The impact of diabetes prevention on labour force participation and income of older Australians: an economic study

Projecting the Health and Economic Burden of Cardiovascular Disease Among People with Type 2 Diabetes, 2022-2031

OBJECTIVE

The aim was to project the health and economic outcomes of cardiovascular disease (CVD) among people with type 2 diabetes from Australian public healthcare and societal perspectives over the next decade.

METHODS

A dynamic multistate model with yearly cycles was developed to project cardiovascular events among Australians with type 2 diabetes aged 40-89 years from 2022 to 2031. CVD risk (myocardial infarction [MI] and stroke) in the type 2 diabetes population was estimated using the 2013 pooled cohort equation, and recurrent cardiovascular event rates in the type 2 diabetes with established CVD population were obtained from the global Reduction of Atherothrombosis for Continued Health (REACH) registry. Costs and utilities were derived from published sources. Outcomes included fatal and non-fatal MI and stroke, years of life lived, quality-adjusted life years (QALYs), total healthcare costs, and total productivity losses. The annual discount rate was 5%, applied to outcomes and costs.

RESULTS

Between 2022 and 2031, a total of 83,618 non-fatal MIs (95% uncertainty interval [UI] 83,170-84,053) and 58,774 non-fatal strokes (95% UI 58,458-59,013) were projected. Total years of life lived and QALYs (discounted) were projected to be 9,549,487 (95% UI 9,416,423-9,654,043) and 6,632,897 (95% UI 5,065,606-7,591,679), respectively. Total healthcare costs and total lost productivity costs (discounted) were projected to be 9.59 billion Australian dollars (AU$) (95% UI 1.90-30.45 billion) and AU$9.07 billion (95% UI 663.53 million-33.19 billion), respectively.

CONCLUSIONS

CVD in people with type 2 diabetes will substantially impact the Australian healthcare system and society over the next decade. Future work to investigate different strategies to optimize the control of risk factors for the prevention and treatment of CVD in type 2 diabetes in Australia is warranted.

Productivity Benefits of Preventing Type 2 Diabetes in Australia: A 10-Year Analysis

OBJECTIVE

Diabetes imposes a heavy burden on both health and productivity. In this study, we sought to estimate the potential productivity gains associated with the prevention of type 2 diabetes over the next 10 years in Australia.

RESEARCH DESIGN AND METHODS

Dynamic life table models were constructed to estimate years of life lived and productivity-adjusted life-years (PALYs) lived by Australians aged 20-69 years over the period from 2020 to 2029. The models distinguished people with and without type 2 diabetes. PALYs were ascribed a financial value equivalent to gross domestic product (GDP) per full-time worker in Australia (∼200,000 Australian dollars [AUD]). The model simulation was first undertaken assuming currently expected trends in the incidence of type 2 diabetes and then repeated assuming hypothetically that the incidence was reduced. The difference between the modeled outputs reflected the impact of new cases of type 2 diabetes on productivity as well as the potential benefits of prevention. An annual 5% discount rate was applied to all outcomes.

RESULTS

Over the next decade, 140 million years of life and 87 million PALYs will be lived by Australians of working age, contributing AUD 18.0 trillion to the country's GDP. A 10% reduction in the incidence of type 2 diabetes would result in a gain of 2,510 PALYs and AUD 532 million in GDP.

CONCLUSIONS

This study illustrates the health and economic impact of type 2 diabetes and the gains that could be potentially achieved from the implementation of effective prevention strategies. However, cost-effectiveness evaluations of these prevention strategies are needed.

Gender Differences in the Relationship between Type 2 Diabetes Mellitus and Employment: Evidence from the Korea Health Panel Study

Previous studies have analyzed the impact of diabetes mellitus on labor market participation by men and women, but gender difference between type 2 diabetes mellitus (T2DM) and employment has not been the focus. This study aims to explore gender differences between T2DM and employment status. Data from the Korea Health Panel Study, 2013-2015 were analyzed by distinguishingT2DM and non-diabetes (N = 11,216). The empirical model was established and the generalized two-stage least squares (2SLS) was estimated, controlling for endogeneity. A family history of diabetes, as an instrumental variable, was related to an individual's genetic predisposition to develop diabetes. The estimated results for the 2SLS showed the interaction effects between T2DM and employment. T2DM had a statistically significant and negative effect on employment for women only. The comparison with non-diabetes showed that women with T2DM had a lower probability of employment by 51.9% (p < 0.05). Exposing gender bias in employment suggests that healthcare policies and disease management programs for diabetic patients should adopt gender-specific remedies.

Needed Improvements in Diabetes Prevention and Management in Brazil

INTRODUCTION

Diabetes mellitus is one of the most common chronic diseases worldwide and its prevalence continues to increase. Adherence to good health behaviors provides better control of the disease. This study analyzed trends in the prevalence of diabetes among Brazilian adults between 2006 and 2016 and compared the frequency of key health behaviors between people with and without diabetes.

METHODS

We analyzed data from 572,437 Brazilian adults interviewed between 2006 and 2016 by using the Surveillance System of Risk and Protection Factors for Chronic Diseases by Telephone Survey (Vigitel). We used regression models to investigate significant trends in the prevalence of diabetes and differences in adherence to health behaviors between people with and without diabetes.

RESULTS

The prevalence of diabetes increased significantly from 5.5% to 8.9% (P <.001), being higher among women, older adults, and those with less than a high school education. The greatest increase was observed among these groups with higher prevalence. People with diabetes had a lower frequency of risk behaviors and a higher frequency of protective behaviors when compared to people without diabetes. The greatest differences were observed in the consumption of soft drinks and sugar-sweetened beverages (9.5% vs 25.0%) and alcoholic beverages (9.0% vs 17.9%), and the smallest differences were related to regular consumption of fruits and vegetables (40.7% vs 34.0%) and meats with excess fat (24.3% vs 32.2%). People with diabetes reported less leisure-time physical activity (PRa, 0.92 [P < .001]) and less transport-related physical activity (PRa, 0.84 [P <.001]) than those without diabetes.

CONCLUSION

Diabetes prevalence increased between 2006 and 2016. People with diabetes had better dietary habits than those without diabetes, but still had risk behaviors such as insufficient physical activity.

The Productivity Burden of Diabetes at a Population Level

OBJECTIVE

Recent studies suggest that diabetes may impact work productivity. In the current study, we sought to estimate the lifetime and population impact of diabetes on productivity using the novel measure of "productivity-adjusted life years" (PALYs).

RESEARCH DESIGN AND METHODS

Using age-specific mortality rates and a productivity index attributable to diabetes (akin to the quality of life index, but which adjusts for reduction in productivity) and life table modeling, we estimated years of life and PALYs lost to diabetes among Australians with diabetes currently aged 20-65 years, with follow-up until 69 years. Life tables were first constructed for the cohort with diabetes and then repeated for the same cohort but with the assumption that they no longer had diabetes. The "nondiabetic" cohort had lower mortality rates and improved productivity. The differences in total years of life lived and PALYs lived between the two cohorts reflected the impact of diabetes.

RESULTS

Overall, diabetes reduced total years of life lived by the cohort by 190,219 years or almost 3%. Diabetes reduced PALYs by 11.6% and 10.5% among men and women, respectively. For both sexes, the impact of diabetes on productivity was lowest in those aged 65-69 years and highest in those 20-24 years. Among the latter, PALYs were reduced by 12.2% and 11.0% for men and women, respectively.

CONCLUSIONS

Elimination of diabetes can prolong life years lived by the whole population and increase the amount of productive years lived. Employers and government should be aware that having diabetes affects work force productivity and implement prevention programs to reduce the impact of diabetes on the workforce.

Out-of-pocket healthcare expenditure and chronic disease - do Australians forgo care because of the cost?

Although we do know that out-of-pocket healthcare expenditure is relatively high in Australia, little is known about what health conditions are associated with the highest out-of-pocket expenditure, and whether the cost of healthcare acts as a barrier to care for people with different chronic conditions. Cross-sectional analysis using linear and logistic regression models applied to the Commonwealth Fund international health policy survey of adults aged 18 years and over was conducted in 2013. Adults with asthma, emphysema and chronic obstructive pulmonary disease (COPD) had 109% higher household out-of-pocket healthcare expenditure than did those with no health condition (95% CI: 50-193%); and adults with depression, anxiety and other mental health conditions had 95% higher household out-of-pocket expenditure (95% CI: 33-187%). People with a chronic condition were also more likely to forego care because of cost. People with depression, anxiety and other mental health conditions had 7.65 times higher odds of skipping healthcare (95% CI: 4.13-14.20), and people with asthma, emphysema and chronic obstructive pulmonary disease had 6.16 times higher odds of skipping healthcare (95% CI: 3.30-11.50) than did people with no health condition. People with chronic health conditions in Canada, the United Kingdom, Germany, France, Norway, Sweden and Switzerland were all significantly less likely to skip healthcare because of cost than were people with a condition in Australia. The out-of-pocket cost of healthcare in Australia acts as a barrier to accessing treatment for people with chronic health conditions, with people with mental health conditions being likely to skip care. Attention should be given to the accessibility and affordability of mental health services in Australia.

The costs of diabetes among Australians aged 45-64 years from 2015 to 2030: projections of lost productive life years (PLYs), lost personal income, lost taxation revenue, extra welfare payments and lost gross domestic product from Health&WealthMOD2030

OBJECTIVES

To project the number of people aged 45-64 years with lost productive life years (PLYs) due to diabetes and related costs (lost income, extra welfare payments, lost taxation revenue); and lost gross domestic product (GDP) attributable to diabetes in Australia from 2015 to 2030.

DESIGN

A simulation study of how the number of people aged 45-64 years with diabetes increases over time (based on population growth and disease trend data) and the economic losses incurred by individuals and the government. Cross-sectional outputs of a microsimulation model (Health&WealthMOD2030) which used the Australian Bureau of Statistics' Survey of Disability, Ageing and Carers 2003 and 2009 as a base population and integrated outputs from two microsimulation models (Static Incomes Model and Australian Population and Policy Simulation Model), Treasury's population and labour force projections, and chronic disease trends data.

SETTING

Australian population aged 45-64 years in 2015, 2020, 2025 and 2030.

OUTCOME MEASURES

Lost PLYs, lost income, extra welfare payments, lost taxation revenue, lost GDP.

RESULTS

18 100 people are out of the labour force due to diabetes in 2015, increasing to 21 400 in 2030 (18% increase). National costs consisted of a loss of $A467 million in annual income in 2015, increasing to $A807 million in 2030 (73% increase). For the government, extra annual welfare payments increased from $A311 million in 2015 to $A350 million in 2030 (13% increase); and lost annual taxation revenue increased from $A102 million in 2015 to $A166 million in 2030 (63% increase). A loss of $A2.1 billion in GDP was projected for 2015, increasing to $A2.9 billion in 2030 attributable to diabetes through its impact on PLYs.

CONCLUSIONS

Individuals incur significant costs of diabetes through lost PLYs and lost income in addition to disease burden through human suffering and healthcare costs. The government incurs extra welfare payments, lost taxation revenue and lost GDP, along with direct healthcare costs.

The indirect costs of back problems (dorsopathies) in Australians aged 45 to 64 years from 2015 to 2030: results from a microsimulation model, Health&WealthMOD2030

This study projected the indirect costs of back problems through lost productive life years (PLYs) from the individual's perspective (lost disposable income), the governmental perspective (reduced taxation revenue, greater welfare spending), and the societal perspective (lost gross domestic product, GDP) from 2015 to 2030, using Health&WealthMOD2030-Australia's first microsimulation model on the long-term impacts of ill-health. Quantile regression analysis was used to examine differences in median weekly income, welfare payments, and taxes of people unable to work due to back problems with working full-time without back problems as comparator. National costs and lost GDP resulting from missing workers due to back problems were also projected. We projected that 90,000 people have lost PLYs due to back problems in 2015, increasing to 104,600 in 2030 (16.2% increase). People with lost PLYs due to back problems are projected to receive AU$340.91 less in total income and AU$339.77 more in welfare payments per week than full-time workers without back problems in 2030 and pay no income tax on average. National costs consisted of a loss of AU$2931 million in annual income in 2015, increasing to AU$4660 million in 2030 (60% increase). For government, extra annual welfare payments are projected to increase from AU$1462 million in 2015 to AU$1709 million in 2030 (16.9% increase), and lost annual taxation revenue to increase from AU$671 million in 2015 to $961 million in 2030 (43.2% increase). We projected losses in GDP of AU$10,543 million in 2015, increasing to AU$14,522 million in 2030 due to back problems.

Pathways between health, education and income in adolescence and adulthood

OBJECTIVE

To quantify the impact of household income, and physical and mental health in adolescence on education attainment, household income and health status in adulthood.

DESIGN

Path analysis and regression models using waves 1-12 of the Household, Income and Labour Dynamics in Australia survey.

PARTICIPANTS

Individuals aged 17 or 18 in 2001, 52% were males (n=655) and 48% were female (52%). Of those participating in wave 1, five did not respond in wave 12.

MAIN OUTCOME MEASURES

Education attainment, household income, physical and mental health at age 29/30.

RESULTS

For females, physical health at age 17/18 was significantly related to level of education attainment at age 29/30 (standardised total effect 0.290, p<0.001), with this influence being greater in magnitude than that of household income at age 17/18 on level of education attainment at age 29/30 (standardised total effect 0.159, p=0.022). Females' physical health at age 17/18 was also significantly related to household income at age 29/30 (standardised total effect 0.09, p=0.018). Both adjusted for initial household income at age 17/18. For males, the total standardised total effect of physical health at age 17/18 had a greater impact than household income at age 17/18 on education attainment at age 29/30 (0.347, p<0.001 for physical health and 0.276, p<0.001 for household income). The OR of achieving a year 12 or higher level of education attainment was 4.72 (95% CI 1.43 to 15.58, p=0.0110) for females with good physical health at age 17/18 and 5.05 (95% CI 1.78 to 14.36, p=0.0024) for males, compared with those with poor physical health at age 17/18.

CONCLUSIONS

As physical health in adolescence appears to have a stronger influence on education attainment in adulthood than household income, equity strategies for education attainment should also target those with poor health.

Therapeutic Use of Metformin in Prediabetes and Diabetes Prevention

People with elevated, non-diabetic, levels of blood glucose are at risk of progressing to clinical type 2 diabetes and are commonly termed 'prediabetic'. The term prediabetes usually refers to high-normal fasting plasma glucose (impaired fasting glucose) and/or plasma glucose 2 h following a 75 g oral glucose tolerance test (impaired glucose tolerance). Current US guidelines consider high-normal HbA1c to also represent a prediabetic state. Individuals with prediabetic levels of dysglycaemia are already at elevated risk of damage to the microvasculature and macrovasculature, resembling the long-term complications of diabetes. Halting or reversing the progressive decline in insulin sensitivity and β-cell function holds the key to achieving prevention of type 2 diabetes in at-risk subjects. Lifestyle interventions aimed at inducing weight loss, pharmacologic treatments (metformin, thiazolidinediones, acarbose, basal insulin and drugs for weight loss) and bariatric surgery have all been shown to reduce the risk of progression to type 2 diabetes in prediabetic subjects. However, lifestyle interventions are difficult for patients to maintain and the weight loss achieved tends to be regained over time. Metformin enhances the action of insulin in liver and skeletal muscle, and its efficacy for delaying or preventing the onset of diabetes has been proven in large, well-designed, randomised trials, such as the Diabetes Prevention Program and other studies. Decades of clinical use have demonstrated that metformin is generally well-tolerated and safe. We have reviewed in detail the evidence base supporting the therapeutic use of metformin for diabetes prevention.

Multiple chronic health conditions and their link with wealth assets

BACKGROUND

There has been little research on the economic status of those with multiple health conditions, particularly on the relationship between multiple health conditions and wealth. This paper will assess the difference in the value and type of wealth assets held by Australians who have multiple chronic health conditions.

METHODS

Using Health&WealthMOD, a microsimulation model of the 45-64-year-old Australian population in 2009, a counterfactual analysis was undertaken. The actual proportion of people with different numbers of chronic health conditions with any wealth, and the value of this wealth was estimated. This was compared with the counterfactual values had the individuals had no chronic health conditions.

RESULTS

There was no change in the proportion of people with one health condition who actually had any wealth, compared to the counterfactual proportion had they had no chronic health conditions. Ninety-four percent of those with four or more health conditions had some accumulated wealth; however, under the counterfactual, 100% would have had some accumulated wealth. There was little change in the value of non-income-producing assets under the counterfactual, regardless of number of health conditions. Those with four or more chronic health conditions had a mean value of $17 000 in income-producing assets; under the counterfactual, the average would have been $78 000.

CONCLUSION

This study has highlighted the variation in the value of wealth according to number of chronic health conditions, and hence the importance of considering multiple morbidities when discussing the relationship between health and wealth.

The economic impact of diabetes through lost labour force participation on individuals and government: evidence from a microsimulation model

Background

Diabetes is a costly and debilitating disease. The aim of the study is to quantify the individual and national costs of diabetes resulting from people retiring early because of this disease, including lost income; lost income taxation, increased government welfare payments; and reductions in GDP.

Methods

A purpose-built microsimulation model, Health&WealthMOD2030, was used to estimate the economic costs of early retirement due to diabetes. The study included all Australians aged 45–64 years in 2010 based on Australian Bureau of Statistics’ Surveys of Disability, Ageing and Carers. A multiple regression model was used to identify significant differences in income, government welfare payments and taxation liabilities between people out of the labour force because of their diabetes and those employed full time with no chronic health condition.

Results

The median annual income of people who retired early because of their diabetes was significantly lower (AU$11 784) compared to those employed full time without a chronic health condition who received almost five times more income. At the national level, there was a loss of AU$384 million in individual earnings by those with diabetes, an extra AU$4 million spent in government welfare payments, a loss of AU$56 million in taxation revenue, and a loss of AU$1 324 million in GDP in 2010: all attributable to diabetes through its impact on labour force participation. Sensitivity analysis was used to assess the impact of different diabetes prevalence rates on estimates of lost income, lost income taxation, increased government welfare payments, and reduced GDP.

Conclusions

Individuals bear the cost of lost income in addition to the burden of the disease. The Government endures the impacts of lost productivity and income taxation revenue, as well as spending more in welfare payments. These national costs are in addition to the Government’s direct healthcare costs.

The impact of diabetes on the labour force participation and income poverty of workers aged 45-64 years in Australia

Objective

To quantify the poverty status and level of disadvantage experienced by Australians aged 45–64 years who have left the labour force due to diabetes in 2010.

Research Design and Methods

A purpose-built microsimulation model, Health&WealthMOD2030, was used to estimate the poverty status and level of disadvantage of those aged 45–64 years who prematurely retire from the workforce due to diabetes. A multiple regression model was used to identify significant differences in rates of income poverty and the degree of disadvantage between those out of the labour force due to diabetes and those employed full- or part-time with no diabetes.

Results

63.9% of people aged 45–64 years who were out of the labour force due to diabetes were in poverty in 2010. The odds of being in poverty for those with no diabetes and employed full-time (OR of being in poverty 0.02 95%CI: 0.01–0.04) or part-time (OR of being in poverty 0.10 95%CI: 0.05–0.23) are significantly lower than those for persons not in the labour force due to diabetes. Amongst those with diabetes, those who were able to stay in either full- or part-time employment were as much as 97% less likely to be in poverty than those who had to retire early because of the condition. Sensitivity analysis was used to assess impacts of different poverty line thresholds and key socioeconomic predictors of poverty.

Conclusions

This study has shown that having diabetes and not being in the labour force because of this condition significantly increases the chances of living in poverty. Intervening to prevent or delay the onset of diabetes is likely to improve their living standards.

The economic burden of cancers on Indian households

We assessed the burden of cancer on households’ out-of-pocket health spending, non-medical consumption, workforce participation, and debt and asset sales using data from a nationally representative health and morbidity survey in India for 2004 of nearly 74 thousand households. Propensity scores were used to match households containing a member diagnosed with cancer (i.e. cancer-affected households) to households with similar socioeconomic and demographic characteristics (controls). Our estimates are based on data from 1,645 households chosen through matching. Cancer-affected households experienced higher levels of outpatient visits and hospital admissions and increased out-of-pocket health expenditures per member, relative to controls. Cancer-affected households spent between Indian Rupees (INR) 66 and INR 85 more per member on healthcare over a 15-day reference period, than controls and additional expenditures (per member) incurred on inpatient care by cancer-affected households annually is equivalent to 36% to 44% of annual household expenditures of matched controls. Members without cancer in cancer-affected households used less health-care and spent less on healthcare. Overall, adult workforce participation rates were lower by between 2.4 and 3.2 percentage points compared to controls; whereas workforce participation rates among adult members without cancer were higher than in control households. Cancer-affected households also had significantly higher rates of borrowing and asset sales for financing outpatient care that were 3.3% to 4.0% higher compared to control households; and even higher for inpatient care.