Abstract
Background
Reports from drug utilization reviews are important tools employed in the assessment of healthcare practices. The objective of this study was to evaluate drug utilization patterns among elderly hospitalized patients on poly-pharmacy regimens in Pakistan.
Methods
A descriptive, non-experimental, cross-sectional study was carried out from December 2015 to March 2016 in six tertiary-care hospitals in the Punjab province of Pakistan. The population under study were patients aged ≥60 years, taking ≥5 medicines per day (i.e., patients on poly-pharmacy) and who were hospitalized in the selected tertiary-care hospitals. In this study, data was collected from 600 hospitalized elderly patients (100 patients per hospital). All medicines prescribed on each in-patient chart were noted on a pre-designed pro-forma sheet and were classified under the Anatomical Therapeutic Chemical (ATC) classification system. Multiple linear regression analysis was used to determine the independent factors associated with poly-pharmacy in this cohort. Statistical Package for Social Sciences (SPSS) was used to analyze the data. P-value < .05 indicated statistical significance.
Results
In 600 hospitalized in-patient (male 52.7% and female 47.3%) medication charts, 3179 medicines were prescribed. The most commonly prescribed drug classes were: A: alimentary tract and metabolism 80% (A02: drugs for acid related disorders 64.5%, A03: drugs for functional gastrointestinal disorders 21.5%), N: nervous system 66.3% (N02: analgesics 67.2%, N03: antiepileptic’s 11.2%), J: anti-infectives for systemic use 62.2% (J01: antibacterial for systemic use 82.5%, J04: antimycobacterials 15.3%) and C: cardiovascular system 48.3% (C07: beta blocking agents 19.8%, C10: lipid modifying agents 16.5%), respectively. The most commonly prescribed active substances were: A02BC01 (omeprazole 51.3%), N02BE01 (paracetamol 50.8%) and J01DD04 (ceftriaxone 40.2%), respectively. In multiple linear regression analysis, male gender (95% CI −.205, −.006, p = .039, B = −.091), being divorced (95% CI −.604, −.136, p = .002, B = −.130) and presence of comorbidity (95% CI .068, .267, p = .001, B = .144) were the independent factors associated with increased drug use among elderly hospitalized patients on poly-pharmacy.
Conclusions
The rational use of medicines is of utmost importance, most particularly in the elderly population. More consideration should be given to rationalizing pharmacotherapy in elderly hospitalized patients who are on poly-pharmacy regimens in Pakistan.
Electronic supplementary material
The online version of this article (doi:10.1186/s40545-017-0112-z) contains supplementary material, which is available to authorized users.
Keywords: Drug use, Drug utilization pattern, Elderly, Hospitalized, Poly-pharmacy
Background
Drug therapy serves as the commonest medical intervention reducing health related risks across numerous diseases [1]. A number of studies have been conducted globally to explore the socio-demographic [2, 3], medical related [3, 4] and health system factors [5] as substantial influencers of drug utilization. Despite this, a limited literature is available on drug utilization within a multivariate framework which considers all of the aforementioned variables [1, 6, 7]; particularly in Pakistan.
The elderly are more prone to chronic illnesses due to aging and physiological changes; with the majority of older people (up to about 80%) suffering from chronic illnesses [8]. Consequently, this group are more likely to have increased drug utilization over the general population [9]. Medical, social and financial changes, both at the individual and societal level, are the consequence of geographical differences and/or changes in medicine use over time, and there is a need to identify, explain and remedy these pharmaco-epidemiological differences.
There is scarce data available on drug utilization among elderly in Pakistan and the investigation of poly-pharmacy (taking ≥5 medicines per day) remains under studied. Elderly people in Pakistan comprise a large proportion drug use and this has led to an increase in total health expenditure. Pakistan’s demographic trends demonstrate that between 1990 and 2010, the population aged ≥60 years increased by 75.1% [10]. A World Health Organization (WHO) report (1998) also reports that 5.6% of Pakistan’s population was over 60 years of age, with a probability of doubling to 11% by the year 2025 [11]. With the lack of literature on drug utilization in older adults in the developing world and the rising global demographic of older adults, this points toward the need for drug utilization studies in this area. Drug utilization research is a valuable tool to guide health policy-makers in making their decisions. Similarly, this facilitates value-added communication amongst healthcare personnel, healthcare authorities and scientists [12]. Drug utilization research assesses the utilization and impact of medicines in the community and plays a key role in prioritizing the medical needs of a given country through guiding selection of medicines for national formularies. Reports from drug utilization reviews are important tools employed in the assessment of healthcare practices. The findings of drug utilization surveys also help to improve the rational use of medicines [13].
As such, the objective of this study was to investigate drug utilization patterns among elderly hospitalized patients on poly-pharmacy regimens in Punjab province, Pakistan. Furthermore, we evaluated the combined effect of various factors (for example, demographic, socioeconomic, and health-related factors) on patients on poly-pharmacy in a representative sample of hospitalized elderly patients.
Methods
Study design
A descriptive, non-experimental, cross-sectional study was carried out in six tertiary-care hospitals in the Punjab province of Pakistan, to evaluate drug utilization patterns among elderly hospitalized patients who were on poly-pharmacy regimens.
For this study, data was collected and evaluated according to the objectives of the study. Elderly patients who had been hospitalized for at least 3 days and who were on ≥5 medicines per day were included in this study. Nutritional supplements, other than vitamins and electrolytes were not considered to be drugs and were not recorded.
Study settings
Six tertiary care hospitals (1: Bahawal Victoria Hospital (BVH), 2: Nishtar hospital, 3: Allied hospital, 4: Mayo hospital, 5: District Headquarter (DHQ) Sargodha, 6: Benazir Bhutto hospital) from different areas of the Punjab province were selected as research sites.
The choice of tertiary-care hospitals was made through systematic random sampling. There are 23 tertiary-care hospitals in the Punjab province [14]. Out of these, six were randomly selected using the random number generator function in Microsoft Excel, thus negating the potential for selection bias. In Pakistan, the tertiary care hospitals are very similar in terms of staff and operations and consequently physicians follow the same prescribing practices. Similarly, the patient population is likely to be the same in tertiary care hospitals. Thus randomly selecting patients from these six hospitals is not expected to create issues with significant bias.
Study population and sample size
The population under study were patients of age ≥ 60 years, who were hospitalized in six selected tertiary-care hospitals. According to the latest Pakistani Census, the population of the surveyed province consists of 91,379,615 individuals [15]. The minimum required sample size was 385, as calculated using the Raosoft sample size calculator [16], with 95% confidence interval (CI) and 5% margin of error [Eq. 1].
| 1 |
Where N is the population size, x is the CI and E is the margin of error.
The study was conducted on the patients who were admitted in selected hospitals due to complications associated with their chronic illness rather than on the patients who were admitted due to an acute episode unrelated to their chronic condition.
Data collection
Over a 3 month period (15 December 2015 to 14 March 2016), a total of 3129 elderly patients were approached in-order to obtain consent from 600 to participate. Data was collected at different intervals from these tertiary-care hospitals irrespective of the date of admission of patients.
A data collection form was designed [Additional file 1], which consisted of four main parts: demographic, socio-economic, health-related characteristics and drug utilization patterns. The reliability and internal consistency of the data collection form was assessed by conducting a pilot study. Piloting was undertaken using data from 60 patients. After piloting, the data collection form was restructured by adding chronic conditions and an area for a list of prescribed medicines, which was not part of the original form. The Cronbach’s alpha value was 0.84 demonstrating excellent reliability.
Measurements
Demographic characteristics
The following categorical variables were recorded; gender (male/female), age (60–74, 75–89, ≥90 years), and civil status (single, married, divorced, widowed).
Socioeconomic characteristics
Education level (primary, secondary and tertiary), annual income (low, middle, upper class), residence (rural, urban), employment status (employed, unemployed) were the four variables measuring the socio-economic status of participants. Those participants who were retired (taking a pension) or running a business were classified as employed. The data was obtained through face to face questioning of all 600 patients.
Health-related characteristics
In-patient charts/medical records were used to collect this data. However, if more information on socio-demographic or health-related characteristics were needed, then patients or caregivers were interviewed. Health-related characteristics included the following; self-reported health (good, moderate, poor), health risks (smoking, alcohol consumption, obesity, none), health service utilization [normal clinic visits (≤3/year), high clinic visits (≤4/year)], and chronic diseases (heart diseases, respiratory, gastrointestinal, diabetes mellitus, joint diseases, hypertension, central nervous system (CNS) disorders, others) and comorbidities (present, absent). Obesity was assessed by the body mass index (BMI), and respondents were regarded as either normal (BMI < 25 kg/m2), overweight (25 ≤ BMI < 30 kg/m2) or obese (BMI ≥ 30 kg/m2) [1].
Drug utilization evaluation
All medicines in each prescription were noted on the pro-forma sheet. For the evaluation of drug utilization patterns, all the medicines from the 600 in-patient charts were classified under the Anatomical Therapeutic Chemical (ATC) classification system [17]. Furthermore, the most commonly prescribed active substances were categorized according to trends in use; low (prescribed to <10% of the selected patients), medium (prescribed to ≥10% of the selected patients but <40%) and high (prescribed to >40% of the selected patients).
Statistical analysis
Statistical Package for Social Sciences (IBM, SPSS Statistics for Windows, version 21.0. Armonk, NY: IBM Corp.) was used for data analysis. Simple linear regression analysis was adopted to determine the association between variables. Multiple linear regression analysis was then carried out for statistically significant variables from the univariate analysis to identify factors associated with increased drug use among elderly hospitalized patients who were on polypharmacy regimens [18]. The 95% CI, beta, standard error, and p-value were described for each factor. Pseudo R square values were included to describe the percentages of variance explained by the model. P-value < .05 was deemed to be statistically signifiant [18].
Results
A total of 3129 elderly hospitalized patients in six tertiary-care hospitals were approached and 600 consented patients (response rate: 19.2%) were included according to the inclusion & exclusion criteria. The response rate was low due to the frailty of the patients and their associated medical conditions meant they often opted not to participate in the study.
Just over half (52.7%, n = 316) of the participants were male and 70.3% (n = 422) were 60–74 years of age. Over three-quarters (77%, n = 462) were married and most (86.8%, n = 521) had primary education level and where of low annual income (79.5%, n = 477). Three-quarters (74.5%, n = 447) were unemployed (or on pensions) and a little over one half (55.8%, n = 335) were domiciled rurally. Self-reported health was moderate in 61.3% (n = 368) and a similar percentage (62%, n = 372) had attended ≤3 clinic visits in the previous year. Just over one-third (37.8%, n = 227) were smokers and co-morbidity was present in over one half (54%, n = 324) of the patients (Table 1).
Table 1.
Characteristics of hospitalized elderly population
| Variables | Male (n = 316) | Female (n = 284) | Total (n = 600) | |
|---|---|---|---|---|
| n (%) | n (%) | n (%) | ||
| Age (years) | 60–74 | 239 (75.6) | 183 (64.4) | 422 (70.3) |
| 75–89 | 57 (18) | 76 (26.8) | 133 (22.2) | |
| ≥90 | 20 (6.3) | 25 (8.8) | 45 (7.5) | |
| Civil Status | Single | 15 (4.7) | 6 (2.1) | 21 (3.5) |
| Married | 284 (89.9) | 178 (62.7) | 462 (77) | |
| Widowed | 13 (4.1) | 78 (27.5) | 91 (15.2) | |
| Divorced | 4 (1.3) | 22 (7.7) | 26 (4.3) | |
| Education level | Primary (≤10 years) | 259 (82) | 262 (92.3) | 521 (86.8) |
| Secondary (11–13 years) | 54 (17.1) | 21 (7.4) | 75 (12.5) | |
| Tertiary (≥14 years) | 3 (0.9) | 1 (0.4) | 4 (0.7) | |
| Annual income | Low class (PKR0–299,999) | 237 (75) | 240 (84.5) | 477 (79.5) |
| Middle class (PKR300,000–999,999) | 63 (19.9) | 41 (14.4) | 104 (17.3) | |
| Upper class (PKR ≥ 1,000,000) | 16 (5.1) | 3 (1.1) | 19 (3.2) | |
| Employment status | Employed | 114 (36.1) | 39 (13.7) | 153 (25.5) |
| Unemployed | 202 (63.9) | 245 (86.3) | 447 (74.5) | |
| Residence | Rural (an area outside of cities and towns) | 177 (56) | 158 (55.6) | 335 (55.8) |
| Urban (a city area considered as the inner city) | 139 (44) | 126 (44.4) | 265 (44.2) | |
| Self-reported health | Good | 14 (4.4) | 20 (7) | 34 (5.7) |
| Moderate | 208 (65.8) | 160 (56.3) | 368 (61.3) | |
| Poor | 94 (29.7) | 104 (36.6) | 198 (33) | |
| Health Service Utilization | Clinic visits ≤3/year | 207 (65.5) | 165 (58.1) | 372 (62) |
| Clinic visits ≥4/year | 109 (34.5) | 119 (41.9) | 228 (38) | |
| Health Risks | Smoking | 189 (59.8) | 38 (13.4) | 227 (37.8) |
| Alcohol Consumption | 5 (1.6) | 0 (0) | 5 (0.8) | |
| Obesity | 48 (15.2) | 166 (58.5) | 214 (35.7) | |
| None | 74 (23.4) | 80 (28.2) | 154 (25.7) | |
| Co-morbidity | Present | 163 (51.6) | 161 (56.7) | 324 (54) |
| Absent | 153 (48.4) | 123 (43.3) | 276 (46) | |
| Number of drugs | 5 | 251 (79.4) | 204 (71.8) | 455 (75.8) |
| 6 | 53 (16.8) | 62 (21.8) | 115 (19.2) | |
| 7 | 11 (3.5) | 15 (5.3) | 26 (4.3) | |
| 8 | 1 (0.3) | 3 (1.1) | 4 (0.7) | |
The most common chronic conditions among participants were; gastrointestinal (37.8%), hypertension (32%) and joint diseases (25.7%) (Table 2).
Table 2.
Chronic conditions associated with elderly hospitalized patients on polypharmacy
| Male (n = 316) | Female (n = 284) | Total (n = 600) | |||
|---|---|---|---|---|---|
| Chronic conditions | n (%) | Chronic conditions | n (%) | Chronic conditions | n (%) |
| Heart diseasesa | 62 (19.6) | Heart diseasesa | 43 (15.1) | Heart diseasesa | 105 (17.5) |
| Respiratoryb | 74 (23.4) | Respiratoryb | 69 (24.3) | Respiratoryb | 143 (23.8) |
| Gastrointestinalc | 123 (38.9) | Gastrointestinalc | 104 (36.6) | Gastrointestinalc | 227 (37.8) |
| Diabetes Mellitus | 41 (13) | Diabetes Mellitus | 71 (25) | Diabetes Mellitus | 112 (18.7) |
| Joint diseasesd | 75 (23.7) | Joint diseasesd | 79 (27.8) | Joint diseasesd | 154 (25.7) |
| Hypertension | 89 (28.2) | Hypertension | 103 (36.3) | Hypertension | 192 (32) |
| CNS disorderse | 63 (19.9) | CNS disorderse | 46 (16.2) | CNS disorderse | 109 (18.2) |
| Others | 46 (14.6) | Others | 49 (17.3) | Others | 95 (15.8) |
a(Heart failure, Coronary ischemic disease, Atrial fibrillation, Stenosis)
b(Chronic bronchitis, Asthma, Chronic obstructive pulmonary disease)
c(Peptic ulcer, Irritable bowel syndrome)
d(Osteoarthritis, Rheumatoid arthritis)
e(Alzheimer’s disease, Epilepsy, Depression, Anxiety)
The most commonly prescribed drug classes were: A: alimentary tract and metabolism (80%), N: nervous system (66.3, J: anti-infectives for systemic use (62.2and C: cardiovascular system (48.3%), respectively. The detailed description about the drug utilization pattern is given in Tables 3 and 4.
Table 3.
Prescription drug utilization in study participants
| Prescription Drug Utilization (%) | ||||||||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Pharmacologic groups | 60–74 years | 75–89 years | ≥90 years | Total | ||||||||||||||
| Male (n = 239) | % | Female (n = 183) | % | Male (n = 57) | % | Female (n = 76) | % | Male (n = 20) | % | Female (n = 25) | % | Male (n = 316) | % | Female (n = 284) | % | Overall (n = 600) | % | |
| A = Alimentary tract and metabolism | 182 | 76.2 | 151 | 82.5 | 44 | 77.2 | 58 | 76.3 | 20 | 100 | 25 | 100 | 246 | 77.8 | 234 | 82.4 | 480 | 80 |
| B = Blood and blood-forming organs | 25 | 10.5 | 19 | 10.4 | 3 | 5.3 | 6 | 7.9 | 1 | 5 | 2 | 8 | 29 | 9.2 | 27 | 9.5 | 56 | 9.3 |
| C = Cardiovascular system | 102 | 42.7 | 81 | 44.3 | 36 | 63.2 | 44 | 57.9 | 12 | 60 | 15 | 60 | 150 | 47.5 | 140 | 49.3 | 290 | 48.3 |
| D = Dermatologic | 10 | 4.2 | 7 | 3.8 | 8 | 14 | 7 | 9.2 | 2 | 10 | 2 | 8 | 20 | 6.3 | 16 | 5.6 | 36 | 6 |
| G = Genitourinary system | 3 | 1.3 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 3 | 0.9 | 0 | 0 | 3 | 0.5 |
| H = Systemic hormonal agents | 10 | 4.2 | 18 | 9.8 | 2 | 3.5 | 2 | 2.6 | 2 | 10 | 2 | 8 | 14 | 4.4 | 22 | 7.7 | 36 | 6 |
| J = Anti-infectives for systemic use | 147 | 61.5 | 133 | 72.7 | 25 | 43.9 | 40 | 52.6 | 13 | 65 | 15 | 60 | 185 | 58.5 | 188 | 66.2 | 373 | 62.2 |
| L = Antineoplastic and immune-modulating agents | 7 | 2.9 | 2 | 1.1 | 1 | 1.8 | 3 | 3.9 | 0 | 0 | 0 | 0 | 8 | 2.5 | 5 | 1.8 | 13 | 2.2 |
| M = Musculoskeletal system | 70 | 29.3 | 56 | 30.6 | 18 | 31.6 | 26 | 34.2 | 2 | 10 | 12 | 48 | 90 | 28.5 | 94 | 33.1 | 184 | 30.7 |
| N = Nervous system | 160 | 66.9 | 134 | 73.2 | 34 | 59.6 | 48 | 63.2 | 12 | 60 | 10 | 40 | 206 | 65.2 | 192 | 67.6 | 398 | 66.3 |
| P = Antiparasitic products | 8 | 3.3 | 1 | 0.5 | 0 | 0 | 2 | 2.6 | 1 | 5 | 0 | 0 | 9 | 2.8 | 3 | 1.1 | 12 | 2 |
| R = Respiratory system | 65 | 27.2 | 54 | 29.5 | 20 | 35.1 | 29 | 38.2 | 5 | 25 | 9 | 36 | 90 | 28.5 | 92 | 32.4 | 182 | 30.3 |
| V = Various | 0 | 0 | 3 | 1.6 | 0 | 0 | 1 | 1.3 | 0 | 0 | 0 | 0 | 0 | 0 | 4 | 1.4 | 4 | 0.7 |
Table 4.
Drug utilization pattern in study participants (ATC level 1–4)
| Drug Utilization pattern (%a) | |||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|
| Level 1 | n | % | Level 2 | n | % | Level 3 | n | % | Level 4 | n | % |
| A | 480 | 80 | A01: Stomatological preparations | 120 | 20 | A01A: Stomatological preparations | 120 | 20 | A01AB: Anti-infectives and antiseptics for local oral treatment | 120 | 20 |
| A02: Drugs for acid related disorders | 387 | 64.5 | A02B: Drugs for peptic ulcer and GERD | 382 | 63.7 | A02BA: H2-receptor antagonists | 60 | 10 | |||
| A02BC: Proton pump inhibitors | 309 | 51.5 | |||||||||
| A03: Drugs for functional gastrointestinal disorders | 129 | 21.5 | A03F: Propulsives | 116 | 19.3 | A03FA: Propulsives | 116 | 19.3 | |||
| A10: Drugs used in diabetes | 117 | 19.5 | A10B: Blood glucose lowering drugs | 72 | 12 | A10BB: Sulfonylureas | 53 | 8.8 | |||
| B | 56 | 9.3 | B01: Antithrombotic agents | 26 | 4.3 | B01A: Antithrombotic agents | 26 | 4.3 | B01AC: Platelet aggregation inhibitors | 23 | 3.8 |
| B05: Blood substitutes | 27 | 4.5 | B05B:I.v. solutions | 27 | 4.5 | B05BC: Osmotic diuresis | 27 | 4.5 | |||
| C | 290 | 48.3 | C03: Diuretics | 75 | 12.5 | C03C: High-ceiling diuretics | 68 | 11.3 | C03CA: Sulfonamides, plain | 68 | 11.3 |
| C07: Beta blocking agents | 119 | 19.8 | C07A: Beta blocking agents | 119 | 19.8 | C07AB: Beta blocking agents, selective | 62 | 10.3 | |||
| C08: Calcium channel blockers | 99 | 16.5 | C08C: Selective calcium channel blockers | 91 | 15.2 | C08CA: Dihydropyridine derivatives | 91 | 15.2 | |||
| C10: Lipid modifying agents | 99 | 16.5 | C10A: Lipid modifying agents, plain | 97 | 16.2 | C10AA: HMG CoA reductase inhibitors | 97 | 16.2 | |||
| D | 36 | 6 | D07: Corticosteroids | 41 | 6.8 | D07A: Corticosteroids, plain | 41 | 6.8 | D07AA: Corticosteroids, weak (group I) | 30 | 5 |
| G | 3 | 0.5 | G04: Urologicals | 4 | 0.7 | G04C: Drugs used in BPH | 2 | 0.3 | G04CA: Alpha-adrenoreceptor antagonists | 2 | 0.3 |
| H | 36 | 6 | H01: Pituitary and hypothalamic hormones | 31 | 5.2 | H01C: Hypothalamic hormones | 31 | 5.2 | H01CB: Somatostatin and analogues | 31 | 5.2 |
| J | 373 | 62.2 | J01: Antibacterials for systemic use | 495 | 82.5 | J01D: Other beta-lactam antibacterials | 266 | 44.3 | J01DD: 3rd-generation cephalosporins | 255 | 42.5 |
| J01 M: Quinolone antibacterials | 68 | 11.3 | J01MA: Fluoroquinolones | 68 | 11.3 | ||||||
| J04: Antimycobacterials | 92 | 15.3 | J04A: Drugs for tuberculosis | 92 | 15.3 | J04AK: Other drugs | 48 | 8 | |||
| L | 13 | 2.2 | L01: Antineoplastic agents | 17 | 2.8 | L01B: Antimetabolites | 04 | 0.7 | L01BA: Folic acid analogues | 03 | 0.5 |
| M | 184 | 30.7 | M01: Anti-inflammatory and antirheumatic products | 188 | 31.3 | M01A: Anti-inflammatory and antirheumatic products, non-steroids | 188 | 31.3 | M01AB: Acetic acid derivatives | 159 | 26.5 |
| N | 398 | 66.3 | N02: Analgesics | 403 | 67.2 | N02B: Other analgesics and antipyretics | 359 | 59.8 | N02BE: Anilides | 305 | 50.8 |
| N03: Antiepileptics | 67 | 11.2 | N03A: Antiepileptics | 67 | 11.2 | N03AX: Other antiepileptics | 32 | 5.3 | |||
| P | 12 | 2 | P01: Antiprotozoals | 13 | 2.2 | P01B: Antimalarials | 12 | 2 | P01BF: Artemisinin and derivatives | 9 | 1.5 |
| R | 182 | 30.3 | R01: Nasal preparations | 84 | 14 | R01A: Decongestants and other | 84 | 14 | R01AD: Corticosteroids | 61 | 10.2 |
| R03: Drugs for obstructive airway diseases | 113 | 18.8 | R03A: Adrenergics, inhalants | 86 | 14.3 | R03AC: Selective beta-2 agonists | 43 | 7.2 | |||
| R06: Antihistamines for systemic use | 67 | 11.2 | R06A: Antihistamines for systemic use | 67 | 11.2 | R06AA: Aminoalkyl ethers | 46 | 7.7 | |||
| V | 4 | 0.7 | V03: Other therapeutic products | 6 | 1 | V03A: Other therapeutic products | 6 | 1 | V03AB: Antidotes | 6 | 1 |
Note: A patient may be prescribed one or more than one drug from level 2, level 3 and level 4 categories
aPercentages given with respect to the total sample size of the patients. GERD Gastroesophageal reflux disease, BPH Benign prostate hyperplasia
The most commonly prescribed active substances were; A02BC01: omeprazole (n = 308, 51.3%), N02BE01: paracetamol (n = 305, 50.8%) and J01DD04: ceftriaxone (n = 241, 40.2%) (Table 5). A detailed description about the usage of all prescribed medicines can be found in Appendix.
Table 5.
Top active substances prescribed to study participants (ATC level 5)
| Name | ATC Code | Frequency (n = 3179) | Percentagea | Trend in use |
|---|---|---|---|---|
| Amlodipine | C08CA01 | 78 | 13 | Medium |
| Aspirin | N02BA01 | 54 | 9 | Low |
| Atenolol | C07AB03 | 62 | 10.3 | Medium |
| Captopril | C09AA01 | 53 | 8.8 | Low |
| Ceftriaxone | J01DD04 | 241 | 40.2 | High |
| Dexamethasone | R01AD03 | 61 | 10.2 | Medium |
| Diclofenac sodium | M01AB05 | 111 | 18.5 | Medium |
| Furosemide | C03CA01 | 68 | 11.3 | Medium |
| Lactulose | A06AD11 | 49 | 8.2 | Low |
| Metformin | A10BA02 | 53 | 8.8 | Low |
| Metoclopramide | A03FA01 | 86 | 14.3 | Medium |
| Metronidazole | A01AB17 | 119 | 19.8 | Medium |
| Omeprazole | A02BC01 | 308 | 51.3 | High |
| Paracetamol | N02BE01 | 305 | 50.8 | High |
| Simvastatin | C10AA01 | 63 | 10.5 | Medium |
aPercentages given with respect to the total sample size of patients
After adjusting the factors associated with increased drug use among elderly hospitalized patients who were on polypharmacy regimens in the univariate analysis, the factors which remained significant in the multiple linear regression were; male gender (95% CI −.205, −.006, p = .039, B = −.091), being divorced (95% CI −.604, −.136, p = .002, B = −.130) and the presence of comorbidity (95% CI .068, .267, p = .001, B = .144) (Table 6).
Table 6.
Factors affecting number of prescribed drugs: multiple linear regression analysis
| Variables | Unstandardized Coefficients | Standardized Coefficients | p-value | 95.0% Confidence Interval for B | |
|---|---|---|---|---|---|
| Std. Error | B | Lower Bound | Upper Bound | ||
| Male | .051 | −.091 | .039 | −.205 | −.006 |
| Widowed | .073 | −.008 | .865 | −.157 | .132 |
| Divorced | .119 | −.130 | .002 | −.604 | −.136 |
| Low income class | .135 | .051 | .592 | −.193 | .339 |
| Middle income class | .144 | −.034 | .716 | −.335 | .230 |
| Moderate self-reported health | .105 | −.075 | .397 | −.296 | .118 |
| Poor self-reported health | .127 | −.034 | .744 | −.292 | .208 |
| ≥4 clinic visits | .076 | .075 | .238 | −.060 | .240 |
| Comorbidity | .051 | .144 | .001 | .068 | .267 |
P-value < .05 was considered statistically significant. Note: Only statistically significant variables in the univariate analysis were entered in the multiple linear regression analysis and are shown in the Table
Model summary: R2 = 0.052, p < 0.0005
Discussions
This large pharmaco-epidemiological study set out to determine drug utilization patterns of elderly patients on poly-pharmacy regimens within six hospitals in Punjab province, Pakistan. Furthermore, the study evaluated the combined effect of factors including; demographic, socioeconomic, and health-related issues on poly-pharmacy in this population. This Discussion compares a summary of the findings with the literature and notes the contribution, outlines the implications for policy, practice and future research and considers the limitations of the study.
Drug utilization patterns
Alimentary tract and metabolism category drugs were the most commonly prescribed class. Pakistan has been afflicted by alimentary tract disorders (ATDs) with an estimated prevalence of 45% [19]. It has been seen that ATDs affect a large number of people (approximately 60 to 70 million people in the US each year) and contribute substantially to morbidity and mortality [20]. According to one estimate, these disorders pose a significant fiscal and societal burden in the US [21]. In 2004 in the US, there were approximately 72 million ambulatory care visits, 4.6 million hospitalizations, 236,000 deaths and an estimated economic burden of $142 billion due to ATDs [22]. A study conducted in Finland reported that Alimentary tract and metabolism category drugs were prescribed to 77% of elderly patients who were on poly-pharmacy regimens [23]. Similarly, another study conducted in Italy reported that ATDs were prescribed to 42% of this population [24]. Thus the common prevalence of ATDs globally has led to increased utilization of alimentary tract and metabolism category drugs.
In the alimentary tract category, the most frequently prescribed sub-classes were; A02: drugs for acid related disorders (64.5%), A03: drugs for functional gastrointestinal disorders (21.5%), A01: stomatological preparations (20%) and A10: drugs used in diabetes (19.5%), respectively (Table 4). Drugs for acid related disorders are most commonly prescribed because they are generally safe and effective medicines used to treat gastric ulcers, heartburn, and gastro-oesophageal reflux disease (GORD). In this category, proton pump inhibitors (PPIs) are the highest-selling drugs worldwide. In addition to the treatment of gastritis, proton pump inhibitors are also very commonly prescribed as a gastro-protectant for patients prescribed antiplatelet and non-steriodal anti-inflammatory drugs (NSAIDs). In some countries they are available over-the-counter (OTC). Nexium (esomeprazole) in the US, earns nearly 6 billion USD and Risek (omeprazole) in Pakistan, earns close to 2 billion PKR, according to IMS Health data from 2012 [25]. It must be kept in mind that the chronic use of PPIs is associated with problems such as osteoporosis, hip fracture, escalated risk of infections, hypergastrinemia, decreased absorption of vitamins and minerals, kidney damage, dementia to name a few [26–28]. Healthcare professionals must adhere to prescribing guidelines and curb the excessive use of PPIs in-order to minimize associated adverse effects and reduce costs.
Nervous system drugs were the second most commonly prescribed class attributed to the high prevalence of neurologic and psychiatric disorders in this study. According to a World Health Organization Report, depression is the leading cause of health-related disability, globally [29]. Furthermore, a study on mood disorders in 30 European countries estimated that approximately 165 million elderly people (38% of the total population of these countries) suffer from significant mental illness [30]. In Pakistan, neurologic and psychiatric disorders are indeed prevalent [31]. A systematic review reported that the mean overall prevalence of anxiety and depressive disorders in the Pakistani population is 34% (range 29–66% for women and 10–33% for men) [32]. Three studies conducted in the Finnish elderly population reported that nervous system drugs were prescribed to between 63% and 89% in this group of patients [23, 33, 34]. Another study in nearby Sweden reported that this class was prescribed to 37% of the elderly population [35]. Thus neurological disorders afflicts both high income and low income countries with comparatively high treatment rates in high income countries [36].
In the nervous system category, frequently prescribed sub-classes in this study were; N02: Analgesics (67.2%), N03: Anti-epileptics (11.2%), respectively (Table 4). Analgesics, main therapy for low back pain, were the most frequently prescribed agents because low back pain is commonplace in the elderly, due to ageing of intervertebral discs [37, 38] with a prevalence of 60–70% in industrialized nations [39]. In 2010 Global Burden of Disease Study estimated that low back pain was one of the top 10 injuries and diseases throughout the world [40]. An American study reported that an estimated 149 million work days were lost due to low back pain, with an economic burden of USD 100 to 200 billion [41, 42].
The third most frequently prescribed drugs in this study were from the anti-infectives for systemic use class. These medicines are prescribed for a variety of infections caused by bacteria, virus, fungi, viroids, prions, nematodes, arthropods and so forth. A range of medicines are used to treat infections including; antivirals, antibiotics, antifungals, antihelminthics, and antiprotozoals. Infectious diseases accounted for 9.2 million deaths worldwide in 2013 (approximately 17% of all deaths) [43]. As in many other developing countries, infectious diseases are common in Pakistan and therefore anti-infectives are commonly prescribed and have a large market size [44]. A study conducted in Italy reported that anti-infectives for systemic use category drugs were prescribed to 41% of the elderly population [24]. Another study conducted in Sweden reported that anti-infectives were prescribed to just over one quarter (27.6%) of the elderly population [35]. It is likely that inter-country variability of infectious diseases is responsible for the varying patterns of global antibiotic use. In the Anti-infectives (for systemic use) category, the most frequently prescribed sub-classes were; J01: anti-bacterials for systemic use (82.5%) and J04: anti-mycobacterials (15.3%) (Table 4). Anti-bacterials/antibiotics are the most widely consumed pharmaceutical group, worldwide [45]. According to one estimate, the utilization of antibiotics has increased by 36% over the 10 years from 2000 to 2010. Russia, China, South Africa, India and Brazil are accountable for 76% of this of the prescribing and the growth [46]. In India, a rise from 29 to 57% was seen in Klebsiella pneumonia between 2008 and 2014. The concerning aspect here is that Klebseilla is becoming increasingly resistant to very potent antibiotics such as carbapenems [45]. The other concern is that this figure is considerably lower in the US and Europe i.e. less than 10% [45]. Generally, for most countries the usage of antibiotics varies with the season [46] and this appears to be no different in Pakistan.
The fourth most frequently prescribed medicines in this study were for cardiovascular diseases. These findings are in line with the fact that cardiovascular disorders (CVDs) are the most prevalent and leading causes of death worldwide [47], resulting in 17.3 million deaths in 2013 [43]. In 2010, the total costs of CVD globally was estimated to be in the vicinity of 315.4 billion USD [48]. Amongst the elderly population, 71% of people aged between 60 and 80 years, and 85% of people over 80 years are estimated to have CVD [49]. Similarly, in Pakistan, amongst the elderly population, 76% of people aged between 60 and 70 years, and 83% of people over 90 years are estimated to have CVD [50]. According to a Danish study of persons aged ≥70 years, cardiovascular drugs (35%) were the most frequently prescribed class [25]. A Danish study revealed that the most commonly prescribed medicines amongst 75 year olds were cardiovascular (25%) drugs [12] where it was almost double this in Sweden with 47% of elderly being prescribed cardiovascular drugs [41]. In the cardiovascular category, the most frequently prescribed sub-classes were; C07: beta blocking agents (19.8%), C08: calcium channel blockers (16.5%), C10: lipid-modifying agents (16.5%) and C03: diuretics (12.5%) respectively (Table 4). Beta-blocking agents are most widely prescribed because they are used to manage cardiac arrhythmias and myocardial infarction, as well as hypertension [51]. Diuretics were the most commonly prescribed class because they represent the first-line treatment for hypertension and the prevalence of hypertension is high throughout the world; it affected between 30 and 45% of the population of Europe in 2013 [52].
In summary, the most commonly prescribed active substances were; omeprazole, paracetamol and ceftriaxone. This follows the developed world with regards omeprazole and paracetamol but the excessive use of potent IV antibiotics such as ceftriaxone warrants serious review.
Factors associated with “poly-pharmacy”
Results from the multiple linear regression analysis revealed that male gender (negatively associated); being divorced (negatively associated) and comorbidity (positively associated) were the main factors associated with increased drug use among elderly hospitalized patients in Pakistan, who were on poly-pharmacy regimens.
The greater ratio of female to male gender amongst these elderly hospitalized patients on poly-pharmacy regimens could be attributed to physiological aspects, which differentially adjust the etiology patterns for females and males. These altered etiology patterns may represent illness behaviors such as being more sensitive to their health and consequently taking more medicines. It is also possible that there are more women in hospital because men simply die at an earlier age, often without even getting to hospital [53]. Such differences explain the use of specific types of medicines by women only. Simultaneously, the societal roles adopted by women in Pakistan, principally as housewives or paid employees also influence these gender differences [43]. Numerous studies have proposed that multiple roles for women, including home-maker, parent, and paid employees are likely to be hectic and harmful to their health [54–56]. Civil status, such as ‘being divorced’ played an important role in the illness behavior in this study. Amongst this subgroup of the elderly, psychological conditions such as depression and anxiety contributed towards poly-pharmacy [56]. Comorbidities are a significant factor associated with poly-pharmacy and increased mortality in older people. The elderly are significantly more prone to comorbidities due to aging and physiological changes; the majority of the older people (up to about 80%) suffer from chronic illnesses [8]. Consequently, they are more likely to have greater drug utilization to manage their chronic illnesses [9].
Implications for policy and practice
There are implications from the findings of this study for pharmaceutical policy and practice in Pakistan.
In terms of policy and practice, there has been very little evaluation of “poly-pharmacy” in the context of Pakistan and so this study contributes significantly to that understanding. This study raises the question of whether prescribing needs to change in some way? Omeprazole was the most commonly prescribed pharmaceutical, followed by paracetamol and this appears to follow the trends in developing countries. What is most concerning, is the very high use of powerful IV antibiotics such as ceftriaxone. A national policy and guidelines need to be put in place to ensure the rational prescribing of potent antibiotics such as this. Further, in Pakistan infrastructure for proper medicine dispensing and patient education is not available within the health system and the availability of pharmacists at public hospital and private pharmacies is negligible. Elderly people who are on polypharmacy regimens are at risk of medicines overuse and adverse effects associated with polypharmacy. There is a global trend to “de-prescribe” in the setting of hospitalized older adults and the government should take the appropriate measures to ensure skilled pharmacists are available to enact this.
Implications for future research
Future research could look at evaluating the impact of pharmacists around implementing and monitoring drug use indicators and clinical guidelines. Further, studies like the current one could be extended to assess potential drug interactions, trends in prevalence and determinants of potentially inappropriate medication use based on the Beer’s Criteria among the elderly population. This study recruited hospitalized elderly patients and it cannot be assumed that the sample is representative in any form of the ambulatory primary care population. Studies need to be conducted in the general community to better understand pharmaco-epidemiological patterns across the wider population. It will be interesting to explore in more detail the impact of being divorced on medicines use in Pakistan as the data (un-expectantly) suggests this is a significant determinant.
Study limitations
There are a few study limitations. First, the population under study was elderly patients being hospitalized in the selected tertiary-care hospitals. Second, only those elderly patients were approached who are taking more than five prescribed medicines per day. Third, DDDs for the prescribed medicines to the hospitalized elderly patients were not calculated. Finally, the study did not investigate the ADRs associated with poly-pharmacy. However, this study provides the baseline information to the researchers regarding the drug utilization pattern among a cohort of hospitalized elderly patients in the Punjab province of Pakistan.
Conclusion and recommendations
The increased use of prescription medicines is commonplace amongst the elderly population worldwide, and this study suggests it is no different in Pakistan. Similarly, the average number of medicines being used by elderly women is on the rise. This study concludes that a series of factors are responsible for “poly-pharmacy” in older adults in Pakistan including; being male, being divorced and the presence of multiple comorbidities. The most common chronic conditions associated with these hospitalized elderly patients were; gastrointestinal, hypertension and joint diseases, respectively. In this study, the most commonly prescribed drug classes reflect that seen in developed nations including; Alimentary tract and metabolism, Nervous system, Antibacterial for systemic use and Cardiovascular system, respectively.
There is no doubt that the usage of medicines is essential; however, poly-pharmacy is likely to also compound in a cyclical manner the associated problems with the use of multiple medicines. It is highly recommended that greater consideration be given to elderly hospitalized patients who are on poly-pharmacy regimens, in Pakistan. To reduce the potential for ADRs associated with poly-pharmacy and to get the maximum benefit of therapy, there is a requirement to use medicines effectively and to evaluate progress at regular intervals based on diagnosis and expected treatment outcomes. The rational use of medicines is of utmost importance, most particularly in the elderly population and the responsibility lies with healthcare professionals to regularly evaluate medicine use.
Unfortunately, geriatrics is not recognized as a specialized area of research in Pakistan and hospital pharmacists are not as commonplace; as they are in developed nations. Under the current scenario, the role of the pharmacist in the management of elderly patients must be enhanced in Pakistan and the impact of this intervention could be tested by employing experimental study designs. The contribution of this paper is that it provides baseline information about the prescribing patterns in the older hospitalized patients in Pakistan and provides a platform for evaluation of policy and practice interventions by Pakistani hospital pharmacists in the future.
Acknowledgements
None.
Funding
No funding was involved in the preparation of this article or in the decision to submit it for publication.
Availability of data and materials
The raw data on which conclusions of this manuscript rely is available upon request. Please contact Muhammad Rehan Sarwar at rehansarwaralvi@gmail.com.
Abbreviations
- ADRs
Adverse Drug Reactions
- ATC
Anatomical Therapeutic Chemical
- ATDs
Alimentary Tract Disorders
- BMI
Body Mass Index
- CNS
Central Nervous System
- CVDs
Cardiovascular Disorders
- DDDs
Defined Daily Doses
- LBP
Low Back Pain
- SPSS
Statistical Package for Social Sciences
- WHO
World Health Organization
Appendix
Table 7.
Description about the usage of all prescribed medicines
| Sr. No | Name | ATC Code | Frequency (N = 3179) |
Percentagea | Trend |
|---|---|---|---|---|---|
| 1 | Aciclovir | J05AB01 | 11 | 1.8 | Low |
| 2 | Acarbose | A10BF01 | 1 | 0.2 | Low |
| 3 | Aclarubicin | L04AB04 | 1 | 0.5 | Low |
| 4 | Adalimumab | L01DB04 | 3 | 0.2 | Low |
| 5 | Alimemazine | R06AD01 | 2 | 0.3 | Low |
| 6 | Allopurinol | M04AA01 | 17 | 2.8 | Low |
| 7 | Amantadine | N04BB01 | 1 | 0.2 | Low |
| 8 | Amiodarone | C01BD01 | 2 | 0.3 | Low |
| 9 | Aminophylline | R03DA05 | 16 | 2.7 | Low |
| 10 | Amitriptyline | N06AA09 | 2 | 0.3 | Low |
| 11 | Amoxicillin | J01CA04 | 27 | 4.5 | Low |
| 12 | Amlodipine | C08CA01 | 78 | 13.0 | Medium |
| 13 | Anastrazole | L02BG03 | 2 | 0.3 | Low |
| 14 | Artemether | P01BF01 | 08 | 1.3 | Low |
| 15 | Aspirin | N02BA01 | 54 | 9.0 | Low |
| 16 | Atenolol | C07AB03 | 62 | 10.3 | Medium |
| 17 | Atropine | A03BA01 | 4 | 0.7 | Low |
| 18 | Atorvastatin | C10AA05 | 15 | 2.5 | Low |
| 19 | Attapulgite | A07BC04 | 1 | 0.2 | Low |
| 20 | Azithromycin | J01FA10 | 13 | 2.2 | Low |
| 21 | Beclomethasone | D07AC15 | 11 | 1.8 | Low |
| 22 | Bisoprolol | C07AB07 | 1 | 0.2 | Low |
| 23 | Bismuth subcitrate | A07BX05 | 1 | 0.2 | Low |
| 24 | Benzyl penicillin | J01 CE01 | 7 | 1.2 | Low |
| 25 | Bleomycin | L01 DC01 | 1 | 0.2 | Low |
| 26 | Bromazepam | N05BA08 | 8 | 1.3 | Low |
| 27 | Bromocriptine | G02CB01 | 3 | 0.5 | Low |
| 28 | Calcium gluconate | A12AA03 | 1 | 0.2 | Low |
| 29 | Captopril | C09AA01 | 53 | 8.8 | Low |
| 30 | Carbamazepine | N03AF01 | 6 | 1.0 | Low |
| 31 | Carvedilol | C07AG02 | 2 | 0.3 | Low |
| 32 | Cefotaxime | J01DD01 | 14 | 2.3 | Low |
| 33 | Ceftriaxone | J01DD04 | 241 | 40.2 | High |
| 34 | Celecoxib | M01AH01 | 5 | 0.8 | Low |
| 35 | Cephradine | J01DB09 | 4 | 0.7 | Low |
| 36 | Cetirizine | R06AE07 | 2 | 0.3 | Low |
| 37 | Cimetidine | A02BA01 | 3 | 0.5 | Low |
| 38 | Chlorhexidine | A01AB03 | 1 | 0.2 | Low |
| 39 | Chloramphenicol | J01BA01 | 6 | 1.0 | Low |
| 40 | Chlorpheniramine | R06AB04 | 11 | 1.8 | Low |
| 41 | Chlorpromazine | N05AA01 | 6 | 1.0 | Low |
| 42 | Colecalciferol | A11CC05 | 6 | 1.0 | Low |
| 43 | Ciprofloxacin | J01MA02 | 34 | 5.7 | Low |
| 44 | Clarithromycin | J01FA09 | 25 | 4.2 | Low |
| 45 | Clindamycin | J01FF01 | 7 | 1.2 | Low |
| 46 | Clopidogrel | B01AC04 | 23 | 3.8 | Low |
| 47 | Clonazepam | N03AE01 | 2 | 0.3 | Low |
| 48 | Clonidine | C02AC01 | 2 | 0.3 | Low |
| 49 | Codeine | R05DA04 | 2 | 0.3 | Low |
| 50 | Colchicine | M04 AC01 | 21 | 3.5 | Low |
| 51 | Cromolyn sodium | A07EB01 | 3 | 0.5 | Low |
| 52 | Cyclosporine | N04 AD01 | 2 | 0.3 | Low |
| 53 | Dacarbazine | L01AX04 | 2 | 0.3 | Low |
| 54 | Darifenacin | G04BD10 | 1 | 0.2 | Low |
| 55 | Drotaverine | A03AD02 | 5 | 0.8 | Low |
| 56 | Dexamethasone | R01AD03 | 61 | 10.2 | Medium |
| 57 | Diclofenac sodium | M01AB05 | 111 | 18.5 | Medium |
| 58 | Dicyclomine | A03AA07 | 1 | 0.2 | Low |
| 59 | Digoxin | C01AA05 | 3 | 0.5 | Low |
| 60 | Diphenhydramine | R06AA02 | 46 | 7.7 | Low |
| 61 | Diphenoxylate | A07DA01 | 3 | 0.5 | Low |
| 62 | Diloxanide | P01AC01 | 1 | 0.2 | Low |
| 63 | Diltiazem | C08DB01 | 1 | 0.2 | Low |
| 64 | Divalproex sodium | N03AG01 | 25 | 4.2 | Low |
| 65 | Dobutamine | C01CA07 | 2 | 0.3 | Low |
| 66 | Domperidone | A03FA03 | 30 | 5.0 | Low |
| 67 | Dopamine | C01CA04 | 2 | 0.3 | Low |
| 68 | Doxycycline | J01AA02 | 2 | 0.3 | Low |
| 69 | Epinephrine | B02BC09 | 4 | 0.7 | Low |
| 70 | Ephedrine | R01AA03 | 1 | 0.2 | Low |
| 71 | Erythromycin | J01FA01 | 1 | 0.2 | Low |
| 72 | Escitalopram | N06AB04 | 18 | 2.8 | Low |
| 73 | Ethambutol | J04AK02 | 25 | 4.2 | Low |
| 74 | Ezitimibe | C10AX09 | 2 | 0.3 | Low |
| 75 | Famotidine | A02BA03 | 20 | 3.3 | Low |
| 76 | Fexofenadine | R06AX26 | 6 | 1.0 | Low |
| 77 | Fluoxetine | N06AB03 | 2 | 0.3 | Low |
| 78 | Formoterol | R03AC13 | 4 | 0.7 | Low |
| 79 | Fosfomycin | J01XX01 | 4 | 0.7 | Low |
| 80 | Furosemide | C03CA01 | 68 | 11.3 | Medium |
| 81 | Gentamicin | J01GB03 | 9 | 1.3 | Low |
| 82 | Glimepiride | A10BB12 | 10 | 1.7 | Low |
| 83 | Glipizide | A10BB07 | 9 | 1.5 | Low |
| 84 | Glyceryl trinitrite | C01DA02 | 9 | 1.5 | Low |
| 85 | Haloperidol | N05 AD01 | 5 | 0.8 | Low |
| 86 | Heparin | B01AB01 | 3 | 0.5 | Low |
| 87 | Hydrocortisone | D07AA02 | 30 | 5.0 | Low |
| 88 | Hydralline | C02DB02 | 15 | 2.5 | Low |
| 89 | Hydralazine | C02DB02 | 6 | 1.0 | Low |
| 90 | Ibuprofen | M01AE01 | 24 | 4.0 | Low |
| 91 | Imipenem | J01DH51 | 3 | 0.5 | Low |
| 92 | Imipramine | N06AA02 | 1 | 0.2 | Low |
| 93 | Indacaterol | R03AC18 | 2 | 0.3 | Low |
| 94 | Indomethacin | M01AB01 | 5 | 0.8 | Low |
| 95 | Insulin | A10AB02 | 40 | 6.7 | Low |
| 96 | Ipratropium | R01AX03 | 19 | 3.2 | Low |
| 97 | Isoniazid | J04 AC01 | 23 | 3.8 | Low |
| 98 | Isosorbide mononitrite | C01DA14 | 3 | 0.5 | Low |
| 99 | Isosorbide dinitrite | C01DA08 | 2 | 0.3 | Low |
| 100 | Ketorolac | M01AB15 | 43 | 7.2 | Low |
| 101 | Lactulose | A06AD11 | 49 | 8.2 | Low |
| 102 | Labetalol | C07AG01 | 3 | 0.5 | Low |
| 103 | Leflunamide | L04AA13 | 3 | 0.5 | Low |
| 104 | Leviteracetam | N03AX14 | 32 | 5.3 | Low |
| 105 | Levodopa | N04BA01 | 4 | 0.7 | Low |
| 106 | Levofloxacin | J01MA12 | 11 | 1.8 | Low |
| 107 | Lisinopril | C09AA03 | 8 | 1.3 | Low |
| 108 | Loperamide | A07DA03 | 5 | 0.8 | Low |
| 109 | Losartan | C09CA01 | 3 | 0.5 | Low |
| 110 | Lovastatin | C10AA02 | 2 | 0.3 | Low |
| 111 | Lumefantrine | P01BF01 | 1 | 0.2 | Low |
| 112 | Mannitol | B05BC01 | 27 | 4.5 | Low |
| 113 | Mebeverine | A03AA04 | 3 | 0.5 | Low |
| 114 | Mecobalamine | B03BA01 | 2 | 0.3 | Low |
| 115 | Midazolam | N05CD08 | 3 | 0.5 | Low |
| 116 | Mercaptopurine | L01BB02 | 1 | 0.2 | Low |
| 117 | Meropenem | J01DH02 | 4 | 0.7 | Low |
| 118 | Metformin | A10BA02 | 53 | 8.8 | Low |
| 119 | Methotrexate | L01BA01 | 3 | 0.5 | Low |
| 120 | Metoclopramide | A03FA01 | 86 | 14.3 | Medium |
| 121 | Metoprolol | C07AB02 | 1 | 0.2 | Low |
| 122 | Metaproterenol | R03AB03 | 1 | 0.2 | Low |
| 123 | Metronidazole | A01AB17 | 119 | 19.8 | Medium |
| 124 | Midazolam | N05CD08 | 15 | 2.5 | Low |
| 125 | Mirtazapine | M06AX11 | 3 | 0.5 | Low |
| 126 | Misoprostol | A02BB01 | 4 | 0.7 | Low |
| 127 | Mg-hydroxide | A02AA04 | 5 | 0.8 | Low |
| 128 | Montelukast sodium | R03DC03 | 16 | 2.7 | Low |
| 129 | Morphine | N02AA01 | 3 | 0.5 | Low |
| 130 | Moxifloxacin | J01MA14 | 23 | 3.8 | Low |
| 131 | Nalbufine | N02AF02 | 13 | 2.2 | Low |
| 132 | Naltrexone | V03AB30 | 6 | 1.0 | Low |
| 133 | Natalizumab | L04AA23 | 1 | 0.2 | Low |
| 134 | Nedocromil | R01AC07 | 3 | 0.5 | Low |
| 135 | Nefazodone | N06AX06 | 1 | 0.2 | Low |
| 136 | Nifedipine | C08CA05 | 2 | 0.3 | Low |
| 137 | Nimodipine | C08CA06 | 11 | 1.8 | Low |
| 138 | Nitroglycerine | C01DA02 | 6 | 1.0 | Low |
| 139 | Nitropruside | C02DD01 | 1 | 0.2 | Low |
| 140 | Nitrazepam | N05CD02 | 2 | 0.3 | Low |
| 141 | Octreotide | H01CB02 | 30 | 5.0 | Low |
| 142 | Omeprazole | A02BC01 | 308 | 51.3 | High |
| 143 | Paracetamol | N02BE01 | 305 | 50.8 | High |
| 144 | Paclitaxel | L01CD01 | 3 | 0.5 | Low |
| 145 | Pantoprazole | A02BC02 | 1 | 0.2 | Low |
| 146 | Phenelzine | N06AF03 | 2 | 0.3 | Low |
| 147 | Phenytoin | N03AB02 | 2 | 0.3 | Low |
| 148 | Pipracilline | J01CA12 | 1 | 0.2 | Low |
| 149 | Prazosin | C02CA01 | 2 | 0.3 | Low |
| 150 | Prednisone | H02AB07 | 1 | 0.2 | Low |
| 151 | Prednisolone | H02AB06 | 1 | 0.2 | Low |
| 152 | Probenecid | M04AB01 | 3 | 0.5 | Low |
| 153 | Procainamide | C01BA02 | 1 | 0.2 | Low |
| 154 | Propoxyphene | N02 AC04 | 2 | 0.3 | Low |
| 155 | Propranolol | C07AA05 | 20 | 3.3 | Low |
| 156 | Propylthiouracil | H03BA02 | 3 | 0.5 | Low |
| 157 | Polymixin | J01XB02 | 2 | 0.3 | Low |
| 158 | Pyrazinamide | J04AK01 | 23 | 3.8 | Low |
| 159 | Pyridoxine | A11HA02 | 17 | 2.8 | Low |
| 160 | Quinine | P01BC01 | 3 | 0.5 | Low |
| 161 | Ranitidine | A02BA02 | 37 | 6.2 | Low |
| 162 | Rifampicin | J04AB02 | 21 | 3.5 | Low |
| 163 | Rifaxamin | D06AX11 | 5 | 0.8 | Low |
| 164 | Reserpine | C02AA02 | 1 | 0.2 | Low |
| 165 | Rituximab | L01XX21 | 1 | 0.2 | Low |
| 166 | Rosuvastatin | C10AA07 | 17 | 2.8 | Low |
| 167 | Salbutamol | R03AC02 | 43 | 7.2 | Low |
| 168 | Sulbactum | J01CG01 | 11 | 1.8 | Low |
| 169 | Salmeterol | R03AC12 | 22 | 3.7 | Low |
| 170 | Simvastatin | C10AA01 | 63 | 10.5 | Medium |
| 171 | Sitagliptin | A10BH01 | 4 | 0.7 | Low |
| 172 | Selegiline | N04BD01 | 2 | 0.3 | Low |
| 173 | Sertraline | N06AB06 | 1 | 0.2 | Low |
| 174 | Somatostatin | H01CB01 | 1 | 0.2 | Low |
| 175 | Solifenacin | G04BD08 | 1 | 0.2 | Low |
| 176 | Spironolactone | C03DA01 | 7 | 1.2 | Low |
| 177 | Streptomycin | J01GA01 | 4 | 0.7 | Low |
| 178 | Sucralfate | A02BX02 | 9 | 1.5 | Low |
| 179 | Sodium picosulfate | A06AB08 | 1 | 0.2 | Low |
| 180 | Sulfasalazine | A07EC01 | 3 | 0.5 | Low |
| 181 | Tamsulosin | G04CA02 | 2 | 0.3 | Low |
| 182 | Tazobactum | J01CG02 | 19 | 3.2 | Low |
| 183 | Theophylline | R03DA04 | 5 | 0.8 | Low |
| 184 | Thioridazine | N05 AC02 | 1 | 0.2 | Low |
| 185 | Thyroxin | H03AA01 | 4 | 0.7 | Low |
| 186 | Tizinidine | M03BX02 | 1 | 0.2 | Low |
| 187 | Terbutaline | R03AC03 | 4 | 0.7 | Low |
| 188 | Tramadol | N02AX02 | 26 | 4.3 | Low |
| 189 | Transemic acid | B02AA02 | 7 | 1.2 | Low |
| 190 | Telmisartan | C09CA07 | 5 | 0.8 | Low |
| 191 | Trustuzumab | L01XC03 | 2 | 0.3 | Low |
| 192 | Vancomycin | J01XA01 | 23 | 3.8 | Low |
| 193 | Venlafaxine | N06AX16 | 1 | 0.2 | Low |
| 194 | Verapamil | C08DA01 | 7 | 1.2 | Low |
| 195 | Vincristine | L01CA02 | 1 | 0.2 | Low |
| 196 | Ziprasidone | N05AE04 | 1 | 0.2 | Low |
| 197 | Zinc sulphate | A12CB01 | 1 | 0.2 | Low |
aPercentages given with respect to total sample size (n = 600)
Additional file
Data collection form. (DOCX 22 kb)
Authors’ contributions
MRS and MAT conceptualized and designed the study. MAT is the research supervisor of MRS. SS, ZB, AS and MQZ also participated in design of the study. MRS collected, analyzed and interpreted the data. MRS, MQZ, ZB, SS, AS and MAT drafted the manuscript. SS made substantial contributions in preparing the revised manuscript. All authors read and approved final version of the manuscript.
Ethics approval and consent to participate
The study was based on the Code of Ethics of the Declaration of Helsinki. The ethical approval was obtained from the Pharmacy Research Ethics Committee (PREC) at the Islamia University Bahawalpur (Reference: 12–2015/PREC, dated October 20, 2015). Before starting the interviews, the data collector explained the purpose of the study to the target participants. Verbal consent was obtained from the agreed participants. Written consent was not possible for most of the respondents either because they were illiterate or they had problems in reading and/or signing the consent document. In this case, written consent was obtained from impartial witness (relatives/friends/guardians). The PREC committee approved this consent procedure. Project approval for this study was also obtained from the Medical Superintendents of each of the six hospitals involved.
Consent for publication
Not applicable.
Competing interests
The authors declare that they have no competing interests.
Publisher’s Note
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The online version of this article (doi:10.1186/s40545-017-0112-z) contains supplementary material, which is available to authorized users.
Contributor Information
Muhammad Rehan Sarwar, Email: rehansarwaralvi@gmail.com.
Muhammad Atif, Email: pharmacist_atif@yahoo.com, Email: muhammad.atif@iub.edu.pk.
Shane Scahill, Email: s.scahill@massey.ac.nz.
Anum Saqib, Email: anumsaqibzaidi@gmail.com.
Muhammad Qamar-uz-Zaman, Email: qamar_pharmacist@hotmail.com.
Zaheer Babar, Email: Z.Babar@hud.ac.uk.
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Data Availability Statement
The raw data on which conclusions of this manuscript rely is available upon request. Please contact Muhammad Rehan Sarwar at rehansarwaralvi@gmail.com.