General Practice Clinical Data Help Identify Dementia Hotspots: A Novel Geospatial Analysis Approach

The Australian National University Alzheimer's Disease Risk Index (ANU-ADRI) score as a predictor for cognitive decline and potential surrogate outcome in the FINGER lifestyle randomized controlled trial

BACKGROUND AND PURPOSE

The complex aetiology of Alzheimer's disease suggests prevention potential. Risk scores have potential as risk stratification tools and surrogate outcomes in multimodal interventions targeting specific at-risk populations. The Australian National University Alzheimer's Disease Risk Index (ANU-ADRI) was tested in relation to cognition and its suitability as a surrogate outcome in a multidomain lifestyle randomized controlled trial, in older adults at risk of dementia.

METHODS

In this post hoc analysis of the Finnish Intervention Study to Prevent Cognitive Impairment and Disability (FINGER), ANU-ADRI was calculated at baseline, 12, and 24 months (n = 1174). The association between ANU-ADRI and cognition (at baseline and over time), the intervention effect on changes in ANU-ADRI, and the potential impact of baseline ANU-ADRI on the intervention effect on changes in cognition were assessed using linear mixed models with maximum likelihood estimation.

RESULTS

A higher ANU-ADRI was significantly related to worse cognition, at baseline (e.g., estimate for global cognition [95% confidence interval] was -0.028 [-0.032 to -0.025]) and over the 2-year study (e.g., estimate for 2-year changes in ANU-ADRI and per-year changes in global cognition [95% confidence interval] was -0.068 [-0.026 to -0.108]). No significant beneficial intervention effect was reported for ANU-ADRI, and baseline ANU-ADRI did not significantly affect the response to the intervention on changes in cognition.

CONCLUSIONS

The ANU-ADRI was effective for the risk prediction of cognitive decline. Risk scores may be crucial for the success of novel dementia prevention strategies, but their algorithm, the target population, and the intervention design should be carefully considered when choosing the appropriate tool for each context.

The role of geographic information system and global positioning system in dementia care and research: a scoping review

Background

Geographic Information System (GIS) and Global Positioning System (GPS), vital tools for supporting public health research, provide a framework to collect, analyze and visualize the interaction between different levels of the health care system. The extent to which GIS and GPS applications have been used in dementia care and research is not yet investigated. This scoping review aims to elaborate on the role and types of GIS and GPS applications in dementia care and research.

Methods

A scoping review was conducted based on Arksey and O’Malley’s framework. All published articles in peer-reviewed journals were searched in PubMed, Scopus, and Web of Science, subject to involving at least one GIS/GPS approach focused on dementia. Eligible studies were reviewed, grouped, and synthesized to identify GIS and GPS applications. The PRISMA standard was used to report the study.

Results

Ninety-two studies met our inclusion criteria, and their data were extracted. Six types of GIS/GPS applications had been reported in dementia literature including mapping and surveillance (n = 59), data preparation (n = 26), dementia care provision (n = 18), basic research (n = 18), contextual and risk factor analysis (n = 4), and planning (n = 1). Thematic mapping and GPS were most frequently used techniques in the dementia field.

Conclusions

Even though the applications of GIS/GPS methodologies in dementia care and research are growing, there is limited research on GIS/GPS utilization in dementia care, risk factor analysis, and dementia policy planning. GIS and GPS are space-based systems, so they have a strong capacity for developing innovative research based on spatial analysis in the area of dementia. The existing research has been summarized in this review which could help researchers to know the GIS/GPS capabilities in dementia research.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12942-022-00308-1.

Dementia Risk Scores and Their Role in the Implementation of Risk Reduction Guidelines

Dementia prevention is a global health priority. In 2019, the World Health Organisation published its first evidence-based guidelines on dementia risk reduction. We are now at the stage where we need effective tools and resources to assess dementia risk and implement these guidelines into policy and practice. In this paper we review dementia risk scores as a means to facilitate this process. Specifically, we (a) discuss the rationale for dementia risk assessment, (b) outline some conceptual and methodological issues to consider when reviewing risk scores, (c) evaluate some dementia risk scores that are currently in use, and (d) provide some comments about future directions. A dementia risk score is a weighted composite of risk factors that reflects the likelihood of an individual developing dementia. In general, dementia risks scores have a wide range of implementations and benefits including providing early identification of individuals at high risk, improving risk perception for patients and physicians, and helping health professionals recommend targeted interventions to improve lifestyle habits to decrease dementia risk. A number of risk scores for dementia have been published, and some are widely used in research and clinical trials e.g., CAIDE, ANU-ADRI, and LIBRA. However, there are some methodological concerns and limitations associated with the use of these risk scores and more research is needed to increase their effectiveness and applicability. Overall, we conclude that, while further refinement of risk scores is underway, there is adequate evidence to use these assessments to implement guidelines on dementia risk reduction.

The Impact of Built and Social Environmental Characteristics on Diagnosed and Estimated Future Risk of Dementia

BACKGROUND

Dementia is a major global health challenge and the impact of built and social environments' characteristics on dementia risk have not yet been fully evaluated.

OBJECTIVE

To investigate associations between built and social environmental characteristics and diagnosed dementia cases and estimated dementia risk.

METHODS

We recruited 25,511 patients aged 65 and older from family physicians' practices. We calculated a dementia risk score based on risk and protective factors for patients not diagnosed with dementia. Our exposure variables were estimated for each statistical area level 1: social fragmentation, nitrogen dioxide, public open spaces, walkability, socio-economic status, and the length of main roads. We performed a multilevel mixed effect linear regression analysis to allow for the hierarchical nature of the data.

RESULTS

We found that a one standard deviation (1-SD) increase in NO2 and walkability score was associated with 10% higher odds of any versus no dementia (95% CI: 1%, 21% for NO2 and 0%, 22% for walkability score). For estimated future risk of dementia, a 1-SD increase in social fragmentation and NO2 was associated with a 1% increase in dementia risk (95% CI: 0, 1%). 1-SD increases in public open space and socioeconomic status were associated with 3% (95% CI: 0.95, 0.98) and 1% decreases (95% CI: 0.98, 0.99) in dementia risk, respectively. There was spatial heterogeneity in the pattern of diagnosed dementia and the estimated future risk of dementia.

CONCLUSION

Associations of neighborhood NO2 level, walkability, public open space, and social fragmentation with diagnosed dementia cases and estimated future risk of dementia were statistically significant, indicating the potential to reduce the risk through changes in built and social environments.

Spatial and temporal analysis of myocardial infarction incidence in Zanjan province, Iran

Background

Myocardial Infarction (MI) is a major important public health concern and has huge burden on health system across the world. This study aimed to explore the spatial and temporal analysis of the incidence of MI to identify potential clusters of the incidence of MI patterns across rural areas in Zanjan province, Iran.

Materials & methods

This was a retrospective and geospatial analysis study of the incidence of MI data from nine hospitals during 2014–2018. Three different spatial analysis methods (Spatial autocorrelation, hot spot analysis and cluster and outlier analysis) were used to identify potential clusters and high-risk areas of the incidence of MI at the study area.

Results

Three thousand eight hundred twenty patients were registered at Zanjan hospitals due to MI during 2014–2018. The overall age-adjusted incidence rate of MI was 343 cases per 100,000 person which was raised from 88 cases in 2014 to 114 cases in 2018 per 100,000 person-year (a 30% increase, P < 0.001). Golabar region had the highest age-adjusted incidence rate of MI (515 cases per 100,000 person). Five hot spots and one high-high cluster were detected using spatial analysis methods.

Conclusion

This study showed that there is a great deal of spatial variations in the pattern of the incidence of MI in Zanjan province. The high incidence rate of MI in the study area compared to the national average, is a warning to local health authorities to determine the possible causes of disease incidence and potential drivers of high-risk areas. The spatial cluster analysis provides new evidence for policy-makers to design tailored interventions to reduce the incidence of MI and allocate health resource to unmet need areas.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12889-021-11695-8.

Investigating spatial convergence of diagnosed dementia, depression and type 2 diabetes prevalence in West Adelaide, Australia

BACKGROUND

Comorbid depression and type 2 diabetes (T2D) is an important risk factor for dementia. This study investigates the factors associated with, the spatial variation and spatial convergence of diagnosed cases of these conditions. This approach may identify areas with unmet needs.

METHODS

We used cross-sectional data (2010 to 2014) from 16 general practices in west Adelaide, Australia. Multi-level modelling accounting for individual-level characteristics nested within statistical area level 1 (SA1) determined covariate associations with these three diseases. Getis-Ord Gi method was used to investigate spatial variation, hot spots and cold spots of these conditions.

RESULTS

1.4% of active patients in west Adelaide aged 45 and above were diagnosed with dementia, 9.6% with depression and 13.3% with T2D. Comorbidity was significant across all three diseases. Elderly age (65+ years) was significantly associated with diagnosed dementia and T2D. Hyperlipidemia or hypertension diagnosis and belonging to lower socioeconomic status were significantly associated with diagnosed T2D and depression. The spatial distribution of each disease varied across west Adelaide. Spatial convergence of the three diseases was observed in two large hot spot clusters and one main cluster of cold spots.

LIMITATIONS

Due to underreporting, potentially significant covariates like alcohol intake were unable to be assessed. There may be a bias towards health-conscious individuals or patients managing diagnosed diseases that actively visit their general practice.

CONCLUSIONS

Patterns of spatial convergence and the shared associations in dementia, depression and diabetes enable policymakers to tailor interventions to the areas where risk of these conditions are greater.

Geospatial Analysis of Environmental Risk Factors for Missing Dementia Patients

BACKGROUND

Dementia-related missing incidents are highly prevalent but still poorly understood. This is particularly true for environmental/geospatial risk factors, which might contribute to these missing incidents.

OBJECTIVE

The study aimed to conduct a retrospective, observational analysis on a large sample of missing dementia patient case records provided by the police (n = 210), covering dates from January 2014 to December 2017. In particular, we wanted to explore 1) whether there were any hotspot regions of missing incidents and 2) the relationship between outdoor landmark density and missing incidents.

METHODS

Global spatial autocorrelation (Moran's I) was used to identify the potential hotspot regions for missing incidents. Meanwhile, spatial buffer and regression modelling were used to determine the relationship between outdoor landmark density and missing incidents.

RESULTS

Our demographics measures replicated and extended previous studies of dementia-related missing incidents. Meanwhile, no hotspot regions for missing incidents were identified, while higher outdoor landmark density led to increased missing incidents.

CONCLUSION

Our results highlight that missing incidents do not occur in isolated hotspots of regions but instead are endemic in patients regardless of location. Higher outdoor landmark density emerges as a significant geospatial factor for missing incidents in dementia, which crucially informs future safeguarding/intervention studies.

Identifying community chronic kidney disease risk profile utilising general practice clinical records and spatial analysis: approach to inform policy and practice

BACKGROUND

Chronic kidney disease (CKD) causes a significant health burden in Australia, and up to 50% of Australians with CKD remain undiagnosed.

AIMS

To estimate the 5-year risk for CKD from general practice (GP) clinical records and to investigate the spatial variation and hot spots of CKD risk in an Australian community.

METHOD

A cross-sectional study was designed using de-identified GP clinical data recorded from 2010 to 2015. A total of 16 GP participated in this study from West Adelaide, Australia. We used health records of 36 565 patients aged 35-74 years, with no prior history of CKD. The 5-year estimated CKD risk was calculated using the QKidney algorithm. Individuals' risk score was aggregated to Statistical Area Level 1 to predict the community CKD risk. A spatial hotspot analysis was applied to identify the communities with greater risk.

RESULTS

The mean estimated 5-year risk for CKD in the sample population was 0.95% (0.93-0.97). Overall, 2.4% of the study population was at high risk of CKD. Significant hot spots and cold spots of CKD risk were identified within the study region. Hot spots were associated with lower socioeconomic status.

CONCLUSIONS

This study demonstrated a new approach to explore the spatial variation of CKD risk at a community level, and implementation of a risk prediction model into a clinical setting may aid in early detection and increase disease awareness in regions of unmet CKD care.

Geospatial Correlation of Amyopathic Dermatomyositis With Fixed Sources of Airborne Pollution: A Retrospective Cohort Study

Objective: Dermatomyositis (DM) may result from exogenous triggers, including airborne pollutants, in genetically susceptible individuals. The United States Environmental Protection Agency's 2011 National Air Toxics Assessment (NATA) models health risks associated with airborne emissions, available by ZIP code tabulation area (ZCTA). Important contributors include point (fixed), on-road, and secondary sources. The objective of this study was to investigate the geospatial distributions of DM and subtypes, classic DM (CDM) and clinically amyopathic DM (CADM), and their associations with airborne pollutants.

Methods: This retrospective cohort study identified 642 adult DM patients from 336 unique ZCTAs. GeoDa v.1.10 was used to calculate global and local Moran's indices and generate local indicator of spatial autocorrelation (LISA) maps. All Moran's indices and LISA maps were permuted 999 times.

Results: Univariate global Moran's indices for DM, CDM, and CADM prevalence were not significant, but LISA maps demonstrated differential local spatial clustering and outliers. CADM prevalence correlated with point sources (bivariate global Moran's index 0.071, pseudo-p = 0.018), in contrast to CDM (−0.0053, pseudo-p = 0.46). Bivariate global Moran's indices for DM, CDM, and CADM prevalence did not correlate with other airborne toxics, but bivariate LISA maps revealed local spatial clustering and outliers.

Conclusion: Prevalence of CADM, but not CDM, is geospatially correlated with fixed sources of airborne emissions. This effect is small but significant and may support the hypothesis that triggering exposures influence disease phenotype. Important limitations are NATA data and ZCTA population estimates were collected from 2011 and ZCTA of residence may not have been where patients had greatest airborne pollutant exposure.