Spatial Disparity and Factors Associated with Dementia Mortality: A Cross-Sectional Study in Zhejiang Province, China

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Regional factors of physical and social features may be influencing this spatial disparity. However, there are few reports on dementia mortality in China, and the true extent of spatial disparity in dementia mortality across small regional localities is unclear. The majority of people who die from dementia are over 60 years old. This study aims to explore the geographical variations in dementia mortality, estimate the relative risk and identify districts at higher risk for intervention and further study. Methods We used surveillance data on deaths from Alzheimer’s disease and other forms of dementia in Zhejiang province from 2015–2019 to estimate the spatial risk of death from dementia using a Bayesian spatial model. We mapped predicted relative risk to visualize the risk of death from Alzheimer’s disease and other forms of dementia and to identify risk factors associated with dementia. Results A total of 30,398 deaths attributable to dementia as the underlying or related cause (multiple causes) were reported in 2015–2019. Counties and districts located in the southeast and west of Zhejiang province had significantly higher standard mortality ratios than others. The predicted mean relative risk was 0.98, with a range of 0.14 ~ 4.37. Counties and districts with a smaller proportion of residents aged 60 years or older, poorer economic status, inferior health resources and worse pollution had a higher risk of dementia death. Conclusions There is spatial disparity in dementia mortality across different districts in Zhejiang. Our study adds new evidence on the association between social and environmental factors and the risk of dementia death. Appropriate preventive health strategies can be developed to reduce such spatial disparity in the risk of dementia mortality. Dementia Mortality Surveillance Geographical Analysis Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Background Dementia is a major public health challenge in China and other low- and middle-income countries. Around 50 million people have dementia worldwide, with nearly 60% of them living in low- and middle-income countries [1]. Dementia, including Alzheimer’s disease and other forms of dementia, is one of the major causes of disability and death among older people, and is now among the top 10 causes of death overall [2]. In the United States, Alzheimer’s disease is officially ranked as the sixth leading cause of death and the fifth leading cause of death for people aged 65 and over [3]. There is often a lack of awareness and understanding of dementia, resulting in stigma and barriers to dementia diagnosis and care, and it may cause even higher numbers of deaths than official sources acknowledge. Few studies have investigated spatial disparity in mortality from Alzheimer’s disease and other forms of dementia, especially in China [4, 5, 6]. One study from China used mortality data from the national Disease Surveillance system and reported dementia mortality is significantly higher in eastern China than in the western and central regions, with a rate ratio of 2.28 (95% CI 1.45-3.60) [4]. The risk of dementia is significantly higher in rural areas than in urban areas [6]. Some multicentre surveys have also shown that the incidence and prevalence of dementia is higher in rural areas of China than in urban areas [7, 8]. However, these study did not examine the relationships between the risk of dementia and social characteristics in small areas and the specific explanation for the spatial disparity in the risk of death from dementia is not yet known. Risk factors for disease may be spatially correlated because individuals in the same region tend to have similar characteristics. Analyses of risk factors for dementia have rarely considered the spatial autocorrelation of disease. One measure of risk of death from disease is SMR. However, in many cases small areas may have extreme SMRs due to small population size or small samples. The use of a Bayesian spatial model is preferred in this case to estimate the disease risk in small areas. It is able to borrow information from neighbouring areas and incorporate covariate information so that extreme values are smoothed or shrunk [9]. Zhejiang province, a province in southeast China, has a population of 11 million people aged 60 or over. The number of deaths from Alzheimer’s disease and other forms of dementia in Zhejiang is about six thousand per year. A cross-sectional survey showed that the age standard prevalence of dementia, Alzheimer’s disease and vascular dementia in Zhejiang province were respectively 13.0, 6.9 and 0.5% [10]. This study used the death data from Alzheimer’s disease and other forms of dementia in Zhejiang province from 2015–2019 provided by the disease surveillance information reporting management system of the Chinese Centre for Disease Prevention. It estimated the spatial risk of dementia deaths using Bayesian spatial models, mapped the predicted relative risk to visualize the risk of dementia death, and identified the associated environmental and social risk factors. Methods Data source and study population The mortality data presented in this study are based on information collected from the 2015–2019 Chronic Disease Surveillance Information and Management System provided by the Zhejiang Provincial Centre for Disease Control and Prevention. This organization has gathered death certificates from all 90 counties and districts in Zhejiang province since 2015. The latest county was created at the end of 2019, so the data for this study includes 89 counties and districts. The death certificates contain medical information, such as causes of death from the immediate cause to underlying causes, and other comorbidities or morbid states. Cause-of-death codes have been classified in terms of the International Classification of Diseases, 10th Revision (ICD–10) since 2000. After physicians upload data to the system, specialists analyse the diagnoses declared on the death cases to ensure data accuracy. Crude mortality rates are calculated based on the population statistical bulletin of Zhejiang published by the Statistical Bureau. There are many different forms of dementia. Alzheimer’s disease is the most common cause of dementia, accounting for an estimated 60–80% of cases [ 11 ]. Other major forms include vascular dementia, dementia with Lewy bodies, and a group of diseases that contribute to frontotemporal dementia. The lines between the different forms of dementia are blurred and mixed forms often coexist. Four categories of dementia were included in this study which adopted internationally definitions of Alzheimer’s disease and other dementia: vascular dementia (F01.0, F01.1, F01.2, F01.3, F01.8, F01.9); unspecified dementia (F03); Alzheimer’s disease (G30.0, G30.1, G30.8, G30.9); and other degenerative diseases of the nervous system not elsewhere classified (G31), including Lewy body disease and frontotemporal dementia[ 12 ]. All the causes of death (both underlying causes and associated causes) mentioned these four categories of dementia on the death certificates were included during the selection. An underlying cause is defined by WHO as the “disease or injury which initiated the train of events leading directly to death, or the circumstances of the accident or violence which produced the fatal injury”. Covariates Previous findings have shown spatial disparity in dementia deaths to be associated with metal elements in drinking water, PM2.5, environmental tobacco smoke, and elements in soil [ 13 , 14 , 15 ].The variable indicators for counties and districts from 2015–2019 used in this study were obtained from the Zhejiang Statistical Yearbook and included the proportion of people aged 60 years or older, GDP per capita, healthcare resource indicators and PM2.5 counts. Statistical analysis We first conducted descriptive statistics of the death cases from Alzheimer’s disease and other forms of dementia in Zhejiang Province from 2015–2019. Then we used a Bayesian geospatial model to estimate the relative risks (RRs) of dementia in 89 counties in Zhejiang province. The traditional statistical approach, frequentist inference, is based on the likelihood function, which is used to derive parameter estimates. Bayesian approaches use probability to measure uncertainty in predictions or inference estimates, and incorporate spatial and temporal dependencies through the specification of prior distributions. Bayesian approaches can overcome modelling issues prevalent in research (nonnormality, small sample size, missing data, clustered data structure) [ 16 , 17 ]. We specify \({Y}_{i}\) to be the counts of dementia death cases in county i and assumed that \({Y}_{i}\) is conditionally independently Poisson distributed. where \({E}_{i}\) is the expected number of dementia death cases in county i , \({\theta }_{i}\) is the relative risk in county i , and n is the number of counties. We implemented a Besag–York–Mollié (BYM) model to obtain relative risk estimates. The BYM model includes both a spatial conditional autoregression component and a heterogeneous random effect component. We expressed the logarithm of \({\theta }_{i}\) as $$log\left({\theta }_{i}\right)={\beta }_{0}+d({x}_{i}{)}^{{\prime }}\beta +{u}_{i}+{v}_{i},$$ where \({\beta }_{0}\) is the intercept parameter that represents the overall risk, \(d(.)\) is a vector of observed covariates, β is the regression coefficients of the covariates, \({u}_{i}\) is a spatial structured effect component modelled with a conditional auto regressive (CAR) distribution as \({u}_{i}|{u}_{-i}\sim N\left({\stackrel{-}{u}}_{{\delta }_{i}},\frac{{\sigma }_{u}^{2}}{n{\delta }_{i}}\right)\) , and \({v}_{i}\) is an unstructured spatial effect defined as [ 18 ]. The relative risk \({\theta }_{i}\) quantifies whether county i has higher ( \({\theta }_{i}>1\) ) or lower ( \({\theta }_{i}<1\) ) risk than the average risk in the standard population. We produced the probabilities of predicted relative risk being greater than a given threshold c (exceedance probabilities, i.e., P ( \({\theta }_{i}>c\) )). We implemented the Poisson model by the recommended strategy (i.e., integrating the nested Laplace approximation (INLA)). Due to the lack of reliable a priori information on all parameters, we used a non-informative approach in selecting the prior and therefore used the default prior available in the R-INLA package. All analyses were performed using the R-INLA package [ 19 ]. Results Death characteristics Alzheimer’s disease or other forms of dementia as the underlying cause or a related cause (multiple causes) of death was mentioned in 30,398 death certificates in Zhejiang in 2015–2019. Of the 28,513 deaths (93.80%) where dementia was the underlying cause of death, 54% were caused by Alzheimer’s disease (Figure 1). 57.89% of the deaths were female (Table 1). The mean age at the time of death was 83.29 years (maximum 112 years), with the mean age of females being 2.23 years higher than that of males. In the majority of cases, the place of death for people with dementia was at home. Most males were married (i.e., wife still alive) at the time of death (63.6% for males and 35.7% for females), while females were usually widowed (63.0% for females and 31.0% for males). Table 1. Characteristics of deaths attributable to Alzheimer’s disease and dementia in 2015–2019. Overall Males Females n=30,398 n=12,800 (42.1%) n=17,598 (57.9%) Mean age 83.3 82.0 84.2 Standard deviation 9.03 9.47 8.57 Marital status Single 658 (2.16%) 541 (4.23%) 117 (0.66%) Married 14,414 (47.4%) 8,138 (63.6%) 6,276 (35.7%) Widow(er) 15,052 (49.5%) 3,968 (31.0%) 11,084 (63.0%) Divorced 212 (0.70%) 119 (0.93%) 93 (0.53%) Unspecified 62 (0.20%) 34 (0.27%) 28 (0.16%) Education degree Postgraduate 9 (0.03%) 5 (0.04%) 4 (0.02%) University 165 (0.54%) 119 (0.93%) 46 (0.26%) Junior college 110 (0.36%) 64 (0.50%) 46 (0.26%) Secondary school 161 (0.53%) 80 (0.62%) 81 (0.46%) Technical school 6 (0.02%) 4 (0.03%) 2 (0.01%) High school 466 (1.53%) 271 (2.12%) 195 (1.11%) Below 29,481 (97.0%) 12,257 (95.8%) 17,224 (97.9%) Place of death Unspecified 16 (0.05%) 8 (0.06%) 8 (0.05%) Medical institution 1,763 (5.80%) 868 (6.78%) 895 (5.09%) The way to the hospital 127 (0.42%) 64 (0.50%) 63 (0.36%) Home 27,495 (90.5%) 11,383 (88.9%) 16,112 (91.6%) Retirement home 778 (2.56%) 351 (2.74%) 427 (2.43%) Other sites 219 (0.72%) 126 (0.98%) 93 (0.53%) Age <60 627 (2.06%) 354 (2.77%) 273 (1.55%) 60–64 574 (1.89%) 310 (2.42%) 264 (1.50%) 65–69 1,025 (3.37%) 544 (4.25%) 481 (2.73%) 70–74 1,725 (5.67%) 877 (6.85%) 848 (4.82%) 75–79 3,379 (11.1%) 1,621 (12.7%) 1,758 (9.99%) 80–84 7,427 (24.4%) 3,289 (25.7%) 4,138 (23.5%) 85–89 8,798 (28.9%) 3,511 (27.4%) 5,287 (30.0%) 90–94 5,301 (17.4%) 1,848 (14.4%) 3,453 (19.6%) 95–99 1,377 (4.53%) 402 (3.14%) 975 (5.54%) 100+ 165 (0.54%) 44 (0.34%) 121 (0.69%) Mapping SMR and relative risk of dementia death in Zhejiang Geographical variations in dementia mortality were found in the study. As can be seen from Figure 2, the standard mortality ratio of counties and districts located in south-eastern and western Zhejiang province is significantly higher than that in other counties and districts. The predicted mean relative risk from the Bayesian spatial model was 0.98, with a range of 0.14 to 4.37. The risk of death from dementia was highest in the southeast of Zhejiang province (Figure 3). Figure 4 shows the predicted probability of the relative risk exceeding 1.5 for counties in Zhejiang province. Risk factors from the spatial models Table 2 shows that the distributions of the characteristics of social factors and environments in the 89 counties and districts in Zhejiang province. In the Bayesian spatial model, the proportion of residents aged above 60 years, GDP per capita, number of hospital beds, number of doctors, and counts of PM2.5 were risk factors independently associated with dementia death risk. Figure 5 shows counties and districts with poorer economic status per capita, inferior health resources and worse pollution are associated with a higher risk of dementia mortality. Table 2. Descriptive statistics of risk factors in the counties and districts (n = 89) in Zhejiang. Factors Min Median Max Mean±SD People aged ≥60 (%) 13.47 21.75 34.29 21.94±3.59 GDP per capita 18053 83598 375963 91894±49460.61 Number of hospital beds 204 2750 14205 3559±2831.40 Number of doctors 181 1748 8842 2292±1710.80 PM2.5 counts 14.88 31.33 66.38 33.66±10.55 Discussion This cross-sectional study used Bayesian geospatial methods to model dementia SMR in Zhejiang, and to evaluate factors associated with high death rate of dementia. Critical risk factors identified in our study included the number of persons over 60 years of age, GDP per capita, number of hospital beds, number of doctors and counts of PM2.5. Our model shows spatial disparity in dementia mortality in the Zhejiang province. The highest risk areas are distributed in the southeast and west. On closer investigation of high-risk districts, we found that these counties and districts share similar circumstance, usually with worse economic levels, inferior local health resources and more polluted environment. Such spatially variations may be attributed to the commons of neighbouring regions, such as the geographical context, social and economic backgrounds, and the similarity of residents’ lifestyles. The mechanisms by which factors affect the relationship between social and economic characteristics and the risk of dementia mortality in small areas are currently unclear. Some studies have reported that residents in circumstances with better economic environments and health resources lead to positive health lifestyles and stimulating behaviours [ 20 ]. One study used data from a cohort design of the 10/66 Dementia Study Group in middle-income countries and found that the absolute and relative risk of death from dementia was significantly higher in rural areas of China than in urban areas, with an estimated median standard mortality ratio for dementia of 2.77 [ 6 ]. Rural areas could be correlate with poor conditions, lower economic levels and inferior local health resources which could give rise to the risk of dementia mortality. Previous reports on spatial disparity in dementia mortality in middle-income countries were limited. A systematic analysis estimates the median standard mortality ratio (SMR) for dementia in China was 1.94 [ 21 ]. Many developed countries have analysed dementia mortality using population-based surveillance data or registration data. Using the US national death certificate data from 2000 and 2010, Xu et al. used a time-scan statistical approach to obtain the spatial and temporal clusters of dementia mortality in the north-eastern US, with a lower relative risk and the most likely temporal cluster of excess mortality located in the Pacific Northwest [ 22 ]. The US Centers for Disease Control and Prevention report on dementia mortality from 2000 to 2017 shows that the age-adjusted mortality rate for dementia in 2017 was 66.7 deaths per 100,000 US standard population, with a higher age-adjusted mortality rate for women (72.7) than for men (56.4) [ 23 ]. These studies can provide policymakers with relevant information to allocate scarce health resources efficiently and equitably. We found that PM2.5 exposure was positively associated with spatial disparity in dementia mortality. Previous evidence suggested that PM2.5 exposure is a major risk factor for death from respiratory disease and cardiovascular disease. Studies of the association between PM2.5 exposure and the risk of death from Alzheimer’s disease and other forms of dementia are limited. Results from a 10-year study of US veterans cohort suggest that excess dementia deaths are associated with PM2.5 concentrations and that the attributable burden of dementia deaths is particularly high among socioeconomically disadvantaged groups in the US [ 24 ]. A meta-analysis of associations between PM2.5 exposure and neurological disorders, which included 80 studies, showed that PM2.5 exposure was strongly associated with increased risk of dementia, Alzheimer’s disease and Parkinson’s disease [ 25 ]. The harmful effects of air pollution exposure on cognitive function, and the risk of cognitive decline, were also validated [ 26 ]. The possible toxicological mechanism by which PM2.5 exposure induces the exacerbation of Alzheimer’s disease and other forms of dementia is neuronal damage caused by PM2.5 through causing oxidative stress, neuroinflammation or altered dopamine levels [ 27 , 28 ]. In addition, exposure to both nitrogen dioxide and carbon monoxide is associated with an increased risk of dementia [ 29 ]. There is a general lack of awareness of the disease burden of dementia in China, as many people still consider dementia as normal ageing and do not have access to dementia health services for old age and care, which places an even heavier burden on the families and communities of those affected. Although dementia primarily affects older people, it is not a normal part of ageing [ 30 ]. In the context of severe ageing, the number of counties and districts at high risk of dementia mortality in Zhejiang province has increased accordingly. One of the important aims of this study is to note high-risk districts for dementia interventions amidst limited public health resources. This is crucial because residential location could act as a marker for the social and environmental factors that relevant to healthcare services. Spatial modelling and mapping provide the required tools to obtain local residents’ health outcomes. Understanding this variation and its attributable factors will contribute to disease prevention, and to monitoring access to health services and planning targeted public health interventions. The mortality case data for this study were obtained from health system records and may underestimate the true mortality rate for dementia. This may produce nondifferential or differential misclassification bias due to the lack of diagnostic capacity for dementia in community hospitals. This study used current-year air pollution indicators as a proxy measure for short-term air pollution exposure and did not do research on the effect of longer-term air pollution exposure on dementia mortality. Furthermore, the pollution indicators in our model only included PM2.5 exposure, which is not sufficiently diverse and could have been analysed for associations with other air pollution indicators such as sulphur dioxide (SO 2 ), carbon monoxide (CO) and nitrogen dioxide (NO 2 ). Limited by the data sources, this study included fewer regional factors of physical and social variables, and future studies could be improved in terms of design, analysis and reporting to provide a solid basis for recommendations and possible interventions. Conclusion Our modelling reveals the spatial trends of dementia death risk in Zhejiang province from 2015–2019, and explores the association between social and environmental factors and the risk of dementia death, as a means to prevent and control excess dementia deaths. We can also provide some different ideas for dementia analysis studies. In addition, we suggest that appropriate measures be implemented to address the key risk factors identified in this study, to reduce spatial disparity in the risk of dementia mortality. Declarations Acknowledgements: We thank the Center of Social Welfare and Governance of Zhejiang University and the Institute of Wenzhou of Zhejiang University offering financial support and thank the Center for Disease Control and Prevention of Zhejiang Province providing the data. Funding: This work was granted by the National Natural Science Foundation of China (72004201), a Project of Humanity and Social Science Youth Foundation of Ministry of Education (20YJC840019), and a Project of Zhejiang Provincial Federation of Social Sciences (2022B 64). Authors’ contributions: Study concept and design: XH, XL, NL and JL. Data analysis: XH, XL, and XZ. Interpretation of data: XH, XL. Drafting of the manuscript: XH, XL and NL. Revision of the draft: XL and JL. All the authors read and approved the final manuscript. Competing interests None declared. Availability of data and materials The data underlying this study are not publicly available due to individual privacy information protection, but are available from the corresponding author upon reasonable request. Ethics approval and consent to participate This study was carried out in accordance with the “Declaration of Helsinki” and was approved by the ethics committee of Zhejiang Provincial Centre for Disease Control and Prevention. The need for Informed Consent was waived by the ethics committee of Zhejiang Provincial Centre for Disease Control and Prevention due to the retrospective nature of the study. Consent for publication Not Applicable. References Nichols E, Szoeke CEI, Vollset SE, Abbasi N, Abd-Allah F, Abdela J, et al. Global, regional, and national burden of Alzheimer’s disease and other dementias, 1990–2016: a systematic analysis for the Global Burden of Disease Study 2016. The Lancet Neurology. 2019;18:88–106. Nichols E, Szoeke CEI, Vollset SE, Abbasi N, Abd-Allah F, Abdela J, et al. Global, regional, and national burden of Alzheimer’s disease and other dementias, 1990–2016: a systematic analysis for the Global Burden of Disease Study 2016. The Lancet Neurology. 2019;18:88–106. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-1935233","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":131316919,"identity":"1fc05bc9-9863-4536-9282-af1fbff515cc","order_by":0,"name":"Xiaotian Heng","email":"","orcid":"","institution":"Zhejiang University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Xiaotian","middleName":"","lastName":"Heng","suffix":""},{"id":131316921,"identity":"8c5e9b8c-3b81-4c86-a62c-685ffff88431","order_by":1,"name":"Xiaoting Liu","email":"","orcid":"","institution":"Zhejiang University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Xiaoting","middleName":"","lastName":"Liu","suffix":""},{"id":131316922,"identity":"73f9ed3e-63e3-411c-95b1-5d334da2be2b","order_by":2,"name":"Na Li","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAnElEQVRIiWNgGAWjYDACdsbGBwxsINYBYrUwMzYbkKqFgU0CooVYYM7M3Fbxo8yOQb7xAOPnAmK0WDYztt3sOZfMwNhwgFl6BjFaDA4ztt1mbDsAdOABNmYeYrUUg7SwkaSFGaSFhxQtzZJAv/BIMBxsliZOy/H2hx+AISYnP+Pwwc9EaYEBHgaJgw2kaAABfpJ1jIJRMApGwUgBADsuKpqYesd1AAAAAElFTkSuQmCC","orcid":"","institution":"Zhejiang Center for Disease Control and Prevention","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"Na","middleName":"","lastName":"Li","suffix":""},{"id":131316923,"identity":"0b155a27-fbf8-4746-8762-f0e4269b2115","order_by":3,"name":"Jie Lin","email":"","orcid":"","institution":"Zhejiang University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Jie","middleName":"","lastName":"Lin","suffix":""},{"id":131316924,"identity":"ceb08600-cc2d-4f4d-a9d9-818bb1578a8e","order_by":4,"name":"Xiaoyan Zhou","email":"","orcid":"","institution":"Zhejiang Center for Disease Control and Prevention","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Xiaoyan","middleName":"","lastName":"Zhou","suffix":""}],"badges":[],"createdAt":"2022-08-06 08:14:15","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-1935233/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-1935233/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":25724904,"identity":"88622e5a-1478-4603-82a3-1008edb02503","added_by":"auto","created_at":"2022-08-26 17:45:13","extension":"jpeg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":58343,"visible":true,"origin":"","legend":"\u003cp\u003eNumber of deaths attributed to dementia and specific types of dementia as an underlying cause of death in Zhejiang, 2015–2019.\u003c/p\u003e\u003cp\u003eNOTE: Dementia deaths are identified according to the International Classification of Diseases, 10th Revision underlying cause-of-death codes: F03 (unspecified dementia), G30 (Alzheimer disease), F01 (vascular dementia), and G31 (other degenerative diseases of nervous system).\u003c/p\u003e","description":"","filename":"Figure1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-1935233/v1/3889570ce4957562496c8640.jpeg"},{"id":25724905,"identity":"1384d43a-265a-4103-a003-07b6f64398a8","added_by":"auto","created_at":"2022-08-26 17:45:13","extension":"jpeg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":325664,"visible":true,"origin":"","legend":"\u003cp\u003eMaps of dementia SMR in Zhejiang counties from 2015–2019.\u003c/p\u003e","description":"","filename":"Figure2.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-1935233/v1/a380cc38c42b9c04da470344.jpeg"},{"id":25724907,"identity":"feef73e9-e3a2-46b9-af55-f632d00f861e","added_by":"auto","created_at":"2022-08-26 17:45:13","extension":"jpeg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":304464,"visible":true,"origin":"","legend":"\u003cp\u003eMaps of dementia relative risk in Zhejiang counties from 2015–2019.\u003c/p\u003e","description":"","filename":"Figure3.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-1935233/v1/d56bd0a35bd24262f58aa6b1.jpeg"},{"id":25725478,"identity":"0ccbc255-fd60-4b3a-9735-f89b88f1c73a","added_by":"auto","created_at":"2022-08-26 17:50:13","extension":"jpeg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":328029,"visible":true,"origin":"","legend":"\u003cp\u003eMaps of dementia exceedance probability of predicted relative risk (i.e., exceeding 1.5).\u003c/p\u003e","description":"","filename":"Figure4.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-1935233/v1/d8c6349d61a75de39af8b7e9.jpeg"},{"id":25725477,"identity":"5860f82b-7fd8-4e79-9150-70f204acff30","added_by":"auto","created_at":"2022-08-26 17:50:13","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":84733,"visible":true,"origin":"","legend":"\u003cp\u003eRisk factors associated with dementia in the spatial model.\u003c/p\u003e","description":"","filename":"Figure5.jpg","url":"https://assets-eu.researchsquare.com/files/rs-1935233/v1/5b3625e9347f2af12ee1ac38.jpg"},{"id":28933345,"identity":"0601848a-7962-460a-978f-e52587aa31cf","added_by":"auto","created_at":"2022-11-11 05:44:31","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":724058,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-1935233/v1/89368394-91d0-44ce-91d8-b888f3400350.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Spatial Disparity and Factors Associated with Dementia Mortality: A Cross-Sectional Study in Zhejiang Province, China","fulltext":[{"header":"Background","content":"Dementia is a major public health challenge in China and other low- and middle-income countries. Around 50 million people have dementia worldwide, with nearly 60% of them living in low- and middle-income countries [1]. Dementia, including Alzheimer’s disease and other forms of dementia, is one of the major causes of disability and death among older people, and is now among the top 10 causes of death overall [2]. In the United States, Alzheimer’s disease is officially ranked as the sixth leading cause of death and the fifth leading cause of death for people aged 65 and over [3]. There is often a lack of awareness and understanding of dementia, resulting in stigma and barriers to dementia diagnosis and care, and it may cause even higher numbers of deaths than official sources acknowledge.\nFew studies have investigated spatial disparity in mortality from Alzheimer’s disease and other forms of dementia, especially in China [4, 5, 6]. One study from China used mortality data from the national Disease Surveillance system and reported dementia mortality is significantly higher in eastern China than in the western and central regions, with a rate ratio of 2.28 (95% CI 1.45-3.60) [4]. The risk of dementia is significantly higher in rural areas than in urban areas [6]. Some multicentre surveys have also shown that the incidence and prevalence of dementia is higher in rural areas of China than in urban areas [7, 8]. However, these study did not examine the relationships between the risk of dementia and social characteristics in small areas and the specific explanation for the spatial disparity in the risk of death from dementia is not yet known. Risk factors for disease may be spatially correlated because individuals in the same region tend to have similar characteristics. Analyses of risk factors for dementia have rarely considered the spatial autocorrelation of disease. One measure of risk of death from disease is SMR. However, in many cases small areas may have extreme SMRs due to small population size or small samples. The use of a Bayesian spatial model is preferred in this case to estimate the disease risk in small areas. It is able to borrow information from neighbouring areas and incorporate covariate information so that extreme values are smoothed or shrunk [9].\nZhejiang province, a province in southeast China, has a population of 11 million people aged 60 or over. The number of deaths from Alzheimer’s disease and other forms of dementia in Zhejiang is about six thousand per year. A cross-sectional survey showed that the age standard prevalence of dementia, Alzheimer’s disease and vascular dementia in Zhejiang province were respectively 13.0, 6.9 and 0.5% [10]. This study used the death data from Alzheimer’s disease and other forms of dementia in Zhejiang province from 2015–2019 provided by the disease surveillance information reporting management system of the Chinese Centre for Disease Prevention. It estimated the spatial risk of dementia deaths using Bayesian spatial models, mapped the predicted relative risk to visualize the risk of dementia death, and identified the associated environmental and social risk factors.\n"},{"header":"Methods","content":"\u003cdiv class=\"Section2\" id=\"Sec2\"\u003e\n \u003ch2\u003eData source and study population\u003c/h2\u003e\n \u003cp\u003eThe mortality data presented in this study are based on information collected from the 2015\u0026ndash;2019 Chronic Disease Surveillance Information and Management System provided by the Zhejiang Provincial Centre for Disease Control and Prevention. This organization has gathered death certificates from all 90 counties and districts in Zhejiang province since 2015. The latest county was created at the end of 2019, so the data for this study includes 89 counties and districts. The death certificates contain medical information, such as causes of death from the immediate cause to underlying causes, and other comorbidities or morbid states. Cause-of-death codes have been classified in terms of the International Classification of Diseases, 10th Revision (ICD\u0026ndash;10) since 2000. After physicians upload data to the system, specialists analyse the diagnoses declared on the death cases to ensure data accuracy. Crude mortality rates are calculated based on the population statistical bulletin of Zhejiang published by the Statistical Bureau.\u003c/p\u003e\n \u003cp\u003eThere are many different forms of dementia. Alzheimer\u0026rsquo;s disease is the most common cause of dementia, accounting for an estimated 60\u0026ndash;80% of cases [\u003cspan class=\"CitationRef\"\u003e11\u003c/span\u003e]. Other major forms include vascular dementia, dementia with Lewy bodies, and a group of diseases that contribute to frontotemporal dementia. The lines between the different forms of dementia are blurred and mixed forms often coexist. Four categories of dementia were included in this study which adopted internationally definitions of Alzheimer\u0026rsquo;s disease and other dementia: vascular dementia (F01.0, F01.1, F01.2, F01.3, F01.8, F01.9); unspecified dementia (F03); Alzheimer\u0026rsquo;s disease (G30.0, G30.1, G30.8, G30.9); and other degenerative diseases of the nervous system not elsewhere classified (G31), including Lewy body disease and frontotemporal dementia[\u003cspan class=\"CitationRef\"\u003e12\u003c/span\u003e]. All the causes of death (both underlying causes and associated causes) mentioned these four categories of dementia on the death certificates were included during the selection. An underlying cause is defined by WHO as the \u0026ldquo;disease or injury which initiated the train of events leading directly to death, or the circumstances of the accident or violence which produced the fatal injury\u0026rdquo;.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv class=\"Section2\" id=\"Sec3\"\u003e\n \u003ch2\u003eCovariates\u003c/h2\u003e\n \u003cp\u003ePrevious findings have shown spatial disparity in dementia deaths to be associated with metal elements in drinking water, PM2.5, environmental tobacco smoke, and elements in soil [\u003cspan class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e15\u003c/span\u003e].The variable indicators for counties and districts from 2015\u0026ndash;2019 used in this study were obtained from the Zhejiang Statistical Yearbook and included the proportion of people aged 60 years or older, GDP per capita, healthcare resource indicators and PM2.5 counts.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv class=\"Section2\" id=\"Sec4\"\u003e\n \u003ch2\u003eStatistical analysis\u003c/h2\u003e\n \u003cp\u003eWe first conducted descriptive statistics of the death cases from Alzheimer\u0026rsquo;s disease and other forms of dementia in Zhejiang Province from 2015\u0026ndash;2019. Then we used a Bayesian geospatial model to estimate the relative risks (RRs) of dementia in 89 counties in Zhejiang province. The traditional statistical approach, frequentist inference, is based on the likelihood function, which is used to derive parameter estimates. Bayesian approaches use probability to measure uncertainty in predictions or inference estimates, and incorporate spatial and temporal dependencies through the specification of prior distributions. Bayesian approaches can overcome modelling issues prevalent in research (nonnormality, small sample size, missing data, clustered data structure) [\u003cspan class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e17\u003c/span\u003e]. We specify \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({Y}_{i}\\)\u003c/span\u003e\u003c/span\u003e to be the counts of dementia death cases in county \u003cem\u003ei\u003c/em\u003e and assumed that \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({Y}_{i}\\)\u003c/span\u003e\u003c/span\u003e is conditionally independently Poisson distributed.\u003c/p\u003e\n \u003cdiv class=\"Equation\" id=\"Equa\"\u003e\n \u003cdiv class=\"mathdisplay\" id=\"FileID_Equa\" name=\"EquationSource\"\u003e\u003cimg src=\"data:image/png;base64,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\"\u003e\u003c/div\u003e\n \u003c/div\u003e\n \u003cp\u003ewhere \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({E}_{i}\\)\u003c/span\u003e\u003c/span\u003e is the expected number of dementia death cases in county \u003cem\u003ei\u003c/em\u003e, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({\\theta }_{i}\\)\u003c/span\u003e\u003c/span\u003e is the relative risk in county \u003cem\u003ei\u003c/em\u003e, and \u003cem\u003en\u003c/em\u003e is the number of counties. We implemented a Besag\u0026ndash;York\u0026ndash;Molli\u0026eacute; (BYM) model to obtain relative risk estimates. The BYM model includes both a spatial conditional autoregression component and a heterogeneous random effect component. We expressed the logarithm of \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({\\theta }_{i}\\)\u003c/span\u003e\u003c/span\u003eas\u003c/p\u003e\n \u003cdiv class=\"Equation\" id=\"Equb\"\u003e\n \u003cdiv class=\"mathdisplay\" id=\"FileID_Equb\" name=\"EquationSource\"\u003e$$log\\left({\\theta }_{i}\\right)={\\beta }_{0}+d({x}_{i}{)}^{{\\prime }}\\beta +{u}_{i}+{v}_{i},$$\u003c/div\u003e\n \u003c/div\u003e\n \u003cp\u003ewhere \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({\\beta }_{0}\\)\u003c/span\u003e\u003c/span\u003e is the intercept parameter that represents the overall risk, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(d(.)\\)\u003c/span\u003e\u003c/span\u003e is a vector of observed covariates, \u003cem\u003e\u0026beta;\u003c/em\u003e is the regression coefficients of the covariates, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({u}_{i}\\)\u003c/span\u003e\u003c/span\u003e is a spatial structured effect component modelled with a conditional auto regressive (CAR) distribution as \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({u}_{i}|{u}_{-i}\\sim N\\left({\\stackrel{-}{u}}_{{\\delta }_{i}},\\frac{{\\sigma }_{u}^{2}}{n{\\delta }_{i}}\\right)\\)\u003c/span\u003e\u003c/span\u003e, and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({v}_{i}\\)\u003c/span\u003e\u003c/span\u003eis an unstructured spatial effect defined as \u003cimg src=\"data:image/png;base64,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\"\u003e [\u003cspan class=\"CitationRef\"\u003e18\u003c/span\u003e]. The relative risk \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({\\theta }_{i}\\)\u003c/span\u003e\u003c/span\u003e quantifies whether county \u003cem\u003ei\u003c/em\u003e has higher (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({\\theta }_{i}\u0026gt;1\\)\u003c/span\u003e\u003c/span\u003e) or lower (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({\\theta }_{i}\u0026lt;1\\)\u003c/span\u003e\u003c/span\u003e) risk than the average risk in the standard population. We produced the probabilities of predicted relative risk being greater than a given threshold \u003cem\u003ec\u003c/em\u003e (exceedance probabilities, i.e., \u003cem\u003eP\u003c/em\u003e (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({\\theta }_{i}\u0026gt;c\\)\u003c/span\u003e\u003c/span\u003e)). We implemented the Poisson model by the recommended strategy (i.e., integrating the nested Laplace approximation (INLA)). Due to the lack of reliable a priori information on all parameters, we used a non-informative approach in selecting the prior and therefore used the default prior available in the R-INLA package. All analyses were performed using the R-INLA package [\u003cspan class=\"CitationRef\"\u003e19\u003c/span\u003e].\u003c/p\u003e\n\u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cem\u003eDeath characteristics\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eAlzheimer\u0026rsquo;s disease or other forms of dementia as the underlying cause or a related cause (multiple causes) of death was mentioned in 30,398 death certificates in Zhejiang in 2015\u0026ndash;2019. Of the 28,513 deaths (93.80%) where dementia was the underlying cause of death, 54% were caused by Alzheimer\u0026rsquo;s disease (Figure 1). 57.89% of the deaths were female (Table 1). The mean age at the time of death was 83.29 years (maximum 112 years), with the mean age of females being 2.23 years higher than that of males. In the majority of cases, the place of death for people with dementia was at home. Most males were married (i.e., wife still alive) at the time of death (63.6% for males and 35.7% for females), while females were usually widowed (63.0% for females and 31.0% for males).\u003c/p\u003e\n\u003cp\u003eTable 1. Characteristics of deaths attributable to Alzheimer\u0026rsquo;s disease and dementia in 2015\u0026ndash;2019.\u003c/p\u003e\n\u003ctable border=\"1\" cellpadding=\"0\" cellspacing=\"0\" width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"31.632653061224488%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"20.408163265306122%\"\u003e\n \u003cp\u003eOverall\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"24.489795918367346%\"\u003e\n \u003cp\u003eMales\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"23.46938775510204%\"\u003e\n \u003cp\u003eFemales\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"31.632653061224488%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"20.408163265306122%\"\u003e\n \u003cp\u003en=30,398\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"24.489795918367346%\"\u003e\n \u003cp\u003en=12,800 (42.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"23.46938775510204%\"\u003e\n \u003cp\u003en=17,598 (57.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"31.632653061224488%\"\u003e\n \u003cp\u003eMean age\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"20.408163265306122%\"\u003e\n \u003cp\u003e83.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"24.489795918367346%\"\u003e\n \u003cp\u003e82.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"23.46938775510204%\"\u003e\n \u003cp\u003e84.2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"31.632653061224488%\"\u003e\n \u003cp\u003eStandard deviation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"20.408163265306122%\"\u003e\n \u003cp\u003e9.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"24.489795918367346%\"\u003e\n \u003cp\u003e9.47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"23.46938775510204%\"\u003e\n \u003cp\u003e8.57\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"31.632653061224488%\"\u003e\n \u003cp\u003eMarital status\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"20.408163265306122%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"24.489795918367346%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"23.46938775510204%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"31.632653061224488%\"\u003e\n \u003cp\u003eSingle\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"20.408163265306122%\"\u003e\n \u003cp\u003e658 (2.16%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"24.489795918367346%\"\u003e\n \u003cp\u003e541 (4.23%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"23.46938775510204%\"\u003e\n \u003cp\u003e117 (0.66%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"31.632653061224488%\"\u003e\n \u003cp\u003eMarried\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"20.408163265306122%\"\u003e\n \u003cp\u003e14,414 (47.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"24.489795918367346%\"\u003e\n \u003cp\u003e8,138 (63.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"23.46938775510204%\"\u003e\n \u003cp\u003e6,276 (35.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"31.632653061224488%\"\u003e\n \u003cp\u003eWidow(er)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"20.408163265306122%\"\u003e\n \u003cp\u003e15,052 (49.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"24.489795918367346%\"\u003e\n \u003cp\u003e3,968 (31.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"23.46938775510204%\"\u003e\n \u003cp\u003e11,084 (63.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"31.632653061224488%\"\u003e\n \u003cp\u003eDivorced\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"20.408163265306122%\"\u003e\n \u003cp\u003e212 (0.70%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"24.489795918367346%\"\u003e\n \u003cp\u003e119 (0.93%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"23.46938775510204%\"\u003e\n \u003cp\u003e93 (0.53%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"31.632653061224488%\"\u003e\n \u003cp\u003eUnspecified\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"20.408163265306122%\"\u003e\n \u003cp\u003e62 (0.20%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"24.489795918367346%\"\u003e\n \u003cp\u003e34 (0.27%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"23.46938775510204%\"\u003e\n \u003cp\u003e28 (0.16%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"31.632653061224488%\"\u003e\n \u003cp\u003eEducation degree\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"20.408163265306122%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"24.489795918367346%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"23.46938775510204%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"31.632653061224488%\"\u003e\n \u003cp\u003ePostgraduate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"20.408163265306122%\"\u003e\n \u003cp\u003e9 (0.03%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"24.489795918367346%\"\u003e\n \u003cp\u003e5 (0.04%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"23.46938775510204%\"\u003e\n \u003cp\u003e4 (0.02%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"31.632653061224488%\"\u003e\n \u003cp\u003eUniversity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"20.408163265306122%\"\u003e\n \u003cp\u003e165 (0.54%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"24.489795918367346%\"\u003e\n \u003cp\u003e119 (0.93%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"23.46938775510204%\"\u003e\n \u003cp\u003e46 (0.26%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"31.632653061224488%\"\u003e\n \u003cp\u003eJunior college\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"20.408163265306122%\"\u003e\n \u003cp\u003e110 (0.36%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"24.489795918367346%\"\u003e\n \u003cp\u003e64 (0.50%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"23.46938775510204%\"\u003e\n \u003cp\u003e46 (0.26%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"31.632653061224488%\"\u003e\n \u003cp\u003eSecondary school\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"20.408163265306122%\"\u003e\n \u003cp\u003e161 (0.53%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"24.489795918367346%\"\u003e\n \u003cp\u003e80 (0.62%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"23.46938775510204%\"\u003e\n \u003cp\u003e81 (0.46%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"31.632653061224488%\"\u003e\n \u003cp\u003eTechnical school\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"20.408163265306122%\"\u003e\n \u003cp\u003e6 (0.02%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"24.489795918367346%\"\u003e\n \u003cp\u003e4 (0.03%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"23.46938775510204%\"\u003e\n \u003cp\u003e2 (0.01%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"31.632653061224488%\"\u003e\n \u003cp\u003eHigh school\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"20.408163265306122%\"\u003e\n \u003cp\u003e466 (1.53%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"24.489795918367346%\"\u003e\n \u003cp\u003e271 (2.12%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"23.46938775510204%\"\u003e\n \u003cp\u003e195 (1.11%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"31.632653061224488%\"\u003e\n \u003cp\u003eBelow\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"20.408163265306122%\"\u003e\n \u003cp\u003e29,481 (97.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"24.489795918367346%\"\u003e\n \u003cp\u003e12,257 (95.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"23.46938775510204%\"\u003e\n \u003cp\u003e17,224 (97.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"31.632653061224488%\"\u003e\n \u003cp\u003ePlace of death\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"20.408163265306122%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"24.489795918367346%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"23.46938775510204%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"31.632653061224488%\"\u003e\n \u003cp\u003eUnspecified\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"20.408163265306122%\"\u003e\n \u003cp\u003e16 (0.05%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"24.489795918367346%\"\u003e\n \u003cp\u003e8 (0.06%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"23.46938775510204%\"\u003e\n \u003cp\u003e8 (0.05%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"31.632653061224488%\"\u003e\n \u003cp\u003eMedical institution\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"20.408163265306122%\"\u003e\n \u003cp\u003e1,763 (5.80%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"24.489795918367346%\"\u003e\n \u003cp\u003e868 (6.78%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"23.46938775510204%\"\u003e\n \u003cp\u003e895 (5.09%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"31.632653061224488%\"\u003e\n \u003cp\u003eThe way to the hospital\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"20.408163265306122%\"\u003e\n \u003cp\u003e127 (0.42%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"24.489795918367346%\"\u003e\n \u003cp\u003e64 (0.50%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"23.46938775510204%\"\u003e\n \u003cp\u003e63 (0.36%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"31.632653061224488%\"\u003e\n \u003cp\u003eHome\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"20.408163265306122%\"\u003e\n \u003cp\u003e27,495 (90.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"24.489795918367346%\"\u003e\n \u003cp\u003e11,383 (88.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"23.46938775510204%\"\u003e\n \u003cp\u003e16,112 (91.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"31.632653061224488%\"\u003e\n \u003cp\u003eRetirement home\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"20.408163265306122%\"\u003e\n \u003cp\u003e778 (2.56%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"24.489795918367346%\"\u003e\n \u003cp\u003e351 (2.74%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"23.46938775510204%\"\u003e\n \u003cp\u003e427 (2.43%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"31.632653061224488%\"\u003e\n \u003cp\u003eOther sites\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"20.408163265306122%\"\u003e\n \u003cp\u003e219 (0.72%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"24.489795918367346%\"\u003e\n \u003cp\u003e126 (0.98%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"23.46938775510204%\"\u003e\n \u003cp\u003e93 (0.53%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"31.632653061224488%\"\u003e\n \u003cp\u003eAge\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"20.408163265306122%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"24.489795918367346%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"23.46938775510204%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"31.632653061224488%\"\u003e\n \u003cp\u003e\u0026lt;60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"20.408163265306122%\"\u003e\n \u003cp\u003e627 (2.06%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"24.489795918367346%\"\u003e\n \u003cp\u003e354 (2.77%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"23.46938775510204%\"\u003e\n \u003cp\u003e273 (1.55%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"31.632653061224488%\"\u003e\n \u003cp\u003e60\u0026ndash;64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"20.408163265306122%\"\u003e\n \u003cp\u003e574 (1.89%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"24.489795918367346%\"\u003e\n \u003cp\u003e310 (2.42%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"23.46938775510204%\"\u003e\n \u003cp\u003e264 (1.50%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"31.632653061224488%\"\u003e\n \u003cp\u003e65\u0026ndash;69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"20.408163265306122%\"\u003e\n \u003cp\u003e1,025 (3.37%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"24.489795918367346%\"\u003e\n \u003cp\u003e544 (4.25%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"23.46938775510204%\"\u003e\n \u003cp\u003e481 (2.73%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"31.632653061224488%\"\u003e\n \u003cp\u003e70\u0026ndash;74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"20.408163265306122%\"\u003e\n \u003cp\u003e1,725 (5.67%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"24.489795918367346%\"\u003e\n \u003cp\u003e877 (6.85%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"23.46938775510204%\"\u003e\n \u003cp\u003e848 (4.82%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"31.632653061224488%\"\u003e\n \u003cp\u003e75\u0026ndash;79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"20.408163265306122%\"\u003e\n \u003cp\u003e3,379 (11.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"24.489795918367346%\"\u003e\n \u003cp\u003e1,621 (12.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"23.46938775510204%\"\u003e\n \u003cp\u003e1,758 (9.99%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"31.632653061224488%\"\u003e\n \u003cp\u003e80\u0026ndash;84\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"20.408163265306122%\"\u003e\n \u003cp\u003e7,427 (24.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"24.489795918367346%\"\u003e\n \u003cp\u003e3,289 (25.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"23.46938775510204%\"\u003e\n \u003cp\u003e4,138 (23.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"31.632653061224488%\"\u003e\n \u003cp\u003e85\u0026ndash;89\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"20.408163265306122%\"\u003e\n \u003cp\u003e8,798 (28.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"24.489795918367346%\"\u003e\n \u003cp\u003e3,511 (27.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"23.46938775510204%\"\u003e\n \u003cp\u003e5,287 (30.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"31.632653061224488%\"\u003e\n \u003cp\u003e90\u0026ndash;94\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"20.408163265306122%\"\u003e\n \u003cp\u003e5,301 (17.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"24.489795918367346%\"\u003e\n \u003cp\u003e1,848 (14.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"23.46938775510204%\"\u003e\n \u003cp\u003e3,453 (19.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"31.632653061224488%\"\u003e\n \u003cp\u003e95\u0026ndash;99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"20.408163265306122%\"\u003e\n \u003cp\u003e1,377 (4.53%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"24.489795918367346%\"\u003e\n \u003cp\u003e402 (3.14%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"23.46938775510204%\"\u003e\n \u003cp\u003e975 (5.54%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"31.632653061224488%\"\u003e\n \u003cp\u003e100+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"20.408163265306122%\"\u003e\n \u003cp\u003e165 (0.54%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"24.489795918367346%\"\u003e\n \u003cp\u003e44 (0.34%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"23.46938775510204%\"\u003e\n \u003cp\u003e121 (0.69%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cem\u003eMapping SMR and relative risk of dementia death in Zhejiang\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eGeographical variations in dementia mortality were found in the study. As can be seen from Figure 2, the standard mortality ratio of counties and districts located in south-eastern and western Zhejiang province is significantly higher than that in other counties and districts. The predicted mean relative risk from the Bayesian spatial model was 0.98, with a range of 0.14 to 4.37. The risk of death from dementia was highest in the southeast of Zhejiang province (Figure 3). Figure 4 shows the predicted probability of the relative risk exceeding 1.5 for counties in Zhejiang province.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eRisk factors from the spatial models\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eTable 2 shows that the distributions of the characteristics of social factors and environments in the 89 counties and districts in Zhejiang province. In the Bayesian spatial model, the proportion of residents aged above 60 years, GDP per capita, number of hospital beds,\u0026nbsp;number of doctors, and counts of PM2.5 were risk factors independently associated with dementia death risk. Figure 5 shows counties and districts with poorer economic status per capita, inferior health resources and worse pollution are associated with a higher risk of dementia mortality.\u003c/p\u003e\n\u003cp\u003eTable 2. Descriptive statistics of risk factors in the counties and districts (n = 89) in Zhejiang.\u003c/p\u003e\n\u003ctable border=\"0\" cellpadding=\"0\" cellspacing=\"0\" width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"28.571428571428573%\"\u003e\n \u003cp\u003eFactors\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"16.3265306122449%\"\u003e\n \u003cp\u003eMin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"16.3265306122449%\"\u003e\n \u003cp\u003eMedian\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"16.3265306122449%\"\u003e\n \u003cp\u003eMax\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"22.448979591836736%\"\u003e\n \u003cp\u003eMean\u0026plusmn;SD\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"28.571428571428573%\"\u003e\n \u003cp\u003ePeople aged \u0026ge;60 (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"16.3265306122449%\"\u003e\n \u003cp\u003e13.47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"16.3265306122449%\"\u003e\n \u003cp\u003e21.75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"16.3265306122449%\"\u003e\n \u003cp\u003e34.29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"22.448979591836736%\"\u003e\n \u003cp\u003e21.94\u0026plusmn;3.59\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"28.571428571428573%\"\u003e\n \u003cp\u003eGDP per capita\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"16.3265306122449%\"\u003e\n \u003cp\u003e18053\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"16.3265306122449%\"\u003e\n \u003cp\u003e83598\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"16.3265306122449%\"\u003e\n \u003cp\u003e375963\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"22.448979591836736%\"\u003e\n \u003cp\u003e91894\u0026plusmn;49460.61\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"28.571428571428573%\"\u003e\n \u003cp\u003eNumber of hospital beds\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"16.3265306122449%\"\u003e\n \u003cp\u003e204\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"16.3265306122449%\"\u003e\n \u003cp\u003e2750\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"16.3265306122449%\"\u003e\n \u003cp\u003e14205\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"22.448979591836736%\"\u003e\n \u003cp\u003e3559\u0026plusmn;2831.40\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"28.571428571428573%\"\u003e\n \u003cp\u003eNumber of doctors\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"16.3265306122449%\"\u003e\n \u003cp\u003e181\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"16.3265306122449%\"\u003e\n \u003cp\u003e1748\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"16.3265306122449%\"\u003e\n \u003cp\u003e8842\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"22.448979591836736%\"\u003e\n \u003cp\u003e2292\u0026plusmn;1710.80\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"28.571428571428573%\"\u003e\n \u003cp\u003ePM2.5 counts\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"16.3265306122449%\"\u003e\n \u003cp\u003e14.88\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"16.3265306122449%\"\u003e\n \u003cp\u003e31.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"16.3265306122449%\"\u003e\n \u003cp\u003e66.38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"22.448979591836736%\"\u003e\n \u003cp\u003e33.66\u0026plusmn;10.55\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis cross-sectional study used Bayesian geospatial methods to model dementia SMR in Zhejiang, and to evaluate factors associated with high death rate of dementia. Critical risk factors identified in our study included the number of persons over 60 years of age, GDP per capita, number of hospital beds, number of doctors and counts of PM2.5. Our model shows spatial disparity in dementia mortality in the Zhejiang province. The highest risk areas are distributed in the southeast and west. On closer investigation of high-risk districts, we found that these counties and districts share similar circumstance, usually with worse economic levels, inferior local health resources and more polluted environment. Such spatially variations may be attributed to the commons of neighbouring regions, such as the geographical context, social and economic backgrounds, and the similarity of residents\u0026rsquo; lifestyles.\u003c/p\u003e \u003cp\u003eThe mechanisms by which factors affect the relationship between social and economic characteristics and the risk of dementia mortality in small areas are currently unclear. Some studies have reported that residents in circumstances with better economic environments and health resources lead to positive health lifestyles and stimulating behaviours [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. One study used data from a cohort design of the 10/66 Dementia Study Group in middle-income countries and found that the absolute and relative risk of death from dementia was significantly higher in rural areas of China than in urban areas, with an estimated median standard mortality ratio for dementia of 2.77 [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Rural areas could be correlate with poor conditions, lower economic levels and inferior local health resources which could give rise to the risk of dementia mortality.\u003c/p\u003e \u003cp\u003ePrevious reports on spatial disparity in dementia mortality in middle-income countries were limited. A systematic analysis estimates the median standard mortality ratio (SMR) for dementia in China was 1.94 [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. Many developed countries have analysed dementia mortality using population-based surveillance data or registration data. Using the US national death certificate data from 2000 and 2010, Xu et al. used a time-scan statistical approach to obtain the spatial and temporal clusters of dementia mortality in the north-eastern US, with a lower relative risk and the most likely temporal cluster of excess mortality located in the Pacific Northwest [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. The US Centers for Disease Control and Prevention report on dementia mortality from 2000 to 2017 shows that the age-adjusted mortality rate for dementia in 2017 was 66.7 deaths per 100,000 US standard population, with a higher age-adjusted mortality rate for women (72.7) than for men (56.4) [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. These studies can provide policymakers with relevant information to allocate scarce health resources efficiently and equitably.\u003c/p\u003e \u003cp\u003eWe found that PM2.5 exposure was positively associated with spatial disparity in dementia mortality. Previous evidence suggested that PM2.5 exposure is a major risk factor for death from respiratory disease and cardiovascular disease. Studies of the association between PM2.5 exposure and the risk of death from Alzheimer\u0026rsquo;s disease and other forms of dementia are limited. Results from a 10-year study of US veterans cohort suggest that excess dementia deaths are associated with PM2.5 concentrations and that the attributable burden of dementia deaths is particularly high among socioeconomically disadvantaged groups in the US [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. A meta-analysis of associations between PM2.5 exposure and neurological disorders, which included 80 studies, showed that PM2.5 exposure was strongly associated with increased risk of dementia, Alzheimer\u0026rsquo;s disease and Parkinson\u0026rsquo;s disease [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. The harmful effects of air pollution exposure on cognitive function, and the risk of cognitive decline, were also validated [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. The possible toxicological mechanism by which PM2.5 exposure induces the exacerbation of Alzheimer\u0026rsquo;s disease and other forms of dementia is neuronal damage caused by PM2.5 through causing oxidative stress, neuroinflammation or altered dopamine levels [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. In addition, exposure to both nitrogen dioxide and carbon monoxide is associated with an increased risk of dementia [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThere is a general lack of awareness of the disease burden of dementia in China, as many people still consider dementia as normal ageing and do not have access to dementia health services for old age and care, which places an even heavier burden on the families and communities of those affected. Although dementia primarily affects older people, it is not a normal part of ageing [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. In the context of severe ageing, the number of counties and districts at high risk of dementia mortality in Zhejiang province has increased accordingly. One of the important aims of this study is to note high-risk districts for dementia interventions amidst limited public health resources. This is crucial because residential location could act as a marker for the social and environmental factors that relevant to healthcare services. Spatial modelling and mapping provide the required tools to obtain local residents\u0026rsquo; health outcomes. Understanding this variation and its attributable factors will contribute to disease prevention, and to monitoring access to health services and planning targeted public health interventions.\u003c/p\u003e \u003cp\u003eThe mortality case data for this study were obtained from health system records and may underestimate the true mortality rate for dementia. This may produce nondifferential or differential misclassification bias due to the lack of diagnostic capacity for dementia in community hospitals. This study used current-year air pollution indicators as a proxy measure for short-term air pollution exposure and did not do research on the effect of longer-term air pollution exposure on dementia mortality. Furthermore, the pollution indicators in our model only included PM2.5 exposure, which is not sufficiently diverse and could have been analysed for associations with other air pollution indicators such as sulphur dioxide (SO\u003csub\u003e2\u003c/sub\u003e), carbon monoxide (CO) and nitrogen dioxide (NO\u003csub\u003e2\u003c/sub\u003e). Limited by the data sources, this study included fewer regional factors of physical and social variables, and future studies could be improved in terms of design, analysis and reporting to provide a solid basis for recommendations and possible interventions.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eOur modelling reveals the spatial trends of dementia death risk in Zhejiang province from 2015\u0026ndash;2019, and explores the association between social and environmental factors and the risk of dementia death, as a means to prevent and control excess dementia deaths. We can also provide some different ideas for dementia analysis studies. In addition, we suggest that appropriate measures be implemented to address the key risk factors identified in this study, to reduce spatial disparity in the risk of dementia mortality.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe thank the Center of Social Welfare and Governance of Zhejiang University and the Institute of Wenzhou of Zhejiang University offering financial support and thank the Center for Disease Control and Prevention of Zhejiang Province providing the data.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was granted by the National Natural Science Foundation of China (72004201), a Project of Humanity and Social Science Youth Foundation of Ministry of Education (20YJC840019), and a Project of Zhejiang Provincial Federation of Social Sciences (2022B 64).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors’ contributions:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eStudy concept and design: XH, XL, NL and JL. Data analysis: XH, XL, and XZ. Interpretation of data: XH, XL. Drafting of the manuscript: XH, XL and NL. Revision of the draft: XL and JL. All the authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNone declared.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data underlying this study are not publicly available due to individual privacy information protection, but are available from the corresponding author upon reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was carried out in accordance with the “Declaration of Helsinki” and was approved by the ethics committee of Zhejiang Provincial Centre for Disease Control and Prevention. The need for Informed Consent was waived by the ethics committee of Zhejiang Provincial Centre for Disease Control and Prevention due to the retrospective nature of the study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot Applicable.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003e\u003cspan\u003eNichols E, Szoeke CEI, Vollset SE, Abbasi N, Abd-Allah F, Abdela J, et al. Global, regional, and national burden of Alzheimer\u0026rsquo;s disease and other dementias, 1990\u0026ndash;2016: a systematic analysis for the Global Burden of Disease Study 2016. The Lancet Neurology. 2019;18:88\u0026ndash;106.\u003c/span\u003e\u003c/li\u003e\n \u003cli\u003e\u003cspan\u003eNichols E, Szoeke CEI, Vollset SE, Abbasi N, Abd-Allah F, Abdela J, et al. 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Alzheimer\u0026rsquo;s \u0026amp; Dementia. 2021;17:327\u0026ndash;406.\u003c/span\u003e\u003c/li\u003e\n \u003cli\u003e\u003cspan\u003eMcKhann G, Drachman D, Folstein M, Katzman R, Price D, Stadlan EM. Clinical diagnosis of Alzheimer\u0026rsquo;s disease: report of the NINCDS-ADRDA Work Group under the auspices of Department of Health and Human Services Task Force on Alzheimer\u0026rsquo;s Disease. Neurology. 1984;34:939\u0026ndash;44.\u003c/span\u003e\u003c/li\u003e\n \u003cli\u003e\u003cspan\u003eKillin LOJ, Starr JM, Shiue IJ, Russ TC. Environmental risk factors for dementia: a systematic review. BMC Geriatrics. 2016;16:175.\u003c/span\u003e\u003c/li\u003e\n \u003cli\u003e\u003cspan\u003eChen R, Wilson K, Chen Y, Zhang D, Qin X, He M, et al. Association between environmental tobacco smoke exposure and dementia syndromes. Occupational and Environmental Medicine. 2013;70:63\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e\n \u003cli\u003e\u003cspan\u003eSun H. 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Proceedings of the National Academy of Sciences. 2018;115:9193\u0026ndash;7.\u003c/span\u003e\u003c/li\u003e\n \u003cli\u003e\u003cspan\u003eVivekanantham S, Shah S, Dewji R, Dewji A, Khatri C, Ologunde R. Neuroinflammation in Parkinson\u0026rsquo;s disease: role in neurodegeneration and tissue repair. International Journal of Neuroscience. 2015;125:717\u0026ndash;25.\u003c/span\u003e\u003c/li\u003e\n \u003cli\u003e\u003cspan\u003ePeters R, Ee N, Peters J, Booth A, Mudway I, Anstey KJ. Air Pollution and Dementia: A Systematic Review. J Alzheimers Dis. 2019;70:S145\u0026ndash;63.\u003c/span\u003e\u003c/li\u003e\n \u003cli\u003e\u003cspan\u003eLivingston G, Huntley J, Sommerlad A, Ames D, Ballard C, Banerjee S, et al. Dementia prevention, intervention, and care: 2020 report of the Lancet Commission. The Lancet. 2020;396:413\u0026ndash;46.\u003c/span\u003e\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Dementia, Mortality, Surveillance, Geographical Analysis","lastPublishedDoi":"10.21203/rs.3.rs-1935233/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-1935233/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground\u003c/strong\u003e\u003c/p\u003e\u003cp\u003eThere is evidence of spatial disparity in mortality from Alzheimer’s disease and other forms of dementia in China. Regional factors of physical and social features may be influencing this spatial disparity. However, there are few reports on dementia mortality in China, and the true extent of spatial disparity in dementia mortality across small regional localities is unclear. The majority of people who die from dementia are over 60 years old. This study aims to explore the geographical variations in dementia mortality, estimate the relative risk and identify districts at higher risk for intervention and further study.\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eMethods \u003c/strong\u003e\u003c/p\u003e\u003cp\u003eWe used surveillance data on deaths from Alzheimer’s disease and other forms of dementia in Zhejiang province from 2015–2019 to estimate the spatial risk of death from dementia using a Bayesian spatial model. We mapped predicted relative risk to visualize the risk of death from Alzheimer’s disease and other forms of dementia and to identify risk factors associated with dementia.\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eResults\u003c/strong\u003e\u003c/p\u003e\u003cp\u003eA total of 30,398 deaths attributable to dementia as the underlying or related cause (multiple causes) were reported in 2015–2019. Counties and districts located in the southeast and west of Zhejiang province had significantly higher standard mortality ratios than others. The predicted mean relative risk was 0.98, with a range of 0.14 ~ 4.37. Counties and districts with a smaller proportion of residents aged 60 years or older, poorer economic status, inferior health resources and worse pollution had a higher risk of dementia death.\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eConclusions\u003c/strong\u003e\u003c/p\u003e\u003cp\u003eThere is spatial disparity in dementia mortality across different districts in Zhejiang. Our study adds new evidence on the association between social and environmental factors and the risk of dementia death. Appropriate preventive health strategies can be developed to reduce such spatial disparity in the risk of dementia mortality.\u003c/p\u003e","manuscriptTitle":"Spatial Disparity and Factors Associated with Dementia Mortality: A Cross-Sectional Study in Zhejiang Province, China","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2022-08-26 17:45:11","doi":"10.21203/rs.3.rs-1935233/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"a14ef7d4-bc58-4aa0-a886-bbcefcc7fa09","owner":[],"postedDate":"August 26th, 2022","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2022-11-11T05:44:14+00:00","versionOfRecord":[],"versionCreatedAt":"2022-08-26 17:45:11","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-1935233","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-1935233","identity":"rs-1935233","version":["v1"]},"buildId":"ApUGefWb6u5IBVtyqm6d5","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}