DOI: 10.6084/m9.figshare.22929242.v1
title: Data set for Assessment of conformity to prescribing writing guidelines and medication errors in paediatrics in three hospitals in Freetown Sierra Leone

description: <strong>Study setting, design and sampling</strong> We conducted this study in the paediatrics departments of the Ola During Children's Hospital (ODCH), Rokupa Government Hospital (RGH), and the King Harman Maternity and Children Hospital (KHMCH) located in Freetown, the Capital city of Sierra Leone. Ola During Children's Hospital is a tertiary teaching hospital and the leading paediatric referral hospital in Sierra Leone. Rokupa Government and KHMCH are secondary hospitals that provide comprehensive emergency obstetric and newborn care inpatient and outpatient paediatric and maternity services. This study was conducted between April 2021 to July 2021 and had two phases. Phase 1 was a descriptive cross-sectional retrospective study of paediatrics prescriptions from the respective pharmacy departments from May 1 to May 31, 2021 that evaluated prescribing errors. In phase 2, we conducted a point prevalence descriptive inpatient chart review that lasted for one week among the paediatric patient population to determine the nature of medication errors. For phase 1 of the study, the population included paediatric prescriptions that came to the respective pharmacy departments in May 2021. Phase 2 included inpatients &lt;16 years irrespective of their working diagnosis and gender. For phase 1 of the study, the sample size was determined using Fisher's formula for single population studies where p=proportion of non-adherence to prescription writing guidelines from a previous study (38%), z = confidence level at 95%, and d = degree of precision of 5%. The minimum sample size was 362. Therefore, a second formula, nf=n/(1+n/N), was used to get the final sample size since the prescription population for May 2021 was 3848 (&lt; 10,000) where; nf =final sample size; ni=initial sample size; N=study population. A final sample size of 366 was obtained after adding 10% for methodological inconsistencies. The Ola During Children's Hospital has ten times more prescriptions for the period under review than the other two hospitals. Hence, the final sample size was divided equally among the three hospitals to distribute the three hospitals better. Simple random sampling was applied using the Research randomizer software, where prescriptions were allocated unique codes. In phase 2, all inpatients were included. Therefore, the sample size depended on the number of patients admitted to the hospitals during the study. <br> <strong>Data collection</strong> For phase 1 of the study, the data collection tool was adapted from the Sierra Leone Pharmacy and Drugs Act 2001, the World Health Organization (WHO) guidelines for prescription writing, and a previous study. Seventeen essential elements were selected for this study and compiled into a single data collection tool. The global accuracy score (GAS) employed in this study is validated and reliable and has been used in two studies in South Africa, although one is unpublished. We manually extracted all data through a review of prescriptions accessed from the pharmacies. The data collection tool for phase 2 was adapted from the WHO guide on reporting and learning systems for medication errors, American Society of Health System Pharmacists (ASHP) guidelines for preventing medication errors in hospitals, and previous studies. Data collection tools were piloted, and feedback was used to develop the final versions used in the study. The treatment charts were reviewed, and the following were extracted and entered into the data collection tool: wrong patient, wrong dose, wrong route, wrong medicine, wrong dosage form, wrong time of administration, contraindication including allergy, wrong duration, dose omitted or delay, wrong frequency, wrong indication, unnecessary medicine, and therapeutic duplication. In addition, nurses were accompanied during the medicine administration rounds, and patients were monitored and observed. <br> Ethical consideration Clearance to conduct the study was obtained from the Research, Innovation and Publication Review Committee of the Faculty of Pharmaceutical Sciences, College of Medicine and Allied Health Sciences, University of Sierra Leone. The management of the hospitals permitted the study to be done in their facilities. Patient information was coded and kept confidential. <br> <strong>Data analysis</strong> The researchers evaluated the completion of the essential elements for each prescription, such as the use of generic names, recommended abbreviations, and prescription legibility. We determined the accuracy score out of 34 total points. Each element was assessed, scoring 0, 1 or 2 for 'not completed', 'partially completed', or 'fully completed', respectively. Legibility was scored subjectively according to the prescription quality index (PQI) as 0, 1, or 2 for 'illegible', 'barely legible' or 'legible', respectively, by two or more persons. The global accuracy score (GAS) for each prescription was determined by calculating the percentage achieved out of 34 possible points for the 17 prescription elements considered and broadly categorised as date, patient, treatment and prescriber identifiers. The GAS was then classified into one of four scores: 100%, 80% – 99%, 40% – 79%, and less than 40%. The desired prescription-writing accuracy score, or gold standard, is 100%. However, from previous studies, attainment of the gold standard was negligible and coupled with the fact that 100% compliance with any standard is physically and cognitively impossible. Therefore, accuracy score ranges of 80% – 100%, 40% – 79% and less than 40% were employed, which are descriptive. The definition and severity categorisation of the National Coordinating Council of Medication Error Reporting and Prevention (NCCMERP) was used. Potential drug-drug interactions (pDDIs) were assessed by the Drug.com interaction checker and classified into no interaction, minor, moderate, and major. The data obtained was cleaned and coded and then entered into Statistical Package for Social Sciences (SPSS) version 20 (IBM Statistics, Armonk, NY, USA) for analysis. Descriptive statistics were applied, and results were presented as frequency, percentages, median, and interquartile range. A Kruskal-Wallis H test was used to show if there was a statistically significant difference in prescription accuracy scores among the different hospitals. The Mann-Whitney U test was used to determine if there is a statistically significant difference in the GAS between the tertiary hospital and the secondary hospitals. A p-value of &lt; 0.05 was considered statistically significant.