Thesis (M. Pharm. (Pharmacy Practice))--North-West University, Potchefstroom Campus, 2009.

Background: According to the Council of Medical Schemes of South Africa (CMS, 2007:52), nearly seventeen percent of the total benefits paid during 2006 were for medicine. Medicine is thus a cost-driving contributor to total healthcare financing. There are various factors influencing and driving medicine usage and cost patterns, including inter alia provider preference, therapeutic committees, marketing and cost. Objectives: The purpose of this study was to identify the top twenty trade name products according to total cost and prevalence in a section of the private health care sector of South Africa, and to identify cost driving products. Methodology: A quantitative, retrospective drug utilisation review (DUR) study was performed on computerised medication records (medicine claims data) for two consecutive years (i.e. 2005 and 2006) that were obtained from a South African pharmaceutical benefit management company (PBM). The study population consisted of 1 218358 and 1 259 099 patients for 2005 and 2006 respectively. A total of 19 860 679 and 21 473017 medicine items that were claimed during 2005 and 2006 were included in the review. Descriptive statistics were used to describe the data, and were analysed using the Statistical Analysis System® SAS 9.1® programme. The cost prevalence index (CPI), developed by Serfontein (1989:180), was used as an indicator of the relative expensiveness of medicine. Resource- and activity driver products (cost driving products) were identified on the database by calculating the total cost of the product, the CPI of the product as well as the prevalence of the product. Variables for analysis included age, gender, prescriber and provider types. Resurts and discussion: A total number of 8 522 574 and 9 046 138 prescriptions were analysed, with an average of 2.33 ± 1.56 and 2.37 ± 1.58 items per prescription during 2005 and 2006 respectively. The average cost per prescription for the total database was R222.16 ± R463.13 for 2005 and R226.25 ± R557.49 for 2006. Members had to co-pay an average of R26.33 ± R102.70 per prescription in 2005 compared to R29.74 ± R103.96 per prescription in 2006. Children under the age of nine accounted for approximately 13% of the total study population, the adolescent age group < 9 and ≥ 19 years) represented 12%, age group three < 19 and ≥ 45 years) represented 38%, age group four < 45 and ≥ 59 years) represented 21% and the geriatric age group (patients older than 59 years) represented 16% of the total study population on the database. About 44% of the study population were male compared to 56% female patients. The top twenty trade name products ranked according to total cost represented about 13% (N=R1 893376 921.00 and N=R2 046 944382.50 in 2005 and 2006 respectively) of the overall medicine cost. The top five trade name products according to total cost for 2005 in descending order were Upitor 1 Omg and 20mg, Fosamax 70mg, Celebrex 200mg and Prexum 4mg. During 2006 the top five trade name products were similar except for Cipralex 10mg in the place of Celebrex 200mg. The CPls for all these products were above one; these products were also all activity drivers. The top twenty trade name products ranked according to prevalence represented about 11% (N=19 860679 and N=21 473074) of the total medicine prevalence for both study periods. The top five trade name products according to prevalence for both years contained Eltroxin 100mcg, Ecotrin 81 mg, Upitor 10mg and Alcophyllex syrup, with Myprodol capsules in 2005 and Mybulen tablets in 2006. Upitor 1 Omg was the only cost driver product in this list. General medical practitioners prescribed the largest quantity of medicine items and represented about 73% of all the medicine items on the database. The medicine prescribed by general medical prescribers accounted for 65% of the overall medicine expenditure on the database. Pharmacies can be seen as the main providers of medicine items. Pharmacies provided approximately 80% of the medicine items and represented over 91% of the total medicine expenditure. Cardiovascular agents were the main pharmacological group that represented the greatest percentage of the total medicine cost, about 19% in both study years. Cardiovascular agents were also positioned 1st according to prevalence and represented about 14% of the overall medicine prevalence in both the study periods. Conclusions and recommendations: Cost driver products can be seen as the products that drives medicine expenditure in the managed health care environment, thus driving the total cost of medicine treatment in the private health care sector of South Africa. Through the implementation of managed health care information- and management instruments medicine expenditure can be reduced. Recommendations for future research have been made. / Thesis (M. Pharm. (Pharmacy Practice))--North-West University, Potchefstroom Campus, 2009.

Due to the fact that the function of dispensing is not the exclusive practice of a single profession, there is much conflict surrounding the issue: it forms the crux of the pharmacy profession but it also forms part of doctors’ scope of practice. Separation of the acts of prescribing and dispensing would prevent the interest of the doctor, who has the potential to profit from selling medicines, being placed above the interest of the patient. It would, however, also affect the essential services that many dispensing doctors provide to pensioners, unemployed patients, those not covered by a medical scheme and those in rural areas. The implications of doctor dispensing are not clear as conflicting evidence suggests that dispensing doctors prescribe more medicine items, injections and antibiotics while preferring certain brand names on the one hand but on the other, evidence shows that dispensing doctors dispensed less expensive medicines compared to other health care providers. The main objective of this study was to analyse the prescribing patterns of dispensing doctors and other medicine providers in a section of the private health care sector of South Africa for 2005 to 2008 by using a medicine claims database. A retrospective drug utilisation review was conducted by extracting data from a medicine claims database for a four–year period, from 1 January 2005 to 31 December 2008. The results revealed that dispensing doctors had a lower cost per prescription compared to other health care providers (R112.66 ± R4.45 vs. R258.48 ± R23.93) and also had a lower cost per medicine item (R39.62 ± R2.18 vs. R112.43 ± R7.56) for the entire study period from 2005 to 2008. Dispensing doctors provided more items per prescription compared to other health care providers (2.85 ± 0.05 items vs. 2.30 ± 0.06 items) but other health care providers claimed more prescriptions per patient per year (7.50 ± 1.15 prescriptions vs. 3.29 ± 0.07 prescriptions). A higher percentage of generic medicine items were provided to patients visiting dispensing doctors. Dispensing doctors treated a majority of patients aged above 19 to 44 years of age while other health care providers treated a majority of patients above 59 years of age. Both dispensing doctors and other health care providers treated a majority of female patients and issued a majority of medicine items to treat acute conditions. The results also revealed that dispensing doctors generally provided relatively inexpensive medicine items, including generic and innovator items, for female and male patients of all ages while other health care providers showed the opposite trend and issued relatively expensive medicine items to these patients. However, when analysing the top twelve pharmacological groups claimed, dispensing doctors had relatively higher costs compared to other health care providers for nine of the pharmacological groups (central nervous system, analgesic, cardio–vascular, ear, nose and throat, dermatological, urinary system, antimicrobial, endocrine system and cytostatic). The pharmacological groups contributing to the highest number of medicine items and highest medicine cost contribution were the antimicrobial group for dispensing doctors and cardio–vascular group for other health care providers. / Thesis (M.Pharm. (Pharmacy Practice))--North-West University, Potchefstroom Campus, 2011.

According to the National Drug Policy one of the health services objectives is to ensure the availability and accessibility of essential drugs to all citizens. An economic objective of the same policy is to promote the cost-effective and rational use of drugs (Department of Health, 1996). Currently, there is no system to scientifically determine the usage of medicines in the public sector and whether the current usage is satisfactory enough (John, 2004:2). The World Health Organization states that "good drug supply management is an essential component of effective and affordable health care services globally (World Health Organization, 1998:1). In the South African context, even though the Essential Drug List helps health care professionals to treat diseases in the public sector, it does not prescribe the minimum guidelines for medicine supply systems (Department of Health, 2006a). The general objective of this study was to develop a therapeutic medicine budget model in a district hospital in the public sector of the North-West Province to control medicine usage. This can be done after analysing the medicine usage patterns and then developing a framework for therapeutic budgeting by evaluating appropriate systems, i.e. the International Code for Disease (ICD-10) classification system, with the therapeutic budget model framework. Retrospective drug utilisation of six months (January 2007 until June 2007) was documented. A random sample population of 25% was selected (n = 1 494). After the data collection period of 9 weeks, the actual study population was only 18.67% (only 1 166 of the 1 494 patients files had a medicine history). All the medicine items prescribed were classified in the therapeutic budget model. Patient confidentially was assured by using a unique pin number on the survey form, so that no names of patients or other biographical details were collected from the patient files onto the survey form, which is in line with the requirements of the Ethics Committee approval conditions for the North-West University. The total number of medicine items dispensed during the study period was 11 768. The average cost per medicine item was R19.36 ± 86.79 for inpatients. The total number of consultations was 3 220. The average number of medicine items per consultation was 3.66 ± 1.98. The total cost of medicine items during the study period was R244 677.11. The average medicine cost per consultation for inpatients was R70.80 ± 177.72. The top three budget groups according to frequency represented 68.11% of all medicine used according to budget groups. The top three pharmacological groups according to total cost represented 61.68% of the total cost of pharmacological groups. The top three therapeutic codes according to frequency represented 18.75% of all therapeutic codes. The top three ICD-10 codes based on total cost represented 59.35% of all medical conditions diagnosed. The total hospital budget for 2007 was predicted at R3 276 750.00. Of this budget, 75% was for pharmaceuticals (R2 457 562.50). The total pharmaceutical medicine cost (excluding surgicals) from the study was R224 677.11 (this was for 18.67% of the total patient visits for six months) which can be calculated at R2 406 824.96 for all patients visits in a full year. The correlation between the actual budget and the projected budget showed a R50 737.54 surplus in the budget of the hospital. A therapeutic budget model can also help in achieving the following: • Proper preparation and planning of budgetary policies in a phased manner based on scientific evidence; • Evaluation of budgetary compliance, cost-efficiency of therapy and standard treatment guidelines (STG) / Essential Drug List (EDL) / formulary compliance; • Better procurement strategies based on demand, expenditure and inventory control; and • Better delivery and maintenance of quality health care by evaluating operational and clinical policies. The therapeutic budget model is a more appropriate manner to use in the projections of budgets and medicine usage. The scope of a therapeutic budget model to be implemented in the hospitals in the public sector of the North-West Province seems to be promising. / Thesis (M. Pharm. (Pharmacy Practice))--North-West University, Potchefstroom Campus, 2009.

Thesis (M. Pharm. (Pharmacy Practice))--North-West University, Potchefstroom Campus, 2009.

Background: According to the Council of Medical Schemes of South Africa (CMS, 2007:52), nearly seventeen percent of the total benefits paid during 2006 were for medicine. Medicine is thus a cost-driving contributor to total healthcare financing. There are various factors influencing and driving medicine usage and cost patterns, including inter alia provider preference, therapeutic committees, marketing and cost. Objectives: The purpose of this study was to identify the top twenty trade name products according to total cost and prevalence in a section of the private health care sector of South Africa, and to identify cost driving products. Methodology: A quantitative, retrospective drug utilisation review (DUR) study was performed on computerised medication records (medicine claims data) for two consecutive years (i.e. 2005 and 2006) that were obtained from a South African pharmaceutical benefit management company (PBM). The study population consisted of 1 218358 and 1 259 099 patients for 2005 and 2006 respectively. A total of 19 860 679 and 21 473017 medicine items that were claimed during 2005 and 2006 were included in the review. Descriptive statistics were used to describe the data, and were analysed using the Statistical Analysis System® SAS 9.1® programme. The cost prevalence index (CPI), developed by Serfontein (1989:180), was used as an indicator of the relative expensiveness of medicine. Resource- and activity driver products (cost driving products) were identified on the database by calculating the total cost of the product, the CPI of the product as well as the prevalence of the product. Variables for analysis included age, gender, prescriber and provider types. Resurts and discussion: A total number of 8 522 574 and 9 046 138 prescriptions were analysed, with an average of 2.33 ± 1.56 and 2.37 ± 1.58 items per prescription during 2005 and 2006 respectively. The average cost per prescription for the total database was R222.16 ± R463.13 for 2005 and R226.25 ± R557.49 for 2006. Members had to co-pay an average of R26.33 ± R102.70 per prescription in 2005 compared to R29.74 ± R103.96 per prescription in 2006. Children under the age of nine accounted for approximately 13% of the total study population, the adolescent age group < 9 and ≥ 19 years) represented 12%, age group three < 19 and ≥ 45 years) represented 38%, age group four < 45 and ≥ 59 years) represented 21% and the geriatric age group (patients older than 59 years) represented 16% of the total study population on the database. About 44% of the study population were male compared to 56% female patients. The top twenty trade name products ranked according to total cost represented about 13% (N=R1 893376 921.00 and N=R2 046 944382.50 in 2005 and 2006 respectively) of the overall medicine cost. The top five trade name products according to total cost for 2005 in descending order were Upitor 1 Omg and 20mg, Fosamax 70mg, Celebrex 200mg and Prexum 4mg. During 2006 the top five trade name products were similar except for Cipralex 10mg in the place of Celebrex 200mg. The CPls for all these products were above one; these products were also all activity drivers. The top twenty trade name products ranked according to prevalence represented about 11% (N=19 860679 and N=21 473074) of the total medicine prevalence for both study periods. The top five trade name products according to prevalence for both years contained Eltroxin 100mcg, Ecotrin 81 mg, Upitor 10mg and Alcophyllex syrup, with Myprodol capsules in 2005 and Mybulen tablets in 2006. Upitor 1 Omg was the only cost driver product in this list. General medical practitioners prescribed the largest quantity of medicine items and represented about 73% of all the medicine items on the database. The medicine prescribed by general medical prescribers accounted for 65% of the overall medicine expenditure on the database. Pharmacies can be seen as the main providers of medicine items. Pharmacies provided approximately 80% of the medicine items and represented over 91% of the total medicine expenditure. Cardiovascular agents were the main pharmacological group that represented the greatest percentage of the total medicine cost, about 19% in both study years. Cardiovascular agents were also positioned 1st according to prevalence and represented about 14% of the overall medicine prevalence in both the study periods. Conclusions and recommendations: Cost driver products can be seen as the products that drives medicine expenditure in the managed health care environment, thus driving the total cost of medicine treatment in the private health care sector of South Africa. Through the implementation of managed health care information- and management instruments medicine expenditure can be reduced. Recommendations for future research have been made. / Thesis (M. Pharm. (Pharmacy Practice))--North-West University, Potchefstroom Campus, 2009.

Due to the fact that the function of dispensing is not the exclusive practice of a single profession, there is much conflict surrounding the issue: it forms the crux of the pharmacy profession but it also forms part of doctors’ scope of practice. Separation of the acts of prescribing and dispensing would prevent the interest of the doctor, who has the potential to profit from selling medicines, being placed above the interest of the patient. It would, however, also affect the essential services that many dispensing doctors provide to pensioners, unemployed patients, those not covered by a medical scheme and those in rural areas. The implications of doctor dispensing are not clear as conflicting evidence suggests that dispensing doctors prescribe more medicine items, injections and antibiotics while preferring certain brand names on the one hand but on the other, evidence shows that dispensing doctors dispensed less expensive medicines compared to other health care providers. The main objective of this study was to analyse the prescribing patterns of dispensing doctors and other medicine providers in a section of the private health care sector of South Africa for 2005 to 2008 by using a medicine claims database. A retrospective drug utilisation review was conducted by extracting data from a medicine claims database for a four–year period, from 1 January 2005 to 31 December 2008. The results revealed that dispensing doctors had a lower cost per prescription compared to other health care providers (R112.66 ± R4.45 vs. R258.48 ± R23.93) and also had a lower cost per medicine item (R39.62 ± R2.18 vs. R112.43 ± R7.56) for the entire study period from 2005 to 2008. Dispensing doctors provided more items per prescription compared to other health care providers (2.85 ± 0.05 items vs. 2.30 ± 0.06 items) but other health care providers claimed more prescriptions per patient per year (7.50 ± 1.15 prescriptions vs. 3.29 ± 0.07 prescriptions). A higher percentage of generic medicine items were provided to patients visiting dispensing doctors. Dispensing doctors treated a majority of patients aged above 19 to 44 years of age while other health care providers treated a majority of patients above 59 years of age. Both dispensing doctors and other health care providers treated a majority of female patients and issued a majority of medicine items to treat acute conditions. The results also revealed that dispensing doctors generally provided relatively inexpensive medicine items, including generic and innovator items, for female and male patients of all ages while other health care providers showed the opposite trend and issued relatively expensive medicine items to these patients. However, when analysing the top twelve pharmacological groups claimed, dispensing doctors had relatively higher costs compared to other health care providers for nine of the pharmacological groups (central nervous system, analgesic, cardio–vascular, ear, nose and throat, dermatological, urinary system, antimicrobial, endocrine system and cytostatic). The pharmacological groups contributing to the highest number of medicine items and highest medicine cost contribution were the antimicrobial group for dispensing doctors and cardio–vascular group for other health care providers. / Thesis (M.Pharm. (Pharmacy Practice))--North-West University, Potchefstroom Campus, 2011.

Background: Warfarin is an anticoagulant that is used for the prophylactic and therapeutic treatment for a wide range of thrombo-embolic disorders. The prescribing and monitoring of warfarin therapy is challenging due to the fact that warfarin exhibits numerous interactions with other drugs and a variety of factors that influence the dosing of warfarin. Objective: The general objective of this study was to investigate the prevalence of drugs prescribed with warfarin that may have a potential drug-drug interaction (DDI) with warfarin. Methods: This was a cross-sectional, observational or qualitative study that was conducted on medicine claims data of a pharmaceutical benefit management company for patients receiving warfarin therapy for a six year period, ranging from 1 January 2005 to 31 December 2010. Drug products that were co-prescribed with warfarin were also identified from the medicine claims database. The total number of prescriptions for all drug products during the study period were analysed and compared to the warfarin dataset. This was done by means of the SAS 9.1® computer package (SAS Institute, 2004). The total number of prescriptions and medicine items claimed from the database during the study period were respectively 49 523 818 and 118 305 941. Potential DDls between warfarin and coprescribed drugs were identified and classified according to a clinically significant rating. The clinically significance ratings of potential DDls are described in three degrees of severity, identified as major, moderate and minor (Tatro, 2011 :xiv). Results: The database consisted of 427 238 warfarin prescriptions and 427 744 warfarin medicine items, which represented 0.9% of the total number of prescriptions and 0.4% of total number of medicine items. The total number of patients who claimed warfarin prescriptions through the database represented 0.9% (n=68 575) of the total number of patients who claimed prescriptions in the total database (2005-2010). General practitioners prescribed the highest frequency of warfarin medicine items, representing 58.3% (n=249 202) of the total number prescribed. The age group that claimed the highest frequency of warfarin prescriptions (n=327 592, 76.6%) and the highest frequency of warfarin medicine items (n=327 984, 76.7%) was age group 4 (consisting of patients 59 years and older). The distribution between females and males regarding warfarin prescriptions claimed (n=205 999, 48.2%; n=221 117, 51.8%) and warfarin medicine items claimed (n=206 232, 48.2%; n=221 390, 51.8%) were almost equal. General practitioners prescribed the highest average PDD (7.01 mg ± 9.86 mg) of warfarin medicine items. Paediatric cardiologists prescribed the lowest average PDD (4.61 mg ± 1.29 mg) of warfarin medicine items. A d-value of 0.1 indicates that there is no practical difference of the average PDD between general practitioners and paediatric cardiologists. The average PDD of warfarin medicine items between females (6.60 mg ± 9.06 mg) and males (6.74 mg± 8.41 mg) was almost equal. The age group who was prescribed the highest average PDD was age group 2 (consisting of patients 20 years to 39 years old) (7.42 mg± 7.42 mg). Age group 4 (consisting of patients 59 years and older) (6.50 mg± 8.90 mg) was prescribed the lowest average PDD of warfarin medicine items. A d-value of 0.1 indicates that there is no practical difference of the average PDDs of warfarin medicine items between these two age groups. The results revealed that drugs with a significance rating (SR) of 1 (n=155 066, 43.3%), 2 (n=30128, 8.4%), 4 (n=137144, 38.3%), and 5 (n=36144, 10.1%) were co-prescribed with warfarin in the six year study period. The five drugs that was co-prescribed with warfarin most frequently was aspirin (n=48 903, 13.6%), thyroxine (n=33 954, 9.5%), amiodarone (n=25 056, 7.0%), simvastatin (n=19 070, 5.3%) and celecoxib (n=10 794, 3.0%). These five drugs have a SR of 1. Conclusions: This study showed that the top five drugs most frequently prescribed with warfarin are aspirin, thyroxine, amiodarone, simvastatin and celecoxib. These drugs can potentially interact with warfarin. The potential interactions of these drugs are rated with a significance rating of 1. This concludes that drugs that can potentially cause life threatening effects and permanent damage are commonly co-prescribed with warfarin. Clinical data concerning the INR or PT must be obtained in order to evaluate whether or not warfarin therapy is changed when a potentially interacting drug is co-prescribed. The age of the patients as well as the duration of warfarin treatment should also be obtained in order to assess whether warfarin treatment is changed with the progression of age. / MPharm (Pharmacy Practice), North-West University, Potchefstroom Campus, 2013

Background: Warfarin is an anticoagulant that is used for the prophylactic and therapeutic treatment for a wide range of thrombo-embolic disorders. The prescribing and monitoring of warfarin therapy is challenging due to the fact that warfarin exhibits numerous interactions with other drugs and a variety of factors that influence the dosing of warfarin. Objective: The general objective of this study was to investigate the prevalence of drugs prescribed with warfarin that may have a potential drug-drug interaction (DDI) with warfarin. Methods: This was a cross-sectional, observational or qualitative study that was conducted on medicine claims data of a pharmaceutical benefit management company for patients receiving warfarin therapy for a six year period, ranging from 1 January 2005 to 31 December 2010. Drug products that were co-prescribed with warfarin were also identified from the medicine claims database. The total number of prescriptions for all drug products during the study period were analysed and compared to the warfarin dataset. This was done by means of the SAS 9.1® computer package (SAS Institute, 2004). The total number of prescriptions and medicine items claimed from the database during the study period were respectively 49 523 818 and 118 305 941. Potential DDls between warfarin and coprescribed drugs were identified and classified according to a clinically significant rating. The clinically significance ratings of potential DDls are described in three degrees of severity, identified as major, moderate and minor (Tatro, 2011 :xiv). Results: The database consisted of 427 238 warfarin prescriptions and 427 744 warfarin medicine items, which represented 0.9% of the total number of prescriptions and 0.4% of total number of medicine items. The total number of patients who claimed warfarin prescriptions through the database represented 0.9% (n=68 575) of the total number of patients who claimed prescriptions in the total database (2005-2010). General practitioners prescribed the highest frequency of warfarin medicine items, representing 58.3% (n=249 202) of the total number prescribed. The age group that claimed the highest frequency of warfarin prescriptions (n=327 592, 76.6%) and the highest frequency of warfarin medicine items (n=327 984, 76.7%) was age group 4 (consisting of patients 59 years and older). The distribution between females and males regarding warfarin prescriptions claimed (n=205 999, 48.2%; n=221 117, 51.8%) and warfarin medicine items claimed (n=206 232, 48.2%; n=221 390, 51.8%) were almost equal. General practitioners prescribed the highest average PDD (7.01 mg ± 9.86 mg) of warfarin medicine items. Paediatric cardiologists prescribed the lowest average PDD (4.61 mg ± 1.29 mg) of warfarin medicine items. A d-value of 0.1 indicates that there is no practical difference of the average PDD between general practitioners and paediatric cardiologists. The average PDD of warfarin medicine items between females (6.60 mg ± 9.06 mg) and males (6.74 mg± 8.41 mg) was almost equal. The age group who was prescribed the highest average PDD was age group 2 (consisting of patients 20 years to 39 years old) (7.42 mg± 7.42 mg). Age group 4 (consisting of patients 59 years and older) (6.50 mg± 8.90 mg) was prescribed the lowest average PDD of warfarin medicine items. A d-value of 0.1 indicates that there is no practical difference of the average PDDs of warfarin medicine items between these two age groups. The results revealed that drugs with a significance rating (SR) of 1 (n=155 066, 43.3%), 2 (n=30128, 8.4%), 4 (n=137144, 38.3%), and 5 (n=36144, 10.1%) were co-prescribed with warfarin in the six year study period. The five drugs that was co-prescribed with warfarin most frequently was aspirin (n=48 903, 13.6%), thyroxine (n=33 954, 9.5%), amiodarone (n=25 056, 7.0%), simvastatin (n=19 070, 5.3%) and celecoxib (n=10 794, 3.0%). These five drugs have a SR of 1. Conclusions: This study showed that the top five drugs most frequently prescribed with warfarin are aspirin, thyroxine, amiodarone, simvastatin and celecoxib. These drugs can potentially interact with warfarin. The potential interactions of these drugs are rated with a significance rating of 1. This concludes that drugs that can potentially cause life threatening effects and permanent damage are commonly co-prescribed with warfarin. Clinical data concerning the INR or PT must be obtained in order to evaluate whether or not warfarin therapy is changed when a potentially interacting drug is co-prescribed. The age of the patients as well as the duration of warfarin treatment should also be obtained in order to assess whether warfarin treatment is changed with the progression of age. / MPharm (Pharmacy Practice), North-West University, Potchefstroom Campus, 2013

“Off-label use” is defined as the use of medicine for indications other than recommended or registered for, e.g. the prescribing of a particular active substance for a patient younger than the substance is recommended or indicated for, or different formulations or dosages of a substance (Ekins-Daukes et al., 2004:349; Stedman’s medical dictionary, 2006). Off-label prescribing is common, and fluctuates by physician, patient and drug (Eguale et al., 2012:781). Drug classes most commonly prescribed off-label include anti-asthmatic, cardiovascular drugs and antidepressants. Lee et al. (2012:140) found that 9 out of 10 antidepressants prescribed were associated with unapproved usage of antidepressants. An antidepressant can be defined as a substance that prevents or relieves depression or depressive episodes (Mosby, 2009:115). There is paucity of information on the off-label prescribing practices of antidepressants in the South African private health sector. According to Eguale et al. (2012:781), the paucity of information on off-label prescribing practices may be, in part, ascribed to the difficulty in the establishment of reasons for treatment. The objective of this study was to determine the prescribing patterns of antidepressants as well as to identify off-label prescribing of antidepressants among adults in a section of the private health sector of South Africa by using a medicine claims database. A quantitative and observational, descriptive cross-sectional design was followed in this study. Data for a period of a year, from January to December 2010 were obtained for analysis. The data set consisted of medicine claims for a total number of 1 220 289 patients, containing a total of 8 515 428 prescriptions and 20 527 777 medicine items. The study population (patients receiving antidepressants 18 years and older) accounted for 14.8% (n = 1 220 289) of the total data set. The average age of patients receiving antidepressants was 56.1 ± 16.6 (median = 56.2) (Inter quartile range = 43.3–68.1). Results of the study showed that antidepressant prescriptions accounted for 8.3% (n = 8 515 428) of all prescriptions claimed during 2010. A total 3.5 % (n = 20 527 777) of antidepressants were claimed during the study period. Using the DU90% method it was established that the majority of antidepressant medicine items were prescribed by general practitioners (i.e. 75.7%, n = 702 285) and psychiatrists (14.9%, n = 702 285). Almost 72% (n = 702 885) of antidepressant medicine items claimed for the study population were for women. The most prescribed antidepressants (based on the DU90%) were amitriptyline (20.6%, n = 702 885), citalopram (19.2%), escitalopram (14.6%), fluoxetine (11.7%), venlafaxine (5.7%), paroxetine (5.2%), duloxetine (4.4%), sertraline (3.8%), bupropion (3.1%) and mirtazapine (2.6%). Amitriptyline accounted for 82.4% of off-label prescriptions (n = 2 635), whereas escitalopram and fluoxetine accounted for 4.2% and 3.8%, respectively. The tricyclic antidepressants (TCAs) were mostly prescribed off-label for migraine, headache and sleep disorders. The off-label prescribing of selective serotonin re-uptake inhibitors (SSRIs) included menopause, schizophrenia and headache. The off-label indicated prescriptions of the serotonin and noradrenaline re-uptake inhibitors (SNRIs) were mostly for schizophrenia and other anxiety disorders. Mirtazapine, a serotonin modulator/tetracyclic antidepressant, was mostly prescribed off-label for anxiety disorders. Off-label prescriptions for bupropion, a noradrenaline and dopamine re-uptake inhibitor mainly included other anxiety disorders and attention deficit hyperactivity disorder (ADHD). Furthermore, the prescribed daily dose (PDD) of each active antidepressant for all off-label indications was determined. In conclusion: This study investigated the off-label prescribing patterns of antidepressants among adults a section of the private health sector of a South Africa, using a large medicine claims database. Recommendations for future research were made. / MPham (Pharmacy Practice), North-West University, Potchefstroom Campus, 2014