Evaluation of the drug utilization pattern at a regional psychiatric hospital, in Benin city, Nigeria.
Drug utilization research involves the prescription and use of drugs with emphasis on the resulting medical, social, and economic consequences.  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.  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  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,  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
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  people.
The Ethics Committee of the hospital reviewed and approved the study protocol.
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  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.
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. 
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)
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.
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.  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,  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  and Benin-City.  An earlier report by Hogerzeil and colleagues showed much lower values of 1.3 and 2.2 for Bangladesh and Lebanon, respectively. 
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  though lower figures have been reported previously in Nigeria [2,21] Ghana,  Lebanon, and Nepal.  In the United Arab Emirates, a much lower value of 4.4% has been reported. 
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%)  and even much lower than the earlier reports in Nigeria [12,14,16] Nepal,  Malawi, Indonesia, Bangladesh, and Tanzania.  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.  from his review of previous studies in developing countries. Also, the result was higher than the value from studies by Guvon et al.  (16%) and Hazra et al.  (45.70%) but very similar to the result of Babalola et al.  (94.16%), Otoom et al.  (93%), and Bosu et al.  (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,  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. 
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.
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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.
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  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|
|Date:||Oct 1, 2013|
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