Evaluation of Medication non-adherence and Associated Factors among Diabetic Patients in Tertiary Care Hospital Lahore, Pakistan

Diabetes is a dangerous disease that affects people all over the world. There is a natural rise in prevalence of the disease, which is a major cause for concern. Diabetes prevalence in patients of all ages was estimated to be 2.8 percent in 2000 and 4.4 percent in 2030 over the world. The global diabetes population is expected to grow from 171 million in 2000 to 366 million by 2030. Men have a higher prevalence of DM than women. Assisting and counselling patients to follow often complicated treatment regimens and achieve strict blood glucose control is a major challenge that must be met at all stages of diabetic treatment. A large number of diabetic outpatients self-administer their prescriptions without consulting a physician or prescribing a medication. As a result, the efficacy and effectiveness of interventions can be strongly influenced by the patient's conduct and trust. Patients' perceptions of the necessity of glycemic control, as well as their ability to self-care, have been examined as factors that may influence patient compliance. Therefore, the goal of this study is to determine the degree of non-adherence, the causes for non-adherence, the prevalence of non-adherence, and the factors that contribute to non-adherence among diabetes patients (Abdulazeez, Omole et al. 2014).

Non-adherence may be caused by patient-related factors such as age, gender, patient education, or therapy-related factors such as route of administration, duration of treatment, difficulty of treatment, and medication side effects (Divya and Nadig 2015).

Non-adherence was found to be anywhere between 21.8 and 25.4 percent in Ethiopia. In a previous study, the percentage of non-adherence was used to indicate various outcomes in diverse settings. These subjects were non-adherent to diabetes therapy in Sudan 55%, Asia 21.9%, Switzerland 80%, Botswana 41.8%, Nigeria 73.64–86.6%, and Ghana 31.5%. Diabetes knowledge, disease length, perception of consequences, psychological issues, and forgetfulness all influence non-adherence. As a result, noncompliance with medication led to more emergency room visits and hospitalizations (Abate 2019).

Diabetes is a metabolic illness with numerous etiologies defined by chronic hyperglycemia and abnormalities in carbohydrate, lipid, and protein metabolism caused by insulin production, insulin action, or both. Pharmacotherapy, dietary adjustments, and lifestyle changes are all part of diabetes management. Oral anti-diabetic medications (OADs) or insulin are used to treat Type 2 diabetes. The main source of energy is blood glucose, which comes from the food you eat (Sweileh, Sa’ed et al. 2014).

Insulin, a hormone produced by the pancreas, aids glucose absorption into your cells for use as energy. Your body may not produce enough or any insulin, or it may not use insulin effectively. Glucose then lingers in your bloodstream, preventing it from reaching your cells. Having too much glucose in your blood might lead to health issues over time. Although there is no cure for diabetes, there are steps you may take to manage your diabetes and stay healthy. Diabetes is sometimes referred to as "a smidgeon of sugar" or "borderline diabetes." There are numerous forms of diabetes. Type 1, Type 2, gestational diabetes, monogenic diabetes, hereditary diabetes, and cystic fibrosis-related diabetes are among them (Lu, Li et al. 2021).

Because of its increased burden and effects, diabetes is one of the four priority non-communicable diseases targeted for action globally. Diabetes affected 30.3 million persons in the United States in 2015, accounting for 9.4% of the population. More than one-fourth of them were unaware that they had the condition. One in in four people over the age of 65 has diabetes. In adults, type 2 diabetes accounts for 90-95 percent of occurrences (Olickal, Chinnakali et al. 2021).

The extent to which a person's conduct in terms of taking drugs or implementing lifestyle modifications in accordance with health professional advice is referred to as drug therapy adherence. Non-adherence occurs when a patient fails to follow a prescription or stops using it for a variety of reasons, both intended and unintended. Non-compliance raises the likelihood of relapse, poor therapy outcomes, and even fatalities (Atinga, Yarney et al. 2018).

Non-compliance with prescribed medication and drug schedules has been a persistently high-profile issue around the world. According to studies on the subject, adherence to drugs for chronic conditions is around 50%, but adherence to lifestyle recommendations is substantially lower. Diabetes is well recognized as one of the most psychologically and behaviorally prevalent chronic illness causes. So, in order to avoid detrimental long-term effects that damage patients' quality of life, increase mortality, morbidity, and societal costs, patients must strictly follow to their prescribed medications in order to limit the disease's burden on health systems. In chronic diseases, non-adherence is defined as failing to complete more than 80% of the prescribed treatment (Kalyango, Owino et al. 2008).

Furthermore, depression is a risk factor for diabetes, and diabetes increases the likelihood of depression developing. Depression is widespread among diabetic patients, and it can lead to poor adherence to medication and dietary changes, physical immobility, poor glycemic control, bad quality of life, disability, and higher health-care costs. A number of studies have shown that a variety of primary care therapies can help people with diabetes and depression. However, only a few of these approaches have been put into practice. For patients with severe, long-term difficulties, a combination of services, doctors, and places is essential to improve quality of treatment, quality of life, client trust, and system efficacy.

It has been assessed that many patients do not stick to their treatment regimens, putting them at risk for a variety of issues. Poor treatment adherence is still a major roadblock to better care, particularly among patients with diabetes and depression. In comparison to people who are not sad, depressed diabetic patients are less likely to stick to drug regimens and have poor diabetes control. To improve adherence to recommended drugs, the therapy of comorbid depression and diabetes should be coordinated and assessed for preference, patience, and clarity. Only two randomized controlled studies for the management of depression and diabetes were found in a review of the literature. Katon and colleagues at Group Health Cooperative, a nonprofit health maintenance organization based in Seattle, Washington, tested an exhausted intervention for adults with major depression, poorly controlled diabetes, and/or coronary heart disease, which was delivered by an advanced-practice nurse. They discovered that the intervention significantly improved medical disease and depression control (Bogner, Morales et al. 2012).

In order to minimize the sugar level, try to cut sugar or processed carbs from your diet, for example, to assist manage diabetes. Work out on a regular basis, drink plenty of water, lose weight if you're obese, quit smoking, eat a very low-carb diet, avoid sedentary habits, eat a high-fiber diet, and boost your vitamin D levels (Bogner, Morales et al. 2012).

The four-item Morisky Green Levine Medication Adherence Scale is one of the most widely used self-report measures of medication nonadherence (MGLS). The MGLS comprises items that analyze the patient's reasons for nonadherence, since it is frequently used for measuring the degree of pharmaceutical non-adherence. As a result, the MGLS fits more appropriately into the category of a measure of the causes of medication non-adherence rather than a measure of the extent of medication non-adherence i.e., an impact indicator model. The consequences of these two models for measure validation are rather different. In an effect indicator model, a measure must have a high inter-item correlation and internal consistency, whereas in a causal indicator model, the items should ideally reflect diverse and possibly unrelated features of the construct. As a result, causal indicator models are unlikely to have strong internal consistency or inter-item correlation. Because the MGLS has been widely utilized as an effect indicator model, we analyze it within this framework to see if practical data support its use as a measure of medication non-adherence. The goal of this study was to assess the MGLS's reliability and factorial validity as a measure of medication non-adherence in an elderly sample from four different locations in the (Beyhaghi, Reeve et al. 2016).

A study employed the four-item MGLS in this study, which consists of four questions with yes/no response alternatives. The MGLS gives a score from 0 to 4, and the creators indicated three categories of medication adherence based on this score: high, medium, and low adherence, with 0, 1 to 2, and 3 to 4 points, respectively. A MGLS-based dichotomous definition of adherence is also popular, with 0 points indicating complete adherence and 1+ points indicating some level of non-adherence. Other indicators of medication adherence utilized in the visit 5 survey, in addition to the MGLS, were: three questions about medication nonadherence with a 4-week recall period, a single question directly asking the percent of time a participant was fully adherent in the previous 4 weeks, and self-reported reasons for medication nonadherence for cohort participants who reported less than 100 percent adherence in the previous 4 weeks (Beyhaghi, Reeve et al. 2016).

Self-report, manual and electronic counts of medication, retrieval of medication from pharmacies, laboratory tests for pharmaceuticals or pharmaceutical metabolites, and questionnaires have all been used to assess low adherence. Despite their low sensitivity and accuracy, questionnaires are more commonly used in large populations due to their inexpensive cost and convenience of use. When combined with other approaches such as computerized pill counts, this equipment can help distinguish between low adherence and non-response to diabetic medication Medication Events Monitoring System- MEMS. The Morisky-Green Test is a method of determining whether or not a person is MGT. With 100 patients, the Brief Medication Questionnaire (BMQ) was validated in English using MEMS as the gold standard. The tool is separated into three screens that detect barriers to drug regimen adherence, patient beliefs, and medication treatment recall. Although it has not yet been tested in diabetes patients, the initial study found that the regimen screen had a sensitivity of 80% and a specificity of 100%. As a result, the BMQ tool looks to be possibly superior to the MGT, particularly in terms of screening for non-adherent behavior, but the two tools were tested in different circumstances. Both instruments' utility in the clinic and in research would be better understood if they were evaluated in the same cohort (Ben, Neumann et al. 2012).

Type 2 diabetes is caused by an insulin secretory malfunction that leads to decreased sensitivity to the effects of current insulin. Fasting and post-prandial hyperglycemia, as well as relative insulin insufficiency, are all symptoms of the condition. Poor blood glucose management can lead to long-term microvascular and macrovascular problems, such as nephropathy, neuropathy, retinopathy, and cardiovascular disease, if left untreated (CVD). Type 2 diabetes is a worldwide epidemic, with rates of occurrence increasing at an alarming rate in both industrialized and developing nations. In the previous 15 years, the global prevalence of type 2 diabetes has increased fivefold. In 2010, 200 million individuals were predicted to have type 2 diabetes, with that number expected to rise to 300 million by 2025 (Jarab, Alqudah et al. 2012).

Diabetes mellitus (DM) and cancer are two diseases that can present an additional clinical burden for patients and health-care professionals, as well as a considerable decline in patient-related outcomes. According to previous research, up to 18 percent of all cancer patients have pre-existing diabetes. Patients who get cancer as a result of diabetes have a reduced response rate to the prescribed treatment. When compared to cancer patients without diabetes, this complicates the management of both conditions by raising the chance of developing glycemic difficulties, hospitalization for chemotherapy-related complications, and greater mortality rates (Al?Taie, Izzettin et al. 2020).

Type 2 diabetes management is complicated, and it necessitates continual medical care as well as patient self-management education and assistance to avoid acute problems and lower the risk of long-term complications. Intensive glycemic management has been linked to better cardiovascular and microvascular outcomes in several observational studies. RCTs have shown that tight glycemic control hemoglobin A1c less than 7% is associated with a lower risk of microvascular consequences in patients with type 2 diabetes (Jarab, Alqudah et al. 2012)

LITERATURE REVIEW

The goal of this study was to detect medication non-adherence and assess the factors that influence it among Type 2 diabetic mellitus DM patients in a tertiary care hospital in South India. A cross-sectional study was conducted on 150 Type 2 diabetes patients. The Morisky Medication Adherence Questionnaire, which has eight items, was used to assess adherence. Age, gender, literacy, diabetes duration, complications, economic concerns, patient awareness of their medicine, and side-effects were all parameters that were investigated using a validated questionnaire. With descriptive statistics, the percentage of non-adherence and the factors that contribute to non-adherence were examined (Divya and Nadig 2015).

A similar Research into why diabetic and hypertensive patients were taken on in which patients who are on long-term pharmacological therapy skip doses or stop taking their medications is sparse. At a Ghanaian teaching hospital, we looked at this phenomenon through the eyes of diabetic and hypertensive patients. We performed a qualitative study between July and December 2015, focusing on caregivers and their patients with chronic diabetes and hypertension who were re-admitted to the Korle Bu Teaching Hospital due to non-adherence to recommended medication. Participants were intentionally sampled and put through in-depth interviews with the use of an interview guide. Thematic network analysis, as advocated by Attride-Stirling, was used to transcribe, manage, and code notes and audio recordings of interviews for themes. So Non-adherence stemmed from the belief that the medications were ineffective in treating the diseases. Because of their ease of availability, perceived efficacy, and affordability, patients with these perceptions rejected drugs and turned to herbal remedies and spiritual healing as therapeutic alternatives (Atinga, Yarney et al. 2018).

In order to determine the diabetic analysis, the data was collected including Males made up more than two-thirds of the patients 69.3%. In total, 82 individuals were found to be non-compliant with their medications 54.66 %, 16 patients 17.08 % were illiterates among the non-adherent patients, 32 patients 39.02% had financial difficulties in purchasing prescribed medications, 53 patient of 64.63% had a lack of information about prescribed medications, 37 patient of 45.12% were unaware of the side effects of the prescribed medication, 59 patient of 71.95% were unaware of what happens when prescribed medications are not taken on a regular basis, and 39 patient of 47.56% were not aware of what happens when prescribed medications are not taken on a regular basis. So Inadequate patient education and awareness regarding the importance of adherence in diabetes care contributes to non-adherence to medications in Type 2 DM patients. As a result, there is a clear need to increase patient adherence through enhancing the health-care system and providing patient and family health education (Divya and Nadig 2015).

A similar study was conducted to determine medication non-adherence and associated variables among adults with diabetes at Felege Hiwot Referral Hospital in Bahir Dar. A cross-sectional study was done among 416 randomly selected diabetes patients at the Felege Hiwot Referral Hospital to overcome this obstacle FHRH. Medication non-adherence was assessed using an eight-item Morisky Medication Adherence Scale questionnaire. The collected data was analyzed using binary logistic regression. Between the dependent and explanatory variables, a P-value less than 0.05 with a 95 percent confidence interval was considered statistically significant. The results showed that 242 of 58.2% of the 416 participants were diabetic men. The study participants' average age SD was 45.4 to 16.7 years. Non-adherence to diabetic treatment was 68.8% on MMAS-8 scale. The age group of 18 to 35 years old includes CI, being single, those having fear of diabetes-related complications, those feeling worsen condition were of 95 % population were all found to be significantly associated with non-adherence to prescribed diabetes medications in the multivariate analysis. As a result, boosting the quality of prescribed drug compliance and adopting a more intense communication plan could help (Abate 2019).

Polypharmacy, tight work schedules, societal norms, poor prescription education by health practitioners, and knowledge and experience with medicine were all recognized as factors that impact non-adherence. The findings imply that while delivering drugs, health caregivers should use treatment approaches that take into account patients' views, values, and norms. Patients and caregivers are sensitized to the consequences of non-adherence during admission, and interventions that monitor and offer feedback mechanisms on patients' medication taking behavior have the potential to improve diabetic and hypertension medication adherence (Atinga, Yarney et al. 2018).

Moreover, as the Diabetes mellitus is a major health disease that affects many people. In individuals with diabetes mellitus, medication adherence is a critical factor of therapeutic success. The goal of this study was to look at medication adherence in people with type 2 diabetes and see if there was any link between beliefs and diabetes knowledge. The research took place in the Al-Makhfia governmental diabetes basic healthcare facility in Nablus, Palestine. The study's primary focus was on medication adherence. To measure beliefs, the Beliefs about Medicines Questionnaire BMQ was employed. Medication adherence was measured using the Morisky Medication Adherence Scale MMSA-8. To assess diabetes-related knowledge, the Michigan diabetes knowledge exam MDKT was employed. Statistical Package for Social Sciences was used to conduct univariate and multivariate analyses SPSS 20. A total of 405 patients were questioned. The participants' mean SD age was 58.3 10.4 years ranges from 28 – 90. Females made up more than half of the participants 53.3%. A total of 42.7 & of the trial participants were non-adherent MMAS-8 score of 6. Diabetic patients with a high knowledge score and strong beliefs in the importance of their anti-diabetic treatments were less likely to be non-adherent and that is of 95 %. Diabetic patients with strong concerns about anti-diabetic drug side effects and those who believe all medicines are dangerous were more likely to be non-adherent also up to 96% respectively. The, BMQ can be used to identify those who are at a higher risk of noncompliance. Patients' understanding of their ailment may have a favorable impact on their medication adherence (Sweileh, Sa’ed et al. 2014).

As we know that Noncompliance with diabetes therapy results in poor glucose control and a higher risk of disease complications. The prevalence and determinants related with non-adherence in resource-constrained settings should be identified in order to reduce the impact of a disease on health systems that are already overburdened with communicable diseases. Therefore, to determine the prevalence and factors associated with non-adherence to diabetes treatment. From February to April 2004, a cross-sectional study was conducted at Mulago Hospital in Uganda. The participants were 402 type 1 and 2 diabetes patients who were randomly recruited from the outpatient diabetic clinic. They had to be at least 18 years old, take diabetes medication for at least one month, and give informed agreement to participate. Patients' self-reports were used to assess non-adherence (Kalyango, Owino et al. 2008).

Mostly the noncommunicable diseases, such as diabetes mellitus, are becoming more prevalent in all countries, regardless of their development status. There is a lot of proof which ensures that, the existence of a gap in the level of diabetes mellitus and the avoidance of its complications in developing countries, there are control measures in place. The goal of this study was to determine the prevalence of diabetes mellitus in both younger and older people in a low-income country, ageing population and to determine the elements that influence them. This is a community-based comparative cross-sectional study that was carried out in Ethiopia, a low-income country. EPI-Info determined the sample size for two populations: the WHO's STEP-wise method for noncommunicable disease surveillance in poor countries and the general public. For sampling, study variable selection, and data collection procedures, nations were used. After an overnight fast, fasting blood glucose levels were tested by finger pricking. The EPI-data computer application version 3.1 was used to enter the data, which was then processed using SPSS version 20 (Animaw and Seyoum 2017).

Non-adherence was found to be 28.9% of the time. Usually, the female gender not understanding the drug regimen well, affording only some or none of the prescribed drugs and longer time since last visit to a health worker were all independently associated with non-adherence. In short, the description diabetic treatment adherence was inadequate. It must be improved by techniques such as assisting patients in understanding their drug regimens, always having drugs available in the hospital so that they do not have to purchase them, and reducing the time between visits to health care providers. More research is needed to determine why females were not adhering to treatment and how to enhance their adherence (Kalyango, Owino et al. 2008).

As the concerned correlations between individuals' diabetes status and potential predictor variables were assessed using bivariate and multivariate logistic regression analyses. A statistically significant level was defined as a P-value of less than 0.05. The study included 1405 people ranging in age from 18 to 97 years old. The average fasting blood glucose level for study participants was 91.16mg/dl, with urban dwellers' levels at 94.73mg/dl and rural dwellers' levels at 87.71mg/dl. Diabetes mellitus affected 3.3 percent of the population, while it affected 2.0 percent of rural residents and 4.6 % of city people. The prevalence of diabetes mellitus and the mean blood glucose level were substantially greater in urban inhabitants than in rural residents. This study found that more than three-quarters of diabetic cases had never been diagnosed before. Urban Diabetes mellitus is more likely among city dwellers, centrally obese, overweight, and hypertensive people. Diabetes mellitus was found to be very common in both the elderly and the young. During this survey, the majority of diabetes patients were discovered unexpectedly. The issue is this because the prevalence of diabetes mellitus and its complications is worrying, special attention should be paid to their control and prevention (Animaw and Seyoum 2017).

The noncompliance with medications may diminish the efficacy of treatments. Thus, link between pharmaceutical nonadherence and death has yet to be investigated outside of clinical trials, to our knowledge. Therefore, a retrospective cohort analysis of 11532 diabetic patients in a managed care organization was conducted. The proportion of days covered for filled oral prescriptions was used to calculate medication adherence. Hypoglycemics, antihypertensives, and statin medications are some of the most common. All-cause hospitalization and all-cause mortality were the major outcomes of interest. To determine the independent variables, multivariable regression analyses were used. There is a link between drug adherence and positive results. Nonadherent patients were younger and had a higher prevalence of 21.3%. When compared to adherent individuals, there are less comorbidities (Ho, Rumsfeld et al. 2006).

Diabetes and cancer occurring at the same time might make managing both disorders more difficult, resulting in a poor prognosis and worsening patient-related outcomes. To see how successful clinical pharmacy services and a pharmacist-led counselling programmed are at improving patient-related outcomes during chemotherapy delivery in patients with diabetes and newly diagnosed cancer. A prospective, randomized, controlled trial was conducted on patients newly diagnosed with cancer who had diabetes throughout chemotherapy delivery in an outpatient oncology environment. Patients were divided into two groups: those who received just normal treatment from oncology care providers and those who received both normal and clinical pharmacy care as part of a thorough oral and written patient education programmed with a three-month follow-up, medication optimization and regular recommendations for diabetes self-care activities were made. Patients in the intervention group had better glycemic control (p =.0049), a significant increase in medication adherence (p =.0049), a significant increase in diabetes self-care activities (p =.037, self-monitoring of blood glucose (p =.027, and foot care (p =.0085), and reported a lower deterioration in quality of life. When compared to the usual treatment group, patients with diabetes and cancer receiving chemotherapy had better patient-related outcomes after clinical pharmacy intervention and counselling (Al?Taie, Izzettin et al. 2020).

A similar type of study was conducted with glycemic targets having hemoglobin 7% are frequently missed despite the availability of numerous viable therapies and the known benefits of glycemic control in people with type 2 diabetes. Long-term microvascular and macrovascular problems are reduced. Several studies have demonstrated the significant benefits of pharmacist-patient interactions. consulted with management on glucose control and other clinical issues improve diabetic individuals' outcomes. Jordan's diabetes prevalence and fatality rates are quickly rising. Clinical pharmacists in Jordan, on the other hand, often do not provide pharmaceutical treatment; instead, their primary responsibilities are dispensing and selling medicinal items to physicians. In an outpatient diabetes clinic, patients with type 2 diabetes were randomly assigned to either usual care or a pharmacist-led pharmaceutical care intervention programmed to assess the primary clinical outcome of glycemic control as well as secondary outcomes such as blood pressure, lipid values, self-reported medication adherence, and self-care activities (Jarab, Alqudah et al. 2012).

Nonadherent individuals had higher levels of glycosylated hemoglobin, systolic and diastolic blood pressure, and low-density lipoprotein cholesterol at the end of the study. Nonadherent patients showed higher all-cause hospitalization 23.2% vs 19.2% and mortality rates in unadjusted analyses, a greater rate of death from any cause 5.9% vs 4.0%. Medication nonadherence was found to be persistent in multivariable analysis. Significantly linked to a higher incidence of all-cause hospitalization and for all-cause mortality shows 95% of the confidence interval. The results were comparable across patient subgroups and varied proportion cutoffs for the proportion of patients with diabetes. So, in brief the medication nonadherence is common among diabetic patients and is linked to complications, having negative consequences. In order for patients to achieve the benefits of medication adherence, interventions are required to complete advantage of prescription treatments (Ho, Rumsfeld et al. 2006).

Concise Moreover, description Patients with type 2 diabetes who attended an outpatient diabetes clinic at a big teaching hospital were recruited and randomly assigned to intervention and usual care groups using the Minim software technique throughout a four-month period from January to April 2011. At the start of the study, the intervention group received face-to-face objective-directed education from a clinical pharmacist about type 2 diabetes, prescription medications, and necessary lifestyle changes, followed by eight weekly phone follow-up calls to discuss and review the prescribed treatment plan and address any patient concerns. Glycemic control was the primary outcome measure, with systolic and diastolic blood pressure, total cholesterol, low-density lipoprotein cholesterol [LDL-C], high-density lipoprotein cholesterol [HDL-C], serum triglycerides, and self-reported medication adherence using 4-item Morisky Scale and self-care activities as secondary measures. Data was collected at the start of the study and again six months afterwards. Biomarker levels were changed from baseline to follow-up, and between-group differences in change amounts were examined using the t test for independent samples. A total of patients 90.6% randomly assigned to the intervention group and patients 91.9% assigned to usual care had baseline and 6-month follow-up values. Compared with baseline values, patients in the intervention group had a mean reduction of 0.8% in a mean increase of 0.1% from baseline in the usual care group P = 0.019. The intervention group compared with the usual care group had small but statistically significant improvements in the secondary measures of fasting blood glucose, systolic and diastolic blood pressure, total cholesterol, LDL-C, serum triglycerides, self-reported medication adherence, and research self-care activities. Between-group differences in changes in the secondary measures of HDL-C and body mass index were not significant. Patients with type 2 diabetes who got pharmacist-led pharmaceutical care in an outpatient diabetes clinic had a drop in A1c after six months, whereas the conventional care group saw little change. Six of the eight secondary biomarkers in the intervention group improved as compared to standard treatment (Jarab, Alqudah et al. 2012).

METHODOLOGY

This was a cross sectional study with patient recruited from July 10^th to September 8^th 2021 at diabetic clinic of Gulab Devi Chest hospital Lahore Pakistan. This hospital provides preventive, promotive, curative and rehabilitative services. So many patients were registered there for follow-up in the previous year. Approximately 150 patients with Diabetes visited there for follow-up in out-patient department every weak. And they visited for every 2 months basis.

The sample size of this study was 100 patients with DM. The inclusion and exclusion criteria were developed for selecting population. Patients of age 18 year to 70 year were included in this study and also patient with all types of DM was included in this study. Patient of age under 18 or above 70 years, Patients with covid-19, Pregnant and nursing mothers, Patients with gestational diabetes were excluded from this study.

Study was conducted after approval from Board of studies of GDEC and Hospital-Institutional Review Board. After approval from these authorities data collection was started.

Convenient and random sampling was used to select eligible participants. The duration of sampling was 1 month based from the population and sample size. The participants were face to face interviewed and collect the data from participants by using Data Collection form.

Data collection form consists of two parts i.e. first part contain the socio-demographic characteristics, pre medication and current medication history and lab values. The other part of DCF includes Green Morisky Levine scale.

In MGL scale, we used four questions that we asked from the patients. These questions have YES/NO response options.  The MGLS results in a score ranging from 0 to 4 and the researcher suggested three level of medication adherence on the basis of the score i.e. high, medium and low adherence with 0,1,2,3 and 4 respectively. 0 indicates perfect adherence, 1+ indicating some level of non-adherence.   

After collecting data from participants it enter in MS Excel and then further calculation done on that data to find the results.

RESULTS

One hundred patients of diabetes were involved in this study. From the result gotten, the number of female patient was 69 (69%) and male patients was 31 (31%) (Table no 1). The most of the patient’s age was between 29 to 61 years which was 84%. And most of the patients were married (93%). Many of the females were house wives (61%) and other patients were employed (39%).Most of the patients were lived in urban areas (57%). The educational level of most of the patients was up to intermediate (76%) (Table no 1).

The diagnosis of the most of the patients was Diabetes type II (77%) and rest of them had Diabetes type I (23%) (Table no 2). Many patients have comorbities in which 48% patients have muscular weakness due to diabetes, 18% patients have hypertension, 12% have joint problem and 8% have congestive heart failure (Table no 2). 63% of the patients have normal blood pressure at follow up day. And 37% have hypertension on the checkup day. All the patients monitored their blood glucose level at regular interval of time (Table no 2).

For evaluating medication non-adherence, we use Morisky Green Levine scale, we obtained the results that the average prevalence of medication non-adherence among diabetic patients was 33.5%. This average was obtained from the values of four different questions that we asked from patients in MGL scale. The prevalence of patients that were adhering with their medication was 66.5% (Table no 3).

The result of MGL scale also shown with different technique, in this we recorded the percentage of the patients that answer to “Yes” with o to 4 questions. 23% patients answered “yes” to 0 items (high adherence), 33% patients answered “yes” 1 items (medium adherence), 30% patients answered “yes” to 2 items (medium adherence), 11% answered “yes” to 3 items (low adherence), 1% answered “yes” to 4 items (Table no 4).

Table no 1:  Frequency distribution and Percentage of demographics

Table no 2: clinical and lab data representation

Table no 3: Response to Morisky Green Levine Adherence scale

Table no 4: Response to medication adherence scale