Health related outcomes among people with type 2 diabetes by country of birth: Result from the 45 and Up Study

Modifiable Risk Factors for Dementia Among Migrants, Refugees and Asylum Seekers in Australia: A Systematic Review

While the prevalence of non-communicable disease risk factors is understood to be higher among migrants than for people born in host nations, little is known about the dementia risk profile of migrants, refugees and asylum seekers. This systematic review examines published literature to understand what is currently reported about 12 identified modifiable risk factors for dementia among migrants, refugees, and asylum seekers residing in Australia. Three literature databases (PubMed/CINAHL/MEDLINE) were systematically searched to find articles reporting excessive alcohol consumption, traumatic brain injury, air pollution, lack of education, hypertension, hearing impairment, smoking, obesity, depression, physical inactivity, diabetes, and limited social contact in Australia's migrant, refugee and asylum seeker population samples. Papers were systematically reviewed following PRISMA guidelines. A total of 763 studies were found, of which 676 articles were excluded, and 79 articles remained. Despite wide variability in study design, size and purpose, the prevalence and correlates of modifiable risk factors of dementia appears markedly different among the studied samples. Compared with Australian-born participants, migrant samples had a higher prevalence of depression, social isolation, physical inactivity and diabetes mellitus. Insufficient information or conflicting evidence prevented inference about prevalence and correlates for the remaining dementia risk factors. A better understanding of the prevalence and correlates of modifiable dementia risk factors is needed in Australia's migrant, refugee and asylum seeker populations. This information, together with a deeper understanding of the contextual and cultural contributing factors affecting people who arrive in Australia through differing pathways is needed before preventive interventions can be realistically targeted and sensitively implemented.

Rapid triage for ischemic stroke: a machine learning-driven approach in the context of predictive, preventive and personalised medicine

BACKGROUND

Recognising the early signs of ischemic stroke (IS) in emergency settings has been challenging. Machine learning (ML), a robust tool for predictive, preventive and personalised medicine (PPPM/3PM), presents a possible solution for this issue and produces accurate predictions for real-time data processing.

METHODS

This investigation evaluated 4999 IS patients among a total of 10,476 adults included in the initial dataset, and 1076 IS subjects among 3935 participants in the external validation dataset. Six ML-based models for the prediction of IS were trained on the initial dataset of 10,476 participants (split participants into a training set [80%] and an internal validation set [20%]). Selected clinical laboratory features routinely assessed at admission were used to inform the models. Model performance was mainly evaluated by the area under the receiver operating characteristic (AUC) curve. Additional techniques-permutation feature importance (PFI), local interpretable model-agnostic explanations (LIME), and SHapley Additive exPlanations (SHAP)-were applied for explaining the black-box ML models.

RESULTS

Fifteen routine haematological and biochemical features were selected to establish ML-based models for the prediction of IS. The XGBoost-based model achieved the highest predictive performance, reaching AUCs of 0.91 (0.90-0.92) and 0.92 (0.91-0.93) in the internal and external datasets respectively. PFI globally revealed that demographic feature age, routine haematological parameters, haemoglobin and neutrophil count, and biochemical analytes total protein and high-density lipoprotein cholesterol were more influential on the model's prediction. LIME and SHAP showed similar local feature attribution explanations.

CONCLUSION

In the context of PPPM/3PM, we used the selected predictors obtained from the results of common blood tests to develop and validate ML-based models for the diagnosis of IS. The XGBoost-based model offers the most accurate prediction. By incorporating the individualised patient profile, this prediction tool is simple and quick to administer. This is promising to support subjective decision making in resource-limited settings or primary care, thereby shortening the time window for the treatment, and improving outcomes after IS.

SUPPLEMENTARY INFORMATION

The online version contains supplementary material available at 10.1007/s13167-022-00283-4.

Social determinants of diabetes-related foot disease among older adults in New South Wales, Australia: evidence from a population-based study

BACKGROUND

Diabetes-related foot is the largest burden to the health sector compared to other diabetes-related complications in Australia, including New South Wales (NSW). Understanding of social determinants of diabetes-related foot disease has not been definitive in Australian studies. This study aimed to investigate the social determinants of diabetes-related foot disease in NSW.

METHODOLOGY

The first wave of the 45 and Up Study survey data was linked with NSW Admitted Patient Data Collection, Emergency Department Data Collection, and Pharmaceutical Benefits Scheme data resulting in 28,210 individuals with diabetes aged 45 years and older in NSW, Australia. Three outcome variables were used: diabetes-related foot disease (DFD), diabetic foot ulcer (DFU), and diabetic foot infection (DFI). They were classified as binary, and survey logistic regression was used to determine the association between each outcome measure and associated factors after adjusting for sampling weights.

RESULTS

The prevalence of DFD, DFU and DFI were 10.8%, 5.4% and 5.2%, respectively, among people with diabetes. Multivariate analyses revealed that the common factors associated with DFD, DFU and DFI were older age (75 years or more), male, single status, background in English speaking countries, and coming from lower-income households (less than AUD 20,000 per year). Furthermore, common lifestyle and health factors associated with DFD, DFU, and DFI were low physical activity (< 150 min of moderate-to-vigorous physical activity per week), history of diabetes for over 15 years, and having cardiovascular disease.

CONCLUSION

Our study showed that about 1 in 10 adults with diabetes aged 45 years and older in NSW reported DFD. Interventions, including the provision of related health services aimed at reducing all forms of DFD in NSW, are recommended to target older individuals with a long history of diabetes, and coming from lower-income households.

Cognitive complaints in age-related chronic conditions: A systematic review

INTRODUCTION

Cognitive complaints in older adults may be indicative of progressive cognitive decline including Alzheimer's disease (AD), but also occur in other age-related chronic conditions, complicating identification of early AD symptoms. To better understand cognitive complaints in aging, we systematically reviewed the evidence to determine their prevalence and characterization among older adults with the most common age-related chronic conditions.

METHODS

This systematic review was conducted in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses statement and the review protocol was prospectively registered with PROSPERO (ID: CRD42020153147). Searches were conducted in PubMed, CINAHL, PsycINFO, Web of Science, and ProQuest Dissertations & Theses A&I in June 2020. Two members of the review team independently determined article eligibility for inclusion and conducted quality appraisal. A narrative synthesis of results was used to integrate findings across studies and draw conclusions regarding the strength of the evidence in each chronic condition category.

RESULTS

Thirty-seven articles met eligibility criteria and were included in the review. Conditions represented were diabetes (n = 20), heart disease (n = 13), hypertension (n = 10), chronic lung disease (n = 5), arthritis (n = 4), heart failure (n = 2), and hyperlipidemia (n = 2). In addition, 16 studies included a measure of multimorbidity. Overall, there was a higher prevalence of cognitive complaints in individuals with higher multimorbidity, including a potential dose-dependent relationship. Findings for specific conditions were inconsistent, but there is evidence to suggest that cross-sectionally, older adults with diabetes, heart disease, chronic lung disease, and arthritis have more cognitive complaints than those without these conditions.

CONCLUSION

There is strong evidence demonstrating that cognitive complaints are more common in older adults with higher multimorbidity, but little research examining these associations over time. Improving our understanding of the longitudinal trajectory of cognitive complaints, multimorbidity, and objective cognition in older age is an important area for future research.

Continuity of Care and the Quality of Life among Patients with Type 2 Diabetes Mellitus: A Cross-Sectional Study in Taiwan

Background: Understanding factors associated with the quality of life (QoL) of patients with type 2 diabetes (T2DM) is an important health issue. This study aimed to explore the correlation between continuity of care and quality of life in patients with T2DM and to probe for important explanatory factors affecting quality of life. Methods: This study used a cross-sectional correlation research design. Convenience sampling was adopted to recruit 157 patients, aged 20–80 years and diagnosed with T2DM in the medical ward of a regional hospital in central Taiwan. Results: The overall mean (standard deviation, SD) QOL score was 53.42 (9.48). Hierarchical regression linear analysis showed that age, depression, two variables of potential disability (movement and depression), and the inability to see a specific physician or maintain relational continuity with medical providers were important predictors that could effectively explain 62.0% of the variance of the overall QoL. Conclusions: The relationship between patients and physicians and maintaining relational continuity with the medical providers directly affect patients’ QoL during hospitalization and should be prioritized clinically. Timely interventions should be provided for older adult patients with T2DM, depression, or an inability to exercise to maintain their QoL.

Will undocumented migrants contribute to change epidemiology, presentation and pharmacologic treatment of diabetes in Western countries?

AIMS

Migrants from countries in which health and social conditions are unsatisfactory, and their offspring, are becoming a growing component of the western population. Available health data show that their morbidity is at least comparable to that of the host country population, with a significant contribution of chronic diseases as diabetes. The possibility that diabetes shows different features in undocumented migrants is the hypothesis that we tried to investigate in this study.

METHODS

We retrospectively analysed the data of 413 patients with type 2 diabetes mellitus (T2DM): 222 patients followed in a diabetes clinic at a University Hospital and 191 undocumented migrants cared for by a Charity in Milan, Italy.

RESULTS

We found that the onset of the disease was earlier in migrants; they showed a significant lower body mass index (BMI) and had lower socioeconomic conditions. They had a worse glycaemic control. The pattern of complications was also different between the two groups, with cardiovascular complications more frequent in Italians. Finally, also pharmacologic treatment differed significantly.

CONCLUSIONS

Age of onset, clinical manifestations and complications of T2DM in undocumented migrants and natives may show significant differences. This is important for both epidemiological and clinical reasons. If these preliminary observations are confirmed by larger studies, we can conclude that undocumented migrants should be screened for T2DM earlier than natives, and that therapies should be tailored to the specific features of their disease.

A hybrid Forecast Cost Benefit Classification of diabetes mellitus prevalence based on epidemiological study on Real-life patient's data

The increasing ratio of diabetes is found risky across the planet. Therefore, the diagnosis is important in population with extreme risk of diabetes. In this study, a decision-making classifier (J48) is applied over a data-mining platform (Weka) to measure accuracy and linear regression on classification results to forecast cost/benefit ratio in diabetes mellitus patients along with prevalence. In total 108 invasive and non-invasive medical features are considered from 251 patients for assessment, and the real-time data are gathered from Pakistan over a time span of June 2017 to April 2018. The results indicate that J48 classifiers achieved the best accuracy of (99.28%), whereas, error rate (0.08%), Kappa stats, PRC, and MCC are (0.98%), precision, recall, and F-matrix are (0.99%). In addition, true positive rate is (0.99%) and false positive is (0.08%). The regression forecast decision indicates blood pressure and glucose level are key features for diabetes. The cost/benefit matrix indicates two predictions for positive test with accuracy (66.68%) and (30.60%), and key attributes with total Gain (118.13%). The study confirmed the proposed prediction is practical for screening of diabetes mellitus patients at the initial stage without invasive medical tests and found effectual in the early diagnosis of diabetes.

An Accurate Clinical Implication Assessment for Diabetes Mellitus Prevalence Based on a Study from Nigeria

The increasing rate of diabetes is found across the planet. Therefore, the diagnosis of pre-diabetes and diabetes is important in populations with extreme diabetes risk. In this study, a machine learning technique was implemented over a data mining platform by employing Rule classifiers (PART and Decision table) to measure the accuracy and logistic regression on the classification results for forecasting the prevalence in diabetes mellitus patients suffering simultaneously from other chronic disease symptoms. The real-life data was collected in Nigeria between December 2017 and February 2019 by applying ten non-intrusive and easily available clinical variables. The results disclosed that the Rule classifiers achieved a mean accuracy of 98.75%. The error rate, precision, recall, F-measure, and Matthew’s correlation coefficient MCC were 0.02%, 0.98%, 0.98%, 0.98%, and 0.97%, respectively. The forecast decision, achieved by employing a set of 23 decision rules (DR), indicates that age, gender, glucose level, and body mass are fundamental reasons for diabetes, followed by work stress, diet, family diabetes history, physical exercise, and cardiovascular stroke history. The study validated that the proposed set of DR is practical for quick screening of diabetes mellitus patients at the initial stage without intrusive medical tests and was found to be effective in the initial diagnosis of diabetes.