Income and economic productivity loss associated with comorbidity: longitudinal analysis of linked individual-level data for a whole country.

OBJECTIVE: To estimate the income loss from having two or more diseases, over and above the independent and separate effects of having a single disease. METHODS: We used linked health income data from 2006-2007 to 2015-2016 for 25-64 years, for the entire New Zealand population. Fixed effects OLS regression was used to estimate within-individual income loss for diseases separately, and to estimate if having two or more diseases together resulted in reduced (subadditive) or additional (superadditive) income impacts (relative to adding together the income impacts for each disease when experienced singly). RESULTS: Of the 169 comorbidity pairs for both sexes, 28 (17%) had a statistically significant superadditive (n=14) or subadditive (n=14) effect of having two diseases. The combined total income gain from deleting all diseases and comorbidities was US$2.269 billion (95% CI US$$2.125 to US$2.389 billion), or a 3.61% (95% CI 3.38% to 3.80%) increase in income. Of this, 8.8% or US$200 million (95% CI US$193 to US$207 million) was attributable to a tendency for comorbidity interactions to increase income loss more than expected for common disease pairings. CONCLUSIONS: This national longitudinal study found that disease is associated with income loss, but most of this impact is due to the distinct and independent impact of separate diseases. Nevertheless, there was a tendency for two or more diseases to disproportionately increase income loss more than the summed impacts of each of these diseases if experienced singly.

Disease-Related Loss to Government Funding: Longitudinal Analysis of Individual-Level Health and Tax Data for an Entire Country.

OBJECTIVES: The objective of this longitudinal analysis was to estimate funding loss in terms of tax revenue to the New Zealand (NZ) government from disease and injury among working age adults. METHODS: Linked national health and tax data sets of the usually resident population between 2006 and 2016 were used to model 40 disease states simultaneously in a fixed-effects regression analysis to estimate population-level tax loss from disease and injury. To estimate tax revenue loss to the NZ government, we modeled a counterfactual scenario where all disease/injury was cause deleted. RESULTS: The estimated tax paid by all 25- to 64-year-olds in the eligible NZ population was $15 773 million (m) per annum (US dollar 2021), or $16 446 m for a counterfactual as though no one had any disease disease-related income loss (a 4.3% or $672.9 m increase in tax revenue per annum). The disease that-if it had no impact on income-generated the greatest impact was mental illness, contributing 34.7% ($233.3 m) of all disease-related tax loss, followed by cardiovascular (14.7%, $99.0 m) and endocrine (10.2%, $68.8 m). Tax revenue gains after deleting all disease/injury increased up to 65 years of age, with the largest contributor occurring among 60- to 64-year-olds ($131.7 m). Varied results were also observed among different ethnicities and differing levels of deprivation. CONCLUSIONS: This study finds considerable variation by disease on worker productivity and therefore tax revenue in this high-income country. These findings strengthen the economic and government case for prevention, particularly the prevention of mental health conditions and cardiovascular disease.

Disease-related income and economic productivity loss in New Zealand: A longitudinal analysis of linked individual-level data.

BACKGROUND: Reducing disease can maintain personal individual income and improve societal economic productivity. However, estimates of income loss for multiple diseases simultaneously with thorough adjustment for confounding are lacking, to our knowledge. We estimate individual-level income loss for 40 conditions simultaneously by phase of diagnosis, and the total income loss at the population level (a function of how common the disease is and the individual-level income loss if one has the disease). METHODS AND FINDINGS: We used linked health tax data for New Zealand as a high-income country case study, from 2006 to 2007 to 2015 to 2016 for 25- to 64-year-olds (22.5 million person-years). Fixed effects regression was used to estimate within-individual income loss by disease, and cause-deletion methods to estimate economic productivity loss at the population level. Income loss in the year of diagnosis was highest for dementia for both men (US$8,882; 95% CI $6,709 to $11,056) and women ($7,103; $5,499 to $8,707). Mental illness also had high income losses in the year of diagnosis (average of about $5,300 per year for males and $4,100 per year for females, for 4 subcategories of: depression and anxiety; alcohol related; schizophrenia; and other). Similar patterns were evident for prevalent years of diagnosis. For the last year of life, cancers tended to have the highest income losses, (e.g., colorectal cancer males: $17,786, 95% CI $15,555 to $20,018; females: $14,192, $12,357 to $16,026). The combined annual income loss from all diseases among 25- to 64-year-olds was US$2.72 billion or 4.3% of total income. Diseases contributing more than 4% of total disease-related income loss were mental illness (30.0%), cardiovascular disease (15.6%), musculoskeletal (13.7%), endocrine (8.9%), gastrointestinal (7.4%), neurological (6.5%), and cancer (4.5%). The limitations of this study include residual biases that may overestimate the effect of disease on income loss, such as unmeasured time-varying confounding (e.g., divorce leading to both depression and income loss) and reverse causation (e.g., income loss leading to depression). Conversely, there may also be offsetting underestimation biases, such as income loss in the prodromal phase before diagnosis that is misclassified to "healthy" person time. CONCLUSIONS: In this longitudinal study, we found that income loss varies considerably by disease. Nevertheless, mental illness, cardiovascular, and musculoskeletal diseases stand out as likely major causes of economic productivity loss, suggesting that they should be prioritised in prevention programmes.

Statin adherence is lower in primary than secondary prevention: A national follow-up study of new users.

BACKGROUND: Maintaining adherence to statins reduces the risk of an initial cardiovascular disease (CVD) event in high-risk individuals (primary prevention) and additional CVD events following the first event (secondary prevention). The effectiveness of statin therapy is limited by the level of adherence maintained by the patient. We undertook a nationwide study to compare adherence and discontinuation in primary and secondary prevention patients. METHODS: Dispensing data from New Zealand community pharmacies were used to identify patients who received their first statin dispensing between 2006 and 2011. The Medication Possession Ratio (MPR) and proportion who discontinued statin medication was calculated for the year following first statin dispensing for patients with a minimum of two dispensings. Adherence was defined as an MPR ≥ 0.8. Previous CVD was identified using hospital discharge records. Multivariable logistic regression was used to control for demographic and statin characteristics. RESULTS: Between 2006 and 2011 289,666 new statin users were identified with 238,855 (82.5%) receiving the statin for primary prevention compared to 50,811 (17.5%) who received it for secondary prevention. The secondary prevention group was 1.55 (95% CI 1.51-1.59) times as likely to be adherent and 0.67 (95% CI 0.65-0.69) times as likely to discontinue statin treatment than the primary prevention group. An early gap in statin coverage increased the odds of discontinuing statin treatment. CONCLUSION: Adherence to statin medication is higher in secondary prevention than primary prevention. Within each group, a range of demographic and treatment factors further influences adherence.