{"paper_id":"4685cd05-97e4-4c85-8595-a80fcfac49c6","body_text":"The profound impact of COVID-19 on the control and care of diabetic patients: a comprehensive retrospective cohort study | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article The profound impact of COVID-19 on the control and care of diabetic patients: a comprehensive retrospective cohort study Fakhria Al Rashdi, Salwa Al Harasi, Mohammed Al Ismaili, AL Ghalia AlYaqoobi, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4662891/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 21 Dec, 2024 Read the published version in BMC Primary Care → Version 1 posted 4 You are reading this latest preprint version Abstract Background The COVID-19 pandemic has led to a significant reallocation of healthcare services, focusing on pandemic response and emergency preparedness. The Oman Ministry of Health has implemented various measures to combat and control COVID-19. However, this shift has harmed routine outpatient appointments, particularly for chronic diseases such as Diabetes mellitus (DM) and hypertension (HTN). Considering this, our study aims to determine the specific effects of the pandemic on diabetes control, focusing on glycated haemoglobin (HbA1c), blood pressure (BP), lipids (mainly low-density lipoprotein (LDL), weight/ Body mass index (BMI), and compare these to pre-pandemic levels. Methods A retrospective cohort study of 223 diabetic patients aged 20–95 years who had a blood workup in 2019 and 2020 and were registered in Al-Khuwair Health Centre from March to December 2020. Data was extracted from the Al Shifa 3plus System and National Diabetic Register (NDR). SPSS was used to analyse the data. Results Out of 260 patients with diabetes, 223 were included in the study, and 37 were excluded (new DM patients and existing patients without follow-up in 2019). The results showed significant HBA1C, Systolic BP, and BMI changes between 2019 and 2020. The mean HbA1c in 2019 (6.9%) was lower than in 2020 (7.2%). Similarly, the mean SBP in 2019 (131.22 mmHg) compared to 2020 (134.84 mmHg), mean BMI in 2019 (30.49), whereas in 2020 (30.80). The LDL and diastolic BP did not change. Conclusion The COVID-19 pandemic affected healthcare systems globally, and it was not only the direct impact of the virus that caused the consequences or mortalities; it could also be the modifications in priorities. Due to the interruptions in inconsistent care, consequences of non-communicable diseases (NCDs) were advertised. Future strategic plans should be prepared and implemented to manage NCD cases in case of pandemics. COVID-19 diabetes primary care Oman Introduction COVID-19 has affected the health systems in low-, middle- and high-income countries in many ways. COVID-19 disrupted health services, including cancer, TB screening, HIV detection, maternal health, children's vaccinations, and non-communicable disease (NCD) mortality. A 40% reduction in outpatient visits was reported in many countries (Arsenault et al., 2022; Xu et al., 2024) (( 1 )). The pandemic had an impact on almost all health aspects, its impact was more significant on NCD in almost all countries. Non-communicable diseases include many types, but diabetes, hypertension, cancer, asthma, and heart and kidney disease are the most prevalent. NCD account for 7 out of 10 major causes of premature death in general (Al-Qudimat et al. 2023)( 2 ). Approximately six months post-COVID-19 pandemic, the World Health Organization (WHO) assessed the impact of the pandemic on healthcare services for NCDs. The study revealed that of the 155 countries surveyed, “53% had partially or entirely disrupted healthcare services for treating hypertension, and 49% for treating diabetes and diabetes-related complications” (WHO.COVID-19)( 3 ) and between 14–44% of COVID-19 victims have diabetes (Al-Qudimat et al).( 2 ) Diabetic patients with SARS-CoV2 infection showed lower survival rates, poorer outcomes and longer hospitalization and mortality (Yan et al., 2020; Giannouchos et al., 2020; Noor,) ( 4 – 6 ). Furthermore, a comparison between patients with COVID-19 who are diabetic or not, showed a reduced chance of survival or recovery in diabetic patients (Al-Qudimat et al., 2023( 2 ) Worse clinical outcomes associated with SARS-CoV-2 infection in patients with diabetes, hypertension, liver conditions, and chronic kidney and respiratory diseases could be attributed to the upregulated expression of angiotensin-converting enzyme 2 ACE2 and cytokine storm. In addition, the comorbidities increase the sensitivity to COVID-19, such as increasing the severity of it by up to 3%by diabetes (Guo et al.) ( 7 ), or the virus can be the cause to develop more severe outcomes (Nikoloski et al., 2021) (( 8 ). A global survey of healthcare professionals found that 80% of chronic patients experienced worsening mental health during the pandemic (Chudasama et al., 2020;) ( 9 ). This could be an indirect impact of delaying or cancelling the patients’ visits. A Brazilian study on diabetic people found that 95.1% prefer to stay at home,38.4% postpone their appointments, and 59.5% have reduced physical activity. Furthermore, 59.4% experienced an increase, decrease, or greater variability in their glucose levels while monitoring blood glucose (Barone et al., 2020) ( 9 ). As a result, patients rely either on home visits or on telemedicine to manage their blood glucose (Al-Qudimat et al., 2022) (( 2 ). In India, COVID-19 lockdowns showed that 26.9% of type 1 DM patients missed their insulin doses, blood glucose monitoring was not performed in 38.5%, and 17.4% were not compliant with the diet during lockdown (Verma et al.2020) ( 10 ). Although telemedicine was employed to control blood glucose, 22% of diabetic patients had an increase in their blood glucose levels (Oraibi et al., 2022) (( 11 ). However, others found that glycaemic control improved in patients with T1D despite the lockdown's limitations. These results suggest that having more time for self-management may help improve glycaemic control in the short term (Fernández et al., 2020) (( 12 ). Lower acute physical activity adversely affected glycaemic control through a reduction in insulin sensitivity and lipid profiles, increased inflammation, and reduced muscle protein synthesis (Martinez-Ferran et al., 2020; Oliver et al., 2023) ( 13 , 14 ) COVID-19 alters the lipid profile with lower TC, TG, LDL and HDL-C, which eventually regulates the cytokine and immune response. These lower profiles are associated with the severity and mortality of the cases (Chidambaram et al., 2022) (( 15 ). Other studies showed that total cholesterol and LDL were significantly higher post-lockdown compared to pre-lockdown (Lia et al., 2022) (( 16 ). Cumulative evidence from different countries showed a high prevalence of hypertension among patients with COVID-19(Shibata et al.2020). (( 17 ) Oman was one of the countries affected by the pandemic, and accordingly, an impact on health services was noted. Most families have elderly members with NCDs. Some isolated them and reduced their visits to avoid being infected with SARS-CoV-2. It was not very clear whether the interruption of DM care during the pandemic has worsened patients’ clinical outcomes or not. Therefore, it is important to evaluate the pandemic’s effects of COVID-19 on diabetic control concerning glycated haemoglobin (HbA1c), blood pressure (BP), lipids mainly low-density lipoprotein (LDL), and weight (Body mass index BMI) and compare them with DM patients’ pre-pandemic, to provide evidence-based recommendations for DM care during pandemics and prepare for sustainability of care in the future. Methods Study Design and Setting: This is a pre-and post-retrospective cohort study of adults with DM followed up in DM primary care clinic. Population: All DM patients registered in the DM Clinic at Al-Khuwair Health Centre were included. The included patients had active follow-ups (physically attended the clinic or had a phone consultation with a blood workup). Inclusion criteria All Omani patients aged > 18 years diagnosed with type 1 or 2 DM and had active follow-ups between January 2019 and December 2020 and had annual blood workups. Exclusion criteria Newly diagnosed type 1 & 2 DM patients or existing DM patients without blood workups or follow-ups in 2019 were excluded. The outcomes of interest are service modification and lockdown effects during the COVID-19 pandemic on their glycaemic control. Data were collected from the National Diabetic Register (NDR) and Al-Shifa System, which was administered by doctors working in ALKhuwair Health Centre. The data were analysed using the SPSS program with the help of a statistician. Outcomes of interest: Glycaemic outcomes of patients followed in the DM clinic. This outcome was measured by determining patients’ clinical information extracted from the Al-Shifa System. The data included socioeconomic info: age, sex, DM risk factors, (BMI), BP, HbA1c, and LDL. The latest follow-up data during the pandemic were used. All clinical parameters were measured using standardised policies and procedures to ensure accuracy and homogeneity in all health centres. Blood HbA1c and LDL are other numerical variables measured in % and g/dL, respectively. General practitioners collected blood samples using standardised techniques and sent them to the laboratory for plasma analysis. The normal targets for DM patients are < 7% for HbA1c and < 2.6 g/dL or < 1.8 for LDL in patients without and with cardiovascular comorbidities, respectively. BMI is a numerical variable measured in kg/m 2 . The measurement was performed by trained nurses using calibrated scales. The patient is considered underweight if their BMI is < 18.5 kg/m 2 , normal weight if their BMI is 18.5–24.9 kg/m 2 , overweight if their BMI is 25–29.9 kg/m 2 , and obese if their BMI is > 30 kg/m 2 . BP is also a numerical variable measured in mmHg. It is measured by triage nurses using calibrated electronic measurement devices. The target BP in DM is < 140/80 for those without complications and < 130/75 for those with cardiac and kidney disease. Data were collected through an electronic data collection sheet created using Google Forms. Age A numerical variable was adjusted for when measuring the impact of DM care during the pandemic on patient clinical outcomes. This information was retrieved from NDR. Sex: A binary variable was retrieved from the NDR and adjusted for in our analyses. Comorbidity: These were categorical variables extracted from the NDR. Statistical analysis: Data were analysed using SPSS. Frequency analyses using the mean, median, and percentage will describe primary outcomes. Clinical outcomes were compared using Chi-square tests. Data collection and management: After ethical approval from the regional research committee in Muscat.The study was performed in accordance with the Declaration of Helsinki. Researchers involved in the research retrieved the data using an electronic sheet, which was guaranteed confidentiality through a password set. Results Out of 260 registered patients with diabetes who attended the DM clinic in 2019 and had a follow-up in 2020, 223 were included in the study, and 37 were excluded (new DM patients and existing patients without follow-up in 2019).The population showed characteristics of 52% were male, and 48% were female, mean age was 48.9% were > 60 years, 45.7% were 41–60 years, and 5.4% were 20–40 years (Table 1 & 2 ). Approximately, patients had type 2 DM (96%) with a mean duration of 10 years. All patients received DM service during the pandemic; 53.4% obtained physical consultations, and 46.6% had phone consultations (Table 3 ). Significant HbA1c, systolic BP, and BMI changes between 2019 and 2020 were observed. The mean HbA1c in 2019 (6.9%) was significantly lower than in 2020 (7.2%), with a mean difference of − 0.30 (95% confidence interval [CI]: −0.47–−0.12; P = 0.0010) ( Table 4 ) . The mean SBP in 2019 (131.22 mmHg) was significantly lower than in 2020 (134.84 mmHg), with a mean difference of − 3.62 (95% CI: −5.21–−2.02; P = 0.0001). The mean BMI in 2019 (30.49) was significantly lower than in 2020 (30.80), with a mean difference of − 0.31 (95% CI: −0.54–−0.08; P = 0.0090). The other metabolic parameters did not change significantly from 2019 to 2020. The mean LDL in 2019 (2.64) and 2020 (2.57) had a mean difference of 0.07 (95% CI: −0.05–0.21; P = 0.2400). The mean diastolic BP in 2019 (78.10) and 2020 (78.21) had a mean difference of − 0.11 (95% CI: −1.27–1.06; P = 0.856). Table 1 Sex percentages included in the study Frequency Percent Valid percent Cumulative percent Valid Male 116 52.0 52.0 52.0 Female 107 48.0 48.0 100 Total 223 100 100 Table 2 Age percentages included in the study Frequency Percent Valid percent Cumulative percent Valid 20–40 years 12 5.4 5.4 5.4 41–60 years 102 45.7 45.7 51.1 > 60 years 109 48.9 48.9 100 Total 223 100 100 Table 3 Phone consultations during the pandemic Frequency Percent Valid percent Cumulative percent Valid No 119 53.4 53.4 53.4 Yes 104 46.6 46.6 100 Total 223 100 100 Table 4 HbA1c values from 2019 to 2020 HbA1c in 2019 HbA1c in 2020 N Valid 219 184 Missing 4 39 Mean 6.96 7.28 Median 6.70 6.80 Standard deviation 1.28 1.71 Minimum 4.30 4.88 Maximum 12.49 16.10 Sex and HbA1c A one-way analysis of covariance (ANOVA) was performed to assess the significance of the difference between males and females in HbA1c-2020 when adjusting for covariates HbA1c-2019 and DM duration Males and females did not differ significantly in HbA1c-2020 after adjusting for covariates HbA1c-2019 and DM duration ( F (1, 177) = 3.187, P = 0.0760). Table 5 & 6 . Table 5 Unadjusted and covariate-adjusted HbA1c-2020 by sex Sex N Unadjusted Adjusted Mean SEM Mean SEM Male 95 7.38 0.20 7.44 0.12 Female 86 7.18 0.15 7.12 0.13 Key: SEM, standard error of the mean. Table 6 ANOVA of HbA1c-2020 by sex with HbA1c-2019 and DM duration as covariates Source Type III sum of squares df Mean square F P Partial η 2 HbA1c-2019 (covariate) 284.336 1 284.336 208.444 0.000 0.541 DM duration (covariate) 0.760 1 0.760 0.557 0.456 0.003 Sex 4.347 1 4.347 3.187 0.076 0.018 Error 241.444 177 1.364 Key: df, degrees of freedom. Unadjusted R 2 = 0.548. Adjusted R 2 = 0.540. Age and HbA1c A one-way ANOVA was performed to assess the significance of differences between the three age groups in HbA1c-2020 when adjusting for covariates HbA1c-2019 and DM duration The three age groups did not differ significantly in HbA1c-2020 after adjusting for covariates HbA1c-2019 and DM duration ( F (2, 176) = 1.881, P = 0.1555. Table 7 & 8 . Table 7 Unadjusted and covariate-adjusted HbA1c-2020 by age Age N Unadjusted Adjusted Mean SEM Mean SEM 20–40 years 10 8.72 0.49 7.84 0.38 41–60 years 86 7.45 0.20 7.39 0.13 > 60 years 88 6.96 0.15 7.12 0.13 Key: SEM, standard error of the mean. Table 8 ANCOVA of HbA1c-2020 by age with HbA1c-2019 and DM duration as covariates Source Type III sum of squares df Mean square F P Partial η 2 HbA1c-2019 (covariate) 252.690 1 252.690 184.809 0.000 0.512 Duration of Diabetes (covariate) 0.802 1 0.802 0.587 0.445 0.003 Age 5.145 2 2.572 1.881 0.155 0.021 Error 240.646 176 1.367 Key: df, degrees of freedom. Unadjusted R 2 = 0.549. Adjusted R 2 = 0.539. Discussion This paper aimed to provide evidence on the impact of COVID-19 on diabetic control, including HBA1C, LDL, BP, and BMI, in diabetic patients attending Alkhuwair Health Centre, comparing 2019 (pre-COVID) and 2020 (post-COVID). The results showed statistically significant HbA1c, systolic BP, and BMI changes between 2019 and 2020. The mean HbA1c in 2019 (6.9%) was significantly lower than in 2020 (7.2%). After resuming the clinic, it was noticed that some patients had higher glycated haemoglobin (HbA1c) levels than last year. SARS-CoV2 virus could interfere with the glucose metabolism pathways by triggering the reprogramming of the glucose metabolism through AMP activating protein kinase with no effect on the pancreas (Rochowski et al.,) ( 18 ). HBA1c results are consistent with a study that found that HbA1c values significantly increased from 7.45–7.53% during the pandemic (Tanji et al., 2021( 19 ). However, a Spanish study on the COVID-19 lockdown’s impact on glycaemic control in patients with type 1 DM found that despite the lockdown, there were improvements in glycaemic control in patients with type 1 DM due to self-management (Fernández et al., 2020;) ( 12 ). Both sex and age groups did not differ significantly in our study regarding HbA1c in 2020. These results were inconsistent with the conclusion of Tanji et al., who noticed that the deterioration in HbA1c values was more apparent in women, patients aged ≥ 65 years, patients with BMI > 25, and patients not using insulin (Tanji et al., 2021)(( 19 ). The mean BMI in 2019 (30.49) was significantly lower than in 2020 (30.80). This finding can be explained by the lockdown, reducing physical activity, and changing lifestyle habits which lead to raising awareness about the importance of nutritional status for a healthy lifestyle (Al Agha et al., 2021; Urzeala et al) ( 20 , 21 ). Considering the extreme weight categories associated with severe COVID-19 complication risk, we noticed that 13.11% of the total sample fell into these vulnerable categories. Being overweight or obese is an independent risk factor in severe COVID-19 patients because enhanced adiposity diminishes pulmonary function (Urzeala et al., 2022) ( 21 ). Healthy lifestyles and choices should be promoted in primary care centres with the help of multidisciplinary teams. Obesity and overweight rates are on the rise, especially in the eastern Mediterranean region, which will cause a further burden on our health systems (Nejadghaderi et al. 2023( 22 ) Another factor that could be implicated here is stress, anxiety, and isolation, especially for older people, which was reported during the COVID-19 pandemic (Palmer et al., 2020; Urzeala et ( 21 , 23 ). Stress is an important factor implicated in the dysfunctionality of the sympathetic nervous system and the hypothalamus that leads to obesity. The other consequence of stress is the tendency to develop eating disorders and lower physical activity. All these factors might explain the increase in BMI noticed in the study cohort (Correia et al., 2021) (( 24 ). Positive relationships were found between dealing with stress related to COVID-19 in patients with NCD and active coping strategies, for example, self-distraction, denial, substance use, behavioral disengagement, venting, planning, religion, and self-blame (Umucu et al.2020)( 25 ) In addition to HBA1c and weight, clinic patients showed high systolic BP and LDL. The results are in line with Akpek 2020, which suggests that infection with SARS-CoV-2 increases systolic and diastolic BP and could lead to hypertension(Akpek 202) ( 26 ). In our study, only systolic BP was significantly higher, but diastolic BP was not. However, the results contradict Feitosa et al., who showed no considerable adverse impact of COVID-19 on office and home BP (Feitosa et al.).( 27 ) Many studies considered an association between antihypertensive medication classes and patient outcomes, but almost all are retrospective investigations or meta-analyses. Therefore, well-conducted research with a considerable number of hypertensive patients is necessary to resolve current controversies about the relationship between hypertension and COVID-19 (Tadic et al., 2021( 28 ) Telemedicine consultation existed at a comparable percentage to physical consultation, which is attributed to the fact that in Oman, the health system, like other countries, adopted many changes in NCD routine management (Bouabida et al., 2022; Habbash et al., 2023; Ullas et al., ( 29 – 31 ). The Directorate of General Health Services in Muscat implemented a telemedicine clinic twice weekly for patient follow-up and consultation in the primary care setting. So, the results align with Chudasama et al., in 2020, which showed that 45% of the participants’ healthcare providers performed telephone ( 32 ). Furthermore, a WHO survey of 155 countries found that 58% now use telemedicine to replace in-person consultation (WHO), ( 3 ). Limitations: Residual confounding can be a challenge in observational studies. To address this, we should include as many confounders as possible in the regression analysis and seek the opinions of clinical experts. Additionally, sensitivity analysis was employed to evaluate any hidden residual confounding, such as mental instability. Negative outcome control could be applied to explore any hidden confounding due to measurement errors for lifestyle factors. Missing data is a major potential limitation. We tried to locate and reference relevant sources of patient information to retrieve any missing data from electronic health records. Furthermore, we performed a sensitivity analysis to manage the missing data Selection bias was another concern. Some of the Omani population receive medical care in private institutions that are not registered in the national registries. Conclusion The COVID-19 pandemic is ongoing with new emerging strains, and many studies have been published exploring COVID-19’s impact on different diseases. In this study, we can conclude that there was a significant adverse effect on glycaemic control in DM patients. This finding suggests that we must provide extra care to patients with non-communicable diseases under normal conditions to prepare them to face such future challenges. This can be achieved by promoting healthy lifestyles and ensuring that our healthcare systems advocate for healthy environments where people can practice a healthy lifestyle and access nutritious foods and activity centres. Additionally, having primary care centres as their support can further facilitate this goal. List of abbreviations Diabetes mellitus (DM) Hypertension (HTN) glycated haemoglobin (HbA1c), Blood pressure (BP) low-density lipoprotein (LDL) Body mass index (BMI) Statistical Package for the Social Sciences (SPSS) National Diabetic Register (NDR) non-communicable diseases (NCDs) World Health Organization (WHO) A one-way analysis of covariance (ANOVA) Declarations Ethics approval and consent to participate: The study was performed in accordance with the Declaration of Helsinki. Written approval from the regional research committee in Muscat region was obtained. (MoH/CSR/21/24309) Consent for publication: Not applicable Availability of data and materials: The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request. Competing interests: The authors declare that they have no competing interests Funding: no funding required Authors' contributions: F.A: wrote the proposal, Data collection and analysis, wrote the whole paper. S.A: wrote the proposal, Data collection and analysis, helped in writing the paper. M.A: Data collection and literature review. G.A: Data collection and literature review C.T: major contributor in writing the manuscript, read and approved the final manuscript Z.A: major contributor in writing the manuscript, read and approved the final manuscript Acknowledgements: Dr. Mezon Tufail, consultant family physician, Ministry of health Oman. Major help in formulating the research idea, gathering the data. Authors' information (optional): Fakhria is senior specialist family physician, graduated from Oman medical specialty board, has MD/MRCGP/OMSB II/Arab board qualifications. 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Telemedicine in non-communicable chronic diseases care during the COVID-19 pandemic: exploring patients’ perspectives. Front Public Health. 2023;11:1270069. Ullas S, Pradeep M, Surendran S, Ravikumar A, Bastine AM, Prasad A, et al. Telemedicine During the COVID-19 Pandemic: A Paradigm Shift in Non-Communicable Disease Management? - A Cross-Sectional Survey from a Quaternary-Care Center in South India. Patient Prefer Adherence. 2021;15:2715–23. Chudasama YV, Gillies CL, Zaccardi F, Coles B, Davies MJ, Seidu S, et al. Impact of COVID-19 on routine care for chronic diseases: A global survey of views from healthcare professionals. Diabetes Metab Syndr. 2020;14(5):965–7. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 21 Dec, 2024 Read the published version in BMC Primary Care → Version 1 posted Editorial decision: Revision requested 03 Jul, 2024 Editor assigned by journal 03 Jul, 2024 Submission checks completed at journal 03 Jul, 2024 First submitted to journal 30 Jun, 2024 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {\"props\":{\"pageProps\":{\"initialData\":{\"identity\":\"rs-4662891\",\"acceptedTermsAndConditions\":true,\"allowDirectSubmit\":false,\"archivedVersions\":[],\"articleType\":\"Research Article\",\"associatedPublications\":[],\"authors\":[{\"id\":322167040,\"identity\":\"253da1e9-1fb6-49c7-8c6b-efc749291d53\",\"order_by\":0,\"name\":\"Fakhria Al Rashdi\",\"email\":\"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABDElEQVRIie2RMWvCQBTHXzhIllDXCMV+hVc6uCVf5Y4MXRwchRZsEeIizvkYugiCw4NAs6R0dXBIFme7FApFfAdxKM1VR8H7wcHdH36P/+MALJYLJCJwSF8Q1Gsp9U3UCfjNCvI5KiOsFThXkW5wTE8ooiRnFUZdL04GVW/T6Y45gecQsKDmXcBFgm2slpMqWavF9uE208lbDPj+YiqG2Q8JiWullUylAlhxCfDDVMzbEdAw0kqflWEqdLL/T/F5JmXOjBVgRQaCEychczHh91nJ1ayoRgHvcp/qRE1jv21YH73xvAR6ijB/rD6/F5u7oJXPy91X2LkpZHMz8ftZD5bGX/lLcxeLxWK5bg7l92ZglfBEAgAAAABJRU5ErkJggg==\",\"orcid\":\"\",\"institution\":\"Imperial College London\",\"correspondingAuthor\":true,\"prefix\":\"\",\"firstName\":\"Fakhria\",\"middleName\":\"Al\",\"lastName\":\"Rashdi\",\"suffix\":\"\"},{\"id\":322167041,\"identity\":\"e6aaa3ba-d2b5-4e64-b4ab-253bbc4d9f59\",\"order_by\":1,\"name\":\"Salwa Al Harasi\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"Ministry of Health\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Salwa\",\"middleName\":\"Al\",\"lastName\":\"Harasi\",\"suffix\":\"\"},{\"id\":322167042,\"identity\":\"2ed3967b-d98a-4046-9310-8085f24c5ef9\",\"order_by\":2,\"name\":\"Mohammed Al Ismaili\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"Ministry of Health\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Mohammed\",\"middleName\":\"Al\",\"lastName\":\"Ismaili\",\"suffix\":\"\"},{\"id\":322167044,\"identity\":\"54021f36-5111-413e-a789-ed8d922ded06\",\"order_by\":3,\"name\":\"AL Ghalia AlYaqoobi\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"Ministry of Health\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"AL\",\"middleName\":\"Ghalia\",\"lastName\":\"AlYaqoobi\",\"suffix\":\"\"},{\"id\":322167046,\"identity\":\"77802579-5095-491b-8964-90b34bd41645\",\"order_by\":4,\"name\":\"Zeenah Atwan\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"Imperial College London\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Zeenah\",\"middleName\":\"\",\"lastName\":\"Atwan\",\"suffix\":\"\"},{\"id\":322167048,\"identity\":\"b3447e18-a864-40df-88e1-b47f15b89828\",\"order_by\":5,\"name\":\"Celine Tabche\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"Imperial College London\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Celine\",\"middleName\":\"\",\"lastName\":\"Tabche\",\"suffix\":\"\"}],\"badges\":[],\"createdAt\":\"2024-06-30 13:27:58\",\"currentVersionCode\":1,\"declarations\":\"\",\"doi\":\"10.21203/rs.3.rs-4662891/v1\",\"doiUrl\":\"https://doi.org/10.21203/rs.3.rs-4662891/v1\",\"draftVersion\":[],\"editorialEvents\":[{\"content\":\"https://doi.org/10.1186/s12875-024-02672-2\",\"type\":\"published\",\"date\":\"2024-12-21T15:57:59+00:00\"}],\"editorialNote\":\"\",\"failedWorkflow\":false,\"files\":[{\"id\":72201864,\"identity\":\"6bcfe28a-5d85-428b-ac43-355ad0c81b53\",\"added_by\":\"auto\",\"created_at\":\"2024-12-23 16:11:22\",\"extension\":\"pdf\",\"order_by\":0,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"manuscript-pdf\",\"size\":578518,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"manuscript.pdf\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-4662891/v1/a52e6f57-2396-4d0a-9167-835ad720952b.pdf\"}],\"financialInterests\":\"No competing interests reported.\",\"formattedTitle\":\"The profound impact of COVID-19 on the control and care of diabetic patients: a comprehensive retrospective cohort study\",\"fulltext\":[{\"header\":\"Introduction\",\"content\":\"\\u003cp\\u003eCOVID-19 has affected the health systems in low-, middle- and high-income countries in many ways. COVID-19 disrupted health services, including cancer, TB screening, HIV detection, maternal health, children's vaccinations, and non-communicable disease (NCD) mortality. A 40% reduction in outpatient visits was reported in many countries (Arsenault et al., 2022; Xu et al., 2024) ((\\u003cspan citationid=\\\"CR1\\\" class=\\\"CitationRef\\\"\\u003e1\\u003c/span\\u003e)). The pandemic had an impact on almost all health aspects, its impact was more significant on NCD in almost all countries. Non-communicable diseases include many types, but diabetes, hypertension, cancer, asthma, and heart and kidney disease are the most prevalent. NCD account for 7 out of 10 major causes of premature death in general (Al-Qudimat et al. 2023)(\\u003cspan citationid=\\\"CR2\\\" class=\\\"CitationRef\\\"\\u003e2\\u003c/span\\u003e). Approximately six months post-COVID-19 pandemic, the World Health Organization (WHO) assessed the impact of the pandemic on healthcare services for NCDs. The study revealed that of the 155 countries surveyed, \\u0026ldquo;53% had partially or entirely disrupted healthcare services for treating hypertension, and 49% for treating diabetes and diabetes-related complications\\u0026rdquo; (WHO.COVID-19)(\\u003cspan citationid=\\\"CR3\\\" class=\\\"CitationRef\\\"\\u003e3\\u003c/span\\u003e) and between 14\\u0026ndash;44% of COVID-19 victims have diabetes (Al-Qudimat et al).(\\u003cspan citationid=\\\"CR2\\\" class=\\\"CitationRef\\\"\\u003e2\\u003c/span\\u003e) Diabetic patients with SARS-CoV2 infection showed lower survival rates, poorer outcomes and longer hospitalization and mortality (Yan et al., 2020; Giannouchos et al., 2020; Noor,) (\\u003cspan additionalcitationids=\\\"CR5\\\" citationid=\\\"CR4\\\" class=\\\"CitationRef\\\"\\u003e4\\u003c/span\\u003e\\u0026ndash;\\u003cspan citationid=\\\"CR6\\\" class=\\\"CitationRef\\\"\\u003e6\\u003c/span\\u003e). Furthermore, a comparison between patients with COVID-19 who are diabetic or not, showed a reduced chance of survival or recovery in diabetic patients (Al-Qudimat et al., 2023(\\u003cspan citationid=\\\"CR2\\\" class=\\\"CitationRef\\\"\\u003e2\\u003c/span\\u003e)\\u003c/p\\u003e \\u003cp\\u003eWorse clinical outcomes associated with SARS-CoV-2 infection in patients with diabetes, hypertension, liver conditions, and chronic kidney and respiratory diseases could be attributed to the upregulated expression of angiotensin-converting enzyme 2 ACE2 and cytokine storm. In addition, the comorbidities increase the sensitivity to COVID-19, such as increasing the severity of it by up to 3%by diabetes (Guo et al.) (\\u003cspan citationid=\\\"CR7\\\" class=\\\"CitationRef\\\"\\u003e7\\u003c/span\\u003e), or the virus can be the cause to develop more severe outcomes (Nikoloski et al., 2021) ((\\u003cspan citationid=\\\"CR8\\\" class=\\\"CitationRef\\\"\\u003e8\\u003c/span\\u003e). A global survey of healthcare professionals found that 80% of chronic patients experienced worsening mental health during the pandemic (Chudasama et al., 2020;) (\\u003cspan citationid=\\\"CR9\\\" class=\\\"CitationRef\\\"\\u003e9\\u003c/span\\u003e). This could be an indirect impact of delaying or cancelling the patients\\u0026rsquo; visits.\\u003c/p\\u003e \\u003cp\\u003eA Brazilian study on diabetic people found that 95.1% prefer to stay at home,38.4% postpone their appointments, and 59.5% have reduced physical activity. Furthermore, 59.4% experienced an increase, decrease, or greater variability in their glucose levels while monitoring blood glucose (Barone et al., 2020) (\\u003cspan citationid=\\\"CR9\\\" class=\\\"CitationRef\\\"\\u003e9\\u003c/span\\u003e). As a result, patients rely either on home visits or on telemedicine to manage their blood glucose (Al-Qudimat et al., 2022) ((\\u003cspan citationid=\\\"CR2\\\" class=\\\"CitationRef\\\"\\u003e2\\u003c/span\\u003e). In India, COVID-19 lockdowns showed that 26.9% of type 1 DM patients missed their insulin doses, blood glucose monitoring was not performed in 38.5%, and 17.4% were not compliant with the diet during lockdown (Verma et al.2020) (\\u003cspan citationid=\\\"CR10\\\" class=\\\"CitationRef\\\"\\u003e10\\u003c/span\\u003e). Although telemedicine was employed to control blood glucose, 22% of diabetic patients had an increase in their blood glucose levels (Oraibi et al., 2022) ((\\u003cspan citationid=\\\"CR11\\\" class=\\\"CitationRef\\\"\\u003e11\\u003c/span\\u003e). However, others found that glycaemic control improved in patients with T1D despite the lockdown's limitations. These results suggest that having more time for self-management may help improve glycaemic control in the short term (Fern\\u0026aacute;ndez et al., 2020) ((\\u003cspan citationid=\\\"CR12\\\" class=\\\"CitationRef\\\"\\u003e12\\u003c/span\\u003e).\\u003c/p\\u003e \\u003cp\\u003eLower acute physical activity adversely affected glycaemic control through a reduction in insulin sensitivity and lipid profiles, increased inflammation, and reduced muscle protein synthesis (Martinez-Ferran et al., 2020; Oliver et al., 2023) (\\u003cspan citationid=\\\"CR13\\\" class=\\\"CitationRef\\\"\\u003e13\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR14\\\" class=\\\"CitationRef\\\"\\u003e14\\u003c/span\\u003e) COVID-19 alters the lipid profile with lower TC, TG, LDL and HDL-C, which eventually regulates the cytokine and immune response. These lower profiles are associated with the severity and mortality of the cases (Chidambaram et al., 2022) ((\\u003cspan citationid=\\\"CR15\\\" class=\\\"CitationRef\\\"\\u003e15\\u003c/span\\u003e). Other studies showed that total cholesterol and LDL were significantly higher post-lockdown compared to pre-lockdown (Lia et al., 2022) ((\\u003cspan citationid=\\\"CR16\\\" class=\\\"CitationRef\\\"\\u003e16\\u003c/span\\u003e). Cumulative evidence from different countries showed a high prevalence of hypertension among patients with COVID-19(Shibata et al.2020). ((\\u003cspan citationid=\\\"CR17\\\" class=\\\"CitationRef\\\"\\u003e17\\u003c/span\\u003e)\\u003c/p\\u003e \\u003cp\\u003eOman was one of the countries affected by the pandemic, and accordingly, an impact on health services was noted. Most families have elderly members with NCDs. Some isolated them and reduced their visits to avoid being infected with SARS-CoV-2.\\u003c/p\\u003e \\u003cp\\u003eIt was not very clear whether the interruption of DM care during the pandemic has worsened patients\\u0026rsquo; clinical outcomes or not. Therefore, it is important to evaluate the pandemic\\u0026rsquo;s effects of COVID-19 on diabetic control concerning glycated haemoglobin (HbA1c), blood pressure (BP), lipids mainly low-density lipoprotein (LDL), and weight (Body mass index BMI) and compare them with DM patients\\u0026rsquo; pre-pandemic, to provide evidence-based recommendations for DM care during pandemics and prepare for sustainability of care in the future.\\u003c/p\\u003e\"},{\"header\":\"Methods\",\"content\":\"\\u003cdiv id=\\\"Sec3\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003eStudy Design and Setting:\\u003c/h2\\u003e \\u003cp\\u003eThis is a pre-and post-retrospective cohort study of adults with DM followed up in DM primary care clinic.\\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec4\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003ePopulation:\\u003c/h2\\u003e \\u003cp\\u003eAll DM patients registered in the DM Clinic at Al-Khuwair Health Centre were included. The included patients had active follow-ups (physically attended the clinic or had a phone consultation with a blood workup).\\u003c/p\\u003e \\u003cp\\u003eInclusion criteria\\u003c/p\\u003e \\u003cp\\u003eAll Omani patients aged\\u0026thinsp;\\u0026gt;\\u0026thinsp;18 years diagnosed with type 1 or 2 DM and had active follow-ups between January 2019 and December 2020 and had annual blood workups.\\u003c/p\\u003e \\u003cp\\u003eExclusion criteria\\u003c/p\\u003e \\u003cp\\u003eNewly diagnosed type 1 \\u0026amp; 2 DM patients or existing DM patients without blood workups or follow-ups in 2019 were excluded.\\u003c/p\\u003e \\u003cp\\u003eThe outcomes of interest are service modification and lockdown effects during the COVID-19 pandemic on their glycaemic control.\\u003c/p\\u003e \\u003cp\\u003eData were collected from the National Diabetic Register (NDR) and Al-Shifa System, which was administered by doctors working in ALKhuwair Health Centre. The data were analysed using the SPSS program with the help of a statistician.\\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec5\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003eOutcomes of interest:\\u003c/h2\\u003e \\u003cp\\u003e\\u003cb\\u003e Glycaemic outcomes of patients followed in the DM clinic.\\u003c/b\\u003e\\u003c/p\\u003e \\u003cp\\u003eThis outcome was measured by determining patients\\u0026rsquo; clinical information extracted from the Al-Shifa System. The data included socioeconomic info: age, sex, DM risk factors, (BMI), BP, HbA1c, and LDL. The latest follow-up data during the pandemic were used. All clinical parameters were measured using standardised policies and procedures to ensure accuracy and homogeneity in all health centres.\\u003c/p\\u003e \\u003cp\\u003eBlood HbA1c and LDL are other numerical variables measured in % and g/dL, respectively. General practitioners collected blood samples using standardised techniques and sent them to the laboratory for plasma analysis. The normal targets for DM patients are \\u0026lt;\\u0026thinsp;7% for HbA1c and \\u0026lt;\\u0026thinsp;2.6 g/dL or \\u0026lt;\\u0026thinsp;1.8 for LDL in patients without and with cardiovascular comorbidities, respectively.\\u003c/p\\u003e \\u003cp\\u003eBMI is a numerical variable measured in kg/m\\u003csup\\u003e2\\u003c/sup\\u003e. The measurement was performed by trained nurses using calibrated scales. The patient is considered underweight if their BMI is \\u0026lt;\\u0026thinsp;18.5 kg/m\\u003csup\\u003e2\\u003c/sup\\u003e, normal weight if their BMI is 18.5\\u0026ndash;24.9 kg/m\\u003csup\\u003e2\\u003c/sup\\u003e, overweight if their BMI is 25\\u0026ndash;29.9 kg/m\\u003csup\\u003e2\\u003c/sup\\u003e, and obese if their BMI is \\u0026gt;\\u0026thinsp;30 kg/m\\u003csup\\u003e2\\u003c/sup\\u003e. BP is also a numerical variable measured in mmHg. It is measured by triage nurses using calibrated electronic measurement devices.\\u003c/p\\u003e \\u003cp\\u003eThe target BP in DM is \\u0026lt;\\u0026thinsp;140/80 for those without complications and \\u0026lt;\\u0026thinsp;130/75 for those with cardiac and kidney disease.\\u003c/p\\u003e \\u003cp\\u003eData were collected through an electronic data collection sheet created using Google Forms.\\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec6\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003eAge\\u003c/h2\\u003e \\u003cp\\u003eA numerical variable was adjusted for when measuring the impact of DM care during the pandemic on patient clinical outcomes. This information was retrieved from NDR.\\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec7\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003eSex:\\u003c/h2\\u003e \\u003cp\\u003eA binary variable was retrieved from the NDR and adjusted for in our analyses.\\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec8\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003eComorbidity:\\u003c/h2\\u003e \\u003cp\\u003eThese were categorical variables extracted from the NDR.\\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec9\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003eStatistical analysis:\\u003c/h2\\u003e \\u003cp\\u003eData were analysed using SPSS. Frequency analyses using the mean, median, and percentage will describe primary outcomes. Clinical outcomes were compared using Chi-square tests.\\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec10\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003eData collection and management:\\u003c/h2\\u003e \\u003cp\\u003e After ethical approval from the regional research committee in Muscat.The study was performed in accordance with the Declaration of Helsinki. Researchers involved in the research retrieved the data using an electronic sheet, which was guaranteed confidentiality through a password set.\\u003c/p\\u003e \\u003c/div\\u003e\"},{\"header\":\"Results\",\"content\":\"\\u003cp\\u003eOut of 260 registered patients with diabetes who attended the DM clinic in 2019 and had a follow-up in 2020, 223 were included in the study, and 37 were excluded (new DM patients and existing patients without follow-up in 2019).The population showed characteristics of 52% were male, and 48% were female, mean age was 48.9% were \\u0026gt;\\u0026thinsp;60 years, 45.7% were 41\\u0026ndash;60 years, and 5.4% were 20\\u0026ndash;40 years (Table\\u0026nbsp;\\u003cspan refid=\\\"Tab1\\\" class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003e\\u0026amp;\\u003cspan refid=\\\"Tab2\\\" class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003e). Approximately, patients had type 2 DM (96%) with a mean duration of 10 years. All patients received DM service during the pandemic; 53.4% obtained physical consultations, and 46.6% had phone consultations (Table\\u0026nbsp;\\u003cspan refid=\\\"Tab3\\\" class=\\\"InternalRef\\\"\\u003e3\\u003c/span\\u003e).\\u003c/p\\u003e \\u003cp\\u003eSignificant HbA1c, systolic BP, and BMI changes between 2019 and 2020 were observed.\\u003c/p\\u003e \\u003cp\\u003eThe mean HbA1c in 2019 (6.9%) was significantly lower than in 2020 (7.2%), with a mean difference of \\u0026minus;\\u0026thinsp;0.30 (95% confidence interval [CI]: \\u0026minus;0.47\\u0026ndash;\\u0026minus;0.12; P\\u0026thinsp;=\\u0026thinsp;0.0010) \\u003cb\\u003e(\\u003c/b\\u003eTable\\u0026nbsp;\\u003cspan refid=\\\"Tab4\\\" class=\\\"InternalRef\\\"\\u003e4\\u003c/span\\u003e\\u003cb\\u003e)\\u003c/b\\u003e.\\u003c/p\\u003e \\u003cp\\u003eThe mean SBP in 2019 (131.22 mmHg) was significantly lower than in 2020 (134.84 mmHg), with a mean difference of \\u0026minus;\\u0026thinsp;3.62 (95% CI: \\u0026minus;5.21\\u0026ndash;\\u0026minus;2.02; \\u003cem\\u003eP\\u0026thinsp;=\\u003c/em\\u003e\\u0026thinsp;0.0001).\\u003c/p\\u003e \\u003cp\\u003eThe mean BMI in 2019 (30.49) was significantly lower than in 2020 (30.80), with a mean difference of \\u0026minus;\\u0026thinsp;0.31 (95% CI: \\u0026minus;0.54\\u0026ndash;\\u0026minus;0.08; \\u003cem\\u003eP\\u003c/em\\u003e\\u0026thinsp;=\\u0026thinsp;0.0090).\\u003c/p\\u003e \\u003cp\\u003eThe other metabolic parameters did not change significantly from 2019 to 2020.\\u003c/p\\u003e \\u003cp\\u003eThe mean LDL in 2019 (2.64) and 2020 (2.57) had a mean difference of 0.07 (95% CI: \\u0026minus;0.05\\u0026ndash;0.21; \\u003cem\\u003eP\\u003c/em\\u003e\\u0026thinsp;=\\u0026thinsp;0.2400). The mean diastolic BP in 2019 (78.10) and 2020 (78.21) had a mean difference of \\u0026minus;\\u0026thinsp;0.11 (95% CI: \\u0026minus;1.27\\u0026ndash;1.06; \\u003cem\\u003eP\\u003c/em\\u003e\\u0026thinsp;=\\u0026thinsp;0.856).\\u003c/p\\u003e \\u003cp\\u003e \\u003cdiv class=\\\"gridtable\\\"\\u003e\\u003ctable float=\\\"Yes\\\" id=\\\"Tab1\\\" border=\\\"1\\\"\\u003e \\u003ccaption language=\\\"En\\\"\\u003e \\u003cdiv class=\\\"CaptionNumber\\\"\\u003eTable 1\\u003c/div\\u003e \\u003cdiv class=\\\"CaptionContent\\\"\\u003e \\u003cp\\u003eSex percentages included in the study\\u003c/p\\u003e \\u003c/div\\u003e \\u003c/caption\\u003e \\u003ccolgroup cols=\\\"6\\\"\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c1\\\" colnum=\\\"1\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c2\\\" colnum=\\\"2\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"char\\\" char=\\\".\\\" class=\\\"colspec\\\" colname=\\\"c3\\\" colnum=\\\"3\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c4\\\" colnum=\\\"4\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c5\\\" colnum=\\\"5\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c6\\\" colnum=\\\"6\\\"\\u003e\\u003c/div\\u003e \\u003cthead\\u003e \\u003ctr\\u003e \\u003cth align=\\\"left\\\" colspan=\\\"2\\\" nameend=\\\"c2\\\" namest=\\\"c1\\\"\\u003e\\u0026nbsp;\\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eFrequency\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003ePercent\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003eValid percent\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003eCumulative percent\\u003c/p\\u003e \\u003c/th\\u003e \\u003c/tr\\u003e \\u003c/thead\\u003e \\u003ctbody\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\" morerows=\\\"2\\\" rowspan=\\\"3\\\"\\u003e \\u003cp\\u003eValid\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eMale\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e116\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e52.0\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e52.0\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e52.0\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eFemale\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e107\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e48.0\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e48.0\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e100\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eTotal\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e223\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e100\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e100\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003c/tr\\u003e \\u003c/tbody\\u003e \\u003c/colgroup\\u003e \\u003c/table\\u003e\\u003c/div\\u003e \\u003c/p\\u003e \\u003cp\\u003e \\u003cdiv class=\\\"gridtable\\\"\\u003e\\u003ctable float=\\\"Yes\\\" id=\\\"Tab2\\\" border=\\\"1\\\"\\u003e \\u003ccaption language=\\\"En\\\"\\u003e \\u003cdiv class=\\\"CaptionNumber\\\"\\u003eTable 2\\u003c/div\\u003e \\u003cdiv class=\\\"CaptionContent\\\"\\u003e \\u003cp\\u003eAge percentages included in the study\\u003c/p\\u003e \\u003c/div\\u003e \\u003c/caption\\u003e \\u003ccolgroup cols=\\\"6\\\"\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c1\\\" colnum=\\\"1\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c2\\\" colnum=\\\"2\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"char\\\" char=\\\".\\\" class=\\\"colspec\\\" colname=\\\"c3\\\" colnum=\\\"3\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c4\\\" colnum=\\\"4\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c5\\\" colnum=\\\"5\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c6\\\" colnum=\\\"6\\\"\\u003e\\u003c/div\\u003e \\u003cthead\\u003e \\u003ctr\\u003e \\u003cth align=\\\"left\\\" colspan=\\\"2\\\" nameend=\\\"c2\\\" namest=\\\"c1\\\"\\u003e\\u0026nbsp;\\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eFrequency\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003ePercent\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003eValid percent\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003eCumulative percent\\u003c/p\\u003e \\u003c/th\\u003e \\u003c/tr\\u003e \\u003c/thead\\u003e \\u003ctbody\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\" morerows=\\\"3\\\" rowspan=\\\"4\\\"\\u003e \\u003cp\\u003eValid\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e20\\u0026ndash;40 years\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e12\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e5.4\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e5.4\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e5.4\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e41\\u0026ndash;60 years\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e102\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e45.7\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e45.7\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e51.1\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e\\u0026gt;\\u0026thinsp;60 years\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e109\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e48.9\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e48.9\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e100\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eTotal\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e223\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e100\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e100\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003c/tr\\u003e \\u003c/tbody\\u003e \\u003c/colgroup\\u003e \\u003c/table\\u003e\\u003c/div\\u003e \\u003c/p\\u003e \\u003cp\\u003e \\u003cdiv class=\\\"gridtable\\\"\\u003e\\u003ctable float=\\\"Yes\\\" id=\\\"Tab3\\\" border=\\\"1\\\"\\u003e \\u003ccaption language=\\\"En\\\"\\u003e \\u003cdiv class=\\\"CaptionNumber\\\"\\u003eTable 3\\u003c/div\\u003e \\u003cdiv class=\\\"CaptionContent\\\"\\u003e \\u003cp\\u003ePhone consultations during the pandemic\\u003c/p\\u003e \\u003c/div\\u003e \\u003c/caption\\u003e \\u003ccolgroup cols=\\\"6\\\"\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c1\\\" colnum=\\\"1\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c2\\\" colnum=\\\"2\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"char\\\" char=\\\".\\\" class=\\\"colspec\\\" colname=\\\"c3\\\" colnum=\\\"3\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c4\\\" colnum=\\\"4\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c5\\\" colnum=\\\"5\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c6\\\" colnum=\\\"6\\\"\\u003e\\u003c/div\\u003e \\u003cthead\\u003e \\u003ctr\\u003e \\u003cth align=\\\"left\\\" colspan=\\\"2\\\" nameend=\\\"c2\\\" namest=\\\"c1\\\"\\u003e\\u0026nbsp;\\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eFrequency\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003ePercent\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003eValid percent\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003eCumulative percent\\u003c/p\\u003e \\u003c/th\\u003e \\u003c/tr\\u003e \\u003c/thead\\u003e \\u003ctbody\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\" morerows=\\\"2\\\" rowspan=\\\"3\\\"\\u003e \\u003cp\\u003eValid\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eNo\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e119\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e53.4\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e53.4\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e53.4\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eYes\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e104\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e46.6\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e46.6\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e100\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eTotal\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e223\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e100\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e100\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003c/tr\\u003e \\u003c/tbody\\u003e \\u003c/colgroup\\u003e \\u003c/table\\u003e\\u003c/div\\u003e \\u003c/p\\u003e \\u003cp\\u003e \\u003cdiv class=\\\"gridtable\\\"\\u003e\\u003ctable float=\\\"Yes\\\" id=\\\"Tab4\\\" border=\\\"1\\\"\\u003e \\u003ccaption language=\\\"En\\\"\\u003e \\u003cdiv class=\\\"CaptionNumber\\\"\\u003eTable 4\\u003c/div\\u003e \\u003cdiv class=\\\"CaptionContent\\\"\\u003e \\u003cp\\u003eHbA1c values from 2019 to 2020\\u003c/p\\u003e \\u003c/div\\u003e \\u003c/caption\\u003e \\u003ccolgroup cols=\\\"4\\\"\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c1\\\" colnum=\\\"1\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c2\\\" colnum=\\\"2\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c3\\\" colnum=\\\"3\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c4\\\" colnum=\\\"4\\\"\\u003e\\u003c/div\\u003e \\u003cthead\\u003e \\u003ctr\\u003e \\u003cth align=\\\"left\\\" colspan=\\\"2\\\" nameend=\\\"c2\\\" namest=\\\"c1\\\"\\u003e\\u0026nbsp;\\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eHbA1c in 2019\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003eHbA1c in 2020\\u003c/p\\u003e \\u003c/th\\u003e \\u003c/tr\\u003e \\u003c/thead\\u003e \\u003ctbody\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\" morerows=\\\"1\\\" rowspan=\\\"2\\\"\\u003e \\u003cp\\u003e\\u003cem\\u003eN\\u003c/em\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eValid\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e219\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e184\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eMissing\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e4\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e39\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colspan=\\\"2\\\" nameend=\\\"c2\\\" namest=\\\"c1\\\"\\u003e \\u003cp\\u003eMean\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e6.96\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e7.28\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colspan=\\\"2\\\" nameend=\\\"c2\\\" namest=\\\"c1\\\"\\u003e \\u003cp\\u003eMedian\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e6.70\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e6.80\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colspan=\\\"2\\\" nameend=\\\"c2\\\" namest=\\\"c1\\\"\\u003e \\u003cp\\u003eStandard deviation\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e1.28\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e1.71\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colspan=\\\"2\\\" nameend=\\\"c2\\\" namest=\\\"c1\\\"\\u003e \\u003cp\\u003eMinimum\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e4.30\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e4.88\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colspan=\\\"2\\\" nameend=\\\"c2\\\" namest=\\\"c1\\\"\\u003e \\u003cp\\u003eMaximum\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e12.49\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e16.10\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003c/tbody\\u003e \\u003c/colgroup\\u003e \\u003c/table\\u003e\\u003c/div\\u003e \\u003c/p\\u003e \\u003cdiv id=\\\"Sec12\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003eSex and HbA1c\\u003c/h2\\u003e \\u003cp\\u003eA one-way analysis of covariance (ANOVA) was performed to assess the significance of the difference between males and females in HbA1c-2020 when adjusting for covariates HbA1c-2019 and DM duration Males and females did not differ significantly in HbA1c-2020 after adjusting for covariates HbA1c-2019 and DM duration (\\u003cem\\u003eF\\u003c/em\\u003e\\u003csub\\u003e(1, 177)\\u003c/sub\\u003e\\u0026thinsp;=\\u0026thinsp;3.187, \\u003cem\\u003eP\\u003c/em\\u003e\\u0026thinsp;=\\u0026thinsp;0.0760). Table \\u003cspan refid=\\\"Tab5\\\" class=\\\"InternalRef\\\"\\u003e5\\u003c/span\\u003e \\u0026amp; \\u003cspan refid=\\\"Tab6\\\" class=\\\"InternalRef\\\"\\u003e6\\u003c/span\\u003e.\\u003c/p\\u003e \\u003cp\\u003e \\u003cdiv class=\\\"gridtable\\\"\\u003e\\u003ctable float=\\\"Yes\\\" id=\\\"Tab5\\\" border=\\\"1\\\"\\u003e \\u003ccaption language=\\\"En\\\"\\u003e \\u003cdiv class=\\\"CaptionNumber\\\"\\u003eTable 5\\u003c/div\\u003e \\u003cdiv class=\\\"CaptionContent\\\"\\u003e \\u003cp\\u003eUnadjusted and covariate-adjusted HbA1c-2020 by sex\\u003c/p\\u003e \\u003c/div\\u003e \\u003c/caption\\u003e \\u003ccolgroup cols=\\\"6\\\"\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c1\\\" colnum=\\\"1\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c2\\\" colnum=\\\"2\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c3\\\" colnum=\\\"3\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c4\\\" colnum=\\\"4\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c5\\\" colnum=\\\"5\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c6\\\" colnum=\\\"6\\\"\\u003e\\u003c/div\\u003e \\u003cthead\\u003e \\u003ctr\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eSex\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e\\u003cem\\u003eN\\u003c/em\\u003e\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colspan=\\\"2\\\" nameend=\\\"c4\\\" namest=\\\"c3\\\"\\u003e \\u003cp\\u003eUnadjusted\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colspan=\\\"2\\\" nameend=\\\"c6\\\" namest=\\\"c5\\\"\\u003e \\u003cp\\u003eAdjusted\\u003c/p\\u003e \\u003c/th\\u003e \\u003c/tr\\u003e \\u003c/thead\\u003e \\u003ctbody\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eMean\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003eSEM\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003eMean\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003eSEM\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eMale\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e95\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e7.38\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e0.20\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e7.44\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e0.12\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eFemale\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e86\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e7.18\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e0.15\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e7.12\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e0.13\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003c/tbody\\u003e \\u003c/colgroup\\u003e \\u003c/table\\u003e\\u003c/div\\u003e \\u003c/p\\u003e \\u003cp\\u003eKey: SEM, standard error of the mean.\\u003c/p\\u003e \\u003cp\\u003e \\u003cdiv class=\\\"gridtable\\\"\\u003e\\u003ctable float=\\\"Yes\\\" id=\\\"Tab6\\\" border=\\\"1\\\"\\u003e \\u003ccaption language=\\\"En\\\"\\u003e \\u003cdiv class=\\\"CaptionNumber\\\"\\u003eTable 6\\u003c/div\\u003e \\u003cdiv class=\\\"CaptionContent\\\"\\u003e \\u003cp\\u003eANOVA of HbA1c-2020 by sex with HbA1c-2019 and DM duration as covariates\\u003c/p\\u003e \\u003c/div\\u003e \\u003c/caption\\u003e \\u003ccolgroup cols=\\\"7\\\"\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c1\\\" colnum=\\\"1\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c2\\\" colnum=\\\"2\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c3\\\" colnum=\\\"3\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c4\\\" colnum=\\\"4\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c5\\\" colnum=\\\"5\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c6\\\" colnum=\\\"6\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c7\\\" colnum=\\\"7\\\"\\u003e\\u003c/div\\u003e \\u003cthead\\u003e \\u003ctr\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eSource\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eType III sum of squares\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003edf\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003eMean square\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e\\u003cem\\u003eF\\u003c/em\\u003e\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e\\u003cem\\u003eP\\u003c/em\\u003e\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003ePartial η\\u003csup\\u003e2\\u003c/sup\\u003e\\u003c/p\\u003e \\u003c/th\\u003e \\u003c/tr\\u003e \\u003c/thead\\u003e \\u003ctbody\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eHbA1c-2019 (covariate)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e284.336\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e1\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e284.336\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e208.444\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e0.000\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e0.541\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eDM duration (covariate)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e0.760\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e1\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e0.760\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e0.557\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e0.456\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e0.003\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eSex\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e4.347\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e1\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e4.347\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e3.187\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003e0.076\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e0.018\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eError\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e241.444\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e177\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e1.364\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colspan=\\\"7\\\" nameend=\\\"c7\\\" namest=\\\"c1\\\"\\u003e \\u003cp\\u003eKey: df, degrees of freedom. Unadjusted \\u003cem\\u003eR\\u003c/em\\u003e\\u003csup\\u003e2\\u003c/sup\\u003e\\u0026thinsp;=\\u0026thinsp;0.548. Adjusted \\u003cem\\u003eR\\u003c/em\\u003e\\u003csup\\u003e2\\u003c/sup\\u003e\\u0026thinsp;=\\u0026thinsp;0.540.\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003c/tbody\\u003e \\u003c/colgroup\\u003e \\u003c/table\\u003e\\u003c/div\\u003e \\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec13\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003eAge and HbA1c\\u003c/h2\\u003e \\u003cp\\u003eA one-way ANOVA was performed to assess the significance of differences between the three age groups in HbA1c-2020 when adjusting for covariates HbA1c-2019 and DM duration The three age groups did not differ significantly in HbA1c-2020 after adjusting for covariates HbA1c-2019 and DM duration (\\u003cem\\u003eF\\u003c/em\\u003e\\u003csub\\u003e(2, 176)\\u003c/sub\\u003e\\u0026thinsp;=\\u0026thinsp;1.881, \\u003cem\\u003eP\\u003c/em\\u003e\\u0026thinsp;=\\u0026thinsp;0.1555.\\u003c/p\\u003e \\u003cp\\u003eTable\\u0026nbsp;\\u003cspan refid=\\\"Tab7\\\" class=\\\"InternalRef\\\"\\u003e7\\u003c/span\\u003e \\u0026amp;\\u003cspan refid=\\\"Tab8\\\" class=\\\"InternalRef\\\"\\u003e8\\u003c/span\\u003e.\\u003c/p\\u003e \\u003cp\\u003e \\u003cdiv class=\\\"gridtable\\\"\\u003e\\u003ctable float=\\\"Yes\\\" id=\\\"Tab7\\\" border=\\\"1\\\"\\u003e \\u003ccaption language=\\\"En\\\"\\u003e \\u003cdiv class=\\\"CaptionNumber\\\"\\u003eTable 7\\u003c/div\\u003e \\u003cdiv class=\\\"CaptionContent\\\"\\u003e \\u003cp\\u003eUnadjusted and covariate-adjusted HbA1c-2020 by age\\u003c/p\\u003e \\u003c/div\\u003e \\u003c/caption\\u003e \\u003ccolgroup cols=\\\"6\\\"\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c1\\\" colnum=\\\"1\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c2\\\" colnum=\\\"2\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c3\\\" colnum=\\\"3\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c4\\\" colnum=\\\"4\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c5\\\" colnum=\\\"5\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c6\\\" colnum=\\\"6\\\"\\u003e\\u003c/div\\u003e \\u003cthead\\u003e \\u003ctr\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eAge\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e\\u003cem\\u003eN\\u003c/em\\u003e\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colspan=\\\"2\\\" nameend=\\\"c4\\\" namest=\\\"c3\\\"\\u003e \\u003cp\\u003eUnadjusted\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colspan=\\\"2\\\" nameend=\\\"c6\\\" namest=\\\"c5\\\"\\u003e \\u003cp\\u003eAdjusted\\u003c/p\\u003e \\u003c/th\\u003e \\u003c/tr\\u003e \\u003c/thead\\u003e \\u003ctbody\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eMean\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003eSEM\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003eMean\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003eSEM\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e20\\u0026ndash;40 years\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e10\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e8.72\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e0.49\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e7.84\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e0.38\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e41\\u0026ndash;60 years\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e86\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e7.45\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e0.20\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e7.39\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e0.13\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u0026gt;\\u0026thinsp;60 years\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e88\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e6.96\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e0.15\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e7.12\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e0.13\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003c/tbody\\u003e \\u003c/colgroup\\u003e \\u003c/table\\u003e\\u003c/div\\u003e \\u003c/p\\u003e \\u003cp\\u003eKey: SEM, standard error of the mean.\\u003c/p\\u003e \\u003cp\\u003e \\u003cdiv class=\\\"gridtable\\\"\\u003e\\u003ctable float=\\\"Yes\\\" id=\\\"Tab8\\\" border=\\\"1\\\"\\u003e \\u003ccaption language=\\\"En\\\"\\u003e \\u003cdiv class=\\\"CaptionNumber\\\"\\u003eTable 8\\u003c/div\\u003e \\u003cdiv class=\\\"CaptionContent\\\"\\u003e \\u003cp\\u003eANCOVA of HbA1c-2020 by age with HbA1c-2019 and DM duration as covariates\\u003c/p\\u003e \\u003c/div\\u003e \\u003c/caption\\u003e \\u003ccolgroup cols=\\\"8\\\"\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c1\\\" colnum=\\\"1\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c2\\\" colnum=\\\"2\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c3\\\" colnum=\\\"3\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c4\\\" colnum=\\\"4\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c5\\\" colnum=\\\"5\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c6\\\" colnum=\\\"6\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c7\\\" colnum=\\\"7\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c8\\\" colnum=\\\"8\\\"\\u003e\\u003c/div\\u003e \\u003cthead\\u003e \\u003ctr\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eSource\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eType III sum of squares\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003edf\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003eMean square\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e\\u003cem\\u003eF\\u003c/em\\u003e\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e\\u003cem\\u003eP\\u003c/em\\u003e\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003ePartial \\u003cem\\u003eη\\u003c/em\\u003e\\u003csup\\u003e2\\u003c/sup\\u003e\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c8\\\"\\u003e\\u0026nbsp;\\u003c/th\\u003e \\u003c/tr\\u003e \\u003c/thead\\u003e \\u003ctbody\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eHbA1c-2019 (covariate)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e252.690\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e1\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e252.690\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e184.809\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e0.000\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e0.512\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eDuration of Diabetes (covariate)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e0.802\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e1\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e0.802\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e0.587\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e0.445\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e0.003\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eAge\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e5.145\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e2\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e2.572\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e1.881\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003e0.155\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e0.021\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eError\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e240.646\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e176\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e1.367\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colspan=\\\"7\\\" nameend=\\\"c7\\\" namest=\\\"c1\\\"\\u003e \\u003cp\\u003eKey: df, degrees of freedom. Unadjusted \\u003cem\\u003eR\\u003c/em\\u003e\\u003csup\\u003e2\\u003c/sup\\u003e\\u0026thinsp;=\\u0026thinsp;0.549. Adjusted \\u003cem\\u003eR\\u003c/em\\u003e\\u003csup\\u003e2\\u003c/sup\\u003e\\u0026thinsp;=\\u0026thinsp;0.539.\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003c/tr\\u003e \\u003c/tbody\\u003e \\u003c/colgroup\\u003e \\u003c/table\\u003e\\u003c/div\\u003e \\u003c/p\\u003e \\u003c/div\\u003e\"},{\"header\":\"Discussion\",\"content\":\"\\u003cp\\u003eThis paper aimed to provide evidence on the impact of COVID-19 on diabetic control, including HBA1C, LDL, BP, and BMI, in diabetic patients attending Alkhuwair Health Centre, comparing 2019 (pre-COVID) and 2020 (post-COVID).\\u003c/p\\u003e \\u003cp\\u003eThe results showed statistically significant HbA1c, systolic BP, and BMI changes between 2019 and 2020. The mean HbA1c in 2019 (6.9%) was significantly lower than in 2020 (7.2%). After resuming the clinic, it was noticed that some patients had higher glycated haemoglobin (HbA1c) levels than last year. SARS-CoV2 virus could interfere with the glucose metabolism pathways by triggering the reprogramming of the glucose metabolism through AMP activating protein kinase with no effect on the pancreas (Rochowski et al.,) (\\u003cspan citationid=\\\"CR18\\\" class=\\\"CitationRef\\\"\\u003e18\\u003c/span\\u003e). HBA1c results are consistent with a study that found that HbA1c values significantly increased from 7.45\\u0026ndash;7.53% during the pandemic (Tanji et al., 2021(\\u003cspan citationid=\\\"CR19\\\" class=\\\"CitationRef\\\"\\u003e19\\u003c/span\\u003e). However, a Spanish study on the COVID-19 lockdown\\u0026rsquo;s impact on glycaemic control in patients with type 1 DM found that despite the lockdown, there were improvements in glycaemic control in patients with type 1 DM due to self-management (Fern\\u0026aacute;ndez et al., 2020;) (\\u003cspan citationid=\\\"CR12\\\" class=\\\"CitationRef\\\"\\u003e12\\u003c/span\\u003e). Both sex and age groups did not differ significantly in our study regarding HbA1c in 2020. These results were inconsistent with the conclusion of Tanji et al., who noticed that the deterioration in HbA1c values was more apparent in women, patients aged\\u0026thinsp;\\u0026ge;\\u0026thinsp;65 years, patients with BMI\\u0026thinsp;\\u0026gt;\\u0026thinsp;25, and patients not using insulin (Tanji et al., 2021)((\\u003cspan citationid=\\\"CR19\\\" class=\\\"CitationRef\\\"\\u003e19\\u003c/span\\u003e).\\u003c/p\\u003e \\u003cp\\u003eThe mean BMI in 2019 (30.49) was significantly lower than in 2020 (30.80). This finding can be explained by the lockdown, reducing physical activity, and changing lifestyle habits which lead to raising awareness about the importance of nutritional status for a healthy lifestyle (Al Agha et al., 2021; Urzeala et al) (\\u003cspan citationid=\\\"CR20\\\" class=\\\"CitationRef\\\"\\u003e20\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR21\\\" class=\\\"CitationRef\\\"\\u003e21\\u003c/span\\u003e). Considering the extreme weight categories associated with severe COVID-19 complication risk, we noticed that 13.11% of the total sample fell into these vulnerable categories. Being overweight or obese is an independent risk factor in severe COVID-19 patients because enhanced adiposity diminishes pulmonary function (Urzeala et al., 2022) (\\u003cspan citationid=\\\"CR21\\\" class=\\\"CitationRef\\\"\\u003e21\\u003c/span\\u003e). Healthy lifestyles and choices should be promoted in primary care centres with the help of multidisciplinary teams. Obesity and overweight rates are on the rise, especially in the eastern Mediterranean region, which will cause a further burden on our health systems (Nejadghaderi et al. 2023(\\u003cspan citationid=\\\"CR22\\\" class=\\\"CitationRef\\\"\\u003e22\\u003c/span\\u003e)\\u003c/p\\u003e \\u003cp\\u003eAnother factor that could be implicated here is stress, anxiety, and isolation, especially for older people, which was reported during the COVID-19 pandemic (Palmer et al., 2020; Urzeala et (\\u003cspan citationid=\\\"CR21\\\" class=\\\"CitationRef\\\"\\u003e21\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR23\\\" class=\\\"CitationRef\\\"\\u003e23\\u003c/span\\u003e). Stress is an important factor implicated in the dysfunctionality of the sympathetic nervous system and the hypothalamus that leads to obesity. The other consequence of stress is the tendency to develop eating disorders and lower physical activity. All these factors might explain the increase in BMI noticed in the study cohort (Correia et al., 2021) ((\\u003cspan citationid=\\\"CR24\\\" class=\\\"CitationRef\\\"\\u003e24\\u003c/span\\u003e). Positive relationships were found between dealing with stress related to COVID-19 in patients with NCD and active coping strategies, for example, self-distraction, denial, substance use, behavioral disengagement, venting, planning, religion, and self-blame (Umucu et al.2020)(\\u003cspan citationid=\\\"CR25\\\" class=\\\"CitationRef\\\"\\u003e25\\u003c/span\\u003e)\\u003c/p\\u003e \\u003cp\\u003eIn addition to HBA1c and weight, clinic patients showed high systolic BP and LDL. The results are in line with Akpek 2020, which suggests that infection with SARS-CoV-2 increases systolic and diastolic BP and could lead to hypertension(Akpek 202) (\\u003cspan citationid=\\\"CR26\\\" class=\\\"CitationRef\\\"\\u003e26\\u003c/span\\u003e). In our study, only systolic BP was significantly higher, but diastolic BP was not. However, the results contradict Feitosa et al., who showed no considerable adverse impact of COVID-19 on office and home BP (Feitosa et al.).(\\u003cspan citationid=\\\"CR27\\\" class=\\\"CitationRef\\\"\\u003e27\\u003c/span\\u003e)\\u003c/p\\u003e \\u003cp\\u003eMany studies considered an association between antihypertensive medication classes and patient outcomes, but almost all are retrospective investigations or meta-analyses. Therefore, well-conducted research with a considerable number of hypertensive patients is necessary to resolve current controversies about the relationship between hypertension and COVID-19 (Tadic et al., 2021(\\u003cspan citationid=\\\"CR28\\\" class=\\\"CitationRef\\\"\\u003e28\\u003c/span\\u003e)\\u003c/p\\u003e \\u003cp\\u003eTelemedicine consultation existed at a comparable percentage to physical consultation, which is attributed to the fact that in Oman, the health system, like other countries, adopted many changes in NCD routine management (Bouabida et al., 2022; Habbash et al., 2023; Ullas et al., (\\u003cspan additionalcitationids=\\\"CR30\\\" citationid=\\\"CR29\\\" class=\\\"CitationRef\\\"\\u003e29\\u003c/span\\u003e\\u0026ndash;\\u003cspan citationid=\\\"CR31\\\" class=\\\"CitationRef\\\"\\u003e31\\u003c/span\\u003e). The Directorate of General Health Services in Muscat implemented a telemedicine clinic twice weekly for patient follow-up and consultation in the primary care setting. So, the results align with Chudasama et al., in 2020, which showed that 45% of the participants\\u0026rsquo; healthcare providers performed telephone (\\u003cspan citationid=\\\"CR32\\\" class=\\\"CitationRef\\\"\\u003e32\\u003c/span\\u003e). Furthermore, a WHO survey of 155 countries found that 58% now use telemedicine to replace in-person consultation (WHO), (\\u003cspan citationid=\\\"CR3\\\" class=\\\"CitationRef\\\"\\u003e3\\u003c/span\\u003e).\\u003c/p\\u003e \\u003cdiv id=\\\"Sec15\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003eLimitations:\\u003c/h2\\u003e \\u003cp\\u003eResidual confounding can be a challenge in observational studies. To address this, we should include as many confounders as possible in the regression analysis and seek the opinions of clinical experts. Additionally, sensitivity analysis was employed to evaluate any hidden residual confounding, such as mental instability. Negative outcome control could be applied to explore any hidden confounding due to measurement errors for lifestyle factors. Missing data is a major potential limitation. We tried to locate and reference relevant sources of patient information to retrieve any missing data from electronic health records. Furthermore, we performed a sensitivity analysis to manage the missing data Selection bias was another concern. Some of the Omani population receive medical care in private institutions that are not registered in the national registries.\\u003c/p\\u003e \\u003c/div\\u003e\"},{\"header\":\"Conclusion\",\"content\":\"\\u003cp\\u003eThe COVID-19 pandemic is ongoing with new emerging strains, and many studies have been published exploring COVID-19\\u0026rsquo;s impact on different diseases. In this study, we can conclude that there was a significant adverse effect on glycaemic control in DM patients. This finding suggests that we must provide extra care to patients with non-communicable diseases under normal conditions to prepare them to face such future challenges. This can be achieved by promoting healthy lifestyles and ensuring that our healthcare systems advocate for healthy environments where people can practice a healthy lifestyle and access nutritious foods and activity centres. Additionally, having primary care centres as their support can further facilitate this goal.\\u003c/p\\u003e\"},{\"header\":\"List of abbreviations\",\"content\":\"\\u003cp\\u003eDiabetes mellitus (DM)\\u003c/p\\u003e \\u003cp\\u003eHypertension (HTN)\\u003c/p\\u003e \\u003cp\\u003eglycated haemoglobin (HbA1c),\\u003c/p\\u003e \\u003cp\\u003eBlood pressure (BP)\\u003c/p\\u003e \\u003cp\\u003elow-density lipoprotein (LDL)\\u003c/p\\u003e \\u003cp\\u003eBody mass index (BMI)\\u003c/p\\u003e \\u003cp\\u003eStatistical Package for the Social Sciences (SPSS)\\u003c/p\\u003e \\u003cp\\u003eNational Diabetic Register (NDR)\\u003c/p\\u003e \\u003cp\\u003enon-communicable diseases (NCDs)\\u003c/p\\u003e \\u003cp\\u003eWorld Health Organization (WHO)\\u003c/p\\u003e \\u003cp\\u003eA one-way analysis of covariance (ANOVA)\\u003c/p\\u003e \"},{\"header\":\"Declarations\",\"content\":\"\\u003cp\\u003eEthics approval and consent to participate:\\u003c/p\\u003e\\n\\u003cp\\u003eThe study\\u0026nbsp;was performed in accordance with the\\u0026nbsp;Declaration of Helsinki.\\u0026nbsp;Written approval from the regional research committee in Muscat region was obtained. (MoH/CSR/21/24309)\\u003c/p\\u003e\\n\\u003cp\\u003eConsent for publication: Not applicable\\u003c/p\\u003e\\n\\u003cp\\u003eAvailability of data and materials: The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.\\u003c/p\\u003e\\n\\u003cp\\u003eCompeting interests:\\u0026nbsp;The authors declare that they have no competing interests\\u003c/p\\u003e\\n\\u003cp\\u003eFunding: no funding required\\u003c/p\\u003e\\n\\u003cp\\u003eAuthors\\u0026apos; contributions:\\u003c/p\\u003e\\n\\u003cp\\u003eF.A: wrote the proposal, Data collection and analysis, wrote the whole paper.\\u003c/p\\u003e\\n\\u003cp\\u003eS.A: wrote the proposal, Data collection and analysis, helped in writing the paper.\\u003c/p\\u003e\\n\\u003cp\\u003eM.A: Data collection and literature review.\\u003c/p\\u003e\\n\\u003cp\\u003eG.A: Data collection and literature review\\u003c/p\\u003e\\n\\u003cp\\u003eC.T:\\u0026nbsp;major contributor in writing the manuscript, read and approved the final manuscript\\u003c/p\\u003e\\n\\u003cp\\u003eZ.A:\\u0026nbsp;major contributor in writing the manuscript, read and approved the final manuscript\\u003c/p\\u003e\\n\\u003cp\\u003eAcknowledgements: Dr. Mezon Tufail, consultant family physician, Ministry of health Oman. Major help in formulating the research idea, gathering the data.\\u003c/p\\u003e\\n\\u003cp\\u003eAuthors\\u0026apos; information (optional): Fakhria is senior specialist family physician, graduated from Oman medical specialty board, has MD/MRCGP/OMSB II/Arab board qualifications. I worked in Oman in primary care for 13 years with skills in treating chronic diseases, emergencies, women health, birth spacing and maternal services at primary care. I worked as in charge of the health center for 4 years. I have experience dealing with Covid cases and covid related problems during covid pandemic. I am currently doing a fellowship in clinical public health at Imperial college London, WHO collaboration center.\\u003c/p\\u003e\"},{\"header\":\"References\",\"content\":\"\\u003col\\u003e\\u003cli\\u003e\\u003cspan\\u003eYan Y, Yang Y, Wang F, Ren H, Zhang S, Shi X et al. Clinical characteristics and outcomes of patients with severe covid-19 with diabetes. 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Hypertens Res. 2020;43(10):1028\\u0026ndash;46.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eRochowski MT, Jayathilake K, Balcerak JM, Selvan MT, Gunasekara S, Miller C et al. Impact of Delta SARS-CoV-2 Infection on Glucose Metabolism: Insights on Host Metabolism and Virus Crosstalk in a Feline Model. Viruses. 2024;16(2).\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eTanji Y, Sawada S, Watanabe T, Mita T, Kobayashi Y, Murakami T, et al. Impact of COVID-19 pandemic on glycemic control among outpatients with type 2 diabetes in Japan: A hospital-based survey from a country without lockdown. Diabetes Res Clin Pract. 2021;176:108840.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eAl Agha AE, Alharbi RS, Almohammadi OA, Yousef SY, Sulimani AE, Alaama RA. Impact of COVID-19 lockdown on glycemic control in children and adolescents. Saudi Med J. 2021;42(1):44\\u0026ndash;8.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eUrzeala C, Duclos M, Chris Ugbolue U, Bota A, Berthon M, Kulik K, et al. COVID-19 lockdown consequences on body mass index and perceived fragility related to physical activity: A worldwide cohort study. Health Expect. 2022;25(2):522\\u0026ndash;31.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eNejadghaderi SA, Grieger JA, Karamzad N, Kolahi AA, Sullman MJM, Safiri S, et al. Burden of diseases attributable to excess body weight in the Middle East and North Africa region, 1990\\u0026ndash;2019. Sci Rep. 2023;13(1):20338.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003ePalmer K, Monaco A, Kivipelto M, Onder G, Maggi S, Michel JP, et al. The potential long-term impact of the COVID-19 outbreak on patients with non-communicable diseases in Europe: consequences for healthy ageing. Aging Clin Exp Res. 2020;32(7):1189\\u0026ndash;94.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eCorreia JC, Locatelli L, Hafner C, Pataky Z, Golay A. [The role of stress in obesity]. Rev Med Suisse. 2021;17(731):567\\u0026ndash;70.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eUmucu E, Lee B. Examining the impact of COVID-19 on stress and coping strategies in individuals with disabilities and chronic conditions. Rehabil Psychol. 2020;65(3):193\\u0026ndash;8.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eAkpek M, Does. COVID-19 Cause. Hypertension? Angiol. 2022;73(7):682\\u0026ndash;7.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eFeitosa FGAM, Feitosa ADM, Paiva AMG, Mota-Gomes MA, Barroso WS, Miranda RD, et al. Impact of the COVID-19 pandemic on blood pressure control: a nationwide home blood pressure monitoring study. Hypertens Res. 2022;45(2):364\\u0026ndash;8.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eTadic M, Saeed S, Grassi G, Taddei S, Mancia G, Cuspidi C. Hypertension and COVID-19: Ongoing Controversies. Front Cardiovasc Med. 2021;8:639222.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eBouabida K, Lebouch\\u0026eacute; B, Pomey MP. Telehealth and COVID-19 Pandemic: An Overview of the Telehealth Use, Advantages, Challenges, and Opportunities during COVID-19 Pandemic. Healthc (Basel). 2022;10(11).\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eHabbash F, Rabeeah A, Huwaidi Z, Abuobaidah H, Alqabbat J, Hayyan F, et al. Telemedicine in non-communicable chronic diseases care during the COVID-19 pandemic: exploring patients\\u0026rsquo; perspectives. Front Public Health. 2023;11:1270069.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eUllas S, Pradeep M, Surendran S, Ravikumar A, Bastine AM, Prasad A, et al. Telemedicine During the COVID-19 Pandemic: A Paradigm Shift in Non-Communicable Disease Management? - A Cross-Sectional Survey from a Quaternary-Care Center in South India. Patient Prefer Adherence. 2021;15:2715\\u0026ndash;23.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eChudasama YV, Gillies CL, Zaccardi F, Coles B, Davies MJ, Seidu S, et al. Impact of COVID-19 on routine care for chronic diseases: A global survey of views from healthcare professionals. Diabetes Metab Syndr. 2020;14(5):965\\u0026ndash;7.\\u003c/span\\u003e\\u003c/li\\u003e\\u003c/ol\\u003e\"}],\"fulltextSource\":\"\",\"fullText\":\"\",\"funders\":[],\"hasAdminPriorityOnWorkflow\":false,\"hasManuscriptDocX\":true,\"hasOptedInToPreprint\":true,\"hasPassedJournalQc\":\"\",\"hasAnyPriority\":false,\"hideJournal\":false,\"highlight\":\"\",\"institution\":\"\",\"isAcceptedByJournal\":true,\"isAuthorSuppliedPdf\":false,\"isDeskRejected\":\"\",\"isHiddenFromSearch\":false,\"isInQc\":false,\"isInWorkflow\":false,\"isPdf\":false,\"isPdfUpToDate\":true,\"isWithdrawnOrRetracted\":false,\"journal\":{\"display\":true,\"email\":\"info@researchsquare.com\",\"identity\":\"bmc-primary-care\",\"isNatureJournal\":false,\"hasQc\":true,\"allowDirectSubmit\":false,\"externalIdentity\":\"famp\",\"sideBox\":\"Learn more about [BMC Primary Care](https://bmcprimcare.biomedcentral.com/)\",\"snPcode\":\"\",\"submissionUrl\":\"https://author-welcome.nature.com/12875\",\"title\":\"BMC Primary Care\",\"twitterHandle\":\"BMC_series\",\"acdcEnabled\":true,\"dfaEnabled\":true,\"editorialSystem\":\"em\",\"reportingPortfolio\":\"BMC Series\",\"inReviewEnabled\":true,\"inReviewRevisionsEnabled\":true},\"keywords\":\"COVID-19, diabetes, primary care, Oman\",\"lastPublishedDoi\":\"10.21203/rs.3.rs-4662891/v1\",\"lastPublishedDoiUrl\":\"https://doi.org/10.21203/rs.3.rs-4662891/v1\",\"license\":{\"name\":\"CC BY 4.0\",\"url\":\"https://creativecommons.org/licenses/by/4.0/\"},\"manuscriptAbstract\":\"\\u003cp\\u003e\\u003cb\\u003eBackground\\u003c/b\\u003e\\u003c/p\\u003e \\u003cp\\u003eThe COVID-19 pandemic has led to a significant reallocation of healthcare services, focusing on pandemic response and emergency preparedness. The Oman Ministry of Health has implemented various measures to combat and control COVID-19. However, this shift has harmed routine outpatient appointments, particularly for chronic diseases such as Diabetes mellitus (DM) and hypertension (HTN). Considering this, our study aims to determine the specific effects of the pandemic on diabetes control, focusing on glycated haemoglobin (HbA1c), blood pressure (BP), lipids (mainly low-density lipoprotein (LDL), weight/ Body mass index (BMI), and compare these to pre-pandemic levels.\\u003c/p\\u003e\\u003cp\\u003e\\u003cb\\u003eMethods\\u003c/b\\u003e\\u003c/p\\u003e \\u003cp\\u003eA retrospective cohort study of 223 diabetic patients aged 20\\u0026ndash;95 years who had a blood workup in 2019 and 2020 and were registered in Al-Khuwair Health Centre from March to December 2020. Data was extracted from the Al Shifa 3plus System and National Diabetic Register (NDR). SPSS was used to analyse the data.\\u003c/p\\u003e\\u003cp\\u003e\\u003cb\\u003eResults\\u003c/b\\u003e\\u003c/p\\u003e \\u003cp\\u003eOut of 260 patients with diabetes, 223 were included in the study, and 37 were excluded (new DM patients and existing patients without follow-up in 2019). The results showed significant HBA1C, Systolic BP, and BMI changes between 2019 and 2020. The mean HbA1c in 2019 (6.9%) was lower than in 2020 (7.2%). Similarly, the mean SBP in 2019 (131.22 mmHg) compared to 2020 (134.84 mmHg), mean BMI in 2019 (30.49), whereas in 2020 (30.80). The LDL and diastolic BP did not change.\\u003c/p\\u003e\\u003cp\\u003e\\u003cb\\u003eConclusion\\u003c/b\\u003e\\u003c/p\\u003e \\u003cp\\u003eThe COVID-19 pandemic affected healthcare systems globally, and it was not only the direct impact of the virus that caused the consequences or mortalities; it could also be the modifications in priorities. Due to the interruptions in inconsistent care, consequences of non-communicable diseases (NCDs) were advertised. Future strategic plans should be prepared and implemented to manage NCD cases in case of pandemics.\\u003c/p\\u003e\",\"manuscriptTitle\":\"The profound impact of COVID-19 on the control and care of diabetic patients: a comprehensive retrospective cohort study\",\"msid\":\"\",\"msnumber\":\"\",\"nonDraftVersions\":[{\"code\":1,\"date\":\"2024-07-25 08:30:19\",\"doi\":\"10.21203/rs.3.rs-4662891/v1\",\"editorialEvents\":[{\"type\":\"communityComments\",\"content\":0},{\"type\":\"decision\",\"content\":\"Revision requested\",\"date\":\"2024-07-03T10:26:56+00:00\",\"index\":\"\",\"fulltext\":\"\"},{\"type\":\"editorAssigned\",\"content\":\"\",\"date\":\"2024-07-03T07:55:35+00:00\",\"index\":\"\",\"fulltext\":\"\"},{\"type\":\"checksComplete\",\"content\":\"\",\"date\":\"2024-07-03T06:21:44+00:00\",\"index\":\"\",\"fulltext\":\"\"},{\"type\":\"submitted\",\"content\":\"BMC Primary Care\",\"date\":\"2024-06-30T13:26:40+00:00\",\"index\":\"\",\"fulltext\":\"\"}],\"status\":\"published\",\"journal\":{\"display\":true,\"email\":\"info@researchsquare.com\",\"identity\":\"bmc-primary-care\",\"isNatureJournal\":false,\"hasQc\":true,\"allowDirectSubmit\":false,\"externalIdentity\":\"famp\",\"sideBox\":\"Learn more about [BMC Primary Care](https://bmcprimcare.biomedcentral.com/)\",\"snPcode\":\"\",\"submissionUrl\":\"https://author-welcome.nature.com/12875\",\"title\":\"BMC Primary Care\",\"twitterHandle\":\"BMC_series\",\"acdcEnabled\":true,\"dfaEnabled\":true,\"editorialSystem\":\"em\",\"reportingPortfolio\":\"BMC Series\",\"inReviewEnabled\":true,\"inReviewRevisionsEnabled\":true}}],\"origin\":\"\",\"ownerIdentity\":\"436f1c56-4dbd-45fc-8ad8-0a4f4d3884f2\",\"owner\":[],\"postedDate\":\"July 25th, 2024\",\"published\":true,\"recentEditorialEvents\":[],\"rejectedJournal\":[],\"revision\":\"\",\"amendment\":\"\",\"status\":\"published-in-journal\",\"subjectAreas\":[],\"tags\":[],\"updatedAt\":\"2024-12-23T16:03:27+00:00\",\"versionOfRecord\":{\"articleIdentity\":\"rs-4662891\",\"link\":\"https://doi.org/10.1186/s12875-024-02672-2\",\"journal\":{\"identity\":\"bmc-primary-care\",\"isVorOnly\":false,\"title\":\"BMC Primary Care\"},\"publishedOn\":\"2024-12-21 15:57:59\",\"publishedOnDateReadable\":\"December 21st, 2024\"},\"versionCreatedAt\":\"2024-07-25 08:30:19\",\"video\":\"\",\"vorDoi\":\"10.1186/s12875-024-02672-2\",\"vorDoiUrl\":\"https://doi.org/10.1186/s12875-024-02672-2\",\"workflowStages\":[]},\"version\":\"v1\",\"identity\":\"rs-4662891\",\"journalConfig\":\"researchsquare\"},\"__N_SSP\":true},\"page\":\"/article/[identity]/[[...version]]\",\"query\":{\"redirect\":\"/article/rs-4662891\",\"identity\":\"rs-4662891\",\"version\":[\"v1\"]},\"buildId\":\"qtupq5eGEP_6zYnWcrvyt\",\"isFallback\":false,\"isExperimentalCompile\":false,\"dynamicIds\":[84888],\"gssp\":true,\"scriptLoader\":[]}","source_license":"CC-BY-4.0","license_restricted":false}