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\def\RunningAuthor{Divya Mol E C et al.}
\firstPage{2233}
\articleType{Original Article}
\receivedDate{12 Jun 2021}
\acceptedDate{31 Jul 2021}
\revisedDate{27 Jul 2021}
\journalVolume{2021, 12}
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\journalDoi{ijrps.v12i3.4839}
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\title{\textbf{Assessment of Health Related Quality of Life and Medication Adherence in the Elderly Population}}
\author{Aishwaria~Jyothi~Bhasu\textsuperscript{1},  
            Mangala~Susmitha\textsuperscript{1},  
            Rhea~Climies\textsuperscript{1},  
            Asmita~Clara~Baral\textsuperscript{1},  
            Divya~Mol~E~C\textsuperscript{*}\textsuperscript{1}, 
            Nandini~Swamy\textsuperscript{2}~\\[5pt]{\textsuperscript{1}Department of Pharmacy Practice, Acharya \& BM Reddy College of Pharmacy, Bengaluru, Karnataka, India}~\\{\textsuperscript{2}Department of General Medicine, ESI MC \& PGIMSR Bengaluru, Karnataka, India}}

\begin{abstract}
The elderly population, aged above 60 years, are prone to various chronic and concurrent diseases. This requires prolonging medication usage, often with complex regimens which affect their medication-taking behavior, compliance, adherence, and overall prognosis of the disease. Thereby, an accurate assessment of adherence behavior and its complimentary factors, prescription analysis are necessary for effective and efficient treatment planning and an overall improvement in the Health Related Quality of Life (HRQOL) of the elderly population. Our study was aimed to comprehend the HRQOL and medication adherence pattern of the elderly. One hundred and thirty-three subjects with a mean age of 66.68 \ensuremath{\pm} 5.49 years were recruited for the study and were assessed for their HRQOL, medication adherence, and the factors influencing medication adherence. Relevant data were collected, questionnaires were administered, and appropriate descriptive and inferential statistics were performed. Our findings demonstrated that there is a noticeable change in the HRQOL of the elderly population. It was also found that subjects were highly adherent to their medications, but certain factors played a major role in influencing medication non-adherence. This implies the importance of determining factors affecting HRQOL, medication adherence, adequate prescription analysis, and promoting and practicing rational use of drugs that can significantly optimize therapy and provide a good prognosis of disease for the elderly population. 
\end{abstract}\def\keywordstitle{Keywords}

\begin{keywords}Compliance,\newline Elderly,\newline Health Related Quality of Life,\newline Medication Adherence,\newline Medication non-adherence
\end{keywords}
\twocolumn[ \maketitle {\printKwdAbsBox}]
\makeatletter{\raggedright\textsuperscript{*}Corresponding Author\par Name:\ Divya~Mol~E~C~\\ Phone:\ +91 9066507062 ~\\  Email:\ divyamolec@acharya.ac.in
        \par}\vspace*{-11pt}\hrulefill\par{\fontsize{12}{14}\selectfont ISSN: 0975-7538}\par% 
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\section{Introduction}
Aging is a universal phenomenon accompanied by physiological and pathological changes, disability, increased risk of disease, a decrease in functional capacity. All the stages of aging have their problems and predicaments. As each level passes, physical strength deteriorates parallel to declining mental stability as well as chronic diseases that pose as an added weight. The likelihood of having two or more significant conditions measures to about 60\% by the age of 75 and more than 75\% by 85 years of age \unskip~\citep{1171358:22828146}. The incidence of a comorbid condition arising grossly depends upon the primary disease and how efficaciously it is managed. Due to chronic illnesses, chronic medication usage is obligatory with multiple dosage regimens. Furthermore, the elderly population is associated with physiological changes that is accompanied by pharmacokinetic and pharmacodynamic alterations of drugs that significantly alter a person's quality of life.

The World Health Organization has defined quality of life as "an individual's perception of life in the context of culture and value system in which he or she lives and concerning his or her goals, expectations, standards and concerns'' \unskip~\citep{1171358:22828149}. It is a broad concept covering the individual's physical health, mental health state, level of independence, social relationships and personal beliefs and their relationship to significant features in the environment. Health Related Quality of Life was derived from the perspective of clinical medicine and health care to evaluate the effects of health on a person's quality of life. It is affected by numerous factors, namely of which are age, educational level, marital status, chronic diseases, economical status, culture and geography. Measures of Health Related Quality of Life help us to find the extent of the effect of a disease on a patient's life. It aids in the assessment of a patient's life satisfaction and reflects the impact of health on an individual's quality of life. 

With a complex prescription and its longevity of usage, poor adherence to prescribed regimens can result in serious health consequences owing to poor compliance. Medication adherence is defined by the World Health Organization as "the degree to which the person's behaviour corresponds with the agreed recommendations from a health care provider'' \unskip~\citep{1171358:22828143}. Rates of non-adherence vary widely in literature, can be high and multifactorial. For instance, patients with chronic conditions are less likely to follow prescription orders than those with acute conditions. A patient's ability and willingness to follow a prescribed regimen directly influence the effectiveness of therapy. Thereby, a strict commitment to medication adherence and compliance is necessary for such cases. The present study is anticipated at determining the HRQOL, medication adherence, and its influencing factors. The results from such a study in the elderly population can help devise policies and strategies regarding therapies aimed at the improvement of patients' HRQOL and their optimal and positive clinical prognosis.
    
\section{Materials and Methods}
\textbf{Study Site}

The study was carried out at ESI MC \& PGIMSR, Rajajinagar, Bengaluru, which is a 500 bedded hospital.\textbf{\space }

\textbf{Study Ethics}

Ethical approval was obtained from the Institutional Ethics Committee (IEC) of ESIC Medical College \& PGIMSR, Rajajinagar, Bengaluru.\textbf{\space }

\textbf{Subject Recruitment and Study Population}

Patients aged 60 years and above who were taking 5 or more drugs during their admission in the in-patient wards of the Department of General Medicine were included in this study. Patients with specific comorbidities like psychiatric disorders, dementia or cancer, or unconscious patients who cannot provide data were excluded. Informed consent was taken from patients who were incorporated and consented to partake in the study.

\textbf{Study Tool}

The demographics, disease information, laboratory results, and medications were obtained on a case report form. 

\textbf{Short Form Health Survey 12 Questionnaire (SF-12)}

\unskip~\citet{1171358:22828139} that consists of 12 questions was used to assess the HRQOL regarding general health and well-being, including the impact of any illness on an extensive range of functional domains

\textbf{Morisky Green Levine Medication Adherence Questionnaire (4-item) (MGLMAQ)}

Medication adherence was measured by the MGLMAQ, which is a self-administered questionnaire consisting of four questions which a scoring system of YES=0 and NO=1 to assess the level of adherence to medications in patients. The items are summed to give a range of scores from 0 to 4 that distinguish high adherence from low adherence. \unskip~\citep{1171358:22828145}

The factors affecting medication adherence were measured by a 10-item self-administered questionnaire that was developed using patient information leaflets and fact sheets. The questions comprised of responses corresponding to YES and NO. The questionnaire was validated by experts in the field (general physician, statistician, faculty members) using the face and content validation method. The questionnaire was analyzed for its accuracy, use of jargon, relevance, and double-ended questions. The reformed questionnaire was then evaluated for its reliability using a pilot study with 20 random subjects and was modified based on the feedback.

All the questionnaires were reformed into English and the local language for easy comprehensibility.\textbf{\space }

The information obtained from the study was recorded in a spreadsheet, and appropriate statistical analysis was performed using online mathematical calculators. All the obtained values were considered significant if the p-value was less than 0.05.
    
\section{Results}
The study included 133 patients identified based on the inclusion and exclusion criteria, out of which 63.2\% were males, and 36.8\% were females. The age range of participants was 60-85 years, and the mean age was 66.68 \ensuremath{\pm} 5.49. The study subjects were categorized based upon their age groups, and the majority belonged to the age category 60-75 years (91\%). It had been observed that the subjects presented with multiple comorbidities, out of which the most commonly presenting condition was of cardiovascular origin (86.5\%).

The 12-Item Short-Form Health Survey (SF-12) was used as a tool to measure the HRQOL. It makes use of two parameters, namely, Physical Composite Score (PCS) and Mental Composite Score (MCS), to determine the overall Health Related Quality of Life of an individual. The Physical Composite Score (PCS) is calculated based on four sub-domains which are as follows: Physical Functioning, Role-Physical, Bodily Pain and General Health. The Mental Composite Score (MCS) is calculated based on four sub-domains which are as follows: Vitality, Social Functioning, Role-Emotional and Mental Health. The composite scores were obtained for these parameters and the descriptive statistics was calculated and analyzed as shown in Table~\ref{tw-f5608079cba6} and Figure~\ref{f-a9938e53ce0a} and Figure~\ref{f-f19a96d90bd1}. The mean PCS score was found to be 37.2 \ensuremath{\pm} 8.8 and the mean MCS was found to be 44 \ensuremath{\pm} 8.9. 


\begin{table*}[!htbp]
\caption{\boldmath {Distribution of the overall PCS and MCS} }
\label{tw-f5608079cba6}
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\tbltoprule \rowcolor{kwdboxcolor}\cAlignHack Parameters & \cAlignHack PCS & \cAlignHack MCS\\
\tblmidrule 
\cAlignHack Mean &
  \cAlignHack 37.2 &
  \cAlignHack 43.9\\
\cAlignHack Median &
  \cAlignHack 37 &
  \cAlignHack 45\\
\cAlignHack Mode &
  \cAlignHack 37 &
  \cAlignHack 48\\
\cAlignHack Standard Deviation &
  \cAlignHack 8.8 &
  \cAlignHack 8.9\\
\cAlignHack Minimum Value &
  \cAlignHack 20 &
  \cAlignHack 24\\
\cAlignHack Maximum Value &
  \cAlignHack 59 &
  \cAlignHack 69\\
\tblbottomrule 
\end{tabulary}\par 
\end{table*}

\begin{table*}[!htbp]
\caption{\boldmath {Mean Scores of SF-12 among the subjects} }
\label{tw-fd1729e9a2bc}
\def\arraystretch{1.1}
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\centering 
\begin{tabulary}{\linewidth}{p{\dimexpr.2272\linewidth-2\tabcolsep}p{\dimexpr.2189\linewidth-2\tabcolsep}p{\dimexpr.25\linewidth-2\tabcolsep}p{\dimexpr.3039\linewidth-2\tabcolsep}}
\tbltoprule \rowcolor{kwdboxcolor}\cAlignHack Scales & \cAlignHack Mean & \cAlignHack 95\% CI & \cAlignHack Standard Error\\
\tblmidrule 
\cAlignHack PCS &
  \cAlignHack 37.1 &
  \cAlignHack 35.7-38.7 &
  \cAlignHack 1.49\\
\cAlignHack MCS &
  \cAlignHack 43.9 &
  \cAlignHack 42.5-45.5 &
  \cAlignHack 1.51\\
\tblbottomrule 
\end{tabulary}\par 
\end{table*}

\begin{table*}[!htbp]
\caption{\boldmath {Distribution of the PCS and MCS of HRQOL based on age-group} }
\label{tw-aaccaec789a6}
\def\arraystretch{1.1}
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\begin{tabulary}{\linewidth}{p{\dimexpr.13379999999999997\linewidth-2\tabcolsep}p{\dimexpr.2999\linewidth-2\tabcolsep}p{\dimexpr.26860000000000003\linewidth-2\tabcolsep}p{\dimexpr.2977\linewidth-2\tabcolsep}}
\tbltoprule \rowcolor{kwdboxcolor}\cAlignHack  & \multicolumn{2}{p{\dimexpr(.5685\linewidth-2\tabcolsep)}}{\cAlignHack Scores (mean \ensuremath{\pm} SD)} & \cAlignHack \\
\rowcolor{kwdboxcolor}\cAlignHack Scales & \multicolumn{2}{p{\dimexpr(.5685\linewidth-2\tabcolsep)}}{\cAlignHack Age group} & \cAlignHack \\
\rowcolor{kwdboxcolor}\cAlignHack  & \cAlignHack 60{\textendash}75 (N = 121) & \cAlignHack 76{\textendash}85 (N = 12) & \cAlignHack Above 85 (N = 0)\\
\tblmidrule 
\cAlignHack PCS &
  \cAlignHack 37.1 \ensuremath{\pm} 8.6 &
  \cAlignHack 42.7 \ensuremath{\pm} 9.4 &
  \cAlignHack none\\
\cAlignHack MCS &
  \cAlignHack 44.3 \ensuremath{\pm} 8.7 &
  \cAlignHack 45.7 \ensuremath{\pm} 9.8 &
  \cAlignHack none\\
\tblbottomrule 
\end{tabulary}\par 
\end{table*}

\begin{table*}[!htbp]
\caption{\boldmath {Mean Scores of SF-12 (Age Group 60-75 years)} }
\label{tw-e3dbb71102f7}
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\tbltoprule \rowcolor{kwdboxcolor}\cAlignHack Scales & \cAlignHack Mean & \cAlignHack 95\% CI & \cAlignHack Standard Error\\
\tblmidrule 
\cAlignHack PCS &
  \cAlignHack 37.1 &
  \cAlignHack 35.6-38.6 &
  \cAlignHack 1.53\\
\cAlignHack MCS &
  \cAlignHack 44.3 &
  \cAlignHack 42.8-45.8 &
  \cAlignHack 1.55\\
\tblbottomrule 
\end{tabulary}\par 
\end{table*}

\begin{table*}[!htbp]
\caption{\boldmath {Mean Scores of SF-12 (Age Group 76-85 years)} }
\label{tw-e8ab9f14f660}
\def\arraystretch{1.1}
\ignorespaces 
\centering 
\begin{tabulary}{\linewidth}{p{\dimexpr.2479\linewidth-2\tabcolsep}p{\dimexpr.2085\linewidth-2\tabcolsep}p{\dimexpr.2314\linewidth-2\tabcolsep}p{\dimexpr.3122\linewidth-2\tabcolsep}}
\tbltoprule \rowcolor{kwdboxcolor}\cAlignHack Scales & \cAlignHack Mean & \cAlignHack 95\% CI & \cAlignHack Standard Error\\
\tblmidrule 
\cAlignHack PCS &
  \cAlignHack 42.7 &
  \cAlignHack 37.4-48 &
  \cAlignHack 5.32\\
\cAlignHack MCS &
  \cAlignHack 45.7 &
  \cAlignHack 40.2-51.2 &
  \cAlignHack 5.54\\
\tblbottomrule 
\end{tabulary}\par 
\end{table*}

\begin{table*}[!htbp]
\caption{\boldmath {Z-score for SF-12} }
\label{tw-9a75b404305f}
\def\arraystretch{1.1}
\ignorespaces 
\centering 
\begin{tabulary}{\linewidth}{p{\dimexpr.3238\linewidth-2\tabcolsep}p{\dimexpr.35279999999999994\linewidth-2\tabcolsep}p{\dimexpr.3234\linewidth-2\tabcolsep}}
\tbltoprule \rowcolor{kwdboxcolor}\cAlignHack Scale & \cAlignHack Z-score & \cAlignHack p-value\\
\tblmidrule 
\cAlignHack PCS &
  \cAlignHack -1.82 &
  \cAlignHack 0.03\\
\cAlignHack MCS &
  \cAlignHack -0.48 &
  \cAlignHack 0.31\\
\tblbottomrule 
\end{tabulary}\par 
\end{table*}

\begin{table*}[!htbp]
\caption{\boldmath {Key Factors Affecting Medication Adherence} }
\label{tw-3dbaa64fd8bf}
\def\arraystretch{1.1}
\ignorespaces 
\centering 
\begin{tabulary}{\linewidth}{p{\dimexpr.12759999999999998\linewidth-2\tabcolsep}p{\dimexpr.35989999999999995\linewidth-2\tabcolsep}p{\dimexpr.11940000000000001\linewidth-2\tabcolsep}p{\dimexpr.3931\linewidth-2\tabcolsep}}
\tbltoprule 
SD1 &
  Complexity of Prescription &
  SD6 &
  Refill of Prescription on Time\\
SD2 &
  Food Habits &
  SD7 &
  Use of OTC Drugs\\
SD3 &
  Family Member's Attitude &
  SD8 &
  Fasting (Religious Views)\\
SD4 &
  Dependence on Bystander &
  SD9 &
  Dose/ Drug Duplication\\
SD5 &
  Knowledge about Prescribed Drugs &
  SD10 &
  Social Stigma\\
\tblbottomrule 
\end{tabulary}\par 
\end{table*}

\bgroup
\fixFloatSize{images/47547225-3a47-4f5e-ae75-a2a0d05379b8-upicture1.png}
\begin{figure}[!htbp]
\centering \makeatletter\IfFileExists{images/47547225-3a47-4f5e-ae75-a2a0d05379b8-upicture1.png}{\includegraphics{images/47547225-3a47-4f5e-ae75-a2a0d05379b8-upicture1.png}}{}
\makeatother 
\caption{\boldmath {Distribution of Physical Composite Score A }}
\label{f-a9938e53ce0a}
\end{figure}
\egroup

\bgroup
\fixFloatSize{images/5b4cd914-52dc-4212-a6c7-74b5ac54dee1-upicture2-u1.png}
\begin{figure}[!htbp]
\centering \makeatletter\IfFileExists{images/5b4cd914-52dc-4212-a6c7-74b5ac54dee1-upicture2-u1.png}{\includegraphics{images/5b4cd914-52dc-4212-a6c7-74b5ac54dee1-upicture2-u1.png}}{}
\makeatother 
\caption{\boldmath {Mental Composite Score B}}
\label{f-f19a96d90bd1}
\end{figure}
\egroup

\bgroup
\fixFloatSize{images/1a0f9217-9e42-42a3-a2a0-1af1c97ef498-upicture3.png}
\begin{figure}[!htbp]
\centering \makeatletter\IfFileExists{images/1a0f9217-9e42-42a3-a2a0-1af1c97ef498-upicture3.png}{\includegraphics{images/1a0f9217-9e42-42a3-a2a0-1af1c97ef498-upicture3.png}}{}
\makeatother 
\caption{\boldmath {Distribution of the pattern of Medication Adherence}}
\label{f-d8721539aca8}
\end{figure}
\egroup

\bgroup
\fixFloatSize{images/8fb3008d-4517-4932-8912-130bb2525daa-upicture4.png}
\begin{figure}[!htbp]
\centering \makeatletter\IfFileExists{images/8fb3008d-4517-4932-8912-130bb2525daa-upicture4.png}{\includegraphics{images/8fb3008d-4517-4932-8912-130bb2525daa-upicture4.png}}{}
\makeatother 
\caption{\boldmath {Distribution of the Factors influencing Medication Adherence}}
\label{f-142caaabf451}
\end{figure}
\egroup
The assessment of HRQOL used simple statistical methods to establish cut-off points for average health, below-average health and above-average health. These methods are based on measures of variability (such as standard deviation and standard error) and the use of confidence intervals to define the average level of health, as shown in Table~\ref{tw-fd1729e9a2bc}.

From the results, it was interpreted that the average scores for PCS and MCS lie within the confidence interval (95\% CI), which included the zero point (average health) to be within the confidence interval. From a graphical analysis of Figure~\ref{f-a9938e53ce0a} and Figure~\ref{f-f19a96d90bd1}, it was observed that the frequency distribution was larger towards 100 (highest level of health) compared to towards 0 (lowest level of health) for both parameters. This indicated that a higher proportion of the subjects exhibited to have a better Health Related Quality of Life without any comparison against socio-demographic factors. 

The PCS and MCS scores of each age category were also obtained, as shown in Table~\ref{tw-aaccaec789a6}. The mean PCS and MCS were also compared between the age groups, as shown in Table~\ref{tw-e3dbb71102f7} and Table~\ref{tw-e8ab9f14f660}. Mann Whitney U~test was applied to determine if there were any significant differences between the two means of PCS and MCS between the age categories. The following statistical results were obtained, as shown in Table~\ref{tw-9a75b404305f}. In regard to PCS, there was a significant difference in the scores for increasing age and a change in HRQOL. But on the contrary, the MCS scores did not show any significant change, indicating the mental parameter is not widely affected with age. Morisky Green Levine Medication Adherence Questionnaire was used to analyze the medication-taking behaviour of the subjects. The study had about 66.2\% of subjects who were highly adherent to their prescribed drugs and only 15.8\% were poorly adherent to their prescriptions, as shown in Figure~\ref{f-d8721539aca8}. The study provided insights into a few factors that had an impact on medication-taking behaviour, as shown in Figure~\ref{f-142caaabf451}  and Table~\ref{tw-3dbaa64fd8bf}. From the results, it was observed that the questions which were responded ``YES'' to have the factor of ``usage of OTC drugs'' (60.1\%) as a major parameter that led the subjects to be non-adherent to their prescribed regimen, followed by "the dependency on their bystander" (57.1\%) to take their medicine. The least influencing factor was observed to be "the attitude of family members towards the elderly" (6\%). The questions which were responded, "NO" to have the factor ``drug or dose duplication'' (72.2\%), followed by ``knowledge of prescribed drugs'' (39.8\%) and ``refill of prescription on time'' (18.8\%) respectively, that played as major influencing factors leading to medication non-adherence. 
    
\section{Discussion}
The study included 133 patients, out of which 63.2\% were males and 36.8\% were females. The age range of participants was 60-85 years, and the mean age was 66.68 \ensuremath{\pm} 5.49. The Physical Composite Score and Mental Composite Score were compiled to determine the overall Health Related Quality of Life of an individual. The mean PCS MCS was found to be, which is similar to the findings in the study performed by \unskip~\cite{1171358:22828148}. In our study in regard to PCS, there was a significant difference in the scores for increasing age and a change in HRQOL. A possible explanation is that, as an individual ages, the physical functioning declines leading to poor Health Related Quality of Life in terms of physical parameters. But on the contrary, the MCS scores did not show any significant change, indicating the mental parameter is not widely affected with age. These scores were lower compared to the study performed by \unskip~\cite{1171358:22828144} and\unskip~\cite{1171358:22828148} respectively. Our study emphasized comorbidities as one of the reasons considered to affect both PCS and MCS score, but the results indicated that the mental parameter did not greatly affect the HRQOL in the elderly. This was seen as a contrast to a study conducted by \unskip~\cite{1171358:22828141} in old people, where HRQOL was not only decreased by aging but also by factors such as gender, literacy, and chronic disease conditions. Medication adherence appears to be one of the major factors that influence treatment outcomes. Medication non-adherence may result in treatment failure and a loss of time and money, finally resulting in changes in Health-Related Quality of Life. Various studies were conducted on medication adherence, and one such study conducted by \unskip~\cite{1171358:22828147}  showed that the level of compliance was high in around 45.41\% of subjects. This determined that the majority of subjects were adherent to their prescriptions. However, this study, as well as ours, was in contrast with the study conducted by \unskip~\cite{1171358:22828140} where it showed low medication adherence showing that the majority were non-adherent to their prescriptions. In this study, 66.2\% of the subjects were adherent to the prescription, and only a few but a significant number of subjects, that is around 15.8\%, showed poor adherence. The factors influencing medication adherence were observed to be about the knowledge about drugs prescribed, the refill parameters, drug duplication, use of OTC medication, and dependency on their bystander. The least important factor that affected medication adherence was family members' attitude towards the elderly. A study conducted by \unskip~\citet{1171358:22828142} showed that the main factors affecting medication adherence were poor knowledge about prescription and polypharmacy, drug storage, lack of knowledge about disease conditions, multimorbidities, and complexity of regimen. 

\textbf{Limitations}

The data was collected from a single hospital and thus may not be characteristic of the whole population. The sample size was less, and the study was carried out for a short duration. There was a lack of follow-up due to the ongoing COVID-19 pandemic.
    
\section{Conclusion}
The elderly population, majorly being inclined to several chronic and comorbid conditions, pose a greater possibility to present with a poorer quality of life. In this study, HRQOL was assessed in the subjects, which showed that the most affected domain was the physical parameter compared to the mental parameter. It also identified that the physical domain is significantly affected by increasing age, whereas the mental domain was not greatly affected with progressing age. The assessment did not include any comparison against socio-demographic factors influencing HRQOL, and this can be a prelude for future studies to determine its contributing elements. Assessment of medication adherence showed that the majority of the subjects presented to be highly adherent to the prescribed regimens, although the level of non-adherence was also significantly high that is contributed by a multitude of factors. Thus, it is suggestive that providing adequate information and counselling to the patients and bystanders about proper drug usage during their hospital stay and discharge can significantly improve medication adherence and compliance.
\section*{Acknowledgement}We are grateful to Dr. Gangaboraiah A, Professor of Statistics, Department of Community Medicine, Kempegowda Institute of Medical Science, Bengaluru, for his contribution to the statistical analysis of our study. We also are grateful for the guidance provided by our institutional and hospital guide, Dr. Divya Mol EC, Department of Pharmacy Practice and Dr. Nandini Swamy, Senior Specialist, Department of General Medicine, ESI MC \& PGIMSR, Bengaluru.

\textbf{Conflict of Interest}

The authors declare that they have no conflict of interest.

\textbf{Funding Support}

The authors declare that they have no funding support for this study. 
    

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