High prevalence of off-label and unlicensed paediatric prescribing in a hospital in Indonesia during the period Aug.—Oct. 2014

Brechkerts Lieske Angruni Tukayo; Bruce Sunderland; Richard Parsons; Petra Czarniak

doi:10.1371/journal.pone.0227687

. 2020 Jan 14;15(1):e0227687. doi: 10.1371/journal.pone.0227687

High prevalence of off-label and unlicensed paediatric prescribing in a hospital in Indonesia during the period Aug.—Oct. 2014

Brechkerts Lieske Angruni Tukayo ^1,², Bruce Sunderland ¹, Richard Parsons ³, Petra Czarniak ^1,^*

Editor: Jinn-Moon Yang⁴

PMCID: PMC6959587 PMID: 31935254

Abstract

Background

Data on off-label and unlicensed prescribing in children in Indonesia is limited. The aims of this study were to determine the prevalence of off-label and unlicensed prescribing for paediatric patients in a public hospital, Indonesia.

Method

A retrospective cross-sectional study of 200 randomly selected paediatric patients admitted to hospital between August and October 2014, collected patient details and all drugs prescribed. Licensed drugs were classified as off-label if there was a non-compliance with the Product Information for age, weight, indication, dose, frequency and route of administration, if there was a contraindication, special precautions or not recommended for children. Unlicensed drugs were those not approved for use in Indonesia. The main outcome was the prevalence of off-label or unlicensed prescribing to infants, children and adolescents and the impact of age group on off-label prescribing.

Results

A total of 200 patients received 1961 medicines of which 1807/1961 (92.1%) were licensed and 154/1961 (7.9%) were unlicensed. There were 1403/1961 (71.5%) drugs prescribed off-label. More than half of the total drugs (n = 1066; 54.4%) were administered parenterally. Every patient was prescribed at least one off-label drug. Indication (n = 810; 34.6%) was the most common reason for off-label prescribing. Ranitidine was the most frequent drug prescribed off label. Darplex^® (dihydroartemisinin and piperaquine), although manufactured in Indonesia, was unlicensed. There was a significant difference between age group and off-label prescribing in that children were prescribed significantly less off-label drugs (p<0.0003).

Conclusion

This study revealed a high prevalence of off-label and unlicensed drug use in paediatric patients in this hospital, exposing them to drug treatments or regimens that had not been approved by regulatory authorities. The high incidence of invasive parenteral prescribing is of concern for paediatric patients. Incentives are needed to encourage specific drug evaluation in paediatric populations.

Introduction

Many drugs prescribed for paediatric patients have undergone limited scientific evaluation with only an estimated one-third of them having been evaluated in clinical trials in this population.[1] Clinical trials that evaluate drug efficacy, safety and quality are required for registration.[2, 3] However, until recently clinical trials were limited in paediatric populations due to perceived ethical concerns regarding patient risk from research.[1, 4] Consequently, many drugs prescribed for children have been assumed to be safe and effective based on studies performed in adult populations.[5]

Children should not be perceived to be ‘small adults’ since their pharmacokinetics and pharmacodynamics vary throughout childhood, leaving large uncertainty about the relative efficacy of drugs with respect to adult populations.[6–8] Despite this, many drugs are used ‘off-label’ or outside their licensed recommendations. The use of off-label medicines refers to the use of a licensed drug at a different dose, indication, age or route of administration from that stated in the approved Product Information (PI).[2, 9] Unlicensed medicines include the reformulation of a licensed drug to provide a modified dosage form considered more suitable for administration but not licensed for paediatric administration, or the specific administration of drugs not licensed in Indonesia.[5]

Despite the introduction of several initiatives to stimulate more research into investigating drug use in children, including the Food and Drug Administration’s (FDA’s) ‘Pharmaceuticals for Children Act’ which was last amended in 2007, and the European Medicines Agency’s (EMA’s) ‘European’s Union’s Paediatric Regulation’ in 2007, it has been reported that children are still under-represented with respect to participating in clinical trials and more work is required to ensure safe use of medicines in children.[10] It is estimated that for the last decade, in hospital settings, the use of off-label drugs ranged from 12.2% to 70.6%.[3] Developing countries are most acutely affected[1] since persons aged between zero to 18 years make up a large proportion of the population[11] and are most vulnerable to diseases.[2]

The prescribing of off-label and unlicensed medicines may be unavoidable in situations where there is no alternative and the benefits potentially outweigh the risks.[1, 2] However, there is a potential public health risk for patients as there is uncertainty about efficacy and toxicity, that may result from off-label or unlicensed medicine use.[12] There may also be a risk associated with compounding drugs to produce a new formulation as there is often no safety information on drug interactions, stability or efficacy due to the potential changes in bioavailability.[2]

Indonesia, a developing country, requires drugs to be licensed by the National Agency of Drug and Food Control before they are marketed. In order to be granted a licensing number, a drug has to be evaluated to ensure it meets the requirements of safety, efficacy and quality. Drug licensing has two stages—pre-licensing and licensing.[13] Drugs prescribed or dispensed off-label are not associated with a statutory responsibility of the prescribing physician or dispensing pharmacist.

The primary aims of this study were to determine the prevalence of off-label and unlicensed prescribing in paediatric patients in a medical ward of a public general hospital in Indonesia and to identify drugs most commonly prescribed off-label and unlicensed.

Materials and methods

Ethical approval

This study was approved by the human research ethics committee at Curtin University (approval number: RHDS-08-15) and by the Director of Abepura’s Hospital, Papua, Indonesia (445/82.7/RSUD-Abe/ll/2015). Individual patient consent was not required as de-identified data were collected retrospectively.

Data collection

This cross-sectional study collected medical record data retrospectively from a three-month period 1^st August to 31^st October 2014 in a paediatric medical ward in Abepura Hospital, Papua, Indonesia, which is a general tertiary hospital. No surgery patients were admitted to the medical ward and all patients were aged from 1 month to 14 years of age.

Of the total 307 patients admitted to the ward over this period, 200 were randomly selected using an on-line randomiser. This number was considered to give a reasonably accurate (±7%) estimate of the prevalence of off-label and unlicensed medicines in this ward. A data collection form was developed and used to collect data from patient medical records. Collected data were transferred to an Excel spread sheet which included information about patient demographics (age, date of birth, weight, gender, length of stay), reasons for admission, past medical history and prescribed drugs (name, indication, strength, dose, frequency, dosage form, route of administration and date of prescription). The following prescribing data were excluded from this study: traditional herbal medicines, standard intravenous replacement solutions, oxygen, total parenteral nutrition and blood products.

Classification

Patients were classified according to the European Medicines Agency (EMA) age classification[14] as newborn infants (0 to 27 days), infants (28 day to 23 months), children (2 to 11 years) and adolescents (12 to 18 years). All drugs were coded using the World Health Organization (WHO) anatomical therapeutic chemical (ATC) classification.[15]

The licensing status of all medicines was determined using the database from the Indonesian National Agency of Drug and Food Control.[16] Drugs with no information on the database were classified unlicensed. Each licensed drug was checked against the Product Information (PI) of the Indonesian MIMS[17, 18] to determine whether or not it was off-label. If there was no information on MIMS, the Indonesian National Medicine Information[19] was consulted. Drugs were classified off-label for the following reasons:

Age/ Weight: Drugs prescribed outside the age or weight range from that in the PI. When there was no information for paediatric use, the drug was also considered off-label for age
Indication: The use of the drug was not for the indication(s) listed in the PI
Dose/ Frequency: Drugs prescribed at a lower or higher dose/ frequency from that listed in the PI. An allowance of ± 10% was permitted for rounding.
Route of administration: Drugs administered via a different route from that stated in the PI
Contraindication/not recommended/special precaution: Drugs where the PI stated they were contraindicated, drugs with unmet special precautions of specific age groups or those not recommended in the PI.

It was possible for a drug to be classified as off-label for more than one reason. If a drug was licensed but prescribed in a formulation that was unlicensed, then it was classified as unlicensed.

Statistical analysis

Descriptive statistics were used for demographic data and to determine the extent of the frequency of off-label and unlicensed prescribing with respect to patients and with respect to the number of medicines prescribed. A Generalized Estimating Equation (GEE) was used to explore factors associated with a drug being given ‘off-label’ as opposed to ‘on-label’ (excluding the relatively small proportion of unlicensed drugs). This model was used because it took into account any correlations between the drugs being prescribed to the same patient. The following independent variables were included in the model: gender, age group, body weight, length of hospital stay, ATC drug category and diagnoses. Initially, all independent variables were included in the model, then the least significant was dropped, one at a time, until all variables remaining in the model were significantly associated with the outcome (a ‘backwards elimination’ strategy). Finally, pairwise interactions between independent variables remaining in the model were tested for statistical significance. All data were analysed using the SAS version 9.2 software, and a p-value <0.05 was taken to indicate a statistically significant association in all tests.

A Pearson’s chi-square test was performed to compare the difference in off-label and unlicensed prescribing between the age groups. Where the numbers were too small for Chi-square to be considered accurate (expected frequency in at least one cell of the table less than 5), Fisher’s Exact test was used instead.

Results

Patient demographics

Of 200 randomly selected medical records, 124 (62.0%) were males and 76 (38.0%) were females. The median age of patients was 1.7 years (range: one month– 13.2 years). The median weight of patients was 9.5 kg (range: 3.8–44.0 kg). The median length of hospital stay was 9 days (range: 2 to 33 days).

With respect to the age of paediatric patients, there were no newborns, 114 (57.0%) infants 80 (40.0%) children and 6 (3.0%) adolescents.

The 200 patients were diagnosed with 430 different indications. Acute gastroenteritis and/or diarrhoea was a common reason for patient admission and was listed as a diagnosis for 106/200 (53.0%) of admissions. Other reasons included dehydration (n = 93/200; 46.5%) and malaria (either Plasmodium falciparum or Plasmodium vivax) (n = 45/200; 22.5%).

Drugs classified as off-label and unlicensed

A total of 1961 drugs were prescribed to 200 patients, of which 1807 (92.1%) were licensed. There were 154 (7.8%) unlicensed drugs and 1403 (71.5%) were prescribed off-label. More than half of the total drugs prescribed (n = 1066; 54.4%) were administered parenterally.

The ten most commonly prescribed drugs overall, including those that were prescribed off-label or unlicensed are shown in Table 1 (the complete list of all drugs is available as S1 Table). Of the 1403 off-label medicines, ranitidine, ondansetron, gentamicin and artesunate showed high levels of this prescribing. Table 2 shows the main reasons, and particular combinations of reasons, for off-label use of the medicines listed in Table 1 (an alphabetical list of all drugs prescribed off-label is included as a table in S2 Table). Several drugs including ranitidine and ondansetron were off-label for several reasons. Both age/weight and indication were high contributors to off-label prescribing. Cefotaxime and ceftriaxone were frequently prescribed in the absence of any indication for their need. This often related to them being prescribed for gastroenteritis. No microbiology data were collected to ascertain any bacterial involvement.

Table 1. The 10 most commonly prescribed drugs with their off-label and unlicensed status.

The numbers in the columns are the number of prescriptions of the drug and the percentage of these which are on/off-label or unlicensed.

Ten most common medications of 1961 prescribed drugs	Prescribed On-label		Prescribed Off-label		Unlicensed medicines
Ten most common medications of 1961 prescribed drugs	N	(%)	n	(%)	n	(%)
Paracetamol (n = 246; 12.5%)	59	24.0	187	76.0	0	0.0
Ranitidine (n = 230; 11.7%)	1	0.4	229	99.6	0	0.0
Cefotaxime (n = 187; 9.5%)	26	13.9	161	86.1	0	0.0
Ondansetron (n = 174; 8.9%)	0	0.0	174	100.0	0	0.0
Zinc sulfate (n = 134; 6.8%)	97	72.4	37	27.6	0	0.0
Liprolac^®^* (n = 119; 6.1%)	88	74.0	31	26.1	0	0.0
Cefixime (n = 115; 5.9%)	12	10.4	88	76.5	15	13.0
Gentamicin (n = 104; 5.3%)	9	8.7	95	91.4	0	0.0
Ceftriaxone (n = 69; 3.5%)	16	23.2	53	76.8	0	0.0
Artesunate (n = 57; 2.9%)	0	0.0	57	100.0	0	0.0

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*Liprolac^® (2.5g powder) sachets consist of viable cells 1,25 x 109 CFU (Streptococcus thermophilus 10 mg, Lactobacillus rhamnosus 3 mg, Lactobacillus acidophilus 3 mg, Bifidobacterium longum 1.25 mg, Bifidobacterium bifidum 1.25 mg), polydextrose 869.63 mg, fructooligosaccharide 375 mg, lactulose mixed powder 125 mg, vitamin C 35 mg, vitamin E 8.125 mg, vitamin A 3.60 mg, pyridoxine HCl 1.13 mg, vitamin B2 0.75 mg, thiamine HCl 0.70 mg).

Table 2. The reasons, or particular combination of reasons, for off-label use of the ten most common medicines shown in Table 1.

Under each drug name are the number of prescriptions (n), and the number of patients (N).

Drug	Reason for off-label classification
Drug	Frequency (n)	Age/ weight	Indication	Dose/ frequency	Contra-indication	Special Precautions
Ranitidine (n = 229 N = 196)	219
	6
	1
	1
	1
	1
Paracetamol (n = 187, N = 138)	148
Paracetamol (n = 187, N = 138)	39
Ondansetron (n = 174, N = 135)	127
	45
	1
	1
Cefotaxime (n = 161, N = 132)	156
	4
	1
Gentamicin (n = 95, N = 85)	56
	23
	16
Cefixime (n = 88, N = 88)	41
	27
	9
	6
	5
Artesunate (n = 57, N = 46)	38
	17
	1
	1
Ceftriaxone (n = 53, N = 48)	51
Ceftriaxone (n = 53, N = 48)	3
Zinc sulfate (n = 37, N = 36)	35
Zinc sulfate (n = 37, N = 36)	2
Liprolac (n = 31, N = 30)	31

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The interpretation of results in Table 2 is as follows—for 23 prescriptions, gentamicin was off-label due to indication. However, for 56 (separate) prescriptions, gentamicin was off-label due to indication as well as dose/ frequency. In total there were 95 off-label prescriptions for gentamicin, for a total of 85 patients.

Based on the total number of 154 unlicensed medicines, the ten drugs most frequently prescribed were Darplex^® (dihydroartemisinin and piperaquine) (n = 30; 19.5%), cough powder ‘14’ (a combination of ambroxol, guaifenesin, chlorpheniramine maleate and vitamin C) (n = 28; 18.2%), primaquine (n = 18; 11.7%), cefixime (n = 15; 9.7%), elemental iron (n = 6; 3.9%), phenobarbitone (n = 6; 3.9%), cough powder ‘3’ (a combination of ambroxol, dexamethasone, salbutamol and tremenza) (n = 5; 3.2%), sucralfate (n = 5; 3.2%), colistin sulphate (n = 4; 2.6%) and fluconazole (n = 4; 2.6%). Among unlicensed medicines, Darplex^® although manufactured in Indonesia was not a registered product in Indonesia. Both sucralfate and elemental iron were imported. All other drugs were compounded preparations.

Drug use based on the anatomical therapeutic chemical (ATC) classification

The most commonly prescribed ATC category was the alimentary tract and metabolism (Class A) (n = 776/1961; 39.6% of all drugs), with ranitidine (n = 230; 29.6% of the 776 drugs within this class) and ondansetron (n = 174/776; 22.4%) most frequently prescribed. Ondansetron was always prescribed off-label and ranitidine 99.6% of the time. The second most frequently prescribed ATC category was anti-infectives (Class J) (n = 533/1961; 27.2%) with cefotaxime, cefixime and gentamicin most commonly prescribed. Usually gentamicin was off-label 95/104 (91.3%). The third most commonly prescribed ATC category was the nervous system (Class N) (n = 274/1961; 14.0%).

Amongst the unlicensed drugs, the most common ATC category was antiparasitic products, insecticides and repellents (Class P) (n = 49, 31.8% of 154 unlicensed drugs) which included Darplex^® (n = 29; 59.2% of the 49 drugs in the class) and primaquine (n = 18/49; 36.7%).

Patient prescribing

The median number of drugs prescribed per person was 9 drugs (range: 4–20). All 200 patients (100%) were given at least one off-label drug and 108 (54%) of them received at least one unlicensed medicine (Table 3). There appeared to be no difference between age groups in the proportion of patients who received at least one unlicensed drug (Fisher’s Exact test; p = 0.38). The distribution of off-label and unlicensed prescribing of the 1961 drugs in infants, children and adolescents is summarized in Table 4. While it appeared that the use of off-label drugs was more common for adolescents than infants, no significant association was evident between age-group and the use of off-label drugs (GEE model, univariate p = 0.090).

Table 3. Level of off-label and unlicensed prescribing to patients according to age classification.

Age groups	Patients with at least one prescribed drug that was:
	Off-label		Unlicensed
	n = 200	(%)	n = 108	(%)
Infants (n = 114; 57.0%)	114	100	60	52.6
Children (n = 80; 40.0%)	80	100	43	53.8
Adolescents (n = 6; 3.0%)	6	100	5	83.3

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Table 4. Frequency of off-label and unlicensed prescribing based on total drugs prescribed.

Age groups	Medicines not off-label or unlicensed		Off-label medicines		Unlicensed medicines
Age groups	n = 404	(%)	n = 1403	(%)	n = 154	(%)
Infants (n = 1144)	236	20.6	824	72.0	84	7.3
Children (n = 755)	163	21.6	527	69.8	65	8.6
Adolescents (n = 62)	5	8.1	52	83.9	5	8.1

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Reasons for off-label and unlicensed prescribing

Of the 1403 drugs which were prescribed ‘off-label’, the most common reason given was indication (diagnosis) (n = 810; 57.7%), followed by age/weight (n = 655; 46.7%), dose/frequency (n = 579; 41.3%) and the use of contraindicated drugs or those with special precautions or not recommended to be used in the patient age prescribed (n = 294; 21.0%). No drug was off-label for route of administration. Neither length of stay (2–3 days vs 4–6 days, p = 0.063; 7+ days vs 4–6 days, p = 0.11) nor gender (p = 0.92) were associated with the rate of off-label prescribing.

The GEE model shows several factors that were independently associated with off-label prescribing (Table 5). While age group appeared to be significantly associated with off-label prescribing based on univariate analysis (Infants: 77.7%; Children: 76.4%; Adolescents: 91.2%; with the p-value for Adolescents vs others p = 0.01), this difference did not persist after adjustment for the other independent variables in the model. As there were no neonates admitted to this ward, it was not possible to determine if off-label prescribing would have been more or less frequent in this age group.

Table 5. Analysis of association with a drug being defined as off-label using a Generalized Estimating Equation (GEE).

(n = 1807 licensed drugs prescribed on or off-label). The “n” in the first column shows the total number of drugs prescribed, while the “n” in the second column shows the number which were off-label.

Variable	Number of off-label drugs n (%)	Odds Ratio	95% Confidence Interval	p-value
Tuberculosis
No (n = 1677)	1316 (78.5)	1 (reference)
Yes (n = 130)	87 (66.9)	0.53	0.35–0.82	0.0037
Malaria
No (n = 1435)	1087 (75.8)	1 (reference)
Yes (n = 372)	316 (85.0)	1.73	1.28–2.34	0.0004
ATC Class
N^* class (n = 266)	200 (75.2)	1 (reference)
A^# class (n = 764)	529 (69.2)	0.68	0.49–0.94	0.0205
Other classes (n = 237)	208 (87.8)	1.92	1.16–3.20	0.0116
ATC Class J^ : Bronchitis**
No: No (n = 1178)	868 (73.7)	1 (reference)
No: Yes (n = 123)	102 (82.9)	1.81	1.08–3.02	0.0231
Yes: No (n = 447)	412 (92.2)	3.87	2.18–6.89	<0.0001
Yes: Yes (n = 59)	21 (35.6)	0.20	0.11–0.35	<0.0001

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* = drugs affecting the nervous system;

^# = alimentary and metabolism drugs;

** = anti-infectives.

Other independent variables in the GEE model were dropped as they appeared to be not significantly associated with the outcome (including gender, body weight, length of hospital stay, ATC drug category and diagnoses). The p-values for the variables which were dropped were: gender (p = 0.92), weight (0.20). We divided LOS into short (2–3 days), medium (4–6 days), and long (7+ days). With medium stay as the reference, the p-value for short was p = 0.063, and for long stay it was p = 0.11, and therefore it was also dropped from the final model. ATC categories and diagnoses which were not included in the final model were dropped because they showed no significant association. It is notable that an indication of tuberculosis was associated with lower off-label prescribing (p = 0.0037), and malaria was associated with a higher rate (p = 0.0004). The specific ATC codes also influenced off-label prescribing, with Class A (alimentary tract drugs) having a lower rate of off-label use than Class N (nervous system drugs), and other classes having a higher rate of off-label use. ATC Class J (anti-infective agents) showed an interaction with bronchitis so that in the absence of bronchitis, Class J drugs were more likely to be used off-label, but when bronchitis was present, Class J drugs were less likely to be used off-label. The number of cases where both bronchitis was present and a Class J drug was used, was small (n = 59), but the difference in use in an off-label fashion was quite clear, and therefore statistically significant.

Discussion

To the best of our knowledge this is the first study to report the prevalence of off-label and unlicensed prescribing in a paediatric medical ward in a public general tertiary hospital setting in Indonesia. Previous studies in Indonesia have reported on the prevalence of off-label and unlicensed prescribing in 67 paediatric inpatients with nephrotic syndrome (aged less than 18 years)[20] and off-label use of anticonvulsants at a private hospital (age not specified).[21] It is not possible to make a direct comparison between the results of these studies with the current findings due to the differences in setting and age of the paediatric patients.

The current study found that off-label and unlicensed prescribing was a common practice in this Indonesian hospital setting. Similar findings have been reported in other studies.[11, 22–28] The median number of drugs prescribed was relatively high at nine per patient (range: 4–20 drugs). In a hospital study in Malaysia (also a Southeast Asian country) in which the median age of the study population was two years, researchers reported that the median number of drugs prescribed per child was four (range 1–52).[28] In our study population, the median age was similar (1.7 years) but the median number of drugs prescribed was much higher. Similar findings were also reported in a study in Finland, in which the median number of drugs in preterm infants and children aged 2–11 years, was 15 (range 2–29) and 9 (range 0–36) respectively.[25] In the current study, there was also a high rate of injection prescribing. Injection rates are often high in developing countries because of a perception that this route of administration is more effective.[29] The high rate of injection prescribing is also of concern to the World Health Organization.[30] These findings raise potential public health issues.

The extent of off-label medicines

A high prevalence of off-label prescribing was found (71.6%) compared to other studies in similar settings in Palestine (31.3%),[11] Germany (31%),[24] Australia (31.8%),[22] Slovak Republic (22%),[31] and Finland (42%).[25] Although these differences may be partly due to the different patient populations with different indications compared to other countries, they may also be due to the use of different definitions of off-label.[12] Unlike some previous studies, this study included as off-label drugs, those which were contraindicated, those with special precautions, and those drugs not recommended for use in children. Moreover, there may be differences in the contents of the Product Information (PI) for the same active ingredient between countries due to differences in registration data submitted.[12, 32]

Off-label drugs

Ranitidine, which was the second most frequently prescribed drug overall, was the most commonly prescribed off-label drug. It was off-label for age and indication because there was no specific age in children at which ranitidine was licensed for parenteral use and it was also prescribed without a clear diagnosis (indication). Several other studies have reported the use of ranitidine among the ten most commonly prescribed off-label drugs. However, these studies were conducted in a surgical ward, PICU, neonate intensive care unit, and paediatric emergency unit.[26, 33] All drugs in the ‘cardiovascular system’ classifications were prescribed off-label due to dose/ frequency and indication.

Reasons for a drug to be off-label

In a recent review, researchers reported that the hospital setting influenced the reason for off-label prescribing, with age and dose most commonly reported reasons for off-label prescribing in neonatal intensive care units; dose, age and indication in paediatric intensive care units; dose ad age in general paediatric wards and dose in the emergency department.[12] In the current study, the most common reason for off-label prescribing was for indication. For example, a parenteral formulation of ondansetron was only indicated for nausea in children with chemotherapy but none of the children on this ward was undergoing chemotherapy hence ondansetron was off-label for all the prescriptions. Other studies have also reported that ondansetron was prescribed off-label due to indication.[34, 35] In addition, the prescribing pattern of antibiotics in this hospital may have contributed significantly to the off-label category based on the indication. For example, cefotaxime was prescribed for many acute gastrointestinal cases although there was no registered indication in the PI for acute gastroenteritis. Age category was associated with a small but statistically significant lower level of off-label prescribing for children (aged 2–11 years) compared to infants or adolescents. The rate of off-label prescribing was however high for all age groups. This is concerning as several studies have reported that off-label prescribing is a risk factor for developing adverse reactions.[36, 37] As highlighted by Bonati et al.,[38] when drugs are prescribed off-label for specific diseases for which indications are not included in the manufacturer’s PI, evidence for their use should be established.[38] Additionally, such prescribing may raise medico-legal issues.[39]

It is notable that the prescribing of antibiotics (often as injections) in the management of gastroenteritis was high. No microbiology testing was performed on any potential pathogens, however antibiotics were not registered for this diagnosis. Where antibiotics were an appropriate selection, a lower level of off-label prescribing occurred. Inappropriate antibiotic prescribing has the potential to lead to resistance.[40]

Unlicensed prescribing

The percentage of unlicensed medicines prescribed in this study (7.9%) conformed within the range of 5.5%–28% reported by other studies in similar settings.[11, 25] A number of studies in developing transitional countries such as Palestine[11] and Malaysia[28] also reported that most unlicensed drugs were extemporaneous preparations produced from capsules or tablets in order to obtain a lower dosage which was not available in the registered products. For instance, the preparation of 3 mg primaquine was made from a 15 mg tablet crushed to aliquot the desired dosage. Such formulations introduce uncertainty of the stability and bioavailability of the mixtures.[2]

The use of extemporaneous preparations was also aimed to reduce the number of oral drug administrations. Darplex^® was the most frequent unlicensed drug because it was unregistered in Indonesia. It is a combination of dihydroartemisinin and piperaquine phosphate, which is one of the WHO recommended combination drugs to treat malaria. The reason why Darplex^® was unregistered in Indonesia is unknown.

Informed consent

There was no informed consent documented for the administration of off-label or unlicensed medicines in this hospital. Informed consent may be appropriate when drugs without the support of clinical data or high quality evidence are prescribed of use in children.[22]

Limitations

Despite the high prevalence of off-label use in paediatric patients reported in this study, there were some limitations. As this was a retrospective study, some data may have been missing. Further, the data were collected in 2015, so it is possible some prescribing has changed since that date. Although the data were collected over a three month period from August to October, as the hospital was located at sea level near the equator, it has a tropical climate and little seasonal variation. Hence potential biased analysis toward seasonal effects is unlikely. As the study did not include neonates and adolescents older than 14 years of age, it may not be an accurate representation of the complete paediatric population. Also, this study may not be comparable to other parts of Indonesia as they may have different patterns of diseases. An evaluation of the safety and efficacy of the drugs prescribed off-label in this study was outside the scope of this research however, there is a need for these evaluations in future studies.

Conclusion

This study reports a high prevalence of both off-label and unlicensed prescribing in paediatric patients in a general medical ward in a hospital setting in Indonesia. Antibiotic prescribing for unlicensed indications was a major contributor and potential public health issue related to these findings. Specific disease states such as tuberculosis resulted in significantly lower off-label prescribing. Prescribers in Indonesia should be made aware of the findings of this study.

Supporting information

S1 Table. All drugs with off-label and unlicensed status.

(DOCX)

Click here for additional data file.^{(36.5KB, docx)}

S2 Table. Reasons, or combination of reasons, for off-label prescribing.

(DOCX)

Click here for additional data file.^{(34.9KB, docx)}

Acknowledgments

Authors wish to acknowledge the assistance of the medical records department of the research hospital for their support and assistance.

Data Availability

The data is available via the following link: http://dx.doi.org/10.4225/06/5a127534c5239.

Funding Statement

The authors received no specific funding for this work.

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PLoS One. doi: 10.1371/journal.pone.0227687.r001

Decision Letter 0

Jinn-Moon Yang

2 Sep 2019

PONE-D-19-20113

High prevalence of off-label and unlicensed paediatric prescribing in a hospital in Indonesia

PLOS ONE

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Reviewer #1: High prevalence of off-label and unlicensed pediatric prescribing in a hospital in Indonesia

In this manuscript, the authors aimed to determine the prevalence of off-label and unlicensed prescribing for pediatric patients in a public hospital, Indonesia. The study is interesting, however, a number of issues should be addressed before its publication.

Major issues:

1. The authors used a GEE model to explore factors associated with a drug being given off-label as opposed to on-label. Several variables (gender, age group, body weight, length of hospital stay, ATC drug category and diagnoses) were included in the model. The results in Table 5 were unclear.

(1) There were no results about gender, body weight, length of hospital stay.

(2) The factors of GEE model in table 5 were not convinced. For example, unreasonable age grouping (infant + adolescent vs child), unknown reasons for ATC class (alimentary and metabolism drugs + anti-infectives + drugs affecting the nervous system vs others).

(3) Why was the variable "tuberculosis" in the model? Since the main indications were acute gastroenteritis, diarrhea, dehydration, and malaria in 430 indications, they should be considered and discussed in the manuscript.

(4) More information about factor "diagnoses" should be addressed.

(5) Were there interaction terms in the GEE model?

2. In table 2, why the number of reasons for ranitidine were combined (age/weight + indication + special precautions - 219), but the number of reasons for paracetamol were separated (age/weight - 148; dose/frequency - 39)? The number of reasons for ondansetron, gentamicin, cefixime, and artesunate were unclear. Some reasons were repeated in the same drug. For example, gentamicin (indication; dose/frequency - 56; indication - 23).

3. The result of the chi-square test was doubtful (line 256, X^2(2) = 2.2 ). More information is required. Since the number of adolescents was few (n = 6), it should be aware of the fact that there might be a low expected value.

4. more information about p-value in table 4.

Minor issues:

1. Lines 150-159 were the reasons that drugs were classified as off-label. Number labeling needs to be modified.

2. Lines 180-181: when the authors did chi-square test, two or three age groups were compared?

3. Require more information about proportions. The author stated many proportions in the manuscript, and the total number changed with different themes. It would be clearer if the denominator of the ratio or the total number is written.

Reviewer #2: The present work provide a descriptive analysis of the prevalence of off-label and unlicensed pediatric prescription in a hospital in Indonesia. The analysis is based on 200 randomly selected patients whose hospital admission were within Aug to Oct 2014. The prevalence and most commonly drugs with reasons for off-label use were reported. Issues of this work are listed below.

1. The size of study population is very small with only 200 patients. Why they consider only 200 randomly selected patients rather than include all patients?

2. The p-value for age group comparison should be further checked given vey similar percentages of the two groups.

3. As they mentioned in the section of Limitations, the data was collected five years ago that may not reflect the actual prevalence of off-label and unlicensed prescriptions. Also, a very short period from Aug to Oct was considered that result in a potentially biased analysis toward seasonal effects. The prevalence calculated in this study is therefore not representative for the status of a hospital in Indonesia. They may consider to enlarge the size of study population and cover at least one year to produce meaningful results.

4. A few grammar errors and typos were found that should be corrected.

5. Instead of showing only the top 10 drugs, they are sugguested to provide a detailed data sheet as supplementory data.

Reviewer #3: In this manuscript, the authors presented the statists of the off-label and unlicensed paediatric prescribing in Indonesia hospital. Data of 200 patients received 1961 medicines were collection and 7.9% and 71.5% of medicines were unlicensed and off-label used, respectively. Especially, the authors indicated that anitidine was the most frequent drug prescribed off label. The topic is interesting but there are some issues about the manuscript which need to be dealt with before accepted:

1. The data was collected only from a 3-month period in a hospital. Is it possible the statists have the seasonality and regionality?

2. The reason which using randomly selected 200 instead of 307 patients is unclear.

3. It would be interesting to have the multi-factor ANOVA or other statistical test using indications and drugs classification.

4. Ranitidine was the most frequent drug prescribed off label. The relationship between Ranitidine and indications should be discussed.

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Reviewer #2: No

Reviewer #3: No

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PLoS One. 2020 Jan 14;15(1):e0227687. doi: 10.1371/journal.pone.0227687.r002

Author response to Decision Letter 0

17 Oct 2019

Please include the following items when submitting your revised manuscript:

• A rebuttal letter that responds to each point raised by the academic editor and reviewer(s). This letter should be uploaded as separate file and labeled 'Response to Reviewers'.

This has been provided.

• A marked-up copy of your manuscript that highlights changes made to the original version. This file should be uploaded as separate file and labeled 'Revised Manuscript with Track Changes'.

This has been provided.

• An unmarked version of your revised paper without tracked changes. This file should be uploaded as separate file and labeled 'Manuscript'.

This has been provided.

Journal Requirements:

When submitting your revision, we need you to address these additional requirements.

We have checked this and consider our manuscript meets the requirements.

We have added the following statement to the manuscript: ‘Individual patient consent was not required as de-identified data were collected retrospectively’

5. Review Comments to the Author

Reviewer #1: High prevalence of off-label and unlicensed pediatric prescribing in a hospital in Indonesia

Major issues:

(1) There were no results about gender, body weight, length of hospital stay.

As described in the Methods section, we included all the independent variables named (including gender, age group, body weight, length of hospital stay, ATC drug category and diagnoses) in the GEE model, but then we dropped variables which appeared to be not significantly associated with the outcome. The p-values for the variables which were dropped were: gender (p=0.92), age group (p=0.09), weight (0.20). We divided LOS into short (2-3 days), medium (4-6 days), and long (7+ days). With medium stay as the reference, the p-value for short was p=0.063, and for long stay it was p=0.11, and therefore it was also dropped from the final model. ATC categories and diagnoses which were not included in the final model were dropped because they showed no significant association. This has been included in the revised manuscript.

Age-group was classified according to the European Medicines Agency, as described in the Methods section. The reason why the Infants and adolescents were initially grouped is that, compared to Infants, the odds ratio associated with adolescents was not statistically significantly different, and therefore the term for adolescents was dropped from the model. We have modified the model, reviewing all possible pairwise interactions, and now we find that the age group does not appear to significantly influence the model, as mentioned above. When including terms for the ATC level, we found that ATC groups A, J and N accounted for 86.6% of the drugs (43%, 28.5% and 15% respectively) with all other classes accounting for 13.4%. No single class (other than A, J or N) accounted for more than 4.5% of the drugs, and therefore this set of drugs were classified as ‘Other ATC group’.

We have subsequently revisited the final model, and found that the interaction between ATC class A and bronchitis was the dominant feature that then led to a simpler interpretation of the results. Tuberculosis is still included in the model because the odds of receiving an off-label drug was lower for those with this condition than those without. This has been modified in the manuscript.

(4) More information about factor "diagnoses" should be addressed.

We used the diagnoses which were recorded in patient medical records, which were transferred to the data sheet (gastro, dehydration, malaria, vomiting, obs febris, bronchitis, respiratory tract infection, tuberculosis, and others. The ‘other’ group included n=79 (18.4%) of diagnoses.

(5) Were there interaction terms in the GEE model?

We have included a statement in the Methods that we examined all pairwise interactions for all the main effects which appeared significantly associated with the outcome. We have revisited the final model. We had previously included pairwise interactions, but after a thorough re-analysis, we found that the interaction between ATC class A and bronchitis was the dominant term. Other interactions appeared not to add further to the model. Table 5 has been updated with the new analysis.

The table shows drugs that were more commonly prescribed off-label for a single reason or for several reasons. For example, for 23 prescriptions, gentamicin was off-label due to indication. However, for 56 (separate) prescriptions, gentamicin was off-label due to indication as well as dose/ frequency.

Table 2 has been modified for clarity and the above example has been included

With the small number of cases in this table from the adolescents, it was found more appropriate to quote the p-value obtained from Fisher’s exact test. This has been updated in the text.

4. more information about p-value in table 4.

A sentence has been added just before the table to explain where this p-value came from (a univariate GEE model).

Minor issues:

1. Lines 150-159 were the reasons that drugs were classified as off-label. Number labeling needs to be modified.

Thankyou. This has been corrected.

2. Lines 180-181: when the authors did chi-square test, two or three age groups were compared?

In Tables 3 and 4, the p-values compare the 3 age groups for the rate of patients or drugs which were off-label vs on-label (unlicensed drugs excluded).

We have reviewed the statements of percentages, and written explicitly what the denominator is (either patients or drugs). Thank you for pointing out this

1. The size of study population is very small with only 200 patients. Why they consider only 200 randomly selected patients rather than include all patients?

A sample of 200 patients was selected for this study as it provides a sufficient sample size for regression and related analysis to be performed. It is evident from this study that we have identified a large number of individual data as off-label and unlicensed prescribing.

Examples of other studies that have included similar numbers of patients include:

• Landwehr C, Richardson J, Bint L, Parsons R, Sunderland B, Czarniak P. Cross-sectional survey of off-label and unlicensed prescribing for patients at a paediatric teaching hospital in Western Australia. PlosOne. 2019; 14(1):e0210237. Doi:10.1371/journal.pone.0210237. eCollection 2019

o The study involved 190 inpatient medication chart records and 1160 prescribed drugs

• Dornalles AD, Calegari LH, de Souza L, Ebone P, Tonelli TS, Carvalho CG. The unlicensed and off-label prescription of medications in general paediatric ward: an observational study. Curr Pediatr Rev. 2019; 15(1): 62-66

o This study involved 157 patients and 1328 prescriptions.

• Tefera YG, Gebresillassis BM, Mekuria AB, Erku DA, Seid N, Beshir HB. Off-label drug use in hospitalised children: a prospective observational study at Gondar University Referral Hospital, Northwestern Ethiopia. Pharmacol Res Perspect. 2017; 5(2):e00304

o The study involved 243 patients and 800 prescribed drugs

• Joret-Descout P, Prot-LabartheS, Brion F, Bataille J, Hartman JF, Bourdon O. Off-label and unlicensed utilisation of medicines in a French paediatric hospital. Int J Clin Pharm. 2015; 37(6): 1222-7

o The study involved 120 patients and a total of 315 prescription medicines

2. The p-value for age group comparison should be further checked given vey similar percentages of the two groups.

In Tables 3 and 4, the p-values compare the 3 age groups for the rate of patients or drugs which were off-label vs on-label (unlicensed drugs excluded).

It was a random sample of data taken over the period of August to October so it would reflect prescribing for that period. However, even though the data were collected over a 3-month period, as the hospital was located at sea level near the equator, it has a tropical climate and little seasonal variation. Hence biased analysis toward seasonal effects are not likely.

We have replaced the following sentence in the section under ‘limitation’:

‘The time frame of this study may not account for any seasonal variation effect on the prescriptions pattern’

with:

‘Although the data were collected over a three month period from August to October, as the hospital was located at sea level near the equator, it has a tropical climate and little seasonal variation. Hence potential biased analysis toward seasonal effects is unlikely.’

This study was part of a Master of Clinical Pharmacy research project. It is therefore not possible to enlarge the size of the study population as the project has been completed.

4. A few grammar errors and typos were found that should be corrected.

Thank you. These have been corrected.

5. Instead of showing only the top 10 drugs, they are sugguested to provide a detailed data sheet as supplementory data.

We have included a supplementary data sheet showing the reasons for prescribing drugs off-label (Supplement 1 - All drugs with their off-label and unlicensed status).

1. The data was collected only from a 3-month period in a hospital. Is it possible the statists have the seasonality and regionality?

Although the data were collected over a three month period from August to October, as the hospital was located at sea level near the equator, it has a tropical climate and little seasonal variation. Hence potential biased analysis toward seasonal effects is unlikely.

Regionality was addressed in the limitations by stating: ‘This study may not be comparable to other parts of Indonesia as they may have different patterns of diseases.’

2. The reason which using randomly selected 200 instead of 307 patients is unclear.

Examples of other studies that have included similar numbers of patients include:

o The study involved 190 inpatient medication chart records and 1160 prescribed drugs

o This study involved 157 patients and 1328 prescriptions.

o The study involved 243 patients and 800 prescribed drugs

o The study involved 120 patients and a total of 315 prescription medicines

3. It would be interesting to have the multi-factor ANOVA or other statistical test using indications and drugs classification.

The ANOVA would be appropriate if the outcome is measured on a continuous scale. However, for the analyses presented, we have a binary outcome (off-label vs on-label). The ‘analagous’ analysis method for a multi-factor analysis of a binary outcome file with correlated measurements is the GEE which we have presented.

4. Ranitidine was the most frequent drug prescribed off label. The relationship between Ranitidine and indications should be discussed.

A table of reasons for the use of ranitidine has been included in the manuscript (Table 3). Note that the total number of reasons is 246, for the 229 administrations of ranitidine, which were off-label.

PLoS One. doi: 10.1371/journal.pone.0227687.r003

Decision Letter 1

Jinn-Moon Yang

12 Nov 2019

PONE-D-19-20113R1

High prevalence of off-label and unlicensed paediatric prescribing in a hospital in Indonesia

PLOS ONE

Dear Dr Czarniak,

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Kind regards,

Jinn-Moon Yang

Academic Editor

PLOS ONE

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Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

1. If the authors have adequately addressed your comments raised in a previous round of review and you feel that this manuscript is now acceptable for publication, you may indicate that here to bypass the “Comments to the Author” section, enter your conflict of interest statement in the “Confidential to Editor” section, and submit your "Accept" recommendation.

Reviewer #1: (No Response)

Reviewer #2: (No Response)

Reviewer #3: (No Response)

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2. Is the manuscript technically sound, and do the data support the conclusions?

Reviewer #1: Partly

Reviewer #2: No

Reviewer #3: Partly

**********

3. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: Yes

Reviewer #2: N/A

Reviewer #3: No

**********

4. Have the authors made all data underlying the findings in their manuscript fully available?

Reviewer #1: Yes

Reviewer #2: Yes

Reviewer #3: No

**********

5. Is the manuscript presented in an intelligible fashion and written in standard English?

Reviewer #1: (No Response)

Reviewer #2: Yes

Reviewer #3: Yes

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6. Review Comments to the Author

Reviewer #1: 1. The authors state that with the small number of cases in this table from the adolescents, it was found more appropriate to quote the p-value obtained from Fisher's exact test. But in the section of statistical analysis of the revised manuscript, they didn't mention about Fisher's exact test.

2. Did the authors consider to exclude six adolescents from GEE model and Fisher's exact test / Chi-square test? Because there were only six adolescent patients in the data, the sample size of the adolescents was small. The situation of six adolescent patients could be discussed separately.

3. The items in Table 2 are confused. Some drugs have two frequencies (e.g., paracetamol, ondansetron, ...). The cumulative frequency of age/weight, indication, dose/frequency, and special precautions for ranitidine is 243, which was not equal to the frequency of ranitidine in Table 2. It might be that the frequency of ranitidine in Table 2 was counted as combination of reasons, but the frequency in Table 3 was not. Even so, it's easy to confuse. Information about Table 2 might be addressed in the context or the footnote, but not in the title of Table 2.

Reviewer #2: The manuscript has been largely improved by addressing most of the issues raised from previous review. But there is still one issue to be solved, the title does not fit the context. They are suggested to append 'during the period Aug.-Oct. 2014' to the title of manuscript to reflect the context and avoid the over-claiming issue.

Reviewer #3: The reason is still unknown why the size of study population is only 200 randomly selected patients rather than include all 307 patients? Although 200 is enough to provides a sufficient sample size for regression and related analysis to be performed, how about all 307 patients? Please show the results of non selected dataset.

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Reviewer #1: No

Reviewer #2: No

Reviewer #3: No

PLoS One. 2020 Jan 14;15(1):e0227687. doi: 10.1371/journal.pone.0227687.r004

Author response to Decision Letter 1

4 Dec 2019

Reviewer #1:

1. The authors state that with the small number of cases in this table from the adolescents, it was found more appropriate to quote the p-value obtained from Fisher's exact test. But in the section of statistical analysis of the revised manuscript, they didn't mention about Fisher's exact test.

Thank you for your comment. A statement addressing this oversight has now been added.

We developed the GEE model using all the data (including the adolescents), but then re-ran the model after excluding the records belonging to these 6 patients. We found that the two models agreed closely, and that the statistical significance of the independent variables identified using the model based on all the records was very similar to those obtained after excluding the adolescents. On this basis, we felt it was justified to include the 57 off-label drug records belonging to these 6 adolescent patients.

We have modified Table 2 to include all of the ten most commonly prescribed drugs in Table 1. For each drug, the reason, or combination of reasons, for off-label prescribing, is shown in Table 2.

Further, we have added a line to the first column to show the number of patients who were taking each drug (in addition to the number of drug records on which each line of the table was based). This may clarify the situation.

We reviewed Table 3, and found that there was an error in the numbers. We consider that the expansion of Table 2 now includes all the information that we wanted to describe in Table 3, and therefore we think it is best to delete Table 3. It was included only as an example, but we thank the reviewer for pointing out the inconsistency in the numbers.

Reviewer #2:

The manuscript has been largely improved by addressing most of the issues raised from previous review. But there is still one issue to be solved, the title does not fit the context. They are suggested to append 'during the period Aug.-Oct. 2014' to the title of manuscript to reflect the context and avoid the over-claiming issue.

Many thanks for your suggestion. The title has been changed to: ‘High prevalence of off-label and unlicensed paediatric prescribing in a hospital in Indonesia during the period Aug. – Oct. 2014’

Reviewer #3:

The reason is still unknown why the size of study population is only 200 randomly selected patients rather than include all 307 patients? Although 200 is enough to provides a sufficient sample size for regression and related analysis to be performed, how about all 307 patients? Please show the results of non selected dataset.

Under the ethical principles related to the conduct of research, it is unethical to collect additional data than is necessary to demonstrate the objectives which were being pursued, as the data is private and confidential information. In this case, a list of a random sample of 200 medical records (from a population of 307 medical records) was prepared before patient medical records were accessed. Data was only collected from the 200 patient medical records which were randomly selected. As this was a random sample, that sample should be representative of the 307 that constituted the sampling frame. Further, a statistician was consulted to ensure the random sample was sufficient for the purpose of the analysis performed.

Attachment

Submitted filename: Response to Reviewers 041219.docx

Click here for additional data file.^{(19.3KB, docx)}

PLoS One. doi: 10.1371/journal.pone.0227687.r005

Decision Letter 2

Jinn-Moon Yang

27 Dec 2019

High prevalence of off-label and unlicensed paediatric prescribing in a hospital in Indonesia during the period Aug. - Oct. 2014

PONE-D-19-20113R2

Dear Dr. Czarniak,

We are pleased to inform you that your manuscript has been judged scientifically suitable for publication and will be formally accepted for publication once it complies with all outstanding technical requirements.

Within one week, you will receive an e-mail containing information on the amendments required prior to publication. When all required modifications have been addressed, you will receive a formal acceptance letter and your manuscript will proceed to our production department and be scheduled for publication.

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With kind regards,

Jinn-Moon Yang

Academic Editor

PLOS ONE

Additional Editor Comments (optional):

Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

Reviewer #1: All comments have been addressed

Reviewer #2: All comments have been addressed

Reviewer #3: (No Response)

**********

2. Is the manuscript technically sound, and do the data support the conclusions?

Reviewer #1: Yes

Reviewer #2: (No Response)

Reviewer #3: Yes

**********

3. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: Yes

Reviewer #2: (No Response)

Reviewer #3: No

**********

4. Have the authors made all data underlying the findings in their manuscript fully available?

Reviewer #1: Yes

Reviewer #2: (No Response)

Reviewer #3: No

**********

5. Is the manuscript presented in an intelligible fashion and written in standard English?

Reviewer #1: Yes

Reviewer #2: (No Response)

Reviewer #3: Yes

**********

6. Review Comments to the Author

Reviewer #1: The revised manuscript is well-presented and all comments have been addressed. I recommend for the acceptance of this manuscript for publication.

Reviewer #2: (No Response)

Reviewer #3: It is not convinced that the reason for using 200 random selected patients instead of whole 307 patients. It is right that the random sample is sufficient for the purpose of the analysis performed, however, it is used when the sample space is large. In this manuscript, the whole sample space is only 307, it is not too big for collecting data or calculating statistics. If the authors insist on the random sample procedure, please use resampling with the bootstrap method to show there is no significant difference among each resampling.

**********

7. PLOS authors have the option to publish the peer review history of their article (what does this mean?). If published, this will include your full peer review and any attached files.

If you choose “no”, your identity will remain anonymous but your review may still be made public.

Do you want your identity to be public for this peer review? For information about this choice, including consent withdrawal, please see our Privacy Policy.

Reviewer #1: No

Reviewer #2: No

Reviewer #3: No

PLoS One. doi: 10.1371/journal.pone.0227687.r006

Acceptance letter

Jinn-Moon Yang

31 Dec 2019

PONE-D-19-20113R2

High prevalence of off-label and unlicensed paediatric prescribing in a hospital in Indonesia during the period Aug. - Oct. 2014

Dear Dr. Czarniak:

I am pleased to inform you that your manuscript has been deemed suitable for publication in PLOS ONE. Congratulations! Your manuscript is now with our production department.

If your institution or institutions have a press office, please notify them about your upcoming paper at this point, to enable them to help maximize its impact. If they will be preparing press materials for this manuscript, please inform our press team within the next 48 hours. Your manuscript will remain under strict press embargo until 2 pm Eastern Time on the date of publication. For more information please contact onepress@plos.org.

For any other questions or concerns, please email plosone@plos.org.

Thank you for submitting your work to PLOS ONE.

With kind regards,

PLOS ONE Editorial Office Staff

on behalf of

Prof. Jinn-Moon Yang

Academic Editor

PLOS ONE

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

S1 Table. All drugs with off-label and unlicensed status.

(DOCX)

Click here for additional data file.^{(36.5KB, docx)}

S2 Table. Reasons, or combination of reasons, for off-label prescribing.

(DOCX)

Click here for additional data file.^{(34.9KB, docx)}

Attachment

Submitted filename: Response to Reviewers 041219.docx

Click here for additional data file.^{(19.3KB, docx)}

Data Availability Statement

The data is available via the following link: http://dx.doi.org/10.4225/06/5a127534c5239.

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PERMALINK

High prevalence of off-label and unlicensed paediatric prescribing in a hospital in Indonesia during the period Aug.—Oct. 2014

Brechkerts Lieske Angruni Tukayo

Bruce Sunderland

Richard Parsons

Petra Czarniak

Roles

Abstract

Background

Method

Results

Conclusion

Introduction

Materials and methods

Ethical approval

Data collection

Classification

Statistical analysis

Results

Patient demographics

Drugs classified as off-label and unlicensed

Table 1. The 10 most commonly prescribed drugs with their off-label and unlicensed status.

Table 2. The reasons, or particular combination of reasons, for off-label use of the ten most common medicines shown in Table 1.

Drug use based on the anatomical therapeutic chemical (ATC) classification

Patient prescribing

Table 3. Level of off-label and unlicensed prescribing to patients according to age classification.

Table 4. Frequency of off-label and unlicensed prescribing based on total drugs prescribed.

Reasons for off-label and unlicensed prescribing

Table 5. Analysis of association with a drug being defined as off-label using a Generalized Estimating Equation (GEE).

Discussion

The extent of off-label medicines

Off-label drugs

Reasons for a drug to be off-label

Unlicensed prescribing

Informed consent

Limitations

Conclusion

Supporting information

Acknowledgments

Data Availability

Funding Statement

References

Decision Letter 0

Jinn-Moon Yang

Roles

Author response to Decision Letter 0

Decision Letter 1

Jinn-Moon Yang

Roles

Author response to Decision Letter 1

Decision Letter 2

Jinn-Moon Yang

Roles

Acceptance letter

Jinn-Moon Yang

Roles

Associated Data

Supplementary Materials

Data Availability Statement

ACTIONS

PERMALINK

RESOURCES

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