DOI: https://doi.org/10.21203/rs.3.rs-1504550/v1
The exposure to risk factors (Smoking, High fasting plasma glucose and High BMI) are considered a major health concern. However, previous studies focused on changes in the global age-specific incidence and mortality, failed to distinguish between cohort and period effects, and did not discuss risk factors separately.
This study to estimate the burden of Alzheimer's disease and other dementias stratified by gender, age, locations, and social-demographic status for 21 regions across the world from 1990 to 2019. Besides, trend analyzes were conducted for major regions and risk factors for DALYs were also analyzed using age-period-cohort model and Join-point model.
Globally, the age standardized incidence rate(ASIR), age standardized death rate(ASDR), and age standardized DALY rates for Alzheimer's disease have an upward trend from 1990 to 2019.(percentage change: 1.5%,3.0%,3.5%, respectively) with average annual percent change. In most regions, indicators (incidence, mortality, and DALYs) increased steadily with SDI increased. By APC analysis, substantially increasing age effects were presented for Alzheimer's disease and other dementias from 40 to 95 years. The cohort effects RRs had a rapid reduction attribute to Smoking, High fasting plasma glucose and High BMI.
As the burden of Alzheimer's disease remains high in areas with low SDI. Therefore, rapid and effective government responses are needed to tackle and control dementia risk factors to reduce the disease burden in middle-low and low SDI countries.
Alzheimer's disease and other dementias is considered to be one of the most common neurodegenerative diseases and one of the most common neurological disorders(1). In 2018, an estimated 50 million people worldwide have Alzheimer's disease, and that number is expected to more than triple by 2050, driven by an aging population, with 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 seen a relatively stable incidence(7, 8). Morbidity and mortality in Alzheimer's disease varies significantly between countries. In the case of 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). A report of 2012, an estimated 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 (10). Based on previous studies in Canada and Europe, the prevalence of dementia alone is estimated at 500,000(11). Besides, Alzheimer's disease is a very serious public health problem in low and middle-income countries. It is widely believed that the prevalence of dementia increases significantly with age, and Sousa et al. said that dementia contributes most to disability in cities in Cuba, the Dominican Republic, Mexico, Peru, and India(12). With advances in health maintenance and increased longevity in developing countries, the dementia incidence will rise to a greater proportion in the coming years(13). Globally, there is still a lack of specific research on the burden and risk factors for Alzheimer's disease and other dementias.
Growing evidence of the presence of risk factors for dementia suggests that lifestyle and other interventions. If implemented effectively, may help delay the onset of dementia and reduce the number of people with dementia in the future(14). The morbidity associated with smoking is not limited to cardiovascular disease, chronic obstructive pulmonary disease, stroke and cancer, but also includes neurobiological and neurocognitive abnormalities of the brain, and there is now substantial epidemiological evidence that smoking is associated with a significantly increased risk of Alzheimer's disease and dementia(15, 16). A higher body mass index (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(17). The main purpose of this study has not only to describe the impact of age, sex, geographic location and socio-demographic index (SDI) associated with AD 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.
The important traditional indicators for cancer surveillance and prevention and control are incidence and mortality, but these two indicators only reflect the degree of harm caused by the disease, while the degree and duration of disability caused by the disease is not reflected. DALYs is a comprehensive measure of population health and is classically defined as the total number of healthy life years lost from onset to death, including Years of Life Lost, (YLL) and Years Lived with Disability, (YLD)(18). Since diseases reduce human life expectancy by causing premature death and disability, DALY can be used as a scientific and comprehensive assessment of disease burden in different diseases and regions, and has become an important indicator commonly used in the international cancer disease burden field in response to the needs of today's biopsychosocial medical paradigm shift. Therefore, we used DALY to model the attributed burden of cause and risk in this study. The secondary analysis of data in this study did not require ethical approval and consent from an institutional review board or ethics committee.
Data Sources
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(19). In this study, data from the IHME website (http://www.healthdata.org/), 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. The socio-demographic index (SDI) was divided countries into five SDI quintiles(20). 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. Alzheimer's disease and other dementias mortality data from vital registrations, oral autopsies, surveillance systems, surveys, censuses or police reports. 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. Standardization of modeling using the Causes of Death Ensemble Model (CODEm), Spatio-Temporal Gaussian Process Regression (ST-GPR), and DisMod-MR. Furthermore, we estimated 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. 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/m 2.
Statistical Analyses
Join-point regression analysis models are often used to identify recent trends in mortality and morbidity data [30]. 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).The connection function used in the model is a logarithmic function, and the year is used as the independent variable. Regression fitting was performed on the natural logarithm of age-standardized morbidity and mortality and to calculate the trend changes of morbidity and mortality, as well as the corresponding APC value for each trend segment. 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]. The above operations were implemented with the Join-point regression program version 4.7.0.0 provided by the National Cancer Institute.
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, 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:
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).
Globally, the age standardized incidence rate(ASIR), age standardized death rate(ASDR), and age standardized DALY rates(ASDR) for Alzheimer's disease have an upward trend from 1990 to 2019. The ASIR of Alzheimer's disease and other dementias is 95.0(107.9,81.6) per 100,000 persons, and the ASDR is 23.0(59.2,5.8) per 100,000 persons in 2019. 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)
| Types | 1990 2019 | ||||
|---|---|---|---|---|---|
| ASIR ASDR DALY ASIR ASDR DALY | |||||
| Overall | 93.6(80.1,106.7) | 22.2(5.5,60.0) | 326.7(143.3,731.0) | 95.0(81.6,107.9) | 23.0(5.8,59.2) 338.6(151.0,731.3) |
| Sex | |||||
| Male | 81.0(68.6,93.0) | 19.7(4.7,55.0) | 289.2(123.2,660.9) | 83.7(71.1,95.7) | 20.7(5.0,55.2) 304.4(131.0,680.0) |
| Female | 101.6(115.7,87.3) | 23.5(62.5,5.9) | 347.9(154.5,765.0) | 103.4(89.2,117.2) | 24.2(6.2,61.5) 361.2(162.8,767.7) |
| Social-demographic index | |||||
| High SDI | 99.6(86.2,113.1) | 22.0(57.5,5.5) | 324.1(147.3,705.6) | 100.6(87.2,113.4) | 22.7(5.9,56.6) 332.4(155.0,688.3) |
| High-middle SDI | 97.6(82.4,111.7) | 22.5(60.5,5.5) | 335.2(148.4,736.4) | 101.7(85.6,116.1) | 22.8(5.7,60.1) 348.5(157.7,754.4) |
| Middle SDI | 88.4(74.8,101.4) | 23.0(63.9,5.5) | 335.3(139.2,770.4) | 93.3(79.5,106.8) | 23.2(5.7,60.2) 346.1(148.6,770.2) |
| Low-middle SDI | 77.3(65.9,88.6) | 20.1(55.7,4.8) | 287.2(672.6,118.6) | 77.1(65.7,88.1) | 21.6(5.2,56.4) 304.8(126.5,702.3) |
| Low SDI | 78.7(67.0,90.5) | 20.9(58.1,4.9) | 295.2(694.7,121.3) | 77.0(66.0,87.9) | 23.0(5.6,62.5) 315.8(129.0,742.9) |
| Region | |||||
| Central Asia | 101.6(86.0,116.8) | 21.6(5.4,59.5) | 326.3(149.4,721.2) | 102.7(87.4,117.4) | 23.4(5.7,64.5) 345.5(155.3,775.1) |
| Central Europe | 104.0(87.6,120.0) | 23.4(5.7,64.7) | 349.9(156.5,779.0) | 106.3(89.8,121.9) | 23.0(5.5,59.4) 348.7(160.9,734.5) |
| Eastern Europe | 98.3(82.0,113.7) | 20.3(4.8,56.0) | 305.5(140.3,679.5) | 101.9(85.5,117.0) | 21.5(5.3,58.3) 324.8(149.9,705.2) |
| Australasia | 96.1(82.5,109.3) | 22.6(5.6,59.7) | 326.9(147.0,726.3) | 93.8(80.5,106.8) | 22.1(5.7,56.5) 319.9(146.3,682.0) |
| High-income Asia Pacific | 96.9(83.5,111.0) | 21.9(5.4,58.5) | 322.6(144.0,710.5) | 108.9(123.8,94.0) | 27.0(6.5,69.5) 385.4(179.3,791.8) |
| High-income North America | 111.1(94.6,126.9) 21.4(5.4,56.4) | 330.8(158.0,700.9) | 105.8(93.8,116.9) | 20.9(5.3,52.1) 317.7(154.0,653.2) | |
| Southern Latin America | 92.7(78.3,106.9) | 20.9(5.0,56.2) | 304.4(137.0,674.7) | 95.2(81.0,109.2) | 21.0(5.1,56.1) 311.9(142.1,674.2) |
| Western Europe | 93.5(105.5,81.0) 22.3(5.5,57.2) | 317.0(141.5,693.9) | 91.5(78.1,104.4) | 21.3(5.4,54.6) 309.7(141.5,663.7) | |
| Andean Latin America | 81.0(69.0,93.4) | 21.5(58.3,5.3) | 306.7(127.3,718.3) | 83.5(71,6,95.5) | 21.8(5.4,55.5) 312.7(133.7,691.7) |
| Caribbean | 77.7(66.4,89.4) | 20.6(55.7,5.0) | 296.0(122.9,685.0) | 78.4(67.2,89.8) | 20.8(5.3,51.9) 299.5(126.9,661.7) |
| Central Latin America | 83.8(71.4,97.0) 23.4(5.8,63.7) | 329.2(133.6,765.8) | 82.9(70.8,95.2) | 23.8(6.0.60.6) 333.2(136.2,747.3) | |
| Tropical Latin America | 102.3(117.0,88.0) | 26.6(6.6,71.1) | 384.4(163.8,871.3) | 104.1(90.5,118.1) | 25.6(6.5,66.2) 390.9(172.0,856.0) |
| North Africa and Middle East | 75.3(64.1,86.5) 26.1(6.4,72.6) | 391.3(170.3,883.1) | 110.2(94.0,116.9) | 25.5(6.3,67.1) 387.0(172.0,848.5) | |
| South Asia | 64.2(73.8,54.6) 16.8(3.8,48.1) | 238.2(96.1,575.6) | 63.6(54.2,73.0) | 19.2(4.6,52.1) 262.1(105.6,617.4) | |
| East Asia | 90.4(76.2,104.0) | 23.3(5.4,63.7) | 347.4(144.0,798.8) | 103.3(87.6,119.3) | 23.2(5.6,61.2) 366.6(164.8,789.1) |
| Oceania | 88.3(74.3,101.8) | 22.2(5.1,60.7) | 332.9(136.7,765.2) | 86.3(73.3,90.0) | 21.5(5.1,59.0) 318.8(133.8,726.9) |
| Southeast Asia | 84.6(72.1,97.2) | 22.8(5.5,62.4) | 333.6(136.5,764.3) | 63.6(54.2,73.0) | 19.2(4.6,52.1) 342.2(141.5,765.1) |
| Central Sub-Saharan Africa | 96.4(83.0,110.2) 23.7(5.5,67.4) | 347.0(149.9,796.7) | 98.3(85.2,112,1) | 26.6(6.7,71.4) 380.6(165.1,857.2) | |
| Eastern Sub-Saharan Africa | 85.8(73.4,98.2) 23.0(5.4,62.8) | 323.3(135.2,746.6) | 85.1(73.4,96.8) | 26.2(6.2,69.5) 356.0(145.0,829.9) | |
| Southern Sub-Saharan Africa | 90.1(77.2,103.3) 21.9(5.2,59.3) | 313.7(135.1,712.2) | 90.0(77.3,102.7) | 22.9(5.5,62.5) 323.8(741.0,138.3) | |
| Western Sub-Saharan Africa | 75.3(64.1,86.5) 22.9(5.5,65.9) | 309.4(124.0,769.1) | 73.5(62.6,83.9) | 25.6(6.3,68.5) 333.4(129.5,774.8) | |
In 2019, Alzheimer's disease and other dementias incidence rate for female were 332.4 million (155.0-688.3) to which the High SDI quintile contributed the most. Correspondingly, the age-standardized DALY rate was the greatest increase in Low SDI quintile (6.9%) and least in the High SDI quintile (2.5%). (Fig. 1)
Results of Joinpoint Regression Analysis
The study presented significant trend changes and the estimated annual percentage change in incidence, death and DALY for periods covered by each trend (Fig. 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 of all SDI quantile regions occurred an increase. Similarly, DALYs also showed a similar increasing trend with ASDR. So 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%.
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 Fig. 3. At each SDI quantile, there was a large amount of heterogeneity between incidence, death and DALY rates due to Alzheimer's disease and other dementias. In many regions, Alzheimer's disease deaths and DALYs decreased as SDI increased. Despite significant improvements in SDI levels, Alzheimer's death rates have not declined in some countries, mainly in South Asia, Central Sub-Saharan Africa and Eastern Sub-Saharan Africa. However, the incidence of Alzheimer's disease has increased in some regions and decreased in others apparently. 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.
Globally, we plotted DALY attributable to the Smoking、High fasting plasma glucose of Alzheimer's disease and other dementias by sex (Fig. 4 and Supplemental Table 2). In 2019, the DALY rates of Alzheimer's disease and other dementias attributable to Smoking for male were more than 3.0 times higher than female in Global. And the DALY rate for Alzheimer's disease and other dementias attributed to high fasting glucose is slightly higher in female than in male. In addition, the DALY rate for Alzheimer's disease and other dementias attribute to obesity was 1.4 times higher in female than in male. 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.
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 years, 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. These five SDI quintiles and global age RR peaked in the 90–94 years age group (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 DALY caused by smoking in high SDI quantile had been the lowest among all GBD regions. However, the coefficients of the age effect on DALY caused by High fasting plasma glucose and High BMI in high-middle SDI quantile had been the highest among all GBD regions.
Among all GBD regions, the period effect in the high-middle SDI quartile had the largest increase in DALY rate due to smoking and high fasting plasma glucose from 1994 to 2019. From the 1904–1908 to the 1979–1983 birth cohorts, the cohort effect showed a consistent downward trend in all these countries. From the earliest birth cohorts to the most recent cohorts, the risk declined for all countries globally (Fig. 5).
A study of GBD 2019 data shows trends and patterns in global Alzheimer's disease and other dementias morbidity, mortality, and DALY, as well as their three most relevant risk factors. The study shows that the burden of Alzheimer's disease and other dementias is high and varies widely between countries and regions, with an increasing trend of DALY rate in all 19 regions except Oceania, West Europe, and High-income North America. ASDR for Alzheimer's disease is on the rise in all countries, with the fastest rise in low SDI countries. DALY rates declined in all regions except middle-low SDI and low SDI. As the world’s population ages, the number of people living with dementia is increasing in middle-low and low SDI countries, and this number is expected to continue to rise.
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(21). In 2010, 58% of people with dementia lived in middle-low and low income countries, with a higher incidence in Latin America (8.5%)(22).The increase of ASDR and DALY in Middle-low SDI and low SDI countries is due to the strain on health resources(23). 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(24). These factors place a multifaceted burden on individuals, families and society(25). This is in addition to the high level of care required by people with dementia. 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(26). 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(27). 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(28). The ASIR for female was 1.17 times higher than male in 2016, indicating that more female was affected by dementia globally(3). 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(29, 30). 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(31). 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(32). 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(33).
Risk factors for Alzheimer's disease and other dementias include Smoking, High fasting plasma glucose and High BMI. Tobacco is the leading cause of many diseases and death worldwide. 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(34). Cigarette smoke also contains neurotoxins, which can increase the risk of Alzheimer's disease(35). 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(36). 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(37, 38). 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(39). 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(40). Recently, evidence suggests that High BMI in midlife is a major risk factor for dementia in later life. This shows an U-shaped relationship between midlife BMI and dementia risk, in line with previous meta-analyses of High BMI and dementias(17). High BMI may also influence dementia risk by affecting other medical conditions such as coronary events and may interact with other cardiovascular risk factors(41). Depression and anxiety may also increase weight, which is associated with an increased risk of dementias(42).
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(22). 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(43). 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. Neurological diseases such as Alzheimer's disease are associated with older age. A systematic study estimated that the prevalence of dementia increased from 1.8–2.6% in the 65–69 age group and from 42.1–60.5% in the 95–99 age group. Consistent with our results, the age RRs reached their highest values in the 90–94 age group(29). 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(44, 45). Alzheimer's disease usually occurs in people over the age of 65, mainly because comorbidities are common. 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(46).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(47). 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(48).
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(49). Thus, cognitive impairment can reduce the burden of disease and the worsening of dementia by promoting early detection. At the same time, education levels in both high SDI and low-income countries have improved and reduced regional inequalities in education. 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(50). 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 (51, 52).
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, morbidity, mortality and DALY of Alzheimer's disease are correlated with SDI levels. Differences in the different indicators of Alzheimer's disease can still be readily seen according to SDI region. 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.
Ethics approval and consent to participate
Not applicable.
Consent for publication
Not applicable.
Availability of data and materials
The list of data sources used is publicly available at the Global Health Data Exchange website (http://ghdx.health data.org/gbd-results-tool), no additional data available
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].
Authors’ contributions
Y C: design this context, acquisition of data or analysis and drafting the article. YY: have access to the data and con-trolled the decision to publish. JS: Revise the article. YW: Revise the article. YM: Revise the article.
Acknowledgments
We would like to thank the Institute for Health Metrics and Evaluation for the data.