Abstract
Objectives
Diabetes requires ongoing monitoring and care to prevent long-term adverse health outcomes. In Canada, quarantine restrictions were put into place to address the coronavirus-2019 (COVID-19) pandemic in March 2020. Primary care diabetes clinics limited their in-person services and were advised to manage type 2 diabetes (T2D) through virtual visits and reduce the frequency of routine diabetes-related lab tests and screening.
Methods
This retrospective cross-sectional study used de-identified patient records from a primary care electronic medical records database in Ontario, Canada, to identify people with T2D who had at least 1 health-care touchpoint between March 1, 2018, and February 28, 2021. Outcomes were described on a monthly or yearly basis: 1) number of people with primary care visits (in-person vs virtual); 2) number of people with referrals; 3) number of people with each of the vital/lab measures; and 4) results of the vital/lab measures.
Results
A total of 16,845 individuals with T2D were included. Compared with the pre-pandemic period, the COVID-19 period had a 16.8% reduction in the T2D population utilizing any primary care and an increase of 330.4% in the number of people with at least 1 virtual visit. Compared with the pre-pandemic period, fewer people had vital/lab measures in the pandemic period. However, among the people with the test results available, the average values for all tests were similar in the pre- and pandemic periods.
Conclusion
Further research is needed to understand the impact of the reduction of in-person clinical care on the entire population with T2D.
Keywords: Canada, COVID-19, diabetes, EMR, treatment
Résumé
Objectifs
Le diabète exige un suivi et des soins continus pour éviter les résultats cliniques indésirables à long terme. En mars 2020, le gouvernement du Canada a mis en place des mesures de quarantaine pour faire face à la pandémie de maladie à coronavirus 2019 (COVID-19). Les cliniques de soins primaires spécialisées en diabète ont restreint leurs services en personne et ont été invitées à offrir des consultations virtuelles pour prendre en charge les personnes diabétiques de type 2 (DT2) et à réduire la fréquence du dépistage du diabète et des examens de laboratoire systématiques.
Méthodes
La présente étude transversale rétrospective reposait sur les dossiers patients dénominalisés issus de la base de données des dossiers médicaux électroniques (DME) sur les soins primaires de l’Ontario, au Canada, pour recenser les personnes DT2 qui avaient eu recours au moins à 1 point de contact de soins de santé entre le 1er mars 2018 et le 28 février 2021. Les résultats cliniques ont été décrits sur une base mensuelle ou annuelle : 1) le nombre de personnes ayant eu des consultations en soins primaires (en personne vs virtuelles); 2) le nombre de personnes ayant obtenu des aiguillages; 3) le nombre de personnes ayant obtenu des mesures des signes vitaux et des examens de laboratoire; 4) les résultats des mesures des signes vitaux et des examens de laboratoire.
Résultats
Un total de 16 845 personnes DT2 ont été retenues. Comparativement à la période prépandémique, la période de COVID-19 a montré une réduction de 16,8 % de la population DT2 qui avait eu recours aux soins primaires et une augmentation de 330,4 % du nombre de personnes qui avaient eu au moins 1 consultation virtuelle. Comparativement à la période prépandémique, un moins grand nombre de personnes avaient eu des mesures des signes vitaux et des examens de laboratoire durant la période pandémique. Toutefois, parmi les personnes dont les résultats d’examens étaient disponibles, les valeurs moyennes de tous les examens réalisés durant les périodes prépandémique et pandémique étaient similaires.
Conclusion
Des recherches subséquentes sont nécessaires pour comprendre les répercussions de la réduction des soins cliniques en personne sur l’ensemble de la population DT2.
Mots clés: Canada, COVID-19, diabète, DME, traitement
Key Messages.
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The COVID-19 pandemic has had an impact on diabetes care in the United States, Canada, and the United Kingdom, including on diagnostics and ongoing treatment.
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Our study corroborates previous studies and adds to the growing literature on the impact of the pandemic restrictions on routine diabetes care.
Introduction
Diabetes affects about 8% of Canadians and requires ongoing monitoring and care to prevent long-term adverse health outcomes (1). In Canada, quarantine restrictions were put into place to address the coronavirus-2019 (COVID-19) pandemic beginning in March 2020 (2). As a result of the pandemic, many primary care and outpatient diabetes clinics limited their in-person services to only the most urgent cases and addressed other cases virtually due to COVID-19–related restrictions and the overburdened health-care system (3). In response, a guide for primary care providers to manage type 2 diabetes (T2D) during COVID-19 was created with an emphasis on delivering diabetes care and support in Canada through virtual visits wherever possible and reducing the frequency of routine diabetes-related lab tests and screening (4).
The impact of the COVID-19 pandemic on diabetes management in the primary care setting has been assessed in several retrospective observational studies (5, 6, 7, 8, 9). In Canada, a study of the population of Ontario showed a reduction in in-person visits by persons with diabetes to their family physicians from 78% to 37% during the pandemic period (10). There were also statistically significant decreases in procedures or tests for people with diabetes, including eye exams, glycated hemoglobin (A1C), and low-density lipoprotein cholesterol (LDL-C) tests. Similarly, a study conducted in the United States (US) showed that, during February and March 2020, A1C and LDL-C testing dropped by 81% to 90%, and new prescriptions of metformin and statins decreased by 52% to 60% when compared with previous months (7). Another US study demonstrated decreased A1C testing during the early pandemic but stable medication fill rates over time. The same study revealed that 29.1% of its 2020 diabetes cohort had at least 1 telemedicine visit, compared with 0.3% in its 2019 cohort (8). In addition, a study using United Kingdom (UK) general practice data reported a 32% reduction in the observed T2D diagnosis rate between March 1 and December 10, 2020, compared with the expected rate, whereas the observed mortality rate in patients with T2D increased by 19% (10). In addition, a dramatic drop in A1C testing was noted in April 2020 (6).
There have been few studies investigating the impact of the COVID-19 pandemic on T2D management in a primary care setting in Canada. In this study, we aimed to describe the impact of the COVID-19 pandemic on T2D care in a primary care network with existing virtual care capabilities in Ontario, Canada.
Methods
Study design and data source
This was a retrospective, cross-sectional study using de-identified patient records extracted from a primary care electronic medical records (EMRs) database. Primary care data for more than 1 million unique persons since 2010 were sourced from a network of clinics across major cities and rural regions in Ontario, Canada’s largest province. The EMR database captures patient-level clinical information over time, including demographics, visits, diagnoses, referrals, Ontario Health Insurance Plan billing, vitals, lab tests, and prescriptions. Before the pandemic, this primary care network had already established some level of virtual care capabilities, such as scheduling and telemedicine technology.
People with T2D who had at least 1 health-care touchpoint between March 1, 2018, and February 28, 2021, were analyzed annually and monthly for the following periods: baseline period from March 1, 2018, to February 28, 2019; pre–COVID-19 period from March 1, 2019, to February 29, 2020; and COVID-19 period from March 1, 2020, to February 28, 2021.
Study population and health-care touchpoints
People with T2D were eligible for this analysis if 1) they had at least 1 prescription for a noninsulin antihyperglycemic medication in their prescription history in the EMR database; 2) they were ≥18 years old at the start of the analysis period (March 1, 2018); and 3) they did not have a pregnancy-related diagnosis during the analysis period (March 1, 2018, to February 28, 2021).
Within the selected T2D population, the following health-care touchpoints were analyzed if they occurred at or after the first noninsulin antihyperglycemic prescription date minus 90 days, and between March 1, 2018, and February 28, 2021: 1) visits (in-person or virtual was determined by Ontario Health Insurance Plan billing codes); 2) referrals with the physician specialty to which the patient was referred; 3) vital measures, including body mass index (BMI) and blood pressure (BP); and 4) lab tests, including A1C, glucose test (all types of plasma glucose tests, e.g. fasting or random), LDL-C, urine albumin-to-creatinine ratio (UACr), and estimated glomerular filtration rate (eGFR).
Study outcomes
The following outcomes were described by aggregating the health-care touchpoints on a monthly or yearly basis: 1) number of people with primary care visits, total and by visit type (in-person vs virtual); 2) number of people with referrals (total and by referral specialty); 3) number of people with each of the vital/lab measures (BMI, BP, A1C, glucose test, LDL-C, UACr, eGFR); and 4) results of the vital/lab measures (BMI, BP, A1C, LDL-C, UACr, eGFR).
Data analysis
Descriptive statistics were used throughout the analysis. Continuous variables are presented as mean with standard deviation and as median with interquartile range. Categorical variables were presented as frequencies with percentages.
SAS version 9.4 (SAS Institute, Cary, North Carolina, United States) was used for data extraction from the database, data manipulation, and data analysis.
Results
Cohort selection and demographics
A total of 16,845 people meeting the selection criteria were included in the analysis between March 1, 2018, and February 28, 2021. Table 1 shows the patient demographics across the baseline, pre–COVID-19, and COVID-19 periods. The mean age and gender distribution was comparable across all 3 periods, with an average age of 60.5 years and 46% female. The COVID-19 period had a 16.8% reduction in the T2D population utilizing any primary care (virtual or in-person) as compared with the pre–COVID-19 period (10,692 visits from March 2019 to February 2020 compared with 8,850 visits from March 2020 to February 2021). The bulk of the reduction in visits occurred during the early phase of the pandemic (March to May 2020) and returned to near pre-pandemic levels by October 2020 (Figure 1 ).
Table 1.
Patients’ demographics
| Baseline: March 2018 to February 2019 | Pre–COVID-19: March 2019 to February 2020 | COVID-19: March 2020 to February 2021 | |
|---|---|---|---|
| People with T2D and ≥1 health-care touchpoint, n | 10,429 | 11,014 | 9,169 |
| Age, years | |||
| Mean (SD) | 60.7 (14.7) | 60.3 (14.9) | 60.5 (14.8) |
| Median (IQR) | 61.0 (52.0–71.0) | 61.0 (51.0–71.0) | 61.0 (51.0–71.0) |
| Age category, n (%) | |||
| 18–24 years | 537 (5.1%) | 641 (5.8%) | 505 (5.5%) |
| 35–49 years | 1,709 (16.4%) | 1,887 (17.1%) | 1,474 (16.1%) |
| 50–64 years | 3,872 (37.1%) | 4,086 (37.1%) | 3,463 (37.8%) |
| 65–79 years | 3,306 (31.7%) | 3,394 (30.8%) | 2,856 (31.1%) |
| ≥80 years | 1,005 (9.6%) | 1,006 (9.1%) | 871 (9.5%) |
| Gender, n (%) | |||
| Female | 4,808 (46.1%) | 5,178 (47.0%) | 4,213 (45.9%) |
| Male | 5,619 (53.9%) | 5,836 (53.0%) | 4,954 (54.0%) |
| Unknown | 2 (0.0%) | 0 (0.0%) | 2 (0.0%) |
| Smoking status, n (%) | |||
| Current smoker | 1,518 (14.6%) | 1,600 (14.5%) | 1,239 (13.5%) |
| Former smoker | 1,132 (10.9%) | 1,171 (10.6%) | 900 (9.8%) |
| Never smoked | 7,219 (69.2%) | 7,716 (70.1%) | 6,452 (70.4%) |
| Unknown | 560 (5.4%) | 527 (4.8%) | 578 (6.3%) |
IQR, interquartile range; SD, standard deviation; T2D, type 2 diabetes mellitus.
Figure 1.
Number of people with at least 1 primary care visit, by visit type, monthly.
In-person vs virtual visits
At the beginning of the pandemic, a sharp drop was observed in number of people with at least 1 in-person visit to the clinics, which was offset by a rise in people with virtual visits (Figure 1). Total numbers of people with in-person or virtual visits were comparable toward the end of the study period.
Compared with the previous year, the COVID-19 period had an increase of 330.4% in the number of people with at least 1 virtual visit, and the average number of virtual visits per person for those who had virtual visits increased from 1.7 to 3.6. On the contrary, 34.8% fewer people had in-person visits during the COVID-19 period, and the average number of in-person visits per person for those who had in-person visits decreased from 4.4 to 3.0.
Referrals
The monthly number of people with at least 1 referral decreased by 17% from the period of March 2019 to February 2020 (4,250 total referrals) to the pandemic period of March 2020 to February 2021 (3,432 total referrals). However, this was similar to the total number of referrals in the March 2018 to February 2019 period (3,716 referrals). Internal medicine was the most common referral across all 3 time periods, but referrals to Family Medicine/General Practice and Endocrinology & Metabolism increased as a proportion of all referrals during the pandemic period. In the periods beginning March 2018 and March 2019, referrals to Family Medicine/General Practice accounted for 11% and 8%, respectively, compared with 18% in the pandemic period. In the periods beginning March 2018 and March 2019, referrals to Endocrinology & Metabolism accounted for 10% and 9%, respectively, compared with 13% in the pandemic period.
Vital measures
Compared with the pre–COVID-19 period, 49.7% and 29.7% fewer people had BMI and BP measures recorded by the primary care clinics during the COVID-19 period, respectively. Among the people with BMI or BP measurements, the proportions of those with high BP (>130/80 mmHg) (75.6%, 76.0%, and 77.5%) and obesity (47.9%, 47.0%, and 44.6%) remained similar across all 3 time periods (Table 2 ).
Table 2.
Number of people with vital/lab measures and their vital/lab results
| Baseline: March 2018 to February 2019 | Pre–COVID-19: March 2019 to February 2020 | COVID-19: March 2020 to February 2021 | |
|---|---|---|---|
| People with ≥1 BP measure, n | 9,190 | 9,909 | 4,988 |
| Diastolic BP, mmHg, mean (SD) | 82.4 (10.5) | 82.4 (10.4) | 82.8 (10.8) |
| Diastolic BP, mmHg, median (IQR) | 82.0 (75.7–88.6) | 82.0 (75.7–88.8) | 82.3 (75.7–89.3) |
| Systolic BP, mmHg, mean (SD) | 134.3 (16.8) | 134.0 (16.9) | 135.6 (17.3) |
| Systolic (mmHg), median (IQR) | 132.8 (123.0–144.0) | 132.9 (123.0–143.6) | 134.0 (124.0–145.3) |
| % with BP >130/80 mmHg | 75.6% | 76.0% | 77.5% |
| People with ≥1 BMI measure, n | 5,904 | 6,734 | 4,736 |
| BMI, kg/m2, mean (SD) | 30.6 (6.5) | 30.5 (6.5) | 30.1 (6.5) |
| BMI, kg/m2, median (IQR) | 29.6 (26.0–34.1) | 29.4 (25.9–34.0) | 29.1 (25.6–33.5) |
| % with BMI ≥30 kg/m2 | 47.9% | 47.0% | 44.6% |
| People with ≥1 A1C test, n | 5,007 | 5,462 | 4,393 |
| A1C%, mean (SD) | 7.3 (1.5) | 7.3 (1.5) | 7.3 (1.5) |
| A1C%, median (IQR) | 7.0 (6.4–7.8) | 7.0 (6.4–7.9) | 6.9 (6.3–7.8) |
| % with A1C >6.5% | 68.0% | 69.4% | 65.1% |
| % with A1C >7.0% | 47.8% | 48.4% | 44.6% |
| People with ≥1 glucose test, n | 4,241 | 4,683 | 3,861 |
| People with ≥1 eGFR test, n | 4,947 | 5,404 | 4,338 |
| eGFR, mL/min/1.73 m2, mean (SD) | 81.0 (22.7) | 81.6 (22.8) | 80.4 (22.9) |
| eGFR, mL/min/1.73 m2, median (IQR) | 84.0 (65.7–98.0) | 85.0 (67.0–98.0) | 83.3 (65.5–97.0) |
| % eGFR <60 mL/min/1.73 m2 | 18.6% | 17.5% | 18.7% |
| People with ≥1 LDL-C test, n | 4,412 | 4,780 | 3,865 |
| LDL-C, mmol/L, mean (SD) | 2.0 (0.9) | 2.1 (0.9) | 2.1 (0.9) |
| LDL-C, mmol/L, median (IQR) | 1.8 (1.4–2.5) | 1.9 (1.4–2.6) | 1.9 (1.4–2.6) |
| % with LDL-C ≥2 mmol/L | 68.0% | 70.8% | 69.2% |
| People with ≥1 UACr test, n | 2,854 | 3,340 | 2,866 |
| UACr, mg/mmol, mean (SD) | 7.4 (16.9) | 7.1 (16.2) | 6.7 (15.4) |
| UACr, mg/mmol, median (IQR) | 1.7 (0.7–5.4) | 1.7 (0.8–5.4) | 1.6 (0.8–4.4) |
| % with UACr ≥2 mg/mmol | 54.1% | 54.8% | 53.8% |
A1C, glycated hemoglobin; BMI, body mass index; BP, blood pressure; eGFR, estimated glomerular filtration rate; IQR, interquartile range; LDL-C, low-density lipoprotein cholesterol; SD, standard deviation; UACr, urine albumin-to-creatinine ratio.
Laboratory tests
During the COVID-19 period, fewer people had A1C (−19.6%), blood glucose (−17.6%), eGFR (−19.7%), LDL-C (−19.1%), and UACr (−14.2%) tests as compared with the pre-COVID period (Table 2). Similar trends were also observed in all other lab tests. Among people with the test results available, the average values for all lab tests were similar across the 3 time periods (Table 2).
Monthly data showed a sharp drop in the number of people getting A1C tests during the period from March to May 2020, which increased but remained below pre–COVID-19 levels toward the end of the study (Figure 2 A). A1C levels were found to be comparable among all 3 time periods, with a large proportion of people (32.0%, 30.4%, and 34.9% for baseline, pre–COVID-19, and COVID-19 periods, respectively) successfully maintaining an A1C of ≤6.5% (Figure 2B). However, a considerable proportion of people were found to have an A1C of >6.5% (68.0%, 69.4%, and 65.1% for baseline, pre–COVID-19, and COVID-19 periods, respectively) or >7.0% (47.8%, 48.4%, and 44.6% for baseline, pre–COVID-19 and COVID-19 periods, respectively).
Figure 2.
(A) Number of people who had at least 1 A1C test, by month. (B) A1C test result distribution within each analysis period. A1C, glycated hemoglobin.
Discussion
In this study, we have described the patterns of primary care for a large population of people with T2D during the COVID-19 pandemic. Overall, fewer people with T2D were found to have health-care touchpoints in the first year of the pandemic, with the majority of the impact observed in the first 7 months. Although the initial phase of the pandemic had fewer people making in-person visits every month, this was mostly offset by a rise in virtual visits. There was a concerning reduction in the measurement and recording of BP, BMI, and blood tests, but the vitals and lab test outcomes did not differ considerably between the baseline, pre–COVID-19, and COVID-19 periods for those who had tests performed. Evidence from other national emergencies have revealed that such disruptions can worsen outcomes in people with acute and chronic diseases, including diabetes, during and after such events (9,11, 12, 13).
During the pandemic, the need to reallocate health-care resources toward managing COVID-19 patients and controlling cross-infection among patients discouraged nonurgent outpatient visits, hospital admissions, and elective surgeries (14). Although the number of patients utilizing health-care touchpoints and undergoing periodic laboratory testing had dropped rapidly and significantly during the initial months of the pandemic, there was a gradual recovery to pre-pandemic levels. However, it is important to note that, even by February 2021, those levels had not completely returned to pre-pandemic levels. This could be due to the ongoing risk of COVID-19 and some pandemic restrictions at that time. The convenience of virtual consultations over in-person visits could also be contributing to the persistence of fewer in-person visits.
Our findings corroborate those of an Ontario study showing that diabetes-related care was impacted during the pandemic and resulted in a reduction of general practitioner and specialist visits as well as a decreased likelihood of important laboratory tests for patients with diabetes (10). Similar to our study, the Ontario findings did not demonstrate worse outcomes for patients with diabetes during the pandemic. Although one interpretation is that there was no impact on health outcomes, it is important to note that there were fewer lab assessments during the pandemic period. Therefore, one can only conclude that, among those who sought health care during the pandemic period, their management was similar to pre-pandemic levels. There is no way to determine the impact on health outcomes among those who did not access health care during that time due to pandemic restrictions.
Other countries have addressed a negative impact on diabetes outcomes due to pandemic restrictions. A UK study revealed an 80% drop in A1C testing in April 2020, including 1.41 million missed/delayed diabetes-monitoring tests, 2.67 million screening tests, and 2.52 million diagnostic tests during the initial 6 months of the pandemic (15). An estimated 76% of patients with diabetes would see their rise in A1C levels to be >1 mmol/mol greater than expected due to disruptions. According to a US study, there was a 70% reduction in A1C during the early months of the pandemic, which correlated with an increase in abnormal A1C results (16). Another study revealed a 66% drop in A1C testing during the first 2 months of the pandemic compared with the previous year, which could possibly translate into delayed/missed testing and further adverse impacts on glycemic management and clinical outcomes (17). These findings highlight the importance of ensuring routine care to the population with diabetes through transitioning to alternative modalities of care such as teleconsultation, in-home visits, or physical consultations in separate clinics to minimize risk of cross-infection, as well as the importance of an uninterrupted supply of medication (18, 19, 20, 21, 22).
Our study, as well as several previous studies, have highlighted the importance of virtual health care during the COVID-19 pandemic. In response to pandemic restrictions implemented by governments in various countries, primary and secondary care through virtual and telephone consultations were advisable over in-person visits (23). Digital health-care aids in treatment adherence and empowers patients to proactively manage their own health (24). In the US, additional efforts were made to communicate with patients at home to ensure continued disease management. The effectiveness of remote management of diabetes has been verified through few studies. One randomized controlled study showed remote management of diabetes to be effective in maintaining BP, fasting blood glucose, and postprandial blood glucose, preventing weight gain and improving patients’ self-management and compliance during the pandemic (25). A US study showed no evidence of a negative association with medication fills and glycemic management, possibly attributed to a small increase in medication fill rates during the pandemic that offered protection against disruptions in diabetes management (8). Mail-order pharmacies and pharmacy delivery services were the key to ensuring a timely supply of medicines to patients (8). The findings also negated any evident association between visit frequency and glycemic management, thus reiterating the efficiency of remote management (8).
The primary care network used for this study already had some virtual care capabilities before the COVID-19 pandemic, although they were not frequently used, as shown by the relatively low number of people who had at least 1 virtual visit before March 2020. The pre-existing facilities seemingly offered a smooth transition from in-person to virtual visits as soon as the risks associated with the pandemic surfaced, thus guaranteeing uninterrupted diabetes care during that period. The increasing number of people with virtual visits in a short period also showed the quick adaptation to pandemic restrictions, which possibly explains the clinical outcomes of patients remaining unaltered during the COVID-19 period.
Although the use of telehealth platforms has been steadily increasing over the past few years, some sections of the population find it challenging to adapt to this approach due to limited internet access as well as the technical skills required for operation (23). Lack of optimal infrastructure required for virtual health platforms and inequal access to quality health services among different regions and nations may have led to varied impacts of the pandemic on patient care, and thus health outcomes, especially in low- to middle-income countries (26). A combination of technology and in-person services helps address the disparity to some extent and, in some cases, a direct combination of both approaches proved necessary (27,28). The availability of new billing codes to support telemedicine made this care more accessible for Ontarians; however, it remains to be seen whether this is a sustained change to health-care delivery in Ontario, especially if these codes do not remain in effect going forward (29).
One of the major strengths of our study was the size of the population, which was included from a major primary care network in Ontario, the most populous province in Canada. Diabetes treatment is similar across provinces in Canada, but there may be regional differences within and between provinces. This study was also comprehensive in documenting real-world vital and lab results of T2D over a 3-year period, where the impact of the pandemic was examined on different aspects of T2D management, including visits, referrals, vital measures, and lab tests.
Our study has some limitations. First, selection of the T2D population from the EMR database involved evaluating the prescription history and noninsulin antihyperglycemic medication used for each person to identify T2D status, which could lead to exclusion of patients getting eligible prescriptions outside of the clinical network. In cases of off-label use of eligible prescriptions, those people may be incorrectly determined to have T2D. Furthermore, this study had an objective to determine the impact of COVID-19 on treatment escalation and therefore focussed on patients who could still escalate to treatment with insulin.
Also, the EMR system did not capture all types of health-care resources accessed by patients: touchpoints with out-of-clinic physicians to which the patient was referred, primary care accessed outside of the medical clinic system, hospital care, diabetes care/services accessed in a pharmacy, or care obtained outside of the province were not included. While analyzing vital and lab results, only people who had tests performed during each time period were included. People not receiving care during the COVID-19 pandemic may have had different health outcomes/health-care–seeking behaviours compared with those with vital/lab tests. Last, the primary care model (e.g. fee-for-service vs capitation) was not assessed in this study. Not all EMRs and clinics have the same capabilities and approaches related to telemedicine and patient access, and therefore may have had different results.
The longer term impact of the COVID-19 pandemic on diabetes-related complications and standard of care is unknown, and it is important to continue to study this relationship. For future studies and analyses, it will be vital to follow people longitudinally to understand the change in T2D management and outcomes at the individual level during the pandemic. This information will be helpful for diabetes treatment planning as virtual medicine may play a larger role in primary care medicine in the future. It is also important to analyze prescription patterns of antihyperglycemic medications before and during the COVID-19 pandemic using pharmacy or claims data. Studies should assess those who were able to access care compared with those who were not able to access care. There may be a systematic difference between these populations that may impact future health outcomes. Finally, survey-based approaches should be implemented to understand the impact of the COVID-19 pandemic on T2D management from a patient and/or physician perspective.
In conclusion, we have highlighted that there was a major decrease in the number of in-person visits to clinics, which was offset by a rise in virtual care visits during the pandemic. We also found that there was a decrease in the number of laboratory tests performed, and these are critical for monitoring patients with diabetes. Although the values of the laboratory tests done were similar across the pre- and pandemic periods, it is important to understand the impact of the reduction of in-person clinical care on the entire population with diabetes.
Acknowledgments
The authors thank Namita Bedi and Kasturi Chatterjee (IQVIA Real World Solutions) for writing support. We also thank Jillian Murray and Atif Kukaswadia (IQVIA Solutions Canada, Inc.) for writing and project management support. This study was sponsored by Novo Nordisk Canada Inc.
Author Disclosures
A.C., S.H., I.K., R.G., and R.T. received advisory fees from IQVIA Solutions Canada, Inc, for contributions to the design and interpretation of the study, but they received no compensation for development of the manuscript. J.H. and A.L. are employees of Novo Nordisk Canada Inc. B.M. and S.G. are employed by IQVIA Solutions Canada, Inc.
Author Contributions
All authors were involved in the design of this study, participated in interpretation of the results and review and revision of the manuscript, and approved the final version submitted for publication.
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