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Drug utilization patterns among elderly hospitalized patients on poly-pharmacy in Punjab, Pakistan

  • Muhammad Rehan Sarwar1,
  • Muhammad Atif1Email author,
  • Shane Scahill2,
  • Anum Saqib1,
  • Muhammad Qamar-uz-Zaman1 and
  • Zaheer Babar3
Journal of Pharmaceutical Policy and Practice201710:23

https://doi.org/10.1186/s40545-017-0112-z

Received: 16 March 2017

Accepted: 1 August 2017

Published: 4 August 2017

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.

Keywords

Drug useDrug utilization patternElderlyHospitalizedPoly-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].
$$ n\kern0.5em =\kern0.5em Nx/\left(\left(N-1\right)E2\kern0.5em +\kern0.5em x\right) $$
(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 [2628]. 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 [5456]. 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.

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

Declarations

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.

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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Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

Authors’ Affiliations

(1)
Department of Pharmacy, The Islamia University of Bahawalpur
(2)
School of Management, Massey University
(3)
Department of Pharmacy, University of Huddersfield

References

  1. Pappa E, et al. Prescribed-drug utilization and polypharmacy in a general population in Greece: association with sociodemographic, health needs, health-services utilization, and lifestyle factors. Eur J Clin Pharmacol. 2011;67(2):185–92.View ArticlePubMedGoogle Scholar
  2. Haider SI, et al. Analysis of the association between polypharmacy and socioeconomic position among elderly aged≥ 77 years in Sweden. Clin Ther. 2008;30(2):419–27.View ArticlePubMedGoogle Scholar
  3. Haider SI, et al. The influence of educational level on Polypharmacy and inappropriate drug use: a register-based study of more than 600,000 older people. J Am Geriatr Soc. 2009;57(1):62–9.View ArticlePubMedGoogle Scholar
  4. Junius-Walker U, Theile G, Hummers-Pradier E. Prevalence and predictors of polypharmacy among older primary care patients in Germany. Fam Pract. 2007;24(1):14–9.View ArticlePubMedGoogle Scholar
  5. Little P, et al. Importance of patient pressure and perceived pressure and perceived medical need for investigations, referral, and prescribing in primary care: nested observational study. BMJ. 2004;328(7437):444.View ArticlePubMedPubMed CentralGoogle Scholar
  6. Furu K, Straume B, Thelle D. Legal drug use in a general population: association with gender, morbidity, health care utilization, and lifestyle characteristics. J Clin Epidemiol. 1997;50(3):341–9.View ArticlePubMedGoogle Scholar
  7. Fillenbaum GG, et al. Factors predicting change in prescription and nonprescription drug use in a community-residing black and white elderly population. J Clin Epidemiol. 1996;49(5):587–93.View ArticlePubMedGoogle Scholar
  8. Milton JC, Hill-Smith I, Jackson SH. Prescribing for older people. BMJ. 2008;336(7644):606–9.View ArticlePubMedPubMed CentralGoogle Scholar
  9. Acurcio FdA, et al. Use of medications by elderly Brazilian retirees: 1-methodology and response rates of a multicenter survey. Cadernos de Saúde Pública. 2006;22(1):87–96.View ArticleGoogle Scholar
  10. Jalal S, Younis MZ. Aging and elderly in Pakistan. Ageing Int. 2014;39(1):4–12.View ArticleGoogle Scholar
  11. Zafar SN, et al. Health and needs assessment of geriatric patients: results of a survey at a teaching hospital in Karachi. J Pak Med Assoc. 2006;56(10):470.PubMedGoogle Scholar
  12. Baksaas I, Lunde P. National drug policies: the need for drug utilization studies. Trends Pharmacol Sci. 1986;7:331–4.View ArticleGoogle Scholar
  13. Laporte J-R, Porta M, Capella D. Drug utilization studies: a tool for determining the effectiveness of drug use. Br J Clin Pharmacol. 1983;16(3):301–4.View ArticlePubMedPubMed CentralGoogle Scholar
  14. Health Department. Health Services. 2016 [cited 2016 29 June]; Available from: http://health.punjab.gov.pk/Services_Package.
  15. The News. Population shoots up by 47 percent since 1998, in the News. 2012. THE NEWS INTERNATIONAL. Google Scholar
  16. Raosoft. Sample size calculator. 2015 [cited 2016 December 2015]; Available from: http://www.raosoft.com/samplesize.html.
  17. World Health Organization. The anatomical therapeutic chemical classification system with defined daily doses (ATC/DDD). Oslo: WHO; 2006.Google Scholar
  18. Field, A., Discovering statistics using SPSS. 2009 London. 2005, SAGE ISBN: 9781847879073.Google Scholar
  19. Lovell RM, Ford AC. Global prevalence of and risk factors for irritable bowel syndrome: a meta-analysis. Clin Gastroenterol Hepatol. 2012;10(7):712–21. e4View ArticlePubMedGoogle Scholar
  20. Shaheen NJ, et al. The burden of gastrointestinal and liver diseases, 2006. Am J Gastroenterol. 2006;101(9):2128–38.View ArticlePubMedGoogle Scholar
  21. Russo MW, et al. Digestive and liver diseases statistics, 2004. Gastroenterol. 2004;126(5):1448–53.View ArticleGoogle Scholar
  22. Peery AF, et al. Burden of gastrointestinal disease in the United States: 2012 update. Gastroenterol. 2012;143(5):1179–87 e1-3.View ArticleGoogle Scholar
  23. Jyrkkä J, et al. Patterns of drug use and factors associated with polypharmacy and excessive polypharmacy in elderly persons. Drugs Aging. 2009;26(6):493–503.View ArticlePubMedGoogle Scholar
  24. Nobili A, et al. Drug utilization and polypharmacy in an Italian elderly population: the EPIFARM-elderly project. Pharmacoepidemiol Drug Saf. 2011;20(5):488–96.View ArticlePubMedGoogle Scholar
  25. Arcidiacono P, et al. Pharmaceutical followers. Int J Ind Organ. 2013;31(5):538–53.View ArticleGoogle Scholar
  26. Targownik LE, et al. Use of proton pump inhibitors and risk of osteoporosis-related fractures. Can Med Assoc J. 2008;179(4):319–26.View ArticleGoogle Scholar
  27. Eusebi LH, et al. Proton pump inhibitors: risks of long-term use. J Gastroenterol Hepatol. 2017;32(7):1295-1302.Google Scholar
  28. Heidelbaugh JJ, et al. Overutilization of proton-pump inhibitors: what the clinician needs to know. Ther Adv Gastroenterol. 2012;5(4):219–32.View ArticleGoogle Scholar
  29. Forsythe P, et al. Mood and gut feelings. Brain Behav Immun. 2010;24(1):9–16.View ArticlePubMedGoogle Scholar
  30. Wittchen HU, et al. The size and burden of mental disorders and other disorders of the brain in Europe 2010. Eur Neuropsychopharmacol. 2011;21(9):655–79.View ArticlePubMedGoogle Scholar
  31. Atif M, Azeem M, Sarwar MR. Potential problems and recommendations regarding substitution of generic antiepileptic drugs: a systematic review of literature. SpringerPlus. 2016;5(1):1.View ArticleGoogle Scholar
  32. Mirza I, Jenkins R. Risk factors, prevalence, and treatment of anxiety and depressive disorders in Pakistan: systematic review. BMJ. 2004;328(7443):794.View ArticlePubMedPubMed CentralGoogle Scholar
  33. Jyrkkä J, et al. Increasing use of medicines in elderly persons: a five-year follow-up of the Kuopio 75+ study. Eur J Clin Pharmacol. 2006;62(2):151–8.View ArticlePubMedGoogle Scholar
  34. Linjakumpu T, et al. Use of medications and polypharmacy are increasing among the elderly. J Clin Epidemiol. 2002;55(8):809–17.View ArticlePubMedGoogle Scholar
  35. Jörgensen T, et al. Prescription drug use, diagnoses, and healthcare utilization among the elderly. Ann Pharmacother. 2001;35(9):1004–9.View ArticlePubMedGoogle Scholar
  36. Whiteford HA, et al. The global burden of mental, neurological and substance use disorders: an analysis from the global burden of disease study 2010. PLoS One. 2015;10(2):e0116820.View ArticlePubMedPubMed CentralGoogle Scholar
  37. Andersson G. The epidemiology of spinal disorders. The adult spine: Principles and practice. 1997;Google Scholar
  38. Phillips FM, et al. Lumbar spine fusion for chronic low back pain due to degenerative disc disease: a systematic review. Spine. 2013;38(7):E409–22.View ArticlePubMedGoogle Scholar
  39. Taimela S, et al. The prevalence of low back pain among children and adolescents: a nationwide, cohort-based questionnaire survey in finland. Spine. 1997;22(10):1132–6.View ArticlePubMedGoogle Scholar
  40. Vos T, et al. Years lived with disability (YLDs) for 1160 sequelae of 289 diseases and injuries 1990–2010: a systematic analysis for the global burden of disease study 2010. Lancet. 2013;380(9859):2163–96.View ArticleGoogle Scholar
  41. Guo HR, et al. Back pain prevalence in US industry and estimates of lost workdays. Am J Public Health. 1999;89(7):1029–35.View ArticlePubMedPubMed CentralGoogle Scholar
  42. Rubin DI. Epidemiology and risk factors for spine pain. Neurol Clin. 2007;25(2):353–71.View ArticlePubMedGoogle Scholar
  43. Naghavi M, et al. Global, regional, and national age-sex specific all-cause and cause-specific mortality for 240 causes of death, 1990-2013: a systematic analysis for the global burden of disease study 2013. Lancet. 2015;385(9963):117–71.View ArticleGoogle Scholar
  44. Atif M, et al. Rights and responsibilities of tuberculosis patients, and the Global Fund: a qualitative study. PLoS One. 2016;11(3):e0151321.View ArticlePubMedPubMed CentralGoogle Scholar
  45. Reardon S. Antibiotic resistance sweeping developing world. Nature. 2014;509(7499):141–2.View ArticlePubMedGoogle Scholar
  46. Van Boeckel TP, et al. Global antibiotic consumption 2000 to 2010: an analysis of national pharmaceutical sales data. Lancet Infect Dis. 2014;14(8):742–50.View ArticlePubMedGoogle Scholar
  47. Mendis S, Puska P, Norrving B. Global atlas on cardiovascular disease prevention and control. Geneva: World Health Organization; 2011.Google Scholar
  48. Go AS, et al. Heart disease and stroke statistics--2014 update: a report from the American Heart Association. Circulation. 2014;129(3):e28.Google Scholar
  49. Go A, et al. Heart disease and stroke statistics-2013 update: a report from the American Heart Association. Circulation. 2013;127(1):e1–e240.View ArticleGoogle Scholar
  50. Williams MA, et al. Secondary prevention of coronary heart disease in the elderly (with emphasis on patients≥ 75 years of age) an American Heart Association scientific statement from the council on clinical cardiology subcommittee on exercise, cardiac rehabilitation, and prevention. Circulation. 2002;105(14):1735–43.View ArticlePubMedGoogle Scholar
  51. Freemantle N, et al. β blockade after myocardial infarction: systematic review and meta regression analysis. BMJ. 1999;318(7200):1730–7.View ArticlePubMedPubMed CentralGoogle Scholar
  52. Taylor J. 2013 ESH/ESC guidelines for the management of arterial hypertension. Eur Heart J. 2013;34(28):2108–9.PubMedGoogle Scholar
  53. Barat I, Andreasen F, Damsgaard EMS. The consumption of drugs by 75-year-old individuals living in their own homes. Eur J Clin Pharmacol. 2000;56(6–7):501–9.View ArticlePubMedGoogle Scholar
  54. Arber S, Cooper H. Gender differences in health in later life: the new paradox? Soc Sci Med. 1999;48(1):61–76.View ArticlePubMedGoogle Scholar
  55. Marmot MG, et al. Contribution of job control and other risk factors to social variations in coronary heart disease incidence. Lancet. 1997;350(9073):235–9.View ArticlePubMedGoogle Scholar
  56. Barnett RC, Hyde JS. Women, men, work, and family. An expansionist theory. Am Psychol. 2001;56(10):781–96.View ArticlePubMedGoogle Scholar

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