Identifying hotspots of type 2 diabetes risk using general practice data and geospatial analysis: an approach to inform policy and practice

Identifying hotspots of seizure-related hospital admissions in Australia

BACKGROUND

This study aimed to: (1) map geographic trends of seizure-related hospitalizations across Australia, (2) identify hotspots in hospitalization rates, and (3) assess geographic inequities in access to specialized services for seizure disorders.

METHODS

Standardized seizure admission ratios (SR) were calculated using publicly available hospital admissions data incorporating the diagnoses of epilepsy, status epilepticus and convulsions for the year 2020-21 in Australia. Forward selection was used to ascertain optimal subset of covariates for spatial regression models. Model fitness was evaluated using Deviance Information Criterion and Watanabe-Akaike Information Criterion. Tiered hospital catchment maps and relationships between hospitals were generated based on proximity and available specialized services. A web-based application was created to view results and includes a search function to identify tiers of hospitals for Australian addresses (https://gntem3.shinyapps.io/epilepsyadmissions/).

RESULTS

Although the absolute number of hospitalizations was low, the Northern Territory had three local government areas (LGAs) with the highest SRs (e.g., MacDonnell LGA (SR 5.29, n = 50)). Hotspots were more frequently observed in regional and remote LGAs but were also present in urban areas (e.g., Geelong LGA (SR 1.24)). The bestperforming spatial regression model incorporated kidney disease, cancer, diabetes, mental health conditions, and the number of family physicians per 100,000 people as significant covariates.

CONCLUSION

Hotspots of seizure-related hospitalizations are often located in areas with limited access to specialized services, underscoring the geographic inequities in care delivery. Addressing these disparities through further modelling of spatial trends and targeted resource allocation is essential for improving equitable healthcare access for seizure disorders.

Hotspots of kidney failure: Analysing Australian metropolitan dialysis demand for service planning

OBJECTIVE

To locate incident hotspots of dialysis demand in Australian capital cities and measure association with prevalent dialysis demand and socioeconomic disadvantage.

METHODS

A retrospective cohort study used Australia and New Zealand Dialysis and Transplant Registry data on people commencing dialysis for kidney failure (KF) resident in an Australian capital city, 1 January 2001 - 31 December 2021. Age-sex-standardised dialysis incidence was estimated by Statistical Area Level 3 (SA3) and dialysis prevalence by SA2.

RESULTS

A total of 32,391 people commencing dialysis were referenced to SA3s within city metropolitan areas based on residential postcode. Incident hotspots were located in Western Sydney. The highest average annual change of standardised incidence was 8.3 per million people (false discovery rate-corrected 95% CI 1.0,15.7) in Mount Druitt, reflecting a 263% increase in absolute demand from 2001-3 to 2019-21. Incident dialysis for diabetic kidney disease contributed substantially to total growth. Incident hotspots were co-located with areas where prevalent dialysis demand was associated with socioeconomic deprivation.

CONCLUSIONS

Novel spatial analyses of geo-referenced registry data located hotspots of kidney failure and associated socio-demographic and comorbid states.

IMPLICATIONS FOR PUBLIC HEALTH

These analyses advance current abilities to plan dialysis capacity at a local level. Hotspots can be targeted for prevention and slowing the progression of kidney disease.

The Development of the Municipal Registry of People with Diabetes in Porto Alegre, Brazil

Background/Objective: Diabetes registries that enhance surveillance and improve medical care are uncommon in low- and middle-income countries, where most of the diabetes burden lies. We aimed to describe the methodological and technical aspects adopted in the development of a municipal registry of people with diabetes using local and national Brazilian National Health System databases. Methods: We obtained data between July 2018 and June 2021 based on eight databases covering primary care, specialty and emergency consultations, medication dispensing, outpatient exam management, hospitalizations, and deaths. We identified diabetes using the International Classification of Disease (ICD), International Classification of Primary Care (ICPC), medications for diabetes, hospital codes for the treatment of diabetes complications, and exams for diabetes management. Results: After data processing and database merging using deterministic and probabilistic linkage, we identified 73,185 people with diabetes. Considering that 1.33 million people live in Porto Alegre, the registry captured 5.5% of the population. Conclusions: With additional data processing, the registry can reveal information on the treatment and outcomes of people with diabetes who are receiving publicly financed care in Porto Alegre. It will provide metrics for epidemiologic surveillance, such as the incidence, prevalence, rates, and trends of complications and causes of mortality; identify inadequacies; and provide information. It will enable healthcare providers to monitor the quality of care, identify inadequacies, and provide feedback as needed.

Creating an interactive map visualising the geographic variations of the burden of diabetes to inform policymaking: An example from a cohort study in Tasmania, Australia

OBJECTIVES

To visualise the geographic variations of diabetes burden and identify areas where targeted interventions are needed.

METHODS

Using diagnostic criteria supported by hospital codes, 51,324 people with diabetes were identified from a population-based dataset during 2004-2017 in Tasmania, Australia. An interactive map visualising geographic distribution of diabetes prevalence, mortality rates, and healthcare costs in people with diabetes was generated. The cluster and outlier analysis was performed based on statistical area level 2 (SA2) to identify areas with high (hot spot) and low (cold spot) diabetes burden.

RESULTS

There were geographic variations in diabetes burden across Tasmania, with highest age-adjusted prevalence (6.1%), excess cost ($2627), and annual costs per person ($5982) in the West and Northwest. Among 98 SA2 areas, 16 hot spots and 25 cold spots for annual costs, and 10 hot spots and 10 cold spots for diabetes prevalence were identified (p<0.05). 15/16 (94%) and 6/10 (60%) hot spots identified were in the West and Northwest.

CONCLUSIONS

We have developed a method to graphically display important diabetes outcomes for different geographical areas.

IMPLICATIONS FOR PUBLIC HEALTH

The method presented in our study could be applied to any other diseases, regions, and countries where appropriate data are available to identify areas where interventions are needed to improve diabetes outcomes.

Associations between dimensions of the social environment and cardiometabolic risk factors: Systematic review and meta-analysis

Aim

The social environment (SE), including social contacts, norms and support, is an understudied element of the living environment which impacts health. We aim to comprehensively summarize the evidence on the association between the SE and risk factors of cardiometabolic disease (CMD).

Methods

We performed a systematic review and meta-analysis based on studies published in PubMed, Scopus and Web of Science Core Collection from inception to 16 February 2021. Studies that used a risk factor of CMD, e.g., HbA1c or blood pressure, as outcome and social environmental factors such as area-level deprivation or social network size as independent variables were included. Titles and abstracts were screened in duplicate. Study quality was assessed using the Newcastle-Ottawa Scale. Data appraisal and extraction were based on the study protocol published in PROSPERO. Data were synthesized through vote counting and meta-analyses.

Results

From the 7521 records screened, 168 studies reported 1050 associations were included in this review. Four meta-analyses based on 24 associations suggested that an unfavorable social environment was associated with increased risk of cardiometabolic risk factors, with three of them being statistically significant. For example, individuals that experienced more economic and social disadvantage had a higher "CVD risk scores" (OR = 1.54, 95%CI: 1.35 to 1.84). Of the 458 associations included in the vote counting, 323 (71%) pointed towards unfavorable social environments being associated with higher CMD risk.

Conclusion

Higher economic and social disadvantage seem to contribute to unfavorable CMD risk factor profiles, while evidence for other dimensions of the social environment is limited.

Prevalence and predictors of undiagnosed type 2 diabetes and pre-diabetes among adult Egyptians: a community-based survey

BACKGROUND

The global prevalence of abnormal glycemic level comprising diabetes mellitus (DM) and pre-diabetes (PDM) is rapidly increasing with special concern for the entity silent or undiagnosed diabetes; those unaware of their condition. Identification of people at risk became much easier with the use of risk charts than the traditional methods. The current study aimed to conduct a community-based screening for T2DM to estimate the prevalence of undiagnosed DM and to assess the AUSDRISK Arabic version as a predictive tool in an Egyptian context.

METHODS

A cross-sectional study was conducted among 719 Adults aging 18 years or more and not known to be diabetics through a population-based household survey. Each participant was interviewed to fill demographic and medical data as well as the AUSDRISK Arabic version risk score and undergo testing for fasting plasma glucose (FPG) and oral glucose tolerance test (OGTT).

RESULTS

The prevalence of DM and PDM were 5% and 21.7% respectively. The multivariate analysis revealed that age, being physically inactive, history of previous abnormal glycemic level and waist circumference were the predictors for having abnormal glycemic level among the studied participants. At cut off points ≥ 13 and ≥ 9, the AUSDRISK respectively discriminated DM [sensitivity (86.11%), specificity (73.35%), and area under the curve (AUC): 0.887, 95% CI: 0.824-0.950] and abnormal glycemic level [sensitivity (80.73%), specificity (58.06%), and AUC: 0.767, 95% CI: 0.727-0.807], p < 0.001.

CONCLUSIONS

Overt DM just occupies the top of an iceberg, its unseen big population have undiagnosed DM, PDM or been at risk of T2DM because of sustained exposure to the influential risk factors. The AUSDRISK Arabic version was proved to be sensitive and specific tool to be used among Egyptians as a screening tool for the detection of DM or abnormal glycemic level. A prominent association has been demonstrated between AUSDRISK Arabic version score and the diabetic status.

Spatial epidemiology of diabetes: Methods and insights

Diabetes mellitus (DM) is a growing epidemic with global proportions. It is estimated that in 2019, 463 million adults aged 20-79 years were living with DM. The latest evidence shows that DM continues to be a significant global health challenge and is likely to continue to grow substantially in the next decades, which would have major implications for healthcare expenditures, particularly in developing countries. Hence, new conceptual and methodological approaches to tackle the epidemic are long overdue. Spatial epidemiology has been a successful approach to control infectious disease epidemics like malaria and human immunodeficiency virus. The implementation of this approach has been expanded to include the study of non-communicable diseases like cancer and cardiovascular diseases. In this review, we discussed the implementation and use of spatial epidemiology and Geographic Information Systems to the study of DM. We reviewed several spatial methods used to understand the spatial structure of the disease and identify the potential geographical drivers of the spatial distribution of DM. Finally, we discussed the use of spatial epidemiology on the design and implementation of geographically targeted prevention and treatment interventions against DM.

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.