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Costs of people with diabetes in relation to average glucose control: an empirical approach controlling for year of onset cohorts

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Abstract

Objective

To estimate the impact of glycaemic control and time since diabetes diagnosis on care costs incurred by people with type 2 diabetes mellitus (T2DM).

Research design and methods

Random-effects linear regression models were run to test the impact of average glucose control (HbA1c) and time since diabetes diagnosis on total care spending in people with T2DM, adjusting for year of onset and other covariates. Two datasets were linked, Vektis (healthcare costs reimbursed by the Dutch mandatory health insurance) and Zodiac (clinical and sociodemographic data). The sample includes 22,612 observations, grouped in 5653 individuals from the Northern part of the Netherlands, covering 4 years (2008–2011).

Results

A 1% point increase in HbA1c is associated with a 2.2% higher total care costs. However, when treatment modality is included, the results are modified. A 1% point increase (11 mol/mol) in HbA1c is significantly associated with 3.4% higher total care costs for individuals without glucose-lowering treatment. Being treated with insulin is significantly associated with an increase in costs of 30–38% for every additional percentage point of HbA1c, depending on the covariates included. Without controlling for year of onset, an additional year of diabetes duration relates to 2.6% higher care costs, while this is 4.9% controlling for year of onset. The effect of HbA1c and diabetes duration differs between types of costs.

Conclusion

HbA1c, insulin treatment and diabetes duration are the main drivers of increasing care costs. The results signal the relevance of controlling for HbA1c together with treatment modality, diabetes duration and year of diagnosis effects.

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Notes

  1. Individuals were classified as having an excellent glucose control if HbA1c level was below or equal to 7% in, at least, 80% of the available measurements during the follow-up period.

  2. The privacy of all patients was assured using a trusted third party (ZorgTTP) to combine the data, while encrypting the personal identity numbers, thus creating a database with detailed information, which could not be traced to a known individual. The researchers performed all analyses at VEKTIS, on a fully anonymous dataset.

  3. Costs that were not included were uninsured care (i.e. informal care), as well as care from any additional insurance (treatments not or only partly covered by the mandatory package; for instance, dental care for people aged 18 years old and above, alternative medical treatments (homoeopathy), or hearing aids).

  4. http://statline.cbs.nl/StatWeb/publication/?VW=T&DM=SLEN&PA=83131ENG&LA=EN.

  5. Diabetes non-related conditions refer to cancer, alcohol abuse, paralysis, chronic pulmonary disease, hypothyroidism, liver disease, peptic ulcer, rheumatoid arthritis, anaemia, psychosis and depression.

  6. Congestive heart failure, acute myocardial infarction, peripheral vascular disease, uncomplicated hypertension, complicated hypertension, stroke, any other cerebrovascular accident, dementia, retinopathy, neuropathy, renal failure, weight loss, obesity were considered the diabetes-related comorbidities.

  7. We also performed the same regressions including Mundlak terms [28] to relax the assumption that observed time-varying variables are uncorrelated with the unobserved variables by adding individual-means of independent variables that do vary over time within individuals. However, the Mundlak terms included were not significant, and the coefficients were quite similar to the ones obtained in the Random-effects linear regression models. Hence, results with Mundlak terms can be found in Table A2 in Online Appendix.

  8. Table A3, Online Appendix, reports the summary statistics for the original sample, before applying the sample selection criteria. There are no big differences when compared to the final sample.

  9. According to the American Diabetes Association guidelines [23], HbA1c levels are at target level if they are equal or below 6.5%. Hence, three categories of HbA1c levels are set according to HbA1c levels: (1) HbA1c ≤ 6.5%; (2) 6.5% < HbA1c ≤ 7.5%; and (3) HbA1c > 7.5%, which indicates uncontrolled diabetes.

  10. A coefficient will be considered statistically significant if it is significant, at least, at 95% confidence level (p value = 0.05).

  11. The average marginal effect and p value of age (and diabetes duration) were calculated using the command lincom from Stata 14.0, using the regression coefficients for age and age squared (diabetes duration and its square) as well as the mean age (diabetes duration) for the overall sample. For example, the average marginal effect of an additional year of age is calculated as follows: coefficient of age + 2 × coefficient of age2 × (mean age) = 0.0174262 + 2 × 0.0000266 × 68.38 = 0.0210593 (z value = 21.68, p value = 0.000).

  12. The coefficient and p value of variables part of interactions in the regression model were obtained using the command lincom from Stata 14.0. For example, the coefficient of insulin is calculated as follows: coefficient of insulin alone + coefficient of the interaction HbA1c # insulin × mean HbA1c = 0.658 − 0.0498 × 6.77. Once we obtained this coefficient and given the logarithmic form of our regression model, we calculated the insulin coefficient as follows: \((e^{\text{coef}} - 1) \times 100 = (e^{0.3208} - 1) \times 100 = 37.83.\). The second part of the calculation was followed for every dummy variable included in the main analysis (treatment modalities and diabetes onset cohorts) and in the sensitivity analysis (Elixhauser comorbidities).

  13. It has already been found in the literature that the incremental costs in the first years after diabetes diagnosis decrease [31] and then start increasing again. In our data, GP costs start to rise after 37 years since diabetes diagnosis.

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Acknowledgements

The authors would like to thank Chantal van Tilburg and Mirte van Galen for their help with the data, as well as Viola Angelini for her contribution and advice when revising the manuscript. We would also like to thank the anonymous reviewers for their useful comments, which have substantially improved the manuscript.

Funding

This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.

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Authors and Affiliations

  1. Department of Economic Analysis and Finance, Faculty of Law and Social Sciences, University of Castilla la Mancha, Cobertizo de San Pedro Mártir s/n, 45071, Toledo, Spain

    Beatriz Rodríguez-Sánchez

  2. Department of Epidemiology, University Medical Centrum of Groningen, Groningen, The Netherlands

    Talitha L. Feenstra

  3. Department of Internal Medicine-Management, University Medical Centrum of Groningen, Groningen, The Netherlands

    Henk J. G. Bilo

  4. Department of Economics, Econometrics and Finance, University of Groningen, Groningen, The Netherlands

    Rob. J. M. Alessie

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  1. Beatriz Rodríguez-Sánchez
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Rodríguez-Sánchez, B., Feenstra, T.L., Bilo, H.J.G. et al. Costs of people with diabetes in relation to average glucose control: an empirical approach controlling for year of onset cohorts. Eur J Health Econ 20, 989–1000 (2019). https://doi.org/10.1007/s10198-019-01072-z

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