Blood Glucose Test Strip Quantity-Limit Policy and Patient Outcomes (2024)

Key Points

Question Did Ontario’s blood glucose test strip (BGTS) quantity-limit policy have any impacts on patient outcomes?

Findings In this population-based observational study, the introduction of reimbursement limits for BGTS had no impact on rates of emergency department visits for hypoglycemia or hyperglycemia or mean hemoglobin A1c levels.

Meaning Other jurisdictions may want to consider introducing BGTS quantity limits because they result in considerable savings, with no indication of patient harm.

Importance Given their high costs, payers have considered implementing quantity limits for reimbursement of blood glucose test strips. The effect of these limits on patient outcomes is unknown.

Objective To determine whether the introduction of quantity limits for blood glucose test strips in August 2013 was associated with changes in clinical outcomes.

Design, Setting, and Participants Cross-sectional time series analysis from April 2008 to March 2015 of residents of Ontario, Canada, aged 19 years and older with diabetes who were eligible for public drug coverage. In a sensitivity analysis, we studied high-volume users of test strips, who were most likely to be affected by the quantity limits.

Exposures Eligible patients were stratified into 4 mutually exclusive groups based on diabetes therapy: insulin, hypoglycemia-inducing oral diabetes agents, nonhypoglycemia-inducing oral diabetes agents, and no drug therapy.

Main Outcomes and Measures The primary outcome was emergency department visits for hypoglycemia or hyperglycemia, and the secondary outcome was mean hemoglobin A1c (HbA1c) levels. Outcomes were measured for all patients in each quarter, stratified by age group (<65 vs ≥65 years) and diabetes therapy.

Results By the end of the study period, 834 309 people met inclusion criteria. Among those younger than 65 years, the rate of hypoglycemia and hyperglycemia declined over the study period (from 4.9 to 3.0 visits per 1000 Ontario drug benefit [ODB]-eligible patients and from 4.2 to 3.6 visits per 1000 ODB-eligible patients, respectively) and was not significantly associated with the introduction of quantity limits (P = .67 and P  = .37, respectively). Similarly, among those aged 65 years and older, rates of hypoglycemia and hyperglycemia declined over the study period (from 2.9 to 1.3 visits per 1000 eligible patients and from 0.8 to 0.5 visits per 1000 eligible patients, respectively) and was not significantly associated with the introduction of quantity limits (P = .12 and P = .24, respectively). Results were consistent for the secondary outcome of mean HbA1c levels and in the sensitivity analysis of high-volume test strip users.

Conclusions and Relevance The imposition of quantity limits for blood glucose test strips was not associated with worsening short-term outcomes, suggesting that these policies can reduce costs associated with test strips without causing patient harm.

Although self-monitoring of blood glucose is recommended practice among patients with diabetes who treat their disease with insulin, recent clinical trials, systematic reviews and meta-analyses have suggested that there is little clinical benefit of regular blood glucose testing among noninsulin treated patients with diabetes.^1-4 These findings have important implications for policy makers who reimburse blood glucose test strips (BGTS), particularly given their costs. In Canada, more than $250 million Canadian dollars was spent on BGTS by public drug programs in 2006,⁵ and more than $1.3 billion US dollars was spent on BGTS among US Medicare beneficiaries in 2012.⁶ Furthermore, BGTS were the second most costly product for the public drug program in Ontario in fiscal year 2012 to 2013, costing the government $139 million Canadian dollars.⁷

Some insurers have considered introducing quantity limit policies in an attempt to control ballooning costs for diabetes treatment.^8-11 Following the release of a guidance document by the Canadian Diabetes Association in 2013, the Ontario public drug program introduced such a policy, restricting annual reimbursement for BGTS to 400 strips for those receiving drugs known to cause hypoglycemia, and 200 strips for all other patients.⁸ A limit of 3000 strips was also introduced among people using insulin, which was designed to introduce little restriction for this patient population. An evaluation of this policy found that annual costs associated with BGTS reimbursement fell by more than 20% in the year following implementation (from $107 million to $83 million Canadian dollars),¹² underscoring the financial appeal of this policy. Furthermore, this study demonstrated that less than 0.3% of all insulin users exceeded 3000 strips both before and after the policy was introduced, suggesting that the quantity limit policy’s impact was largely concentrated among noninsulin treated patients with diabetes.

Despite the demonstrable cost savings associated with quantity limit policies for BGTS, these policies have been the subject of considerable debate. Some have argued that limiting patients’ ability to monitor blood glucose would lead to poor glycemic control and worsening clinical outcomes, while others suggest that these quantity limits are an opportunity to help prevent overuse of a costly intervention offering limited clinical benefit.^13-17 Given the growing adoption of quantity limit policies for BGTS across North America,^9-11 evidence is needed regarding their impact on patient outcomes. Herein we report the impact of a BGTS quantity limit policy on patient outcomes among adults with diabetes in Ontario, Canada.

We conducted a population-based, cross-sectional time series analysis of all patients aged 19 years and older with diabetes who were eligible for public drug coverage between April 1, 2008 and March 31, 2015 in Ontario, Canada. This study was approved by the research ethics board of Sunnybrook Health Sciences Centre, Toronto.

In each quarter (January-March, April-June, July-September, and October-December) of the 7-year study period, we identified all patients aged 19 years and older who were eligible for public drug benefits and diagnosed with diabetes prior to the start of the quarter. Patients are eligible for public drug coverage in Ontario if they receive social assistance, disability support or home care services, are aged 65 years or older, or reside in a government-sponsored long-term care home. Therefore, among those aged 19 years to 65 years, eligibility was determined on the basis of receipt of any drug prescription dispensed from a community-based pharmacy in the 181 to 365 days prior to the beginning of each study quarter.

All patients meeting the inclusion criteria were stratified into 1 of 4 mutually exclusive categories based on their diabetes drug therapy in the 1 year prior to the end of the quarter. Diabetes therapy groups were assigned hierarchically as patients treated with: (1) insulin (regardless of other diabetes therapy received), (2) oral hypoglycemic agents (OHAs) known to induce hypoglycemia (sulfonylureas, repaglinide), (3) oral diabetes drugs not generally associated with hypoglycemia (metformin, acarbose, thiazolidinediones), and (4) no drug therapy.

We used the Ontario Diabetes Database, a validated administrative data registry with a sensitivity of 86% and a specificity of 97%, to identify all patients with a diagnosis of diabetes¹⁸ and the Ontario Drug Benefit (ODB) database to identify all diabetes medications and BGTS dispensed to eligible patients. We used the Ontario Health Insurance Plan (OHIP) Registered Persons Database to determine patient demographics and vital status, and the Canadian Institute for Health Information’s National Ambulatory Care Reporting System to capture diagnostic information from all emergency department (ED) visits in Ontario over the study period. Finally, the Dynacare Medical Laboratories (DML)-CArdiovascular HEalth in Ambulatory care Research Team (CANHEART) database was used to capture all HbA1c test results for a subgroup of patients tested at DML.¹⁹ Dynacare Medical Laboratories is an outpatient, community laboratory that provides testing for approximately one-third of the Ontario population. These data sets were linked using unique, encoded identifiers, were analyzed at the Institute for Clinical Evaluative Sciences (ICES, http://www.ices.on.ca), and are used regularly to assess the impact of policies in the Ontario health care system.^20-22

The primary outcomes of interest were ED visits for hypoglycemia (International Classification of Disease, 10th Revision [ICD-10] codes E15, E16.0, E16.1, E16.2, E10.63, E11.63, E13.63, E14.63) or hyperglycemia (ICD-10 codes E10.0, E10.1, E11.0, E11.1, E13.0, E13.1, E14.0, E14.1, E13.68, E14.68, R73.9), excluding suspected diagnoses and planned hospital visits.²³ We also included a secondary outcome of mean HbA1c levels. Owing to incomplete ascertainment of laboratory tests for all Ontarians, mean HbA1c levels were calculated only among those patients in our cohort who had at least 1 HbA1c test result recorded at DML in a given quarter. All outcomes were ascertained quarterly over the study period, and were stratified by age and diabetes therapy group.

We used interventional time series autoregressive integrated moving average (ARIMA) models to examine the impact of Ontario’s quantity limit policy (August 2013) on each of our outcomes. We used a ramp function to assess the effect of the policy in the models and the augmented Dickey-Fuller test to assess stationarity. We examined the residual autocorrelation, partial autocorrelation, and inverse autocorrelation correlograms for model parameter selection and appropriateness, and assessed remaining residual autocorrelation using the Ljung-Box χ² test to ensure model fit. All analyses were stratified by age (<65 and ≥65 years) and were performed at the Institute for Clinical Evaluative Sciences (http://www.ices.on.ca) using SAS statistical software (SAS version 9.3 and SAS EG 6.1; SAS Institute) and a type 1 error rate of 0.05 as the threshold for statistical significance.

We conducted a sensitivity analysis, restricting our cohort to a subgroup of high-volume BGTS users who were most likely to be affected by the introduction of quantity limits. In this analysis, we determined the BGTS dispensing history for all members of the primary cohort in the year prior to the introduction of the quantity limit policy (between July 2012 and June 2013). The BGTS dispensed in the month immediately preceding policy implementation (July 2013) were not included owing to anomalous dispensing patterns observed at that time, suggestive of hoarding test strips in anticipation of the policy change.¹² Patients were included in the high-volume sensitivity analysis if their total BGTS use over the year exceeded 400 strips (if receiving hypoglycemia-inducing OHAs) or 200 strips (if receiving nonhypoglycemia-inducing OHAs or no drug therapy). Because only a small number of patients treated with insulin exceeded the 3000 strip quantity limit, insulin-treated patients were not included in this sensitivity analysis.

Over the 7-year study period, the number of people aged 19 years and older who had diabetes and were eligible for drug coverage rose 51.0% from 552 523 to 834 309 patients. In the last quarter of the study period (Q1 2015), the average age of eligible patients was 72.3 years (standard deviation 11.7), 707 397 (84.7%) of the 834 309 patients were aged 65 years or older, and 420 751 (50.4%) were men.

In our primary analysis, we found that rates of ED visits for hypoglycemia fell consistently for both age groups over the study period, decreasing by 38.8% among those younger than 65 years (from 4.9 to 3.0 visits per 1000 ODB-eligible) and by 55.2% among those aged 65 years and older (from 2.9 to 1.3 visits per 1000 ODB-eligible). Similarly, rates of ED visits for hyperglycemia decreased in both age groups over this period (14.3% reduction, from 4.2 to 3.6 visits per 1000 ODB-eligible, and 37.5% reduction, from 0.8 to 0.5 visits per 1000 ODB-eligible among younger and older patients with diabetes, respectively). Despite an overall trend toward lower rates over our study period, the introduction of BGTS quantity limits led to no significant change in rates of ED visits for hypoglycemia (P for ramp intervention function in August 2013 = .67 for patients aged <65 years and P = .12 for patients aged ≥65 years) or hyperglycemia (P = .37 and P = .24 for patients aged <65 years and ≥65 years, respectively; Figure 1).

In our secondary analysis among a subcohort of 83 347 individuals with laboratory data available, after a slight rise from 7.2 in Q2 2008 to 7.5 in Q2 2009, mean HbA1c levels remained stable (range from 7.4 to 7.7) over the remainder of the study period among individuals younger than 65 years and was not impacted by the BGTS policy (P = .80) (Figure 2). We observed a similar pattern among those aged 65 years and older, with a slight rise in mean HbA1c levels from 6.7 to 7.0 from Q2 2008 to Q2 2009 after which HbA1c levels stabilized and ranged between 7.0 and 7.2 over the remainder of the study period. There was no evidence of an impact of the BGTS policy on this trend (P= .97; Figure 2). When stratified by diabetes therapy group, rates of hypoglycemia and hyperglycemia were higher among insulin treated patients and lowest among those treated with nonhypoglycemia-inducing OHAs and those receiving no drug therapy (Figure 3 and Figure 4). All outcomes were stable over the study period after stratifying by diabetes therapy group (Figure 3 and Figure 4) (see eFigure in the Supplement).

In our sensitivity analysis among 140 118 high-volume BGTS users, the average age of included patients was 73.6 years and 47.9% were men. In this cohort, the rates of ED visits for hypoglycemia, hyperglycemia, and mean HbA1c levels were considerably lower compared with our primary analysis (Figure 2 and Figure 5). Similar to the primary analysis, in the 18 months following the policy’s introduction, we found no significant change in any of the outcomes measured (P > .05 for all models) (see eTable in the Supplement for details of all models).

In this population-based study spanning 7 years, we found that the introduction of BGTS quantity limits had no immediate impact on ED visits for hyperglycemia or hypoglycemia, or on HbA1c levels. Furthermore, these findings were consistent when restricting our cohort to patients who were dispensed a large number of BGTS prior to the policy’s implementation and who therefore are most likely to have been influenced by the quantity limits.

Over the past several years, systematic reviews and economic analyses have questioned the utility of routine use of BGTS among noninsulin treated patients with type 2 diabetes, and suggest that this practice may not be cost-effective.^1-4,24 Despite this evidence, clinical practice guidelines have been slow to recommend specific optimal testing frequencies in this population, and have instead recommended that testing frequency be individualized based on factors such as diabetes agents used, level of glycemic control, and history of hypoglycemia.^25,26 Therefore, policy-makers tasked with providing access to evidence-based diabetes agents within constrained budgets have had little direction as to which reimbursement limits may be suitable to reduce inappropriate testing while ensuring that patient safety is maintained. These findings provide policy-makers and clinicians with valuable information regarding the impact of a specific quantity limit policy on patient outcomes. In particular, this suggests that the reimbursement limits Ontario introduced in 2013 that resulted in considerable cost savings and aligned with guidance from the Canadian Diabetes Association may be appropriate for other jurisdictions seeking to promote use of BGTS in a manner that does not jeopardize patient safety.

Strengths of our study include its large, population-based design, and the linkage of drug reimbursement data with health service and laboratory outcomes. However, several limitations merit discussion. First, we assessed outcomes for only 1.5 years following implementation of the policy. Thus, we can draw no inferences about long-term consequences. However, outcome rates observed in this analysis were relatively stable, and we found no indication of worsening patient outcomes in the latter quarters of our study period. This is reassuring because it suggests no immediate impact of the policy on the outcomes we measured, however, longer-term studies are needed. Second, our analysis is restricted to BGTS reimbursed through the public drug program, and we do not know whether patients who reached their quantity limits acquired additional test strips through other means. Third, because our analysis is restricted to seniors and social assistance recipients, the findings may not be generalizable to other populations. However, given that Ontario’s public drug plan covers the entire population aged 65 years and older, it is likely that our findings in this age group are more widely generalizable. Finally, while our findings suggest no impact of the quantity limit policy among all patients with diabetes, a subset of high-volume users, and by diabetes therapy group, we cannot preclude the possibility that there is another small subset of the population that may have been negatively impacted by restricted access to these strips, or that the policy had impacts on other outcomes that we cannot measure such as quality of life.

In this large, population-based policy evaluation, we found no indication of short-term worsening in rates of hypoglycemia, hyperglycemia, or changes in mean HbA1c levels after implementation of a quantity limit policy for BGTS that focused restrictions on noninsulin treated patients. This suggests that these quantity limits represent an important opportunity for policy makers to achieve considerable cost savings without introducing patient harm.

Corresponding Author: Tara Gomes, MHSc, Li Ka Shing Knowledge Institute, St Michael's Hospital, 30 Bond St, Toronto, Ontario M5B1W8, Canada (gomest@smh.ca).

Acceptance Date: September 17, 2016.

Correction: This article was corrected on July 24, 2017, to correct title errors in Figure 3 and Figure 4.

Published Online: November 7, 2016. doi:10.1001/jamainternmed.2016.6851

Author Contributions: Ms Martins had full access to all the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis.

Concept and design: Gomes, Martins, Tadrous, Paterson, Juurlink, Mamdani.

Acquisition, analysis, or interpretation of data: Gomes, Martins, Tadrous, Paterson, Shah, Tu, Chu, Mamdani.

Drafting of the manuscript: Gomes, Tadrous.

Critical revision of the manuscript for important intellectual content: Martins, Tadrous, Paterson, Shah, Tu, Juurlink, Chu, Mamdani.

Statistical analysis: Gomes, Martins, Juurlink, Chu.

Obtained funding: Gomes.

Administrative, technical, or material support: Gomes, Tu.

No additional contributions: Tadrous, Paterson, Shah.

Conflict of Interest Disclosures: Dr Muhammad Mamdani has received honoraria from Boehringer Ingelheim, Pfizer, Sanofi, Bristol-Myers Squibb, Astra-Zeneca, GlaxoSmithKline, Novo-Nordisk, Eli Lilly, Merck, and Bayer. No other disclosures are reported.

Funding/Support: This study was funded by a grant from the Ontario Ministry of Health and Long-Term Care (MOHLTC) Health System Research Fund. This study was also supported by the Institute for Clinical Evaluative Sciences (ICES), which is funded by an annual grant from the Ontario MOHLTC, and by operating grants from the Institute for Circulatory and Respiratory Health-Canadian Institutes of Health Research Chronic Diseases Team.

Role of the Funder/Sponsor: The funders had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; or decision to submit the manuscript for publication.

Disclaimer: The opinions, results, and conclusions reported in this paper are those of the authors and are independent from the funding sources. No endorsement by ICES or the Ontario MOHLTC is intended or should be inferred. Parts of this material are based on data and information compiled and provided by CIHI. However, the analyses, conclusions, opinions and statements expressed herein are those of the author, and not necessarily those of CIHI.

Additional Contributions: We thank IMS Brogan Inc for use of their Drug Information Database, and Gamma-Dynacare Medical Laboratories for providing access to the laboratory data. They were not compensated for their contribution.