Evaluating the Effect of the COVID-19 Pandemic on Hypertension and Diabetes Care in South Korea: An Interrupted Time Series Analysis

Evaluating the Effect of the COVID-19 Pandemic on Hypertension and Diabetes Care in South Korea: An Interrupted Time Series Analysis | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Evaluating the Effect of the COVID-19 Pandemic on Hypertension and Diabetes Care in South Korea: An Interrupted Time Series Analysis Boram Sim, Sun-Mi Kim, Eun Woo Nam This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-1923481/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 12 Aug, 2023 Read the published version in BMC Public Health → Version 1 posted 10 You are reading this latest preprint version Abstract Background Access to healthcare services is important, especially for patients with chronic diseases. We evaluated the effect of COVID-19 pandemic on outpatient visits and medication for patients with hypertension and diabetes in South Korea. Methods Nationwide claims data were extracted for patients with hypertension and diabetes from January 2019 to July 2020. We used an interrupted time series (ITS) analysis to evaluate the pandemic’s impact on outpatient care using the number of outpatient visits and days of medication supplied per visit. We identified the change in continuity of care in medication, a consequence of change in outpatient care, using the Medication Possession Ratio (MPR). Results The number of outpatient visits for diabetes significantly declined in February 2020, when community transmission began. However, when high-intensity social distancing was relaxed in April 2020, outpatient visits for hypertension and diabetes rebounded significantly. Moreover, when the outpatient visits declined, the number of days of medication supplied per visit increased. Consequently, the average MPRs significantly increased compared to 2019, increasing the ratio of patients with appropriate medication supply (MPR ≥ 0.8). Conclusions Outpatient visits decreased immediately when COVID-19 spread to local communities. However, the number of days of medication supplied per visit increased to compensate for the longer intervals between visits. Rather, the change in continuity of care in medication improved; thus, the temporary decrease in outpatient visits might have had limited negative impact on health outcomes. COVID-19 Pandemic Chronic Disease Outpatient Care Access to Care Interrupted Time Series Figures Figure 1 Figure 2 1. Background Since its discovery in Wuhan, China, in December 2019, coronavirus disease 2019 (COVID-19) has spread rapidly worldwide, prompting the World Health Organization to declare it a pandemic [1]. South Korea reported its first confirmed case of COVID-19 on January 20, 2020. The country experienced its first wave of the pandemic starting with the 31st case reported on February 18 and ending in April 2020 [2]. During this period, the Korean government rapidly sorted through suspected and confirmed cases using speedy and large-scale contact tracing and diagnostic testing. In addition, it attempted to contain the spread of the virus in local communities by implementing a social distancing policy [3]. Consequently, the number of new confirmed cases soon peaked at 909 on February 29, 2020, and gradually declined thereafter [2]. However, the COVID-19 outbreak and the government's response affected many aspects of daily life. A public survey conducted during the first wave of the pandemic showed that daily life had progressively worsened with the following events: the first confirmed case in Korea (January 2020), beginning of local transmission (February), and implementation of a stringent social distancing policy (March) [4]. Consequently, access to healthcare services was also greatly disrupted [5–7]. This calls for caution, as patients in need of healthcare services may not have had proper access to them [8]. Access to healthcare services is important, especially for patients with chronic diseases. A lack of timely healthcare service can increase the risk of emergency room visits or hospitalization due to deterioration of conditions, onset of complications, and so on [9]. Chronic diseases are responsible for 80% of all deaths in Korea and impose considerable socioeconomic burden. In addition, for patients with underlying conditions, the risk of severe COVID-19 illness such as death and ICU admission is higher [10–11]. Thus, from an epidemiological perspective of COVID-19, continuous care for chronic diseases is crucial. Research has found changes in healthcare utilization during the COVID-19 pandemic in both Korea and abroad [12–14]. Korean studies has reported that while overall healthcare utilization decreased in 2020, healthcare utilization by patients with chronic diseases was higher than expected [15, 16]. However, these studies have some limitation. First, they are cross-sectional and do not examine the changes over time. Instead, time-series analysis can help us understand the changes in healthcare utilization over time, and reflect the trends before or during the pandemic. Second, studies have focused on changes in the volume of healthcare services, such as the number of patients or outpatient visit days. This makes it difficult to identify other aspects of continuity of care, such as medication. Patients with hypertension and diabetes need to ensure continuity of care by not only regularly visiting healthcare institutions for management of their conditions but also by taking medicine to maintain their blood pressure or keep blood sugar levels under control. Here, we examined the impact of COVID-19 pandemic on two aspects of outpatient care for patients with hypertension and diabetes: outpatient visits and medication. Furthermore, we identified the change in continuity of care in medication, which is the result of changes in outpatient care, before and after the pandemic. 2. Methods 2.1 Study Design First, we used an interrupted time-series (ITS) analysis to evaluate the impact of the pandemic on outpatient care. ITS analysis is useful for evaluating the impact of a policy, intervention, or event when the policy or event affects the entire population, such as the pandemic; under such circumstances, it is impossible to set a control group [17]. Furthermore, to evaluate the continuity of care in medication, we identified changes in the Medication Possession Ratio (MPR) in 2019 and 2020. 2.2 Data Source Nationwide claims data were extracted from the Health Insurance Review and Assessment Service (HIRA) of South Korea for patients who made outpatient visits to medical institutions with hypertension (I10–I14) or diabetes (E11–E15) as the principal or secondary diagnosis from January 2019 to July 2020. Owing to universal population coverage in Korea, the HIRA database covers the entire population and all healthcare providers in the country. Only patients aged 20 years or more were included. If a claim case included both hypertension and diabetes, it was included in both groups. Among the 124,275,685 total cases, 97,906,143 and 38,330,462 cases were included in the hypertension and diabetes groups, respectively. 2.3 Outcome Variables We used the number of outpatient visits and days of medication supplied per visit as outcome variables. Note that ITS analysis uses aggregated data collected over equally spaced time intervals. Here, the number of outpatient visits was summed up on a weekly basis. Then, considering that possible variations in the number of holidays in each week, we calculated the average daily number of outpatient visits per week by dividing the total number of outpatient visits by the number of working days [18] in a week. We defined the days of medication supplied per visit as the number of days of supply of the relevant medication provided to the patient for a prescription. Thus, when determining the days of medication supplied, only claims cases that had prescriptions of antihypertensive or antihyperglycemic agents were included. Again, the average number of days of medication supplied per visit was obtained on a weekly basis. 2.4 Covariates Patient demographic factors (sex and age) and level of medical need (complications and comorbidities) were chosen as covariates. To adjust for the severity of hypertension or diabetes, the presence of any complications was checked using the diagnosis codes. The presence of comorbidities was expressed using the Charlson Comorbidity Index (CCI) score [19]. For this, patients' inpatient and outpatient data during the year prior to the study period were obtained and checked for comorbidities. Categorical variables were described as percentages in the model. 2.5 Medication Possession Ratio (MPR) MPR was calculated as the total number of days of medication within the observation period divided by the total number of days in the observation period [20]. We adjusted oversupply (MPR > 1.0) 1.0 and computed the average MPR for each year. We defined a patient with MPR not less than 0.8 as “appropriate-medication supply” and determined the portion of such patients in each year. 2.6 Statistical Analysis The time interval was in weeks and a total of 83 time points were included in the analysis. There were two events of interest: The first was the occurrence of case No. 31 (week 60), who caused the spread of COVID-19 to local communities, resulting in the first wave of the pandemic in Korea. The second was the end of high-intensity social distancing (week 68), which was implemented to contain the spread of the first wave. The analysis model is as follows: Log (Y t ) = intercept + β 0 × time t + β 1 × event1 t + β 2 × time after event1 t + β 3 × event2 t + β 4 × time after event2 t + X t λ + ε t where Y t denotes the outcome variable at time t. Since the data for both outcome variables did not follow a normal distribution, they were log-transformed. In addition, time represents the change in time from January 2019; event1 and event2 are dummies representing the events of interest (before (0) and/or after (1) weeks 60 or 68); time after event1 and event2 are the change in time following weeks 60 or 68, respectively; X indicates covariates. β 1 and β 3 refer to the changes in the level of the outcome variable immediately following each event, and β 2 and β 4 refer to the changes in the trend of the outcome variables. To evaluate significant differences in the continuity of care in medication, the average MPR and portion of “appropriate-medication supply” patients were compared for 2019 and 2020 using a t-test and chi-square test. All analyses were performed using SAS Enterprise Guide 7.15. 3. Results 3.1 Characteristics of Patients Table 1 presents the results of the comparison between the composition of patients in 2019 and 2020. For both hypertension and diabetes, the ratio of female patients decreased by 0.4 percentage point from 2019 to 2020, whereas the average age increased. Although the ratio of patients with complications decreased from 2019 to 2020 for both hypertension and diabetes, the proportion of diabetic patients with complications was approximately 10 times greater than that of hypertensive patients. The average CCI score decreased for both hypertension and diabetes. Table 1 Characteristics of participants (Unit: Case, %) Category Hypertension Diabetes Jan–Dec 2019 Jan–Jul 2020 Jan–Dec 2019 Jan–Jul 2020 Sex Male 29,420,241 (47.9) 17,626,093 (48.3) 12,952,902 (53.8) 7,739,673 (54.3) Female 32,013,303 (52.1) 18,846,506 (51.7) 11,110,855 (46.2) 6,527,032 (45.8) Age 64.6 (avg.) 64.8 (avg.) 63.3 (avg.) 63.4 (avg.) Presence of complications Yes 2,724,006 ( 4.4) 1,507,479 ( 4.1) 10,266,954 (42.7) 6,046,326 (42.4) No 58,709,538 (95.6) 34,965,120 (95.9) 13,796,803 (57.3) 8,220,379 (57.6) Charlson Comorbidity Index (CCI) score 0 12,306,129 (20.0) 8,054,053 (22.1) 5,291,583 (22.0) 3,388,911 (23.8) 1 15,717,344 (25.6) 9,430,201 (25.9) 7,304,929 (30.4) 4,360,741 (30.6) 2 13,849,572 (22.5) 8,043,250 (22.1) 5,608,304 (23.3) 3,261,841 (22.9) 3 or higher 19,560,499 (31.8) 10,945,095 (30.0) 5,858,941 (24.4) 3,255,212 (22.8) Total 61,433,544 (100.0) 36,472,599 (100.0) 24,063,757 (100.0) 14,266,705 (100.0) 3.2 Impact of COVID-19 on the Number of Outpatient Visits The average number of outpatient visits (log) for hypertension showed a gradually increasing trend starting in 2019 (Fig. 1 ). In February 2020, when the spread of COVID-19 to local communities began (①), the number of outpatient visits declined by 9.0%; however, this decrease was not statistically significant (Table 2 ). However, when high-intensity social distancing was relaxed in April 2020 (②), this number showed a statistically significant rebound of 19.0%. Table 2 Impact of the COVID-19 on outpatient care for hypertension and diabetes patients Category Variable Hypertension Diabetes Exp (β) p-value Exp (β) p-value Number of outpatient visits Time (β 0 ) 1.004 0.1443 1.004 0.2058 Intervention 1 (β 1 ) 0.913 0.1289 0.870 0.0161* Time after intervention 1 (β 2 ) 0.996 0.7521 0.993 0.5325 Intervention 2 (β 3 ) 1.185 0.0057** 1.163 0.0124* Time after intervention 2 (β 4 ) 0.994 0.6306 1.001 0.9121 Days of medication supplied per visits Time (β 0 ) 1.001 0.0083 1.001 0.0077** Intervention 1 (β 1 ) 1.033 < 0.0001*** 1.042 < 0.0001*** Time after intervention 1 (β 2 ) 0.998 0.0368 0.998 0.0492* Intervention 2 (β 3 ) 0.995 0.3504 0.992 0.1538 Time after intervention 2 (β 4 ) 1.003 0.0063* 1.002 0.034* * p < 0.05, ** p < 0.01, *** p < 0.001 Regarding diabetes, the average number of outpatient visits (log) started increasing in 2019 (Fig. 1 ). However, this number significantly decreased by 13.0% when the spread of COVID-19 to local communities began (①) and then rebounded by 16.0% immediately following the easing of high-intensity social distancing (②) (Table 2 ). For both hypertension and diabetes, the trend following these two time points did not significantly change. 3.3 Impact of COVID-19 on the Days of Medication Supplied Per Visit The average number of days of medication supplied per visit (log) for hypertension showed a gradually increasing trend starting in 2019 (Fig. 2 ). When the spread of COVID-19 to local communities began in February 2020 (①), this number significantly increased by approximately 3.3% (Table 2 ). However, there was no significant change in the trend thereafter. At the end of the high-intensity social distancing (②), the level of days of medication supplied did not significantly change, but the trend of the slope significantly increased by 0.3%. The average number of days of medication supplied per visit (log) for diabetes also showed a steady increase starting in 2019 (Fig. 2 ). When community transmission began (①), this number significantly increased by 4.2%, whereas the increasing trend of the slope slowed down by 0.2% (Table 2 ). When high-intensity social distancing was relaxed (②), the level of days of medication supplied did not significantly change and the trend of the slope increased significantly by 0.2%, as was the case for hypertension. Medication Possession Ratio (MPR) The average MPRs of hypertension and diabetes patients in 2020 were 0.81 and 0.79, respectively; compared to 2019, these were statistically significant increases of 0.03 and 0.04, respectively (p < 0.0001) (Table 3 ). The ratio of hypertension and diabetes patients with appropriate medication supply (MPR ≥ 0.8) in 2020 was 75.0% and 70.0%, respectively; compared to 2019, these were statistically significant increases of 6.5 and 7.2 percentage points, respectively (p < 0.0001) (Table 3 ). Table 3 Medication Possession Ratio (MPR) and ratio of patients with appropriate-medication supply among hypertension and diabetes patients Category Disease 2019 (A) 2020 (B) Change (B-A) p-value MPR (Mean ± S.D.) Hypertension 0.79% (± 0.28) 0.82% (± 0.23) 0.03 percentage points < 0.0001*** Diabetes 0.76% (± 0.30) 0.79% (± 0.26) 0.04 percentage points < 0.0001*** Patients with appropriate medication supply † (%, N) Hypertension 68.5% (N = 4,051,064) 75.0% (N = 4,852,268) 6.5 percentage points < 0.0001*** Diabetes 62.9% (N = 1,314,013) 70.1% (N = 1,618,746) 7.2 percentage points < 0.0001*** * p < 0.05, ** p < 0.01, *** p < 0.001 †Patients with appropriate medication supply: Patients whose MPR is not less than 0.8. 4. Discussion This study demonstrated the impact of COVID-19 on two aspects of outpatient care for patients with hypertension and diabetes: outpatient visits and medication. We found that the number of outpatient visits by patients decreased immediately when COVID-19 spread to local communities, in line with results observed in other countries during the pandemic [5, 13]. The most noteworthy reason for this reduction is the patients' fear of contracting COVID-19 [21]. According to the Medical Service Experience Survey of Korea, 15.6% of respondents in the first half of 2020 answered that they felt infection-related anxiety while using medical services; this is more than a two-fold increase year-over-year from the same period in 2019 [22]. A public perception survey conducted in 2020 also showed that 73.2% of the respondents had avoided using healthcare [23]. Patients seem to have made decisions on using healthcare services by weighing the risks and benefits of doing so during that period [24]. Here, we found that the number of outpatient visits by diabetes patients significantly decreased when COVID-19 began spreading to local communities; however, no significant differences were observed for hypertension patients. This is may be because the ratio of diabetes patients with complications is higher than that of hypertension patients; hence, the former perceived the risk of COVID-19 infection as a greater threat. These perceptions may have been escalated by the government's pandemic-related policies, such as social distancing [21]. We found that the number of outpatient visits, which suppressed when the high-intensity social distancing policy was enforced, rebounded significantly once the policy ended. This suggests that although this policy contributed positively to curbing the spread of COVID-19, it may have negatively affected the disease management of non-COVID-19 patients [25, 26]. In response, the Korean government took necessary measures, such as temporarily allowing teleconsultation and designating "National Safe Hospitals". In these hospitals, the treatment of patients with respiratory conditions is separated to an independent area. However, these measures failed to offset the reduction in healthcare utilization in the end. This may be because the portion of teleconsultation is very low; in this study, among the claim cases of hypertension and diabetes from 2020, very few (about 0.45% and 0.52%, respectively) were teleconsultation cases. A previous study indicated that the teleconsultation rate in Korea is very low compared to other centuries due to low awareness of teleconsultation of both doctors and patients [27]. However, even in the United States, where telemedicine was considerably expanded during the pandemic, its use failed to counteract the overall decrease in healthcare utilization [14]. Thus, further research is needed on the causes for disruptions in healthcare services and the corresponding policy responses. Research from the perspective of healthcare service supply also shows that relatively low-priority healthcare services for non-COVID-19 patients were postponed or canceled due to the surge in COVID-19 [28]. However, Korea may have experienced minimal impact in this regard because its healthcare system was not overwhelmed by the COVID-19 pandemic [29]. Nevertheless, the interval between outpatient visits may have increased because of changes in doctors’ practice. For example, doctors were wary of patients becoming infected with COVID-19 while visiting their healthcare institutions [9]. In an unprecedented situation such as the pandemic, doctors took the flexible approach of providing long-term prescriptions and postponing patients' subsequently scheduled outpatient visits. We also found a significant increase in the number of days of medication supplied per visit while the number of outpatient visits were decreasing. Other countries show similar shifts in approach, which is a strategy recommended at the national level [30, 31]. Furthermore, these changes in healthcare utilization may vary depending on the risk perception of COVID-19 infection. The subgroup analysis results by age and region in Supplementary Tables shows that the reduction in the number of outpatient visits and increase in the days of medication supplied per visit was greater for the high-risk groups (i.e., patients aged 65 years or older), and patients in the Daegu and Gyeongbuk regions, which were the epicenter of the first wave of the COVID-19 pandemic in Korea. In line with research [7, 30], those at higher risk of COVID-19 showed more flexible responses to healthcare utilization from the perspective of both supply and demand. Finally, this study found continuity of care in medication increased significantly from 2019 to 2020. This may be because the aforementioned long-term prescription strategy, in addition to the increasing number of days of medication supplied per visit. Studies have also reported improvements in medication adherence during the COVID-19 pandemic [32, 33]. Thus, this study indicate that patients with hypertension and diabetes may have continued their medication during the pandemic;the temporary decrease in outpatient visits may have had limited negative impact on health outcomes. Note that MPR was determined from administrative data alone and may not be fully representative of whether the patient actually took the medication; still, possession of a sufficient amount of medication carries an important meaning as it is the first step in ensuring patients' ability to continue their medication [34]. Further research from a long-term perspective is necessary to determine how the delay in regular visits to the doctor or lab tests influenced disease management, and whether longer-term prescriptions were an effective strategy. This study has several limitations. First, we analyzed data up to July 2020 and focused on changes in healthcare utilization during the first wave of the COVID-19 pandemic in Korea (February 2020 to April 2020). Thus, our work does not reflect other COVID-19 waves, such as the spread of the Omicron variant, or changes in the pandemic policy, such as the stepwise return to normal life that followed our study period. Nevertheless, note that the first wave of the pandemic was the period where other factors may have had the least effect, such as pandemic-policy fatigue. Hence, it may be the period that most clearly shows the impact of COVID-19. Second, since this study aimed to investigate average changes in outpatient care, the results may differ from those at the individual level. For example, we cannot rule out that individual patients missed opportunities to improve their health by failing to use healthcare services in a timely manner. Nevertheless, our work is meaningful in that it examined changes in the services provided (medication supplied) as well as outpatient visits using nationally representative data. This is especially significant as we show that the continuity of medication as a proxy indicator to assess the impact of COVID-19 on health outcomes. 5. Conclusions The finding from the study revealed that the number of outpatient visits and the days of medication supplied per visits fluctuated when COVID-19 spread to local communities. However, this study suggests that patients may have continued their medication during the pandemic despites the temporary decrease in outpatient visits. Nevertheless, health crisis such as the COVID-19 pandemic affects healthcare utilization by patients with chronic diseases. Consequently, we need corresponding response measures to emerging and re-emerging infectious diseases. For example, the government needs to implement transparent and reliable risk communication strategies to prevent individuals from experiencing excessive anxiety during health crises. A change in the healthcare delivery system is also required to provide healthcare services to patients in a timely manner. Although many countries, including Korea, quickly introduced and expanded the use of teleconsultation because of the pandemic, its routine use must be actively promoted if it is to be effective in an infectious disease crisis. Abbreviations CCI: Charlson Comorbidity Index COVID-19: coronavirus disease 2019 HIRA: Health Insurance Review and Assessment Service ITS: Interrupted time-series MPR : Medication Possession Ratio Declarations Ethics approval and consent to participate The study was reviewed and has been granted an exemption from requiring ethics approval by the Institutional Review Board (IRB) of Health Insurance Review and Assessment Service (HIRA) (IRB No. 2020-089-001). All methods were carried out in accordance to relevant guidelines and regulation. Access to the data was granted and approved by HIRA. The IRB of HIRA (IRB No. 2020-089-001) granted an exemption for requirement for informed consent because data were all anonymized and un-identifiable". Consent for publication Not Applicable Availability of data and materials The data that support the findings of this study are available from the Health Insurance Review and Assessment Service but restrictions apply to the availability of these data, which were used under license for the current study, and so are not publicly available. Data are however available from the authors upon reasonable request and with permission of the Health Insurance Review and Assessment Service. Competing interests The authors declare that they have no competing interests. Funding None Authors' contributions BRS contributed to the conceptualization and design the study, performed all statistical analyses, and wrote the first draft. SMK contributed to the data interpretation and revised the manuscript. EWN contributed to the conceptualization and design the study, and revised the manuscript. The authors read and approved the final manuscript. Acknowledgements Not Applicable References World Health Organization. WHO Director-General’s opening remarks at the media briefing on COVID-19; 2020 [cited 2022 July 13] Available from: https://www.who.int/dg/speeches/detail/who-director-general-s-opening-remarks-atthe-media-briefing-on-covid-19.11-march-2020. Task Force for Tackling COVID-19. All about Korea’s Response to COVID-19. Seoul: Ministry of Foreign Affairs, Korea; October 7, 2020 [cited 2020 July 13]. Available from: https://www.mofa.go.kr/eng/brd/m_22591/view.do?seq=35&page=1. Central Disaster Management Headquarters. Press release: Extending Intensive "Social Distancing" for Another 2 Weeks; April 4, 2020 [cited 2022 July 13] Available from: http://ncov.mohw.go.kr/en/tcmBoardView.do?brdId=12&brdGubun=125&dataGubun=&ncvContSeq=353953&contSeq=353953&board_id=1365&gubun=. Myoungsoon Y. Trends in Korean Society 2020: Risk perception and behaviors related to COVID-19. Statistics Research Institute 2020:113–124. Mehrotra A, Chernewm M, Linetsky D, Hatch H, Cutler D. The Impact of the COVID-19 Pandemic on Outpatient Visits: Practices Are Adapting to the New Normal. New York: Commonwealth Fund; June 2020. Rodríguez-Leor O, Cid-Alvarez B, Ojeda S, Martin-Moreiras J, Rumoroso JR, L´opez-Palop R, et al. Impact of the COVID-19 pandemic on interventional cardiology activity in Spain. REC Interv Cardiol 2020. Zhang YN, Chen Y, Wang Y, Li F, Pender M, Wang N, et al. Reduction in healthcare services during the COVID-19 pandemic in China. BMJ Glob Health 2020;5:e003421. Blecker S, Jones SA, Petrilli CM, Admon A, Weerahandi H, Francois F, et al. Hospitalizations for chronic disease and acute conditions in the time of COVID-19. JAMA Intern Med 2021;181(2):269–271. DOI:10.1001/jamainternmed.2020.3978 Wright A, Salazar A, Mirica M, Volk L, Schiff GD. The invisible epidemic: neglected chronic disease management during COVID-19. J Intern Med 2020; 35(9):2816–2817. Kompaniyets L, Pennington AF, Goodman AB, Rosenblum HG, Belay B, Ko JY, et al. Underlying Medical Conditions and Severe Illness Among 540,667 Adults Hospitalized With COVID-19, March 2020-March 2021. Prev Chronic Dis. 2021; 18:E66. doi: 10.5888/pcd18.210123. Centers for Disease Control and Prevention. People who are at higher risk for severe illness; 2020 [accessed 17 Feb 2022]. Available from: www.cdc.gov/coronavirus/2019-ncov/need-extra-precautions/people-at-higher-risk.html. Barone MTU, Villarroel D, de Luca PV, Harnik SB, de Souza Lima BL, Wieselberg RJP, et al. COVID-19 impact on people with diabetes in South and Central America (SACA region). Diabetes Res Clin Pract 2020;166:108301. Liu C, You J, Zhu W, Chen Y, Li S, Zhu Y, et al. The COVID-19 outbreak negatively affects the delivery of care for patients with diabetic foot ulcers. Diabetes Care 2020;43(10):e125–e126. DOI: 10.2337/dc20-1581 Patel SY, Mehrotra A, Huskamp HA, Uscher-Pines L, Ganguli I, Barnett ML. Trends in outpatient care delivery and telemedicine during the COVID-19 pandemic in the US. JAMA Intern Med 2021;181(3):388–391. Kim J. Changes in health insurance medical expenses before and after COVID-19 and their implications. Nabo Focus 2020;(26):1–4 Oh J-Y, Cho S-J, Choi J- Changes in health care utilization during the COVID-19 pandemic. Health Policy Manag 2021;31(4):508–517 Shadish WR, Cook TD, Campbell DT. Experimental and quasi-experimental designs for generalized causal inference. Boston: Houghton Mifflin; 2002. Choi J, Oh JY, Lee YS, Min KH, Hur GY, Lee SY, et al. Harmful impact of air pollution on severe acute exacerbation of chronic obstructive pulmonary disease: particulate matter is hazardous. Int J Chron Obstruct Pulmon Dis 2018;13:1053. Quan H, Sundararajan V, Halfon P, Fong A, Burnand B, Luthi JC, et al. Coding algorithms for defining comorbidities in ICD-9-CM and ICD-10 administrative data. Med Care 2005:1130–1139. Lee H, Park JH, Floyd JS, Park S, Kim HC. Combined Effect of Income and Medication Adherence on Mortality in Newly Treated Hypertension: Nationwide Study of 16 Million Person‐Years. J Am Heart Assoc 2019;8(16):e013148. Mantica G, Riccardi N, Terrone C, Gratarola A. Non-COVID-19 visits to emergency departments during the pandemic: the impact of fear. Public Health 2020;183:40–41. Shin J, Moon S, Jung S. COVID-19 and medical service utilization experience. Health and Welfare Issue & Focus. Korea Institute for Health and Social Affairs. April 12, 2021, p. 1–8. Lee M, You M. Avoidance of Healthcare Utilization in South Korea during the Coronavirus Disease 2019 (COVID-19) Pandemic. Int J Environ Res Public Health 2021;18:4363. Lee SY, Khang Y-H, Lim H-K. Impact of the 2015 Middle East respiratory syndrome outbreak on emergency care utilization and mortality in South Korea. Yonsei Med J 2019;60:796–803. Kluge HHP, Wickramasinghe K, Rippin HL, Mendes R, Peters DH, Kontsevaya A, et al. Prevention and control of non-communicable diseases in the COVID-19 response. Lancet 2020;395(10238):1678–1680. Katulanda P, Dissanayake HA, Ranathunga I, Ratnasamy V, Wijewickrama PS, Yogendranathan N, et al. Prevention and management of COVID-19 among patients with diabetes: an appraisal of the literature. Diabetologia 2020;63(8):1440–1452. Kim H, Lee H, Park CS, Kim S, Cho SA, Yoo SM, et al. Preliminary Results of Teleconsultations Temporarily Allowed during the COVID-19 Pandemic. Yonsei Med J. 2021;62(9):850-857. Lin S, Sattler A, Smith M. Retooling primary care in the COVID-19 era. Mayo Clin Proc 2020;95(9):1831–1834. Sim B, Nam EW. The Impact of COVID-19 Pandemic on Outpatient Visits for All-Cause and Chronic Diseases in Korea: A Nationwide Population-Based Study. Int J Environ Res Public Health 2022;19:5674. https://doi.org/10.3390/ijerph19095674 Osawa I, Goto T, Asami Y, Itoh N, Kaga Y, Yamamoto Y, et al. Physician visits and medication prescriptions for major chronic diseases during the COVID-19 pandemic in Japan: retrospective cohort study. BMJ Open 2021;11:e050938. Martini N, Piccinni C, Pedrini A, Maggioni, A. CoViD-19 e malattie croniche: conoscenze attuali, passi futuri e il progetto MaCroScopio [CoViD-19 and chronic diseases: current knowledge, future steps and the MaCroScopio project]. Recenti Prog Med 2020;111:198–201 (Italian). Kaye L, Theye B, Smeenk I, Gondalia R, Barrett MA, Stempel DA. Changes in medication adherence among patients with asthma and COPD during the COVID-19 pandemic. J Allergy Clinl Immunol Pract 2020;8(7):2384–2385. https://doi.org/10.1016/j.jaip.2020.04.053 Nguyen A, Williams T, Kangethe A, Polson M. Impacts of COVID-19 on the adherence to oral anti-diabetic medications in commercially insured adult patients with type 2 diabetes mellitus; 2021 [cited 2022 July 31]. Available from: https://www1.magellanrx.com/documents/2021/03/impacts-of-covid-19-on-the-adherence-to-oral-anti-diabetic-medications-in-commercially-insured-adult-patients-with-type-2-diabetes-mellitus.pdf/. Hong J, Kang H. Oral antihyperglycemic medication adherence and its associated factors among ambulatory care with adult type 2 diabetes patients in Korea. Health Policy and Management 2010;20(2):128–143. Additional Declarations No competing interests reported. Supplementary Files 4.SupplementaryTablesBMC0802.docx Cite Share Download PDF Status: Published Journal Publication published 12 Aug, 2023 Read the published version in BMC Public Health → Version 1 posted Editorial decision: Major revision 27 Jun, 2023 Reviews received at journal 23 Jun, 2023 Reviewers agreed at journal 09 Jun, 2023 Reviews received at journal 05 Nov, 2022 Reviewers agreed at journal 25 Oct, 2022 Reviewers invited by journal 30 Aug, 2022 Editor assigned by journal 30 Aug, 2022 Editor invited by journal 27 Aug, 2022 Submission checks completed at journal 27 Aug, 2022 First submitted to journal 02 Aug, 2022 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-1923481","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":132092290,"identity":"b02bdc4d-3a6f-4a1b-a3f1-289974a36bf5","order_by":0,"name":"Boram Sim","email":"","orcid":"","institution":"HIRA Research Institute, Health Insurance Review and Assessment Service (HIRA)","correspondingAuthor":false,"prefix":"","firstName":"Boram","middleName":"","lastName":"Sim","suffix":""},{"id":132092291,"identity":"787c36ba-5788-415d-b30c-247a7a18b7fd","order_by":1,"name":"Sun-Mi Kim","email":"","orcid":"","institution":"Healthcare Resource Assessment Division, Health Insurance Review and Assessment Service (HIRA)","correspondingAuthor":false,"prefix":"","firstName":"Sun-Mi","middleName":"","lastName":"Kim","suffix":""},{"id":132092292,"identity":"39ee8efc-c0ee-44bc-ae16-91e99d05bf6f","order_by":2,"name":"Eun Woo Nam","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA1ElEQVRIiWNgGAWjYNCCCgh1AERIEKflDMlaGNuQOAS18EvkHvx0c95hOXP+wwcPMNTYMUjOPoBfi+SMvGTp3G2HjS1npCUcYDiWzCDNl4Bfi8HtHAOQlsQNN3gMDjCwHWCQ4yHgMPvbOca/c+cAtZw//+EAwz8itBhI55hJ5zYAtRzIYTjA2HaAQZqQFon7b8ysc46lGxvcSDM4kNiXzCPZQ0ALf88Z49s5NdZyBucPP/7w4ZudnMQZAlqgoBlCJTAwEHIWHNQRq3AUjIJRMApGIgAAFUpCcMTO9qoAAAAASUVORK5CYII=","orcid":"","institution":"Yonsei University","correspondingAuthor":true,"prefix":"","firstName":"Eun","middleName":"Woo","lastName":"Nam","suffix":""}],"badges":[],"createdAt":"2022-08-03 01:29:14","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-1923481/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-1923481/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s12889-023-16430-z","type":"published","date":"2023-08-12T21:54:48+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":25895500,"identity":"e6c055cf-fc5f-4376-9fbd-4f4534ad2ffb","added_by":"auto","created_at":"2022-08-31 16:09:07","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":469444,"visible":true,"origin":"","legend":"\u003cp\u003eImpact of the COVID-19 pandemic on the outpatient visits for hypertension and diabetes\u003c/p\u003e","description":"","filename":"Figure1BMC0802.jpg","url":"https://assets-eu.researchsquare.com/files/rs-1923481/v1/ae578bbef5ad8fb41329c3d8.jpg"},{"id":25895499,"identity":"46779969-8f51-4b49-9dc9-5a8756401312","added_by":"auto","created_at":"2022-08-31 16:09:07","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":489500,"visible":true,"origin":"","legend":"\u003cp\u003eImpact of the COVID-19 pandemic on the days of medication supplied for hypertension and diabetes\u003c/p\u003e","description":"","filename":"Figure2BMC0802.jpg","url":"https://assets-eu.researchsquare.com/files/rs-1923481/v1/e08599c6c2c1f9ffd0b2122f.jpg"},{"id":44735826,"identity":"5c61ac96-7913-40e0-abad-cfebce92f9b4","added_by":"auto","created_at":"2023-10-16 22:27:39","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":559842,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-1923481/v1/f2fdd50a-0c6d-4764-a992-6fa7d27e447a.pdf"},{"id":25895498,"identity":"45bd2f0a-b31f-44fe-a28c-0fcbf1af6a64","added_by":"auto","created_at":"2022-08-31 16:09:07","extension":"docx","order_by":7,"title":"","display":"","copyAsset":false,"role":"supplement","size":30526,"visible":true,"origin":"","legend":"","description":"","filename":"4.SupplementaryTablesBMC0802.docx","url":"https://assets-eu.researchsquare.com/files/rs-1923481/v1/98e6641835c89def0aa27114.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Evaluating the Effect of the COVID-19 Pandemic on Hypertension and Diabetes Care in South Korea: An Interrupted Time Series Analysis","fulltext":[{"header":"1. Background","content":"\u003cp\u003eSince its discovery in Wuhan, China, in December 2019, coronavirus disease 2019 (COVID-19) has spread rapidly worldwide, prompting the World Health Organization to declare it a pandemic [1]. South Korea reported its first confirmed case of COVID-19 on January 20, 2020. The country experienced its first wave of the pandemic starting with the 31st case reported on February 18 and ending in April 2020 [2]. During this period, the Korean government rapidly sorted through suspected and confirmed cases using speedy and large-scale contact tracing and diagnostic testing. In addition, it attempted to contain the spread of the virus in local communities by implementing a social distancing policy [3]. Consequently, the number of new confirmed cases soon peaked at 909 on February 29, 2020, and gradually declined thereafter [2].\u003c/p\u003e \u003cp\u003eHowever, the COVID-19 outbreak and the government's response affected many aspects of daily life. A public survey conducted during the first wave of the pandemic showed that daily life had progressively worsened with the following events: the first confirmed case in Korea (January 2020), beginning of local transmission (February), and implementation of a stringent social distancing policy (March) [4]. Consequently, access to healthcare services was also greatly disrupted [5\u0026ndash;7]. This calls for caution, as patients in need of healthcare services may not have had proper access to them [8].\u003c/p\u003e \u003cp\u003eAccess to healthcare services is important, especially for patients with chronic diseases. A lack of timely healthcare service can increase the risk of emergency room visits or hospitalization due to deterioration of conditions, onset of complications, and so on [9]. Chronic diseases are responsible for 80% of all deaths in Korea and impose considerable socioeconomic burden. In addition, for patients with underlying conditions, the risk of severe COVID-19 illness such as death and ICU admission is higher [10\u0026ndash;11]. Thus, from an epidemiological perspective of COVID-19, continuous care for chronic diseases is crucial.\u003c/p\u003e \u003cp\u003eResearch has found changes in healthcare utilization during the COVID-19 pandemic in both Korea and abroad [12\u0026ndash;14]. Korean studies has reported that while overall healthcare utilization decreased in 2020, healthcare utilization by patients with chronic diseases was higher than expected [15, 16]. However, these studies have some limitation. First, they are cross-sectional and do not examine the changes over time. Instead, time-series analysis can help us understand the changes in healthcare utilization over time, and reflect the trends before or during the pandemic. Second, studies have focused on changes in the volume of healthcare services, such as the number of patients or outpatient visit days. This makes it difficult to identify other aspects of continuity of care, such as medication. Patients with hypertension and diabetes need to ensure continuity of care by not only regularly visiting healthcare institutions for management of their conditions but also by taking medicine to maintain their blood pressure or keep blood sugar levels under control.\u003c/p\u003e \u003cp\u003eHere, we examined the impact of COVID-19 pandemic on two aspects of outpatient care for patients with hypertension and diabetes: outpatient visits and medication. Furthermore, we identified the change in continuity of care in medication, which is the result of changes in outpatient care, before and after the pandemic.\u003c/p\u003e"},{"header":"2. Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Study Design\u003c/h2\u003e \u003cp\u003eFirst, we used an interrupted time-series (ITS) analysis to evaluate the impact of the pandemic on outpatient care. ITS analysis is useful for evaluating the impact of a policy, intervention, or event when the policy or event affects the entire population, such as the pandemic; under such circumstances, it is impossible to set a control group [17]. Furthermore, to evaluate the continuity of care in medication, we identified changes in the Medication Possession Ratio (MPR) in 2019 and 2020.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Data Source\u003c/h2\u003e \u003cp\u003eNationwide claims data were extracted from the Health Insurance Review and Assessment Service (HIRA) of South Korea for patients who made outpatient visits to medical institutions with hypertension (I10\u0026ndash;I14) or diabetes (E11\u0026ndash;E15) as the principal or secondary diagnosis from January 2019 to July 2020. Owing to universal population coverage in Korea, the HIRA database covers the entire population and all healthcare providers in the country. Only patients aged 20 years or more were included. If a claim case included both hypertension and diabetes, it was included in both groups. Among the 124,275,685 total cases, 97,906,143 and 38,330,462 cases were included in the hypertension and diabetes groups, respectively.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Outcome Variables\u003c/h2\u003e \u003cp\u003eWe used the number of outpatient visits and days of medication supplied per visit as outcome variables. Note that ITS analysis uses aggregated data collected over equally spaced time intervals. Here, the number of outpatient visits was summed up on a weekly basis. Then, considering that possible variations in the number of holidays in each week, we calculated the average daily number of outpatient visits per week by dividing the total number of outpatient visits by the number of working days [18] in a week.\u003c/p\u003e \u003cp\u003eWe defined the days of medication supplied per visit as the number of days of supply of the relevant medication provided to the patient for a prescription. Thus, when determining the days of medication supplied, only claims cases that had prescriptions of antihypertensive or antihyperglycemic agents were included. Again, the average number of days of medication supplied per visit was obtained on a weekly basis.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4 Covariates\u003c/h2\u003e \u003cp\u003ePatient demographic factors (sex and age) and level of medical need (complications and comorbidities) were chosen as covariates. To adjust for the severity of hypertension or diabetes, the presence of any complications was checked using the diagnosis codes. The presence of comorbidities was expressed using the Charlson Comorbidity Index (CCI) score [19]. For this, patients' inpatient and outpatient data during the year prior to the study period were obtained and checked for comorbidities. Categorical variables were described as percentages in the model.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.5 Medication Possession Ratio (MPR)\u003c/h2\u003e \u003cp\u003eMPR was calculated as the total number of days of medication within the observation period divided by the total number of days in the observation period [20]. We adjusted oversupply (MPR\u0026thinsp;\u0026gt;\u0026thinsp;1.0) 1.0 and computed the average MPR for each year. We defined a patient with MPR not less than 0.8 as \u0026ldquo;appropriate-medication supply\u0026rdquo; and determined the portion of such patients in each year.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e2.6 Statistical Analysis\u003c/h2\u003e \u003cp\u003eThe time interval was in weeks and a total of 83 time points were included in the analysis. There were two events of interest: The first was the occurrence of case No. 31 (week 60), who caused the spread of COVID-19 to local communities, resulting in the first wave of the pandemic in Korea. The second was the end of high-intensity social distancing (week 68), which was implemented to contain the spread of the first wave. The analysis model is as follows:\u003c/p\u003e \u003cp\u003eLog (Y\u003csub\u003et\u003c/sub\u003e)\u0026thinsp;=\u0026thinsp;intercept\u0026thinsp;+\u0026thinsp;β\u003csub\u003e0\u003c/sub\u003e\u0026thinsp;\u0026times;\u0026thinsp;time\u003csub\u003et\u003c/sub\u003e\u0026thinsp;+\u0026thinsp;β\u003csub\u003e1\u003c/sub\u003e\u0026thinsp;\u0026times;\u0026thinsp;event1\u003csub\u003et\u003c/sub\u003e\u0026thinsp;+\u0026thinsp;β\u003csub\u003e2\u003c/sub\u003e\u0026thinsp;\u0026times;\u0026thinsp;time after event1\u003csub\u003et\u003c/sub\u003e\u0026thinsp;+\u0026thinsp;β\u003csub\u003e3\u003c/sub\u003e\u0026thinsp;\u0026times;\u0026thinsp;event2\u003csub\u003et\u003c/sub\u003e\u0026thinsp;+\u0026thinsp;β\u003csub\u003e4\u003c/sub\u003e\u0026thinsp;\u0026times;\u0026thinsp;time after event2\u003csub\u003et\u003c/sub\u003e\u0026thinsp;+\u0026thinsp;X\u003csub\u003et\u003c/sub\u003eλ\u0026thinsp;+\u0026thinsp;ε\u003csub\u003et\u003c/sub\u003e\u003c/p\u003e \u003cp\u003ewhere Y\u003csub\u003et\u003c/sub\u003e denotes the outcome variable at time t. Since the data for both outcome variables did not follow a normal distribution, they were log-transformed. In addition, time represents the change in time from January 2019; event1 and event2 are dummies representing the events of interest (before (0) and/or after (1) weeks 60 or 68); time after event1 and event2 are the change in time following weeks 60 or 68, respectively; X indicates covariates. β\u003csub\u003e1\u003c/sub\u003e and β\u003csub\u003e3\u003c/sub\u003e refer to the changes in the level of the outcome variable immediately following each event, and β\u003csub\u003e2\u003c/sub\u003e and β\u003csub\u003e4\u003c/sub\u003e refer to the changes in the trend of the outcome variables.\u003c/p\u003e \u003cp\u003eTo evaluate significant differences in the continuity of care in medication, the average MPR and portion of \u0026ldquo;appropriate-medication supply\u0026rdquo; patients were compared for 2019 and 2020 using a t-test and chi-square test. All analyses were performed using SAS Enterprise Guide 7.15.\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\n\u003ch2\u003e3.1 Characteristics of Patients\u003c/h2\u003e\n\u003cp\u003eTable\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e presents the results of the comparison between the composition of patients in 2019 and 2020. For both hypertension and diabetes, the ratio of female patients decreased by 0.4 percentage point from 2019 to 2020, whereas the average age increased. Although the ratio of patients with complications decreased from 2019 to 2020 for both hypertension and diabetes, the proportion of diabetic patients with complications was approximately 10 times greater than that of hypertensive patients. The average CCI score decreased for both hypertension and diabetes.\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003ctable id=\"Tab1\" border=\"1\"\u003e\u003ccaption\u003e\n\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n\u003cdiv class=\"CaptionContent\"\u003e\n\u003cp\u003eCharacteristics of participants (Unit: Case, %)\u003c/p\u003e\n\u003c/div\u003e\n\u003c/caption\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth rowspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eCategory\u003c/p\u003e\n\u003c/th\u003e\n\u003cth colspan=\"4\" align=\"left\"\u003e\n\u003cp\u003eHypertension\u003c/p\u003e\n\u003c/th\u003e\n\u003cth colspan=\"4\" align=\"left\"\u003e\n\u003cp\u003eDiabetes\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003cth colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eJan\u0026ndash;Dec 2019\u003c/p\u003e\n\u003c/th\u003e\n\u003cth colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eJan\u0026ndash;Jul 2020\u003c/p\u003e\n\u003c/th\u003e\n\u003cth colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eJan\u0026ndash;Dec 2019\u003c/p\u003e\n\u003c/th\u003e\n\u003cth colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eJan\u0026ndash;Jul 2020\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"9\" align=\"left\"\u003e\n\u003cp\u003eSex\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eMale\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e29,420,241\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(47.9)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e17,626,093\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(48.3)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e12,952,902\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(53.8)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e7,739,673\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(54.3)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eFemale\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e32,013,303\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(52.1)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e18,846,506\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(51.7)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e11,110,855\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(46.2)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e6,527,032\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(45.8)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eAge\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e64.6\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(avg.)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e64.8\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(avg.)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e63.3\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(avg.)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e63.4\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(avg.)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"9\" align=\"left\"\u003e\n\u003cp\u003ePresence of complications\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eYes\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2,724,006\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e( 4.4)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1,507,479\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e( 4.1)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e10,266,954\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(42.7)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e6,046,326\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(42.4)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNo\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e58,709,538\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(95.6)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e34,965,120\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(95.9)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e13,796,803\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(57.3)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e8,220,379\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(57.6)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"9\" align=\"left\"\u003e\n\u003cp\u003eCharlson Comorbidity Index (CCI) score\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e12,306,129\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(20.0)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e8,054,053\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(22.1)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e5,291,583\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(22.0)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3,388,911\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(23.8)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e15,717,344\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(25.6)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e9,430,201\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(25.9)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e7,304,929\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(30.4)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e4,360,741\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(30.6)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e13,849,572\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(22.5)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e8,043,250\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(22.1)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e5,608,304\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(23.3)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3,261,841\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(22.9)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3\u003c/p\u003e\n\u003cp\u003eor higher\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e19,560,499\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(31.8)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e10,945,095\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(30.0)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e5,858,941\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(24.4)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3,255,212\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(22.8)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eTotal\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e61,433,544\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(100.0)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e36,472,599\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(100.0)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e24,063,757\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(100.0)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e14,266,705\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(100.0)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\n\u003ch2\u003e3.2 Impact of COVID-19 on the Number of Outpatient Visits\u003c/h2\u003e\n\u003cp\u003eThe average number of outpatient visits (log) for hypertension showed a gradually increasing trend starting in 2019 (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e). In February 2020, when the spread of COVID-19 to local communities began (①), the number of outpatient visits declined by 9.0%; however, this decrease was not statistically significant (Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e). However, when high-intensity social distancing was relaxed in April 2020 (②), this number showed a statistically significant rebound of 19.0%.\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003ctable id=\"Tab2\" border=\"1\"\u003e\u003ccaption\u003e\n\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n\u003cdiv class=\"CaptionContent\"\u003e\n\u003cp\u003eImpact of the COVID-19 on outpatient care for hypertension and diabetes patients\u003c/p\u003e\n\u003c/div\u003e\n\u003c/caption\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd rowspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eCategory\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd rowspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eVariable\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eHypertension\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eDiabetes\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eExp (\u0026beta;)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ep-value\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eExp (\u0026beta;)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ep-value\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd rowspan=\"5\" align=\"left\"\u003e\n\u003cp\u003eNumber of outpatient visits\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eTime (\u0026beta;\u003csub\u003e0\u003c/sub\u003e)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.004\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.1443\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.004\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.2058\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eIntervention 1 (\u0026beta;\u003csub\u003e1\u003c/sub\u003e)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.913\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.1289\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.870\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.0161*\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eTime after intervention 1 (\u0026beta;\u003csub\u003e2\u003c/sub\u003e)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.996\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.7521\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.993\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.5325\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eIntervention 2 (\u0026beta;\u003csub\u003e3\u003c/sub\u003e)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.185\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.0057**\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.163\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.0124*\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eTime after intervention 2 (\u0026beta;\u003csub\u003e4\u003c/sub\u003e)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.994\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.6306\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.001\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.9121\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd rowspan=\"5\" align=\"left\"\u003e\n\u003cp\u003eDays of medication supplied\u003c/p\u003e\n\u003cp\u003eper visits\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eTime (\u0026beta;\u003csub\u003e0\u003c/sub\u003e)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.001\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.0083\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.001\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.0077**\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eIntervention 1 (\u0026beta;\u003csub\u003e1\u003c/sub\u003e)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.033\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;0.0001***\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.042\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;0.0001***\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eTime after intervention 1 (\u0026beta;\u003csub\u003e2\u003c/sub\u003e)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.998\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.0368\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.998\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.0492*\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eIntervention 2 (\u0026beta;\u003csub\u003e3\u003c/sub\u003e)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.995\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.3504\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.992\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.1538\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eTime after intervention 2 (\u0026beta;\u003csub\u003e4\u003c/sub\u003e)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.003\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.0063*\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.002\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.034*\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003ctfoot\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"6\"\u003e* p\u0026thinsp;\u0026lt;\u0026thinsp;0.05, ** p\u0026thinsp;\u0026lt;\u0026thinsp;0.01, *** p\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tfoot\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/div\u003e\n\u003cp\u003eRegarding diabetes, the average number of outpatient visits (log) started increasing in 2019 (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e). However, this number significantly decreased by 13.0% when the spread of COVID-19 to local communities began (①) and then rebounded by 16.0% immediately following the easing of high-intensity social distancing (②) (Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e). For both hypertension and diabetes, the trend following these two time points did not significantly change.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\n\u003ch2\u003e3.3 Impact of COVID-19 on the Days of Medication Supplied Per Visit\u003c/h2\u003e\n\u003cp\u003eThe average number of days of medication supplied per visit (log) for hypertension showed a gradually increasing trend starting in 2019 (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e). When the spread of COVID-19 to local communities began in February 2020 (①), this number significantly increased by approximately 3.3% (Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e). However, there was no significant change in the trend thereafter. At the end of the high-intensity social distancing (②), the level of days of medication supplied did not significantly change, but the trend of the slope significantly increased by 0.3%.\u003c/p\u003e\n\u003cp\u003eThe average number of days of medication supplied per visit (log) for diabetes also showed a steady increase starting in 2019 (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e). When community transmission began (①), this number significantly increased by 4.2%, whereas the increasing trend of the slope slowed down by 0.2% (Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e). When high-intensity social distancing was relaxed (②), the level of days of medication supplied did not significantly change and the trend of the slope increased significantly by 0.2%, as was the case for hypertension.\u003c/p\u003e\n\u003cp\u003e\u003cspan class=\"ItalicUnderline\"\u003eMedication Possession Ratio (MPR)\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003eThe average MPRs of hypertension and diabetes patients in 2020 were 0.81 and 0.79, respectively; compared to 2019, these were statistically significant increases of 0.03 and 0.04, respectively (p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001) (Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e). The ratio of hypertension and diabetes patients with appropriate medication supply (MPR\u0026thinsp;\u0026ge;\u0026thinsp;0.8) in 2020 was 75.0% and 70.0%, respectively; compared to 2019, these were statistically significant increases of 6.5 and 7.2 percentage points, respectively (p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001) (Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003ctable id=\"Tab3\" border=\"1\"\u003e\u003ccaption\u003e\n\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\n\u003cdiv class=\"CaptionContent\"\u003e\n\u003cp\u003eMedication Possession Ratio (MPR) and ratio of patients with appropriate-medication supply among hypertension and diabetes patients\u003c/p\u003e\n\u003c/div\u003e\n\u003c/caption\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eCategory\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eDisease\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003e2019 (A)\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003e2020 (B)\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eChange\u003c/p\u003e\n\u003cp\u003e(B-A)\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003ep-value\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd rowspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eMPR\u003c/p\u003e\n\u003cp\u003e(Mean\u0026thinsp;\u0026plusmn;\u0026thinsp;S.D.)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eHypertension\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.79%\u003c/p\u003e\n\u003cp\u003e(\u0026plusmn;\u0026thinsp;0.28)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.82%\u003c/p\u003e\n\u003cp\u003e(\u0026plusmn;\u0026thinsp;0.23)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.03 percentage points\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;0.0001***\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eDiabetes\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.76%\u003c/p\u003e\n\u003cp\u003e(\u0026plusmn;\u0026thinsp;0.30)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.79%\u003c/p\u003e\n\u003cp\u003e(\u0026plusmn;\u0026thinsp;0.26)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.04 percentage points\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;0.0001***\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd rowspan=\"2\" align=\"left\"\u003e\n\u003cp\u003ePatients with appropriate medication supply\u003csup\u003e\u0026dagger;\u003c/sup\u003e\u003c/p\u003e\n\u003cp\u003e(%, N)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eHypertension\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e68.5%\u003c/p\u003e\n\u003cp\u003e(N\u0026thinsp;=\u0026thinsp;4,051,064)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e75.0%\u003c/p\u003e\n\u003cp\u003e(N\u0026thinsp;=\u0026thinsp;4,852,268)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e6.5 percentage points\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;0.0001***\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eDiabetes\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e62.9%\u003c/p\u003e\n\u003cp\u003e(N\u0026thinsp;=\u0026thinsp;1,314,013)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e70.1%\u003c/p\u003e\n\u003cp\u003e(N\u0026thinsp;=\u0026thinsp;1,618,746)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e7.2 percentage points\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;0.0001***\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003ctfoot\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"6\"\u003e\u003csup\u003e*\u003c/sup\u003e p\u0026thinsp;\u0026lt;\u0026thinsp;0.05, \u003csup\u003e**\u003c/sup\u003e p\u0026thinsp;\u0026lt;\u0026thinsp;0.01, \u003csup\u003e***\u003c/sup\u003e p\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"6\"\u003e\u0026dagger;Patients with appropriate medication supply: Patients whose MPR is not less than 0.8.\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tfoot\u003e\n\u003c/table\u003e\n\u003c/div\u003e\n\u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eThis study demonstrated the impact of COVID-19 on two aspects of outpatient care for patients with hypertension and diabetes: outpatient visits and medication. We found that the number of outpatient visits by patients decreased immediately when COVID-19 spread to local communities, in line with results observed in other countries during the pandemic [5, 13]. The most noteworthy reason for this reduction is the patients' fear of contracting COVID-19 [21]. According to the Medical Service Experience Survey of Korea, 15.6% of respondents in the first half of 2020 answered that they felt infection-related anxiety while using medical services; this is more than a two-fold increase year-over-year from the same period in 2019 [22]. A public perception survey conducted in 2020 also showed that 73.2% of the respondents had avoided using healthcare [23]. Patients seem to have made decisions on using healthcare services by weighing the risks and benefits of doing so during that period [24]. Here, we found that the number of outpatient visits by diabetes patients significantly decreased when COVID-19 began spreading to local communities; however, no significant differences were observed for hypertension patients. This is may be because the ratio of diabetes patients with complications is higher than that of hypertension patients; hence, the former perceived the risk of COVID-19 infection as a greater threat.\u003c/p\u003e \u003cp\u003eThese perceptions may have been escalated by the government's pandemic-related policies, such as social distancing [21]. We found that the number of outpatient visits, which suppressed when the high-intensity social distancing policy was enforced, rebounded significantly once the policy ended. This suggests that although this policy contributed positively to curbing the spread of COVID-19, it may have negatively affected the disease management of non-COVID-19 patients [25, 26]. In response, the Korean government took necessary measures, such as temporarily allowing teleconsultation and designating \"National Safe Hospitals\". In these hospitals, the treatment of patients with respiratory conditions is separated to an independent area. However, these measures failed to offset the reduction in healthcare utilization in the end. This may be because the portion of teleconsultation is very low; in this study, among the claim cases of hypertension and diabetes from 2020, very few (about 0.45% and 0.52%, respectively) were teleconsultation cases. A previous study indicated that the teleconsultation rate in Korea is very low compared to other centuries due to low awareness of teleconsultation of both doctors and patients [27]. However, even in the United States, where telemedicine was considerably expanded during the pandemic, its use failed to counteract the overall decrease in healthcare utilization [14]. Thus, further research is needed on the causes for disruptions in healthcare services and the corresponding policy responses.\u003c/p\u003e \u003cp\u003eResearch from the perspective of healthcare service supply also shows that relatively low-priority healthcare services for non-COVID-19 patients were postponed or canceled due to the surge in COVID-19 [28]. However, Korea may have experienced minimal impact in this regard because its healthcare system was not overwhelmed by the COVID-19 pandemic [29]. Nevertheless, the interval between outpatient visits may have increased because of changes in doctors\u0026rsquo; practice. For example, doctors were wary of patients becoming infected with COVID-19 while visiting their healthcare institutions [9]. In an unprecedented situation such as the pandemic, doctors took the flexible approach of providing long-term prescriptions and postponing patients' subsequently scheduled outpatient visits. We also found a significant increase in the number of days of medication supplied per visit while the number of outpatient visits were decreasing. Other countries show similar shifts in approach, which is a strategy recommended at the national level [30, 31].\u003c/p\u003e \u003cp\u003eFurthermore, these changes in healthcare utilization may vary depending on the risk perception of COVID-19 infection. The subgroup analysis results by age and region in Supplementary Tables shows that the reduction in the number of outpatient visits and increase in the days of medication supplied per visit was greater for the high-risk groups (i.e., patients aged 65 years or older), and patients in the Daegu and Gyeongbuk regions, which were the epicenter of the first wave of the COVID-19 pandemic in Korea. In line with research [7, 30], those at higher risk of COVID-19 showed more flexible responses to healthcare utilization from the perspective of both supply and demand.\u003c/p\u003e \u003cp\u003eFinally, this study found continuity of care in medication increased significantly from 2019 to 2020. This may be because the aforementioned long-term prescription strategy, in addition to the increasing number of days of medication supplied per visit. Studies have also reported improvements in medication adherence during the COVID-19 pandemic [32, 33]. Thus, this study indicate that patients with hypertension and diabetes may have continued their medication during the pandemic;the temporary decrease in outpatient visits may have had limited negative impact on health outcomes. Note that MPR was determined from administrative data alone and may not be fully representative of whether the patient actually took the medication; still, possession of a sufficient amount of medication carries an important meaning as it is the first step in ensuring patients' ability to continue their medication [34]. Further research from a long-term perspective is necessary to determine how the delay in regular visits to the doctor or lab tests influenced disease management, and whether longer-term prescriptions were an effective strategy.\u003c/p\u003e \u003cp\u003eThis study has several limitations. First, we analyzed data up to July 2020 and focused on changes in healthcare utilization during the first wave of the COVID-19 pandemic in Korea (February 2020 to April 2020). Thus, our work does not reflect other COVID-19 waves, such as the spread of the Omicron variant, or changes in the pandemic policy, such as the stepwise return to normal life that followed our study period. Nevertheless, note that the first wave of the pandemic was the period where other factors may have had the least effect, such as pandemic-policy fatigue. Hence, it may be the period that most clearly shows the impact of COVID-19. Second, since this study aimed to investigate average changes in outpatient care, the results may differ from those at the individual level. For example, we cannot rule out that individual patients missed opportunities to improve their health by failing to use healthcare services in a timely manner. Nevertheless, our work is meaningful in that it examined changes in the services provided (medication supplied) as well as outpatient visits using nationally representative data. This is especially significant as we show that the continuity of medication as a proxy indicator to assess the impact of COVID-19 on health outcomes.\u003c/p\u003e"},{"header":"5. Conclusions","content":"\u003cp\u003eThe finding from the study revealed that the number of outpatient visits and the days of medication supplied per visits fluctuated when COVID-19 spread to local communities. However, this study suggests that patients may have continued their medication during the pandemic despites the temporary decrease in outpatient visits. Nevertheless, health crisis such as the COVID-19 pandemic affects healthcare utilization by patients with chronic diseases. Consequently, we need corresponding response measures to emerging and re-emerging infectious diseases. For example, the government needs to implement transparent and reliable risk communication strategies to prevent individuals from experiencing excessive anxiety during health crises. A change in the healthcare delivery system is also required to provide healthcare services to patients in a timely manner. Although many countries, including Korea, quickly introduced and expanded the use of teleconsultation because of the pandemic, its routine use must be actively promoted if it is to be effective in an infectious disease crisis.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003e\u003cstrong\u003eCCI:\u003c/strong\u003e Charlson Comorbidity Index\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCOVID-19:\u003c/strong\u003e coronavirus disease 2019\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eHIRA:\u003c/strong\u003e Health Insurance Review and Assessment Service\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eITS:\u003c/strong\u003e Interrupted time-series\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMPR\u003c/strong\u003e: Medication Possession Ratio\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003e\u003cem\u003eEthics approval and consent to participate\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study was reviewed and has been granted an exemption from requiring ethics approval by the Institutional Review Board (IRB) of Health Insurance Review and Assessment Service (HIRA) (IRB No. 2020-089-001). All methods were carried out in accordance to relevant guidelines and regulation. Access to the data was granted and approved by HIRA. The IRB of HIRA (IRB No. 2020-089-001) granted an exemption for requirement for informed consent because data were all anonymized and un-identifiable\u0026quot;.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eConsent for publication\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot Applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eAvailability of data and materials\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data that support the findings of this study are available from the Health Insurance Review and Assessment Service but restrictions apply to the availability of these data, which were used under license for the current study, and so are not publicly available. Data are however available from the authors upon reasonable request and with permission of the Health Insurance Review and Assessment Service.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eCompeting interests\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eFunding\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eNone\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eAuthors\u0026apos; contributions\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eBRS contributed to the conceptualization and design the study, performed all statistical analyses, and wrote the first draft. SMK contributed to the data interpretation and revised the manuscript. EWN contributed to the conceptualization and design the study, and revised the manuscript. The authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eAcknowledgements\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot Applicable\u003c/p\u003e"},{"header":"References","content":"\u003col \u003e\n\u003cli\u003eWorld Health Organization. WHO Director-General\u0026rsquo;s opening remarks at the media briefing on COVID-19; 2020 [cited 2022 July 13] Available from: https://www.who.int/dg/speeches/detail/who-director-general-s-opening-remarks-atthe-media-briefing-on-covid-19.11-march-2020.\u003c/li\u003e\n\u003cli\u003eTask Force for Tackling COVID-19. All about Korea\u0026rsquo;s Response to COVID-19. Seoul: Ministry of Foreign Affairs, Korea; October 7, 2020 [cited 2020 July 13]. Available from: https://www.mofa.go.kr/eng/brd/m_22591/view.do?seq=35\u0026amp;page=1.\u003c/li\u003e\n\u003cli\u003eCentral Disaster Management Headquarters. Press release: Extending Intensive \u0026quot;Social Distancing\u0026quot; for Another 2 Weeks; April 4, 2020 [cited 2022 July 13] Available from: http://ncov.mohw.go.kr/en/tcmBoardView.do?brdId=12\u0026amp;brdGubun=125\u0026amp;dataGubun=\u0026amp;ncvContSeq=353953\u0026amp;contSeq=353953\u0026amp;board_id=1365\u0026amp;gubun=.\u003c/li\u003e\n\u003cli\u003eMyoungsoon Y. Trends in Korean Society 2020: Risk perception and behaviors related to COVID-19. Statistics Research Institute 2020:113\u0026ndash;124.\u003c/li\u003e\n\u003cli\u003eMehrotra A, Chernewm M, Linetsky D, Hatch H, Cutler D. The Impact of the COVID-19 Pandemic on Outpatient Visits: Practices Are Adapting to the New Normal. New York: Commonwealth Fund; June 2020.\u003c/li\u003e\n\u003cli\u003eRodr\u0026iacute;guez-Leor O, Cid-Alvarez B, Ojeda S, Martin-Moreiras J, Rumoroso JR, L\u0026acute;opez-Palop R, et al. Impact of the COVID-19 pandemic on interventional cardiology activity in Spain. REC Interv Cardiol 2020.\u003c/li\u003e\n\u003cli\u003eZhang YN, Chen Y, Wang Y, Li F, Pender M, Wang N, et al. Reduction in healthcare services during the COVID-19 pandemic in China. BMJ Glob Health 2020;5:e003421.\u003c/li\u003e\n\u003cli\u003eBlecker S, Jones SA, Petrilli CM, Admon A, Weerahandi H, Francois F, et al. Hospitalizations for chronic disease and acute conditions in the time of COVID-19. JAMA Intern Med 2021;181(2):269\u0026ndash;271. DOI:10.1001/jamainternmed.2020.3978\u003c/li\u003e\n\u003cli\u003eWright A, Salazar A, Mirica M, Volk L, Schiff GD. The invisible epidemic: neglected chronic disease management during COVID-19. J Intern Med 2020; 35(9):2816\u0026ndash;2817.\u003c/li\u003e\n\u003cli\u003eKompaniyets L, Pennington AF, Goodman AB, Rosenblum HG, Belay B, Ko JY, et al. Underlying Medical Conditions and Severe Illness Among 540,667 Adults Hospitalized With COVID-19, March 2020-March 2021. Prev Chronic Dis. 2021; 18:E66. doi: 10.5888/pcd18.210123.\u003c/li\u003e\n\u003cli\u003eCenters for Disease Control and Prevention. People who are at higher risk for severe illness; 2020 [accessed 17 Feb 2022]. Available from: www.cdc.gov/coronavirus/2019-ncov/need-extra-precautions/people-at-higher-risk.html.\u003c/li\u003e\n\u003cli\u003eBarone MTU, Villarroel D, de Luca PV, Harnik SB, de Souza Lima BL, Wieselberg RJP, et al. COVID-19 impact on people with diabetes in South and Central America (SACA region). Diabetes Res Clin Pract 2020;166:108301.\u003c/li\u003e\n\u003cli\u003eLiu C, You J, Zhu W, Chen Y, Li S, Zhu Y, et al. The COVID-19 outbreak negatively affects the delivery of care for patients with diabetic foot ulcers. Diabetes Care 2020;43(10):e125\u0026ndash;e126. DOI: 10.2337/dc20-1581\u003c/li\u003e\n\u003cli\u003ePatel SY, Mehrotra A, Huskamp HA, Uscher-Pines L, Ganguli I, Barnett ML. Trends in outpatient care delivery and telemedicine during the COVID-19 pandemic in the US. JAMA Intern Med 2021;181(3):388\u0026ndash;391.\u003c/li\u003e\n\u003cli\u003eKim J. Changes in health insurance medical expenses before and after COVID-19 and their implications. Nabo Focus 2020;(26):1\u0026ndash;4\u003c/li\u003e\n\u003cli\u003eOh J-Y, Cho S-J, Choi J- Changes in health care utilization during the COVID-19 pandemic. Health Policy Manag 2021;31(4):508\u0026ndash;517\u003c/li\u003e\n\u003cli\u003eShadish WR, Cook TD, Campbell DT. Experimental and quasi-experimental designs for generalized causal inference. Boston: Houghton Mifflin; 2002.\u003c/li\u003e\n\u003cli\u003eChoi J, Oh JY, Lee YS, Min KH, Hur GY, Lee SY, et al. Harmful impact of air pollution on severe acute exacerbation of chronic obstructive pulmonary disease: particulate matter is hazardous. Int J Chron Obstruct Pulmon Dis 2018;13:1053.\u003c/li\u003e\n\u003cli\u003eQuan H, Sundararajan V, Halfon P, Fong A, Burnand B, Luthi JC, et al. Coding algorithms for defining comorbidities in ICD-9-CM and ICD-10 administrative data. Med Care 2005:1130\u0026ndash;1139.\u003c/li\u003e\n\u003cli\u003eLee H, Park JH, Floyd JS, Park S, Kim HC. Combined Effect of Income and Medication Adherence on Mortality in Newly Treated Hypertension: Nationwide Study of 16 Million Person‐Years. J Am Heart Assoc 2019;8(16):e013148.\u003c/li\u003e\n\u003cli\u003eMantica G, Riccardi N, Terrone C, Gratarola A. Non-COVID-19 visits to emergency departments during the pandemic: the impact of fear. Public Health 2020;183:40\u0026ndash;41.\u003c/li\u003e\n\u003cli\u003eShin J, Moon S, Jung S. COVID-19 and medical service utilization experience. Health and Welfare Issue \u0026amp; Focus. Korea Institute for Health and Social Affairs. April 12, 2021, p. 1\u0026ndash;8.\u003c/li\u003e\n\u003cli\u003eLee M, You M. Avoidance of Healthcare Utilization in South Korea during the Coronavirus Disease 2019 (COVID-19) Pandemic. Int J Environ Res Public Health 2021;18:4363.\u003c/li\u003e\n\u003cli\u003eLee SY, Khang Y-H, Lim H-K. Impact of the 2015 Middle East respiratory syndrome outbreak on emergency care utilization and mortality in South Korea. Yonsei Med J 2019;60:796\u0026ndash;803.\u003c/li\u003e\n\u003cli\u003eKluge HHP, Wickramasinghe K, Rippin HL, Mendes R, Peters DH, Kontsevaya A, et al. Prevention and control of non-communicable diseases in the COVID-19 response. Lancet 2020;395(10238):1678\u0026ndash;1680.\u003c/li\u003e\n\u003cli\u003eKatulanda P, Dissanayake HA, Ranathunga I, Ratnasamy V, Wijewickrama PS, Yogendranathan N, et al. Prevention and management of COVID-19 among patients with diabetes: an appraisal of the literature. Diabetologia 2020;63(8):1440\u0026ndash;1452.\u003c/li\u003e\n\u003cli\u003eKim H, Lee H, Park CS, Kim S, Cho SA, Yoo SM, et al. Preliminary Results of Teleconsultations Temporarily Allowed during the COVID-19 Pandemic. Yonsei Med J. 2021;62(9):850-857.\u003c/li\u003e\n\u003cli\u003eLin S, Sattler A, Smith M. Retooling primary care in the COVID-19 era. Mayo Clin Proc 2020;95(9):1831\u0026ndash;1834.\u003c/li\u003e\n\u003cli\u003eSim B, Nam EW. The Impact of COVID-19 Pandemic on Outpatient Visits for All-Cause and Chronic Diseases in Korea: A Nationwide Population-Based Study. Int J Environ Res Public Health 2022;19:5674. https://doi.org/10.3390/ijerph19095674\u003c/li\u003e\n\u003cli\u003eOsawa I, Goto T, Asami Y, Itoh N, Kaga Y, Yamamoto Y, et al. Physician visits and medication prescriptions for major chronic diseases during the COVID-19 pandemic in Japan: retrospective cohort study. BMJ Open 2021;11:e050938.\u003c/li\u003e\n\u003cli\u003eMartini N, Piccinni C, Pedrini A, Maggioni, A. CoViD-19 e malattie croniche: conoscenze attuali, passi futuri e il progetto MaCroScopio [CoViD-19 and chronic diseases: current knowledge, future steps and the MaCroScopio project]. Recenti Prog Med 2020;111:198\u0026ndash;201 (Italian). \u003c/li\u003e\n\u003cli\u003eKaye L, Theye B, Smeenk I, Gondalia R, Barrett MA, Stempel DA. Changes in medication adherence among patients with asthma and COPD during the COVID-19 pandemic. J Allergy Clinl Immunol Pract 2020;8(7):2384\u0026ndash;2385. https://doi.org/10.1016/j.jaip.2020.04.053\u003c/li\u003e\n\u003cli\u003eNguyen A, Williams T, Kangethe A, Polson M. Impacts of COVID-19 on the adherence to oral anti-diabetic medications in commercially insured adult patients with type 2 diabetes mellitus; 2021 [cited 2022 July 31]. Available from: https://www1.magellanrx.com/documents/2021/03/impacts-of-covid-19-on-the-adherence-to-oral-anti-diabetic-medications-in-commercially-insured-adult-patients-with-type-2-diabetes-mellitus.pdf/.\u003c/li\u003e\n\u003cli\u003eHong J, Kang H. Oral antihyperglycemic medication adherence and its associated factors among ambulatory care with adult type 2 diabetes patients in Korea. Health Policy and Management 2010;20(2):128\u0026ndash;143.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-public-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"pubh","sideBox":"Learn more about [BMC Public Health](http://bmcpublichealth.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/pubh/default.aspx","title":"BMC Public Health","twitterHandle":"@BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"COVID-19, Pandemic, Chronic Disease, Outpatient Care, Access to Care, Interrupted Time Series","lastPublishedDoi":"10.21203/rs.3.rs-1923481/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-1923481/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eAccess to healthcare services is important, especially for patients with chronic diseases. We evaluated the effect of COVID-19 pandemic on outpatient visits and medication for patients with hypertension and diabetes in South Korea.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eNationwide claims data were extracted for patients with hypertension and diabetes from January 2019 to July 2020. We used an interrupted time series (ITS) analysis to evaluate the pandemic\u0026rsquo;s impact on outpatient care using the number of outpatient visits and days of medication supplied per visit. We identified the change in continuity of care in medication, a consequence of change in outpatient care, using the Medication Possession Ratio (MPR).\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eThe number of outpatient visits for diabetes significantly declined in February 2020, when community transmission began. However, when high-intensity social distancing was relaxed in April 2020, outpatient visits for hypertension and diabetes rebounded significantly. Moreover, when the outpatient visits declined, the number of days of medication supplied per visit increased. Consequently, the average MPRs significantly increased compared to 2019, increasing the ratio of patients with appropriate medication supply (MPR\u0026thinsp;\u0026ge;\u0026thinsp;0.8).\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eOutpatient visits decreased immediately when COVID-19 spread to local communities. However, the number of days of medication supplied per visit increased to compensate for the longer intervals between visits. Rather, the change in continuity of care in medication improved; thus, the temporary decrease in outpatient visits might have had limited negative impact on health outcomes.\u003c/p\u003e","manuscriptTitle":"Evaluating the Effect of the COVID-19 Pandemic on Hypertension and Diabetes Care in South Korea: An Interrupted Time Series Analysis","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2022-08-31 16:09:04","doi":"10.21203/rs.3.rs-1923481/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Major revision","date":"2023-06-27T05:13:28+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2023-06-23T14:54:10+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"2396efcb-2bc6-449f-9df4-cfc0c63f9867","date":"2023-06-09T08:41:04+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2022-11-05T11:32:18+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"db5d4720-ac27-4aa5-b37d-b17b7cefbe01","date":"2022-10-26T01:14:50+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2022-08-30T15:23:30+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2022-08-30T15:20:45+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2022-08-27T07:02:01+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2022-08-27T07:00:16+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Public Health","date":"2022-08-03T01:21:58+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-public-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"pubh","sideBox":"Learn more about [BMC Public Health](http://bmcpublichealth.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/pubh/default.aspx","title":"BMC Public Health","twitterHandle":"@BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"512a88bd-5330-4d56-90ec-5bc5e5904b96","owner":[],"postedDate":"August 31st, 2022","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2023-10-16T22:15:36+00:00","versionOfRecord":{"articleIdentity":"rs-1923481","link":"https://doi.org/10.1186/s12889-023-16430-z","journal":{"identity":"bmc-public-health","isVorOnly":false,"title":"BMC Public Health"},"publishedOn":"2023-08-12 21:54:48","publishedOnDateReadable":"August 12th, 2023"},"versionCreatedAt":"2022-08-31 16:09:04","video":"","vorDoi":"10.1186/s12889-023-16430-z","vorDoiUrl":"https://doi.org/10.1186/s12889-023-16430-z","workflowStages":[]},"version":"v1","identity":"rs-1923481","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-1923481","identity":"rs-1923481","version":["v1"]},"buildId":"_2-kVJe1T_tPrBINL-cwx","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}