Discussion
Observed utilization rates in 2014 in Switzerland of four strongly recommended measures in diabetes management were 69.6% for biannual HbA1c testing, 44.3% for annual eye examination, 44.3% for annual kidney function examination, and 55.5% for annual LDL testing (in patients below 75 years). Associations between health insurance preferences and utilization were consistent across the four measures. Having supplementary insurance, choosing a lower deductible level, and choosing a managed care insurance model were positively associated with being adherent to the recommendations. After adjusting for all available influencing factors and spatial autocorrelation, the unexplained regional variation was only moderate. There was no common pattern of spatial clustering visible across the four studied measures.
The observed utilization rates suggest that the underlying recommendations were not being followed perfectly. In a previous study using year 2011 to 2013 data from the same data source, similar utilization rates were reported: 70.0% of patients had biannual HbA1c testing, 44.2% an annual eye examination, 12% both serum creatinine and albuminuria testing annually, and 59.0% an annual lipid profile (total cholesterol, HDL and LDL, and triglycerides).13 The much higher rate of annual kidney function examination found in our study was mainly due to the use of a different definition of kidney function examination—a serum creatinine and/or albuminuria test.
Overall, few studies assessing the utilization of management measures recommended for diabetes patients exist, and some with discrepant findings. Some studies from the USA, Japan and Italy are directly comparable with ours as they reported on the utilization of at least one of our four measures of interest. For HbA1c testing, one study conducted in Texas, USA, reported a 54.8% biannual utilization rate,36 while the utilization rate in an Italian study was relatively low (33.9%).37 By contrast, a study from Japan using claims data found an annual utilization rate of 95.8%.38 For eye examination, studies in the USA reported utilization rates of 15.3% (using claims data),39 70% (using telephone survey data),40 and 75% (data from rural Latinos).41 A Japanese study reported a utilization rate of 35.6%,38 and the rate in the Italian study was even lower (15.6%).37 The Italian study also reported a utilization rate of LDL testing of 52.1%, which was similar to the finding in our study.37 However, these different reports may not be entirely comparable with our study since the data sources and definitions of adherence to recommendations were different.
Patients’ sociodemographics were associated with healthcare utilization. The probability of undergoing the four recommended measures was generally high between age 50 and 80 years, and decreased strongly thereafter. This was expected because the elderly may have more barriers to accessing healthcare services due to poor health status. Moreover, the measures may become less important in the elderly as comorbidities and life expectancy affect priority setting and the benefit of preventing long-term complications. Women were more likely to undergo eye examination and kidney function examination in our study, which was consistent with previous findings.38 42 43 However, women were less likely to undergo annual LDL testing, which might be due to more attention to the risk of cardiovascular disease in men. Myocardial infarction and related conditions have traditionally been perceived as predominantly male diseases. Living in an urban area was positively associated with more utilization of annual kidney function examination and LDL testing, which may be partly explained by easier access to healthcare facilities than in rural areas. The language region effects on the utilization of the four measures found in the present study indicated that the language region plays an important role in influencing healthcare utilization, which might be due to different culture and norms in each language region.44 45
One of the key findings in the present study was the association between health insurance preferences and utilization of diabetes management measures, in a setting with mandatory insurance and universal access to care. Very few studies have explored the effect of health insurance–related factors on services utilization in patients with diabetes. Most of the available studies only concluded that uninsured patients were less likely to use healthcare services than insured patients or patients with private insurance.36 40 While non-insurance does practically not occur in Switzerland, foregoing healthcare due to out-of-pocket payments is a well-documented phenomenon.46 This is one of the first studies to look into potential influences of health insurance characteristics on utilization of measures on diabetes management in detail. Overall, we found consistent effects of health insurance characteristics on utilization across the four measures of interest, and they persisted after controlling for other important influences such as age, health status, and to some extent income (defined by regional purchasing power index). Patients with higher deductibles tend to be healthier and willing to take more risks, and some invoices may be missed in these patients, which may partially explain our observation of lower utilization of the measures of interest. However, higher out-of-pocket costs may also make patients more reluctant to use these measures, which would make high deductibles a financial barrier to recommended healthcare.47 Similarly, patients having supplementary insurance may be wealthier, and on average more health conscious. Thus, they may tend to seek care more frequently and regularly, as observed in our study. Patients choosing a managed care model had more utilization of the measures studied than patients choosing an insurance model offering completely free physician choice. This finding is of great interest because it may indicate that strengthening a coordinative role of primary care physicians in managed care and providing financial incentives to the insured for choosing such models may also positively impact certain healthcare utilization indicators or outcomes. More health insurance incentives for participation in managed care models could be considered to achieve optimized healthcare utilization.
Presence of comorbidities was associated with more utilization, which may be due to more health awareness and regular contact with healthcare providers. The finding of the lower uptake of LDL testing among prevalent cases was unexpected, as we would have expected prevalent patients to be more adherent to disease management and treatment compared with new patients.48 The ophthalmologist density covariate reflected the access to eye examination services, and thus partly explained the higher utilization of eye examinations in patients living in regions with more ophthalmologists.
The unexplained geographic variation of utilization across small regions after adjusting for all available factors was only moderate for all four measures. One possible reason could be that we were unable to control for locally specific factors in our models. For example, physician-level factors such as age, years in practice, and the awareness of and attitude toward clinical guidelines and recommendations vary across physicians and could affect the communication with patients and finally the patients’ behaviors.49 In addition, some patient-level characteristics were not captured in our data source, for example, educational level or marital status, as well as patients’ preferences, which were demonstrated to be potentially related to the utilization of healthcare services.40 By mapping out the unexplained spatial variation, we noted that the spatial patterns of regional variation were inconsistent across the four measures studied. These patterns indicated that the utilization of the four measures strongly recommended to patients with diabetes differ substantially within Switzerland. The spatial variation of utilization might be even less prominent after controlling for more potentially influential factors unmeasured in the present study, such as physician characteristics which could not be captured from the claims data. Combination of different data sources may serve as a promising approach in future studies.
In addition to the limitations mentioned above, further potential weaknesses should be noted about this study. First, the health insurance claims data have limited clinical information; for example, outpatient diagnoses are lacking. The study population was selected according to the prescription of any diabetes medication, which may have led to some misclassification of prevalent and incident cases; this might partially account for the unexpected finding of more utilization of LDL testing in incident patients. It was impossible to distinguish between type 1 and type 2 diabetes. Patients with type 1 diabetes are a small fraction (approximately 8% in Switzerland in 201450); they normally get the illness when they are young, tend to be well treated by specialists, and are generally better at self-management. Due to the high costs of insulin injections and the associated medical supplies and devices, choosing a low deductible level is expected in patients with type 1 diabetes. Such different behaviors may have had an impact on utilization of the four measures and influenced our results to a certain degree. Besides, the laboratory test results were not available from claims data and it was impossible to estimate the proportion of targets achieved for the diabetes management measures. Second, we used claims data from a single health insurer. Enrollees of other health insurers might theoretically have different characteristics and show different healthcare use patterns. However, the results presented were based on a population of 1.2 million covering all regions in Switzerland. The benefit package of the mandatory health insurance is defined at the federal level and is the same for all health insurers. Thus, we expect little deviation of enrollees’ features compared with the total Swiss population, and the results should be generalizable to the whole of Switzerland.
In conclusion, we observed that the utilization of four diabetes management measures was not optimal in Switzerland although these measures have been recommended broadly and are based on strong evidence. Sociodemographics, health insurance preferences, and clinical characteristics were associated with their utilization. The presence of supplementary insurance, a lower deductible level, and participation in a managed care plan were associated with higher utilization, consistently across the four measures. After controlling for available factors and spatial autocorrelation, maps of remaining variation indicated inconsistent patterns of utilization in the four measures. Our findings indicate that the uptake of strongly recommended measures for diabetes management could possibly be optimized by providing further incentives to insured and care providers through insurance scheme design. By contrast, due to the absence of marked regional variation patterns, we conclude that there may be only limited potential for improvement by targeted regional intervention (eg, awareness and promotion campaigns). Moreover, our novel approach aids in the identification of geographic variation and influencing factors of healthcare services use in Switzerland and comparable settings worldwide.