Diabetes Management during the COVID-19 pandemic: A Study on Contributory Health Service Scheme Beneficiaries from Mumbai, India

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India, ranked third globally in confirmed COVID-19 cases, imposed strict lockdowns from March 24, 2020. This study aims to understand diabetes management during the pandemic within the framework of a uniform contributory healthcare scheme. Data and Methods: This study analyses healthcare utilisation patterns among 653 diabetes patients from a retrospective cohort of 835 individuals with Type 2 Diabetes based in Mumbai, India. Data spanning pre-pandemic and pandemic phases are compared across various healthcare usages. Trends in glycemic control during COVID-19 are also evaluated relative to pre-COVID levels. Results: We identified discernible trends in healthcare usage: there was a notable decrease during the initial wave of the pandemic, a subsequent rise, and followed by a decrease during the second wave. This trend was evident across various healthcare services, including outpatient department (OPD) visits, inpatient department (IPD) admissions, laboratory tests, and pharmaceutical purchases. Healthcare use remained higher among individuals with comorbidities throughout the pandemic period. Individuals aged 75 and above did not show the same level of recovery in OPD visits during the intermittent period as younger age groups, suggesting increased vulnerability and avoidance of in-person care among older adults. A gendered pattern was also observed in lab test utilisation: among women, testing rates halved during the initial phase, recovered to 80% during the intermittent period, and declined again during the second wave. In contrast, among men, lab test usage remained suppressed throughout the pandemic period. The 45–59 and 60-74 age groups showed the most substantial recovery in lab test rates during the intermittent period. Additionally, hospitalisation rates among women and individuals aged 60–74 exceeded pre-pandemic levels during this phase, suggesting delayed care-seeking or a rise in severe cases. Despite these shifts in healthcare utilisation, the overall average values of HbA1c and fasting plasma glucose (FPG) remained relatively stable in men and the oldest age group (75 and above). An exception was noted among women, who exhibited more variability in glycaemic indicators. Importantly, there was no strong correlation between the frequency of HbA1c testing or OPD visits with the corresponding HbA1c values. However, a significant association was found between HbA1c values recorded prior to the pandemic and those during successive waves, indicating that individuals with poor glycaemic control before the pandemic continued to face challenges during it. COVID-19 pandemic Healthcare utilisation Type 2 Diabetes Retrospective cohort Electronic Health Records (EHR) Figures Figure 1 Figure 2 Figure 3 Figure 4 Introduction The COVID-19 pandemic has profoundly disrupted healthcare systems around the world, with individuals living with chronic conditions such as diabetes mellitus bearing a disproportionate burden. Diabetes, a complex and lifelong condition, affects over 536 million adults globally, including an estimated 74.2 million in India alone, according to the 2021 annual report by the International Diabetes Federation. ( 1 ) Effective diabetes management relies heavily on consistent self-care, regular monitoring, lifestyle modifications, medication adherence, and timely access to healthcare services. However, the pandemic severely compromised these essential aspects of care.( 2 ) Amid lockdowns and strained health infrastructure, routine diabetes management—including in-person consultations, diagnostic testing, and follow-up care—became increasingly difficult to maintain.( 3 – 5 ) Individuals with diabetes faced multiple challenges, such as reduced physical activity, dietary shifts, restricted healthcare access, treatment discontinuation due to fear of infection, postponed medical interventions, and elevated psychological stress.( 6 – 9 ) While self-reported studies suggest that these factors negatively influenced glycemic control and metabolic health, a key limitation is the absence of clinical and laboratory-based evidence (Ghosal, Arora, et al., 2020; Holman et al., 2020; Singhai et al., 2020). Beyond the general disruptions, individuals with diabetes faced an elevated risk of adverse outcomes from COVID-19 itself, including worsened glucose regulation and increased morbidity and mortality.( 10 ) Even in the absence of infection, poor disease management can precipitate deteriorating glycemic control, increasing the likelihood of long-term complications, disability, and premature death. Chronic hyperglycemia has also been associated with greater susceptibility to infections and poorer clinical outcomes. ( 11 , 12 ) India recorded its first confirmed case of COVID-19 on January 30, 2020, in the state of Kerala. ( 13 ) Following this initial case, the virus gradually spread to other parts of the country, initially linked primarily to individuals with a travel history to affected nations and their contacts. ( 14 ) Community transmission soon became evident, leading to an escalating number of cases across various states. ( 15 , 16 ) Faced with the exponential rise in COVID-19 cases and the threat of overwhelming the healthcare system, the Government of India implemented stringent public health measures.( 16 , 17 ) The most significant of these was a nationwide lockdown announced on March 24, 2020. ( 18 ) This initial 21-day lockdown was subsequently extended multiple times with varying degrees of restrictions. ( 19 ) Measures included the suspension of all international and domestic travel (flights, trains, interstate buses) and the closure of educational institutions, non-essential businesses, public spaces, and religious gatherings. ( 20 ) Strict stay-at-home orders were enforced, movement was severely restricted, and mandatory mask-wearing and social distancing norms were introduced.( 21 , 22 ) Subsequent phases involved gradual "unlocking" with localised containment zones based on case prevalence.( 23 ) . Despite the magnitude of this public health crisis, there remains a gap in research that leverages electronic health records (EHRs) to examine the real-world impact of the pandemic on diabetes care and outcomes. Furthermore, few studies have explored how glycemic control among Indian diabetes patients was managed during this period. This study seeks to bridge these gaps by offering a comprehensive, data-driven assessment of healthcare utilisation and diabetes management before and during the COVID-19 pandemic in an Indian context. Study Population The study is based on a retrospective cohort comprising 835 individuals diagnosed with diabetes, selected from the CHIPS study database.( 24 ) This database is compiled using electronic health records of individuals enrolled in the government’s Contributory Health Services Scheme (CHSS). Under the CHSS, the government employer provides comprehensive health care coverage to its employees and their dependents, which includes services such as outpatient consultations, hospitalisations, both surgical and non-surgical medical interventions, medications, and diagnostic tests. This cohort is tracked from their initial diagnosis of diabetes in either 2011 or 2012 to 2021. Each cohort member possesses comprehensive medical records, encompassing detailed documentation of laboratory findings, pharmaceutical usage, and exhaustive records of both outpatient and inpatient interactions throughout the study's designated timeline. The laboratory test records include values for Fasting Plasma Glucose (FPG) and Haemoglobin A1c (HbA1c) tests for the entire cohort study period, thereby also providing a thorough dataset for analysis during the period studied in this paper. Attritions were noted during the 10-year retrospective follow-up of cohorts, leading to a reduction in the overall cohort size available for analysis. Some individuals within the cohort experienced mortality, while some discontinued CHSS due to various reasons. In addition, the study excluded those currently grappling with acute medical conditions such as End Stage Renal Disease, individuals undergoing hemodialysis, and those diagnosed with Acute Coronary Syndrome. These acute health conditions are known to induce significant perturbations in glycemic control, potentially introducing outlier data points that could skew the study results. Consequently, the analytical dataset consists of 653 diabetes patients who continued to access healthcare services within the defined study window, spanning from March 11, 2019, to June 28, 2021. An essential facet to highlight is the healthcare services’ structure underpinning this study. Notably, the healthcare services provided operate within a contributory framework, wherein the government employer assumes partial financial responsibility. This approach is pivotal in mitigating potential inequalities stemming from financial constraints. COVID Data Collection and Periodisation: The official data pertaining to the daily count of COVID-19 cases is sourced from the Government of India website. ( 25 ) The collection spans from the date of the first recorded COVID-19 case in Mumbai, commencing on March 11, 2020, to the conclusion of the second wave on May 28, 2021. Due to the potential influence of weekends or holidays on reporting, where cases reported on the present day are documented on the following day, a weekly aggregation approach is adopted. The week is demarcated from Monday to Sunday, and the average count of active COVID-19 cases per day within each week is computed for the study population. The entire study period is segmented into five distinct phases, each holding temporal significance in relation to the pandemic's progression. These phases are delineated as follows: Pre-COVID Phase (March 11, 2019, to March 12, 2020) This span predates the onset of the pandemic, serving as a baseline reference. First COVID Cases Phase (March 11, 2020, to May 3, 2020) Encompassing the advent of COVID-19 in the region, this phase characterises the initial emergence of cases. First Wave (May 4, 2020, to January 10, 2021) Signifying the primary wave of the pandemic, this phase extends from the conclusion of the initial cases phase to the subsequent decrease in case numbers. Intermittent Period between Waves (January 11, 2021, to February 14, 2021) This interlude represents the transition between the first and second waves. Second Wave (February 15, 2021, to May 28, 2021) Portraying the resurgence of cases, this phase encapsulates the secondary wave of COVID-19 transmission. To facilitate analytical convenience, distinct waves of COVID-19 transmission are demarcated based on the average count of active cases per week. A wave's initiation is established when the weekly average surpasses 700 cases, while its cessation is defined by the occurrence of four consecutive weeks during which the weekly average remains below 700 cases and exhibits no subsequent peak. This temporal criterion, roughly equivalent to one month, guides the identification of discrete wave patterns within the data. Statistical Analysis: Medical records of 653 diabetic individuals from the Cohort study database are sourced in order to comprehend healthcare utilisation patterns across distinct periods. These records contain hospitalisation (IPD), outpatient (OPD) visits, laboratory test results, and pharmaceutical purchases. During the COVID-19 pandemic, teleconsultations conducted by physicians have been categorised as OPD visits. As the duration of each phase under comparison is not constant, person-months are used as the denominator. To facilitate standardised comparison, healthcare usage rates are expressed as events per 1,000 person-months. $$\:Healthcare\:Usage\:Rate=\left(\frac{Number\:of\:events\:in\:thereference\:period\:}{Total\:person-months\:in\:the\:regerence\:period}\right)\times\:1000$$ Where: Number of events are hospitalisation, OPD visits, laboratory tests, or pharmaceutical purchases. Total person-months refers to the sum of the months that each person is observed. For the analysis of hospitalisation rates among Diabetes patients during COVID and non-COVID periods, it's important to note a methodological adjustment in the denominator used for calculation. Unlike other healthcare utilisation rates measured in this paper, which typically use a denominator of 1,000 patient months, the hospitalisation rates are presented per 10,000 patient months. This adjustment is made due to the relatively rare nature of hospitalisation events, necessitating a larger denominator to provide a more accurate and meaningful representation of these occurrences. Additionally, the rates of FPG and HbA1c testing across different age and sex groups are tabulated. The study population is stratified by age groups (30–44, 45–59, 60–74, and 75 + years), sex, the presence of diabetes-related comorbidities (dyslipidaemia, hypertension, and thyroid disorders), and frequency of OPD visits in the three years preceding the COVID-19 pandemic (categorised as low, medium, and high). Rate ratios are used to compare healthcare usage rates during the COVID-19 period with pre-COVID rates. A comparative exploration of average HbA1c and FPG values was performed across age and sex subgroups within each defined period. To ensure comparability, the means of HbA1c and FPG were age-sex standardised based on the age-sex structure of the pre-COVID population. Lastly, Spearman’s rank correlation was utilised to identify patterns and trends in HbA1c levels between the pre-COVID period and the first and second waves of COVID-19. Additionally, we examined the correlation between OPD visits and HbA1c levels during the first and second COVID waves. Furthermore, we assessed the correlation between the number of days of COVID-related restrictions (within the 90 days preceding the HbA1c test) and HbA1c values during these waves. Results The study population consists of 653 individuals, with 276 men and 377 women. Most participants do not have thyroid conditions (491), but a notable portion (162) do, predominantly women. Dyslipidemia is prevalent in 633 individuals. Hypertension is also common, seen in 598 participants. Regarding pre-COVID healthcare usage rates, the population is evenly distributed across low, medium, and high usage categories, with a slight predominance of women in each group. Table 1 Baseline Characteristics of the Study Cohort Men Women Total Age Category 30–44 13 40 53 45–59 101 172 273 60–74 116 128 244 75+ 46 37 83 Thyroid No 245 246 491 Yes 31 131 162 Dyslipidemia No 8 12 20 Yes 268 365 633 Hypertension No 16 39 55 Yes 260 338 598 Pre-COVID Healthcare usage Low 92 134 226 Medium 97 115 212 High 87 128 215 Total 276 377 653 1. Comparison of Healthcare utilisation by Diabetes patients during pre-COVID and COVID-19 phases OPD visit rates In our study population, men and women exhibit similar OPD visit rates during the pre-COVID period, with men (1,527 visits) having a slightly lower rate than women (1,587 visits). Analysing by age groups, we observe an inverse relationship between OPD visit rates and age, with younger age groups consistently showing higher rates compared to older age groups throughout the study. During the first COVID wave, OPD visit rates in each subgroup decreased by half. However, during the intermittent period between the two COVID waves, these rates increase to approximately 80 per cent of the pre-COVID levels. Specifically, OPD rates for women rise to 1,296, while for men, the rate is 1,207. By the second COVID wave, OPD visit rates decreased again, with women's rates decreasing to 767, which is lower than that of men at 885. Additionally, individuals with comorbidities have consistently higher OPD usage rates compared to those without comorbidities throughout the study period. Table 2 Number of OPD visits per 1000 Patient Months in diabetes patients across various waves during pre-COVID and post-COVID-19 phases Pre COVID First cases First wave Intermittent period Second wave OPD Rate Rate Rate ratio Rate Rate ratio Rate Rate ratio Rate Rate ratio Sex Men 1527 937 0.61 883 0.58 1207 0.79 885 0.58 Women 1587 750 0.47 810 0.51 1296 0.82 767 0.48 Age group 30–44 1771 822 0.46 817 0.46 1609 0.91 981 0.55 45–59 1681 893 0.53 910 0.54 1345 0.80 922 0.55 60–74 1425 751 0.53 791 0.56 1180 0.83 699 0.49 75+ 1424 848 0.60 769 0.54 961 0.68 704 0.49 Thyroid No 1487 811 0.55 806 0.54 1211 0.81 801 0.54 Yes 1787 885 0.50 946 0.53 1402 0.78 866 0.48 Dyslipidemia No 1547 833 0.54 807 0.52 1048 0.68 739 0.48 Yes 1562 829 0.53 842 0.54 1264 0.81 820 0.52 Hypertension No 1515 730 0.48 850 0.56 992 0.65 805 0.53 Yes 1566 838 0.54 840 0.54 1283 0.82 818 0.52 Pre-COVID healthcare usage Low 710 393 0.55 439 0.62 660 0.93 436 0.61 Medium 1393 841 0.60 761 0.55 1053 0.76 748 0.54 High 2589 1222 0.47 1294 0.50 2025 0.78 1247 0.48 Total 1562 829 0.53 841 0.54 1258 0.81 817 0.52 Pharmacy visit rates Our study shows that pre-COVID, women had a slightly higher rate of pharmacy visits (1,190) than men (1,127). During the first COVID wave, these rates nearly halved for both groups, dropping to 716 for men and 704 for women. In the period between the two COVID waves, rates partially recovered, with men’s rate increasing to 965 while women’s rate was slightly higher at 1,040. However, in the second COVID wave, rates halved again, with men at 616 and women at 590. When examining age groups, individuals aged 30–44 had the highest pre-COVID pharmacy visit rate (1,203), higher than any older group. Across all age groups, pharmacy visit rates declined during the COVID-19 phases. In the first wave, rates dropped to 714 for the 30–44 age group and to 754 for the 45–59 group. Older groups had lower rates, with 681 for those aged 60–74 and 635 for those 75+. During the intermittent period, rates rose to about 81 per cent of pre-COVID levels, with the 45–59 age group reaching the highest rate (1,052). In the second COVID wave, rates declined again, but younger age groups still had higher rates: 603 for the 30–44 group and 634 for the 45–59 group, compared to 577 for ages 60–74 and 558 for those 75+. Our study shows that individuals with comorbidities consistently have higher pharmacy visit rates compared to those without. Specifically, individuals with dyslipidemia had a pre-COVID pharmacy visit rate of 1,172, while those without dyslipidemia had a rate of 874. Similarly, those with hypertension had a pre-COVID rate of 1,191 compared to 864 for those without hypertension. During the first COVID wave, these rates dropped to approximately 60 per cent of their pre-COVID levels. However, during the intermittent period between waves, the rates increased again, with individuals having dyslipidemia and hypertension reaching nearly 87 per cent and 88 per cent of their pre-COVID rates, respectively. By the second wave, the rates decreased once more, with those with dyslipidemia dropping to 605 and those without dyslipidemia to 476. Similarly, for hypertension, the rates dropped to 613 for those with hypertension and 464 for those without hypertension. Table 3 Number of pharmacy visits per 1000 patient months in diabetes patients during pre-COVID and COVID-19 phases Pre COVID First cases First wave Intermittent period Second wave Drug Purchase Rate Rate Rate ratio Rate Rate ratio Rate Rate ratio Rate Rate ratio Sex Men 1127 699 0.62 716 0.64 965 0.86 616 0.55 Women 1190 661 0.56 704 0.59 1040 0.87 590 0.50 Age group 30–44 1203 744 0.62 714 0.59 997 0.83 603 0.50 45–59 1154 732 0.63 754 0.65 1052 0.91 634 0.55 60–74 1162 580 0.50 681 0.59 980 0.84 577 0.50 75+ 1176 736 0.63 635 0.54 950 0.81 558 0.47 Thyroid No 1122 641 0.57 678 0.60 988 0.88 578 0.52 Yes 1289 785 0.61 803 0.62 1071 0.83 669 0.52 Dyslipidemia No 874 741 0.85 569 0.65 667 0.76 476 0.54 Yes 1172 675 0.58 713 0.61 1019 0.87 605 0.52 Hypertension No 864 610 0.71 598 0.69 571 0.66 464 0.54 Yes 1191 683 0.57 719 0.60 1049 0.88 613 0.51 Pre-COVID healthcare usage Low 546 317 0.58 344 0.63 570 1.04 327 0.60 Medium 1108 666 0.60 666 0.60 886 0.80 562 0.51 High 1841 1022 0.56 1091 0.59 1542 0.84 898 0.49 Total 1163 677 0.58 709 0.61 1008 0.87 601 0.52 Lab test rates During pre-COVID, men had slightly higher lab test rates (217) compared to women (203). During the first COVID wave, lab test rates for men dropped to about 50 per cent of pre-COVID levels (110) and continued to decrease slightly through the intermittent period (108) and further during the second wave (104). In contrast, for women, the rates fell to 101 during the first wave, then surged to 171 (84 per cent of the pre-COVID level) in the intermittent period before sharply declining to 80 in the second wave. Pre-COVID lab test rates were similar across all age groups. However, during the first wave and intermittent periods, individuals aged 45–59 and 60–74 had higher lab test rates than the youngest ( 30 – 44 ) and oldest (75+) age groups. By the second wave, lab test rates had decreased to approximately 43 per cent of pre-COVID levels across all age groups: 30–44 (101), 45–59 (100), 60–74 (84), and 75+ (70). Individuals with high healthcare usage rates pre-COVID consistently had higher lab test rates, highlighting a trend where diabetes patients requiring more intensive care maintained relatively higher levels of healthcare engagement throughout the pandemic. Table 4 Number of lab tests per 1000 patient months among diabetes patients during pre-COVID and COVID-19 phases Pre COVID First cases First wave Intermittent period Second wave Lab Tests Rate Rate Rate ratio Rate Rate ratio Rate Rate ratio Rate Rate ratio Sex Men 217 109 0.50 110 0.51 108 0.50 104 0.48 Women 203 54 0.27 101 0.50 171 0.84 80 0.39 Age group 30–44 221 33 0.15 103 0.47 70 0.32 101 0.46 45–59 225 78 0.35 108 0.48 168 0.75 100 0.44 60–74 191 87 0.46 107 0.56 158 0.83 84 0.44 75+ 200 75 0.38 88 0.44 69 0.35 70 0.35 Thyroid No 195 85 0.43 96 0.49 127 0.65 84 0.43 Yes 252 56 0.22 133 0.53 197 0.78 110 0.44 Dyslipidemia No 111 185 1.66 60 0.53 190 1.71 100 0.90 Yes 212 74 0.35 106 0.50 143 0.67 90 0.42 Hypertension No 202 65 0.32 112 0.56 67 0.33 80 0.39 Yes 210 79 0.37 104 0.50 151 0.72 91 0.44 Pre-COVID healthcare usage Low 104 47 0.45 72 0.69 94 0.91 56 0.54 Medium 212 46 0.22 77 0.37 92 0.43 61 0.29 High 312 138 0.44 163 0.52 244 0.78 153 0.49 Total 209 77 0.37 105 0.50 144 0.69 90 0.43 Hospitalisation rates Hospitalisation rates were initially higher for men (255) compared to women (203) before the COVID-19 pandemic. During the first COVID wave, the hospitalisation rates for men decreased to 62 per cent of the pre-COVID rate, further declined to 26 per cent during the intermittent period, and then increased to 73 per cent in the second wave. In contrast, for women, the rates decreased to 49 per cent of pre-COVID levels during the first wave, increased significantly to 147 per cent during the intermittent period, and then dropped again to 52 per cent in the second wave. Age-wise, the hospitalisation rate was directly proportional to age, with the youngest group (30–44 years) having the lowest rate (125) and the oldest group (75+) having the highest rate (261) pre-COVID. This pattern persisted during the first wave, with rates of 48, 70, 181, and 193 for the 30–44, 45–59, 60–74, and 75 + age groups, respectively. However, during the intermittent period, the 60–74 age group saw the highest hospitalisation rate (346), exceeding pre-COVID levels, while the youngest group had no recorded hospitalisations. In the second wave, hospitalisation rates remained below 60 per cent of pre-COVID levels for all age groups except 60–74, which had 83 per cent of the pre-COVID rate. For individuals with high pre-COVID healthcare usage, hospitalisation rates were consistently higher, exceeding pre-COVID levels during the intermittent period. Table 5 Number of hospitalisations per 10,000 patient months in diabetes patients during pre-COVID and COVID-19 phases Pre COVID First cases First wave Intermittent period Second wave Hospitalisations Rate Rate Rate ratio Rate Rate ratio Rate Rate ratio Rate Rate ratio Sex Men 255 65 0.26 159 0.62 67 0.26 186 0.73 Women 203 64 0.31 99 0.49 297 1.47 105 0.52 Age group 30–44 125 0 - 48 0.38 0 - 46 0.37 45–59 217 0 - 70 0.32 134 0.62 98 0.45 60–74 244 99 0.41 181 0.74 346 1.42 202 0.83 75+ 261 225 0.86 193 0.74 116 0.44 152 0.58 Thyroid No 227 49 0.22 129 0.57 170 0.75 135 0.59 Yes 218 111 0.51 111 0.51 290 1.33 152 0.70 Dyslipidemia No 129 309 2.40 66 0.51 0 - 251 1.95 Yes 228 57 0.25 126 0.55 206 0.90 136 0.60 Hypertension No 91 0 - 0 - 0 - 88 0.97 Yes 237 70 0.30 136 0.57 218 0.92 144 0.61 Pre-COVID healthcare usage Low 127 0 1.13 143 1.06 135 71 0.56 Medium 184 108 0.59 81 0.44 42 0.23 77 0.42 High 364 81 0.22 150 0.41 420 1.15 267 0.73 Total 225 64 0.29 124 0.55 200 0.89 139 0.62 2. Glucose Levels among diabetes patients during pre-COVID and COVID phases Figure 1 and Fig. 2 present the mean HbA1c levels in the study population across pre-COVID and COVID phases, first stratified by sex and then by age groups. In the study population, the average HbA1c was initially 7.1 (SE ± 0.0), which gradually increased to 7.2 (SE ± 0.1) during the first COVID-19 wave and reached 7.4 (SE ± 0.1) in the second wave. Men demonstrated better glucose management than women throughout the pandemic. For men, the average HbA1c was 7.2 (SE ± 0.1) during the pre-COVID period, remained stable at 7.2 (SE ± 0.1) during the first wave, and increased by 0.1 to 7.3 (SE ± 0.2) during the second wave. In contrast, women had a slightly lower average HbA1c of 7.1 (SE ± 0.1) during the pre-COVID period, but their levels rose more significantly during the pandemic, reaching 7.2 (SE ± 0.1) during the first wave and climbing further to 7.5 (SE ± 0.2) in the second wave. Examining the HbA1c and FPG levels by age groups pre-COVID, we observe an inverse relationship with age. Older age groups (60 and above) exhibited better glucose control compared to younger groups. For the pre-COVID period, the average HbA1c for the 30–44 age group was 7.4 (SE ± 0.2), 7.3 (SE ± 0.1) for the 45–59 age group, 7.0 (SE ± 0.1) for the 60–74 age group, and 6.7 (SE ± 0.1) for those aged 75 and above. During the COVID phases, we see variations in HbA1c levels among different age groups. For the 30–44 age group, HbA1c increased to 7.9 (SE ± 0.2) during the intermittent period and then slightly decreased but remained elevated at 7.7 (SE ± 0.4) during the second COVID phase. During the second COVID wave, in the 45–59 age group, the HbA1c level rose to 7.5 (SE ± 0.3), while for the 60–74 age group, it increased to 7.3 (SE ± 0.2) from the pre-COVID level. Notably, in the 75 + age group, the HbA1c increased to 7.0 (SE ± 0.2) during the first COVID wave but then declined to 6.7 (SE ± 0.2) in the subsequent phase. Figure 3 and Fig. 4 highlight the mean FPG levels in the study population across pre-COVID and COVID phases, first stratified by sex and then by age groups. The average FPG of the study population was initially 127.5 mg/dl (SE ± 1.2), which increased to 131.8 mg/dl (SE ± 2.3) during the first COVID-19 wave and further rose to 136.3 mg/dl (SE ± 4.4) during the second wave. Pre-COVID, women had better FPG levels than men, with an average of 124.8 mg/dl (SE ± 1.6) in women compared to 130.8 mg/dl (SE ± 1.7) in men. However, during the pandemic, the trends seen in HbA1c levels were reflected in FPG levels as well. Men managed to maintain their FPG close to the pre-COVID baseline, with an increase to 131.6 mg/dl (SE ± 3.4) during the first wave, then slightly decreasing to 130.0 mg/dl (SE ± 5.9) by the second wave. In contrast, FPG levels for women continued to rise, reaching 131.6 mg/dl (SE ± 3.4) during the first wave and further increasing to 141.9 mg/dl (SE ± 6.4) during the second wave. Pre-COVID, the 30–44 age group had an average FPG level of 138.1 mg/dl (SE ± 5.4), the 45–59 age group had 131.9 mg/dl (SE ± 1.8), the 60–74 age group had 121.3 mg/dl (SE ± 1.4), and the 75 + age group had 115.7 mg/dl (SE ± 2.4). During the second COVID wave, these FPG values increased to 156.2 mg/dl (SE ± 11.8) in the 30–44 age group, 138.5 mg/dl (SE ± 6.6) in the 45–59 age group, and 133.5 mg/dl (SE ± 8.3) in the 60–74 age group. However, for the 75 + age group, FPG levels initially increased to 125.0 mg/dl (SE ± 5.5) during the first COVID wave but then decreased to 111.1 mg/dl (SE ± 4.8) during the second wave. Discussion The COVID-19 pandemic placed an unprecedented strain on healthcare systems worldwide. Hospitals globally faced surges of critically ill COVID-19 patients, leading to shortages of essential resources. ( 26 – 28 ) In response, healthcare systems were forced to rapidly reallocate resources, often diverting staff and infrastructure away from non-COVID-related services, including routine chronic disease management and elective surgeries.( 29 – 31 ) For people with diabetes, this disruption meant difficulties in regular follow-ups, essential blood glucose monitoring, obtaining timely refills of medications (like insulin and oral hypoglycemic agents), and accessing specialised care such as diabetic foot clinics or retinopathy screenings. A study from Ahmedabad, Gujarat, reported that 55.7 per cent faced delays in regular checkups, 39.8 per cent used telemedicine to avoid travelling to the hospital for fear of getting COVID-19 infection, and 7.8 per cent faced delays in consuming medicines due to unavailability. Regular intake of medicines/insulin was altered for 40.5 per cent of study participants. Additionally, 54.3 per cent of participants expressed fear while visiting the laboratory to measure their blood glucose levels. ( 32 ) Several studies across the globe also reported similar findings. A mixed-method study conducted in Northern Jordan revealed that both the quality and accessibility of healthcare services were adversely affected for patients with diabetes mellitus (DM) and chronic respiratory diseases (CRD) during the COVID-19 pandemic.( 33 ) Another study from the United States reported that 50.8 per cent of individuals with Type 2 diabetes experienced diabetes distress. Furthermore, 21.1 per cent reported experiencing delayed medical care, while 15.1 per cent indicated that they did not receive medical care.( 34 ) One of these notable consequences was a significant decrease in face-to-face doctor visits. ( 35 – 37 ). By analysing the OPD visit rates, our study indirectly assessed the impact of these organisational changes that occurred during the pandemic. Our study demonstrated a significant reduction in outpatient department (OPD) visits during the initial phases of the pandemic, but later, this hurdle was surpassed by teleconsultations, which are equivalent to face-to-face doctor visits. Studies report that the pandemic accelerated the adoption of telemedicine for diabetes management.( 38 ) A study highlighted that telemedicine became a crucial tool for managing diabetes during lockdowns, offering remote consultations and continuous care. ( 39 ) It is also important to note that we did not find a statistically significant association between the decrease in OPD visits and an increase in HbA1c levels. (Supplementary Table 7) This finding aligns with a study conducted by Patel et al., where no conclusive evidence of a negative correlation between the decline in outpatient visits and medication fills or glycemic control was observed. ( 40 ) The impact of lockdown measures to mitigate the spread of the COVID-19 pandemic on glycaemic control in individuals with Type 2 Diabetes Mellitus has demonstrated considerable heterogeneity. Two studies conducted in Turkey revealed that despite reduced adherence to dietary and exercise regimens, as well as challenges in accessing medication, diabetic patients did not experience significant alterations in weight, fasting plasma glucose (FPG), or HbA1c levels. ( 41 , 42 ) Furthermore, a study conducted at outpatient diabetes clinics in a city in the Southern part of India also concluded that the lockdown did not result in a substantial disruption of glycemic control, lifestyle, or psychosocial well-being within their patient population. This stability was attributed to a general increase in the adoption of healthier eating habits. ( 43 ) In a cohort of 114 individuals with Diabetes, Biancalana et al. reported no significant changes in glucose control, although 26 per cent of participants exhibited a 0.3 per cent increase in HbA1c.( 44 )The findings of these studies resonate with the trend reported among men in this study population, where, unlike women, no significant change in HbA1c and FPG was noticed. Tanji et al. also reported deterioration in glycemic levels among women compared to men. ( 45 ) Contrarily, Indian individuals, as reported by Khare and Jindal, experienced a deterioration in glycaemic control during the initial phase of the lockdown.( 46 ) Anjana et al., in a survey encompassing 205 T2DM patients, observed a noteworthy increase in HbA1c following the lockdown.( 47 ) Another study showed that among those subjected to lockdown measures, only retired patients demonstrated an improvement in HbA1c levels.( 48 ) Our study also reported better glycemic control among the oldest age group. The study by D’Onofrio et al. suggested that individuals with poor psychological well-being may experience worsened glycemic control due to the restrictions imposed by lockdown measures.( 48 ) This observation aligns with our findings, where we identified a positive association between pre-COVID HbA1c levels and those during the first and second COVID-19 waves, indicating that individuals with pre-existing poor glycemic control are more susceptible to elevated HbA1c and FPG levels during pandemic phases. Several studies documented a decline in glycemic control, particularly during the days of lockdown( 49 – 53 ). Interestingly, a simulation model study employing multivariate regression analysis to assess the impact of previous natural disasters on patients with diabetes predicted a linear relationship between the duration of lockdown and the exacerbation of diabetes-related complications. ( 52 ). On the contrary, in our study, no significant association was found between HbA1c levels and the number of days under COVID restrictions during the 90 days preceding the HbA1c test, indicating a lack of a linear relationship. (Supplementary Table 7) A study on healthcare utilisation patterns among veterans in the United States during the COVID-19 pandemic observed that initially, during the first three months following the onset of the pandemic, there were significant reductions in both the monthly rates of HbA1c measurements and in-person outpatient visits. Later, these utilisation rates gradually rebounded to pre-pandemic levels.( 54 ) This observed trajectory closely aligns with the trends seen in our study population, where healthcare utilisation rates declined during the first wave of COVID-19, partially rebounded in the intermittent period, and approached baseline levels by the second wave. Our study findings resonate with a growing body of research on the global impact of COVID-19 on healthcare systems. For instance, a study conducted by Chen, Krupp, and Lo in 2022 also observed declines in the proportion of patients receiving HbA1c testing, decreases in the percentage of patients engaging in diabetes-related in-person office visits, and a reduction in the number of visits per patient. ( 55 ) Furthermore, the overall utilisation of non-emergent outpatient visits declined, reflecting the broader trend of decreased healthcare engagement during the pandemic. Similarly, research by Palanca and colleagues highlighted the effects of full lockdown measures; approximately 50% of participants experienced a reduction in HbA1c testing, with older individuals being particularly affected. ( 56 ) Our study highlighted a notable decrease in FPG and HbA1c testing rates at the pandemic's onset. This decline can be attributed to both the redirection of medical resources towards immediate pandemic needs and patients' reluctance to visit medical facilities, fearing infection. This aligns with Boserup et al.'s (2020) observations of reduced non-emergency department visits in the pandemic's early months. ( 57 ) The 45–59 age group and women showed a significant resurgence in OPD visit rates and Pharmacy visit rates during the intermittent phases. This might be interpreted as a heightened awareness or prioritisation of regular health check-ups amidst the pandemic. ( 58 ) In contrast, the elderly (aged 75 and above) demonstrated a continued decline in healthcare usage rates, perhaps due to their increased risk of severe COVID-19 complications. ( 59 ) A cross-sectional study conducted in India surveyed 636 individuals to assess their willingness to visit hospitals during the COVID-19 pandemic. The findings revealed that 74.8 per cent of respondents were reluctant to seek hospital care unless their symptoms were severe. The primary reasons cited included fear of contracting COVID-19 from other patients (72.6 per cent), apprehension about leaving home during the pandemic (31.1 per cent), and concerns about infection from medical equipment (24.5 per cent). ( 60 ) In a nutshell, our research noted that the study population gradually returned to pre-COVID levels of healthcare utilisation during the intermittent phase, demonstrating the positive impact of diabetes awareness, coordination, and availability facilitated by the compact yet well-connected healthcare services in this population. Limitations Our study is subject to some inherent limitations caused by the absence of an exhaustive assessment of multifaceted variables that could potentially influence patients' glycemic control. Notably, factors encompassing alterations in lifestyle patterns during periods of isolation, adherence to dietary protocols, variations in body mass index, the duration of diabetes treatment, blood pressure parameters, and the impact of stressors have not been comprehensively examined within the confines of our study. Conclusion Our research examined healthcare utilisation patterns and diabetes management during various stages of the COVID-19 pandemic. We identified discernible trends in healthcare usage: there was a notable decrease during the initial wave of the pandemic, a subsequent rise, and followed by a decrease during the second wave. This trend was evident across various healthcare services, including outpatient department (OPD) visits, inpatient department (IPD) admissions, laboratory tests, and pharmaceutical purchases. Those with comorbidities had higher healthcare usage rates, and the need for additional care continued even during the pandemic. Interestingly, while we observed these shifts in healthcare access, our data did not show significant changes in the average HbA1c and FPG values among men. However, the values increased among women. Moreover, there wasn’t a strong correlation between the frequency of HbA1c tests or OPD visits and the actual HbA1c values. A significant association was found between HbA1c values prior to the pandemic and during its successive waves. This highlights the possibility that individuals who had challenges in managing their glucose levels before the pandemic faced continued challenges during it. Declarations Ethics approval: This study was conducted using de-identified secondary data collected as part of routine healthcare delivery. As the data are not publicly available and do not contain personally identifiable information, individual informed consent was not required. The requirement for informed consent was waived by the Institutional Ethics Committee of Bhabha Atomic Research Centre, Mumbai, India, which also granted ethical clearance for the study. The study adhered to the principles of the Declaration of Helsinki and complied with all relevant ethical guidelines and national regulations. Consent for publication: Not applicable. This study does not include any individual-level identifiable information or images requiring consent for publication. Availability of data and materials: The data sets generated and analysed during this study are not publicly available but may be accessed through the corresponding author upon reasonable request. Competing interests: The authors declare that they have no competing interests or relevant disclosures. Funding: This research received no specific grant from any funding agency. Authors' contributions: Puja Goswami: Formulation of research objectives, data preprocessing, statistical analysis, and manuscript writing. Thandassery R. Dilip: Conceptualisation of the study, formulation of research objectives, editing, and critical revision of the manuscript. Yogesh Shejul: Guidance in data preprocessing, manuscript review, and revision. Anjali Kulkarni: Guidance in data preprocessing, manuscript review, and revision. Acknowledgement: Not applicable References International Diabetes Federation. IDF Diabetes Atlas. 2021. Chudasama Y V., 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 & Metabolic Syndrome: Clinical Research & Reviews [Internet]. 2020 Sep 1 [cited 2023 Sep 1];14(5):965–7. Available from: https://www.sciencedirect.com/science/article/pii/S1871402120302113 Gregg EW, Sattar N, Ali MK. The changing face of diabetes complications. Lancet Diabetes Endocrinol [Internet]. 2016 Jun 1 [cited 2023 Sep 1];4(6):537–47. Available from: http://www.thelancet.com/article/S2213858716300109/fulltext Morrison F, Shubina M, Turchin A. Encounter Frequency and Serum Glucose Level, Blood Pressure, and Cholesterol Level Control in Patients With Diabetes Mellitus. Arch Intern Med [Internet]. 2011 Sep 26 [cited 2023 Sep 1];171(17):1542–50. Available from: https://jamanetwork.com/journals/jamainternalmedicine/fullarticle/1105940 Mohseni M, Ahmadi S, Azami-Aghdash S, Mousavi Isfahani H, Moosavi A, Fardid M, et al. Challenges of routine diabetes care during COVID-19 era: A systematic search and narrative review. Prim Care Diabetes [Internet]. 2021 Dec 1 [cited 2023 Sep 1];15(6):918–22. Available from: https://www.sciencedirect.com/science/article/pii/S1751991821001340 Rosenbaum L. The Untold Toll — The Pandemic’s Effects on Patients without Covid-19. New England Journal of Medicine [Internet]. 2020 Jun 11 [cited 2023 Sep 1];382(24):2368–71. Available from: https://www.nejm.org/doi/full/10.1056/NEJMms2009984 Bornstein SR, Rubino F, Khunti K, Mingrone G, Hopkins D, Birkenfeld AL, et al. Practical recommendations for the management of diabetes in patients with COVID-19. Lancet Diabetes Endocrinol [Internet]. 2020 Jun 1 [cited 2023 Sep 1];8(6):546–50. Available from: http://www.thelancet.com/article/S2213858720301522/fulltext Koliaki C, Tentolouris A, Eleftheriadou I, Melidonis A, Dimitriadis G, Tentolouris N. Clinical Management of Diabetes Mellitus in the Era of COVID-19: Practical Issues, Peculiarities and Concerns. Journal of Clinical Medicine 2020, Vol 9, Page 2288 [Internet]. 2020 Jul 18 [cited 2023 Sep 1];9(7):2288. Available from: https://www.mdpi.com/2077-0383/9/7/2288/htm Banerjee M, Chakraborty S, Pal R. Diabetes self-management amid COVID-19 pandemic. Diabetes & Metabolic Syndrome: Clinical Research & Reviews [Internet]. 2020 Jul 1 [cited 2023 Sep 1];14(4):351–4. Available from: https://www.sciencedirect.com/science/article/pii/S1871402120300783 Rastogi A, Hiteshi P, Bhansali A. Improved glycemic control amongst people with long-standing diabetes during COVID-19 lockdown: a prospective, observational, nested cohort study. Int J Diabetes Dev Ctries [Internet]. 2020 Oct 1 [cited 2023 Sep 1];40(4):476–81. Available from: https://link.springer.com/article/10.1007/s13410-020-00880-x Rajagopalan S. Serious infections in elderly patients with diabetes mellitus. Clinical Infectious Diseases [Internet]. 2005 Apr 1 [cited 2023 Sep 1];40(7):990–6. Available from: https://dx.doi.org/10.1086/427690 Badawi A, Ryoo SG. Prevalence of comorbidities in the Middle East respiratory syndrome coronavirus (MERS-CoV): a systematic review and meta-analysis. International Journal of Infectious Diseases [Internet]. 2016 Aug 1 [cited 2023 Sep 1];49:129–33. Available from: https://www.sciencedirect.com/science/article/pii/S1201971216311006 Andrews MA, Areekal B, Rajesh K, Krishnan J, Suryakala R, Krishnan B, et al. First confirmed case of COVID-19 infection in India: A case report. Indian Journal of Medical Research [Internet]. 2020 May 1 [cited 2025 May 18];151(5):490–2. Available from: https://journals.lww.com/ijmr/fulltext/2020/51050/first_confirmed_case_of_covid_19_infection_in.17.aspx Jahan N, Rubeshkumar P, Karuppiah M, Sambath I, Sendhilkumar M, Ilangovan K, et al. Entry and initial spread of COVID-19 in India: Epidemiological analysis of media surveillance data, India, 2020. Clin Epidemiol Glob Health [Internet]. 2021 Jan 1 [cited 2025 May 18];9:347–54. Available from: https://www.sciencedirect.com/science/article/pii/S2213398420302281 WHO. Coronavirus disease 2019 (COVID-19) Situation Report-63 HIGHLIGHTS. 2019; Gupta M, Mohanta R, Rao A, Parameswaran GG, Agarwal M, Arora M, et al. Transmission dynamics of the COVID-19 epidemic in India and modeling optimal lockdown exit strategies. International Journal of Infectious Diseases [Internet]. 2021 Feb 1 [cited 2025 May 18];103:579–89. Available from: https://www.sciencedirect.com/science/article/pii/S1201971220325224 Mahajan P, Kaushal J. Epidemic Trend of COVID-19 Transmission in India During Lockdown-1 Phase. J Community Health [Internet]. 2020 Dec 1 [cited 2025 May 18];45(6):1291–300. Available from: https://link.springer.com/article/10.1007/s10900-020-00863-3 Government of India. Government of India issues Orders prescribing lockdown for containment of COVID-19 Epidemic in the country: Press Information Bureau [Internet]. 2020 [cited 2025 May 18]. Available from: https://www.pib.gov.in/PressReleasePage.aspx?PRID=1607997 Kumar S, Sonkar S, Atam I, Gupta H, Parmar K, Verma S, et al. Socio-economic impact of first 21 days nationwide lockdown- 1 on the spread of SARS-COV-2 in India in relation to health. J Family Med Prim Care [Internet]. 2020 [cited 2025 May 18];9(9):4557. Available from: https://journals.lww.com/jfmpc/fulltext/2020/09090/socio_economic_impact_of_first_21_days_nationwide.12.aspx Bairwa M, Behera S, Chavda PD, Kulkarni P, Mohapatra A, Ramraj B, et al. Combating the COVID-19 pandemic in a resource-constrained setting: insights from initial response in India. BMJ Glob Health [Internet]. 2020 Nov 13 [cited 2025 May 18];5(11). Available from: https://gh.bmj.com/content/5/11/e003416 Bhadoria AS, Mishra S, Mohapatra A, Singh A, Kant R, Nundy S. Physical distancing amidst social connectivity: Time to re-visit ‘social distancing’ as India fights COVID-19 and a few concerns. Curr Med Res Pract [Internet]. 2020 May [cited 2025 May 18];10(3):85. Available from: https://pmc.ncbi.nlm.nih.gov/articles/PMC7255194/ Schaner S, Theys N, Angrisani M, Banerjee J, Khobragade PY, Petrosyan S, et al. Adherence to COVID-19 protective behaviours in India from May to December 2020: evidence from a nationally representative longitudinal survey. BMJ Open [Internet]. 2022 Feb 1 [cited 2025 May 18];12(2):e058065. Available from: https://bmjopen.bmj.com/content/12/2/e058065 Saha J, Chouhan P. Lockdown and unlock for the COVID-19 pandemic and associated residential mobility in India. International Journal of Infectious Diseases [Internet]. 2021 Mar 1 [cited 2025 May 18];104:382–9. Available from: https://www.sciencedirect.com/science/article/pii/S1201971220325029 Kulkarni A, Dilip TR, Shejul YK, Bhandarkar P, Goswami P, Sharma P, et al. Progression and management of diabetes in Indian settings with universal access to health care: Protocol and plans for CHIPS cohort study. medRxiv [Internet]. 2023 Jan 28 [cited 2023 Oct 21];2023.01.24.23284975. Available from: https://www.medrxiv.org/content/10.1101/2023.01.24.23284975v1 Covid19 Dashboard | Home [Internet]. 2023 [cited 2024 Dec 22]. Available from: https://covid19dashboard.mohfw.gov.in/ Janke AT, Venkatesh AK. Understanding and Addressing the US Hospital Bed Shortage—Build, Baby, Build. JAMA Netw Open [Internet]. 2025 Feb 3 [cited 2025 May 18];8(2):e2460652–e2460652. Available from: https://jamanetwork.com/journals/jamanetworkopen/fullarticle/2830389 McCabe R, Kont MD, Schmit N, Whittaker C, Løchen A, Baguelin M, et al. Modelling intensive care unit capacity under different epidemiological scenarios of the COVID-19 pandemic in three Western European countries. Int J Epidemiol [Internet]. 2021 Jul 9 [cited 2025 May 18];50(3):753–67. Available from: https://dx.doi.org/10.1093/ije/dyab034 Beitler JR, Mittel AM, Kallet R, Kacmarek R, Hess D, Branson R, et al. Ventilator sharing during an acute shortage caused by the COVID-19 pandemic. Am J Respir Crit Care Med [Internet]. 2020 Aug 15 [cited 2025 May 18];202(4):600–4. Available from: /doi/pdf/10.1164/rccm.202005-1586LE?download=true Atary M, Abu-Rmeileh NME. Deferral of elective surgeries during the COVID-19 pandemic and its impact on Palestinian patients: a cross-sectional study. Confl Health [Internet]. 2023 Dec 1 [cited 2025 May 19];17(1):1–16. Available from: https://link.springer.com/articles/10.1186/s13031-023-00509-w Herrera CA, Juárez-Ramírez C, Reyes-Morales H, Bedregal P, Reartes-Peñafiel DL, Díaz-Portillo SP, et al. COVID-19 Disruption To Routine Health Care Services: How 8 Latin American And Caribbean Countries Responded. https://doi.org/101377/hlthaff202300694 [Internet]. 2023 Dec 4 [cited 2025 May 18];42(12):1667–74. Available from: /doi/pdf/10.1377/hlthaff.2023.00694 Filip R, Gheorghita Puscaselu R, Anchidin-Norocel L, Dimian M, Savage WK. Global Challenges to Public Health Care Systems during the COVID-19 Pandemic: A Review of Pandemic Measures and Problems. Journal of Personalized Medicine 2022, Vol 12, Page 1295 [Internet]. 2022 Aug 7 [cited 2025 May 18];12(8):1295. Available from: https://www.mdpi.com/2075-4426/12/8/1295/htm Mehta S, Puwar T, Patel Y, Patel M, Shah V, Patel K, et al. Understanding Health-Seeking Behavior of People with Diabetes during COVID-19 Pandemic: A Facility Based Cross-Sectional Study Conducted in Ahmedabad, India. Clinical Diabetology [Internet]. 2023 [cited 2025 May 18];12(3):141–9. Available from: https://journals.viamedica.pl/clinical_diabetology/article/view/DK.a2023.0013/73045 Al-Bataineh R, Al-Hammouri M, Al-Jaraideh W. The accessibility and quality of health services for diabetes mellitus and chronic respiratory disease patients during Covid-19 in Northern Jordan: A mixed method study. PLoS One [Internet]. 2023 Nov 1 [cited 2025 May 18];18(11):e0294655. Available from: https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0294655 Saseetharran A, Patel SA. COVID-19 pandemic-related healthcare interruptions and diabetes distress: a national study of US adults with diabetes. BMC Public Health [Internet]. 2024 Dec 1 [cited 2025 May 18];24(1):1–8. Available from: https://link.springer.com/articles/10.1186/s12889-024-17921-3 Levene LS, Seidu S, Greenhalgh T, Khunti K. Pandemic threatens primary care for long term conditions. BMJ [Internet]. 2020 Oct 5 [cited 2023 Sep 1];371. Available from: https://www.bmj.com/content/371/bmj.m3793 Joy M, McGagh D, Jones N, Liyanage H, Sherlock J, Parimalanathan V, et al. Reorganisation of primary care for older adults during COVID-19: a cross-sectional database study in the UK. British Journal of General Practice [Internet]. 2020 Aug 1 [cited 2023 Sep 1];70(697):e540–7. Available from: https://bjgp.org/content/70/697/e540 Solans O, Vidal-Alaball J, Cabo PR, Mora N, Coma E, Simó JMB, et al. Characteristics of Citizens and Their Use of Teleconsultations in Primary Care in the Catalan Public Health System Before and During the COVID-19 Pandemic: Retrospective Descriptive Cross-sectional Study. J Med Internet Res 2021;23(5):e28629 https://www.jmir.org/2021/5/e28629 [Internet]. 2021 May 27 [cited 2023 Sep 1];23(5):e28629. Available from: https://www.jmir.org/2021/5/e28629 Dhediya R, Chadha M, Bhattacharya AD, Godbole S, Godbole S. Role of Telemedicine in Diabetes Management. J Diabetes Sci Technol [Internet]. 2023 May 1 [cited 2025 May 18];17(3):775–81. Available from: https://scholar.google.com/scholar_url?url=https://journals.sagepub.com/doi/pdf/10.1177/19322968221081133&hl=en&sa=T&oi=ucasa&ct=ufr&ei=ThAqaOWaC7Oi6rQPvJGpyAE&scisig=AAZF9b_AhEyQIkNlFSe1g6hRL6F7 Ghosh A, Gupta R, Misra A. Telemedicine for diabetes care in India during COVID19 pandemic and national lockdown period: Guidelines for physicians. Diabetes & Metabolic Syndrome: Clinical Research & Reviews [Internet]. 2020 Jul 1 [cited 2025 May 18];14(4):273–6. Available from: https://www.sciencedirect.com/science/article/pii/S1871402120300606 Patel SY, McCoy RG, Barnett ML, Shah ND, Mehrotra A. Diabetes Care and Glycemic Control During the COVID-19 Pandemic in the United States. JAMA Intern Med [Internet]. 2021 Oct 1 [cited 2023 Sep 1];181(10):1412–4. Available from: https://jamanetwork.com/journals/jamainternalmedicine/fullarticle/2781811 Selek A, Gezer E, Altun E, Sözen M, Topaloğlu Ö, Köksalan D, et al. The impact of COVID-19 pandemic on glycemic control in patients with diabetes mellitus in Turkey: a multi-center study from Kocaeli. J Diabetes Metab Disord [Internet]. 2021 Dec 1 [cited 2023 Sep 1];20(2):1461–7. Available from: https://link.springer.com/article/10.1007/s40200-021-00888-y Önmez A, Gamsızkan Z, Özdemir Ş, Kesikbaş E, Gökosmanoğlu F, Torun S, et al. The effect of COVID-19 lockdown on glycemic control in patients with type 2 diabetes mellitus in Turkey. Diabetes & Metabolic Syndrome: Clinical Research & Reviews [Internet]. 2020 Nov 1 [cited 2023 Sep 1];14(6):1963–6. Available from: https://www.sciencedirect.com/science/article/pii/S1871402120303957 Sankar P, Ahmed WN, Mariam Koshy V, Jacob R, Sasidharan S. Effects of COVID-19 lockdown on type 2 diabetes, lifestyle and psychosocial health: A hospital-based cross-sectional survey from South India. Diabetes & Metabolic Syndrome: Clinical Research & Reviews [Internet]. 2020 Nov 1 [cited 2023 Sep 1];14(6):1815–9. Available from: https://www.sciencedirect.com/science/article/pii/S1871402120303544 Biancalana E, Parolini F, Mengozzi A, Solini A. Short-term impact of COVID-19 lockdown on metabolic control of patients with well-controlled type 2 diabetes: a single-centre observational study. Acta Diabetol [Internet]. 2021 Apr 1 [cited 2023 Sep 1];58(4):431–6. Available from: https://link.springer.com/article/10.1007/s00592-020-01637-y Tanji 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 [Internet]. 2021 Jun 1 [cited 2023 Sep 3];176:108840. Available from: /pmc/articles/PMC8084613/ Khare J, Jindal S. Observational study on Effect of Lock Down due to COVID 19 on glycemic control in patients with Diabetes: Experience from Central India. Diabetes Metab Syndr [Internet]. 2020 Aug 20 [cited 2023 Sep 1];14(6):1571–4. Available from: https://europepmc.org/articles/PMC7438221 Anjana RM, Pradeepa R, Deepa M, Jebarani S, Venkatesan U, Parvathi SJ, et al. Acceptability and Utilization of Newer Technologies and Effects on Glycemic Control in Type 2 Diabetes: Lessons Learned from Lockdown. https://home.liebertpub.com/dia [Internet]. 2020 Jun 30 [cited 2023 Sep 1];22(7):527–34. Available from: https://www.liebertpub.com/doi/10.1089/dia.2020.0240 D’Onofrio L, Pieralice S, Maddaloni E, Mignogna C, Sterpetti S, Coraggio L, et al. Effects of the COVID-19 lockdown on glycaemic control in subjects with type 2 diabetes: the glycalock study. Diabetes Obes Metab [Internet]. 2021 Jul 1 [cited 2023 Sep 1];23(7):1624–30. Available from: https://onlinelibrary.wiley.com/doi/full/10.1111/dom.14380 Biamonte E, Pegoraro F, Carrone F, Facchi I, Favacchio G, Lania AG, et al. Weight change and glycemic control in type 2 diabetes patients during COVID-19 pandemic: the lockdown effect. Endocrine [Internet]. 2021 Jun 1 [cited 2023 Sep 1];72(3):604–10. Available from: https://link.springer.com/article/10.1007/s12020-021-02739-5 Karatas S, Yesim T, Beysel S. Impact of lockdown COVID-19 on metabolic control in type 2 diabetes mellitus and healthy people. Prim Care Diabetes [Internet]. 2021 Jun 1 [cited 2023 Sep 1];15(3):424–7. Available from: https://www.sciencedirect.com/science/article/pii/S1751991821000036 Fisher L, Polonsky W, Asuni A, Jolly Y, Hessler D. The early impact of the COVID-19 pandemic on adults with type 1 or type 2 diabetes: A national cohort study. J Diabetes Complications [Internet]. 2020 Dec 1 [cited 2023 Sep 1];34(12):107748. Available from: https://www.sciencedirect.com/science/article/pii/S1056872720305298 Ghosal S, Sinha B, Majumder M, Misra A. Estimation of effects of nationwide lockdown for containing coronavirus infection on worsening of glycosylated haemoglobin and increase in diabetes-related complications: A simulation model using multivariate regression analysis. Diabetes & Metabolic Syndrome: Clinical Research & Reviews. 2020 Jul 1;14(4):319–23. Khader MA, Jabeen T, Namoju R. A cross sectional study reveals severe disruption in glycemic control in people with diabetes during and after lockdown in India. Diabetes & Metabolic Syndrome: Clinical Research & Reviews. 2020 Nov 1;14(6):1579–84. Adhikari S, Titus AR, Baum A, Lopez P, Kanchi R, Orstad SL, et al. Disparities in routine healthcare utilization disruptions during COVID-19 pandemic among veterans with type 2 diabetes. BMC Health Serv Res [Internet]. 2023 Dec 1 [cited 2025 May 29];23(1):1–10. Available from: https://link.springer.com/articles/10.1186/s12913-023-09057-8 Chen JL, Krupp GR, Lo JY. The COVID-19 Pandemic and Changes in Health Care Utilization Among Patients With Type 2 Diabetes. 2022 [cited 2023 Sep 2]; Available from: http://diabetesjournals.org/care/article-pdf/doi/10.2337/dc21-2248/642680/dc212248.pdf Palanca A, Quinones-Torrelo C, Girbés J, Real JT, Ampudia-Blasco FJ. Impact of COVID-19 lockdown on diabetes management and follow-up in a broad population in Spain. Eur J Clin Invest [Internet]. 2022 Jun 1 [cited 2023 Sep 2];52(6):e13771. Available from: https://onlinelibrary.wiley.com/doi/full/10.1111/eci.13771 Boserup B, McKenney M, Elkbuli A. The impact of the COVID-19 pandemic on emergency department visits and patient safety in the United States. Am J Emerg Med [Internet]. 2020 Sep 1 [cited 2023 Oct 10];38(9):1732–6. Available from: https://www.sciencedirect.com/science/article/pii/S0735675720304848 Hoffer-Hawlik MA, Moran AE, Burka D, Kaur P, Cai J, Frieden TR, et al. Leveraging Telemedicine for Chronic Disease Management in Low- and Middle-Income Countries During Covid-19. Glob Heart [Internet]. 2020 [cited 2023 Oct 10];15(1):63. Available from: /pmc/articles/PMC7500231/ Czeisler MÉ, Marynak K, Clarke KEN, Salah Z, Shakya I, Thierry JM, et al. Delay or Avoidance of Medical Care Because of COVID-19–Related Concerns — United States, June 2020. Morbidity and Mortality Weekly Report [Internet]. 2020 Sep 9 [cited 2023 Oct 10];69(36):1250. Available from: /pmc/articles/PMC7499838/ Shamsundar M, Choudhary S. A Cross-Sectional Study of Attitude and Behaviour of Individuals Towards Visiting the Hospital During the COVID-19 Pandemic in India. The Brown Journal of Hospital Medicine [Internet]. 2022 Jun 6 [cited 2025 May 18];1(2):36121. Available from: https://pmc.ncbi.nlm.nih.gov/articles/PMC11878854/ Additional Declarations No competing interests reported. Supplementary Files SupplementaryTables.docx Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 06 Jan, 2026 Reviews received at journal 31 Jul, 2025 Reviewers agreed at journal 24 Jul, 2025 Reviews received at journal 17 Jul, 2025 Reviewers agreed at journal 17 Jul, 2025 Reviewers invited by journal 10 Jul, 2025 Editor invited by journal 16 Jun, 2025 Editor assigned by journal 30 May, 2025 Submission checks completed at journal 29 May, 2025 First submitted to journal 29 May, 2025 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. 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Goswami","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABD0lEQVRIiWNgGAWjYHCCBCjN2ABCciDmgQekaDEGa0nAqRodALUkNiAbgw2YSyQ8/Piloi6xf9rhBsavOw6nzw87/BBoi52cbgN2LZYzEpKlZc4cTpxxO7GBWfbM4dyNt9MMgFqSjc0OYNdicCMhQVqy7UBuA0iLZBtQy+wEkJYDidtwa0n+LdlWlzsfqiXdcHb6B0Ja0iQ/tjHnbgBqYfzYdjhBXjoHvy2WPQ/SrBnOHK7fCNRymLEt3XCDdE7BgQQD3H4xZ89Jvvmjos5Y7nb6w4c/26zl5Wenb/7wocJODqf3GXgSmHmgnMM8DM0MBgcg4jiBAQP7AcYfUA6QUccg34Bb9SgYBaNgFIxMAAAzK28rDers4AAAAABJRU5ErkJggg==","orcid":"","institution":"International Institute for Population Sciences","correspondingAuthor":true,"prefix":"","firstName":"Puja","middleName":"","lastName":"Goswami","suffix":""},{"id":483552490,"identity":"17be4917-5e4c-4494-9c75-eee19f2a6361","order_by":1,"name":"Dilip Thandassery.R","email":"","orcid":"","institution":"International Institute for Population Sciences","correspondingAuthor":false,"prefix":"","firstName":"Dilip","middleName":"","lastName":"Thandassery.R","suffix":""},{"id":483552491,"identity":"5d6a5610-820a-428e-9c5a-5aa8d61d6c06","order_by":2,"name":"Yogesh Shejul","email":"","orcid":"","institution":"Bhabha Atomic Research Centre","correspondingAuthor":false,"prefix":"","firstName":"Yogesh","middleName":"","lastName":"Shejul","suffix":""},{"id":483552492,"identity":"7c3ad349-a2c2-48b5-a2b7-b7856f705baa","order_by":3,"name":"Anjali Kulkarni","email":"","orcid":"","institution":"Bhabha Atomic Research Centre","correspondingAuthor":false,"prefix":"","firstName":"Anjali","middleName":"","lastName":"Kulkarni","suffix":""}],"badges":[],"createdAt":"2025-05-12 05:08:25","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6642930/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6642930/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":86673836,"identity":"f0bd07a7-a778-49ac-a9fe-804d4a331a12","added_by":"auto","created_at":"2025-07-14 11:50:58","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":58620,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eAverage HbA1c values of diabetes patients across the study period by sex\u003c/em\u003e\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-6642930/v1/0be5cdbdf0d1a32c7c8e2843.png"},{"id":86675466,"identity":"d403e4d7-11a8-4d45-85e2-b5225e4121b2","added_by":"auto","created_at":"2025-07-14 11:58:58","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":43651,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eAverage HbA1c values of diabetes patients across the study period by age groups\u003c/em\u003e\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-6642930/v1/6cd7f9af32062223347a2819.png"},{"id":86673831,"identity":"ccc8e7c7-a3fd-4fad-9967-718471b97d63","added_by":"auto","created_at":"2025-07-14 11:50:57","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":45019,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eAverage FPG values of diabetes patients across the study period by sex\u003c/em\u003e\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-6642930/v1/3246e92e20f0ec70cc11b6a0.png"},{"id":86673834,"identity":"b26a5407-7d81-4567-8220-76eddce0b7a3","added_by":"auto","created_at":"2025-07-14 11:50:58","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":50234,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eAverage FPG values of diabetes patients across the study period by age groups\u003c/em\u003e\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-6642930/v1/f9709f48755c1da45734f63f.png"},{"id":86676690,"identity":"6235291a-24f3-45c2-aad6-9be05cc1cac9","added_by":"auto","created_at":"2025-07-14 12:14:59","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2288199,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6642930/v1/11bdb484-dabb-4572-ba9f-574e66ceac97.pdf"},{"id":86675712,"identity":"73efc7df-64e8-4b24-8ac2-91192ca68799","added_by":"auto","created_at":"2025-07-14 12:06:57","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":269056,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryTables.docx","url":"https://assets-eu.researchsquare.com/files/rs-6642930/v1/57f8def96e7f93b29c8a8879.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Diabetes Management during the COVID-19 pandemic: A Study on Contributory Health Service Scheme Beneficiaries from Mumbai, India","fulltext":[{"header":"Introduction","content":"\u003cp\u003eThe COVID-19 pandemic has profoundly disrupted healthcare systems around the world, with individuals living with chronic conditions such as diabetes mellitus bearing a disproportionate burden. Diabetes, a complex and lifelong condition, affects over 536\u0026nbsp;million adults globally, including an estimated 74.2\u0026nbsp;million in India alone, according to the 2021 annual report by the International Diabetes Federation. (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e) Effective diabetes management relies heavily on consistent self-care, regular monitoring, lifestyle modifications, medication adherence, and timely access to healthcare services. However, the pandemic severely compromised these essential aspects of care.(\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e)\u003c/p\u003e\u003cp\u003eAmid lockdowns and strained health infrastructure, routine diabetes management\u0026mdash;including in-person consultations, diagnostic testing, and follow-up care\u0026mdash;became increasingly difficult to maintain.(\u003cspan additionalcitationids=\"CR4\" citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e) Individuals with diabetes faced multiple challenges, such as reduced physical activity, dietary shifts, restricted healthcare access, treatment discontinuation due to fear of infection, postponed medical interventions, and elevated psychological stress.(\u003cspan additionalcitationids=\"CR7 CR8\" citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e) While self-reported studies suggest that these factors negatively influenced glycemic control and metabolic health, a key limitation is the absence of clinical and laboratory-based evidence (Ghosal, Arora, et al., 2020; Holman et al., 2020; Singhai et al., 2020).\u003c/p\u003e\u003cp\u003eBeyond the general disruptions, individuals with diabetes faced an elevated risk of adverse outcomes from COVID-19 itself, including worsened glucose regulation and increased morbidity and mortality.(\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e) Even in the absence of infection, poor disease management can precipitate deteriorating glycemic control, increasing the likelihood of long-term complications, disability, and premature death. Chronic hyperglycemia has also been associated with greater susceptibility to infections and poorer clinical outcomes. (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e)\u003c/p\u003e\u003cp\u003eIndia recorded its first confirmed case of COVID-19 on January 30, 2020, in the state of Kerala. (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e) Following this initial case, the virus gradually spread to other parts of the country, initially linked primarily to individuals with a travel history to affected nations and their contacts. (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e) Community transmission soon became evident, leading to an escalating number of cases across various states. (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e)\u003c/p\u003e\u003cp\u003eFaced with the exponential rise in COVID-19 cases and the threat of overwhelming the healthcare system, the Government of India implemented stringent public health measures.(\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e) The most significant of these was a nationwide lockdown announced on March 24, 2020. (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e) This initial 21-day lockdown was subsequently extended multiple times with varying degrees of restrictions. (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e) Measures included the suspension of all international and domestic travel (flights, trains, interstate buses) and the closure of educational institutions, non-essential businesses, public spaces, and religious gatherings. (\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e) Strict stay-at-home orders were enforced, movement was severely restricted, and mandatory mask-wearing and social distancing norms were introduced.(\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e) Subsequent phases involved gradual \"unlocking\" with localised containment zones based on case prevalence.(\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e) .\u003c/p\u003e\u003cp\u003eDespite the magnitude of this public health crisis, there remains a gap in research that leverages electronic health records (EHRs) to examine the real-world impact of the pandemic on diabetes care and outcomes. Furthermore, few studies have explored how glycemic control among Indian diabetes patients was managed during this period. This study seeks to bridge these gaps by offering a comprehensive, data-driven assessment of healthcare utilisation and diabetes management before and during the COVID-19 pandemic in an Indian context.\u003c/p\u003e"},{"header":"Study Population","content":"\u003cp\u003eThe study is based on a retrospective cohort comprising 835 individuals diagnosed with diabetes, selected from the CHIPS study database.(\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e) This database is compiled using electronic health records of individuals enrolled in the government\u0026rsquo;s Contributory Health Services Scheme (CHSS). Under the CHSS, the government employer provides comprehensive health care coverage to its employees and their dependents, which includes services such as outpatient consultations, hospitalisations, both surgical and non-surgical medical interventions, medications, and diagnostic tests.\u003c/p\u003e\u003cp\u003eThis cohort is tracked from their initial diagnosis of diabetes in either 2011 or 2012 to 2021. Each cohort member possesses comprehensive medical records, encompassing detailed documentation of laboratory findings, pharmaceutical usage, and exhaustive records of both outpatient and inpatient interactions throughout the study's designated timeline. The laboratory test records include values for Fasting Plasma Glucose (FPG) and Haemoglobin A1c (HbA1c) tests for the entire cohort study period, thereby also providing a thorough dataset for analysis during the period studied in this paper.\u003c/p\u003e\u003cp\u003eAttritions were noted during the 10-year retrospective follow-up of cohorts, leading to a reduction in the overall cohort size available for analysis. Some individuals within the cohort experienced mortality, while some discontinued CHSS due to various reasons. In addition, the study excluded those currently grappling with acute medical conditions such as End Stage Renal Disease, individuals undergoing hemodialysis, and those diagnosed with Acute Coronary Syndrome. These acute health conditions are known to induce significant perturbations in glycemic control, potentially introducing outlier data points that could skew the study results.\u003c/p\u003e\u003cp\u003eConsequently, the analytical dataset consists of 653 diabetes patients who continued to access healthcare services within the defined study window, spanning from March 11, 2019, to June 28, 2021.\u003c/p\u003e\u003cp\u003eAn essential facet to highlight is the healthcare services\u0026rsquo; structure underpinning this study. Notably, the healthcare services provided operate within a contributory framework, wherein the government employer assumes partial financial responsibility. This approach is pivotal in mitigating potential inequalities stemming from financial constraints.\u003c/p\u003e\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003eCOVID Data Collection and Periodisation:\u003c/h2\u003e\u003cp\u003eThe official data pertaining to the daily count of COVID-19 cases is sourced from the Government of India website. (\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e) The collection spans from the date of the first recorded COVID-19 case in Mumbai, commencing on March 11, 2020, to the conclusion of the second wave on May 28, 2021. Due to the potential influence of weekends or holidays on reporting, where cases reported on the present day are documented on the following day, a weekly aggregation approach is adopted. The week is demarcated from Monday to Sunday, and the average count of active COVID-19 cases per day within each week is computed for the study population.\u003c/p\u003e\u003cp\u003eThe entire study period is segmented into five distinct phases, each holding temporal significance in relation to the pandemic's progression. These phases are delineated as follows:\u003c/p\u003e\u003cp\u003e\u003cstrong\u003ePre-COVID Phase (March 11, 2019, to March 12, 2020)\u003c/strong\u003e\u003cp\u003eThis span predates the onset of the pandemic, serving as a baseline reference.\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eFirst COVID Cases Phase (March 11, 2020, to May 3, 2020)\u003c/strong\u003e\u003cp\u003eEncompassing the advent of COVID-19 in the region, this phase characterises the initial emergence of cases.\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eFirst Wave (May 4, 2020, to January 10, 2021)\u003c/strong\u003e\u003cp\u003eSignifying the primary wave of the pandemic, this phase extends from the conclusion of the initial cases phase to the subsequent decrease in case numbers.\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eIntermittent Period between Waves (January 11, 2021, to February 14, 2021)\u003c/strong\u003e\u003cp\u003eThis interlude represents the transition between the first and second waves.\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eSecond Wave (February 15, 2021, to May 28, 2021)\u003c/strong\u003e\u003cp\u003ePortraying the resurgence of cases, this phase encapsulates the secondary wave of COVID-19 transmission.\u003c/p\u003e\u003c/p\u003e\u003cp\u003eTo facilitate analytical convenience, distinct waves of COVID-19 transmission are demarcated based on the average count of active cases per week. A wave's initiation is established when the weekly average surpasses 700 cases, while its cessation is defined by the occurrence of four consecutive weeks during which the weekly average remains below 700 cases and exhibits no subsequent peak. This temporal criterion, roughly equivalent to one month, guides the identification of discrete wave patterns within the data.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\u003ch2\u003eStatistical Analysis:\u003c/h2\u003e\u003cp\u003eMedical records of 653 diabetic individuals from the Cohort study database are sourced in order to comprehend healthcare utilisation patterns across distinct periods. These records contain hospitalisation (IPD), outpatient (OPD) visits, laboratory test results, and pharmaceutical purchases. During the COVID-19 pandemic, teleconsultations conducted by physicians have been categorised as OPD visits.\u003c/p\u003e\u003cp\u003eAs the duration of each phase under comparison is not constant, person-months are used as the denominator. To facilitate standardised comparison, healthcare usage rates are expressed as events per 1,000 person-months.\u003cdiv id=\"Equa\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equa\" name=\"EquationSource\"\u003e\n$$\\:Healthcare\\:Usage\\:Rate=\\left(\\frac{Number\\:of\\:events\\:in\\:thereference\\:period\\:}{Total\\:person-months\\:in\\:the\\:regerence\\:period}\\right)\\times\\:1000$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eWhere:\u003c/p\u003e\u003cp\u003e\u003cul\u003e\u003cli\u003e\u003cp\u003eNumber of events are hospitalisation, OPD visits, laboratory tests, or pharmaceutical purchases.\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eTotal person-months refers to the sum of the months that each person is observed.\u003c/p\u003e\u003c/li\u003e\u003c/ul\u003e\u003c/p\u003e\u003cp\u003eFor the analysis of hospitalisation rates among Diabetes patients during COVID and non-COVID periods, it's important to note a methodological adjustment in the denominator used for calculation. Unlike other healthcare utilisation rates measured in this paper, which typically use a denominator of 1,000 patient months, the hospitalisation rates are presented per 10,000 patient months. This adjustment is made due to the relatively rare nature of hospitalisation events, necessitating a larger denominator to provide a more accurate and meaningful representation of these occurrences. Additionally, the rates of FPG and HbA1c testing across different age and sex groups are tabulated.\u003c/p\u003e\u003cp\u003eThe study population is stratified by age groups (30\u0026ndash;44, 45\u0026ndash;59, 60\u0026ndash;74, and 75\u0026thinsp;+\u0026thinsp;years), sex, the presence of diabetes-related comorbidities (dyslipidaemia, hypertension, and thyroid disorders), and frequency of OPD visits in the three years preceding the COVID-19 pandemic (categorised as low, medium, and high).\u003c/p\u003e\u003cp\u003eRate ratios are used to compare healthcare usage rates during the COVID-19 period with pre-COVID rates.\u003c/p\u003e\u003cp\u003eA comparative exploration of average HbA1c and FPG values was performed across age and sex subgroups within each defined period. To ensure comparability, the means of HbA1c and FPG were age-sex standardised based on the age-sex structure of the pre-COVID population.\u003c/p\u003e\u003cp\u003eLastly, Spearman\u0026rsquo;s rank correlation was utilised to identify patterns and trends in HbA1c levels between the pre-COVID period and the first and second waves of COVID-19. Additionally, we examined the correlation between OPD visits and HbA1c levels during the first and second COVID waves. Furthermore, we assessed the correlation between the number of days of COVID-related restrictions (within the 90 days preceding the HbA1c test) and HbA1c values during these waves.\u003c/p\u003e\u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003eThe study population consists of 653 individuals, with 276 men and 377 women. Most participants do not have thyroid conditions (491), but a notable portion (162) do, predominantly women. Dyslipidemia is prevalent in 633 individuals. Hypertension is also common, seen in 598 participants. Regarding pre-COVID healthcare usage rates, the population is evenly distributed across low, medium, and high usage categories, with a slight predominance of women in each group.\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\u003eBaseline Characteristics of the Study Cohort\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=\"char\" char=\".\" 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=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMen\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eWomen\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eTotal\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAge Category\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e30\u0026ndash;44\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e13\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e40\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e53\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e45\u0026ndash;59\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e101\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e172\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e273\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e60\u0026ndash;74\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e116\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e128\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e244\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e75+\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e46\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e37\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e83\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eThyroid\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e245\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e246\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e491\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e31\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e131\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e162\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eDyslipidemia\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e20\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e268\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e365\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e633\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eHypertension\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e16\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e39\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e55\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e260\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e338\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e598\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003ePre-COVID Healthcare usage\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLow\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e92\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e134\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e226\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMedium\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e97\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e115\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e212\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHigh\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e87\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e128\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e215\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTotal\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e276\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e377\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e653\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\n\u003ch3\u003e1. Comparison of Healthcare utilisation by Diabetes patients during pre-COVID and COVID-19 phases\u003c/h3\u003e\n\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\u003ch2\u003eOPD visit rates\u003c/h2\u003e\u003cp\u003eIn our study population, men and women exhibit similar OPD visit rates during the pre-COVID period, with men (1,527 visits) having a slightly lower rate than women (1,587 visits). Analysing by age groups, we observe an inverse relationship between OPD visit rates and age, with younger age groups consistently showing higher rates compared to older age groups throughout the study.\u003c/p\u003e\u003cp\u003eDuring the first COVID wave, OPD visit rates in each subgroup decreased by half. However, during the intermittent period between the two COVID waves, these rates increase to approximately 80 per cent of the pre-COVID levels. Specifically, OPD rates for women rise to 1,296, while for men, the rate is 1,207. By the second COVID wave, OPD visit rates decreased again, with women's rates decreasing to 767, which is lower than that of men at 885.\u003c/p\u003e\u003cp\u003eAdditionally, individuals with comorbidities have consistently higher OPD usage rates compared to those without comorbidities throughout the study period.\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\u003eNumber of OPD visits per 1000 Patient Months in diabetes patients across various waves during pre-COVID and post-COVID-19 phases\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"10\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" 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=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003ePre COVID\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u003cp\u003e\u003cem\u003eFirst cases\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u003cp\u003e\u003cem\u003eFirst wave\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e\u003cp\u003e\u003cem\u003eIntermittent period\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e\u003cp\u003e\u003cem\u003eSecond wave\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eOPD\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eRate\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cem\u003eRate\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cem\u003eRate ratio\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cem\u003eRate\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cem\u003eRate ratio\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cem\u003eRate\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u003cp\u003e\u003cem\u003eRate ratio\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c9\"\u003e\u003cp\u003e\u003cem\u003eRate\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c10\"\u003e\u003cp\u003e\u003cem\u003eRate ratio\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eSex\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eMen\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1527\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e937\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e0.61\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e883\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e0.58\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e1207\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e\u003cb\u003e0.79\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e885\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e\u003cb\u003e0.58\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eWomen\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1587\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e750\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e0.47\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e810\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e0.51\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e1296\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e\u003cb\u003e0.82\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e767\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e\u003cb\u003e0.48\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eAge group\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\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\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003e30\u0026ndash;44\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1771\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e822\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e0.46\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e817\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e0.46\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e1609\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.91\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e981\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e\u003cb\u003e0.55\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003e45\u0026ndash;59\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1681\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e893\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e0.53\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e910\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e0.54\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e1345\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e\u003cb\u003e0.80\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e922\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e\u003cb\u003e0.55\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003e60\u0026ndash;74\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1425\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e751\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e0.53\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e791\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e0.56\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e1180\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e\u003cb\u003e0.83\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e699\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e\u003cb\u003e0.49\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003e75+\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1424\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e848\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e0.60\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e769\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e0.54\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e961\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e\u003cb\u003e0.68\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e704\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e\u003cb\u003e0.49\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eThyroid\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\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\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eNo\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1487\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e811\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e0.55\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e806\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e0.54\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e1211\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e\u003cb\u003e0.81\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e801\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e\u003cb\u003e0.54\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eYes\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1787\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e885\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e0.50\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e946\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e0.53\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e1402\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e\u003cb\u003e0.78\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e866\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e\u003cb\u003e0.48\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eDyslipidemia\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\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\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eNo\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1547\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e833\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e0.54\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e807\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e0.52\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e1048\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.68\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e739\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e\u003cb\u003e0.48\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eYes\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1562\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e829\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e0.53\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e842\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e0.54\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e1264\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e\u003cb\u003e0.81\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e820\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e\u003cb\u003e0.52\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eHypertension\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\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\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eNo\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1515\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e730\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e0.48\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e850\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e0.56\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e992\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e\u003cb\u003e0.65\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e805\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e\u003cb\u003e0.53\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eYes\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1566\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e838\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e0.54\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e840\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e0.54\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e1283\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e\u003cb\u003e0.82\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e818\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e\u003cb\u003e0.52\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003ePre-COVID healthcare usage\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\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\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eLow\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e710\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e393\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e0.55\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e439\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e0.62\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e660\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.93\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e436\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e\u003cb\u003e0.61\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eMedium\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1393\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e841\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e0.60\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e761\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e0.55\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e1053\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e\u003cb\u003e0.76\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e748\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e\u003cb\u003e0.54\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eHigh\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2589\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1222\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e0.47\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1294\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e0.50\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e2025\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e\u003cb\u003e0.78\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e1247\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e\u003cb\u003e0.48\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eTotal\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1562\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e829\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e0.53\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e841\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e0.54\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e1258\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e\u003cb\u003e0.81\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e817\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e\u003cb\u003e0.52\u003c/b\u003e\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=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003ePharmacy visit rates\u003c/h2\u003e\u003cp\u003eOur study shows that pre-COVID, women had a slightly higher rate of pharmacy visits (1,190) than men (1,127). During the first COVID wave, these rates nearly halved for both groups, dropping to 716 for men and 704 for women. In the period between the two COVID waves, rates partially recovered, with men\u0026rsquo;s rate increasing to 965 while women\u0026rsquo;s rate was slightly higher at 1,040. However, in the second COVID wave, rates halved again, with men at 616 and women at 590.\u003c/p\u003e\u003cp\u003eWhen examining age groups, individuals aged 30\u0026ndash;44 had the highest pre-COVID pharmacy visit rate (1,203), higher than any older group. Across all age groups, pharmacy visit rates declined during the COVID-19 phases. In the first wave, rates dropped to 714 for the 30\u0026ndash;44 age group and to 754 for the 45\u0026ndash;59 group. Older groups had lower rates, with 681 for those aged 60\u0026ndash;74 and 635 for those 75+. During the intermittent period, rates rose to about 81 per cent of pre-COVID levels, with the 45\u0026ndash;59 age group reaching the highest rate (1,052). In the second COVID wave, rates declined again, but younger age groups still had higher rates: 603 for the 30\u0026ndash;44 group and 634 for the 45\u0026ndash;59 group, compared to 577 for ages 60\u0026ndash;74 and 558 for those 75+.\u003c/p\u003e\u003cp\u003eOur study shows that individuals with comorbidities consistently have higher pharmacy visit rates compared to those without. Specifically, individuals with dyslipidemia had a pre-COVID pharmacy visit rate of 1,172, while those without dyslipidemia had a rate of 874. Similarly, those with hypertension had a pre-COVID rate of 1,191 compared to 864 for those without hypertension. During the first COVID wave, these rates dropped to approximately 60 per cent of their pre-COVID levels. However, during the intermittent period between waves, the rates increased again, with individuals having dyslipidemia and hypertension reaching nearly 87 per cent and 88 per cent of their pre-COVID rates, respectively. By the second wave, the rates decreased once more, with those with dyslipidemia dropping to 605 and those without dyslipidemia to 476. Similarly, for hypertension, the rates dropped to 613 for those with hypertension and 464 for those without hypertension.\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\u003eNumber of pharmacy visits per 1000 patient months in diabetes patients during pre-COVID and COVID-19 phases\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"10\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" 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=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003ePre COVID\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u003cp\u003e\u003cem\u003eFirst cases\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u003cp\u003e\u003cem\u003eFirst wave\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e\u003cp\u003e\u003cem\u003eIntermittent period\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e\u003cp\u003e\u003cem\u003eSecond wave\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eDrug Purchase\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eRate\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cem\u003eRate\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cem\u003eRate ratio\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cem\u003eRate\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cem\u003eRate ratio\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cem\u003eRate\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u003cp\u003e\u003cem\u003eRate ratio\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c9\"\u003e\u003cp\u003e\u003cem\u003eRate\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c10\"\u003e\u003cp\u003e\u003cem\u003eRate ratio\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eSex\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eMen\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1127\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e699\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e0.62\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e716\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e0.64\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e965\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e\u003cb\u003e0.86\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e616\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e\u003cb\u003e0.55\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eWomen\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1190\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e661\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e0.56\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e704\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e0.59\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e1040\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e\u003cb\u003e0.87\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e590\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e\u003cb\u003e0.50\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eAge group\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\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\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003e30\u0026ndash;44\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1203\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e744\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e0.62\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e714\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e0.59\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e997\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.83\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e603\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e\u003cb\u003e0.50\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003e45\u0026ndash;59\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1154\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e732\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e0.63\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e754\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e0.65\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e1052\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.91\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e634\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e\u003cb\u003e0.55\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003e60\u0026ndash;74\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1162\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e580\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e0.50\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e681\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e0.59\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e980\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e\u003cb\u003e0.84\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e577\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e\u003cb\u003e0.50\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003e75+\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1176\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e736\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e0.63\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e635\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e0.54\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e950\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.81\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e558\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e\u003cb\u003e0.47\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eThyroid\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\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\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eNo\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1122\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e641\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e0.57\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e678\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e0.60\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e988\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e\u003cb\u003e0.88\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e578\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e\u003cb\u003e0.52\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eYes\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1289\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e785\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e0.61\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e803\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e0.62\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e1071\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e\u003cb\u003e0.83\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e669\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e\u003cb\u003e0.52\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eDyslipidemia\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\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\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eNo\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e874\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e741\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.85\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e569\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e0.65\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e667\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.76\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e476\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e\u003cb\u003e0.54\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eYes\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1172\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e675\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e0.58\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e713\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e0.61\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e1019\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e\u003cb\u003e0.87\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e605\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e\u003cb\u003e0.52\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eHypertension\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\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\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eNo\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e864\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e610\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e0.71\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e598\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e0.69\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e571\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e\u003cb\u003e0.66\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e464\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e\u003cb\u003e0.54\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eYes\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1191\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e683\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e0.57\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e719\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e0.60\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e1049\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e\u003cb\u003e0.88\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e613\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e\u003cb\u003e0.51\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003ePre-COVID healthcare usage\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\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\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eLow\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e546\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e317\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e0.58\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e344\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e0.63\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e570\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e1.04\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e327\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e\u003cb\u003e0.60\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eMedium\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1108\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e666\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e0.60\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e666\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e0.60\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e886\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e\u003cb\u003e0.80\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e562\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e\u003cb\u003e0.51\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eHigh\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1841\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1022\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e0.56\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1091\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e0.59\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e1542\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e\u003cb\u003e0.84\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e898\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e\u003cb\u003e0.49\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eTotal\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1163\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e677\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e0.58\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e709\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e0.61\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e1008\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e\u003cb\u003e0.87\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e601\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e\u003cb\u003e0.52\u003c/b\u003e\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\n\u003ch3\u003eLab test rates\u003c/h3\u003e\n\u003cp\u003eDuring pre-COVID, men had slightly higher lab test rates (217) compared to women (203). During the first COVID wave, lab test rates for men dropped to about 50 per cent of pre-COVID levels (110) and continued to decrease slightly through the intermittent period (108) and further during the second wave (104). In contrast, for women, the rates fell to 101 during the first wave, then surged to 171 (84 per cent of the pre-COVID level) in the intermittent period before sharply declining to 80 in the second wave.\u003c/p\u003e\u003cp\u003ePre-COVID lab test rates were similar across all age groups. However, during the first wave and intermittent periods, individuals aged 45\u0026ndash;59 and 60\u0026ndash;74 had higher lab test rates than the youngest (\u003cspan additionalcitationids=\"CR31 CR32 CR33 CR34 CR35 CR36 CR37 CR38 CR39 CR40 CR41 CR42 CR43\" citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e) and oldest (75+) age groups. By the second wave, lab test rates had decreased to approximately 43 per cent of pre-COVID levels across all age groups: 30\u0026ndash;44 (101), 45\u0026ndash;59 (100), 60\u0026ndash;74 (84), and 75+ (70).\u003c/p\u003e\u003cp\u003eIndividuals with high healthcare usage rates pre-COVID consistently had higher lab test rates, highlighting a trend where diabetes patients requiring more intensive care maintained relatively higher levels of healthcare engagement throughout the pandemic.\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\u003eNumber of lab tests per 1000 patient months among diabetes patients during pre-COVID and COVID-19 phases\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"10\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" 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=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003ePre COVID\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u003cp\u003e\u003cem\u003eFirst cases\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u003cp\u003e\u003cem\u003eFirst wave\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e\u003cp\u003e\u003cem\u003eIntermittent period\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e\u003cp\u003e\u003cem\u003eSecond wave\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eLab Tests\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eRate\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cem\u003eRate\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cem\u003eRate ratio\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cem\u003eRate\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cem\u003eRate ratio\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cem\u003eRate\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u003cp\u003e\u003cem\u003eRate ratio\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c9\"\u003e\u003cp\u003e\u003cem\u003eRate\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c10\"\u003e\u003cp\u003e\u003cem\u003eRate ratio\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eSex\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eMen\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e217\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e109\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e0.50\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e110\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e0.51\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e108\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e\u003cb\u003e0.50\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e104\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e\u003cb\u003e0.48\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eWomen\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e203\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e54\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e0.27\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e101\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e0.50\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e171\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.84\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e80\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e\u003cb\u003e0.39\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eAge group\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\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\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003e30\u0026ndash;44\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e221\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e33\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e0.15\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e103\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e0.47\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e70\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e\u003cb\u003e0.32\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e101\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e\u003cb\u003e0.46\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003e45\u0026ndash;59\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e225\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e78\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e0.35\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e108\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e0.48\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e168\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e\u003cb\u003e0.75\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e100\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e\u003cb\u003e0.44\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003e60\u0026ndash;74\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e191\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e87\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e0.46\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e107\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e0.56\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e158\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.83\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e84\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e\u003cb\u003e0.44\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003e75+\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e200\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e75\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e0.38\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e88\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e0.44\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e69\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e\u003cb\u003e0.35\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e70\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e\u003cb\u003e0.35\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eThyroid\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\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\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eNo\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e195\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e85\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e0.43\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e96\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e0.49\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e127\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e\u003cb\u003e0.65\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e84\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e\u003cb\u003e0.43\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eYes\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e252\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e56\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e0.22\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e133\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e0.53\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e197\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.78\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e110\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e\u003cb\u003e0.44\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eDyslipidemia\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\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\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eNo\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e111\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e185\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.66\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e60\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.53\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e190\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e1.71\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e100\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e0.90\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eYes\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e212\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e74\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e0.35\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e106\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e0.50\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e143\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e\u003cb\u003e0.67\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e90\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e\u003cb\u003e0.42\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eHypertension\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\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\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eNo\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e202\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e65\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e0.32\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e112\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e0.56\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e67\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e\u003cb\u003e0.33\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e80\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e\u003cb\u003e0.39\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eYes\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e210\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e79\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e0.37\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e104\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e0.50\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e151\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e\u003cb\u003e0.72\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e91\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e\u003cb\u003e0.44\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003ePre-COVID healthcare usage\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\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\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eLow\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e104\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e47\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e0.45\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e72\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e0.69\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e94\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.91\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e56\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e\u003cb\u003e0.54\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eMedium\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e212\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e46\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e0.22\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e77\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e0.37\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e92\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e\u003cb\u003e0.43\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e61\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e\u003cb\u003e0.29\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eHigh\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e312\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e138\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e0.44\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e163\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e0.52\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e244\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.78\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e153\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e\u003cb\u003e0.49\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eTotal\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e209\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e77\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e0.37\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e105\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e0.50\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e144\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e\u003cb\u003e0.69\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e90\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e\u003cb\u003e0.43\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\n\u003ch3\u003eHospitalisation rates\u003c/h3\u003e\n\u003cp\u003eHospitalisation rates were initially higher for men (255) compared to women (203) before the COVID-19 pandemic. During the first COVID wave, the hospitalisation rates for men decreased to 62 per cent of the pre-COVID rate, further declined to 26 per cent during the intermittent period, and then increased to 73 per cent in the second wave. In contrast, for women, the rates decreased to 49 per cent of pre-COVID levels during the first wave, increased significantly to 147 per cent during the intermittent period, and then dropped again to 52 per cent in the second wave.\u003c/p\u003e\u003cp\u003eAge-wise, the hospitalisation rate was directly proportional to age, with the youngest group (30\u0026ndash;44 years) having the lowest rate (125) and the oldest group (75+) having the highest rate (261) pre-COVID. This pattern persisted during the first wave, with rates of 48, 70, 181, and 193 for the 30\u0026ndash;44, 45\u0026ndash;59, 60\u0026ndash;74, and 75\u0026thinsp;+\u0026thinsp;age groups, respectively. However, during the intermittent period, the 60\u0026ndash;74 age group saw the highest hospitalisation rate (346), exceeding pre-COVID levels, while the youngest group had no recorded hospitalisations. In the second wave, hospitalisation rates remained below 60 per cent of pre-COVID levels for all age groups except 60\u0026ndash;74, which had 83 per cent of the pre-COVID rate.\u003c/p\u003e\u003cp\u003eFor individuals with high pre-COVID healthcare usage, hospitalisation rates were consistently higher, exceeding pre-COVID levels during the intermittent period.\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\u003eNumber of hospitalisations per 10,000 patient months in diabetes patients during pre-COVID and COVID-19 phases\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"10\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" 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=\"char\" char=\".\" 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=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003ePre COVID\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u003cp\u003e\u003cem\u003eFirst cases\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u003cp\u003e\u003cem\u003eFirst wave\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e\u003cp\u003e\u003cem\u003eIntermittent period\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e\u003cp\u003e\u003cem\u003eSecond wave\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eHospitalisations\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eRate\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cem\u003eRate\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cem\u003eRate ratio\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cem\u003eRate\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cem\u003eRate ratio\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cem\u003eRate\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u003cp\u003e\u003cem\u003eRate ratio\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c9\"\u003e\u003cp\u003e\u003cem\u003eRate\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c10\"\u003e\u003cp\u003e\u003cem\u003eRate ratio\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eSex\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eMen\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e255\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e65\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e0.26\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e159\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e0.62\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e67\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e\u003cb\u003e0.26\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e186\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e0.73\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eWomen\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e203\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e64\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e0.31\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e99\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e0.49\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e297\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e1.47\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e105\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e\u003cb\u003e0.52\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eAge group\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\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\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003e30\u0026ndash;44\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e125\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e48\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.38\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e46\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e0.37\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003e45\u0026ndash;59\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e217\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e70\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e0.32\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e134\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.62\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e98\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e\u003cb\u003e0.45\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003e60\u0026ndash;74\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e244\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e99\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.41\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e181\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.74\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e346\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e1.42\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e202\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e0.83\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003e75+\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e261\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e225\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.86\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e193\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.74\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e116\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.44\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e152\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e0.58\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eThyroid\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\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\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eNo\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e227\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e49\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e0.22\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e129\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e0.57\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e170\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.75\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e135\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e\u003cb\u003e0.59\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eYes\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e218\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e111\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.51\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e111\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e0.51\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e290\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e1.33\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e152\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e0.70\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eDyslipidemia\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\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\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eNo\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e129\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e309\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.40\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e66\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.51\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e251\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e1.95\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eYes\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e228\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e57\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e0.25\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e126\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e0.55\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e206\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.90\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e136\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e\u003cb\u003e0.60\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eHypertension\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\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\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eNo\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e91\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e88\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e0.97\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eYes\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e237\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e70\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e0.30\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e136\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e0.57\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e218\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.92\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e144\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e\u003cb\u003e0.61\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003ePre-COVID healthcare usage\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\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\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eLow\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e127\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.13\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e143\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.06\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e135\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e71\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e0.56\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eMedium\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e184\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e108\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.59\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e81\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e0.44\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e42\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.23\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e77\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e\u003cb\u003e0.42\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eHigh\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e364\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e81\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e0.22\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e150\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e0.41\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e420\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e1.15\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e267\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e0.73\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eTotal\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e225\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e64\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e0.29\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e124\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e0.55\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e200\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.89\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e139\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e\u003cb\u003e0.62\u003c/b\u003e\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=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003e2. Glucose Levels among diabetes patients during pre-COVID and COVID phases\u003c/h2\u003e\u003cp\u003eFigure \u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e and Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e present the mean HbA1c levels in the study population across pre-COVID and COVID phases, first stratified by sex and then by age groups. In the study population, the average HbA1c was initially 7.1 (SE\u0026thinsp;\u0026plusmn;\u0026thinsp;0.0), which gradually increased to 7.2 (SE\u0026thinsp;\u0026plusmn;\u0026thinsp;0.1) during the first COVID-19 wave and reached 7.4 (SE\u0026thinsp;\u0026plusmn;\u0026thinsp;0.1) in the second wave.\u003c/p\u003e\u003cp\u003eMen demonstrated better glucose management than women throughout the pandemic. For men, the average HbA1c was 7.2 (SE\u0026thinsp;\u0026plusmn;\u0026thinsp;0.1) during the pre-COVID period, remained stable at 7.2 (SE\u0026thinsp;\u0026plusmn;\u0026thinsp;0.1) during the first wave, and increased by 0.1 to 7.3 (SE\u0026thinsp;\u0026plusmn;\u0026thinsp;0.2) during the second wave. In contrast, women had a slightly lower average HbA1c of 7.1 (SE\u0026thinsp;\u0026plusmn;\u0026thinsp;0.1) during the pre-COVID period, but their levels rose more significantly during the pandemic, reaching 7.2 (SE\u0026thinsp;\u0026plusmn;\u0026thinsp;0.1) during the first wave and climbing further to 7.5 (SE\u0026thinsp;\u0026plusmn;\u0026thinsp;0.2) in the second wave.\u003c/p\u003e\u003cp\u003eExamining the HbA1c and FPG levels by age groups pre-COVID, we observe an inverse relationship with age. Older age groups (60 and above) exhibited better glucose control compared to younger groups. For the pre-COVID period, the average HbA1c for the 30\u0026ndash;44 age group was 7.4 (SE\u0026thinsp;\u0026plusmn;\u0026thinsp;0.2), 7.3 (SE\u0026thinsp;\u0026plusmn;\u0026thinsp;0.1) for the 45\u0026ndash;59 age group, 7.0 (SE\u0026thinsp;\u0026plusmn;\u0026thinsp;0.1) for the 60\u0026ndash;74 age group, and 6.7 (SE\u0026thinsp;\u0026plusmn;\u0026thinsp;0.1) for those aged 75 and above. During the COVID phases, we see variations in HbA1c levels among different age groups. For the 30\u0026ndash;44 age group, HbA1c increased to 7.9 (SE\u0026thinsp;\u0026plusmn;\u0026thinsp;0.2) during the intermittent period and then slightly decreased but remained elevated at 7.7 (SE\u0026thinsp;\u0026plusmn;\u0026thinsp;0.4) during the second COVID phase. During the second COVID wave, in the 45\u0026ndash;59 age group, the HbA1c level rose to 7.5 (SE\u0026thinsp;\u0026plusmn;\u0026thinsp;0.3), while for the 60\u0026ndash;74 age group, it increased to 7.3 (SE\u0026thinsp;\u0026plusmn;\u0026thinsp;0.2) from the pre-COVID level. Notably, in the 75\u0026thinsp;+\u0026thinsp;age group, the HbA1c increased to 7.0 (SE\u0026thinsp;\u0026plusmn;\u0026thinsp;0.2) during the first COVID wave but then declined to 6.7 (SE\u0026thinsp;\u0026plusmn;\u0026thinsp;0.2) in the subsequent phase.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eFigure \u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e and Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e highlight the mean FPG levels in the study population across pre-COVID and COVID phases, first stratified by sex and then by age groups. The average FPG of the study population was initially 127.5 mg/dl (SE\u0026thinsp;\u0026plusmn;\u0026thinsp;1.2), which increased to 131.8 mg/dl (SE\u0026thinsp;\u0026plusmn;\u0026thinsp;2.3) during the first COVID-19 wave and further rose to 136.3 mg/dl (SE\u0026thinsp;\u0026plusmn;\u0026thinsp;4.4) during the second wave.\u003c/p\u003e\u003cp\u003ePre-COVID, women had better FPG levels than men, with an average of 124.8 mg/dl (SE\u0026thinsp;\u0026plusmn;\u0026thinsp;1.6) in women compared to 130.8 mg/dl (SE\u0026thinsp;\u0026plusmn;\u0026thinsp;1.7) in men. However, during the pandemic, the trends seen in HbA1c levels were reflected in FPG levels as well. Men managed to maintain their FPG close to the pre-COVID baseline, with an increase to 131.6 mg/dl (SE\u0026thinsp;\u0026plusmn;\u0026thinsp;3.4) during the first wave, then slightly decreasing to 130.0 mg/dl (SE\u0026thinsp;\u0026plusmn;\u0026thinsp;5.9) by the second wave. In contrast, FPG levels for women continued to rise, reaching 131.6 mg/dl (SE\u0026thinsp;\u0026plusmn;\u0026thinsp;3.4) during the first wave and further increasing to 141.9 mg/dl (SE\u0026thinsp;\u0026plusmn;\u0026thinsp;6.4) during the second wave.\u003c/p\u003e\u003cp\u003ePre-COVID, the 30\u0026ndash;44 age group had an average FPG level of 138.1 mg/dl (SE\u0026thinsp;\u0026plusmn;\u0026thinsp;5.4), the 45\u0026ndash;59 age group had 131.9 mg/dl (SE\u0026thinsp;\u0026plusmn;\u0026thinsp;1.8), the 60\u0026ndash;74 age group had 121.3 mg/dl (SE\u0026thinsp;\u0026plusmn;\u0026thinsp;1.4), and the 75\u0026thinsp;+\u0026thinsp;age group had 115.7 mg/dl (SE\u0026thinsp;\u0026plusmn;\u0026thinsp;2.4). During the second COVID wave, these FPG values increased to 156.2 mg/dl (SE\u0026thinsp;\u0026plusmn;\u0026thinsp;11.8) in the 30\u0026ndash;44 age group, 138.5 mg/dl (SE\u0026thinsp;\u0026plusmn;\u0026thinsp;6.6) in the 45\u0026ndash;59 age group, and 133.5 mg/dl (SE\u0026thinsp;\u0026plusmn;\u0026thinsp;8.3) in the 60\u0026ndash;74 age group. However, for the 75\u0026thinsp;+\u0026thinsp;age group, FPG levels initially increased to 125.0 mg/dl (SE\u0026thinsp;\u0026plusmn;\u0026thinsp;5.5) during the first COVID wave but then decreased to 111.1 mg/dl (SE\u0026thinsp;\u0026plusmn;\u0026thinsp;4.8) during the second wave.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe COVID-19 pandemic placed an unprecedented strain on healthcare systems worldwide. Hospitals globally faced surges of critically ill COVID-19 patients, leading to shortages of essential resources. (\u003cspan additionalcitationids=\"CR27\" citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e) In response, healthcare systems were forced to rapidly reallocate resources, often diverting staff and infrastructure away from non-COVID-related services, including routine chronic disease management and elective surgeries.(\u003cspan additionalcitationids=\"CR30\" citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e) For people with diabetes, this disruption meant difficulties in regular follow-ups, essential blood glucose monitoring, obtaining timely refills of medications (like insulin and oral hypoglycemic agents), and accessing specialised care such as diabetic foot clinics or retinopathy screenings. A study from Ahmedabad, Gujarat, reported that 55.7 per cent faced delays in regular checkups, 39.8 per cent used telemedicine to avoid travelling to the hospital for fear of getting COVID-19 infection, and 7.8 per cent faced delays in consuming medicines due to unavailability. Regular intake of medicines/insulin was altered for 40.5 per cent of study participants. Additionally, 54.3 per cent of participants expressed fear while visiting the laboratory to measure their blood glucose levels. (\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e)\u003c/p\u003e\u003cp\u003eSeveral studies across the globe also reported similar findings. A mixed-method study conducted in Northern Jordan revealed that both the quality and accessibility of healthcare services were adversely affected for patients with diabetes mellitus (DM) and chronic respiratory diseases (CRD) during the COVID-19 pandemic.(\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e) Another study from the United States reported that 50.8 per cent of individuals with Type 2 diabetes experienced diabetes distress. Furthermore, 21.1 per cent reported experiencing delayed medical care, while 15.1 per cent indicated that they did not receive medical care.(\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e)\u003c/p\u003e\u003cp\u003eOne of these notable consequences was a significant decrease in face-to-face doctor visits. (\u003cspan additionalcitationids=\"CR36\" citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e). By analysing the OPD visit rates, our study indirectly assessed the impact of these organisational changes that occurred during the pandemic. Our study demonstrated a significant reduction in outpatient department (OPD) visits during the initial phases of the pandemic, but later, this hurdle was surpassed by teleconsultations, which are equivalent to face-to-face doctor visits. Studies report that the pandemic accelerated the adoption of telemedicine for diabetes management.(\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e) A study highlighted that telemedicine became a crucial tool for managing diabetes during lockdowns, offering remote consultations and continuous care. (\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e)\u003c/p\u003e\u003cp\u003eIt is also important to note that we did not find a statistically significant association between the decrease in OPD visits and an increase in HbA1c levels. (Supplementary Table\u0026nbsp;7) This finding aligns with a study conducted by Patel et al., where no conclusive evidence of a negative correlation between the decline in outpatient visits and medication fills or glycemic control was observed. (\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e)\u003c/p\u003e\u003cp\u003eThe impact of lockdown measures to mitigate the spread of the COVID-19 pandemic on glycaemic control in individuals with Type 2 Diabetes Mellitus has demonstrated considerable heterogeneity. Two studies conducted in Turkey revealed that despite reduced adherence to dietary and exercise regimens, as well as challenges in accessing medication, diabetic patients did not experience significant alterations in weight, fasting plasma glucose (FPG), or HbA1c levels. (\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e, \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e) Furthermore, a study conducted at outpatient diabetes clinics in a city in the Southern part of India also concluded that the lockdown did not result in a substantial disruption of glycemic control, lifestyle, or psychosocial well-being within their patient population. This stability was attributed to a general increase in the adoption of healthier eating habits. (\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e) In a cohort of 114 individuals with Diabetes, Biancalana et al. reported no significant changes in glucose control, although 26 per cent of participants exhibited a 0.3 per cent increase in HbA1c.(\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e)The findings of these studies resonate with the trend reported among men in this study population, where, unlike women, no significant change in HbA1c and FPG was noticed. Tanji et al. also reported deterioration in glycemic levels among women compared to men. (\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e)\u003c/p\u003e\u003cp\u003eContrarily, Indian individuals, as reported by Khare and Jindal, experienced a deterioration in glycaemic control during the initial phase of the lockdown.(\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e) Anjana et al., in a survey encompassing 205 T2DM patients, observed a noteworthy increase in HbA1c following the lockdown.(\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e) Another study showed that among those subjected to lockdown measures, only retired patients demonstrated an improvement in HbA1c levels.(\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e) Our study also reported better glycemic control among the oldest age group. The study by D\u0026rsquo;Onofrio et al. suggested that individuals with poor psychological well-being may experience worsened glycemic control due to the restrictions imposed by lockdown measures.(\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e) This observation aligns with our findings, where we identified a positive association between pre-COVID HbA1c levels and those during the first and second COVID-19 waves, indicating that individuals with pre-existing poor glycemic control are more susceptible to elevated HbA1c and FPG levels during pandemic phases.\u003c/p\u003e\u003cp\u003eSeveral studies documented a decline in glycemic control, particularly during the days of lockdown(\u003cspan additionalcitationids=\"CR50 CR51 CR52\" citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e). Interestingly, a simulation model study employing multivariate regression analysis to assess the impact of previous natural disasters on patients with diabetes predicted a linear relationship between the duration of lockdown and the exacerbation of diabetes-related complications. (\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e). On the contrary, in our study, no significant association was found between HbA1c levels and the number of days under COVID restrictions during the 90 days preceding the HbA1c test, indicating a lack of a linear relationship. (Supplementary Table\u0026nbsp;7)\u003c/p\u003e\u003cp\u003eA study on healthcare utilisation patterns among veterans in the United States during the COVID-19 pandemic observed that initially, during the first three months following the onset of the pandemic, there were significant reductions in both the monthly rates of HbA1c measurements and in-person outpatient visits. Later, these utilisation rates gradually rebounded to pre-pandemic levels.(\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e) This observed trajectory closely aligns with the trends seen in our study population, where healthcare utilisation rates declined during the first wave of COVID-19, partially rebounded in the intermittent period, and approached baseline levels by the second wave. Our study findings resonate with a growing body of research on the global impact of COVID-19 on healthcare systems. For instance, a study conducted by Chen, Krupp, and Lo in 2022 also observed declines in the proportion of patients receiving HbA1c testing, decreases in the percentage of patients engaging in diabetes-related in-person office visits, and a reduction in the number of visits per patient. (\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e) Furthermore, the overall utilisation of non-emergent outpatient visits declined, reflecting the broader trend of decreased healthcare engagement during the pandemic. Similarly, research by Palanca and colleagues highlighted the effects of full lockdown measures; approximately 50% of participants experienced a reduction in HbA1c testing, with older individuals being particularly affected. (\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e)\u003c/p\u003e\u003cp\u003eOur study highlighted a notable decrease in FPG and HbA1c testing rates at the pandemic's onset. This decline can be attributed to both the redirection of medical resources towards immediate pandemic needs and patients' reluctance to visit medical facilities, fearing infection. This aligns with Boserup et al.'s (2020) observations of reduced non-emergency department visits in the pandemic's early months. (\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e)\u003c/p\u003e\u003cp\u003eThe 45\u0026ndash;59 age group and women showed a significant resurgence in OPD visit rates and Pharmacy visit rates during the intermittent phases. This might be interpreted as a heightened awareness or prioritisation of regular health check-ups amidst the pandemic. (\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e) In contrast, the elderly (aged 75 and above) demonstrated a continued decline in healthcare usage rates, perhaps due to their increased risk of severe COVID-19 complications. (\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e) A cross-sectional study conducted in India surveyed 636 individuals to assess their willingness to visit hospitals during the COVID-19 pandemic. The findings revealed that 74.8 per cent of respondents were reluctant to seek hospital care unless their symptoms were severe. The primary reasons cited included fear of contracting COVID-19 from other patients (72.6 per cent), apprehension about leaving home during the pandemic (31.1 per cent), and concerns about infection from medical equipment (24.5 per cent). (\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e)\u003c/p\u003e\u003cp\u003eIn a nutshell, our research noted that the study population gradually returned to pre-COVID levels of healthcare utilisation during the intermittent phase, demonstrating the positive impact of diabetes awareness, coordination, and availability facilitated by the compact yet well-connected healthcare services in this population.\u003c/p\u003e\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\u003ch2\u003eLimitations\u003c/h2\u003e\u003cp\u003eOur study is subject to some inherent limitations caused by the absence of an exhaustive assessment of multifaceted variables that could potentially influence patients' glycemic control. Notably, factors encompassing alterations in lifestyle patterns during periods of isolation, adherence to dietary protocols, variations in body mass index, the duration of diabetes treatment, blood pressure parameters, and the impact of stressors have not been comprehensively examined within the confines of our study.\u003c/p\u003e\u003c/div\u003e"},{"header":"Conclusion","content":"\u003cp\u003eOur research examined healthcare utilisation patterns and diabetes management during various stages of the COVID-19 pandemic. We identified discernible trends in healthcare usage: there was a notable decrease during the initial wave of the pandemic, a subsequent rise, and followed by a decrease during the second wave. This trend was evident across various healthcare services, including outpatient department (OPD) visits, inpatient department (IPD) admissions, laboratory tests, and pharmaceutical purchases. Those with comorbidities had higher healthcare usage rates, and the need for additional care continued even during the pandemic.\u003c/p\u003e\u003cp\u003eInterestingly, while we observed these shifts in healthcare access, our data did not show significant changes in the average HbA1c and FPG values among men. However, the values increased among women. Moreover, there wasn\u0026rsquo;t a strong correlation between the frequency of HbA1c tests or OPD visits and the actual HbA1c values. A significant association was found between HbA1c values prior to the pandemic and during its successive waves. This highlights the possibility that individuals who had challenges in managing their glucose levels before the pandemic faced continued challenges during it.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval:\u0026nbsp;\u003c/strong\u003eThis study was conducted using de-identified secondary data collected as part of routine healthcare delivery. As the data are not publicly available and do not contain personally identifiable information, individual informed consent was not required. The requirement for informed consent was waived by the Institutional Ethics Committee of Bhabha Atomic Research Centre, Mumbai, India, which also granted ethical clearance for the study. The study adhered to the principles of the Declaration of Helsinki and complied with all relevant ethical guidelines and national regulations.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication:\u0026nbsp;\u003c/strong\u003eNot applicable. This study does not include any individual-level identifiable information or images requiring consent for publication.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials:\u0026nbsp;\u003c/strong\u003eThe data sets generated and analysed during this study are not publicly available but may be accessed through the corresponding author upon reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests:\u003c/strong\u003e The authors declare that they have no competing interests or relevant disclosures.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding:\u0026nbsp;\u003c/strong\u003eThis research received no specific grant from any funding agency.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026apos; contributions:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003ePuja Goswami:\u003c/em\u003e Formulation of research objectives, data preprocessing, statistical analysis, and manuscript writing.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eThandassery\u0026nbsp;\u003c/em\u003e\u003cem\u003eR. Dilip:\u003c/em\u003e Conceptualisation of the study, formulation of research objectives, editing, and critical revision of the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eYogesh Shejul:\u003c/em\u003e Guidance in data preprocessing, manuscript review, and revision.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eAnjali Kulkarni:\u003c/em\u003e Guidance in data preprocessing, manuscript review, and revision.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgement:\u003c/strong\u003e Not applicable\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eInternational Diabetes Federation. IDF Diabetes Atlas. 2021. \u003c/li\u003e\n\u003cli\u003eChudasama Y V., 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 \u0026amp; Metabolic Syndrome: Clinical Research \u0026amp; Reviews [Internet]. 2020 Sep 1 [cited 2023 Sep 1];14(5):965\u0026ndash;7. Available from: https://www.sciencedirect.com/science/article/pii/S1871402120302113\u003c/li\u003e\n\u003cli\u003eGregg EW, Sattar N, Ali MK. The changing face of diabetes complications. Lancet Diabetes Endocrinol [Internet]. 2016 Jun 1 [cited 2023 Sep 1];4(6):537\u0026ndash;47. Available from: http://www.thelancet.com/article/S2213858716300109/fulltext\u003c/li\u003e\n\u003cli\u003eMorrison F, Shubina M, Turchin A. Encounter Frequency and Serum Glucose Level, Blood Pressure, and Cholesterol Level Control in Patients With Diabetes Mellitus. Arch Intern Med [Internet]. 2011 Sep 26 [cited 2023 Sep 1];171(17):1542\u0026ndash;50. Available from: https://jamanetwork.com/journals/jamainternalmedicine/fullarticle/1105940\u003c/li\u003e\n\u003cli\u003eMohseni M, Ahmadi S, Azami-Aghdash S, Mousavi Isfahani H, Moosavi A, Fardid M, et al. Challenges of routine diabetes care during COVID-19 era: A systematic search and narrative review. Prim Care Diabetes [Internet]. 2021 Dec 1 [cited 2023 Sep 1];15(6):918\u0026ndash;22. Available from: https://www.sciencedirect.com/science/article/pii/S1751991821001340\u003c/li\u003e\n\u003cli\u003eRosenbaum L. The Untold Toll \u0026mdash; The Pandemic\u0026rsquo;s Effects on Patients without Covid-19. New England Journal of Medicine [Internet]. 2020 Jun 11 [cited 2023 Sep 1];382(24):2368\u0026ndash;71. Available from: https://www.nejm.org/doi/full/10.1056/NEJMms2009984\u003c/li\u003e\n\u003cli\u003eBornstein SR, Rubino F, Khunti K, Mingrone G, Hopkins D, Birkenfeld AL, et al. Practical recommendations for the management of diabetes in patients with COVID-19. Lancet Diabetes Endocrinol [Internet]. 2020 Jun 1 [cited 2023 Sep 1];8(6):546\u0026ndash;50. Available from: http://www.thelancet.com/article/S2213858720301522/fulltext\u003c/li\u003e\n\u003cli\u003eKoliaki C, Tentolouris A, Eleftheriadou I, Melidonis A, Dimitriadis G, Tentolouris N. Clinical Management of Diabetes Mellitus in the Era of COVID-19: Practical Issues, Peculiarities and Concerns. Journal of Clinical Medicine 2020, Vol 9, Page 2288 [Internet]. 2020 Jul 18 [cited 2023 Sep 1];9(7):2288. Available from: https://www.mdpi.com/2077-0383/9/7/2288/htm\u003c/li\u003e\n\u003cli\u003eBanerjee M, Chakraborty S, Pal R. Diabetes self-management amid COVID-19 pandemic. Diabetes \u0026amp; Metabolic Syndrome: Clinical Research \u0026amp; Reviews [Internet]. 2020 Jul 1 [cited 2023 Sep 1];14(4):351\u0026ndash;4. Available from: https://www.sciencedirect.com/science/article/pii/S1871402120300783\u003c/li\u003e\n\u003cli\u003eRastogi A, Hiteshi P, Bhansali A. Improved glycemic control amongst people with long-standing diabetes during COVID-19 lockdown: a prospective, observational, nested cohort study. Int J Diabetes Dev Ctries [Internet]. 2020 Oct 1 [cited 2023 Sep 1];40(4):476\u0026ndash;81. Available from: https://link.springer.com/article/10.1007/s13410-020-00880-x\u003c/li\u003e\n\u003cli\u003eRajagopalan S. Serious infections in elderly patients with diabetes mellitus. Clinical Infectious Diseases [Internet]. 2005 Apr 1 [cited 2023 Sep 1];40(7):990\u0026ndash;6. Available from: https://dx.doi.org/10.1086/427690\u003c/li\u003e\n\u003cli\u003eBadawi A, Ryoo SG. Prevalence of comorbidities in the Middle East respiratory syndrome coronavirus (MERS-CoV): a systematic review and meta-analysis. International Journal of Infectious Diseases [Internet]. 2016 Aug 1 [cited 2023 Sep 1];49:129\u0026ndash;33. Available from: https://www.sciencedirect.com/science/article/pii/S1201971216311006\u003c/li\u003e\n\u003cli\u003eAndrews MA, Areekal B, Rajesh K, Krishnan J, Suryakala R, Krishnan B, et al. First confirmed case of COVID-19 infection in India: A case report. Indian Journal of Medical Research [Internet]. 2020 May 1 [cited 2025 May 18];151(5):490\u0026ndash;2. Available from: https://journals.lww.com/ijmr/fulltext/2020/51050/first_confirmed_case_of_covid_19_infection_in.17.aspx\u003c/li\u003e\n\u003cli\u003eJahan N, Rubeshkumar P, Karuppiah M, Sambath I, Sendhilkumar M, Ilangovan K, et al. Entry and initial spread of COVID-19 in India: Epidemiological analysis of media surveillance data, India, 2020. Clin Epidemiol Glob Health [Internet]. 2021 Jan 1 [cited 2025 May 18];9:347\u0026ndash;54. Available from: https://www.sciencedirect.com/science/article/pii/S2213398420302281\u003c/li\u003e\n\u003cli\u003eWHO. Coronavirus disease 2019 (COVID-19) Situation Report-63 HIGHLIGHTS. 2019; \u003c/li\u003e\n\u003cli\u003eGupta M, Mohanta R, Rao A, Parameswaran GG, Agarwal M, Arora M, et al. Transmission dynamics of the COVID-19 epidemic in India and modeling optimal lockdown exit strategies. International Journal of Infectious Diseases [Internet]. 2021 Feb 1 [cited 2025 May 18];103:579\u0026ndash;89. Available from: https://www.sciencedirect.com/science/article/pii/S1201971220325224\u003c/li\u003e\n\u003cli\u003eMahajan P, Kaushal J. Epidemic Trend of COVID-19 Transmission in India During Lockdown-1 Phase. J Community Health [Internet]. 2020 Dec 1 [cited 2025 May 18];45(6):1291\u0026ndash;300. Available from: https://link.springer.com/article/10.1007/s10900-020-00863-3\u003c/li\u003e\n\u003cli\u003eGovernment of India. Government of India issues Orders prescribing lockdown for containment of COVID-19 Epidemic in the country: Press Information Bureau [Internet]. 2020 [cited 2025 May 18]. Available from: https://www.pib.gov.in/PressReleasePage.aspx?PRID=1607997\u003c/li\u003e\n\u003cli\u003eKumar S, Sonkar S, Atam I, Gupta H, Parmar K, Verma S, et al. Socio-economic impact of first 21 days nationwide lockdown- 1 on the spread of SARS-COV-2 in India in relation to health. J Family Med Prim Care [Internet]. 2020 [cited 2025 May 18];9(9):4557. Available from: https://journals.lww.com/jfmpc/fulltext/2020/09090/socio_economic_impact_of_first_21_days_nationwide.12.aspx\u003c/li\u003e\n\u003cli\u003eBairwa M, Behera S, Chavda PD, Kulkarni P, Mohapatra A, Ramraj B, et al. Combating the COVID-19 pandemic in a resource-constrained setting: insights from initial response in India. BMJ Glob Health [Internet]. 2020 Nov 13 [cited 2025 May 18];5(11). Available from: https://gh.bmj.com/content/5/11/e003416\u003c/li\u003e\n\u003cli\u003eBhadoria AS, Mishra S, Mohapatra A, Singh A, Kant R, Nundy S. Physical distancing amidst social connectivity: Time to re-visit \u0026lsquo;social distancing\u0026rsquo; as India fights COVID-19 and a few concerns. Curr Med Res Pract [Internet]. 2020 May [cited 2025 May 18];10(3):85. Available from: https://pmc.ncbi.nlm.nih.gov/articles/PMC7255194/\u003c/li\u003e\n\u003cli\u003eSchaner S, Theys N, Angrisani M, Banerjee J, Khobragade PY, Petrosyan S, et al. Adherence to COVID-19 protective behaviours in India from May to December 2020: evidence from a nationally representative longitudinal survey. BMJ Open [Internet]. 2022 Feb 1 [cited 2025 May 18];12(2):e058065. Available from: https://bmjopen.bmj.com/content/12/2/e058065\u003c/li\u003e\n\u003cli\u003eSaha J, Chouhan P. Lockdown and unlock for the COVID-19 pandemic and associated residential mobility in India. International Journal of Infectious Diseases [Internet]. 2021 Mar 1 [cited 2025 May 18];104:382\u0026ndash;9. Available from: https://www.sciencedirect.com/science/article/pii/S1201971220325029\u003c/li\u003e\n\u003cli\u003eKulkarni A, Dilip TR, Shejul YK, Bhandarkar P, Goswami P, Sharma P, et al. Progression and management of diabetes in Indian settings with universal access to health care: Protocol and plans for CHIPS cohort study. medRxiv [Internet]. 2023 Jan 28 [cited 2023 Oct 21];2023.01.24.23284975. Available from: https://www.medrxiv.org/content/10.1101/2023.01.24.23284975v1\u003c/li\u003e\n\u003cli\u003eCovid19 Dashboard | Home [Internet]. 2023 [cited 2024 Dec 22]. Available from: https://covid19dashboard.mohfw.gov.in/\u003c/li\u003e\n\u003cli\u003eJanke AT, Venkatesh AK. Understanding and Addressing the US Hospital Bed Shortage\u0026mdash;Build, Baby, Build. JAMA Netw Open [Internet]. 2025 Feb 3 [cited 2025 May 18];8(2):e2460652\u0026ndash;e2460652. Available from: https://jamanetwork.com/journals/jamanetworkopen/fullarticle/2830389\u003c/li\u003e\n\u003cli\u003eMcCabe R, Kont MD, Schmit N, Whittaker C, L\u0026oslash;chen A, Baguelin M, et al. Modelling intensive care unit capacity under different epidemiological scenarios of the COVID-19 pandemic in three Western European countries. Int J Epidemiol [Internet]. 2021 Jul 9 [cited 2025 May 18];50(3):753\u0026ndash;67. Available from: https://dx.doi.org/10.1093/ije/dyab034\u003c/li\u003e\n\u003cli\u003eBeitler JR, Mittel AM, Kallet R, Kacmarek R, Hess D, Branson R, et al. Ventilator sharing during an acute shortage caused by the COVID-19 pandemic. Am J Respir Crit Care Med [Internet]. 2020 Aug 15 [cited 2025 May 18];202(4):600\u0026ndash;4. Available from: /doi/pdf/10.1164/rccm.202005-1586LE?download=true\u003c/li\u003e\n\u003cli\u003eAtary M, Abu-Rmeileh NME. Deferral of elective surgeries during the COVID-19 pandemic and its impact on Palestinian patients: a cross-sectional study. Confl Health [Internet]. 2023 Dec 1 [cited 2025 May 19];17(1):1\u0026ndash;16. Available from: https://link.springer.com/articles/10.1186/s13031-023-00509-w\u003c/li\u003e\n\u003cli\u003eHerrera CA, Ju\u0026aacute;rez-Ram\u0026iacute;rez C, Reyes-Morales H, Bedregal P, Reartes-Pe\u0026ntilde;afiel DL, D\u0026iacute;az-Portillo SP, et al. COVID-19 Disruption To Routine Health Care Services: How 8 Latin American And Caribbean Countries Responded. https://doi.org/101377/hlthaff202300694 [Internet]. 2023 Dec 4 [cited 2025 May 18];42(12):1667\u0026ndash;74. Available from: /doi/pdf/10.1377/hlthaff.2023.00694\u003c/li\u003e\n\u003cli\u003eFilip R, Gheorghita Puscaselu R, Anchidin-Norocel L, Dimian M, Savage WK. Global Challenges to Public Health Care Systems during the COVID-19 Pandemic: A Review of Pandemic Measures and Problems. Journal of Personalized Medicine 2022, Vol 12, Page 1295 [Internet]. 2022 Aug 7 [cited 2025 May 18];12(8):1295. Available from: https://www.mdpi.com/2075-4426/12/8/1295/htm\u003c/li\u003e\n\u003cli\u003eMehta S, Puwar T, Patel Y, Patel M, Shah V, Patel K, et al. Understanding Health-Seeking Behavior of People with Diabetes during COVID-19 Pandemic: A Facility Based Cross-Sectional Study Conducted in Ahmedabad, India. Clinical Diabetology [Internet]. 2023 [cited 2025 May 18];12(3):141\u0026ndash;9. Available from: https://journals.viamedica.pl/clinical_diabetology/article/view/DK.a2023.0013/73045\u003c/li\u003e\n\u003cli\u003eAl-Bataineh R, Al-Hammouri M, Al-Jaraideh W. The accessibility and quality of health services for diabetes mellitus and chronic respiratory disease patients during Covid-19 in Northern Jordan: A mixed method study. PLoS One [Internet]. 2023 Nov 1 [cited 2025 May 18];18(11):e0294655. Available from: https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0294655\u003c/li\u003e\n\u003cli\u003eSaseetharran A, Patel SA. COVID-19 pandemic-related healthcare interruptions and diabetes distress: a national study of US adults with diabetes. BMC Public Health [Internet]. 2024 Dec 1 [cited 2025 May 18];24(1):1\u0026ndash;8. Available from: https://link.springer.com/articles/10.1186/s12889-024-17921-3\u003c/li\u003e\n\u003cli\u003eLevene LS, Seidu S, Greenhalgh T, Khunti K. Pandemic threatens primary care for long term conditions. BMJ [Internet]. 2020 Oct 5 [cited 2023 Sep 1];371. Available from: https://www.bmj.com/content/371/bmj.m3793\u003c/li\u003e\n\u003cli\u003eJoy M, McGagh D, Jones N, Liyanage H, Sherlock J, Parimalanathan V, et al. Reorganisation of primary care for older adults during COVID-19: a cross-sectional database study in the UK. British Journal of General Practice [Internet]. 2020 Aug 1 [cited 2023 Sep 1];70(697):e540\u0026ndash;7. Available from: https://bjgp.org/content/70/697/e540\u003c/li\u003e\n\u003cli\u003eSolans O, Vidal-Alaball J, Cabo PR, Mora N, Coma E, Sim\u0026oacute; JMB, et al. Characteristics of Citizens and Their Use of Teleconsultations in Primary Care in the Catalan Public Health System Before and During the COVID-19 Pandemic: Retrospective Descriptive Cross-sectional Study. J Med Internet Res 2021;23(5):e28629 https://www.jmir.org/2021/5/e28629 [Internet]. 2021 May 27 [cited 2023 Sep 1];23(5):e28629. Available from: https://www.jmir.org/2021/5/e28629\u003c/li\u003e\n\u003cli\u003eDhediya R, Chadha M, Bhattacharya AD, Godbole S, Godbole S. Role of Telemedicine in Diabetes Management. J Diabetes Sci Technol [Internet]. 2023 May 1 [cited 2025 May 18];17(3):775\u0026ndash;81. Available from: https://scholar.google.com/scholar_url?url=https://journals.sagepub.com/doi/pdf/10.1177/19322968221081133\u0026amp;hl=en\u0026amp;sa=T\u0026amp;oi=ucasa\u0026amp;ct=ufr\u0026amp;ei=ThAqaOWaC7Oi6rQPvJGpyAE\u0026amp;scisig=AAZF9b_AhEyQIkNlFSe1g6hRL6F7\u003c/li\u003e\n\u003cli\u003eGhosh A, Gupta R, Misra A. Telemedicine for diabetes care in India during COVID19 pandemic and national lockdown period: Guidelines for physicians. Diabetes \u0026amp; Metabolic Syndrome: Clinical Research \u0026amp; Reviews [Internet]. 2020 Jul 1 [cited 2025 May 18];14(4):273\u0026ndash;6. Available from: https://www.sciencedirect.com/science/article/pii/S1871402120300606\u003c/li\u003e\n\u003cli\u003ePatel SY, McCoy RG, Barnett ML, Shah ND, Mehrotra A. Diabetes Care and Glycemic Control During the COVID-19 Pandemic in the United States. JAMA Intern Med [Internet]. 2021 Oct 1 [cited 2023 Sep 1];181(10):1412\u0026ndash;4. Available from: https://jamanetwork.com/journals/jamainternalmedicine/fullarticle/2781811\u003c/li\u003e\n\u003cli\u003eSelek A, Gezer E, Altun E, S\u0026ouml;zen M, Topaloğlu \u0026Ouml;, K\u0026ouml;ksalan D, et al. The impact of COVID-19 pandemic on glycemic control in patients with diabetes mellitus in Turkey: a multi-center study from Kocaeli. J Diabetes Metab Disord [Internet]. 2021 Dec 1 [cited 2023 Sep 1];20(2):1461\u0026ndash;7. Available from: https://link.springer.com/article/10.1007/s40200-021-00888-y\u003c/li\u003e\n\u003cli\u003e\u0026Ouml;nmez A, Gamsızkan Z, \u0026Ouml;zdemir Ş, Kesikbaş E, G\u0026ouml;kosmanoğlu F, Torun S, et al. The effect of COVID-19 lockdown on glycemic control in patients with type 2 diabetes mellitus in Turkey. Diabetes \u0026amp; Metabolic Syndrome: Clinical Research \u0026amp; Reviews [Internet]. 2020 Nov 1 [cited 2023 Sep 1];14(6):1963\u0026ndash;6. Available from: https://www.sciencedirect.com/science/article/pii/S1871402120303957\u003c/li\u003e\n\u003cli\u003eSankar P, Ahmed WN, Mariam Koshy V, Jacob R, Sasidharan S. Effects of COVID-19 lockdown on type 2 diabetes, lifestyle and psychosocial health: A hospital-based cross-sectional survey from South India. Diabetes \u0026amp; Metabolic Syndrome: Clinical Research \u0026amp; Reviews [Internet]. 2020 Nov 1 [cited 2023 Sep 1];14(6):1815\u0026ndash;9. Available from: https://www.sciencedirect.com/science/article/pii/S1871402120303544\u003c/li\u003e\n\u003cli\u003eBiancalana E, Parolini F, Mengozzi A, Solini A. Short-term impact of COVID-19 lockdown on metabolic control of patients with well-controlled type 2 diabetes: a single-centre observational study. Acta Diabetol [Internet]. 2021 Apr 1 [cited 2023 Sep 1];58(4):431\u0026ndash;6. Available from: https://link.springer.com/article/10.1007/s00592-020-01637-y\u003c/li\u003e\n\u003cli\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 [Internet]. 2021 Jun 1 [cited 2023 Sep 3];176:108840. Available from: /pmc/articles/PMC8084613/\u003c/li\u003e\n\u003cli\u003eKhare J, Jindal S. Observational study on Effect of Lock Down due to COVID 19 on glycemic control in patients with Diabetes: Experience from Central India. Diabetes Metab Syndr [Internet]. 2020 Aug 20 [cited 2023 Sep 1];14(6):1571\u0026ndash;4. Available from: https://europepmc.org/articles/PMC7438221\u003c/li\u003e\n\u003cli\u003eAnjana RM, Pradeepa R, Deepa M, Jebarani S, Venkatesan U, Parvathi SJ, et al. Acceptability and Utilization of Newer Technologies and Effects on Glycemic Control in Type 2 Diabetes: Lessons Learned from Lockdown. https://home.liebertpub.com/dia [Internet]. 2020 Jun 30 [cited 2023 Sep 1];22(7):527\u0026ndash;34. Available from: https://www.liebertpub.com/doi/10.1089/dia.2020.0240\u003c/li\u003e\n\u003cli\u003eD\u0026rsquo;Onofrio L, Pieralice S, Maddaloni E, Mignogna C, Sterpetti S, Coraggio L, et al. Effects of the COVID-19 lockdown on glycaemic control in subjects with type 2 diabetes: the glycalock study. Diabetes Obes Metab [Internet]. 2021 Jul 1 [cited 2023 Sep 1];23(7):1624\u0026ndash;30. Available from: https://onlinelibrary.wiley.com/doi/full/10.1111/dom.14380\u003c/li\u003e\n\u003cli\u003eBiamonte E, Pegoraro F, Carrone F, Facchi I, Favacchio G, Lania AG, et al. Weight change and glycemic control in type 2 diabetes patients during COVID-19 pandemic: the lockdown effect. Endocrine [Internet]. 2021 Jun 1 [cited 2023 Sep 1];72(3):604\u0026ndash;10. Available from: https://link.springer.com/article/10.1007/s12020-021-02739-5\u003c/li\u003e\n\u003cli\u003eKaratas S, Yesim T, Beysel S. Impact of lockdown COVID-19 on metabolic control in type 2 diabetes mellitus and healthy people. Prim Care Diabetes [Internet]. 2021 Jun 1 [cited 2023 Sep 1];15(3):424\u0026ndash;7. Available from: https://www.sciencedirect.com/science/article/pii/S1751991821000036\u003c/li\u003e\n\u003cli\u003eFisher L, Polonsky W, Asuni A, Jolly Y, Hessler D. The early impact of the COVID-19 pandemic on adults with type 1 or type 2 diabetes: A national cohort study. J Diabetes Complications [Internet]. 2020 Dec 1 [cited 2023 Sep 1];34(12):107748. Available from: https://www.sciencedirect.com/science/article/pii/S1056872720305298\u003c/li\u003e\n\u003cli\u003eGhosal S, Sinha B, Majumder M, Misra A. Estimation of effects of nationwide lockdown for containing coronavirus infection on worsening of glycosylated haemoglobin and increase in diabetes-related complications: A simulation model using multivariate regression analysis. Diabetes \u0026amp; Metabolic Syndrome: Clinical Research \u0026amp; Reviews. 2020 Jul 1;14(4):319\u0026ndash;23. \u003c/li\u003e\n\u003cli\u003eKhader MA, Jabeen T, Namoju R. A cross sectional study reveals severe disruption in glycemic control in people with diabetes during and after lockdown in India. Diabetes \u0026amp; Metabolic Syndrome: Clinical Research \u0026amp; Reviews. 2020 Nov 1;14(6):1579\u0026ndash;84. \u003c/li\u003e\n\u003cli\u003eAdhikari S, Titus AR, Baum A, Lopez P, Kanchi R, Orstad SL, et al. Disparities in routine healthcare utilization disruptions during COVID-19 pandemic among veterans with type 2 diabetes. BMC Health Serv Res [Internet]. 2023 Dec 1 [cited 2025 May 29];23(1):1\u0026ndash;10. Available from: https://link.springer.com/articles/10.1186/s12913-023-09057-8\u003c/li\u003e\n\u003cli\u003eChen JL, Krupp GR, Lo JY. The COVID-19 Pandemic and Changes in Health Care Utilization Among Patients With Type 2 Diabetes. 2022 [cited 2023 Sep 2]; Available from: http://diabetesjournals.org/care/article-pdf/doi/10.2337/dc21-2248/642680/dc212248.pdf\u003c/li\u003e\n\u003cli\u003ePalanca A, Quinones-Torrelo C, Girb\u0026eacute;s J, Real JT, Ampudia-Blasco FJ. Impact of COVID-19 lockdown on diabetes management and follow-up in a broad population in Spain. Eur J Clin Invest [Internet]. 2022 Jun 1 [cited 2023 Sep 2];52(6):e13771. Available from: https://onlinelibrary.wiley.com/doi/full/10.1111/eci.13771\u003c/li\u003e\n\u003cli\u003eBoserup B, McKenney M, Elkbuli A. The impact of the COVID-19 pandemic on emergency department visits and patient safety in the United States. Am J Emerg Med [Internet]. 2020 Sep 1 [cited 2023 Oct 10];38(9):1732\u0026ndash;6. Available from: https://www.sciencedirect.com/science/article/pii/S0735675720304848\u003c/li\u003e\n\u003cli\u003eHoffer-Hawlik MA, Moran AE, Burka D, Kaur P, Cai J, Frieden TR, et al. Leveraging Telemedicine for Chronic Disease Management in Low- and Middle-Income Countries During Covid-19. Glob Heart [Internet]. 2020 [cited 2023 Oct 10];15(1):63. Available from: /pmc/articles/PMC7500231/\u003c/li\u003e\n\u003cli\u003eCzeisler M\u0026Eacute;, Marynak K, Clarke KEN, Salah Z, Shakya I, Thierry JM, et al. Delay or Avoidance of Medical Care Because of COVID-19\u0026ndash;Related Concerns \u0026mdash; United States, June 2020. Morbidity and Mortality Weekly Report [Internet]. 2020 Sep 9 [cited 2023 Oct 10];69(36):1250. Available from: /pmc/articles/PMC7499838/\u003c/li\u003e\n\u003cli\u003eShamsundar M, Choudhary S. A Cross-Sectional Study of Attitude and Behaviour of Individuals Towards Visiting the Hospital During the COVID-19 Pandemic in India. The Brown Journal of Hospital Medicine [Internet]. 2022 Jun 6 [cited 2025 May 18];1(2):36121. Available from: https://pmc.ncbi.nlm.nih.gov/articles/PMC11878854/\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-health-services-research","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bhsr","sideBox":"Learn more about [BMC Health Services Research](http://bmchealthservres.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/BHSR/default.aspx","title":"BMC Health Services Research","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"COVID-19 pandemic, Healthcare utilisation, Type 2 Diabetes, Retrospective cohort, Electronic Health Records (EHR)","lastPublishedDoi":"10.21203/rs.3.rs-6642930/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6642930/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eBackground: The COVID-19 pandemic significantly disrupted healthcare utilisation, particularly for individuals with chronic conditions like diabetes. India, ranked third globally in confirmed COVID-19 cases, imposed strict lockdowns from March 24, 2020. This study aims to understand diabetes management during the pandemic within the framework of a uniform contributory healthcare scheme.\u003c/p\u003e\n\u003cp\u003eData and Methods: This study analyses healthcare utilisation patterns among 653 diabetes patients from a retrospective cohort of 835 individuals with Type 2 Diabetes based in Mumbai, India. Data spanning pre-pandemic and pandemic phases are compared across various healthcare usages. Trends in glycemic control during COVID-19 are also evaluated relative to pre-COVID levels.\u003c/p\u003e\n\u003cp\u003eResults: We identified discernible trends in healthcare usage: there was a notable decrease during the initial wave of the pandemic, a subsequent rise, and followed by a decrease during the second wave. This trend was evident across various healthcare services, including outpatient department (OPD) visits, inpatient department (IPD) admissions, laboratory tests, and pharmaceutical purchases. Healthcare use remained higher among individuals with comorbidities throughout the pandemic period.\u003c/p\u003e\n\u003cp\u003eIndividuals aged 75 and above did not show the same level of recovery in OPD visits during the intermittent period as younger age groups, suggesting increased vulnerability and avoidance of in-person care among older adults.\u003c/p\u003e\n\u003cp\u003eA gendered pattern was also observed in lab test utilisation: among women, testing rates halved during the initial phase, recovered to 80% during the intermittent period, and declined again during the second wave. In contrast, among men, lab test usage remained suppressed throughout the pandemic period. The 45–59 and 60-74 age groups showed the most substantial recovery in lab test rates during the intermittent period. Additionally, hospitalisation rates among women and individuals aged 60–74 exceeded pre-pandemic levels during this phase, suggesting delayed care-seeking or a rise in severe cases.\u003c/p\u003e\n\u003cp\u003eDespite these shifts in healthcare utilisation, the overall average values of HbA1c and fasting plasma glucose (FPG) remained relatively stable in men and the oldest age group (75 and above). An exception was noted among women, who exhibited more variability in glycaemic indicators. Importantly, there was no strong correlation between the frequency of HbA1c testing or OPD visits with the corresponding HbA1c values. However, a significant association was found between HbA1c values recorded prior to the pandemic and those during successive waves, indicating that individuals with poor glycaemic control before the pandemic continued to face challenges during it.\u003c/p\u003e","manuscriptTitle":"Diabetes Management during the COVID-19 pandemic: A Study on Contributory Health Service Scheme Beneficiaries from Mumbai, India","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-07-14 11:50:53","doi":"10.21203/rs.3.rs-6642930/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-01-06T11:24:14+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-07-31T10:57:15+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"42359249902602791893134182694702042438","date":"2025-07-24T22:31:18+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-07-18T03:28:51+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"94398604409216338873172221145484210371","date":"2025-07-18T03:21:22+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-07-10T08:50:20+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-06-16T13:08:21+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-05-30T08:24:49+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-05-29T18:05:52+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Health Services Research","date":"2025-05-29T18:02:20+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-health-services-research","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bhsr","sideBox":"Learn more about [BMC Health Services Research](http://bmchealthservres.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/BHSR/default.aspx","title":"BMC Health Services Research","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"5c2f6118-84a0-421c-ae44-f61266a5b859","owner":[],"postedDate":"July 14th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-01-22T19:23:38+00:00","versionOfRecord":[],"versionCreatedAt":"2025-07-14 11:50:53","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6642930","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6642930","identity":"rs-6642930","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}