key: cord-0721515-4ayjlfez
authors: Moin, John S.; Troke, Natalie; Plumptre, Lesley; Anderson, Geoffrey M.
title: The Impact of the COVID-19 Pandemic on Diabetes Care for Adults with Type 2 Diabetes in Ontario, Canada
date: 2022-05-02
journal: Can J Diabetes
DOI: 10.1016/j.jcjd.2022.04.009
sha: 4c7272d2a1a93310bfb44a7749b035d1a0894754
doc_id: 721515
cord_uid: 4ayjlfez

nan

The COVID-19 pandemic was declared by the World Health Organization on March 11 th , 2020, and according to preliminary reports, resulted in major disruptions to routine medical services worldwide, especially for those living with diabetes and chronic diseases (1) (2) (3) . On March 15 th the Chief Medical Officer of Health in Ontario directed healthcare organizations and providers to stop or substantially scale back all nonessential or elective services until further notice (4, 5) . As a result, physicians and allied health networks were required to postpone routine patient visits, which included those living with diabetes and other chronic diseases, to reduce the risk of COVID-19 infections. The public, also worried about contracting the virus in clinical and hospital settings, canceled or drastically reduced their appointments and daily travel, especially in the first few months of the pandemic (3, 4, 6, 7) . Living with diabetes requires extensive self-management routines, lifestyle adjustments, medicines, and regular contact with healthcare professionals, most of which takes place in primary care settings (8) (9) (10) . According to the 2018 guidelines published by Diabetes Canada, high quality diabetes care should include regular physician visits that provide opportunities to reduce the risk of diabetes complications through appropriate physical examination such as foot and eye examination, careful monitoring of glucose control and lipid levels through laboratory tests and prescriptions of drugs that can reduce risk of cardiovascular and kidney complications (10, 11) . Accepted clinical guidelines for evidenced-based care, routine public reporting of quality-of-care measures for those living with diabetes, including physician visits, key processes of care measures and health outcomes that might be avoided with appropriate care has become routine in many jurisdictions including Ontario (12) (13) (14) (15) . Moreover, diabetes has important implication for healthcare costs and health complications (10, (16) (17) (18) , making this patient population especially vulnerable to disruptions in routine care.

Social distancing and reduced access to medical care during the COVID-19 pandemic could have important impacts on quality of care for those with diabetes (4, 6) . It is also important to note that many studies have shown that diabetes is one of the major comorbidities associated with the development of severe COVID-19-related adverse outcomes and mortality (19) (20) (21) (22) (23) (24) . Thus, decreases in quality of care for diabetes could have immediate impacts on morbidity and mortality related to COVID-19 infections as well as longer term impacts on mortality and morbidity due to diabetes itself. There has been some work based on surveys of providers and patients that have raised concerns about quality of care for those with diabetes during the COVID-19 pandemic (2, 3) but evidence has been minimal at the population level using accepted markers of quality of care (25) . Only one recent publication was found with a focus on diabetic foot complications and related procedures in Ontario, Canada (26) . In this study, we use well defined and accepted quality of diabetes care measures of structure, process, and outcomes and population-based data from Ontario, Canada to evaluate the extent to which the quality of care for those with diabetes was changed during the first wave of COVID-19 pandemic (March 1, 2020 -August 31, 2020). We hope this study can inform our understanding of the impacts of the COVID-19 pandemic on care for those with diabetes and guide efforts to improve and maintain quality.

We conducted a population-based pre-and-post study using linked provincial administrative health databases to assess changes in total diabetes-related visits in primary care, specialists, emergency department and hospital settings, including procedures, testing, and prescriptions, for all residents of Ontario, Canada living with diabetes. We compared rates of use of these outcomes in the first six months of the COVID-19 pandemic (March 2020 to September 2020) to two previous six-month periods -(March 2019 to September 2019 and October 2019 to February 2020). Ontario is the most populated province in Canada with an estimated 2020 population of 14,734,014 (27) . All permanent residents in the province have full coverage for necessary physician, hospital and diagnostic services without copayments or deductibles.

We conducted the study using linked health administrative databases at ICES (formerly known as the Institute for Clinical Evaluative Sciences) Central, Toronto, Ontario. The Ontario Health Insurance Plan (OHIP) claims database provides records of all healthcare services delivered by physicians to patients eligible for coverage. The Registered Person Database provides demographic information for all patients covered under OHIP, including neighbourhood income quintiles generated by the Postal Code Conversion File. The ICESderived Ontario Diabetes Database (ODD) allows for the identification of persons living with diabetes. The Canadian Institute for Health Information Discharge Abstract Database (DAD) and National Ambulatory Care Reporting System (NACRS) contain records on all inpatient hospital admissions, and all hospital-and community-based ambulatory care including emergency department visits. The Ontario Drug Benefit Claims Database (ODB) captures drug benefit claims for seniors and low-income recipients. These datasets were linked using unique encoded identifiers and analyzed at ICES. ICES is an independent, non-profit research institute whose legal status under Ontario's health information privacy law allows it to collect and analyze health care and demographic data, without consent, for health system evaluation and improvement. Many of the measures employed in this study have been used in previous research and public reporting on diabetes and primary care metrics in Ontario (4, 6, 13, (28) (29) (30) (31) .

Three study cohorts were constructed by identifying all community-dwelling residents in Ontario, diagnosed with non-gestational diabetes within at least two years prior to the first day of entry into each cohort (i.e., index dates: March 1, 2019; September 1, 2019; March 1, 2020 respectively), 20 years of age or older as of index date, eligible for OHIP coverage as of index date, resided within the community (i.e., not living in long-term care facilities at any time during the study period), and alive at the end of cohort timeframe (Supplemental Figure 1 ). The algorithm used to identify persons with diabetes from ODD has a sensitivity of 86% and specificity of 97% (32) . More information on the algorithm has been published elsewhere (32) . We excluded those who were non-Ontario residents, 19 years of age or younger, had missing or invalid birthday/sex information, missing health card number, as well as those who died during the study period. We identified common comorbidities: Hypertension, Congestive Heart Failure, Acute Myocardial Infarction, Chronic Obstructive Pulmonary Diseases, Asthma, Dementia, and other mental health issues, within this patient population using OHIP, DAD, and NACRS (33, 34) .

The outcomes in the study were identified using ICES databases and ICES validated disease-specific registries (28) (29) (30) (31) . Study outcomes were organized using Donabedian's 'Structure, Process, Outcome' framework (35):

1) Structure (access to care and context measures): a) total general practitioners/family physician (GP/FP) visits, including in-person and virtual visits; b) total specialist visits, including in-person and virtual visits.

2) Process (processes of diabetes care metrics): a) eye exams, defined as those 40+ years of age who had a retinal exam within each cohort timeframe; b) The hemoglobin A1c (HbA1c) tests for those 40+ years of age; c) Low-density lipoproteins (LDL) test for those 40+ years of age; d) Angiotensin-converting enzyme (ACE) inhibitor, Angiotensin II receptor blocker (ARB) and Statin scripts filled within each cohort for those 65+ years of age.

3) Outcomes (health/utilization metrics): a) acute complications of diabetes, defined as having at least one visit to the Emergency Department (ED) or hospital admission with diagnosis for the following procedures during each cohort timeframe: hyperglycemia, hypoglycemia, soft tissue infection; b) chronic complication of diabetes, defined as having at least one visit to ED or hospital admission with diagnosis for one of the following during each cohort timeframe: cardiovascular disease, chronic renal disease, amputations.

Descriptive statistics for the sampled data and study cohorts are presented, with frequency and percentages. The structure, process and outcome measures were dichotomized and treated as binary dependant variables (event either occurred or did not) within the timeframe for follow-up in each cohort as follows: reference cohort 1 (March 1, 2019 to August 31, 2019); reference cohort 2 (September 1, 2019 to February 29, 2020) and the pandemic cohort (March 1, 2020 to August 31, 2020). Standardized differences were calculated to ensure that all three cohorts were balanced and minimize potential confounding. A standardized difference less than 0.1 suggest that cohorts are not substantially different based on the predictors being examined (36) . More information on the methodology and interpretation of this approach can be found elsewhere (36) .

A multivariate logistic regression analysis was used to model binary outcomes. Generalized Estimation Equations (GEE) with exchangeable covariance structure were used to account for the repeated measures within patients. The adjusted regression analysis included the covariates: cohorts, age, sex, income quintile, individual comorbidities, and health regions measured at the index date for each time period. Adjusted risk for each outcome and each cohort was calculated using our multivariate logistic regression model. Then the change in adjusted J o u r n a l P r e -p r o o f risks and their 95% confidence intervals (CI) for each outcome for the comparison of the pandemic period to each of the two prior periodsreference cohort 1 (to account for potential seasonality) and reference cohort 2 (to account for potential temporal trends) were calculated. SAS 9.4 statistical software was used for the analysis, utilizing the GENMOD procedure with binary distribution and log link. All tests were two-sided with p-values less than (p <0.05) considered statistically significant.

This study was conducted in accordance with Research Ethics Board (REB) guidelines and policies at the University of Toronto and received REB approval (# 41386). Furthermore, all studies carried out within ICES are subject to a privacy impact assessment and approval from ICES Privacy and Legal Office. The protocol for this study was approved by ICES and the data sufficiently deidentified and small cells suppressed to protect privacy. All analyses for this study were conducted using an encrypted remote connection to Data Access Services at ICES, a secure server where the data and analytical software are housed. Table 1 summarizes each study cohort and characteristics of all community-dwelling residents who are OHIP insured and been diagnosed with diabetes. The standardized difference calculated between cohorts did not yield any numerical value over 0.1 indicating that they are balanced.

Data on frequency and percent of structure, process, and outcomes measures by study cohorts are reported in the Supplemental Table 1 . Table 2 provides a summary of the adjusted and unadjusted estimates of the relative risks for changes in each of the measures between the pandemic period and two pre-pandemic periods. The probability of total visits to GPs and specialists went down 12% (Confidence Interval CI:12%, 12%, P-value <0.001) and 13% (CI:13%, 13%) respectively, with probability of an inperson visit to a GP decreasing by almost half 47% (CI:47%, 47%). There were large increases in the probability of virtual visits to both types of providers. The probability of having an eye exam went down by about 43% (CI:44%, 43%) and the probability of a HbA1c 28% (CI:29%, 28%) and lipid blood test 31% (CI:31%, 31%). The probability of a filled prescriptions for preventative drug therapy were basically unchanged. There were some differences in the probability complication rates across the two comparison periods, but the overall pattern was of a lower probability of visits for acute 16% (CI: 17%, 14%) and chronic 9% (CI: 10%-7%), and both ED 18% (CI: 19%, 16%) and hospital complications 16% (CI: 15%, 11%). 

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This pre-and-post study demonstrated that there were major disruptions to structures and processes of diabetes care. We observed substantial reductions in the number of people with diabetes who saw their GP/FP in-person over six months, dropping from 77.6% prior to the pandemic to 36.7% during the pandemic. In-person specialist visits were also reduced by about 8%. There were major increases in virtual care for both GP/FP and specialists, consistent with other studies examining the shift from in-person to virtual care (4, 6) . However, many critical processes of diabetes care were disrupted during this timeframe as observed in the 5% absolute decrease in eye-exams, 19% drop in HbA1c tests, and 15% drop in LDL tests during the pandemic.

Acute and chronic complications of diabetes were used as proxies for health outcomes and were both largely unchanged. It is too early to know with certainty actual health outcomes associated with the first wave of COVID-19, however, we suspect that diabetes complications have not gone down but rather the reductions in these outcomes suggest that people were less likely to seek care. This is further supported by the relative drop in acute and chronic complications within ED and hospital settings for any cause in those with diabetes compared to previous timepoints. Therefore, we argue that there were observed disruptions to both structures and processes of diabetes care in the province during the first wave of COVID-19 pandemic. Furthermore, that the reductions in visits for diabetes complications observed are consistent with possible barriers to ED and hospital care, driven either by reluctance of those with diabetes to seek care or institutional care systems overwhelmed with COVID-19, meant that those individuals were unable to seek or receive care.

With regard to disruptions in processes of diabetes care; we argue that while virtual visits increased, there were major decrease for in-person services such as: eye exams, HbA1c and LDL testing, and ED visits, many of which are critical components of diabetes care. Services such as eye and foot examinations are difficult to administer remotely and may have lasting implications for diabetes-related complications in the near-and long-term. This is in spite of the fact that eye examination rates for diabetics in Ontario were already suboptimal prior to COVID-19, potentially increasing the risk of complications, such as diabetic retinopathy (37) . Another study had observed increases in diabetes-related foot amputations prior to COVID-19 in Ontario (38) . Given that about one-third of diabetic foot ulcers fail to heal and many with non healing ulcers progress to lower-extremity amputations is worrying (39) . Other studies have also noted that stay-at-home orders and lockdowns have created new norms in health behaviours and living that may be particularly damaging in the diabetic population, such as isolation, unhealthy diets, decreased physical activity, stress and mental health related concerns, as well as delaying careseeking due to fears of contracting COVID-19 (9, (40) (41) (42) . The findings in this study also support this assertion that there were indicators of reduced care-seeking across other hospital and ED services, as was observed in our study within the diabetic population (43) (44) (45) . How patient outcomes related to in-person service disruptions, weight gain, de-stabilized glucose control, retinopathy, nephropathy, foot amputations and other related complications warrants future impact studies.

Promising signs indicating some continuity of care were the shifts to virtual care and the ability for patients to refill needed medicines. It was also noted in other studies that with regard to virtual care, many of the services rebounded to pre-pandemic numbers, months after the initial lockdowns (4, 6) . In terms of age and income, there were no major technological or financial barriers to access in this respect, as 91.2% of virtual visits were provided by telephone, which are readily available (6) . Moreover, the rate of virtual visits increased similarly across all chronic conditions (including diabetes) and income quintiles (6) . Older patients were the highest users of virtual care, a similar trend observed in our population (6) . However, lower level of virtual care seen among younger and rural residents may warrant further attention (4, 6) . It is important to consider the limitations of virtual care, especially over the phone. Some disadvantages of virtual visits are physicians' inability to conduct physical examinations, establish therapeutic physicianpatient relationships to foster support, and observe nonverbal cues such as body language (4). Also, low uptake of smartphones and video may indicate possible age, financial, education, digital, or other health system barriers that fail to capitalize on optimal virtual care delivery. Therefore, while there has been a large uptake of virtual care, its appropriate roll in diabetes care and extent of care remains to be seen. High-quality care for those with diabetes can have important impacts on health and healthcare costs. Given that the prevalence of diabetes is expected to increase in Canada (46) , is a major cause of death and poses risks for serious long term complications including: blindness, cardiovascular disease, end stage renal disease, hypertension, stroke, neuropathy, lower limb amputations, and premature death (18) , warrants the continual evaluation and monitoring of care quality being delivered during and after the pandemic.

Strengths of this study are its population-wide coverage, use of the most up-to-date health administrative data, validated diseases cohorts and service utilization algorithms. There were some limitations in our study. The ODD does not differentiate between type 1 or type 2 diabetes, however it is known that 90-95% of the population are type 2 (28) . We were unable to differentiate the type of virtual visits (text, phone, video, etc.), however as noted above it is expected that about 90% were delivered by telephone. Diabetes and COVID-19 disproportionally impact racialized individuals (22, 28, 40) , and how those disparities impact access or barriers to care were not examined in this study. Diabetes care and access to care may be slightly or entirely distinct in different jurisdictions, therefore our results while representative of the Ontario population may not be generalizable elsewhere. Due to the nature of the data and the way cohorts were constructed, we were unable to differentiate between outcomes in the spring and summer 2020 for the pandemic cohort. Diabetics have been known to suffer from increased mental health conditions and dental diseases, however, these outcomes were not assessed here. Due to the unavailability of cause of death at the time of this study and death being a competing risk for outcome measures, we only analyzed individuals living with diabetes and excluded those who died during the observation period. Therefore, mortality due to diabetes complications and service disruptions were not examined. Time itself was not assessed within the analysis as in a time-varying autoregressive (ARIMA) model due to limited number of time points, which may partially bias findings. Lastly, while the impact of COVID-19 lockdowns on diabetes care in the first wave were examined, it was too early to assess health outcomes and consequences of structural care barriers and processes; a follow-up study will be conducted in this regard.

Nevertheless, we reported the extent to which diabetes-related care had been impacted during the initial months of COVID-19, particularly within the context of reduced in-person GP/FP and specialist visits, reflecting structural barriers to care. We noted process barriers to diabetes care, particularly those requiring in-person visitations, such as eye examinations, testing, and possibly physical examinations, many of which are critical components of diabetes care. While there was a drastic increase in virtual care, it is unlikely that many of the essential services and testing were adequately supplemented. While our early health outcomes suggest some reductions in diabetes-related complications, we argue that this was due to reductions in care seeking and obliging by public stay-at-home orders. Actual health impacts and consequences of these care disruptions during the early months of the pandemic and beyond, require further study. 

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