Non-adherence to depression treatment is a common clinical problem globally. However, limited research is available from India. This cross-sectional study aimed to assess non-adherence to prescribed treatment among patients with unipolar depression at a psychiatric out-patient department (OPD) of a tertiary hospital in Kolkata, India. The Morisky Medication Adherence Scale (MMAS) was used and a questionnaire designed by the Principal Investigator (PI) was administered. A total of 239 patients with unipolar depression were interviewed of whom 66.9% (160) were non-adherent and 33.1% (79) were adherent to treatment. The difference was significant (Fisher’s Exact
Adherence (“extent to which a person’s behaviour corresponds with medical or health advice provided by a health care provider”) [
Non-adherence to treatment is a well-documented issue in the care of unipolar or major depression. According to World Health Report, 1999 [
The World Health Organisation (WHO) categorised the determinants of non-adherence into five dimensions: social and economic, health system-related, therapy-related, condition-related, and patient-related [
A meta-analysis of studies conducted between 1975 and 1996 in the USA showed that patients on antidepressants took an average of more than three-fifths (65.0%) of the prescribed amount as compared to 76.0% adherence in physical disorders [
Few studies from India have aimed to comprehensively assess factors affecting non-adherence to prescribed treatment among those diagnosed with unipolar depression. This motivated the current study which aimed to assess correlates (sociodemographic variables, therapy associated factors, impact of multi-drug treatment and co-morbidities, locus of control, reasons) of non-adherence among patients diagnosed with unipolar depression in a psychiatric department of a tertiary hospital in Kolkata, India.
This cross-sectional study was conducted among adult patients visiting the psychiatric out-patient department (OPD) of a tertiary hospital in Kolkata. This is one of the oldest medical colleges and tertiary care public hospitals well connected by all modes of transportation-buses, trains, trams, and the underground metro. Easy accessibility makes it one of the busiest hospitals in the city. The psychiatric OPD in the hospital is open on six days a week. Patients pay a nominal fee of Rs 2.00 for a card registering them for consultation. Prescribed medicines are written on this OPD card and can be used on multiple occasions. Medications such as amitriptyline, fluoxetine, sertaline (antidepressants), and clonazepam (anxiolytic) among others are dispensed free of cost to the patient from the hospital dispensary. The psychiatric department (including indoor facilities) and the OPD are housed in two separate buildings. The clinical team comprises 10 senior psychiatrists including the head of department, professors and medical officers. Other staffs include interns, post-graduate trainees, nurses, security staff and a documentation officer. Although there is no provision for counsellors or psychiatric social workers in the department, there is one social worker for the entire hospital. In the absence of psychiatric social workers and the busy schedule of the doctors, by rotation the nursing staffs assist in the OPD by explaining to patients how prescribed medicines have to be taken. To aid the process of medication intake, medicines are dispensed in small white paper packets (separate ones for different medicines) on which nurses write instructions in Bengali. Bengali is the common language spoken and understood by all—both patients and medical professionals.
The sample size planned for this study was 305 which arrived based on information provided by a study conducted by Chakraborty and colleagues in 2009 [
Adult patients, both men and women attending the psychiatric OPD and had been diagnosed with unipolar depression at least six months prior to the commencement of the study, were approached. The upper age limit of 60 years was selected to rule out the possibility of including patients with dementia. Only those willing to participate were recruited in the study. All physicians were made aware of the study and they agreed to send patients who met the inclusion criteria. In spite of best efforts, some patients may have been missed from this overcrowded OPD but, this number was negligible. Moreover, the register containing patient details recorded the patient prescription number only (no diagnosis). The problem was further compounded by the fact that each individual was not provided with a unique identification number whereby tracing patients missed from the OPD register could not be considered as an option for inviting them to participate in the research.
Patients meeting ICD-10 [ Men and women between 18–60 years of age. In treatment for at least six months prior to commencement of study. Currently in remission—diagnosed as “unipolar depression in remission” (the definition of remission offered by Zimmerman and colleagues), 2006 was followed: symptomatic remission judged by the clinician and functional recovery (e.g., presence of features of positive mental health such as optimism and self-confidence; a return to one’s usual, normal self; and a return to usual level of functioning) as judged by the patient [ Other co-morbid conditions excluding patients with psychotic features, diagnosed bipolar affective disorder. Willing to participate and provide written informed consent.
Information exclusively from care givers. Individuals refusing to participate.
Data collection was carried out by the principal investigator (PI) only after obtaining informed/understood consent. The 8-item Morisky Medication Adherence Scale (MMAS), a validated instrument, was administered to measure adherence. The MMAS is a reliable tool that has been widely used to measure adherence particularly among hypertensive patients. It was developed in 2008 and is a modified version of the 4-item MMAS. The MMAS is an 8-item structured instrument where seven questions have dichotomous (Yes, No) responses. There are five response options in the eighth question. It has a reliability of 0.83 along with good predictive validity. Although the application of this tool is limited in the domain of mental health, nevertheless it is an appropriate instrument for measuring specific medication behaviour in chronic diseases [
The cross-language equivalence procedure was followed to adapt the Bengali version of MMAS. The tool was translated in Bengali and back translated into English by two independent persons, one a social scientist and the other a school teacher, none of whom were part of the research. Their agreement was 98.6%. Face validation of MMAS was carried out by two psychiatrists, one from Kolkata and the other from United Kingdom (UK), none of whom were involved with the study and their agreement was 99.1%.
In addition, a semi-structured interview schedule was developed by the PI to explore demographic and treatment-related factors, the impact of multi-drug treatment, co-morbidities, and LOC on adherence. The interview schedule was designed after considerable literature review and consultations with social scientists, epidemiologists, psychiatrists, and psychologists at Sree Chitra Tirunal Institute for Medical Sciences and Technology (SCTIMST), Achutha Menon Centre for Health Sciences Studies (AMCHSS), and a consultant psychiatrist from UK. The interview schedule was translated into Bengali by two independent individuals, one a social scientist and the other a school teacher, none of whom were involved in the research endeavour. The interview schedule was pretested in the OPD which took no more than 30 minutes. Modifications and suggestions were incorporated in the final version of the interview schedule. The interview schedule was divided into four sections, namely, demography (Section
Ethical clearance was obtained from both Technical Advisory Committee (TAC), Institutional Ethics Committee (IEC) of SCTIMST and the collaborating institute. Permission to use the MMAS for the present study was requested and granted. Participants were provided written and verbal communication about the purpose of the study, contacts of persons concerned, respondent’s right to discontinue the interview at any time they so deemed without affecting their treatment benefits.
Data were entered by the PI in SPSS (Statistical Package for Social Sciences) version 17.0 for windows (SPSS, Inc., Chicago, IL). Data were cleaned and random checks were done for missing values and inconsistencies with the hard copy. Hard copies of the questionnaire and signed consent forms are in safe and secure custody of the PI.
Data analysis was also carried out by the PI using SPSS 17.0 for windows (SPSS, Inc., Chicago, IL). Univariate analysis of all variables was carried out to describe the sample characteristics. Further analysis has been carried out with non-adherence as the outcome of interest. Bivariate analyses of independent variables with dependent/outcome variable were done by cross-tabulation and testing with Pearson Chi square. A multivariate model was fitted using Binary Logistic Regression (enter method) with non-adherence as outcome variables. Variables found significant in the bivariate analysis and considered to have an interaction effect were included. This was done to adjust for possible confounding and interactions to arrive at model explaining the dependent variable. Measure of association was presented as Odds Ratio (OR) with 95.0% Confidence Interval (CI). Sex disaggregated analysis was attempted. Multivariate analysis was attempted with high and moderate (0) and poor adherence (1) as the outcome variable.
A total of 246 participants were approached of whom 239 signed the consent form and 7 declined. Thus, the response rate was calculated as
Among the total of 239 subjects, nearly three quarters (176; 73.6%) were women. One-third of the study participants were in the age group of 31–40 years (71, 29.8%). Majority of the participants were Muslims (137; 57.3%), and the remaining 102 (42.7%) were Hindus. The study subjects were predominantly married (173; 72.4%), and majority of them had some education (124; 51.9%). More than half (128; 53.6%) of the respondents were residing in rural areas. The most frequently reported occupation was being a housewife or performing household chores (145; 60.7%). More than three quarters of the respondents reported belonging to nuclear families (185; 77.4%). The median age of the sample was 40.0
Sociodemographic characteristics of the respondents (
Sociodemographic characteristics |
|
% | Men | % | Women | % |
---|---|---|---|---|---|---|
Sex | ||||||
Men | 63 | 26.4 | ||||
Women | 176 | 73.6 | ||||
Age (years) | ||||||
18–30 | 55 | 23.0 | 14 | 22.2 | 41 | 23.3 |
31–40 | 71 | 29.8 | 16 | 25.4 | 55 | 31.2 |
41–50 | 67 | 28.0 | 20 | 31.7 | 47 | 26.7 |
51–60 | 46 | 19.2 | 13 | 20.7 | 33 | 18.8 |
Religion | ||||||
Hindus | 102 | 42.7 | 38 | 60.3 | 64 | 36.4 |
Muslims | 137 | 57.3 | 25 | 39.7 | 112 | 63.6 |
Marital Status | ||||||
Single (Never married) | 17 | 7.1 | 11 | 17.5 | 6 | 3.4 |
Married | 173 | 72.4 | 47 | 74.5 | 126 | 71.6 |
Widow/er | 35 | 14.6 | 3 | 4.8 | 32 | 18.2 |
Separated | 14 | 5.9 | 2 | 3.2 | 12 | 6.8 |
Education (in completed years) | ||||||
No education (0) | 93 | 38.9 | 14 | 22.2 | 77 | 43.8 |
Some education (1–10) | 124 | 51.9 | 36 | 57.2 | 90 | 51.1 |
Higher education (>10) | 22 | 9.2 | 13 | 20.6 | 9 | 5.1 |
Occupation | ||||||
Unemployed | 21 | 8.8 | 16 | 25.4 | 5 | 2.8 |
Housewife/household chores | 145 | 60.7 | 5 | 7.9 | 140 | 79.5 |
Irregular employment | 29 | 12.1 | 18 | 28.6 | 11 | 6.3 |
Regular employment | 21 | 8.8 | 14 | 22.2 | 7 | 4.0 |
Agricultural work | 11 | 4.6 | 9 | 14.3 | 2 | 1.1 |
Others (cooks, maids, etc.) | 12 | 5.0 | 1 | 1.6 | 11 | 6.3 |
Residence | ||||||
Rural | 128 | 53.6 | 31 | 49.2 | 97 | 55.1 |
Peri-urban | 38 | 15.9 | 12 | 19.0 | 26 | 14.8 |
Urban | 73 | 30.5 | 20 | 31.7 | 53 | 30.1 |
Family type | ||||||
Nuclear | 185 | 77.4 | 49 | 77.8 | 136 | 77.3 |
Joint/extended | 42 | 17.6 | 12 | 19.0 | 30 | 17.0 |
Others | 12 | 5.0 | 2 | 3.2 | 10 | 5.7 |
Per capita income (INR; |
||||||
Below 2500 | 74 | 30.9 | 1 | 1.6 | 1 | 0.6 |
2500–4000 | 69 | 28.9 | 19 | 30.2 | 53 | 30.1 |
Above 4000–6000 | 43 | 18.0 | 17 | 27.0 | 52 | 29.5 |
Above 6000 | 53 | 22.2 | 17 | 27.0 | 36 | 20.5 |
Based on the scoring of the MMAS, majority of the patients were found to have poor adherence (160; 66.9%). A few (33; 13.8%) were moderately adherent while 46 (19.2%) were highly adherent. The median score was
Hospital-based prevalence of adherence (
An overwhelming majority (214; 89.5%) had no idea about the diagnosis of the condition for which they were seeking treatment. A mere 23 (9.6%) had some idea about their condition of which only 2 (0.9%) used the term “depression.” The average duration (in months) of treatment was
The mean number of medications advised was
Commonly prescribed medicines (
Types of medicines |
|
% |
---|---|---|
Antidepressants | ||
Selective serotonin reuptake inhibitors (SSRIs) | 106 | 44.4 |
Others (Tricyclic, Tetracyclic) | 133 | 55.6 |
Other medications | ||
Benzodiazepine | 224 | 93.7 |
Others (Vitamins, antihypertensive, etc.) | 15 | 6.3 |
Most of the study participants were recommended to take medicines thrice a day (108; 45.2%) followed by twice a day (Table
Recommended frequency of medication intake (
Medication intake |
|
% |
---|---|---|
Once a day | 38 | 15.9 |
Twice a day | 93 | 38.9 |
Thrice a day | 108 | 45.2 |
A total of 163 (68.2%) respondents confirmed having missed follow-up treatment while 76 (31.8%) stated they were able to be present at the hospital as per their scheduled visit. Of the 163 who were unable to be present on days of their consultation few reported having missed their follow up treatment usually (27; 16.6%) (Table
Patient reported frequency of consultations missed (
Frequency of consultations missed |
|
% |
---|---|---|
Rarely | 72 | 44.2 |
Sometimes | 64 | 39.2 |
Usually | 27 | 16.6 |
Of the 239 patients interviewed most of the patients reported using multiple therapy. Nearly three quarters (174; 72.8%) of the patients reported having taken more medicines than prescribed. A few reported visiting healing temples (73; 30.5%), healers of other medical system. One of the reasons they offered for seeking extra medical help was dissatisfaction with the current management of their condition (Table
Multiple forms of treatment as reported by patients (
Multiple forms of treatment | Yes | % | No | % |
---|---|---|---|---|
Visit healing temple | 73 | 30.5 | 166 | 69.5 |
Take more pills | 174 | 72.8 | 65 | 27.2 |
Seek medical advice from other physicians due to dissatisfaction with current treatment | 230 | 96.2 | 9 | 3.8 |
Consult health-care providers practicing alternative medicine | 19 | 7.9 | 220 | 92.1 |
A total of 43 (18.0%) individuals reported having another health condition for which medical treatment was being sought—34 (17.3%) reported having one, 7 (3.6%) reported two and 2 (1.0%) reported three physical conditions respectively (Table
Patient reported co-morbidities (
Co-morbidities | Yes | % | No | % |
---|---|---|---|---|
Any other health condition | 43 | 18.0 | 196 | 82.0 |
Hypertension | 22 | 9.2 | 217 | 90.8 |
Thyroid related illnesses | 11 | 4.6 | 228 | 95.4 |
Diabetes | 5 | 2.1 | 234 | 97.9 |
Cardiovascular diseases | 5 | 2.1 | 234 | 97.9 |
Others (bronchial asthma, etc.) | 8 | 3.3 | 231 | 96.7 |
Difficulty in adherence due to treatment regimen of co-morbid conditions (
Degree of difficulty |
|
% |
---|---|---|
Not at all | 33 | 76.7 |
Sometimes | 6 | 14.0 |
Rarely | 4 | 9.3 |
Half the respondents (120; 50.2%) stated that they were responsible for their own health and 110 (46.0%) maintained that remaining healthy depended on one’s own self. However, as less as one thirds of the respondents (72, 30.1%) believed in having command of their own health or recovery in case of illness. Beliefs about bad luck and karma as causes of illness were voiced by many. A lesser number of people were of the opinion that God’s will or evil eye were responsible for the illness (76; 31.8%). While majority of the participants held on to the notion that cure depended on luck/fate a lesser number believed in God’s contribution in the recovery process (Table
Locus of control and health outcomes (
Locus of control | Yes | % | No | % |
---|---|---|---|---|
Internal locus of control | ||||
Directly responsible for own health | 120 | 0.2 | 119 | 49.8 |
Remaining healthy depends on self | 110 | 46.0 | 129 | 54.0 |
In control of own health | 75 | 31.4 | 164 | 68.6 |
If sick, recovery depends on self | 72 | 30.1 | 167 | 69.9 |
External locus of control | ||||
Illness as a result of bad luck/ill fate | 145 | 60.7 | 94 | 39.3 |
Illness as a result of karma | 107 | 44.8 | 132 | 55.2 |
Illness as a result of God’s will | 76 | 31.8 | 163 | 68.2 |
Illness as a result of evil eye | 43 | 18.0 | 196 | 82.0 |
Recovery depends upon luck/fate | 143 | 59.8 | 96 | 40.2 |
Recovery depends upon God | 109 | 45.6 | 130 | 54.4 |
Not only medication but ability to follow advice is also considered an important component of adherence to prescribed treatment and hence participant’s views about the issue were explored. Of the 239 respondents only 23 (9.6%) reported that they were given some advice apart from prescribed medicines for example, blood test, particularly for ruling out thyroid related illnesses (8; 34.8%), counselling (3; 13.4%), life style modifications (morning walks, healthy diet—3; 13.4%), and others (9; 39.1%). Of these 23 individuals approximately half (11; 47.8%) reported inability to follow suggested counsel while the remaining 12 (52.2%) expressed that they had no problem in doing so. Financial constraint was the most frequently reported reason for not being able to follow advice while lack of time and space were reported as reasons for not being able to change lifestyle.
For the purpose of further analysis adherence was converted into a binary variable which based on the MMAS scores were classified as adherent (0; high and moderate adherence) and non-adherent (1; low adherence). Based on this classification, 79 (33.1%) were found to be adherent and more than three-fifths (160; 66.9%) were found to be non-adherent.
In addition, to the MMAS when patients were asked if they missed medication in the last thirty days, 145 (60.7%) gave positive response while 94 (39.3%) replied negatively.
Bivariate analysis of outcome variable was done with other independent variables. Adherence scores were used to categorise respondents into a binary variable adherent and non-adherent. The association was considered significant if “
Factors associated with poor adherence: results of bivariate analysis.
Variables | Non-adherent | Adherent | Total | % | Odds ratio | 95% CI | ||
---|---|---|---|---|---|---|---|---|
|
% |
|
% | |||||
Sex** | ||||||||
Men | 33 | 52.4 | 30 | 47.6 | 63 | 26.4 | 1 | |
Women | 127 | 72.2 | 49 | 27.8 | 176 | 73.6 | 2.4 | 1.4–4.3 |
Stopped medication (30 days)** | ||||||||
No | 30 | 31.9 | 64 | 68.1 | 94 | 39.3 | 1 | |
Yes | 130 | 89.7 | 15 | 10.3 | 145 | 60.7 | 18.5 | 9.2–36.8 |
Take more medicines** | ||||||||
No | 110 | 63.2 | 64 | 36.8 | 174 | 72.8 | 1 | |
Yes | 50 | 76.9 | 15 | 23.1 | 65 | 27.2 | 1.9 | 1.0–3.7 |
Internal LOC** | ||||||||
Yes | 25 | 41.0 | 36 | 59.0 | 61 | 25.5 | 1 | |
No | 135 | 75.8 | 43 | 24.2 | 178 | 74.5 | 4.5 | 2.4–8.3 |
Factors associated with poor adherence: multiple logistic regression.
Variables | Adjusted odds ratio | 95% CI |
|
---|---|---|---|
Sex* | |||
Men | 1 | ||
Women | 2.7 | 1.1–7.1 |
|
Age group | |||
18–30 | 1 | ||
31–40 | 0.8 | 0.3–2.1 | 0.632 |
41–50 | 0.4 | 0.1–1.2 | 0.149 |
51–60 | 0.3 | 0.1–1.0 | 0.051 |
Education (years completed) | |||
0 | 1 | ||
1–10 | 1.3 | 0.6–2.8 | 0.486 |
>10 | 0.2 | 0.1–0.6 | 0.131 |
Religion | |||
Hindus | 1 | ||
Muslims | 1.1 | 0.4–2.9 | 0.917 |
Marital status | |||
Never married | 1 | ||
Married | 0.4 | 0.1–1.9 | 0.241 |
Separated | 2.8 | 0.2–41.6 | 0.562 |
Widow/er | 0.3 | 0.1–2.3 | 0.243 |
Occupation | |||
Unemployed | 1 | ||
Housewife/household chores | 0.4 | 0.8–2.1 | 0.446 |
Irregular employment | 1.9 | 0.2–19.8 | 0.681 |
Regular employment | 2.8 | 0.3–31.5 | 0.403 |
Agricultural work | 0.1 | 0.1–0.8 | 0.031 |
Others (cooks, maids) | 0.2 | 0.2–1.3 | 0.192 |
Family type | |||
Nuclear | 1 | ||
Joint/extended | 0.8 | 0.2–3.1 | 0.842 |
Others | 6.7 | 0.6–79.8 | 0.119 |
Take more medicines** | |||
No | 1 | ||
Yes | 2.8 | 1.1–7.3 |
|
Visited healing temple | |||
No | 1 | ||
Yes | 0.6 | 0.2–1.5 | 0.371 |
Average treatment duration (months) | |||
<12 | 1 | ||
12–24 | 0.4 | 0.1–0.9 | 0.010 |
>24 | 0.6 | 0.2–1.6 | 0.467 |
External LOC | |||
Yes | 1 | ||
No | 0.1 | 0.2–3.7 | 0.951 |
Internal LOC** | |||
Yes | 1 | ||
No | 4.5 | 2.4–8.3 |
|
Since medication non-adherence is a common feature of any chronic disease, information regarding factors hindering adherence is crucial in designing interventions promoting adherence to treatment for those suffering from chronic diseases. Considering this aspect a set of additional questions were asked to 193 patients who reported experiencing difficulty in complying with the prescribed treatment. Distinction was made in classifying barriers to adherence which were patient related and those which were not. The following section documents some of the most frequently reported reasons for missing medication. Table
Patient reported barriers to adherence of prescribed treatment (
Patient reported barriers to adherence | Yes | % | No | % |
---|---|---|---|---|
Patient related and social factors | ||||
Forget to take medicines | 109 | 56.5 | 84 | 43.5 |
Difficult to take medicines at specified time | 72 | 37.3 | 121 | 62.7 |
Did not feel like taking medicines (medication fatigue) | 63 | 32.6 | 130 | 67.4 |
Too much of work at home | 55 | 28.5 | 138 | 71.5 |
Unable to visit hospital working hours due to loss of wages | 44 | 22.8 | 149 | 77.2 |
Stop medication for religious reasons | 43 | 22.3 | 150 | 77.7 |
Did not get leave from work | 26 | 13.5 | 167 | 86.5 |
Medicines were costly to buy | 24 | 12.4 | 169 | 87.6 |
No one at home to remind about taking medications | 21 | 10.9 | 172 | 89.1 |
Did not want others to know | 19 | 9.8 | 174 | 90.2 |
Suggested by someone within family to stop medication | 19 | 9.8 | 174 | 90.2 |
No one to accompany me to the hospital | 19 | 9.8 | 174 | 90.2 |
Did not feel current treatment is effective | 16 | 8.2 | 177 | 91.8 |
Afraid of side effects | 13 | 6.7 | 180 | 93.3 |
Personal other illness (gall bladder operation, asthma, etc.) | 11 | 5.7 | 182 | 94.3 |
Do not believe medicines will improve condition | 10 | 5.2 | 183 | 94.8 |
Someone outside the family suggested stopping medicines | 9 | 4.7 | 184 | 95.3 |
Could not arrange money to come to the hospital | 7 | 3.6 | 186 | 96.4 |
Medication related | ||||
Afraid of medication dependency | 53 | 27.5 | 140 | 72.5 |
Feel worse after taking medication | 36 | 18.7 | 157 | 81.3 |
Lack of proper explanation on how to take medicines | 26 | 13.5 | 167 | 86.5 |
Too many medicines to take | 22 | 11.4 | 171 | 88.6 |
Health-facility related | ||||
Hospital is far from home | 92 | 47.7 | 101 | 52.3 |
Long waiting hours in the hospital | 61 | 31.6 | 132 | 68.4 |
No direct transportation to the hospital; change many times | 47 | 24.4 | 146 | 75.6 |
Doctor did not spend enough time to examine | 15 | 7.8 | 178 | 92.2 |
Medicines provided by hospital not available at the facility | 5 | 2.6 | 188 | 97.4 |
Miscellaneous | ||||
Inclement weather particularly rains | 19 | 9.8 | 174 | 90.2 |
Difficulty in swallowing medicines | 6 | 3.1 | 187 | 96.9 |
Medical store did not have supply of medicines prescribed | 5 | 2.6 | 188 | 97.4 |
Although, it was not an explicit objective of the current study, the sample nevertheless warranted a sex-disaggregated analysis which is reported in the current section. As reported earlier there were 63 men and 176 women in the study.
There were an equal proportion of men and women in all age categories. While more than the half the men were Hindus (38, 60.3%), Muslim women accounted for 63.6% of the study population More than seventy percent of both men and women were married. A larger proportion of men were single (never married) compared to women while the number of widows outnumbered their male counterparts. The number of women with no education was nearly twice as men in the same category. More than half then men and women had some education (1 to 10 years). However, men were four times more likely to have higher education (greater than 10 years) compared to women. Only 5 (2.8%) of women reported being unemployed compared to 16 (25.4%) men. Being a housewife or involvement with household activities was reported as the most common occupation by the women (140; 79.5%) while the most commonly mentioned occupation among men was irregular employment (18; 28.6%). Exactly twice as many men were involved in regular employment as compared to women. There was nearly equal proportion of men and women from rural areas as were from the peri-urban and urban areas. More than three-fourths of both men and women belonged to nuclear families (Table
These observations suggest that gender differentials in adherence are very important but generally unaddressed currently. There is a need for gender-sensitive research in future studies.
This study has made one of the earliest attempts to document factors adversely affecting adherence to treatment among those diagnosed with unipolar depression in a psychiatric department of a tertiary hospital in Kolkata. Unipolar depression is one of the most common mental disorders: point prevalence is 1.9% for men and 3.2% for women [
Based on the findings of available literature, the aim of this work was to explore the problem of adherence associated with depression, a chronic disease. This research findings are notable with respect to six issues: the hospital based prevalence of non-adherence, significant risk of women being non-adherent, internal locus of control, medication intake, no knowledge about the diagnosis of the condition for which treatment is being sought and inclusion of a single question to address adherence.
The hospital based prevalence of non-adherence to antidepressants in this study was found to be 66.9%. This is considerably higher than the reported rates of non-adherence to depression medication which range from 10.0% to 60.0% (median 40.0%) [
In this study, women were found to be more non-adherent compared to men (OR 2.7; 95% CI 1.1–7.1). Available literature on this issue is inconsistent. Similar to this study finding, studies from Canada reported better compliance to treatment among men [
This study noted that adherence was moderated by locus of control. Consistent with existing literature a low locus of control was noted among the non-adherent in this study. The relationship between LOC and adherence in other health conditions, particularly diabetes is well documented [
An overwhelming majority of the study participants were not aware of the diagnosis of the health condition for which they were seeking health care. When probed, patients reported knowing the symptoms for which they were being treated but not the exact diagnosis (except in two instances). The possible reasons for this finding may be physician apathy to explain the diagnosis to the patients or patient inability to comprehend explanations offered. As mentioned above engaging additional personnel may resolve this problem. It is not uncommon for those afflicted by a stigmatising illness to refer to the symptoms rather than the diagnosis to make their illness more socially acceptable. In Asian cultures the explanatory models of mental health experiences highlight the associations of depression with common complaints such as excessive vaginal discharge [
Non-adherent patients (160; 66.9%) were more likely to take more medicines than the prescribed dose than their adherent counterparts. While most studies on adherence to antidepressants focus on discontinuation of medication as a feature of non-adherence, a few studies have nevertheless focused on the issue of additional intake of prescribed medicines [
Although the variable adherence was made operational using MMAS, an additional probe was used as a supportive variable—this turned out to have a strong independent association, suggesting that a simple question like “Have you ever missed or stopped medication in the last thirty days” would be able to elicit the same kind of information and may be found to be more appropriate in settings lacking adequate health personnel.
Despite limitations imposed by the cross-sectional nature of the survey, clinical setting, retrospective study design and patient reported adherence, the study offered a strong ethical advantage as the patient was not exposed to an experience different from that of routine care provision. Mental health continues to remain a neglected public health concern. From this perspective this study makes some important contributions. The use of a semi-structured interview schedule allowed for documenting both quantitative and qualitative data. Data collection was carried out by a single investigator thereby eliminating the possibility of interobserver bias. This epidemiological study makes the first attempt using the 8 item MMAS for measuring adherence to prescribed depression therapy in India.
This study has enumerated health-related issues that are typically neglected but which routinely confront health services. Non-adherence to prescribed therapy in a clinical out-patient setting was found to be a common problem among those diagnosed with unipolar depression, which was characterised by discontinuation of medicines without consulting the treating physician. Measures have been suggested to increase adherence and improve management of depression. This study has highlighted that non-adherent patients are likely not to follow the drug regimen prescribed to them. This finding emphasises the need to sensitise the patients about the importance or correct and proper drug intake. Additional resources in terms of personnel should be allocated to enhance communication with patients encouraging them to be adherent.
This study also indicates the need for intersectoral programmes linking health departments with other departments such as public works department (improvements in transportation and communication system) to ameliorate the problem of non-adherence of depression therapy. Mental health policies addressing the specific gender needs will contribute to increasing adherence. For instance, modifications in working days and hours of health facilities may increase presentation of cases with depression in the hospitals and increase adherence to treatment. Preliminary findings of this study merit further research, and intersectoral systems oriented approach to improve adherence is needed for effective strategies in India and settings elsewhere where non-adherence to depression therapy is an important public health problem.
The authors declare that they have no conflict of interests.
The authors would like to thank all the participants of the study, Dr. D. G. Mukherjee, the head of the department of the collaborating institute, Drs. G. Bandyopadhyay, S. Ghosh, and A. Ray and other medical officers. At the home institute, the collaborative support of senior scientists Drs. K. R. Thankappan, V. Raman Kutty, P. S. Sarma, T. K. Sundari Ravindran, K. Srinivasan, M. Ramanathan, B. Soman, and M. Nair are gratefully acknowledged. The valuable inputs by Mrs. A. Alexander, Dr. M. P. R., and Professor A. N. Chowdhury are much appreciated.