\documentclass[10pt]{article}

\usepackage{fullpage}
\usepackage{setspace}
\usepackage{parskip}
\usepackage{titlesec}
\usepackage[section]{placeins}
\usepackage{xcolor}
\usepackage{breakcites}
\usepackage{lineno}
\usepackage{hyphenat}





\PassOptionsToPackage{hyphens}{url}
\usepackage[colorlinks = true,
            linkcolor = blue,
            urlcolor  = blue,
            citecolor = blue,
            anchorcolor = blue]{hyperref}
\usepackage{etoolbox}
\makeatletter
\patchcmd\@combinedblfloats{\box\@outputbox}{\unvbox\@outputbox}{}{%
  \errmessage{\noexpand\@combinedblfloats could not be patched}%
}%
\makeatother


\usepackage{natbib}




\renewenvironment{abstract}
  {{\bfseries\noindent{\abstractname}\par\nobreak}\footnotesize}
  {\bigskip}

\titlespacing{\section}{0pt}{*3}{*1}
\titlespacing{\subsection}{0pt}{*2}{*0.5}
\titlespacing{\subsubsection}{0pt}{*1.5}{0pt}


\usepackage{authblk}


\usepackage{graphicx}
\usepackage[space]{grffile}
\usepackage{latexsym}
\usepackage{textcomp}
\usepackage{longtable}
\usepackage{tabulary}
\usepackage{booktabs,array,multirow}
\usepackage{amsfonts,amsmath,amssymb}
\providecommand\citet{\cite}
\providecommand\citep{\cite}
\providecommand\citealt{\cite}
% You can conditionalize code for latexml or normal latex using this.
\newif\iflatexml\latexmlfalse
\providecommand{\tightlist}{\setlength{\itemsep}{0pt}\setlength{\parskip}{0pt}}%

\AtBeginDocument{\DeclareGraphicsExtensions{.pdf,.PDF,.eps,.EPS,.png,.PNG,.tif,.TIF,.jpg,.JPG,.jpeg,.JPEG}}

\usepackage[utf8]{inputenc}
\usepackage[english]{babel}
\usepackage{CJKutf8}



\usepackage{float}








\begin{document}

\title{Evaluation of medication adherence in hypertension, dyslipidemia, and
diabetes using health insurance claims: a population-based cohort study
in Japan}



\author[1]{Minako Matsumoto}%
\author[1]{Sei Harada}%
\author[1]{Harufumi Ikuta}%
\author[1]{Miho Iida}%
\author[1]{Suzuka Kato}%
\author[1]{Mizuki Sata}%
\author[1]{Shibuki Takuma}%
\author[1]{Yoshiki Ishibashi}%
\author[1]{Naoko Miyagawa}%
\author[1]{Moeko Hisamatsu}%
\author[1]{Aya Hirata}%
\author[1]{Kazuyo Kuwabara}%
\author[2]{Ayano Takeuchi}%
\author[3]{Daisuke Sugiyama}%
\author[1]{Sayo Suzuki}%
\author[1]{Tomonori Nakamura}%
\author[1]{Tomonori Okamura}%
\author[1]{Toru Takebayashi}%
\affil[1]{Keio Gijuku Daigaku}%
\affil[2]{Chuo Daigaku}%
\affil[3]{Keio Gijuku Daigaku Kango Iryo Gakubu Daigakuin Kenko Management Kenkyuka}%


\vspace{-1em}



  
  \date{June 17, 2024}


\begingroup
\let\center\flushleft
\let\endcenter\endflushleft
\maketitle
\endgroup





\selectlanguage{english}
\begin{abstract}
\textbf{Purpose:} Hypertension (HT), dyslipidemia (DL), and diabetes
mellitus (DM) are major risk factors for cardiovascular diseases.
Despite the wide availability of medications to reduce this risk, poor
adherence to medications remains an issue. The aim of this study is to
evaluate medication adherence of prevalent users in these disease
medications (HT, DL, DM) using claims data. Factors associated with
non-adherence were also examined. \textbf{Methods:} Of 7,538
participants of the Tsuruoka Metabolomics Cohort Study, 3,737(HT:
2,724, DL: 2,137, DM: 665) were identified as prevalent users of these
disease medications. Information on lifestyle was collected through a
questionnaire. Adherence was assessed by a proportion of days covered
(PDC) and participants with PDC [?] 0.8 were defined as adherent.
Predictors of non-adherence were determined by performing multivariable
logistic regression. \textbf{Results:} Medication adherence differed by
medication groups. Those with DL-only showed the lowest adherence
(79.6\%), followed by those with DM-only (81.7\%) and those with HT-only
(89.8\%). The associations were observed especially among those with no
comorbidities. Determinants associated with non-adherence in each
medication group were skipping breakfast and poor understanding of
medications among those with HT, females, having comorbidities, having
the medical history of stroke, and drinking habit among those with DL,
and skipping breakfast among those with DM. \textbf{Conclusion:} While
participants showed high medication adherence, differences were observed
across medication groups. The identified predictors of non-adherence
could help target those in need of adherence support.%
\end{abstract}%



\sloppy


Evaluation of medication adherence in hypertension, dyslipidemia, and
diabetes using health insurance claims: a population-based cohort study
in Japan

\textbf{Running title:} Evaluation of medication adherence in
hypertension, dyslipidemia, and diabetes (9/50)

\emph{Minako Matsumoto} \textsuperscript{1}, Sei
Harada\textsuperscript{1}, Harufumi Ikuta\textsuperscript{2}, Miho
Iida\textsuperscript{1}, Suzuka Kato\textsuperscript{1}, Mizuki
Sata\textsuperscript{1}, Takuma Shibuki\textsuperscript{1}, Yoshiki
Ishibashi\textsuperscript{1}, Naoko Miyagawa\textsuperscript{1}, Moeko
Hisamatsu\textsuperscript{2}, Aya Hirata\textsuperscript{1}, Kazuyo
Kuwabara\textsuperscript{1}, Ayano Takeuchi\textsuperscript{3}, Daisuke
Sugiyama\textsuperscript{4}, Sayo Suzuki\textsuperscript{2}, Tomonori
Nakamura\textsuperscript{2}, Tomonori Okamura\textsuperscript{1}, Toru
Takebayashi\textsuperscript{1}

\textsuperscript{1} Department of Preventive Medicine, Public Health,
Keio University School of Medicine, Tokyo, Japan

\textsuperscript{2} Division of Pharmaceutical Care Sciences, Keio
University Faculty of Pharmacy, Tokyo, Japan

\textsuperscript{3} Faculty of Science and Engineering, Chuo University,
Tokyo, Japan

\textsuperscript{4} Faculty of Nursing and Medical Care and Graduate
School of Health Management, Keio University, Fujisawa, Japan

Corresponding author: Toru Takebayashi

Department of Preventive Medicine and Public Health, Keio University
School of Medicine,

35 Shinanomachi, Shinjuku-ku, Tokyo 160-8582, Japan.

\emph{ttakebayashi@a3.keio.jp}

ORCID: 0000-0002-8268-8026

\textbf{Funding:} This study was supported in part by research funds
from the Yamagata Prefectural Government and the City of Tsuruoka and by
JSPS KAKENHI Grant Number JP16H06277, JP18H06377, JP19K21458

\textbf{Conflict of interest} : None declared

\textbf{Ethics statement} : The study was approved by the Medical Ethics
Committee of the School of Medicine, Keio University, Tokyo, Japan
(Approval No 20110264).

\textbf{Patient consent statement} : All individual participants in this
study provided written informed consent.

\textbf{Data availability statement} : The datasets generated and/or
analyzed during the current study are not publicly available due to a
license agreement with Tsuruoka City but are available from the
corresponding author on reasonable request.

\textbf{Acknowledgements:} The authors would like to acknowledge the
contribution of Tsuruoka City for providing access to health insurance
claims and Yoshinori Takizawa as an advisor on the use of claims data.

\textbf{Keywords:} adherence, pharmacoepidemiology, health insurance
claims data, cohort-study

\textbf{Word Count:} 2,961/3,000\textbf{Abstract Word Count:} 232/250

\textbf{Key points Word Count:} 99/100

\textbf{Plain Language Summary Word Count:} 199/200\textbf{Tables:} 3;
\textbf{Figures:} 2; \textbf{Supplemental Materials} : 6

\textbf{Key points}

\begin{itemize}
\tightlist
\item
  Medication adherence and factors associated with non-adherence among
  prevalent users were evaluated across multiple diseases in a
  population-based cohort study in Japan.
\item
  Adherence differed by medication groups (hypertension, dyslipidemia,
  and diabetes). Those with dyslipidemia-only showed the lowest
  adherence, followed by those with diabetes-only and those with
  hypertension-only. The associations were observed especially among
  those with no comorbidities.
\item
  Determinants associated with non-adherence were skipping breakfast and
  poor understanding of medications among those with hypertension,
  females, having comorbidities, having the medical history of stroke,
  and drinking habit among those with dyslipidemia, and skipping
  breakfast among those with diabetes.
\end{itemize}

\textbf{Plain Language Summary}

Hypertension, dyslipidemia, and diabetes are risk factors for
cardiovascular diseases (CVD). Despite the wide availability of
medications to reduce this risk, poor adherence to medications remains
an issue. Our aim was to evaluate medication adherence of prevalent
users in these disease medications using claims data. Factors associated
with non-adherence were also examined. Of 7,538 participants of the
Tsuruoka Metabolomics Cohort Study, 3,737 were identified as prevalent
users. Information on lifestyle was collected through a questionnaire.
Adherence was assessed by a proportion of days covered (PDC) and
participants with PDC [?] 0.8 were defined as adherent. Adherence differed
by medication groups (hypertension, dyslipidemia, and diabetes). Those
with dyslipidemia-only showed the lowest adherence (79.6\%), followed by
those with diabetes-only (81.7\%) and those with hypertension-only
(89.8\%). The associations were observed especially among those with no
comorbidities. Predictors of non-adherence in each medication group were
determined by performing multivariable logistic regression. Determinants
associated with non-adherence were skipping breakfast and poor
understanding of medications among those with hypertension, females,
having comorbidities, having the medical history of stroke, and drinking
habit among those with dyslipidemia, and skipping breakfast among those
with diabetes. The identified predictors of non-adherence could help
target those in need of adherence support.

\begin{CJK}{UTF8}{gbsn}\textbf{Abstract　}

\end{CJK}\selectlanguage{english}\textbf{Purpose:}

Hypertension (HT), dyslipidemia (DL), and diabetes mellitus (DM) are
major risk factors for cardiovascular diseases. Despite the wide
availability of medications to reduce this risk, poor adherence to
medications remains an issue. The aim of this study is to evaluate
medication adherence of prevalent users in these disease medications
(HT, DL, DM) using claims data. Factors associated with non-adherence
were also examined.

\textbf{Methods:}

Of 7,538 participants of the Tsuruoka Metabolomics Cohort Study,
3,737(HT: 2,724, DL: 2,137, DM: 665) were identified as prevalent users
of these disease medications. Information on lifestyle was collected
through a questionnaire. Adherence was assessed by a proportion of days
covered (PDC) and participants with PDC [?] 0.8 were defined as adherent.
Predictors of non-adherence were determined by performing multivariable
logistic regression.

\textbf{Results:}

Medication adherence differed by medication groups. Those with DL-only
showed the lowest adherence (79.6\%), followed by those with DM-only
(81.7\%) and those with HT-only (89.8\%). The associations were observed
especially among those with no comorbidities. Determinants associated
with non-adherence in each medication group were skipping breakfast and
poor understanding of medications among those with HT, females, having
comorbidities, having the medical history of stroke, and drinking habit
among those with DL, and skipping breakfast among those with DM.

\textbf{Conclusion:}

While participants showed high medication adherence, differences were
observed across medication groups. The identified predictors of
non-adherence could help target those in need of adherence support.

\textbf{I} \textbf{ntroduction}

Hypertension (HT), dyslipidemia (DL), and diabetes mellitus (DM) are
major risk factors for cardiovascular diseases (CVD)
\textsuperscript{1--3}, and their coexistence is often observed in
clinical practice\textsuperscript{4}. Although medication, in addition
to diet and exercise therapy, is crucial for strict control of these
risk factors, some patients show poor adherence to medications
\textsuperscript{5--7}. Thus, to guarantee the maximum efficacy of
medications, it is important to understand medication adherence and the
factors associated with non-adherence.

Although many adherence studies have been conducted, few have
investigated medication adherence in a population with multiple
diseases\textsuperscript{8,9} and of prevalent users. A study which
compares adherence in various diseases in the same study population is
required. In pharmacoepidemiology, many studies using claims data have
determined medication adherence with new user design which only includes
patients with newly prescribed medications. The new user design reduces
confounding by indication, however, it is not representative of
real-world medical care. In more realistic settings such as
population-based cohort study, prevalent user design will have greater
applicability. Therefore, using prevalent user design in our study is
very meaningful to examine multiple medications adherence which is the
leading risk factor for CVD. Furthermore, while previous studies have
reported several factors associated with adherence, such as
social-related, disease-related, therapy-related, and
participant-related factors, few studies have evaluated these factors
together \textsuperscript{10}.

In our previous study, we conducted the validity assessment of
medication use in multiple diseases by linking our population-based
cohort data with claims data \textsuperscript{11}. We propose that our
database which is likely to be broadly applicable to more realistic
environments is a powerful tool for studying medication adherence. In
the present study, we assessed medication adherence of prevalent users
in our population-based Tsuruoka Metabolomics Cohort Study(TMCS)using
health insurance claims. Disease-related, participant-related, and
therapy-related factors associated with non-adherence were also
examined.

\textbf{2. Material and methods}

\emph{Study population}

This study involved participants of the TMCS, an ongoing prospective
cohort study conducted in Tsuruoka City, Yamagata Prefecture, Japan. A
total of 11,002 participants aged 35-74 years were recruited during the
baseline period (April-March 2012 to 2015). Details have been reported
previously \textsuperscript{12--16}. Briefly, the TMCS has two different
populations: a resident population(RP) and a worksite population(WP).
The RP includes participants attending a municipal health check-up, and
the WP includes those from an occupational health check-up. In this
study, 7,538 individuals from the resident cohort who participated in
the follow-up survey from April 2016 to March 2020 were selected for
analysis due to the non-availability of claims data on WP at this time.

Figure 1 shows a study design. We defined the period of 90 days
following the day participants joined the survey as the base line
period. The day of the first dispensing claims for designated
medications during the base line period was defined as the index date.
The preceding 180 days from the index date were defined as the look back
period and 365 days following the index date were defined as the follow
up period. Figure 2 shows a flow of the selection and exclusion
criteria. Prevalent users were defined as those who had dispensing
claims for designated medications during the look back period and the
base line period. To ensure that participants were taking the
medications for HT, DL, and/or DM, only those with diagnoses of these
diseases were included. Those who did not complete the questionnaire and
whose claims data was not fully available were excluded.

All data were recorded using anonymized participant linkers. The study
was approved by the Medical Ethics Committee of the School of Medicine,
Keio University, Tokyo, Japan (Approval No 20110264). All individual
participants in this study provided written informed consent.

\emph{Diagnosis and medication information from health insurance claims}

Diagnosis and medication information was extracted from claims data
provided by Tsuruoka City for the November 2014 to September 2021
period. Diagnoses of diseases were determined according to the
International Classification of Diseases, 10th revision (ICD-10) codes:
HT (I10), DL (E78), and DM (E11, E14). Medications were classified
according to the World Health Organization's Anatomical Therapeutic
Chemical (ATC) classification system \textsuperscript{17}. Medications
with no ATC codes offered in Japan were assigned the closest minimum
codes based on their medication category (Supplement Table
1-3)\textsuperscript{11,18,19}.

\emph{Outcomes: Proportion of days covered (PDC)}

Medication adherence was evaluated using the PDC, which was calculated
by dividing the total number of days covered with medications by the
number of days in the follow-up period \textsuperscript{20}. If
participants took two or three medication groups, we calculated the PDC
for each individual medication group and then divided the sum of each
individual PDC by the total number of medication groups. The existence
of concomitant medications and medication switching were also considered
in this study. The days covered by the drugs were counted without
duplication. Participants with PDC \textless{} 0.8 were categorized as
non-adherent, and those with PDC [?] 0.8 as adherent \textsuperscript{21}.
A PDC threshold near 0.8 is reportedly needed to prevent or reduce the
risk of cardiovascular-related mortality \textsuperscript{22}.

\emph{Definition of potential predictors of medication adherence}

Information on socio-demographics, lifestyle, and medical histories was
collected through a standardized self-administered
questionnaire\textsuperscript{23,24}, while other measurements (height,
weight, blood pressure, and laboratory data) were collected during the
health check-up. Participants who had graduated from a technical
college, junior college, university or graduate school were classified
as having \textgreater{} 12 years of education. Current smokers were
defined as those who currently smoked cigarettes and current drinkers as
those who consumed alcohol [?] 20 g every day. Those who maintained a
habit of performing moderate-intensity exercise for at least 30 minutes
[?] 2 times per week and kept the habit for [?] 1 year were defined as
regular exercisers. Sleep status was classified as `good sleep quality'
if a participant answered `Yes' to the question, `Do you sleep well and
enough? '. Participants who habitually skipped breakfast [?] 3 times per
week were classified as the skipping breakfast group. Eating speed was
classified as `fast eaters' if a participant answered `Quicker' to the
question, `Is your eating speed quicker than others? '. Those who
indicated they had dinner two hours before going to bed [?] 3 times weekly
were categorized into the late dinner group. In terms of attitude toward
lifestyle improvement, participants who answered `I have no intention of
improving my lifestyle' to the question, `Do you want to try to improve
your lifestyle such as through exercise or dietary habits?' were
categorized into the unmotivated group. In contrast, participants who
answered `yes' or `I have already started improving my lifestyle' were
categorized into the motivated group. If participants experienced
cerebral hemorrhage or brain infarction, they were defined as those with
medical histories of stroke. If participants experienced angina pectoris
or myocardial infarction, they were defined as those with heart disease.
Participants whose medications were confirmed by claims data and who
answered the applicable questions on medical histories in the
questionnaire were defined as the understanding of medications group.
Those with HT medications and their systolic blood pressure [?] 140 mm Hg
or diastolic blood pressure [?] 90 mm Hg were defined as participants with
poorly controlled HT \textsuperscript{25}, those with DL medications and
their low-density lipoprotein cholesterol [?] 140 mg/dL or high-density
lipoprotein cholesterol \textless{} 40 mg/dL or triglyceride [?] 150 mg/dL
were defined as participants with poorly controlled
DL\textsuperscript{26} , and those with DM medications and their fasting
blood glucose [?] 126 mg/dL were defined as participants with poorly
controlled DM \textsuperscript{27} .

\emph{Statistical methods}

We analyzed 3,737 prevalent users of HT, DL, and/or DM medications
(Group1). Differences between non-adherent and adherent were determined
by using Student's t-test for continuous variables and Chi-square test
for categorized variables. To examine factors associated with
non-adherent in each medication group, multivariable logistic regression
analysis was performed adjusting sex, age, comorbidities, medical
history of stroke, education years, smoking status, drinking status,
skipping breakfast, motivation to improving lifestyle, and understanding
of medications. Adjusted odds ratios (ORs) with 95\% confidence
intervals (CIs) were calculated. Furthermore, subgroup analyses were
also performed excluding medications that were not prescribed more than
180 days during the follow-up period(Group2). A two-tailed p value
\textless{} 0.05 was considered statistically significant. All
statistical analyses were performed using SAS 9.4 (SAS Institute Inc.,
Cary, NC, USA).

\textbf{3. Results}

\emph{3.1. Basic characteristics}

Of the 3,737 (1,774 males and 1,963 females) prevalent users, 80.8\%
were aged 65 and older (Table 1). The most commonly prescribed
medication group was HT. The most frequent concurrent therapeutic areas
were HT-only (34.7\%), followed by HT and DL (26.8\%) and DL-only
(20.7\%). Those with poorly controlled DL or DM were less adherent with
their medications than those with controlled. The similar tendencies
were observed in each medication group and group2 (Table 2, Supplement
Table 4,5).

\emph{3.2. PDC}

The proportion of participants with adherent was 86.5\%. Medication
adherence differed by medication groups. Those with DL-only showed the
lowest adherence (79.6\%), followed by those with DM-only (81.7\%) and
those with HT-only (89.8\%). Comparisons of medication adherence were
statistically significant between those with DL-only and those with
HT-only (p<0.0001) (data not shown). The associations were observed
especially among those with no comorbidities. The similar tendencies
were observed in the subgroup analysis with group2. The proportion of
participants with adherent was 88.1\%. Those with DL-only (81.4\%) and
DM-only (81.3\%) showed lower adherence than those with HT-only
(91.0\%).

\emph{3.3. Factors influencing non-adherence}

Table 3 shows the ORs with 95\% CIs for factors associated with
non-adherence in each medication group. Age, skipping breakfast, and
understanding of medications were associated with non-adherence among
those with HT. After multivariable adjustment, skipping breakfast (1.79;
1.07-3.00) and understanding of medications (0.46; 0.25-0.83) were still
associated with non-adherence. Among those with DL, age, comorbidities,
medical history of stroke, education years, and skipping breakfast were
associated with non-adherence. After multivariable adjustment,
comorbidities (0.61; 0.48-0.78) and medical history of stroke (0.53;
0.35-0.81) were still associated with non-adherence. Although sex and
drinking status showed no association without adjustment, it showed an
association after multivariable adjustment: males (0.73; 0.54-0.99) and
current drinkers (1.57; 1.07-2.31). Among those with DM, medical history
of stroke, smoking status, and skipping breakfast were associated with
non-adherence. Skipping breakfast (2.47; 1.06-5.77) was still related to
non-adherence after multivariable adjustment. The associations were
still observed after multivariable adjustment in group2 (Supplement
Table 6). Furthermore, the following determinants were associated with
non-adherence after multivariable adjustment in group2: age 65 and older
(0.70; 0.50-0.98) among those with HT, comorbidities (0.55; 0.31-0.96)
and current smokers (1.88; 1.00-3.54) among those with DM.

\textbf{Discussion}

This study examines the medication adherence of prevalent users with HT,
DL, and/or DM in population-based Tsuruoka Metabolomics Cohort Study by
using health insurance claims. Medication adherence differed by
medication groups. Those with DL-only showed the lowest adherence,
followed by those with DM-only and those with HT-only. The associations
were observed especially among those with no comorbidities.
Disease-related, participant-related, and therapy-related factors
associated with non-adherence were also examined in each medication
group. Namely, skipping breakfast and poor understanding of medications
were related to non-adherence among those with HT, females, having
comorbidities of HT and/or DM, having the medical history of stroke, and
current drinkers among those with DL, and skipping breakfast among those
with DM. To our knowledge, the present study is one of few
population-based studies to link participants' claims data in Japan and
to explore medication adherence across multiple diseases.

Those with DL-only showed the lowest adherence among the medications.
Although a previous study from Korea which examined the medication
adherence in elderly patients reported a lower value of adherent in the
DL group (69.1\%), HT (71.0\%), and DM (74.0\%) \textsuperscript{28},
the proportion of adherent with DL medications was lowest among these
medications as well. The reason for the difference in values of
non-adherent is likely due to the different measurement of medication
adherence. The Korean study determined the medication adherence using
medication possession ratio (MPR), which is reported to estimate higher
adherence than PDC \textsuperscript{8}. Furthermore, there is a
possibility that medication systems might have led the results to the
difference. In Japan, most outpatients receive their medications in
courses of 90 days or less on the day of the prescription, which expires
in 4 days. Moreover, a medical service called the ``one dose package
service \textsuperscript{29}'' is offered to those with comorbidities
and who have to take many medications. ``One dose package
service\textsuperscript{29}'' refers to combining multiple medications
into one pack at each dosing time. This unique service could lead to
different results in Japan compared to other counties.

Although those with DL-only showed the lowest adherence among the
medications, those with comorbidities and those with medical histories
of stroke were found to have less chances to be non-adherent among those
with DL, which is consistent with prior studies \textsuperscript{9,32}.
A study from the US reported that while the number of cardiometabolic
conditions was not a significant predictor of HT medication refill
adherence, a greater number of conditions was linked to improved DL
medication refill adherence \textsuperscript{9}. A plausible explanation
for this is self-recognition of one's health condition. As participants
with DL-only are asymptomatic, it can be difficult for them to realize
the efficacy of therapy and a life-long therapy is
required\textsuperscript{33}. In contrast, those with comorbidities or
taking the medication for the purpose of secondary prevention of stroke
might perceive the risk and be more likely to take their medications as
indicated. As a factor associated with non-adherence, the skipping
breakfast group was identified among those with HT and DM. One possible
explanation is that as those medications are often instructed to take
after breakfast, some participants might not take the medications when
they skip the breakfast. As for the medication that can be taken without
regard to meals, it is suggested to tell patients that it is possible to
take the medications even if they skip the breakfast. Moreover, those
who did not understand the medications they took were found to be a
predictor of non-adherent among those with HT. We assume that the
participants with poor understanding of their medications take the
medications not knowing the efficacy, due to poor health awareness or
lack of interest in treatment. Many studies have explored the
association between sex and medication adherence in chronic diseases and
some studies reported that males are more adherent than
females\textsuperscript{30--32}. We observed the same tendency among
those with DL in the present study. Reports on the association between
alcohol consumption and medication adherence have been
consistent\textsuperscript{28}. We assume that current drinkers take
their medications with poor health awareness or a lack of knowledge.

\emph{Study strengths and weaknesses}

Previous studies have explored medication adherence and factors
associated with non- adherence, but few studies have identified them in
multiple diseases in the same study population \textsuperscript{28}. The
major strength of this study was the linkage of population-based cohort
data with claims data. Our database enabled us to explore various
factors related to medication adherence across different diseases.

However, several limitations also need to be acknowledged. First, as
medication adherence was examined based on claims data, it may differ
from participants' actual behavior. However, the PDC is considered a
conservative measure of adherence \textsuperscript{20}, and an
association between adherence measured with the PDC and blood
concentrations has been shown \textsuperscript{34}. Previous study
compared adherence measured using claims data with PDC to adherence
using patient interviews \textsuperscript{35}. The study showed that
adherence measured based on claims data was lower than adherence
reported in patient interviews. The study suggested that a major
explanation as to how claims data may underestimate adherence was
misclassification of non-adherence, which can arise due to appropriate
discontinuation or changes to medications according to the prescribers'
direction, and having alternative sources for obtaining
medications\textsuperscript{35}. The author calculated the positive
predictive value of claims data for identifying non-adherent patients to
be 0.72. In our study, we were able to overcome the weaknesses of the
prior study\textsuperscript{35} by analyzing each participant's data in
detail and pursuing the sources of the medications. Second, the
comorbidity variable was constructed as with or without diseases(HT,
DL, and/or DM). We could not explore the determinants of non-adherent
in detail according to the concurrent therapeutic areas, due to a small
number of participants. Third, because we only examined participants
from the resident cohort, the selection process may have led to
inclusion of an older demographic. Further studies should examine
participants of working age, which should include mostly participants
aged below 65 years. Fourth, we conducted the study with three
medications; however, further study will be needed with other
medications by increasing the number of participants. Finally, the
generalizability of this study to other populations might be limited.

Conclusion

Prevalent users of HT, DL, and DM medications in Japan showed high
medication adherence. However, adherence differed by medication group.
Those with DL-only showed the lowest adherence, followed by those with
DM-only and those with HT-only. The associations were observed
especially among those with no comorbidities. Moreover, factors
associated with non-adherence differed by medication group. Skipping
breakfast and poor understanding of medications were related to
non-adherence among those with HT, females, having comorbidities, having
the medical history of stroke, and current drinkers among those with DL,
and skipping breakfast among those with DM. The identified predictors of
non-adherence could help target those in need of adherence support.

\textbf{Acknowledgments}

The authors would like to acknowledge the contribution of Tsuruoka City
for providing access to health insurance claims and Yoshinori Takizawa
as an advisor on the use of claims data. This study was supported in
part by research funds from the Yamagata Prefectural Government and the
City of Tsuruoka and by JSPS KAKENHI Grant Number JP16H06277,
JP18H06377, JP19K21458. Conflicts of interest: None declared.

1. Catapano AL, Graham I, De Backer G, et al. 2016 ESC/EAS Guidelines
for the Management of Dyslipidaemias. \emph{Eur Heart J} .
2016;37(39):2999-3058.

2. Grundy SM, Stone NJ, Bailey AL, et al. 2018
AHA/ACC/AACVPR/AAPA/ABC/ACPM/ADA/AGS/APhA/ASPC/NLA/PCNA Guideline on the
Management of Blood Cholesterol: A Report of the American College of
Cardiology/American Heart Association Task Force on Clinical Practice
Guidelines. \emph{J Am Coll Cardiol} . 2019;73(24):e285-e350.

3. Kinoshita M, Yokote K, Arai H, et al. Japan Atherosclerosis Society
(JAS) Guidelines for Prevention of Atherosclerotic Cardiovascular
Diseases 2017. \emph{J Atheroscler Thromb} . 2018;25(9):846-984.

4. Otsuka T, Takada H, Nishiyama Y, et al. Dyslipidemia and the Risk of
Developing Hypertension in a Working-Age Male Population. \emph{J Am
Heart Assoc} . 2016;5(3).

5. Lee HJ, Jang SI, Park EC. Effect of adherence to antihypertensive
medication on stroke incidence in patients with hypertension: a
population-based retrospective cohort study. \emph{BMJ Open} .
2017;7(6).

6. Rodriguez F, Maron DJ, Knowles JW, Virani SS, Lin S, Heidenreich PA.
Association of Statin Adherence with Mortality in Patients with
Atherosclerotic Cardiovascular Disease. \emph{JAMA Cardiol} .
2019;4(3):206-213.

7. Simard P, Presse N, Roy L, et al. Association Between Metformin
Adherence and All-Cause Mortality Among New Users of Metformin: A Nested
Case-Control Study. \emph{Ann Pharmacother} . 2018;52(4):305-313.

8. Oung AB, Kosirog E, Chavez B, Brunner J, Saseen JJ. Evaluation of
medication adherence in chronic disease at a federally qualified health
center. \emph{Ther Adv Chronic Dis} . 2017;8(8-9):113-120.

9. Hansen RA, Voils CI, Farley JF, et al. Prescriber continuity and
medication adherence for complex patients. \emph{Ann Pharmacother} .
2015;49(3):293-302.

10. Gast A, Mathes T. Medication adherence influencing factors-an
(updated) overview of systematic reviews. \emph{Syst Rev} . 2019;8(1).

11. Matsumoto M, Harada S, Iida M, et al. Validity assessment of
self-reported medication use for hypertension, diabetes, and
dyslipidemia in a pharmacoepidemiologic study by comparison with health
insurance claims. \emph{J Epidemiol} . 2021;31(9):495-502.

12. Harada S, Takebayashi T, Kurihara A, et al. Metabolomic profiling
reveals novel biomarkers of alcohol intake and alcohol-induced liver
injury in community-dwelling men. \emph{Environ Health Prev Med} .
2016;21(1):18-26.

13. Iida M, Harada S, Kurihara A, et al. Profiling of plasma metabolites
in postmenopausal women with metabolic syndrome. \emph{Menopause} .
2016;23(7):749-758.

14. Fukai K, Harada S, Iida M, et al. Metabolic Profiling of Total
Physical Activity and Sedentary Behavior in Community-Dwelling
Men.\emph{PLoS One} . 2016;11(10).

15. Harada S, Hirayama A, Chan Q, et al. Reliability of plasma polar
metabolite concentrations in a large-scale cohort study using capillary
electrophoresis-mass spectrometry. \emph{PLoS One} . 2018;13(1).

16. Harada S, Sata M, Matsumoto M, et al. Changes in Smoking Habits and
Behaviors Following the Introduction and Spread of Heated Tobacco
Products in Japan and Its Effect on FEV 1 Decline: A Longitudinal Cohort
Study. \emph{J Epidemiol} . 2022;32(4):180-187.

17. WHO. Guideline for ATC classification and DDD assignment 2022.

18. Takahisa F, Yazaki Y. \emph{Therapeutics Manual 2019} . Igaku-shoin;
2019.

19. Takahisa F, Yazaki Y. \emph{Therapeutics Manual 2022} . Igaku-shoin;
2022.

20. Raebel MA, Schmittdiel J, Karter AJ, Konieczny JL, Steiner JF.
Standardizing terminology and definitions of medication adherence and
persistence in research employing electronic databases. \emph{Med Care}
. 2013;51(8 Suppl 3).

21. Umeda T, Hayashi A, Fujimoto G, Piao Y, Matsui N, Tokita S.
Medication Adherence/Persistence and Demographics of Japanese
Dyslipidemia Patients on Statin-Ezetimibe as a Separate Pill Combination
Lipid-Lowering Therapy - An Observational Pharmacy Claims Database
Study. \emph{Circ J} . 2019;83(8):1689-1697.

22. Zongo A, Simpson S, Johnson JA, Eurich DT. Optimal threshold of
adherence to lipid lowering drugs in predicting acute coronary syndrome,
stroke, or mortality: A cohort study. \emph{PLoS One} . 2019;14(9).

23. Nakajima K, Iwane T, Higuchi R, et al. Kanagawa Investigation of the
Total Check-up Data from the National database (KITCHEN): Protocol for
data-driven population-based repeated cross-sectional and 6-year cohort
studies. \emph{BMJ Open} . 2019;9(2):1-11.

24. Ministry of Health, Labour, and Welfare. Health Examination and
Guidance Program for Japanese Adults. 2008. Accessed December 18, 2023.
https://www.mhlw.go.jp/stf/seisakunitsuite/bunya/0000161103.html

25. Umemura S, Arima H, Arima S, et al. The Japanese Society of
Hypertension Guidelines for the Management of Hypertension (JSH
2019).\emph{Hypertens Res} . 2019;42(9):1235-1481.

26. Teramoto T, Sasaki J, Ishibashi S, et al. Diagnosis of
atherosclerosis: Executive summary of the Japan Atherosclerosis Society
(JAS) guidelines for the diagnosis and prevention of atherosclerotic
cardiovascular diseases in Japan - 2012 version. \emph{J Atheroscler
Thromb} . 2014;21(4):296-298.

27. Haneda M, Noda M, Origasa H, et al. \emph{Japanese Clinical Practice
Guideline for Diabetes 2016} . Vol 9.; 2018.

28. Han E, Sohn HS, Lee JY, Jang S. Health Behaviors and Medication
Adherence in Elderly Patients. \emph{Am J Heal Promot} .
2017;31(4):278-286.

29. Nakai K, Yamamoto N, Kamei M, Fujita M. The effect of one-dose
package on medication adherence for the elderly care in
Japan.\emph{Pharm Pract (Granada)} . 2009;7(1):59-62.

30. Wiegand P, McCombs JS, Wang JJ. Factors of hyperlipidemia medication
adherence in a nationwide health plan. \emph{Am J Manag Care} .
2012;18(4):193-199.

31. Shaha KC, Sultana S, Saha SK, Shahidullah SM, Jyoti BK. Patient
Characteristics Associated With Medication Adherence to Anti-Diabetic
Drugs. \emph{Mymensingh Med J} . 2019;28(2):423-428.

32. Mann DM, Woodward M, Muntner P, Falzon L, Kronish I. Predictors of
nonadherence to statins: a systematic review and meta-analysis.\emph{Ann
Pharmacother} . 2010;44(9):1410-1421.

33. Tsuyuki RT, Bungard TJ. Poor adherence with hypolipidemic drugs: a
lost opportunity. \emph{Pharmacotherapy} . 2001;21(5):576-582.

34. Steiner JF, Prochazka A V. The assessment of refill compliance using
pharmacy records: methods, validity, and applications. \emph{J Clin
Epidemiol} . 1997;50(1):105-116.

35. Glassberg MB, Trygstad T, Wei D, Robinson T, Farley JF. Accuracy of
Prescription Claims Data in Identifying Truly Nonadherent
Patients.\emph{J Manag care Spec Pharm} . 2019;25(12):1349-1356.

\textbf{Table 1. Characteristics of the study population}\selectlanguage{english}
\begin{longtable}[]{@{}llll@{}}
\toprule
& & \textbf{PDC \textless{} 0.8} & \textbf{PDC [?] 0.8}\tabularnewline
N & 3,737 & 505(13.5 \%) & 3,232(86.5 \%)\tabularnewline
\textbf{Participant-related factors} & & &\tabularnewline
Sex (Male, \%) & 1,774 (47.5 \%) & 234 (46.3 \%) & 1,540 (47.6
\%)\tabularnewline
Age[?] 65 years (\%) & 3,018 (80.8 \%) & 382 (75.6 \%) & 2,636(81.6
\%)\tabularnewline
SBP (mmHg) \textsuperscript{a, c} & 135.1 (17.2) & 135.1 (17.6) & 135.1
(17.2)\tabularnewline
DBP (mmHg) \textsuperscript{a, c} & 76.8 (10.9) & 77.1 (11.0) & 76.7
(10.9)\tabularnewline
LDL-C (mg/dL) \textsuperscript{a, d} & 108.5 (27.4) & 114.1 (31.2) &
107.4 (26.5)\tabularnewline
HDL-C (mg/dL) \textsuperscript{a, d} & 65.6 (16.2) & 65.1 (16.4) & 65.7
(16.2)\tabularnewline
Triglycerides (mg/dL) \textsuperscript{b, d} & 101 (28-5,040) & 103
(28-903) & 100 (30-5,040)\tabularnewline
Fasting blood glucose (mg/dL) \textsuperscript{b, e} & 130.9 (28.1) &
132.4 (26.7) & 130.7 (28.3)\tabularnewline
BMI[?]25 (kg/m\textsuperscript{2}) (\%) & 1,361 (36.4 \%) & 181 (35.8 \%)
& 1,180 (36.5 \%)\tabularnewline
Education\textgreater{} 12 years (\%) & 2,822 (75.5 \%) & 398 (78.8 \%)
& 2,424 (75.0 \%)\tabularnewline
Current smoker (\%) & 307 (8.2 \%) & 49 (9.7 \%) & 258 (8.0
\%)\tabularnewline
Current drinker (\%) & 820 (21.9 \%) & 113 (22.4 \%) & 707 (21.9
\%)\tabularnewline
Regular exerciser (\%) & 1,209 (32.4 \%) & 164 (32.5 \%) & 1,045 (32.3
\%)\tabularnewline
Good sleep quality (\%) & 3,005 (80.4 \%) & 398 (78.8 \%) & 2,607 (80.7
\%)\tabularnewline
Skipping breakfast (\%) & 158 (4.2 \%) & 34 (6.7 \%) & 124 (3.8
\%)\tabularnewline
Fast eater (\%) & 1,011 (27.1 \%) & 127 (25.1 \%) & 884 (27.4
\%)\tabularnewline
Late dinner (\%) & 559 (15.0 \%) & 81 (16.0 \%) & 478 (14.8
\%)\tabularnewline
Attitude toward lifestyle improvement, (Unmotivated, \%) & 1,349 (36.1
\%) & 176 (37.4 \%) & 1,173 (37.7 \%)\tabularnewline
Understanding of medications (\%) & 3,267 (87.4 \%) & 436 (94.3 \%) &
2,831 (96.9 \%)\tabularnewline
\textbf{Treatment-related factors} & & &\tabularnewline
Poorly controlled hypertension \textsuperscript{c} & 1,075 (39.5 \%) &
121 (39.2 \%) & 954 (39.5 \%)\tabularnewline
Poorly controlled dyslipidemia \textsuperscript{d} & 612 (28.6 \%) & 117
(35.5 \%) & 495 (27.4 \%)\tabularnewline
Poorly controlled diabetes mellitus \textsuperscript{e} & 336 (50.5 \%)
& 49 (55.1 \%) & 287 (49.8 \%)\tabularnewline
\textbf{Disease-related factors} & & &\tabularnewline
Medication group & & &\tabularnewline
Hypertension & 2,724 (72.9 \%) & 262 (51.9 \%) & 2,462 (76.2
\%)\tabularnewline
Dyslipidemia & 2,137 (57.2 \%) & 334 (66.1 \%) & 1,803 (55.8
\%)\tabularnewline
Diabetes mellitus & 665 (17.8 \%) & 88 (17.4 \%) & 577 (17.9
\%)\tabularnewline
\bottomrule
\end{longtable}

Abbreviations: BMI, body mass index; DBP, diastolic blood pressure;
HDL-C, high-density lipoprotein cholesterol; LDL-C, low-density
lipoprotein cholesterol; SBP, systolic blood pressure.

\textsuperscript{a} Reported as mean (standard deviation).

\textsuperscript{b} Reported as median (range).

\textsuperscript{c} Data were collected from those taking hypertension
medications.

\textsuperscript{d} Data were collected from those taking dyslipidemia
medications.

\textsuperscript{e} Data were collected from those taking diabetes
mellitus medications.

\textbf{Table 1. Characteristics of the study population (Continued.)}\selectlanguage{english}
\begin{longtable}[]{@{}llll@{}}
\toprule
& & \textbf{PDC \textless{} 0.8} & \textbf{PDC [?] 0.8}\tabularnewline
N & 3,737 & 505(13.5 \%) & 3,232(86.5 \%)\tabularnewline
Concurrent therapeutic areas \textsuperscript{f} & & &\tabularnewline
Hypertension-only & 1,297 (34.7 \%) & 133 (10.3 \%) & 1,164 (89.8
\%)\tabularnewline
Dyslipidemia-only & 773 (20.7 \%) & 158 (20.4 \%) & 615 (79.6
\%)\tabularnewline
Diabetes mellitus-only & 120 (3.2 \%) & 22 (18.3 \%) & 98 (81.7
\%)\tabularnewline
Hypertension + Dyslipidemia & 1,002 (26.8 \%) & 125 (12.5 \%) & 877
(87.5 \%)\tabularnewline
Hypertension + Diabetes mellitus & 183 (4.9 \%) & 20 (10.9 \%) & 163
(89.1 \%)\tabularnewline
Dyslipidemia + Diabetes mellitus & 120 (3.2 \%) & 16 (13.3 \%) & 104
(86.7 \%)\tabularnewline
Hypertension + Dyslipidemia + Diabetes mellitus & 242 (6.5 \%) & 31
(12.8 \%) & 211 (87.2 \%)\tabularnewline
Medical histories & & &\tabularnewline
Stroke & 157 (4.2 \%) & 14 (2.8 \%) & 143 (4.4 \%)\tabularnewline
Heart disease & 496 (13.3 \%) & 46 (9.1 \%) & 450 (13.9
\%)\tabularnewline
Chronic kidney disease & 10 (0.3 \%) & 3 (0.6 \%) & 7 (0.2
\%)\tabularnewline
\bottomrule
\end{longtable}

\textsuperscript{f} Proportion of being adherent and non-adherent were
calculated based on the number of participants of each concurrent
therapeutic area as a denominator.

\textbf{Table 2. Characteristics of the study population according to
each medication group}\selectlanguage{english}
\begin{longtable}[]{@{}lllllllll@{}}
\toprule
& \textbf{Hypertension}(\textbf{N=2,724}) &
\textbf{Hypertension}(\textbf{N=2,724}) & &
\textbf{Dyslipidemia}(\textbf{N=2,137}) &
\textbf{Dyslipidemia}(\textbf{N=2,137}) & &
\textbf{Diabetes}(\textbf{N=665}) &
\textbf{Diabetes}(\textbf{N=665})\tabularnewline
& \textbf{PDC \textless{} 0.8} & \textbf{PDC [?] 0.8} & & \textbf{PDC
\textless{} 0.8} & \textbf{PDC [?] 0.8} & & \textbf{PDC \textless{} 0.8} &
\textbf{PDC [?] 0.8}\tabularnewline
N & 262(9.6 \%) & 2,462(90.4 \%) & & 334(15.6 \%) & 1,803(84.4
\%) & & 88(13.2 \%) & 577(86.8 \%)\tabularnewline
\textbf{Participant-related factors} & & & & & & & &\tabularnewline
Sex (Male, \%) & 146(55.7 \%) & 1,243(50.5 \%) & & 118(35.3 \%) &
725(40.2 \%) & & 63(71.6 \%) & 369(64.0 \%)\tabularnewline
Age[?] 65 years (\%) & 202(77.1 \%) & 2,036(82.7 \%) & & 256(76.6
\%) & 1,495(82.9 \%) & & 65(73.9 \%) & 477(82.7
\%)\tabularnewline
SBP (mmHg) \textsuperscript{a, c} & 135.6(17.8) & 135.1(17.2) & & &
& & &\tabularnewline
DBP (mmHg) \textsuperscript{a, c} & 77.6(11.1) & 76.7(10.9) & & & &
& &\tabularnewline
LDL-C (mg/dL) \textsuperscript{a, d} & & & & 113.7(31.4) &
107.5(26.4) & & &\tabularnewline
HDL-C (mg/dL) \textsuperscript{a, d} & & & & 65.4(16.3) & 65.6(16.2)
& & &\tabularnewline
Triglycerides (mg/dL) \textsuperscript{b, d} & & & & 103(28-903) &
100(30-5,040) & & &\tabularnewline
Fasting blood glucose (mg/dL) \textsuperscript{b, e} & & & & & & &
134.5(27.1) & 130.3(28.2)\tabularnewline
BMI[?]25 (kg/m\textsuperscript{2}) (\%) & 110(42.0 \%) & 985(40.0 \%)
& & 121(36.2 \%) & 667(37.0 \%) & & 46(52.3 \%) & 273(47.3
\%)\tabularnewline
Education\textgreater{} 12 years (\%) & 202(77.1 \%) & 1,834(74.5
\%) & & 268(80.2 \%) & 1,343(74.5 \%) & & 59(67.0 \%) & 423(73.3
\%)\tabularnewline
Current smoker (\%) & 21(8.0 \%) & 212(8.6 \%) & & 27(8.1 \%) &
114(6.3 \%) & & 20(22.7 \%) & 68(11.8 \%)\tabularnewline
Current drinker (\%) & 75(28.6 \%) & 637(25.9 \%) & & 57(17.1 \%)
& 261(14.5 \%) & & 25(28.4 \%) & 125(21.7 \%)\tabularnewline
Regular exerciser (\%) & 78(29.8 \%) & 793(32.2 \%) & & 109(32.6
\%) & 599(33.2 \%) & & 27(30.7 \%) & 191(33.1 \%)\tabularnewline
Good sleep quality (\%) & 205(78.2 \%) & 1,999(81.2 \%) & &
262(78.4 \%) & 1,443(80.0 \%) & & 77(87.5 \%) & 466(80.8
\%)\tabularnewline
Skipping breakfast (\%) & 19(7.3 \%) & 100(4.1 \%) & & 20(6.0 \%)
& 66(3.7 \%) & & 10(11.4 \%) & 26(4.5 \%)\tabularnewline
Fast eater (\%) & 62(23.7 \%) & 669(27.2 \%) & & 91(27.2 \%) &
501(27.8 \%) & & 22(25.0 \%) & 182(31.5 \%)\tabularnewline
Late dinner (\%) & 43(16.4 \%) & 401(16.3 \%) & & 50(15.0 \%) &
241(13.4 \%) & & 18(20.5 \%) & 93(16.1 \%)\tabularnewline
Attitude toward lifestyle improvement, (Unmotivated, \%) & 98(37.4 \%)
& 927(37.7 \%) & & 102(30.5 \%) & 599(33.2 \%) & & 35(39.8 \%) &
171(29.6 \%)\tabularnewline
Understanding of medications (\%) & 247(94.3 \%) & 2,386(96.9 \%) &
& 297(88.9 \%) & 1,583(87.8 \%) & & 84(95.5 \%) & 554(96.0
\%)\tabularnewline
\bottomrule
\end{longtable}

Abbreviations: BMI, body mass index; DBP, diastolic blood pressure;
HDL-C, high-density lipoprotein cholesterol; LDL-C, low-density
lipoprotein cholesterol; SBP, systolic blood pressure.

\textsuperscript{a} Reported as mean (standard
deviation).\textsuperscript{b} Reported as median (range).
\textsuperscript{c} Data were collected from those taking hypertension
medications.

\textsuperscript{d} Data were collected from those taking dyslipidemia
medications. \textsuperscript{e} Data were collected from those taking
diabetes mellitus medications.



\textbf{Table 2. Characteristics of the study population according to
each medication group (Continued)}\selectlanguage{english}
\begin{longtable}[]{@{}lllllllll@{}}
\toprule
& \textbf{Hypertension}(\textbf{N=2,724}) &
\textbf{Hypertension}(\textbf{N=2,724}) & &
\textbf{Dyslipidemia}(\textbf{N=2,137}) &
\textbf{Dyslipidemia}(\textbf{N=2,137}) & &
\textbf{Diabetes}(\textbf{N=665}) &
\textbf{Diabetes}(\textbf{N=665})\tabularnewline
& \textbf{PDC \textless{} 0.8} & \textbf{PDC [?] 0.8} & & \textbf{PDC
\textless{} 0.8} & \textbf{PDC [?] 0.8} & & \textbf{PDC \textless{} 0.8} &
\textbf{PDC [?] 0.8}\tabularnewline
N & 262(9.6 \%) & 2,462(90.4 \%) & & 334(15.6 \%) & 1,803(84.4
\%) & & 88(13.2 \%) & 577(86.8 \%)\tabularnewline
Treatment-related factors & & & & & & & &\tabularnewline
Poorly controlled hypertension \textsuperscript{c} & 108(41.2 \%) &
967(39.3 \%) & & & & & &\tabularnewline
Poorly controlled dyslipidemia \textsuperscript{d} & & & & 116(34.7
\%) & 496(27.5 \%) & & &\tabularnewline
Poorly controlled diabetes mellitus \textsuperscript{e} & & & & & & &
52(59.1 \%) & 284(49.2 \%)\tabularnewline
Disease-related factors & & & & & & & &\tabularnewline
Comorbidities of HT and/or DL and/or DM & 129(49.2 \%) & 1,298(52.7
\%) & & 176(52.7 \%) & 1,188(65.9 \%) & & 66(75.0 \%) & 479(83.0
\%)\tabularnewline
Medical histories & & & & & & & &\tabularnewline
Stroke & 10(3.8 \%) & 113(4.6 \%) & & 9(2.7 \%) & 93(5.2 \%) & &
4(4.5 \%) & 31(5.4 \%)\tabularnewline
Heart disease & 34(13.0 \%) & 376(15.3 \%) & & 27(8.1 \%) &
282(15.6 \%) & & 6(6.8 \%) & 86(14.9 \%)\tabularnewline
Chronic kidney disease & 1(0.4 \%) & 7(0.3 \%) & & 2(0.6 \%) &
4(0.2 \%) & & 0(0.0 \%) & 1(0.2 \%)\tabularnewline
\bottomrule
\end{longtable}

Abbreviations: DL, dyslipidemia; DM, diabetes mellitus; HT,
hypertension.

\textsuperscript{c} Data were collected from those taking hypertension
medications.

\textsuperscript{d} Data were collected from those taking dyslipidemia
medications.

\textsuperscript{e} Data were collected from those taking diabetes
mellitus medications.



\textbf{Table 3. Odds ratios (95\% confidence intervals) for factors
associated with non-adherence in each medication group}\selectlanguage{english}
\begin{longtable}[]{@{}lllllllll@{}}
\toprule
\begin{minipage}[t]{0.08\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.08\columnwidth}\raggedright\strut
\textbf{Hypertension}\strut
\end{minipage} & \begin{minipage}[t]{0.08\columnwidth}\raggedright\strut
\textbf{Hypertension}\strut
\end{minipage} & \begin{minipage}[t]{0.08\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.08\columnwidth}\raggedright\strut
\textbf{Dyslipidemia}\strut
\end{minipage} & \begin{minipage}[t]{0.08\columnwidth}\raggedright\strut
\textbf{Dyslipidemia}\strut
\end{minipage} & \begin{minipage}[t]{0.08\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.08\columnwidth}\raggedright\strut
\textbf{Diabetes}\strut
\end{minipage} & \begin{minipage}[t]{0.08\columnwidth}\raggedright\strut
\textbf{Diabetes}\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.11\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.11\columnwidth}\raggedright\strut
\textbf{OR} \textbf{(95\% CI)}\strut
\end{minipage} & \begin{minipage}[t]{0.11\columnwidth}\raggedright\strut
\textbf{Adjusted OR (95\% CI)}\strut
\end{minipage} & \begin{minipage}[t]{0.11\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.11\columnwidth}\raggedright\strut
\textbf{OR} \textbf{(95\% CI)}\strut
\end{minipage} & \begin{minipage}[t]{0.11\columnwidth}\raggedright\strut
\textbf{Adjusted OR (95\% CI)}\strut
\end{minipage} & \begin{minipage}[t]{0.11\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.11\columnwidth}\raggedright\strut
\textbf{OR} \textbf{(95\% CI)}\strut
\end{minipage} & \begin{minipage}[t]{0.11\columnwidth}\raggedright\strut
\textbf{Adjusted OR (95\% CI)}\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.08\columnwidth}\raggedright\strut
Male\strut
\end{minipage} & \begin{minipage}[t]{0.08\columnwidth}\raggedright\strut
1.23 (0.96-1.60)\strut
\end{minipage} & \begin{minipage}[t]{0.08\columnwidth}\raggedright\strut
1.26 (0.93-1.71)\strut
\end{minipage} & \begin{minipage}[t]{0.08\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.08\columnwidth}\raggedright\strut
0.81 (0.64-1.04)\strut
\end{minipage} & \begin{minipage}[t]{0.08\columnwidth}\raggedright\strut
0.73 (0.54-0.99)\strut
\end{minipage} & \begin{minipage}[t]{0.08\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.08\columnwidth}\raggedright\strut
1.42 (0.87-2.33)\strut
\end{minipage} & \begin{minipage}[t]{0.08\columnwidth}\raggedright\strut
1.09 (0.62-1.92)\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.08\columnwidth}\raggedright\strut
Age[?] 65 years\strut
\end{minipage} & \begin{minipage}[t]{0.08\columnwidth}\raggedright\strut
0.70 (0.52-0.96)\strut
\end{minipage} & \begin{minipage}[t]{0.08\columnwidth}\raggedright\strut
0.73 (0.53-1.01)\strut
\end{minipage} & \begin{minipage}[t]{0.08\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.08\columnwidth}\raggedright\strut
0.68 (0.51-0.90)\strut
\end{minipage} & \begin{minipage}[t]{0.08\columnwidth}\raggedright\strut
0.85 (0.63-1.15)\strut
\end{minipage} & \begin{minipage}[t]{0.08\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.08\columnwidth}\raggedright\strut
0.59 (0.35-1.00)\strut
\end{minipage} & \begin{minipage}[t]{0.08\columnwidth}\raggedright\strut
0.67 (0.38-1.18)\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.08\columnwidth}\raggedright\strut
Comorbidities\strut
\end{minipage} & \begin{minipage}[t]{0.08\columnwidth}\raggedright\strut
0.87 (0.67-1.12)\strut
\end{minipage} & \begin{minipage}[t]{0.08\columnwidth}\raggedright\strut
0.90 (0.69-1.17)\strut
\end{minipage} & \begin{minipage}[t]{0.08\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.08\columnwidth}\raggedright\strut
0.58 (0.46-0.73)\strut
\end{minipage} & \begin{minipage}[t]{0.08\columnwidth}\raggedright\strut
0.61 (0.48-0.78)\strut
\end{minipage} & \begin{minipage}[t]{0.08\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.08\columnwidth}\raggedright\strut
0.61 (0.36-1.04)\strut
\end{minipage} & \begin{minipage}[t]{0.08\columnwidth}\raggedright\strut
0.65 (0.37-1.14)\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.08\columnwidth}\raggedright\strut
Medical history of stroke\strut
\end{minipage} & \begin{minipage}[t]{0.08\columnwidth}\raggedright\strut
0.83 (0.57-1.21)\strut
\end{minipage} & \begin{minipage}[t]{0.08\columnwidth}\raggedright\strut
0.76 (0.51-1.13)\strut
\end{minipage} & \begin{minipage}[t]{0.08\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.08\columnwidth}\raggedright\strut
0.47 (0.31-0.72)\strut
\end{minipage} & \begin{minipage}[t]{0.08\columnwidth}\raggedright\strut
0.53 (0.35-0.81)\strut
\end{minipage} & \begin{minipage}[t]{0.08\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.08\columnwidth}\raggedright\strut
0.42 (0.18-0.99)\strut
\end{minipage} & \begin{minipage}[t]{0.08\columnwidth}\raggedright\strut
0.46 (0.19-1.12)\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.08\columnwidth}\raggedright\strut
Education\textgreater{} 12 years\strut
\end{minipage} & \begin{minipage}[t]{0.08\columnwidth}\raggedright\strut
1.14 (0.84-1.54)\strut
\end{minipage} & \begin{minipage}[t]{0.08\columnwidth}\raggedright\strut
1.06 (0.77-1.44)\strut
\end{minipage} & \begin{minipage}[t]{0.08\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.08\columnwidth}\raggedright\strut
1.38 (1.03-1.84)\strut
\end{minipage} & \begin{minipage}[t]{0.08\columnwidth}\raggedright\strut
1.26 (0.93-1.70)\strut
\end{minipage} & \begin{minipage}[t]{0.08\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.08\columnwidth}\raggedright\strut
0.75 (0.46-1.22)\strut
\end{minipage} & \begin{minipage}[t]{0.08\columnwidth}\raggedright\strut
0.67 (0.40-1.13)\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.08\columnwidth}\raggedright\strut
Current smoker\strut
\end{minipage} & \begin{minipage}[t]{0.08\columnwidth}\raggedright\strut
0.93 (0.58-1.48)\strut
\end{minipage} & \begin{minipage}[t]{0.08\columnwidth}\raggedright\strut
0.76 (0.46-1.24)\strut
\end{minipage} & \begin{minipage}[t]{0.08\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.08\columnwidth}\raggedright\strut
1.30 (0.84-2.02)\strut
\end{minipage} & \begin{minipage}[t]{0.08\columnwidth}\raggedright\strut
1.25 (0.78-2.00)\strut
\end{minipage} & \begin{minipage}[t]{0.08\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.08\columnwidth}\raggedright\strut
2.20 (1.26-3.85)\strut
\end{minipage} & \begin{minipage}[t]{0.08\columnwidth}\raggedright\strut
1.62 (0.87-3.02)\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.08\columnwidth}\raggedright\strut
Current drinker\strut
\end{minipage} & \begin{minipage}[t]{0.08\columnwidth}\raggedright\strut
1.15 (0.87-1.52)\strut
\end{minipage} & \begin{minipage}[t]{0.08\columnwidth}\raggedright\strut
0.99 (0.70-1.38)\strut
\end{minipage} & \begin{minipage}[t]{0.08\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.08\columnwidth}\raggedright\strut
1.22 (0.89-1.66)\strut
\end{minipage} & \begin{minipage}[t]{0.08\columnwidth}\raggedright\strut
1.57 (1.07-2.31)\strut
\end{minipage} & \begin{minipage}[t]{0.08\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.08\columnwidth}\raggedright\strut
1.44 (0.87-2.38)\strut
\end{minipage} & \begin{minipage}[t]{0.08\columnwidth}\raggedright\strut
1.26 (0.71-2.24)\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.08\columnwidth}\raggedright\strut
Skipping breakfast\strut
\end{minipage} & \begin{minipage}[t]{0.08\columnwidth}\raggedright\strut
1.85 (1.11-3.07)\strut
\end{minipage} & \begin{minipage}[t]{0.08\columnwidth}\raggedright\strut
1.79 (1.07-3.00)\strut
\end{minipage} & \begin{minipage}[t]{0.08\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.08\columnwidth}\raggedright\strut
1.68 (1.00-2.80)\strut
\end{minipage} & \begin{minipage}[t]{0.08\columnwidth}\raggedright\strut
1.64 (0.97-2.79)\strut
\end{minipage} & \begin{minipage}[t]{0.08\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.08\columnwidth}\raggedright\strut
2.72 (1.26-5.85)\strut
\end{minipage} & \begin{minipage}[t]{0.08\columnwidth}\raggedright\strut
2.47 (1.06-5.77)\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.11\columnwidth}\raggedright\strut
Having the motivation for lifestyle improvement\strut
\end{minipage} & \begin{minipage}[t]{0.11\columnwidth}\raggedright\strut
1.01 (0.78-1.32)\strut
\end{minipage} & \begin{minipage}[t]{0.11\columnwidth}\raggedright\strut
1.01 (0.77-1.33)\strut
\end{minipage} & \begin{minipage}[t]{0.11\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.11\columnwidth}\raggedright\strut
1.13 (0.88-1.46)\strut
\end{minipage} & \begin{minipage}[t]{0.11\columnwidth}\raggedright\strut
1.14 (0.88-1.48)\strut
\end{minipage} & \begin{minipage}[t]{0.11\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.11\columnwidth}\raggedright\strut
0.64 (0.40-1.01)\strut
\end{minipage} & \begin{minipage}[t]{0.11\columnwidth}\raggedright\strut
0.68 (0.42-1.10)\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.08\columnwidth}\raggedright\strut
Understanding of medications\strut
\end{minipage} & \begin{minipage}[t]{0.08\columnwidth}\raggedright\strut
0.52 (0.30-0.93)\strut
\end{minipage} & \begin{minipage}[t]{0.08\columnwidth}\raggedright\strut
0.46 (0.25-0.83)\strut
\end{minipage} & \begin{minipage}[t]{0.08\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.08\columnwidth}\raggedright\strut
1.12 (0.77-1.61)\strut
\end{minipage} & \begin{minipage}[t]{0.08\columnwidth}\raggedright\strut
0.94 (0.64-1.38)\strut
\end{minipage} & \begin{minipage}[t]{0.08\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.08\columnwidth}\raggedright\strut
0.87 (0.29-2.58)\strut
\end{minipage} & \begin{minipage}[t]{0.08\columnwidth}\raggedright\strut
0.96 (0.31-3.00)\strut
\end{minipage}\tabularnewline
\bottomrule
\end{longtable}

Abbreviations: CI, confidence interval; DM, diabetes mellitus; DL,
dyslipidemia; HT, hypertension; OR, odds ratio.

The following factors were used as controls: female sex, age 35-64
years, no comorbidities(only-HT, only-DL, and only-DM), no medical
history of stroke, education[?] 12 years, non-current smoker, non-current
drinker, non-skipping breakfast group, lack of motivation for lifestyle
improvement and lack of understanding of medications.

\selectlanguage{english}
\FloatBarrier
\end{document}

