Stratification strategy for evaluating the influence of diabetes complication severity index on the risk of hospitalization: a record linkage data in Western Australia

Socioeconomic inequalities and diabetes complications: an analysis of administrative data from Hungary

Diabetes complications are associated with increased healthcare costs and worsened patient outcomes. In this paper, we analyse how individual-level demographic and territorial-level socioeconomic and healthcare variables influence the presence and severity of diabetes complications and their relationship with mortality. Our study utilizes anonymized administrative healthcare data on all diabetes patients of Hungary between 2010 and 2017. We construct settlement-year level and individual-year level panel datasets to analyse diabetes prevalence, incidence and complications, employing Poisson and logit models to explore associations between complications and the explanatory variables. The adapted Diabetes Complications Severity Index (aDCSI) is employed to quantitatively evaluate the severity of complications by aggregating individual complication scores from ICD-10 diagnosis codes. We find that diabetes prevalence and incidence are higher in settlements with above-median unemployment rates, where patients exhibit more severe complications, as shown by higher average aDCSI scores. Among socioeconomic factors, unemployment rate is particularly associated with increased aDCSI scores, while better healthcare access is associated with lower aDCSI scores in unadjusted but with higher scores in adjusted models. The presence and severity of complications, especially renal, cardiovascular and peripheral vascular ones, substantially increase 5-year inpatient mortality. Most of the mortality difference by settlement-level unemployment rate disappears when complications are accounted for. We conclude that socioeconomic inequalities, particularly higher unemployment rates, are strongly linked to diabetes complications and associated mortality risk. Addressing these disparities through improved healthcare accessibility and targeted public health strategies could play a crucial role in reducing the burden of diabetes-related complications and improving patient outcomes.

Type 2 diabetes severity in the workforce: An occupational sector analysis using German claims data

BACKGROUND

Individuals of working age spend a significant amount of time at the workplace making it an important context for disease prevention and management. The temporal development and prevalence of T2D have been shown to differ in the working population based on gender, age group and occupational sector regardless of socioeconomic status. Given potential differences in risk factors associated with different work environments, this study aims to define vulnerable occupational groups by examining T2D severity and its trends in working men and women with T2D of two age groups and among nine occupational sectors.

METHODS

The study is based on claims data of the statutory health insurance provider AOKN. The study population consisted of all insured working individuals with T2D. T2D severity was measured using the adapted diabetes complications severity index-complication count (DCSI-CC). Mean DCSI-CC scores were calculated over four time periods between 2012 and 2019 for men and women of the age groups 18-45 and 46+ years and among nine occupational sectors. Trends of DCSI-CC were investigated using ordinal logistic regression analyses to examine the effect of time-period on the odds of having higher DCSI scores.

RESULTS

Overall, there was a significant rise in T2D severity over time in working men and women of the older age group. Moreover, the study displayed occupational sector differences in T2D severity and its trends. Over all, working men of all sectors had higher DCSI-CC scores compared to working women. Individuals working in the sector "Transport, logistics, protection and security" and "Construction, architecture, measuring and building technology" had higher T2D severity, while those working in the "Health sector, social work, teaching & education" had relatively lower T2D severity. There was a gender-specific significant increase over time in T2D severity in the above-mentioned occupational sectors.

CONCLUSION

The study displayed gender, age group and occupational sector differences in T2D severity and its trends. Working individuals could thus benefit from personalized prevention interventions that consider occupational contexts. As a next step, examining T2D trends and severity in specific occupations within the vulnerable occupational sectors is needed.

A Comprehensive Systematic Review of Data Linkage Publications on Diabetes in Australia

Aims

Our study aimed to identify the common themes, knowledge gaps and to evaluate the quality of data linkage research on diabetes in Australia.

Methods

This systematic review was developed in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (the PRISMA Statement). Six biomedical databases and the Australian Population Health Research Network (PHRN) website were searched. A narrative synthesis was conducted to comprehensively identify the common themes and knowledge gaps. The guidelines for studies involving data linkage were used to appraise methodological quality of included studies.

Results

After screening and hand-searching, 118 studies were included in the final analysis. Data linkage publications confirmed negative health outcomes in people with diabetes, reported risk factors for diabetes and its complications, and found an inverse association between primary care use and hospitalization. Linked data were used to validate data sources and diabetes instruments. There were limited publications investigating healthcare expenditure and adverse drug reactions (ADRs) in people with diabetes. Regarding methodological assessment, important information about the linkage performed was under-reported in included studies.

Conclusions

In the future, more up to date data linkage research addressing costs of diabetes and its complications in a contemporary Australian setting, as well as research assessing ADRs of recently approved antidiabetic medications, are required.

Time protective effect of contact with a general practitioner and its association with diabetes-related hospitalisations: a cohort study using the 45 and Up Study data in Australia

OBJECTIVES

To evaluate the relationship between the proportion of time under the potentially protective effect of a general practitioner (GP) captured using the Cover Index and diabetes-related hospitalisation and length of stay (LOS).

DESIGN

An observational cohort study over two 3-year time periods (2009/2010-2011/2012 as the baseline and 2012/2013-2014/2015 as the follow-up).

SETTING

Linked self-report and administrative health service data at individual level from the 45 and Up Study in New South Wales, Australia.

PARTICIPANTS

A total of 21 965 individuals aged 45 years and older identified with diabetes before July 2009 were included in this study.

MAIN OUTCOME MEASURES

Diabetes-related hospitalisation, unplanned diabetes-related hospitalisation and LOS of diabetes-related hospitalisation and unplanned diabetes-related hospitalisation.

METHODS

The average annual GP cover index over a 3-year period was calculated using information obtained from Australian Medicare and hospitalisation. The effect of exposure to different levels of the cover on the main outcomes was estimated using negative binomial models weighted for inverse probability of treatment weight to control for observed covariate imbalance at the baseline period.

RESULTS

Perfect GP cover was observed among 53% of people with diabetes in the study cohort. Compared with perfect level of GP cover, having lower levels of GP cover including high (incidence rate ratio (IRR) 2.8, 95% CI 2.6 to 3.0), medium (IRR 3.2, 95% CI 2.7 to 3.8) and low (IRR 3.1, 95% CI 2.0 to 4.9) were significantly associated with higher number of diabetes-related hospitalisation. Similar association was observed between the different levels of GP cover and other outcomes including LOS for diabetes-related hospitalisation, unplanned diabetes-related hospitalisation and LOS for unplanned diabetes-related hospitalisation.

CONCLUSIONS

Measuring longitudinal continuity in terms of time under cover of GP care may offer opportunities to optimise the performance of primary healthcare and reduce secondary care costs in the management of diabetes.

Diabetes Disease Severity Was Not Associated with Risk of Deep Infection or Revision After Shoulder Arthroplasty

BACKGROUND

Prior studies have identified diabetes and disease severity (defined using hemoglobin A1c [HbA1c]) as potential risk factors for complications after shoulder arthroplasty. Evaluations of diabetes status and risk of adverse outcomes beyond the 30-day window either are limited or have not accounted for disease severity. Further, measures of diabetes severity other than HbA1c have yet to be investigated in a shoulder arthroplasty population.

QUESTIONS/PURPOSES

(1) Are diabetes status and glycemic control associated with adverse events, including deep infection, all-cause revision, and 90-day readmission after shoulder arthroplasty? (2) Is postoperative HbA1c associated with revision risk? (3) Is there a threshold of preoperative HbA1c that best identifies patients with diabetes who are at higher risk of 3-year deep infection, 1-year all-cause revision, or 90-day readmission? (4) Can the Adapted Diabetes Complications Severity index (aDCSI) be used as an alternative measure of diabetes severity in evaluating the risk of deep infection, all-cause revision, and 90-day readmission and identification of patients with diabetes at higher risk for these events? (5) Is there a difference between elective and traumatic shoulder arthroplasty patients?

METHODS

We conducted a retrospective registry-based cohort study using Kaiser Permanente's Shoulder Arthroplasty Registry (2005-2015). Primary shoulder arthroplasties were classified as patients with and without diabetes. Patients with diabetes were further evaluated using two disease severity measures (1) HbA1c, with good glycemic control classified as preoperative HbA1c < 7.0% and poor control defined as HbA1c ≥ 7.0%; and (2) aDCSI, classified as mild (score of 0-2) or severe (score ≥ 3) diabetes. Cox regression was used to evaluate the risk of deep infection and revision according to diabetes status and disease severity; conditional logistic regression was used for 90-day readmission. Time-dependent 1-year postoperative HbA1c was used to evaluate revision risk in Cox regression. All models were adjusted for covariates and stratified by elective versus trauma shoulder arthroplasty. Receiver operating characteristic curves were generated for HbA1c and aDCSI to determine whether a threshold exists to identify patients at higher risk of deep infection, all-cause revision, or 90-day readmission. The study sample consisted of 8819 patients; 7353 underwent elective shoulder arthroplasty and 1466 underwent shoulder arthroplasty due to trauma. For elective shoulder arthroplasty, 1430 patients (19%) had diabetes, and among the patients who underwent arthroplasty due to trauma, 444 (30%) had diabetes.

RESULTS

Patients with diabetes who underwent elective shoulder arthroplasty and had poor glycemic control had a higher likelihood of 90-day readmission compared with patients without diabetes (OR, 1.5; 95% CI, 1.0-2.1; p = 0.032). No association was found for patients with diabetes who underwent shoulder arthroplasty due to trauma. No association was found between postoperative HbA1c and revision risk. Receiver operating characteristic curve analysis suggested preoperative HbA1c performed poorly at differentiating adverse events. When using aDCSI, patients with severe diabetes who underwent both elective and traumatic shoulder arthroplasty had a higher likelihood of 90-day readmission compared with patients without diabetes (OR, 1.6; 95% CI, 1.2-2.2; p = 0.001 and OR, 1.8; 95% CI, 1.2-2.7; p = 0.005, respectively). Similar to HbA1c, the aDCSI was a poor classifier in differentiating adverse events.

CONCLUSIONS

Of the longer-term outcomes evaluated, more-severe diabetes was only found to be associated with an increase in 90-day readmissions after shoulder arthroplasty; a stronger association was found when using the aDCSI in identifying diabetes severity. Arbitrary cutoffs in HbA1c may not be the best method for determining risk of postoperative outcomes. Future work investigating perioperative diabetes management should work to identify and validate measures, such as the aDCSI, that better identify patients at higher risk for postoperative outcomes and, more importantly, whether outcomes can be improved by modifying these measures with targeted interventions.

LEVEL OF EVIDENCE

Level III, therapeutic study.

A time-duration measure of continuity of care to optimise utilisation of primary health care: a threshold effects approach among people with diabetes

BACKGROUND

Literature highlighted the importance of timely access and ongoing care provided at primary care settings in reducing hospitalisation and health care resource uses. However, the effect of timely access to primary care has not been fully captured in most of the current continuity of care indices. This study aimed to develop a time-duration measure of continuity of primary care ("cover index") capturing the proportion of time an individual is under the potentially protective effect of primary health care contacts.

METHODS

An observational study was conducted on 36,667 individuals aged 45 years or older with diabetes mellitus extracted from Western Australian linked administrative data. Threshold effect models were used to determine the maximum time interval between general practitioner (GP) visits that afforded a protective effect against avoidable hospitalisation across complication cohorts. The optimal maximum time interval was used to compute a cover index for each individual. The cover was evaluated using descriptive statistics stratified by population socio-demographic characteristics.

RESULTS

The optimal maximum time between GP visits was 9-13 months for people with diabetes with no complication, 5-11 months for people with diabetes with 1-2 complications, and 4-9 months for people with diabetes with 3+ complications. The cover index was lowest among those aged 75+ years, males, Indigenous people, socio-economically disadvantaged and those in very remote areas.

CONCLUSIONS

This study developed a new measure of continuity of primary care that adds a time parameter to capturing longitudinal continuity. Cover has the potential to better capture underuse of primary care and will significantly contribute to the sparsely available methods for analysis of linked administrative data in evaluating continuity of care for people with chronic conditions.

Trends in comorbidity burden and treatment patterns in type 2 diabetes: Longitudinal data from a US cohort from 2006 to 2014

AIMS

To gather real-world data on treatment characteristics and comorbidity progression in patients with newly-diagnosed type 2 diabetes (T2D) and evaluate differences by patient age.

METHODS

Retrospective analysis of a US administrative claims database including 16,950 subjects with newly-diagnosed T2D in 2006 and a baseline Diabetes Complications Severity Index (DCSI) score of 0. Patients were categorized by DCSI score at year 8 (0, 1-2, or ≥3) and comparatively analyzed based on demographic variables, drug usage, and diabetes-related comorbidities.

RESULTS

Year 8 DCSI score distribution was 0 (29.9%), 1-2 (36.2%), and ≥3 (33.9%). The highest DCSI score subgroup (≥3) was characterized by a significantly greater percentage of males, older age at T2D diagnosis, and higher Medicare enrollment. DCSI progressed most rapidly in the oldest age group (≥65). Among all subjects at year 8, insulin use was significantly highest among subjects with DCSI ≥3 compared with those having a lower DCSI. However, for subjects with DCSI ≥3, insulin use was lower among those in the oldest age group (≥65) relative to younger age groups.

CONCLUSIONS

These real-world data suggest a relationship between age at T2D diagnosis and disease progression based on comorbidity burden and lower usage of injectable therapies in older patients.

Identifying patterns of general practitioner service utilisation and their relationship with potentially preventable hospitalisations in people with diabetes: The utility of a cluster analysis approach

AIMS

We aimed to characterise use of general practitioners (GP) simultaneously across multiple attributes in people with diabetes and examine its impact on diabetes related potentially preventable hospitalisations (PPHs).

METHODS

Five-years of panel data from 40,625 adults with diabetes were sourced from Western Australian administrative health records. Cluster analysis (CA) was used to group individuals with similar patterns of GP utilisation characterised by frequency and recency of services. The relationship between GP utilisation cluster and the risk of PPHs was examined using multivariable random-effects negative binomial regression.

RESULTS

CA categorised GP utilisation into three clusters: moderate; high and very high usage, having distinct patient characteristics. After adjusting for potential confounders, the rate of PPHs was significantly lower across all GP usage clusters compared with those with no GP usage; IRR = 0.67 (95%CI: 0.62-0.71) among the moderate, IRR = 0.70 (95%CI 0.66-0.73) high and IRR = 0.76 (95%CI 0.72-0.80) very high GP usage clusters.

CONCLUSIONS

Combination of temporal factors with measures of frequency of use of GP services revealed patterns of primary health care utilisation associated with different underlying patient characteristics. Incorporation of multiple attributes, that go beyond frequency-based approaches may better characterise the complex relationship between use of GP services and diabetes-related hospitalisation.