smoking attributable to DALYs are high-income countries. The area with the highest cumulative DALY rates caused by the three risk factors was North Africa and Middle East.
Results of Age-Period-Cohort Analysis
In different SDI Quintiles, the age effect coefficient of DALY for Alzheimer’s disease caused by smoking, high BMI, and high fasting plasma glucose increased from 40-45 to 90-94 age group, with the most significantly increase in high fasting plasma glucose. The relative risk of Alzheimer’s disease and other dementias exponentially increases with age for both sex, with the highest age-specific risk observed in the 90-94 years age group across the five SDI quintiles and globally (Supplemental Table 3).For smoking, the significant increased for DALYs in each region occurred between 40-95 age groups, with the most considerable increased observed in the Low-middle SDI quantile. The coefficients of age effect on DALYs caused by smoking in the high SDI quantile were the lowest among all GBD regions. However, the coefficients of the age effect on DALYs caused by high fasting plasma glucose and high BMI in the high-middle SDI quantile were the highest among all GBD regions.
The period effect in the high-middle SDI quartile had the most significant increase in DALY rate due to smoking and high fasting plasma glucose from 1994 to 2019. Additionally, the cohort effect showed a consistent downward trend in all countries from the 1904-1908 to the 1979-1983 birth cohorts. From the earliest birth cohorts to the most recent cohorts, the risk declined for all countries globally (Figure 5).
A study based on GBD 2019 data reveals the trends and patterns in global morbidity, mortality, and DALY associated with Alzheimer’s disease and other dementias, as well as the three most relevant risk factors. The study highlights that the burden of Alzheimer’s disease and other dementias is substantial and varies significantly across countries and regions. The DALY rate is increasing in all 19 regions, except for Oceania, West Europe, and High-income North America. Additionally, the ASDR for Alzheimer’s disease is rising in all countries, with the fastest increase occurring in low SDI countries. Although the DALY rates have decreased in most regions, they have increased in middle-low SDI and low SDI regions. As the global population continues to age, the number of people living with dementia is rising in middle-low and low SDI countries, and this trend is expected to continue in the future.
Alzheimer’s disease Burden in Different Regions and Groups
This section discusses Alzheimer’s disease burden in Different Regions and Groups. The IHME has divided GBD 2019 into 21 regions based on epidemiological similarity and geographical proximity (28). In 2010, 58% of people with dementia lived in middle-low and low income countries, with a higher incidence in Latin America (8.5%) (29). The increase of ASDR and DALY in Middle-low SDI and low SDI countries is due to the strain on health resources (30). They can’t to seek medical help as frequently as patients in high SDI countries, and the fact that there is little training at all levels of health services in the recognition and management of dementia (31). These factors place a multifaceted burden on individuals, families and society (32). In contrast, the reliability and accessibility of home care systems in low SDI and low SDI countries is poor and families are able to provide less support (33). The higher incidence of neurological diseases in high SDI countries can be explained by their longer life expectancy compared to the global level as a whole, as well as their case surveillance and case reporting which allows for earlier diagnosis of disorders (34). In addition to this, these adverse lifestyle trends (e.g., Overweight, fasting blood glucose levels) may be additional factors contributing to some of the increased burden of Alzheimer’s disease (35). The ASIR for female was 1.17 times higher than male in 2016, indicating that more female was affected by dementia globally (26). In this study, the ASIR was higher in Female than in male, not only because the relatively longer life expectancy of women, but also because of the inherent biological differences between men and women, which is consistent with previous studies (36, 37). Additionally, female was more likely than male to develop Alzheimer’s disease and other dementias, likely due to differences in brain structure, brain development, and function (38). The increased incidence of Alzheimer’s disease appears to be more pronounced in Japan than in the United States. This may be partly due to the larger gender gap in life expectancy in Japan compared to the US in 1990 and 2018 (39). Furthermore, we can also see that women with high SDI have higher ASIR than women with high-middle SDI, which may be due to the higher obesity rate in High SDI women (40).
Risk Factors Attributable to Alzheimer’s disease Burden
The tobacco industry started targeting the third world to increase tobacco consumption there and strengthen their industry. This may be the main reason for the increased DALY rates for Alzheimer’s disease in low SDI regions and smokers (41). Cigarette smoke also contains neurotoxins, which can increase the risk of Alzheimer’s disease (42). We observed gender and area-specific distribution of Alzheimer’s disease attributable burdens due to smoking in East Asia. Higher fasting plasma glucose is associated with cerebellar atrophy. Therefore, monitoring and management of blood sugar levels may also have an impact on brain health (43). Fasting plasma glucose is one of the most important risk factors for Alzheimer’s disease, and studies have shown that metabolic disorders as early as childhood may affect brain health and cognitive function decades later (44, 45). In addition to this, numerous studies have reported that fasting blood glucose at different stages of life is associated with cognitive brain health in mid-life (46). For high BMI levels, there was little difference between different SDI regions in risk effects. Obesity is also an identified risk factor for neurocognitive impairment. The obesity rate in European and American countries is at the highest level in the world. The U.S. has the highest death rate in terms of high body mass index (47). Recently, evidence suggests that High BMI in midlife is a major risk factor for dementia in later life. This shows a U-shaped relationship between midlife BMI and dementia risk, in line with previous meta-analyses of High BMI and dementias (20). High BMI may also influence dementia risk by affecting other medical conditions such as coronary events and may interact with other cardiovascular risk factors (48). Depression and anxiety may also increase weight, which is associated with an increased risk of dementias (49).
The RR of the age effect shows an exponential upward trend. Since 1990, studies have observed that the global increase in the burden of Alzheimer’s disease and other dementias has been associated with an increase in life expectancy, with the burden growing fastest in populations aged 60 years or older due to increased longevity and declining birth rates (29). A definitive study has shown that a number of risk factors that are effective in preventing dementia can account for up to 35% of the burden of dementia. They included hearing loss, education, depression, lack of physical activity, smoking, obesity, etc (50). Our findings also found an exponential increase in age RRs for dementia DALYs over the age of 50, making it even more important that we adopt effective risk factors to control the prevalence of dementia. A systematic study estimated that the prevalence of dementia increased from 1.8% to 2.6% in the 65-69 age group and from 42.1% to 60.5% in the 95-99 age group. Consistent with our results, the age RRs reached their highest values in the 90-94 age group (36). Many studies have shown that most people will notice a number of cognitive changed as they get older. A variety of physiological disease-related mechanisms lead to a decline in cognitive function with ageing (51, 52). Physical health problems and dementia occur very frequently together in older people, whose bodies are already prone to diabetes and high blood pressure. This made them at an increased risk of developing Alzheimer’s disease and dementia (53). Period RRs for Alzheimer’s disease and other dementias have been rising, likely due to dramatic changes in lifestyle factors across regions, including impaired sleep hygiene and unhealthy eating habits (9). Over the past decade, increasing chronic disease risk has been strongly associated with Alzheimer’s disease (54). Besides, the higher period RR for Alzheimer’s disease in High-middle than in High SDI may due to an increase in obesity and related diseases in middle-age which is expected to a 19% in dementia in China and a 9% in the US (55).
The cohort RRs for Alzheimer’s disease and other Dementias have been declining. It showed that different regions and SDI quartiles have increased investment in chronic disease prevention and increased the public awareness of chronic disease prevention and treatment in recent decades (56). Thus, cognitive impairment can reduce the burden of disease and the worsening of dementia by promoting early detection. For example, our country’s nine-year free compulsory education policy has also contributed to an increase in the average years of schooling, which is critical to education for all (57). Finally, the investments of people in education and health services contribute to improvement of their mental and physical health. It also can effectively reduce the risks of developing dementia in later life (58, 59).
Our current study has limitations as it is based on the regional Alzheimer’s disease data. Although the GBD provides a high quality estimate of the global burden of disease, there are still some limitations. If we could have information at the national level on all factors associated with Alzheimer’s disease, the results would be more conducive to improving public policy and diagnostic and treatment facilities. In addition, if we had quantitative information on data such as BMI, number of cigarettes per day, etc., a more accurate association could be measured based on mortality rates.
In conclusion, there is a clear association between the incidence, mortality, and DALY rates of Alzheimer’s disease and SDI levels, with differences observed among different SDI regions. Countries in middle-low and low SDI regions have higher levels of risk factor exposure, such as smoking, fasting plasma glucose and High BMI are positively associated with an increased risk of death in cases of dementia, leading to a higher burden of Alzheimer’s disease. Age and gender are important influencing factors in Alzheimer’s disease. Population aging is the direct cause of the disease. Women have a higher incidence than men due to physiological differences. Therefore, it is necessary to control Alzheimer’s disease risk factors and decrease health disparities in countries with different development levels.
Acknowledgments: We would like to thank the Institute for Health Metrics and Evaluation for the data.
Declaration of Competing interests: The authors declare that there are no relationships or activities that might bias, or be perceived to bias, their work.
Funding: This work was funded by the National Natural Science Foundations (NSFC) of China [grant number 81973153, 81673276, and 81373101]. This study was supported by the Fundamental Research Funds for the Central Universities of Central South University [grant number 2022ZZTS0845].
Ethical standards: None.
1. Global, regional, and national burden of neurological disorders during 1990-2015: a systematic analysis for the Global Burden of Disease Study 2015. The Lancet Neurology. Nov 2017;16(11):877-897. doi:10.1016/s1474-4422(17)30299-5
2. Weidner WS, Barbarino P. THE STATE OF THE ART OF DEMENTIA RESEARCH: NEW FRONTIERS. Alzheimer’s and Dementia. 2019;15(7):P1473.
3. Global, regional, and national incidence, prevalence, and years lived with disability for 328 diseases and injuries for 195 countries, 1990-2016: a systematic analysis for the Global Burden of Disease Study 2016. Lancet (London, England). Sep 16 2017;390(10100):1211-1259. doi:10.1016/s0140-6736(17)32154-2
4. Matthews FE, Arthur A, Barnes LE, et al. A two-decade comparison of prevalence of dementia in individuals aged 65 years and older from three geographical areas of England: results of the Cognitive Function and Ageing Study I and II. Lancet (London, England). 2013;382(9902):1405-1412.
5. Chengxuan, Qiu, Eva, et al. Twenty-year changes in dementia occurrence suggest decreasing incidence in central Stockholm, Sweden. Neurology. 2013;
6. Kenneth M, Langa, Eric B, et al. A Comparison of the Prevalence of Dementia in the United States in 2000 and 2012. JAMA internal medicine. 2017;
7. Dodge HH, Buracchio TJ, Fisher GG, et al. Trends in the Prevalence of Dementia in Japan. Int J Alzheimers Dis. 2012;2012:956354.
8. Gao S, Ogunniyi A, Hall KS, et al. Dementia incidence declined in African-Americans but not in Yoruba. Alzheimer’s & dementia : the journal of the Alzheimer’s Association. Mar 2016;12(3):244-51. doi:10.1016/j.jalz.2015.06.1894
9. Bo Z, Wan Y, Meng SS, et al. The temporal trend and distribution characteristics in mortality of Alzheimer’s disease and other forms of dementia in China: Based on the National Mortality Surveillance System (NMS) from 2009 to 2015. PloS one. 2019;14(1):e0210621. doi:10.1371/journal.pone.0210621
10. Huang Y, Wang Y, Wang H, et al. Prevalence of mental disorders in China: a cross-sectional epidemiological study. The lancet Psychiatry. Mar 2019;6(3):211-224. doi:10.1016/s2215-0366(18)30511-x
11. Wu YT, Ali GC, Guerchet M, et al. Prevalence of dementia in mainland China, Hong Kong and Taiwan: an updated systematic review and meta-analysis. International journal of epidemiology. Jun 1 2018;47(3):709-719. doi:10.1093/ije/dyy007
12. Thies W, Bleiler L. 2012 Alzheimer’s disease facts and figures. Alzheimers & Dementia the Journal of the Alzheimers Association. 2012;8(2):131-168.
13. Impact T. Rising Tide : The Impact of Dementia on Canadian Society. 2010;
14. Sousa RM, Ferri CP, Acosta D, et al. Contribution of chronic diseases to disability in elderly people in countries with low and middle incomes: a 10/66 Dementia Research Group population-based survey. Lancet (London, England). Nov 28 2009;374(9704):1821-30. doi:10.1016/s0140-6736(09)61829-8
15. Sabat SR. Dementia in developing countries: a tidal wave on the horizon. The Lancet. 2009/11/28/ 2009;374(9704):1805-1806. doi:https://doi.org/10.1016/S0140-6736(09)62037-7
16. Estimation of the global prevalence of dementia in 2019 and forecasted prevalence in 2050: an analysis for the Global Burden of Disease Study 2019. The Lancet Public health. Feb 2022;7(2):e105-e125. doi:10.1016/s2468-2667(21)00249-8
17. Elwood P, Galante J, Pickering J, et al. Healthy lifestyles reduce the incidence of chronic diseases and dementia: evidence from the Caerphilly cohort study. PLoS One. 2013;8(12):e81877. doi:10.1371/journal.pone.0081877
18. Durazzo TC, Insel PS, Weiner MW. Greater regional brain atrophy rate in healthy elderly subjects with a history of cigarette smoking. Alzheimer’s & dementia : the journal of the Alzheimer’s Association. Nov 2012;8(6):513-9. doi:10.1016/j.jalz.2011.10.006
19. Durazzo TC, Mattsson N, Weiner MW, Alzheimer’s Disease Neuroimaging I. Smoking and increased Alzheimer’s disease risk: a review of potential mechanisms. Alzheimer’s & dementia : the journal of the Alzheimer’s Association. 2014;10(3 Suppl):S122-S145. doi:10.1016/j.jalz.2014.04.009
20. Anstey KJ, Cherbuin N, Budge M, Young J. Body mass index in midlife and late-life as a risk factor for dementia: a meta-analysis of prospective studies. Obesity reviews : an official journal of the International Association for the Study of Obesity. May 2011;12(5):e426-37. doi:10.1111/j.1467-789X.2010.00825.x
21. Global, regional, and national comparative risk assessment of 84 behavioural, environmental and occupational, and metabolic risks or clusters of risks, 1990-2016: a systematic analysis for the Global Burden of Disease Study 2016. Lancet (London, England). Sep 16 2017;390(10100):1345-1422. doi:10.1016/s0140-6736(17)32366-8
22. Zhou T, Guan H, Yao J, Xiong X, Ma A. The quality of life in Chinese population with chronic non-communicable diseases according to EQ-5D-3L: a systematic review. Quality of life research : an international journal of quality of life aspects of treatment, care and rehabilitation. Nov 2018;27(11):2799-2814. doi:10.1007/s11136-018-1928-y
23. Global burden of 87 risk factors in 204 countries and territories, 1990-2019: a systematic analysis for the Global Burden of Disease Study 2019. Lancet (London, England). Oct 17 2020;396(10258):1223-1249. doi:10.1016/s0140-6736(20)30752-2
24. Ezzati M, Lopez AD, Rodgers A, Vander Hoorn S, Murray CJ. Selected major risk factors and global and regional burden of disease. Lancet (London, England). Nov 2 2002;360(9343):1347-60. doi:10.1016/s0140-6736(02)11403-6
25. Lozano R, Naghavi M, Foreman K, et al. Global and regional mortality from 235 causes of death for 20 age groups in 1990 and 2010: a systematic analysis for the Global Burden of Disease Study 2010. Lancet (London, England). Dec 15 2012;380(9859):2095-128. doi:10.1016/s0140-6736(12)61728-0
26. Yang Y, Schulhofer-Wohl S, Land KC. the intrinsic estimator for age-period- cohort analysis: what it is and how to use it 1. 2017;
27. Luo L. Assessing Validity and Application Scope of the Intrinsic Estimator Approach to the Age-Period-Cohort Problem. Demography. 2013;50(6):1945-1967.
28. Murray CJ, Ezzati M, Flaxman AD, et al. GBD 2010: design, definitions, and metrics. Lancet (London, England). Dec 15 2012;380(9859):2063-6. doi:10.1016/s0140-6736(12)61899-6
29. Prince M, Bryce R, Albanese E, Wimo A, Ribeiro W, Ferri CP. The global prevalence of dementia: A systematic review and metaanalysis. Alzheimer’s & Dementia. 2013/01/01/ 2013;9(1):63-75.e2. doi:https://doi.org/10.1016/j.jalz.2012.11.007
30. Chan KY, Wang W, Wu JJ, et al. Epidemiology of Alzheimer’s disease and other forms of dementia in China, 1990–2010: a systematic review and analysis. The Lancet. 2013/06/08/ 2013;381(9882):2016-2023. doi:https://doi.org/10.1016/S0140-6736(13)60221-4
31. Organization WH. World Health Organization, Neurological Disorders: Public health challenges. 2006;
32. Wortmann M. Dementia: Alzheimer’s Disease International. 2010;
33. Prince M, Patel V, Saxena S, et al. No health without mental health. The Lancet. 2012;370(9590):859-877.
34. Collaborators GUND, Feigin VL, Vos T, et al. Burden of Neurological Disorders Across the US From 1990-2017: A Global Burden of Disease Study. JAMA Neurol. 2021;78(2):165-176. doi:10.1001/jamaneurol.2020.4152
35. Mokdad AH, Ballestros K, Echko M, et al. The State of US Health, 1990-2016: Burden of Diseases, Injuries, and Risk Factors Among US States. Jama. Apr 10 2018;319(14):1444-1472. doi:10.1001/jama.2018.0158
36. Chan KY, Wang W, Wu JJ, et al. Epidemiology of Alzheimer’s disease and other forms of dementia in China, 1990-2010: a systematic review and analysis. Lancet (London, England). Jun 8 2013;381(9882):2016-23. doi:10.1016/s0140-6736(13)60221-4
37. Ballard C, Gauthier S, Corbett A, Brayne C, Aarsland D, Jones E. Alzheimer’s disease. Lancet (London, England). Mar 19 2011;377(9770):1019-31. doi:10.1016/s0140-6736(10)61349-9
38. Oveisgharan S, Arvanitakis Z, Yu L, Farfel J, Schneider JA, Bennett DA. Sex differences in Alzheimer’s disease and common neuropathologies of aging. Acta Neuropathologica. 2018;136
39. Dodge HH, Buracchio TJ, Fisher GG, et al. Trends in the prevalence of dementia in Japan. Int J Alzheimers Dis. 2012;2012:956354. doi:10.1155/2012/956354
40. Webber L, Kilpi F, Marsh T, Rtveladze K, McPherson K, Brown M. Modelling obesity trends and related diseases in Eastern Europe. Obesity reviews : an official journal of the International Association for the Study of Obesity. Aug 2012;13(8):744-51. doi:10.1111/j.1467-789X.2012.00999.x
41. Sebrié E, Glantz SA. The tobacco industry in developing countries. BMJ (Clinical research ed). Feb 11 2006;332(7537):313-4. doi:10.1136/bmj.332.7537.313
42. Swan GE, Lessov-Schlaggar CN. The effects of tobacco smoke and nicotine on cognition and the brain. Neuropsychology review. Sep 2007;17(3):259-73. doi:10.1007/s11065-007-9035-9
43. Cherbuin N, Sachdev P, Anstey KJ. Higher normal fasting plasma glucose is associated with hippocampal atrophy: The PATH Study. Neurology. Sep 4 2012;79(10):1019-26. doi:10.1212/WNL.0b013e31826846de
44. Koletzko B, Decsi T, Molnár D, Hunty ADL. Early Nutrition Programming and Health Outcomes in Later Life. Springer Netherlands. 2009;
45. Carmichael O, Stuchlik P, Pillai S, et al. High-Normal Adolescent Fasting Plasma Glucose Is Associated With Poorer Midlife Brain Health: Bogalusa Heart Study. The Journal of clinical endocrinology and metabolism. Oct 1 2019;104(10):4492-4500. doi:10.1210/jc.2018-02750
46. Jack CR, Jr., Knopman DS, Jagust WJ, et al. Tracking pathophysiological processes in Alzheimer’s disease: an updated hypothetical model of dynamic biomarkers. The Lancet Neurology. Feb 2013;12(2):207-16. doi:10.1016/s1474-4422(12)70291-0
47. Mubarik S, Liu X, Malik SS, Wang L, Yu Y, Yu C. Evaluation of lifestyle risk factor differences in global patterns of breast cancer mortality and DALYs during 1990-2017 using hierarchical age-period-cohort analysis. Environmental science and pollution research international. Sep 2021;28(36):49864-49876. doi:10.1007/s11356-021-14165-1
48. Gorospe EC, Dave JK. The risk of dementia with increased body mass index. Age and ageing. Jan 2007;36(1):23-9. doi:10.1093/ageing/afl123
49. Gallacher J, Bayer A, Fish M, et al. Does anxiety affect risk of dementia? Findings from the Caerphilly Prospective Study. Psychosomatic medicine. Jul 2009;71(6):659-66. doi:10.1097/PSY.0b013e3181a6177c
50. Livingston G, Sommerlad A, Orgeta V, et al. Dementia prevention, intervention, and care. Lancet (London, England). Dec 16 2017;390(10113):2673-2734. doi:10.1016/s0140-6736(17)31363-6
51. van Harten AC, Mielke MM, Swenson-Dravis DM, et al. Subjective cognitive decline and risk of MCI: The Mayo Clinic Study of Aging. Neurology. Jul 24 2018;91(4):e300-e312. doi:10.1212/wnl.0000000000005863
52. Jessen F, Amariglio RE, Buckley RF, et al. The characterisation of subjective cognitive decline. Lancet Neurol. 2020;19(3):271-278. doi:10.1016/S1474-4422(19)30368-0
53. Carone M, Jewell MA, P. N. Estimating the Lifetime Risk of Dementia in the Canadian Elderly Population Using Cross-Sectional Cohort Survival Data. Journal of the American Statistical Association. 2014;
54. Matthews FE, Arthur A, Barnes LE, et al. A two-decade comparison of prevalence of dementia in individuals aged 65 years and older from three geographical areas of England: results of the Cognitive Function and Ageing Study I and II. Lancet (London, England). Oct 26 2013;382(9902):1405-12. doi:10.1016/s0140-6736(13)61570-6
55. Loef M, Walach H. Midlife obesity and dementia: meta-analysis and adjusted forecast of dementia prevalence in the United States and China. Obesity (Silver Spring, Md). Jan 2013;21(1):E51-5. doi:10.1002/oby.20037
56. Bundy JD, He J. Hypertension and Related Cardiovascular Disease Burden in China. Annals of Global Health. 2016;82(2):227-233.
57. Bai R, Dong W. Trends in Mortality Rates for Alzheimer’s Disease and Other Dementias Over 30 Years in China. American Journal of Alzheimer’s Disease & Other Dementias®. 2021/01/01 2021;36:15333175211044884. doi:10.1177/15333175211044884
58. Ording AG, Christensen LB, Bjørge T, et al. Birthweight and all-cause mortality after childhood and adolescent leukemia: a cohort of children with leukemia from Denmark, Norway, Sweden, and Washington State. Acta oncologica (Stockholm, Sweden). Aug 2020;59(8):949-958. doi:10.1080/0284186x.2020.1738546
59. Cohen AK, Syme SL. Education: a missed opportunity for public health intervention. American journal of public health. Jun 2013;103(6):997-1001. doi:10.2105/ajph.2012.300993
© Serdi 2023