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Evaluation of the drug utilization pattern at a regional psychiatric hospital, in Benin city, Nigeria.

INTRODUCTION

Drug utilization research involves the prescription and use of drugs with emphasis on the resulting medical, social, and economic consequences. [1] Medicines are essential in health care delivery, therefore, the availability and affordability of good quality and efficacious drugs in addition to their rational use is a sine qua none to an effective health care delivery system. [2] However, irrational and inappropriate use of medicines are frequent occurrences in many countries, particularly the developing ones. [2,3] According to the WHO, more than half of all medicines are either prescribed and dispensed irrationally or sold inappropriately, also half of all patients prescribed medications fail to take them correctly [4] leading to poor treatment outcomes.

In Nigeria and other developing nations, researches evaluating drug utilization habits have been conducted using the WHO drug use indicators, that showed very high rates of polypharmacy, [5] overuse of antibiotics and injections, and lack of prescribing from essential drugs list. [2,4,6-9]

This study was aimed at determining the drug utilization patterns at a regional neuropsychiatric hospital, in Benin City, Nigeria using some of the WHO core drug use indicators, and to identify other drug use grey areas such as availability of key essential medicines, to which future drug use intervention programs could be centered on.

MATERIALS AND METHODS

Study site

This study was conducted at a regional tertiary psychiatric facility in South-south Nigeria.

The hospital has a 220-bed capacity and serves a catchment population of about 13 million [10] people.

Ethical approval

The Ethics Committee of the hospital reviewed and approved the study protocol.

Study design

The study design employed for this study was retrospective. It was a descriptive study that utilized relevant data from the prescription records of patients seen at the Out-Patient Pharmacy Unit of the hospital, from September 2007 to August 2012. Data on some of the WHO core drug use indicators [11] and the percentage of drugs prescribed but not available (i.e., out of stock) were collected during the study.

Data collection process

Systematic random sampling was adopted in the data collection. The prescription sheets of patients seen over the study period were collected and collated chronologically and later separated according to the year of prescription. For the purpose of this study, September 2007 to August 2008 is referred to as year 1, September 2008 to August 2009 as year 2, September 2009 to august 2010 as year 3, September 2010 to August 2011 as year 4, and September 2011 to August 2012 as year 5. The total number of prescriptions over the five-year period was 108,000 with an average of 48,57,60, 60 and 75 prescriptions per day giving rise to 17 280, 20 520, 21 600, 21 600, and 27 000 prescriptions, respectively, for each year. From the 108,000 total prescriptions that was collated and classified according to the year of prescription, 3 prescriptions were selected at random by picking 1 in every 16 prescriptions for the first year, 1 in every 19 prescriptions for the second year, 1 in every 20 prescriptions for the third and fourth years, and 1 in every 25 prescriptions for the fifth year amounting to 1080 prescriptions per year and 5,400 sample prescriptions used in this study. The relevant information on the sampled prescriptions was entered into a structured data collection form. The information that were extracted from the prescriptions included: Date of prescription, age and sex of the patient, number of drugs per prescription, number of drugs prescribed by generic name, number of prescriptions with antibiotics, number of drugs prescribed from the essential drugs list, number of drugs prescribed but not available. In addition, the total numbers of each drug prescribed within the study period as well as the frequency of such prescriptions were captured using a proforma designed by the authors.

Data analysis

Extracted information from the prescription sheets were entered into the data collection form and sorted with the aid of Microsoft Excel 2007 and summarized as mean and frequencies. The prescribing indicators were calculated using the WHO guideline, including average number of drugs per encounter, percentage of drugs prescribed by generic name or from essential drugs list, and percentage of encounters during which an antibiotic was prescribed.

Average number of drugs per encounter was calculated by dividing the total number of different drug products prescribed by the total number of encounters surveyed. Percentage of drug prescribed by generic name was determined by dividing the number of drugs prescribed by generic by the total number of drugs multiplied by 100. Percentage of encounter with an antibiotic prescribed was calculated by dividing the number of patient encounters during which an antibiotic was prescribed by the total number of encounters surveyed multiplied by 100. Percentage of drugs prescribed from essential drugs list was determined by dividing the total number of products prescribed from the hospital's formulary by the total number of drugs prescribed multiplied by 100. Percentage of drugs prescribed but not available was determined by dividing the number of encounters during which at least a drug was out of stock by the total number of encounters multiplied by 100.

The Drug Utilization 90% (DU 90%) segment shows the number of drugs that account for 90% of all the drugs used in that facility and comprises the drugs whose percentage adds up to 90.

The DDD/1000 inhabitants/day which provides a rough estimate of the proportion of the study population treated daily with a particular drug or group of drugs was calculated using the Anatomic Therapeutic Chemical (ATC) classification and Defined Daily Dose (DDD) assignment as given by WHO collaborating center for drug statistics methodology Oslo, Norway. [12]

Formula for DDD/1000 inhabitants/day

Amount of drugs used in 1 yr (mg) x 1000/DDD (mg) x population x study duration (in days)

RESULTS

A total number of 5,400 prescriptions were used to assess the pattern of drug utilization in this study. As shown on [Table 1], more than half of the prescriptions; 2833 (53%) were for females.

The majority of the prescriptions; 3836 (71%) were for adults aged 18-49 years while 584 (10.81%) prescriptions did not have any age information.

The pattern of prescription revealed that an average of 2.88 drugs were prescribed per encounter, 94.38% of the drugs were prescribed by their generic names. The percentage of encounters with antibiotics prescribed was 3.2% while 99.2% of all the drugs were prescribed from the essential drugs list [Table 2].

Out of the 5400 prescriptions encountered, 3826 (70.85%) had all the drugs prescribed available in the hospital pharmacy, whereas at least a drug was out of stock in 1574 (29.15%) prescriptions [Table 3].

The drugs whose utilization accounted for about 90% of the entire drug use (DU 90%) include haloperidol, amitriptyline, benzhexol, trifluoperazine, chlorpromazine, and carbamazepine. The DDD/1000 inhabitants/day for each drug as well as the actual number of population on the average that consume each drug daily is also shown on Table 4.

Haloperidol was the most utilized drug in the setting with a DDD/1000 inhabitants/day of 5 and about 28 patients being placed daily on this drug while the least utilized drug was paroxetine with the DDD/1000 inhabitants/day of 0.001 and about 0.007 patients being on the drug daily.

DISCUSSION

Prescriptions reflect physician's attitude towards the disease being managed, training and drug availability, as well as the nature of the prevailing health care systems. [13] Using the WHO indicators for rational drug use, this study provides insight into the prescribing practices at the Federal Neuropsychiatric Hospital, Benin City and has shown areas that need improvement.

Whereas the WHO guidelines on rational use of drugs in the region recommends a range of 1.6-1.8 drugs per encounter, [11] an average of 2.88 drugs were prescribed per encounter by clinicians in this facility. Over 50% of the prescriptions had at least 3 drugs. However, high values of 3.3 and 3.5 were in reports from Northern Nigeria, [14,15] and even higher values of 3.99 and 4.4 were reported from Ilorin [16] and Benin-City. [13] An earlier report by Hogerzeil and colleagues showed much lower values of 1.3 and 2.2 for Bangladesh and Lebanon, respectively. [17]

The number of drugs taken has a direct relationship with the number of incidence of new hospital admissions per year due to adverse drug reactions, inappropriate medication use, and mortality. [18,19] Drug-food interactions and therapeutic duplication errors are some of the other problems associated with polypharmacy.

Prescribing all drugs by generic names is the recommendation of the WHO. The high level of generic prescription observed in this study; (94.38%) is a good trend. Increased generic prescribing will reduce the cost of medications and promote medication adherence. Similarly, high values of 75.0% and 99.8% of generic prescribing were reported by studies in Bangladesh and Cambodia [20] though lower figures have been reported previously in Nigeria [2,21] Ghana, [22] Lebanon, and Nepal. [23] In the United Arab Emirates, a much lower value of 4.4% has been reported. [23]

The average percentage of encounters with antibiotics found in this study was 3.2%. This value is lower than the WHO reference point (20.0-26.8%) [11] and even much lower than the earlier reports in Nigeria [12,14,16] Nepal, [24] Malawi, Indonesia, Bangladesh, and Tanzania. [25] This low antibiotic use is also a pointer to the relative rational prescribing practiced in this facility, and it could also be attributable to the fact that the center is a specialized facility and, therefore, most patients with some other physical ailments that would warrant the use of antibiotics are appropriately referred to other health care facilities.

Percentage of drugs prescribed from the essential drug list (99.2%) was higher than the average value of 84.60% recorded by Melinda et al. [26] from his review of previous studies in developing countries. Also, the result was higher than the value from studies by Guvon et al. [27] (16%) and Hazra et al. [28] (45.70%) but very similar to the result of Babalola et al. [29] (94.16%), Otoom et al. [30] (93%), and Bosu et al. [31] (97%).

A possible explanation for the high percentage of prescriptions from the Essential Drug List is the availability, in all the hospital consulting rooms, of the hospital drug bulletin, which was adapted from the Essential Drugs List.

The most utilized drugs in the facility studied that fall within the DU 90% segment, i.e., the drugs whose use accounted for about 90% of all the drugs used in the study site [Table 4], included: Amitriptyline (22.3%), trifluoperazine (20.3%), haloperidol (15.5%), chlorpromazine (15.2%), benzhexol (12.6%), and carbamazepine (7.9%). However, haloperidol was found to be the most prescribed drug because, out of about 60 patients seen in the OPD pharmacy daily, 28 (46.7%) of them had haloperidol on their prescriptions. As reported in an earlier study, [32] this study identified a gradual but steady decline in the use of typical antipsychotics as well as anticholinergics while the use of atypical antipsychotics such as olanzapine and risperidone is on the increase.

In about 70% of the prescriptions encountered, all the drugs prescribed were available in the hospital pharmacy. This is, however, lower than that reported from a study conducted in northern Nigeria where there was about 91.7% drug availability at the facility studied. [21]

CONCLUSION

The study found that the prescription patterns at the hospital studied were not in conformity with the WHO guidelines. Polypharmacy is still commonly practiced at the study site. There is a need to introduce interventional strategies geared towards improving the prescribing practices of the prescribers in this facility.

The most utilized psychotropic drug at the study site was haloperidol, accounting for about 46.7% of all the drugs prescribed daily.

The level of availability of the key essential drugs in the facility was poor.

Limitations of the study

The prescriptions used in assessing the pattern of prescription were those of the patients who purchased their medications from the hospital; therefore, the result of this research might not be generalizable to patients who prefer to purchase their drugs outside the hospital. In addition, this study was conducted with the outpatient prescriptions in one institution; therefore, the result might not apply to outpatients in other federal psychiatric hospitals.

DOI: 10.4103/2045-080X.123222

REFERENCES

[1.] World Health Organization: Introduction to Drug utilization research. What is Drug Utilization research and why is it needed? Norway: World Health Organization; 2003. p. 9.

[2.] Enato EF, Chima IE. Evaluation of drug utilization pattern and patient care practices. West Afr J Pharm 2011;22:36-40.

[3.] World Health Organization: Medicines strategy 2008-2013. Geneva: WHO; 2008.

[4.] Adebayo ET, Hussain NA. Pattern of Prescription drug use in a Nigerian Army Hospital. Ann Afr Med 2010;9:152-8.

[5.] Kaplan HI, Sadock BJ. Kaplan and Saddock's Synopsis of psychiatry. 8th ed. Baltimore: Lippincott Williams and Wilkins; 1998. p. 940-1.

[6.] Stimac D, Culig J. Out-patient utilization of psychopharmacenticals in the city of Zagreb 2001-2006. Psychiatr Danub 2009;21:56-64.

[7.] Osterberg L, Blaschke T. Adherence to medication. N Engl J Med 2005;353:487-97.

[8.] Cramer JA, Rosenbeck R. Compliance with medication regimens for mental and physical disorders. Psychiatr Serv 1998;49:196-201.

[9.] Matsui DM. Drug compliance in pediatrics. Clinical and research issues. Pediatr Clin North Am 1997;44:1-14.

[10.] Agbonile IO, Famuyiwa O. Psychiatric drug prescribing in a Nigerian Psychiatric Hospital. Int Psychiatry 2009;6:96-7.

[11.] Isah AO, Laing R, Quick J, Mabadeje AF, Santoso B, Hogerzeil H, et al. The Development of Reference Values for the World Health Organization (WHO) Health Facility Core Prescribing Indicators. West Afr J Pharmacol Drug Res 2002;18:6-11.

[12.] Guidelines for ATC Classification and DDD assignment. 13th ed. Oslo, Norway; 2010. p.18-27.

[13.] Laporte JR. Towards a healthy use of pharmaceuticals. Dev Dialogue 1985;2:48-55.

[14.] Chedi BA, Abdu-Aguye I, Kwanashie HO. WHO Core Prescription Indicators: Field Experience in Public Health Facilities in Kano, Nigeria. Bio and Environ Sci J Trop J 2004;6:66-70.

[15.] Ibrahim MT. Physicians' Prescribing behaviour in two tertiary health care facilities in North Western Nigeria. Analysis of 518 prescriptions. Sahel Med J 2004;7:115-8.

[16.] Akande TM, Oloye MO. Prescription pattern at a secondary health care facility in Ilorin, Nigeria. Ann Afr Med 2007;6:186-9.

[17.] Hogerzeil HV, Bimo, Ross-Degnan D, Laing RO, Ofori-Adjei D, Santoso B, et al. Field tests for rational drug use in twelve developing countries. Lancet 1993;342:1408-10.

[18.] Grymonpre RE, Mitenko PA, Sitar DS, Aoki FY, Montgomery PR. Drug associated hospital admissions in older patients. J Am Geriatr Soc 1988;36:1092-8.

[19.] Lazarou J, Pomeranze BH, Corey PN. Incidence of adverse drug reactions in hospitalized patients: A meta-analysis of prospective studies. JAMA 1998;279:1200-5.

[20.] Chareonkul C, Khun VI, Boonshuyar C. Rational drug use in Cambodia: Study of three pilot health centers in Kampong Thorn Province. Southeast Asian J Trop Med Public Health 2002;33:418-24.

[21.] Igbiks T, Joseph OF. Drug prescription pattern in a Nigerian Tertiary Hospital. Trop J Pharm Res 2012;11:146-52.

[22.] Owusu-Daaku FT, Sablah J. The essential drug list and drug use indicators at two university hospitals: KNUST and Legon, Ghana. West Afr J Pharm 2004;18:53-7.

[23.] Sharif SI, Al-Shaqra M, Hajjar H, Shamout A, Wess L. Patterns of drug prescribing in a hospital in Dubai, United Arab Emirates. Libyan J Med 2008;3:10-2.

[24.] Kafle KK, Karkee SB, Prasad RR. INRUD Drug Use Indicators in Nepal: Practice patterns in health post in four districts. INRUD News 1992;3:15.

[25.] Massele AY, Nsimbi SE, Rimoy G. Prescribing habits in church-owned primary health care facilities in Dar ES Salaam and other Tanzanian Coast regions. East Afr Med J 2001;78:510-4.

[26.] Melinda P, Talgat N, Grace H, Farruh Y, Richard L. Prescribing practices of primary health care physicians in Uzvekistan. J Trop Med Int Health 2003;8:182-90.

[27.] Guvon AB, Barman A, Ahmed JU, Ahmed UA, Alam MS. Baseline survey on use of drugs at the primary health care level in Bangladesh. Bull World Health Organ 1994;72:265-71.

[28.] Hazra A, Tripathi SK, Alam MS. Prescribing dispensing activities at the health facilities of a non-government organization. Natl Med J India 2000;13:177-82.

[29.] Babalola CP, Awoleye SA, Akinyemi JO, Kotila OA. Evaluation of prescription pattern in Osun State (southwest) Nigeria. J Public Health Epidemiol 2011;3:94-8.

[30.] Otoom S, Batieha A, Hadidi H, Hasan M, Al-Saudi K. Evaluation of drug use in Jordan, using WHO Prescribing indicators. East Mediterr Health J 2002;8:537-43.

[31.] Bosu WK, Ofori-Adjei D. An audit of prescribing practices in Health Care Facilities of the Wassa west district of Ghana. West Afr J Med 2000;19:298-303.

[32.] Adesola AO, Anozie IG, Erohubie P, James BO. Prevalence and correlates of "High Dose" antipsychotic prescribing: Findings from a hospital audit. Ann Med Health Sci Res 2013;3:62-6.

Hillary O. Odo, Sunday O. Olotu (1), Imafidon O. Agbonile1, Peter O. Esan, Bawo O. James (1)

Departments of Pharmacy and (1) Clinical Services, Federal Neuropsychiatry Hospital, Benin, Edo, Nigeria

Address for correspondence:

Odo O. Hillary, Department of Pharmacy, Federal

Neuropsychiatric Hospital, P.M.B 1108, Benin, Edo, Nigeria.

E-mail: hilaryonyenkem@yahoo.com
Table 1: Patient demographics

Patient variables     No. of patients (n)   Percentage

Total                        5400              100

Gender
Male                         2567               47
Female                       2833               53

Age group (years)
5-10 (Children)                22             0.4074
11-17 (Adolescents)           135             2.5000
18-49 (Adults)               3836             71.037
>49 (elderly)                 823             15.240

No age info                   584             10.815

Table 2: Prescribing pattern, based on WHO core
drug use indicators [11]

Prescribing                                 FNHB    Reference
indicator                                             value

Average number of drugs per encounter        2.88    1.6-1.8
Percentage of drugs prescribed by generic   94.38      100
Percentage of encounter with antibiotic      3.2    20.0-26.8
Percentage prescribed from EDL               99.2      100

FNHB=Federal neuropsychiatric hospital, Benin, EDL=Essential
drug list, WHO=World health organization

Table 3: Drug availability

No. of drugs   No. of prescriptions   Percentage
out of stock       encountered

0                      3826            70.85
1                      1183            21.91
2                      315             5.833
3                      50              0.926
4                      26              0.481
Total                 5400             100

Table 4: Utilization of psychotropic drugs expressed as
percentages, DDD/1000 inhabitants/day and the actual
number of population

ATC code   Drug                Total no.   Percentage    STR (mg)
                               of doses

N06AA09    Amitriptyline *      120583     22.29113442      25
N05AB06    Trifluoperazine *    109723     20.28354032      5
N05AD01    Haloperidol *        83811      15.49341339      5
N05AA01    Chlorpromazine *     82036      15.16528452     100
N04AA01    Benzhexol *          68366      12.63823006      5
N03AF01    Carbamazepine *      42678      7.889512077     200
                                           93.76111479
N03AG01    Sodium valproate      9364      1.731041546     200
N06AA02    Imipramine            5258      0.972000902      25
N05AH03    Olanzapine            5156      0.953145046      5
N05AX08    Risperidone           3729      0.68934792       2
N06AB06    Sertralline           2219      0.410207304      50
N05AC02    Thioridazine          1839      0.339959996     100
N06AB04    Citalopram            1805      0.333674711      20
N06AB03    Fluoxetine            1501      0.277476865      20
N05AB02    Fluphenazine dec      1489      0.275258529      25
N05BA01    Diazepam              1239      0.229043195      10
N05AF01    Flupentixol            99       0.018301272      20
N04AA02    Biperiden              37       0.006839869      5
N06AB05    Paroxetine             14       0.002588059      20
           Total                540946         100

ATC code   Drug                DDD (mg)   DDD/1000/day

N06AA09    Amitriptyline *        75      4.076339026
N05AB06    Trifluoperazine *      20       2.78191047
N05AD01    Haloperidol *          8       5.312347876
N05AA01    Chlorpromazine *      300      2.773247874
N04AA01    Benzhexol *            10      3.466695063
N03AF01    Carbamazepine *       1000     0.865644396

N03AG01    Sodium valproate      1500     0.126620962
N06AA02    Imipramine            100      0.133311022
N05AH03    Olanzapine             10       0.26144984
N05AX08    Risperidone            5       0.151271754
N06AB06    Sertralline            50       0.22504158
N05AC02    Thioridazine          300      0.062167863
N06AB04    Citalopram             20      0.183055454
N06AB03    Fluoxetine             20      0.152225062
N05AB02    Fluphenazine dec       1       3.775201817
N05BA01    Diazepam               10      0.125654132
N05AF01    Flupentixol            4       0.050200803
N04AA02    Biperiden              10      0.001876192
N06AB05    Paroxetine             20      0.001419821
           Total

ATC code   Drug                % population   Actual no. of
                                               population

N06AA09    Amitriptyline *     0.407633903     22.01223074
N05AB06    Trifluoperazine *   0.278191047     15.02231654
N05AD01    Haloperidol *       0.531234788     28.68667853
N05AA01    Chlorpromazine *    0.277324787     14.97553852
N04AA01    Benzhexol *         0.346669506     18.72015334
N03AF01    Carbamazepine *      0.08656444     4.674479737

N03AG01    Sodium valproate    0.012662096     0.683753195
N06AA02    Imipramine          0.013331102     0.719879518
N05AH03    Olanzapine          0.026144984     1.411829135
N05AX08    Risperidone         0.015127175     0.81686747
N06AB06    Sertralline         0.022504158     1.215224535
N05AC02    Thioridazine        0.006216786     0.335706462
N06AB04    Citalopram          0.018305545     0.988499452
N06AB03    Fluoxetine          0.015222506     0.822015334
N05AB02    Fluphenazine dec    0.377520182     20.38608981
N05BA01    Diazepam            0.012565413     0.678532311
N05AF01    Flupentixol          0.00502008     0.271084337
N04AA02    Biperiden           0.000187619     0.010131435
N06AB05    Paroxetine          0.000141982     0.007667032
           Total

* Drugs that fall within the DU 90% segment. DDD=Defined daily
dose, ATC=Anatomic therapeutic chemical, STR=Strength
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Title Annotation:Original Article
Author:Odo, Hillary O.; Olotu, Sunday O.; Agbonile, Imafidon O.; Esan, Peter O.; James, Bawo O.
Publication:Archives of Pharmacy Practice
Article Type:Report
Geographic Code:6NIGR
Date:Oct 1, 2013
Words:3468
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