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Influence of the COVID-19 pandemic on regular clinic visits and medication prescriptions among people with diabetes: Retrospective cohort analysis of health care claims

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The aim of this study was to investigate the effect of the COVID-19 pandemic on regular clinic visits among people with diabetes and to elucidate the factors related to visit patterns among these patients during the pandemic. This was a longitudinal study using anonymized insurance claims data from the Joint Health Insurance Society in Tokyo from October 2017 to September 2020. First, we identified patients with diabetes who were fully enrolled in the health plan from fiscal year 2017 until September 2020 and who were regularly receiving glucose-lowering medications (every 1–3 months) from October 2017 to September 2018. We divided follow-up into the pre-pandemic period (October 2018 to March 2020) and the pandemic period (April 2020 to September 2020). A multilevel logistic regression model was used to determine the risks of delayed clinic visits/medication prescriptions (i.e., >3 months after a previous visit/prescription) during the pandemic period. We identified 1118 study participants. The number of delayed clinic visits/medication prescriptions during the pre-pandemic and pandemic periods was 188/3354 (5.6%) and 125/1118 (11.2%), respectively. There was a significant increase in delayed clinic visits during the pandemic (adjusted odds ratio 3.68 (95% confidence interval 2.24 to 6.04, P < .001), even after controlling for confounding factors. We also found a significant interaction between sex and delayed visits; women had significantly fewer clinic visits during the COVID-19 pandemic than men. We clarified the relationship of the COVID-19 pandemic with delays in regular clinic visits and medication prescriptions among people with diabetes. The response to the COVID-19 pandemic differed between men and women.
Observational Study
1
Medicine®
Influence of the COVID-19 pandemic on regular
clinic visits and medication prescriptions among
people with diabetes
Retrospective cohort analysis of health care claims
ToshikiMaeda, MD, PhD, MPHa,* , TakumiNishi, PhD, MPHa,b, MasatakaHarada, PhDc,
KozoTanno, MD, PhDd,e, NaoyukiNishiya, PhDe,f, KeiAsayama, MD, PhDg,h,i, NagakoOkuda, MD, PhDj,
DaisukeSugiyama, MD, PhDk, HiroshiYatsuya, MD, PhDl, AkiraOkayama, MD, PhDm, HisatomiArima, MD, PhDa
Abstract
The aim of this study was to investigate the effect of the COVID-19 pandemic on regular clinic visits among people with diabetes
and to elucidate the factors related to visit patterns among these patients during the pandemic.
This was a longitudinal study using anonymized insurance claims data from the Joint Health Insurance Society in Tokyo from
October 2017 to September 2020. First, we identified patients with diabetes who were fully enrolled in the health plan from fiscal
year 2017 until September 2020 and who were regularly receiving glucose-lowering medications (every 1–3 months) from October
2017 to September 2018. We divided follow-up into the pre-pandemic period (October 2018 to March 2020) and the pandemic
period (April 2020 to September 2020). A multilevel logistic regression model was used to determine the risks of delayed clinic
visits/medication prescriptions (i.e., >3 months after a previous visit/prescription) during the pandemic period.
We identified 1118 study participants. The number of delayed clinic visits/medication prescriptions during the pre-pandemic
and pandemic periods was 188/3354 (5.6%) and 125/1118 (11.2%), respectively. There was a significant increase in delayed
clinic visits during the pandemic (adjusted odds ratio 3.68 (95% confidence interval 2.24 to 6.04, P < .001), even after controlling
for confounding factors. We also found a significant interaction between sex and delayed visits; women had significantly fewer
clinic visits during the COVID-19 pandemic than men.
We clarified the relationship of the COVID-19 pandemic with delays in regular clinic visits and medication prescriptions among
people with diabetes. The response to the COVID-19 pandemic differed between men and women.
Abbreviations: CI = confidence interval, COVID-19 = coronavirus disease 2019, DPP-4-I = dipeptidyl peptidase 4 inhibitors,
FY = fiscal year, αGI = alpha-glucosidase inhibitors, GLP-1RA = glucagon-like peptide 1 receptor agonists, JPY = Japanese yen,
OR = odds ratio, SD = standard deviation, SGLT2-I = sodium-glucose cotransporter-2 inhibitors, SU = sulfonyl urea.
Keywords: COVID-19, diabetes mellitus, disease management
This study was supported by JSPS KAKENHI, grant numbers 19H03879, 20K20674, and 21K17317.
This study was approved by the institutional review board of Fukuoka University Clinical Research and Ethics Center (U21-01-010). All methods were carried out in
accordance with the Declaration of Helsinki. Informed consent was waived according to the Ethical Guidelines for Medical and Health Research Involving Human Subjects
and ethics committee. Fukuoka University Clinical Research & Ethics Centre (FU-CREC) also granted the exemption. The members of FU-CREC involved in the decision
were as follows: Fumihito Hirai, Shinichiro Yasunaga, Teruaki Izaki, Ryoko Sakuma, Satoshi Imaizumi, Kohichiro Kawashima, Toshiyasu Ikuta, Maho Oishi, and Yuri Kusunose.
The authors have no conflicts of interest to disclose.
The datasets generated and/or analyzed during the current study are not publicly available owing to privacy policy of the data provider but are available from the
corresponding author on reasonable request.
Supplemental Digital Content is available for this article.
aDepartment of Preventive Medicine and Public Health, Faculty of Medicine, Fukuoka University, Fukuoka, Japan, bDepartment of Research Planning and Information
Management, Fukuoka Institute of Health and Environmental Sciences, Fukuoka, Japan, cDepartment of Industrial Economics, Faculty of Economics, Fukuoka University,
Fukuoka, Japan, dDepartment of Hygiene and Preventive Medicine, Iwate Medical University, Morioka, Iwate, Japan, eIwate Tohoku Medical Megabank Organization,
Iwate Medical University, Morioka Iwate, Japan, fDivision of Integrated Information for Pharmaceutical Sciences, Department of Clinical Pharmacy, Iwate Medical
University School of Pharmacy, Iwate, Japan, gDepartment of Hygiene and Public Health, Teikyo University School of Medicine, Tokyo, Japan, hKU Leuven Department
of Cardiovascular Sciences, University of Leuven, Leuven, Belgium, iTohoku Institute for Management of Blood Pressure, Sendai, Japan, jDepartment of Health Science,
Kyoto Prefectural University, Kyoto, Japan, kFaculty of Nursing And Medical Care, Keio University, Tokyo, Japan, lDepartment of Public Health and Health Systems,
Graduate School of Medicine, Nagoya University, Aichi, Japan, mResearch Institute of Strategy for Prevention, Tokyo, Japan.
*Correspondence: Toshiki Maeda, Department of Preventive Medicine and Public Health, Faculty of Medicine, Fukuoka University, 8-19-1 Nanakuma, Jonan-ku, Fukuoka
814-0180, Japan (e-mail: tmaeda@fukuoka-u.ac.jp).
Copyright © 2022 the Author(s). Published by Wolters Kluwer Health, Inc.
This is an open-access article distributed under the terms of the Creative Commons Attribution-Non Commercial License 4.0 (CCBY-NC), where it is permissible to
download, share, remix, transform, and buildup the work provided it is properly cited. The work cannot be used commercially without permission from the journal.
How to cite this article: Maeda T, Nishi T, Harada M, Tanno K, Nishiya N, Asayama K, Okuda N, Sugiyama D, Yatsuya H, Okayama A, Arima H. Influence of the COVID-19
pandemic on regular clinic visits and medication prescriptions among people with diabetes: retrospective cohort analysis of health care claims. Medicine. 2022;101:29(e29458).
Received: 24 November 2021 / Received in final form: 25 April 2022 / Accepted: 25 April 2022
http://dx.doi.org/10.1097/MD.0000000000029458
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Maeda et al. Medicine (2022) 101:29 Medicine
1. Introduction
Since the novel coronavirus disease 2019 (COVID-19) was rst
reported in Wuhan, China on 31 December, 2019,[1,2] the out-
break has rapidly spread all over the world. As of 29 September,
2021, the cumulative number of infected people stood at over
232 million, and nearly 5 million people have died worldwide.[3]
In Japan as of the same date in 2021, approximately 1.7 million
people have been infected and there have been approximately
17,000 deaths attributed to COVID-19.[4] In the United States,
excess deaths attributed to COVID-19 are estimated to be as
high as 72.4%.[5] As effective vaccines have become available,
the prevalence of infection in highly vaccinated areas appears
to be declining.[3] However, COVID-19 remains a serious threat
to people around the world and continues to disrupt the nor-
mal lives of people globally. The pandemic has also seriously
damaged the global economy, social solidarity, and people's
daily life. Owing to fear of catching and spreading the infec-
tion, people have stopped meeting with family and close friends,
traveling, and enjoying entertainment. As for health care, the
COVID-19 pandemic has had impacts on elective surgery[6,7]
and routine health care,[8] as well as medical care for COVID-19
itself. According to a report from the United States, one-third of
people surveyed said that they have avoided routine health care
during the COVID-19 pandemic.[8] However, such avoidance of
routine care could lead to poor control of diseases and espe-
cially chronic, lifestyle-related diseases like diabetes mellitus.
Diabetes is prevalent worldwide, with an estimated 422 mil-
lion people diagnosed with diabetes. The disease has directly
caused 1.5 million deaths.[9] Diabetes is also prevalent in Japan,
where approximately 19.7% of men and 10.8% of women
have diagnosed or suspected diabetes.[10] Diabetes causes seri-
ous health complications,[9,11] which lead to not only prema-
ture mortality[12,13] but also a heavy economic burden.[14,15] To
prevent the progression of diabetes, strict disease management
and regular follow-up are essential.[16–18] Indeed, several stud-
ies have reported that regular or a high frequency of health
care visits[17–19] is related to well-controlled diabetes. However,
the COVID-19 pandemic might have affected the behavior of
patients with diabetes, causing them to avoid routine care, espe-
cially during the rst wave of the pandemic when there was
insufcient information and vaccines were not yet available.
This is concerning because diabetes mellitus is a primary risk
factor for complications and death owing to COVID-19.[20–22]
Although it is understandable that these patients might post-
pone routine care, delays in care can lead to a poor or deterio-
rating health condition. However, few studies have investigated
the effects of the COVID-19 pandemic on the pattern of health
care visits among patients with diabetes during the period
before vaccines were available. Studies examining the behavior
patterns of these patients would contribute to preparation for a
similar crisis in the future.
The aim of this study was to investigate the effect of the
COVID-19 pandemic on patterns of clinic visits among people
with diabetes and to elucidate those factors related to these pat-
terns among patients with diabetes during the pandemic, espe-
cially during the period when vaccines were unavailable.
2. Methods
2.1. Study design and participants
This was a retrospective cohort study conducted from October
2017 to September 2020, as part of a project aiming to eluci-
date the effect of COVID-19 on patients with diabetes during
the rst wave of the pandemic in Japan. The main objectives
of that project were as follows: rstly, to investigate the effect
of the COVID-19 pandemic on patterns of clinic visits, and
nally to determine the starting point of delayed regular visits
and patients’ behavioral reactions to COVID-19. The present
study was related to the rst objective and another study[23]
involved the second objective. We used anonymized insurance
claim data from the Joint Health Insurance Society, which com-
prises several freight transportation service companies located
in Tokyo. The Japanese government has established univer-
sal health insurance coverage that is generally divided into
employee-based plans (Employee's Health Insurance), commu-
nity-based plans (National Health Insurance), and Late Elders’
Health Insurance.[24,25] The Health Insurance Society is a type
of employee-based plan. All insured are under 75 years old
because those aged more than 75 years old are mandated to
join the Late Elders’ Health Insurance, according to Japanese
law.[25] We used monthly data for clinic visits and prescriptions
because insurance claims are submitted each month. The Health
Insurance Society included 84,907 people in scal year (FY)
2019 (from April 1, 2019 to March 31, 2020). Patients with
diabetes were rst identied using International Classication
of Diseases, Tenth Revision (ICD-10) codes that correspond to
diabetes (E10,11,12,13 and 14) and/or prescriptions for dia-
betes medications (N = 3753). From among these patients, we
selected those fully enrolled in the health plan from FY 2017
until September 2020 (N = 3014). Consequently, we included
patients who completed regular visits and those who were pre-
scribed glucose-lowering medications every 1–3 months or more
than four times within 1 year from October 2017 to September
2018 as study participants (N = 1118) (Supplemental Digital
Content 1, http://links.lww.com/MD/G760). We divided the fol-
low-up into pre-pandemic period 1 (October 2018 to March
2019), pre-pandemic period 2 (April 2019 to September 2019),
pre-pandemic period 3 (October 2019 to March 2020), and the
pandemic period (April 2020 to September 2020). We dened
the pandemic period as starting from April 2020 because the
government declared a State of Emergency for some large
prefectures on 7 April 2020, which was then extended to all
of Japan on 16 April, 2020.[26] A patient was dened as hav-
ing delayed clinic visits/medication prescriptions during any
period when they failed to receive a medication prescription
for more than 3 consecutive months; the interval of prescrip-
tions in Japan is largely within 3 months.[18] We compared the
number of delayed clinic visits and/or medication prescriptions
during the pandemic period with those during the pre-pandemic
period. Details of the study design are depicted in Figure1. We
also changed the denition of delayed clinic visits/medication
prescriptions from more than 3 to 4 months, to check robust-
ness. COVID-19 vaccines became available in February 2021
in Japan;[27] the data used in the study were prior to the period
when vaccines against COVID-19 became available.
This study was approved by the institutional review board of
Fukuoka University Clinical Research and Ethics Center (U21-
01-010). Informed consent was waived according to the Ethical
Guidelines for Medical and Health Research Involving Human
Subjects.
2.2. Definition of variables
Medications included brand-name as well as generic medica-
tions. Medications were categorized into alpha-glucosidase
inhibitors (αGI), biguanide, dipeptidyl peptidase 4 inhibitors
(DPP-4-I), glinide, glucagon-like peptide 1 receptor agonists
(GLP-1RA), insulin, sodium-glucose cotransporter-2 inhibitors
(SGLT2-I), sulfonyl urea (SU), thiazolidine, and compounding
agents. Participants’ age was the age at the end of each FY.
We obtained standard monthly income data, used for the pay-
ment of insurance premiums in the nal year because this was
reported to have an association with clinic visits among patients
with diabetes.[18] We used these data, unchanged, throughout the
study period because of limited data; income was considered
not to have changed dramatically. Health insurance can apply to
employees as well as dependent family members. We considered
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Maeda et al. Medicine (2022) 101:29 www.md-journal.com
that each could react differently to a threat such as a pandemic;
therefore, we created a variable of qualication (employees and
dependents) and used them for adjustment.
2.3. Statistical analysis
We performed chi-square tests for categorical variables. The
data had a person-period data structure and we analyzed out-
come (T) using explanatory variables of the previous period (T
− 1) to maintain temporality.[28] For instance, when we analyzed
delayed visits in pre-pandemic period 3, we used the explana-
tory variables of pre-pandemic period 2. We used a multilevel
logistic regression model to examine the effect of the COVID-19
pandemic on regular visits by patients with diabetes using per-
son-period data, with four periods per person. Variables used
for adjustment were age, sex, income, qualication, biannual
variation (October to March vs April to September), and med-
ications (αGI, biguanide, DPP-4-I, glinide, GLP-1RA, insulin,
SGLT2-I, SU, thiazolidine, and compounding agents).
We investigated the relationships between the impact of
the COVID-19 pandemic on delayed clinic visits/medication
prescriptions and age, sex, income, qualication (employee,
dependent), biannual variation (October to March, April
to September), and medication, using interaction terms. We
treated age and income as continuous variables in the main
analysis. To examine interaction, we treated continuous
variables as binary at the mean value (age: <57, ≥57 years;
income: <370, ≥370 (×103) JPY). We then conducted further
analysis to check robustness by: rstly, changing the deni-
tion of delayed clinic visits/medication prescriptions from
an interval of more than 3 months to one of 4 months; sec-
ondly, replacing 160 missing data points for standard monthly
income with mean imputation; and nally, performing panel
data analysis with a random-effects model rather than mul-
tilevel analysis. Furthermore, the rate of delayed visits was
expected to be increased in accordance with a patient's diabe-
tes history. Therefore, we used difference-in-differences anal-
ysis under the hypothesis that the trend of delayed visits was
constant. We compared delayed visits between pre-pandemic
period 3 (October 2019 to March 2020) and the pandemic
period (April 2020 to September 2020) (“exposure arm”)
with delayed visits between pre-pandemic period 1 (October
2018 to March 2019) and pre-pandemic period 2 (April 2019
to September 2019) (“control arm”). We used a multilevel lin-
ear model with adjustment for age, sex, income, qualication,
and medications. Stata release 16 (StataCorp LLC, College
Station, TX) was used for the statistical analyses. All reported
p values were two-tailed, and the level of signicance was set
at P < .05.
3. Results
3.1. Participant characteristics
We identied 1118 study participants; the characteristics of par-
ticipants are presented in Table1. Participants’ mean age (stan-
dard deviation; SD) in 2018 was 56.2 (8.6) years, and 77.7%
were men (n = 869). Employees visited a health care facility more
often than their dependents. Mean standard monthly income
×103) JPY was 370.0 (SD 18.7). Table2 shows the number of
prescribed medications per period. Biguanide, DPP-4, glinide,
insulin, SGLT2-I, SU, and compounding agents were more fre-
quently used than other medicines. The frequency of prescrip-
tion for each medication did not vary signicantly through the
study period, except for DPP-4-I.
3.2. Effect of the COVID-19 pandemic on delayed clinic
visits/medication prescriptions
Figure 2 shows the rate of delayed clinic visits/medication pre-
scriptions in each period. The number of delayed clinic visits/
medication prescriptions among the total 1118 participants was
52 in pre-pandemic period 1, 63 in pre-pandemic period 2, 73
in pre-pandemic period 3, and 125 during the pandemic period.
There was a signicant association between delayed clinic visits/
medication prescriptions and period (P < 0.001). Table3 shows
the results of univariate and multivariate analyses. The number
(percentage) of delayed clinic visits/medication prescriptions in the
pre-pandemic and pandemic periods was 188/3354 (5.6%) and
125/1118 (11.2%), respectively. There was a signicant increase in
delayed clinic visits/medication prescriptions during the pandemic
period compared with the pre-pandemic period (adjusted odds
ratio [OR] 3.68, 95% condence interval [CI] 2.24 to 6.04; P <
.001), even when controlling for sex, age, qualication (employee
or dependent), standard monthly income, biannual variation
(October to March, April to September), and medication.
Figure 1. Study design. Study participants were individuals enrolled in the health insurance throughout the follow-up period. We counted the number of discon-
tinuities in each period. We also adjusted for biannual variation (from October and March vs. from April to September) in multivariable analysis.
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Maeda et al. Medicine (2022) 101:29 Medicine
3.3. Interaction between variables and the COVID-19
pandemic
We examined the interaction between the period of the COVID-
19 pandemic and each variable (Fig. 3). Women had signi-
cantly fewer clinic visits during the COVID-19 pandemic than
men (men: adjusted OR 2.65, 95% CI 1.55 to 4.52; women:
adjusted OR 19.31, 95% CI 5.24 to 71.15; P = .013 for inter-
action). Furthermore, older people and dependents tended to
delay routine care visits, although this was not statistically sig-
nicant. There was no signicant interaction for income or for
each medication.
3.4. Analysis of robustness
We also changed the interval of irregular medication from more
than 3 months to 4 months for further analysis and obtained
similar results. The number (percentage) of gaps in each
period was 28 (2.5%) in pre-pandemic period 1, 49 (4.4%)
in pre-pandemic period 2, 50 (4.5%) in pre-pandemic period
3, and 102 (9.1%) during the pandemic period (P < .001);
the adjusted OR for irregular visits in the pandemic period
was 4.95 (95% CI 2.54 to 9.66; P < .001). When we imputed
the mean value of income (369.876 [×103] JPY) for missing
data (adjusted OR 3.79, 95% CI 2.32 to 6.18; P < .001), the
results did not change. The results of panel data analysis using
a random-effects model showed an adjusted OR for irregular
visits in the pandemic period of 3.45 (95% CI 2.21 to 5.40;
P < .001), which was similar to the main results. Regarding
the results for difference in differences, there was a signicant
difference between the control arm and the exposure arm (β
.035, 95% CI .012 to .057; P=.002) (Fig.4); this result was
consistent with our other ndings.
4. Discussion
In this study, we revealed that the COVID-19 pandemic had a
negative impact on regular health care visits and medication
prescriptions among people with diabetes during the rst wave
of the pandemic in Japan. We also found that the response to the
pandemic was signicantly different by sex.
Diabetes mellitus is a main risk factor for complications and
death owing to COVID-19;[20–22] thus, it has been proposed that
people with diabetes have avoided routine health care for fear of
severe COVID-19 infection, especially during the period before
vaccines were available. Some studies have reported decreased
diagnosis rates among individuals with stroke,[29] acute heart
failure,[30] and pulmonary embolism[31] during the COVID-19
pandemic, probably because of a decline in consultations.[32]
However, those reports are mainly related to acute care.
Furthermore, those studies have only compared the diagnosis
rates between the periods before and during the COVID-19 pan-
demic. In contrast, we dened delayed visits and longitudinally
followed individuals with chronic diseases such as diabetes,
consequently demonstrating that routine health care declined
among people with diabetes in Japan owing to the COVID-19
pandemic.
Some studies have reported that irregular clinic visits have
a negative impact on several health outcomes. Research
Table 1
Participant characteristics at the start of the study period
(October 2018).
Variables N or mean
Sex
Men, n (%) 869 (77.7)
Women, n (%) 249 (22.3)
Age (years), mean (SD) 56.2 (8.6)
Qualification
Employee, n (%) 934 (83.5)
Dependent, n (%) 184 (16.5)
Standard monthly income, JPY (×103), mean (SD) 370.0 (18.7)
JPY = Japanese yen, SD = standard deviation.
Table 2
Prescribed medications in each period.
Pre-pandemic period 1
(Oct 2018 to Mar 2019)
Pre-pandemic period 2
(Apr 2019 to Sep 2019)
Pre-pandemic period 3
(Oct 2019 to Mar 2020)
Pandemic period
(Apr 2020 to Sep 2020) P*
aGI, n (%) 169 (15.1) 160 (14.3) 167 (14.9) 156 (14.0) .852
Biguanide, n (%) 489 (43.7) 494 (44.2) 493 (44.1) 511 (45.7) .794
DPP-4-I, n (%) 651 (58.2) 616 (55.1) 576 (51.5) 551 (49.3) <.001
Glinide, n (%) 408 (4.4) 52 (4.7) 54 (4.8) 52 (4.7) .968
GLP-1RA, n (%) 57 (5.1) 59 (5.3) 66 (5.9) 68 (6.1) .696
Insulin, n (%) 183 (16.4) 186 (16.6) 194 (17.4) 193 (17.3) .908
SGLT2-I, n (%) 331 (29.6) 344 (30.8) 355 (31.8) 377 (33.7) .193
SU, n (%) 311 (27.8) 314 (28.1) 303 (27.1) 288 (25.8) .607
Thiazolidine, n (%) 104 (9.3) 103 (9.2) 105 (9.4) 108 (9.7) .986
Compounding agents, n (%) 425 (38.0) 422 (37.8) 402 (36.0) 394 (35.2) .450
αGI = alpha-glucosidase inhibitor, DPP-4-I = dipeptidyl peptidase IV inhibitor, GLP-1RA = glucagon-like peptide 1 receptor agonist, SGLT2-I = sodium–glucose cotransporter-2 inhibitor, SU = sulfonyl urea.
*Pearson's chi-square test.
Figure 2. Rate of delayed clinic visits/medication prescriptions over the study
period. The denominator is the population at risk (N = 1118). The numerator is
the number of cases of irregular medication prescriptions (P < .001).
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Maeda et al. Medicine (2022) 101:29 www.md-journal.com
investigating the impact of irregular visits on diabetes out-
comes using propensity score matching suggests that people
with irregular health care visits tend to have poor glycemic con-
trol[18] and a higher risk of hospitalization for ischemic heart
disease and stroke.[17] Other research has revealed a strong
dose–response relationship between encounter frequency and
metabolic outcomes, including glycemic control.[19] These nd-
ings could be because frequent visits make it possible to adjust
the medication dose and provide lifestyle coaching or other
health education, which leads to better diabetes control.[19]
More frequent health care encounters are also associated with
better medication adherence.[33,34] This study revealed that
delayed clinic visits/medication prescriptions occurred during
the COVID-19 pandemic in Japan. Such behavior could lead
to poorer control of diabetes; however, we did not investi-
gate delayed clinic visits/medication prescriptions in terms
of clinical outcome. Further research is needed to investigate
the effects of the COVID-19 pandemic on health outcomes in
patients with diabetes.
Our study also revealed that older people tended to delay rou-
tine care visits. Older age is a risk factor for severe COVID-19
infection,[20] so it is not surprising that older people have tended
to avoid routine care for fear of infection. Interestingly, the
response to the COVID-19 pandemic differed between men and
women. Although the reason for this is unknown, one underly-
ing mechanism could be sex differences in response to a threat.
It has been reported that women are generally more risk-averse
than men,[35,36] although these results are mainly derived from
studies addressing nancial risk. Another reason is that the data
used in this study were claims data derived from an employ-
ee-based plan. Nearly all the men in this study were employees
(99%) and only 30% of women were employees; the remaining
70% of women were classied as dependents. Thus, workers
might be more strongly encouraged by occupational physicians
to attend regular health care visits and receive their medica-
tion. Dependents, such as homemakers, might have postponed
visits to a health care facility during the COVID-19 pandemic.
However, when we analyzed only employees, there was still a
signicant interaction between response to the pandemic and
patient sex (P = .037 for interaction; data not shown). Further
research is needed to investigate sex differences in the response
to threats such as a pandemic.
Some studies have reported that telehealth is effective for
chronic disease management, such as diabetes management.[37–39]
The importance of telehealth was especially emphasized during
the COVID-19 pandemic.[40] There was a sharp increase in
Table 3
Effects of the coronavirus disease 2019 pandemic on irregular medication and visits.
Cases/total (%) Crude OR (95% CI) P Adjusted OR (95% CI) P
Period
Pre-pandemic period 188/3354 (5.6) 1.00 (Reference) 1.00 (Reference)
Pandemic period 125/1118 (11.2) 4.64 (2.96 to 7.28) <.001 3.68 (2.24 to 6.04) <.001
Multilevel logistic regression model was used for person-period data, with four periods per person. Variables used for adjustment included sex, age, qualification, standard monthly income, biannual
variation, and medication.
CI = confidence interval, OR = odds ratio.
Figure 3. Interaction between COVID-19 pandemic and each variable. Age and income were categorized at the mean value. The X-axis is shown on a loga-
rithmic scale. Glinide and glucagon-like peptide 1 receptor agonist not shown in the figure. αGI = alpha-glucosidase inhibitor, DPP-4-I = dipeptidyl peptidase
IV inhibitor, SGLT2-I = sodium-glucose cotransporter-2 inhibitor, SU = sulfonylurea, compounding: compounding agents. The unit of income was (×103) JPY.
6
Maeda et al. Medicine (2022) 101:29 Medicine
the use of telehealth during the COVID-19 pandemic in many
countries compared with before the pandemic.[41–44] A similar
trend was also observed in Japan, although the use of online
consultation was limited to fewer than 10,000 at the peak in
May 2020; that number has decreased and plateaued at approx-
imately 5000–7000 during the following period in Japan as a
whole.[45,46] The reason for the limited use of telehealth might
be owing to regulations,[44,47] reimbursement,[44,47,48] patients’
or provider's literacy or preference, as well as the availability
of technology.[44,48,49] Evidence regarding telehealth is scarce in
Japan; therefore, further research to evaluate the effect of tele-
health is essential.
Although this study revealed the effect of the COVID-19
pandemic on delayed clinic visits/medication prescriptions for
diabetes in practice, there are some limitations. We did not dif-
ferentiate type 1 and type 2 diabetes in the analysis, which is an
important limitation. The reason why we did not differentiate
the type of diabetes is that it is not necessary that a provider
differentiate type 1 and type 2 diabetes for reimbursement. In
addition, disease coding is not sufcient for differentiating type
1 and type 2 diabetes because of its low sensitivity;[50] thus, there
could be some cases in which it is unknown whether the patient
had type 1 or type 2 diabetes. As a side note, we excluded diag-
noses of type 1 diabetes (ICD10:E10) at least once (N = 66), and
a similar result was obtained (adjusted OR 4.11, 95% CI 2.43
to 6.95, P < .001). However, differentiation between type 1 and
type 2 diabetes remained somewhat incomplete; further investi-
gations are needed. The data were obtained prior to the period
when vaccines against COVID-19 became available; therefore,
the ndings of this study are not necessarily relevant to the sit-
uation in the post-vaccine era. Study participants were limited
to a single health insurance society for a specic transportation
industry. Therefore, the results may not be generalizable to other
populations because our participants might be more careful
about their health so as to be able to drive safely. Additionally,
there was a notable imbalance with respect to participants’ sex,
which could also be owing to characteristics of the transporta-
tion industry. We could not use critical inuential factors such
as physiologic data (for example, body mass index, blood sugar,
and glycated hemoglobin), level of education, and period of
treatment because these data were limited. Although we dened
delayed clinic visits/medication prescriptions as patients who
failed to receive a medication prescription for more than 3 con-
secutive months, it would be more accurate to use the discrep-
ancy between prescription intervals and the time between visits;
however, this information was unavailable.
5. Conclusion
We revealed a negative impact on regular health care visits and
medication prescriptions among people with diabetes during the
rst wave of the COVID-19 pandemic in Japan. The response
to the COVID-19 pandemic differed between men and women.
Acknowledgments
We are grateful to Tokyo Kamotsu-Unso Kenko-Hoken-Kumiai
for providing us with the valuable data. We thank Analisa Avila,
MPH, ELS, of Edanz (https://jp.edanz.com/ac) for editing a
draft of this manuscript.
Author contributions
Akira Okayama contributed to acquisition of the data. Kei
Asayama, Nagako Okuda, Akira Okayama, Daisuke Sugiyama,
Hiroshi Yatsuya, Akira Okayama, Hisatomi Arima contrib-
uted to conception of the work. Toshiki Maeda, Takumi Nishi,
Masataka Harada contributed to analysis and interpretation
of the data and drafting the manuscript. All authors critically
revised the draft for important intellectual content, approved
the nal version of the manuscript to be published, and agree
to be accountable for all aspects of the work in ensuring that
questions related to the accuracy or integrity of any part of the
work are appropriately investigated and resolved.
Conceptualization: Akira Okayama, Daisuke Sugiyama, Hiroshi
Yatsuya, Kei Asayama, Nagako Okuda
Data curation: Kozo Tanno, Masataka Harada, Naoyuki
Nishiya, Takumi Nishi
Figure 4. The results for difference in differences analysis. Comparisons were made between pre-pandemic period 3 (October 2019 to March 2020) and the
pandemic period (April 2020 to September 2020) (“exposure arm”) with delayed visits between pre-pandemic period 1 (October 2018 to March 2019) and
pre-pandemic period 2 (April 2019 to September 2019) (“control arm”). Variables used for adjustment were age, sex, income, qualification, and medications.
7
Maeda et al. Medicine (2022) 101:29 www.md-journal.com
Formal analysis: Masataka Harada, Takumi Nishi, Toshiki
Maeda
Funding acquisition: Akira Okayama
Investigation: Toshiki Maeda
Methodology: Akira Okayama
Project administration: Akira Okayama, Toshiki Maeda
Resources: Kozo Tanno, Naoyuki Nishiya
Supervision: Akira Okayama, Hisatomi Arima
Writing – original draft: Toshiki Maeda
Writing – review & editing: Akira Okayama, Daisuke Sugiyama,
Hiroshi Yatsuya, Hisatomi Arima, Kei Asayama, Kozo
Tanno, Masataka Harada, Nagako Okuda, Naoyuki Nishiya,
Takumi Nishi
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