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392 EVA KOPECNA et al.Table 3. Health conditions for using medicines by students duringlast 6 months*.Health conditions Number (%)Headache 406 (41.5)Cold and sore throat 373 (38.1)Dysmenorrhoea 276 (28.2)Fever 248 (25.3)Vomiting/Diarrhoea 81 (8.3)Migraine 79 (8.1)Toothache 53 (5.4)Muscle/Joint aches 50 (5.1)Stomach aches 35 (3.6)Contraception 3 (3.4)Others 115 (11.7)* Students indicated all health problems they suffered during last6 months. In total 796 students responded.medicines, and 205 (26%) took only prescriptionmedicines. For subsequent question related healthconditions for which students used medication duringlast 6 months, the students could enter all healthconditions and medicine used. Thus, the data collectedfor this section represented prevalence ofeach of health condition during last 6 months andmedicines taken to treat this condition. Among themost frequent health conditions for using medicinesduring last 6 months reported by students wereheadache 406 (41.5%), cold and sore throat 373(38.1%), dysmenorrhoea 276 (28.2%), fever 248(25.3%), vomiting/diarrhoea 81 (8.3%), migraine 79(8.1%), toothache 53 (5.4%), muscle/joint aches 50(5.1%), stomach aches 35 (3.6%), contraception 33(3.4%) and the others 115 (11.7%) (Table 3).Out of 796 students 685 (86%) rememberedthe exact names of the medicines they used. Wherethe students indicated a brand name of a medicine,the generic names were used for statistical purposes.Nonsteroidal anti-inflammatory medicines, ibuprofenand paracetamol, were the most frequently usedmedicines in general (Table 4).About 10% of students (99) noted that theynever used OTC medicines, 3% (29) used OTCmedicines every day and a high majority of studentsuse OTC medicines occasionally or sometimes.Absolute results and results per different categories(sex, type of school and students with and withoutchronic disease) are summarized in Table 5.Out of 979 students, 737 (75.3%) admittedself-administration of medicines during their lifetime.The most frequent medicines that studentsused were ibuprofen (n = 470) and paracetamol (n =263). The three most common reasons for self medicationreported were immediate help and relief (n =381), the simplest solution in that situation (n =189), and familiarity with the safe use of medicines(n = 140). The studentís mother was the most frequentperson who recommended and dispensed themedicines to students (n = 412), followed be physician(n = 275), and pharmacist (n = 173). The mainsource of medication was the home pharmacy cabinet(n = 681) and pharmacy (n = 162). Of the femalestudents, 446 admitted use of medicines for menstrualdiscomfort, with ibuprofen (n = 389), paracetamol(n = 53), and metamizole (n = 47) the mostfrequently chosen.The data showed statistically significant associationbetween the medicines used during last sixmonths and type of school, sex and chronic diseases.Students who attended nursing schools tended to usefewer medicines than the students from other typesof schools; females and students with chronic diseaseused more medicines. Students from nursingschools and students with chronic disease used lessOTC medicines than other students during last sixmonths. As regards studentsí self-medication duringtheir lifetime, a statistically significant associationwas found for female students (Table 6).DISCUSSIONStatistical analysesThe most common method of modelling binomialhealth data in cross-sectional studies today islogistical analysis, which works very well if onewants to model the ratio of odds instead of the ratioof probabilities. It also yields a good approximateanalysis if one is interested in the ratio of probabilitiesof a rare event. However, if the event is not rare,and one is interested in the ratio of probabilities (i.e.,prevalence ratio), then the logistic approximationwill be poor because the odds ratio is a poor estimatorof the probability ratio. Several authors recommendedusing the log-binomial model (i.e., binomialmodel with log link function), which directly modelsthe prevalence ratio. However, this model is notwidely used as it often fails to converge. Instead,Poisson regression may be a different natural choicefor fitting a log-linear model, since it estimatesprevalence ratio and since most medical applicationsof the Poisson distribution arise via the Poissonapproximation to the binomial distribution (19). Theestimating equations for Poisson regression are unbiasedwhen the response variable is binary rather than
Use of medicines among students of high schools in the Czech Republic 393Table 4. Medicines taken during last 6 months.Health condition Generic name Number ATC codeHeadache Ibuprofen 321 M01AE01(n = 406) Paracetamol 100 N02BE01Naproxen 5 M01AE02Nimesulide 5 M01AX17Cold and sore throat Dichlorobenzyl alcohol + Amylmetacresol 68 R02AA20(n = 373) Benzalkonium chloride + Menthol +Essential oils 40 R02AA20Antibiotics 35 J01Benzydamine 30 A01AD02Butamirate + Guaifenesin 28 R05FB02Benzoxonium chloride + Lidocaine hydrochloride 26 R02AA20Paracetamol 23 N02BE01Paracetamol + Phenylephrine 22 N02BE51Fever Paracetamol 186 N02BE01(n = 248) Ibuprofen 51 M01AE01Aspirin 14 N02BA01Antibiotics 9 J01Dysmenorhoea Ibuprofen 214 M01AE01(n = 276) Paracetamol 26 N02BE01Metamizol + Pitofenone 25 A03DA02Nimesulide 8 M01AX17Vomiting/ Diarrhoea Carbo medicinalis 29 A07BA5(n = 81) Other intestinal adsorbents 27 A07BC05Intestinal anti-infectives 22 A07AXAntidiarrheal microorganisms 4 A07FA01Loperamide 3 A07DA03Migraine Ibuprofen 55 M01AE01(n = 79) Paracetamol 9 N02BE01Metamizole 4 N02BB02Sumatriptan 3 N02CC01Nimesulide 3 M01AX17Muscle/Joint ache Ibuprofen 19 M01AE01(n = 50) Magnesium sulfate 5 A12CC30Ketoprofen 4 M01AE03Paracetamol 3 N02BE01Toothache Ibuprofen 45 M01AE01(n = 53) Paracetamol 3 N02BE01Nimesulide 2 M01AX17Paracetamol + Propyfenazon 2 N02BE51Stomach ache Other intestinal adsorbents 7 A07BC05(n = 35) Ibuprofen 5 M01AE01Calcium carbonate 4 A02AD01Metamizole 4 N02BB02Others Contraceptives 33 G03(n = 148) Cetirizine 21 R06AE07Antibiotics 16 J01Loratadine 7 R06AX13Desloratadine 6 R06AX27Levocetirizine 5 R06AE09
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PHARMACEUTICAL TECHNOLOGY347. £uka
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