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The indirect impact of the COVID-19 pandemic on people with type 2 diabetes mellitus and without COVID-19 infection: Systematic review and meta-analysis
1 Zhuoran Hu and Hin Moi Youn contributed equally to the manuscript.
Zhuoran Hu
Footnotes
1 Zhuoran Hu and Hin Moi Youn contributed equally to the manuscript.
Affiliations
Department of Family Medicine and Primary Care, School of Clinical Medicine, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong Special Administrative Region of China
1 Zhuoran Hu and Hin Moi Youn contributed equally to the manuscript.
Hin Moi Youn
Footnotes
1 Zhuoran Hu and Hin Moi Youn contributed equally to the manuscript.
Affiliations
Department of Family Medicine and Primary Care, School of Clinical Medicine, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong Special Administrative Region of China
Department of Family Medicine and Primary Care, School of Clinical Medicine, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong Special Administrative Region of China
Department of Family Medicine and Primary Care, School of Clinical Medicine, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong Special Administrative Region of China
Department of Family Medicine and Primary Care, School of Clinical Medicine, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong Special Administrative Region of China
Department of Family Medicine and Primary Health Care, United Christian Hospital and Tseung Kwan O Hospital, Hong Kong Hospital Authority, Hong Kong Special Administrative Region of China
Centre for Safe Medication Practice and Research, Department of Pharmacology and Pharmacy, Li Ka Shing Faculty of Medicine, University of Hong Kong, Hong Kong Special Administrative Region of ChinaLaboratory of Data Discovery for Health (D24H), Hong Kong Science and Technology Park, Hong Kong Special Administrative Region of ChinaResearch Department of Practice and Policy, School of Pharmacy, University College London, London, United KingdomAston Pharmacy School, Aston University, Birmingham, United Kingdom
Department of Medicine, School of Clinical Medicine, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong Special Administrative Region of ChinaState Key Laboratory of Brain and Cognitive Sciences, The University of Hong Kong, Hong Kong Special Administrative Region of China
Department of Medicine, School of Clinical Medicine, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong Special Administrative Region of China
Department of Family Medicine and Primary Care, School of Clinical Medicine, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong Special Administrative Region of China
Corresponding author at: Department of Family Medicine and Primary Care, School of Clinical Medicine, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong Special Administrative Region of China.
Department of Family Medicine and Primary Care, School of Clinical Medicine, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong Special Administrative Region of ChinaCentre for Safe Medication Practice and Research, Department of Pharmacology and Pharmacy, Li Ka Shing Faculty of Medicine, University of Hong Kong, Hong Kong Special Administrative Region of ChinaLaboratory of Data Discovery for Health (D24H), Hong Kong Science and Technology Park, Hong Kong Special Administrative Region of China
Investigates the indirect impact of the pandemic on T2DM patients without infection.
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Meta-analysis shows that hemoglobin A1c and body mass index changed insignificantly.
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Meta-analysis shows that triglyceride and total cholesterol might worsen.
Abstract
Background
The effect directly from the coronavirus disease 2019 (COVID-19) infection on health and fatality has received considerable attention, particularly among people with type 2 diabetes mellitus (T2DM). However, evidence on the indirect impact of disrupted healthcare services during the pandemic on people with T2DM is limited. This systematic review aims to assess the indirect impact of the pandemic on the metabolic management of T2DM people without a history of COVID-19 infection.
Methods
PubMed, Web of Science, and Scopus were systematically searched for studies that compared diabetes-related health outcomes between pre-pandemic and during-pandemic periods in people with T2DM and without the COVID-19 infection and published from January 1, 2020, to July 13, 2022. A meta-analysis was performed to estimate the overall effect on the diabetes indicators, including hemoglobin A1c (HbA1c), lipid profiles, and weight control, with different effect models according to the heterogeneity.
Results
Eleven observational studies were included in the final review. No significant changes in HbA1c levels [weighted mean difference (WMD), 0.06 (95% CI −0.12 to 0.24)] and body weight index (BMI) [0.15 (95% CI −0.24 to 0.53)] between the pre-pandemic and during-pandemic were found in the meta-analysis. Four studies reported lipid indicators; most reported insignificant changes in low-density lipoprotein (LDL, n = 2) and high-density lipoprotein (HDL, n = 3); two studies reported an increase in total cholesterol and triglyceride.
Conclusions
This review did not find significant changes in HbA1c and BMI among people with T2DM after data pooling, but a possible worsening in lipids parameters during the COVID-19 pandemic. There were limited data on long-term outcomes and healthcare utilization, which warrants further research.
The coronavirus disease 2019 (COVID-19) pandemic has been a major global public health emergency since December 2019, leading to a widespread shift in healthcare priorities [
]. People with type 2 diabetes mellitus (T2DM) are more vulnerable to developing severe symptoms, complications, or deaths that may be directly or indirectly related to the pandemic [
]. Moreover, control measures to contain the infection, such as social distancing, lockdowns, and healthcare resource re-allocation, are likely to have indirect consequences on disease prognosis and complications [
The indirect effect of the pandemic on T2DM mostly arises from changes in routine care, such as disrupted delivery and the use of health services. During the pandemic, non-essential, non-emergent, elective health services were postponed or canceled in response to the pandemic [
Impact of COVID-19 pandemic on daily life, physical exercise, and general health among older people with type 2 diabetes: a qualitative interview study.
]. For people with T2DM who require consistent monitoring of symptoms and timely treatment adjustment, such as personalized glycemic control protocol [
], the disruption of care created new barriers to managing the disease, which may have resulted in poor health outcomes or incidence of diabetes-related complications. The indirect impact is also recognized from alterations in daily life due to infection control measures, such as lockdowns and social distancing. Decreased mobility or physical activities, increased psychological stress, and changed food intake pose additional challenges to retaining adequate diabetes management and usual healthy habits, which would potentially affect diabetes control of glycemic level, blood lipid, and weight during the pandemic [
Increased stress, weight gain and less exercise in relation to glycemic control in people with type 1 and type 2 diabetes during the COVID-19 pandemic.
Glycemic parameters in patients with new-onset diabetes during COVID-19 pandemic are more severe than in patients with new-onset diabetes before the pandemic: NOD COVID India Study.
]. However, the extent of the indirect consequences on people with T2DM and without a history of the infection is yet to be investigated. Considering that health resources were mostly diverted to the care of those with COVID-19 during the early period of the pandemic [
], those without the infection might be less convenient to access health services. Thus, in this review, we focus on people with T2DM who do not have a history of the infection to eliminate factors that could potentially affect the outcome, aiming to evaluate the true indirect impact of the pandemic on people with T2DM by reviewing studies that specifically target those not infected with COVID-19.
2. Materials and methods
2.1 Data sources and searching strategy
This systematic review was conducted using PICOS (Population, Intervention, Comparison, Outcome, and Study design) and PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) tools. We systematically searched PubMed, Web of Science Core Collection, and Scopus published between January 2020 and July 2022, using Medical Subject Headings (MeSH) and text search terms “diabetes mellitus, type 2” [MeSH] AND “COVID-19” [MeSH] OR “SARS-CoV-2” [MeSH] OR coronavirus [MeSH] AND (“glycemic control” [MeSH] OR ‘indirect*’ [tiab] OR ‘spill-over*’ [tiab]). The detailed search strategy is provided in Supplementary Tables 1 and 2. In addition, we used Google Scholar to manually conduct a forward search of included studies until July 13, 2022.
2.2 Study selection
We included studies that (1) targeted people who were diagnosed with T2DM, (2) excluded people who had COVID-19 infection (3) compared diabetes-related outcomes between pre-pandemic and during-pandemic periods. Studies were excluded if they included subjects with other types of diabetes or confirmed COVID-19 cases. We excluded studies that measured non-diabetes-related health outcomes, such as mental health.
2.3 Screening process, quality assessment, and data extraction
Using these eligibility criteria, two independent investigators (HZR and LSL) screened all studies at the titles and abstracts and subsequently performed full-text screening to ensure that all primarily selected studies were satisfied with the selection criteria. Two independent investigators (HZR and YHM) assessed the methodological quality using Joanna Briggs Institute (JBI) Critical Appraisal Tools [
] for cohort assessment of the research quality (supplementary Table 3) and conducted the data extraction.
2.4 Outcomes of interest
The primary outcome was glycemic control, including HbA1c and mean fasting blood glucose if HbA1c was not reported. Although glycemic control is the center of diabetes management, as a metabolic disease, a good prognosis of diabetes people still requires a multiple-dimensional control of metabolic parameters because the high prevalence of hypertension and dyslipidemia in diabetes people [
] requires physicians to monitor and control blood pressure and lipid. We extracted the data as secondary outcomes if the included studies reported (1) body weight (kg) or BMI (kg/m2), (2) lipid profile (mg/dL) included triglyceride (TG), total cholesterol (TC), low-density lipoprotein (LDL), high-density lipoprotein (HDL) and (3) blood pressure (BP, mmHg). Moreover, only the pre-pandemic and during-pandemic data from the same study subjects were extracted (supplementary Fig. 1).
2.5 Data synthesis and analysis
We performed meta-analyses for each dependent outcome, including HbA1c, BMI, and lipid profiles, to assess the indirect impact of COVID-19 on people with T2DM. Inverse-variance weighting mean difference (WMD) was calculated separately for parameters extracted from before and during the COVID-19 pandemic. For studies that measured and reported outcomes multiple times during the investigation period, we selected the last measurement because it covered the whole length of observation.
Statistical heterogeneity between the studies was assessed before choosing effect models for WMD. I2 values of > 50% and a significant Cochran Q-test (p < 0.05) indicate significant inconsistency. Publication bias was assessed using funnel plots and Egger’s tests [
] to adjust the funnel plot and recalculated the results if there was publication bias. Sensitivity analyses were also conducted using the leave-one-out strategy to examine whether a single study could have markedly affected the results’ stability. Univariable and multivariable meta-regression analyses were conducted to discover which factors brought high heterogeneity. If the heterogeneity is still significant after removing the influential studies based on the sensitivity analysis, we performed subgroup analyses using significant moderators obtained from the meta-regression as grouping factors. Any discrepancies were resolved through discussion with another investigator (WYF). All median values with interquartile range (IQR) [
]. All parameters were reported with the same units. The statistical significance level was set at p < 0.05. For all outcomes, 95% confidence intervals (CIs) were calculated. We performed the analysis using the R (version 4.1.0) package ‘metafor’ and showed the meta-analysis results with forest plots. No missing data were identified among included studies.
3. Results
3.1 Literature collection and general data
The PRISMA flowchart in Fig. 1 summarizes the search results and selection process of all studies included in the analysis. Overall, the number of records identified by our database searches was 6506. Of these records, 223 were screened for the full-text review after removing 6263 studies from the title and abstract screening process. Finally, a total of eleven studies were included in the final review.
Table 1 presents the clinical characteristics. Eleven studies provide clinical data from an aggregate sample of 3192 subjects in 6 countries. The mean age of the subjects was between 50 and 80 years. Nine studies measured the lockdown where duration and restrictions vary depending on countries. Turkey, for example, enforced a lockdown for three months from March to June 2020. The investigation period ranged between 1 and 8 months after the first wave of the pandemic or the lockdown initiation.
Table 1Clinical characteristics of included studies.
Source
Country
Study design
Sample size, n
Male, %
Mean age ± SD, years
Control (Pre-pandemic)
Span
Endpoint (Pandemic)
Outcome
Glycemic parameter
Lipid profile
BMI
Munekawa et al., Sep 2020
Kyoto, Japan
Retrospective cohort
183
62.1
67.4 ± 11.3
Jan-Feb, 2020
Apr-May 2020
Increased
No significant change*
Rastogi et al., Oct 2020
Chandigarh, India
Prospective cohort
442
71.8
median (range) 58 (52–64)
Pre-lockdown
Immediately after the lockdown lifted
Decreased
No significant change
Onmez et al., Oct 2020
Duzce, Turkey
Retrospective†
101
56.5
55 ± 13
Pre-lockdown
1 month after the lockdown lifted
No significant change
No significant change
Karatas et al. Jan 2021
Istanbul, Turkey
Case-control†
85
31.8
55.8 ± 10.5
Pre-lockdown
6 months after pre-lockdown
Increased
Increased
Increased
D’Onofrio et al. Mar 2021
Lazio, Italy
Retrospective, case-control†
141
61
median (range) 68 (61–74)
December 9 2019 – March 9 2020
1 month after the lockdown lifted
No significant difference
No significant difference
No significant difference
Falcetta et al. Mar 2021
Pisa, Italy
Retrospective cohort
304
65
69.1 ± 9.2
Pre-lockdown
1 month after the lockdown lifted
No significant difference
Decreased
Increased
Biamonte et al. Jun 2021
Rozzano, Italy
Retrospective†
128
57.8
median (range) 70 (40–91)
Pre-lockdown
1 month after the lockdown lifted
Increased
Increased
Verma et al. Oct 2021
Bathinda, India
Cross-sectional†
20
61.2
51.5 ± 11.3
Pre-lockdown
1–3 months after lockdown lifted
Decreased*
Jin et al. Oct 2021
Daegu, Korea
Retrospective†
996
60.5
62.9 ± 11.3
January 1–18 Feb 2020
Jul- Aug 2020
Increased
Selek et al. Dec 2021
Kocaeli, Turkey
Retrospective†
283
47
58.0 ± 12.1
Pre-pandemic
15 days - 5 months after lockdown lifted
No significant difference
Increased*
No significant difference
Alowainati et al. Dec 2021
Doha, Qatar
Retrospective†
509
41.3
55.8 ± 13.4
Pre-lockdown
Immediately after the lockdown lifted
No significant difference
*: body weight or mean fasting glucose. Unless otherwise specified: glycemic parameter means HbA1c (%), body weight means BMI (Kg/m2); lipid profile (mg/dL) included triglyceride, total cholesterol, low-density lipoprotein, and high-density lipoprotein, HDL. †: included cohort follow-up data. Only extracted and analyzed the cohort data.
Lockdown policies in different countries were different.
All studies reported clinical parameters (Fig. 2), eleven reported glycemic control indicators, six reported BMI, four reported lipid profiles, and one reported blood pressure. No significant changes were found in most included studies. Except for glycemic parameters, LDL and TC, no improvements were reported in the remaining parameters.
Fig. 2Changes in clinical parameters compared with the pre-pandemic HbA1%: glycated hemoglobin; BP: blood pressure; BMI: body mass index; TC: total cholesterol; TG: triglyceride; LDL: low-density lipoprotein; HDL: high-density lipoprotein.
The impact of lockdown and changes in clinical practice on glycemic control during the COVID-19 pandemic: analysis of data from the National Diabetes Center, Doha, Qatar.
Effect of coronavirus disease 2019 pandemic on the lifestyle and glycemic control in patients with type 2 diabetes: a cross-section and retrospective cohort study.
] were reported in Japan and Korea, which did not implement lockdown, and in Italy and Turkey, which imposed strict lockdown regulations. Studies from India all showed improved HbA1c% [
Effect of lockdown on diabetes care during the COVID-19 pandemic: result of a telephone-based survey among patients attending a diabetic clinic in northern India.
The meta-analysis pooled data and showed no significant change in HbA1c levels during the COVID-19 period compared with the pre-pandemic, with a WMD of 0.06 (95% CI −0.12 to 0.24), p = 0.544, I2= 78.2% based on ten papers (Fig. 3). No significant publication bias was observed in the funnel plots and Egger’s test (p = 0.695, Supplement Fig. 2). Leave-one-out sensitivity analysis demonstrated that no included studies were required to be removed.
Fig. 3Meta-analysis to assess the indirect impact of COVID-19 on HbA1c (%).
Multivariable meta-regression analysis showed that study location (odd ratio, OR: 1.73, 95% CI 1.05–2.85, p < 0.05) and lockdown enforcement (OR: 0.55, 95% CI 0.31–0.98, p < 0.05) were interactively associated with HbA1c. However, study location (subgroup difference, p = 0.18) and lockdown implementation (subgroup difference, p = 0.79) did not affect HbA1c significantly after the pandemic after subgroup analysis.
3.3 Effect on lipid profiles
Fig. 4 shows the results of the meta-analysis for blood lipids. Lipid panel (mg/dl) was reported in 4 European studies. Two studies on TC showed improvement [
], while the pooled data using the random-effect model showed no significant changes (WMD: 20.91, 95% CI −8.89 to 50.71). No improvements in HDL were reported in the included papers (stable [
Effect of coronavirus disease 2019 pandemic on the lifestyle and glycemic control in patients with type 2 diabetes: a cross-section and retrospective cohort study.
] but gained an insignificant mean difference [0.15 (95% CI −0.24 to 0.53)] from the fixed-effect model (Fig. 5). However, potential publication bias can be observed in the funnel plots and Egger’s test (p = 0.008). After adjusting the funnel plot with the ‘trim-and-fill’ method, the recalculated WMD is 0.04 (95% CI −0.31 to 0.39). The WMD of WC [0.08 (95% CI −0.68 to 2.29)] also shows no significance (Supplementary Fig. 9).
Fig. 5Meta-analysis to assess the indirect impact of COVID-19 on BMI (Kg/m2). *: converted from median (range).
One study reported that the self-reported proportion of poorly controlled hypertension increased (pre-COVID-19 vs. COVID-19: 22% vs. 27%, p < 0.001), but without quantitative data [
During the COVID-19 pandemic, various measures to control the spread of the virus have affected the elective healthcare and daily life of people with T2DM. While most studies have examined health effects directly related to COVID-19, this is the first systematic review to investigate the indirect impact of the pandemic explicitly in T2DM people without a history of the infection. A total of 11 studies that satisfied the criteria were included in the final analysis. Overall, we found no significant changes in HbA1c% and BMI between pre-pandemic and during-pandemic but significant changes in TC and TG from lipid panels.
The previous systematic reviews and meta-analysis have shown inconsistent results regarding changes in the level of glycated hemoglobin and lipid profiles in diabetes people during the pandemic. For example, Ojo et al. [
] (OR 0.14, 95% CI −0.13 to 0.40, p > 0.05) found no statistically significant changes. Similar to our findings, Ojo’s review also illustrated improvements in lipid profiles, with TC (OR −0.53, 95% CI −0.56 to −0.50), TG (OR −0.06, 95% CI −0.09 to −0.04), LDL (OR −0.11, 95% CI −0.56 to −0.50), and HDL (OR 3.69, 95% CI 3.27 4.11). The difference in study designs may explain the inconsistency in the findings of our review compared with the previous ones. The primary difference is that we only included the studies where participants were strictly selected without the COVID-19 infection because it could affect the outcome when evaluating the indirect impact of the pandemic. People with T2DM and COVID-19 infection may have fewer barriers to accessing health services than those without the infection, potentially mitigating the effect of disruptions in care [
]. Moreover, the SARS-CoV-2 would activate immune factors, leading to multiple organ injuries, especially the liver, and pancreas, which regulate blood sugar [
]. Another difference is that most review studies compared pre-pandemic and during-pandemic data from different subjects, but we only extracted the studies which followed up on the same subjects generating conservative results.
4.1 Effect on HbA1c and BMI
Although no significant changes in HbA1c and BMI in people with T2DM were observed, this may be possibly attributed to variations in pandemic containment policies and implementation approaches across different settings. First, lockdown measures differ in each country regarding stringent levels or durations. For example, Italy [
Promoting physical activity during school closures imposed by the first wave of the COVID-19 pandemic: physical education teachers’ behaviors in France, Italy and Turkey.
]. Moreover, various lockdown and social restrictions measures were introduced and lifted in order to adapt to the constantly changing situations during the pandemic [
]. For example, a lockdown in India mandated not leaving home at the peak of the COVID-19 pandemic but gradually eased and maintained a semi-lockdown about two weeks after the declaration of national lock, allowing people to go out for medical and other necessary activities [
]. Second, the variation in investigation periods to assess HbA1c may also affect the results. The follow-up periods in the included studies varied from 1 to 8 months, but HbA1c is commonly used to monitor at least three months of glycemic control among diabetes people [
]. Therefore, an extended investigation period may be able to provide more information. For example, a significant deterioration of HbA1c was observed in the studies with a 6-month investigation period [
], even though the pooled WMD was insignificant [0.36, 95% CI (−0.25 to 0.97), Supplementary Fig. 4], suggesting future studies with a long observational period with a large study population are required.
4.2 Effect on lipid panel
Increased TG and declined HDL are well-recognized characteristics of T2DM dyslipidemia [
], suggesting TG may be a more sensitive indicator for suboptimal glycemic control. Thus, after meta-analysis, the increased TG and decreased TC, which might be attributed to the declined HDL, may indicate the pandemic’s adverse impact on glycemic control. However, more studies on the pandemic effect of blood lipids are excepted since the limited number of included studies may decrease the power of this meta-analysis, especially for TC.
4.3 Effect on healthcare disruption
Disruption of healthcare is a widely discussed impact of the pandemic. A universally decreased health service utilization for non-COVID-19 conditions [
Impact of COVID-19 pandemic on daily life, physical exercise, and general health among older people with type 2 diabetes: a qualitative interview study.
Preliminary estimate of excess mortality during the COVID-19 outbreak – New York City, March 11–May 2, 2020, MMWR Morb. Mortal. Wkly. Rep., 69, 2020, pp. 603–605.
], characterized by reduced health service hours due to lockdowns, dwindling human resource availability due to medical source re-allocation, and hesitancy to seek care due to fears of contracting the virus during the pandemic; such change may result in challenging management of diabetes in the long term, even though it takes time to be revealed. One study [
] examined the impact of the 2003 SARS epidemic retrospectively and found a short-term dramatic decline in healthcare utilization would lead to increased diabetic mortality one year after the outbreak [relative risk:1.08 (CI: 1.03 – 1.15)]. This previous experience raises similar concerns during the present pandemic, as the present pandemic has lasted more extended time and affected more globally than the SARS epidemic, with the on-and-off disease-control policies, like repeated lockdown implementation when facing emerging virus variants [
]. This study provides a window to view if disruptions in care or re-allocation of resources and daily life alterations may have affected diabetes management, informing in response to this burden. Although our results did not significantly impact glycemic and weight parameters in people with T2DM, possibly attributable to the limited data and modifications, some findings from lipid profiles send a wake-up call. Further studies are required, and developing a concrete response to improve the adaptability of healthcare systems and the daily life aid for diabetes populations will be crucial for similar future events.
4.4 Effect on lifestyle changes
Lockdown and social distancing during the pandemic could negatively affect physical activity and diet habits, as shown by three included studies [
]. Nevertheless, there is a possibility that people with diabetes rethink their lifestyles and compensate for decreased physical activity by enhancing medication adherence [
Effect of lockdown on diabetes care during the COVID-19 pandemic: result of a telephone-based survey among patients attending a diabetic clinic in northern India.
Changes in lifestyle, behaviors, and risk factors for cognitive impairment in older persons during the first wave of the coronavirus disease 2019 pandemic in Finland: results from the FINGER study.
]. In particular, the included studies were conducted during the first wave outbreak when people were most concerned about infection. More quantitative evidence on lifestyle changes and its consequence on glycemic control is needed because of the conflicting results.
4.5 Strengths and limitations
The unprecedented situation in diabetes care calls for prioritizing efforts to address any challenges for effective disease management. This review provides the first overview of the indirect impact of the COVID-19 pandemic on people with T2DM without infection. We adjusted potential publication bias and improved the robustness of the meta-analysis by calculating the mean difference with conservative effect models and heterogeneity-lowering methods.
This review had several limitations. First, all available evidence included in the review was assessed during the early period of the pandemic. The long-term impact caused by disease-control policies during the early stages of the pandemic remains to be determined. For instance, the healthcare disruption may predispose diabetes patients to a greater risk of future marco- and micro cardiovascular complications [
]. Second, after the first vaccine became available to the public in November 2020, governments began to relax restrictions that could be affecting disease management patterns for diabetes patients once again. Third, despite adjusting the heterogeneity and publication bias, the intrinsic difference between the study population and the small study effect suggests sensitivity and caution in interpreting the findings and drawing definitive conclusions. Lastly, we did not include studies that measured other non-diabetes-specific complications or comorbidities, such as mental health. Depression, anxiety, and stress are important determinants affecting disease management, particularly self-care behaviors. Future research is needed to understand the effect of mental health during the prolonged period of the pandemic.
5. Conclusion
The changes in some routine clinical indicators for T2DM during the COVID-19 pandemic were insignificant compared with the pre-pandemic period, besides total cholesterol and triglyceride, which showed an improvement and an impairment, respectively. Future studies are required to understand the extent to which disruptions to care and usual routines may have affected diabetes control during the pandemic so that we can produce information for strategy development to guide adequate care for those who suffered from the pandemic and to prepare for future health emergencies.
Funding
This work was supported by the Hong Kong Food and Health Bureau Commissioned Research on the Novel Coronavirus Disease (COVID-19) [Grant numbers: COVID19F08].
Ethics approval and consent to participate
Not applicable.
Consent for publication
Not applicable.
CRediT authorship contribution statement
Zhuoran Hu: Investigation, Validation, Visualization, Formal analysis, Investigation, Writing – original draft; Hin Moi Youn: Formal analysis, Validation, Investigation, Writing – review & editing; Lily Luk Siu Lee: Validation, Investigation; Esther Yee Tak Yu, Ivy Lynn Mak, David Vai-Kiong Chao, Welchie Wai Kit Ko, Gary Kui Kai Lau, Chak Sing Lau: Writing – review & editing; Ian Chi Kei Wong, Jianchao Quan: Writing – Supervision, Funding acquisition, Writing – review & editing; Eric Yuk Fai Wan Wan and Cindy Lo Kuen Lam: Supervision, Conceptualization, Methodology, Funding acquisition, Writing – review & editing. All authors read and approved the final manuscript.
Declaration of Competing Interest
ICKW reports research funding from Amgen, Bristol Myers Squibb, Pfizer, Janssen, Bayer, GSK, Novartis, the Hong Kong Research Grants Council, the Hong Kong Health and Medical Research Fund, the National Institute for Health Research in England, the European Commission, and the National Health and Medical Research Council in Australia, outside the submitted work; and is a non-executive director of Jacobson Medical in Hong Kong and a consultant to IQVIA and World Health Organization. Other authors declare that they have no competing interests.
Impact of COVID-19 pandemic on daily life, physical exercise, and general health among older people with type 2 diabetes: a qualitative interview study.
Increased stress, weight gain and less exercise in relation to glycemic control in people with type 1 and type 2 diabetes during the COVID-19 pandemic.
Glycemic parameters in patients with new-onset diabetes during COVID-19 pandemic are more severe than in patients with new-onset diabetes before the pandemic: NOD COVID India Study.
The impact of lockdown and changes in clinical practice on glycemic control during the COVID-19 pandemic: analysis of data from the National Diabetes Center, Doha, Qatar.
Effect of coronavirus disease 2019 pandemic on the lifestyle and glycemic control in patients with type 2 diabetes: a cross-section and retrospective cohort study.
Effect of lockdown on diabetes care during the COVID-19 pandemic: result of a telephone-based survey among patients attending a diabetic clinic in northern India.
Promoting physical activity during school closures imposed by the first wave of the COVID-19 pandemic: physical education teachers’ behaviors in France, Italy and Turkey.
Preliminary estimate of excess mortality during the COVID-19 outbreak – New York City, March 11–May 2, 2020, MMWR Morb. Mortal. Wkly. Rep., 69, 2020, pp. 603–605.
Changes in lifestyle, behaviors, and risk factors for cognitive impairment in older persons during the first wave of the coronavirus disease 2019 pandemic in Finland: results from the FINGER study.
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