Reliability and Validity of Parent/Caregiver Reported Hydroxyurea Evaluation of Adherence for Life Scale in Patients with Sickle Cell Disease in Ghana

Reliability and Validity of Parent/Caregiver Reported Hydroxyurea Evaluation of Adherence for Life Scale in Patients with Sickle Cell Disease in Ghana | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Reliability and Validity of Parent/Caregiver Reported Hydroxyurea Evaluation of Adherence for Life Scale in Patients with Sickle Cell Disease in Ghana Evans Xorse Amuzu, Lawrence Osei-Tutu, Yaa Gyamfua Oppong-Mensah, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7221221/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Sickle Cell Disease (SCD) is an inherited disorder of the red blood cells with highest burden in sub-Saharan Africa. The burden of SCD pain episodes can be reduced by the appropriate use of hydroxyurea (HU). Non-adherence to medication leads to increased health service utilization and costs and further impairs adherence and prognosis. Adherence assessment methods mostly involve laboratory tests which are expensive, and fraught with delays in low resource settings. Other assessment methods like psychometric tools, which are quicker and cheaper, are hardly used in these settings. In Ghana, HU adherence has only been reported in Accra but no study has focused on evaluating a psychometric tool specifically developed for HU adherence assessment. The study evaluated the adherence to HU using the Hydroxyurea Evaluation for Adherence for Life (HEAL) tool in paediatric SCD patients in Kumasi and assessed the tool’s reliability and validity compared to the Mean Corpuscular Volume (MCV) values. We enrolled twenty-eight (28) SCD-SS patients, who had been on HU for at least six (6) months at the Komfo Anokye Teaching Hospital (KCSCD-KATH). Their MCV values were obtained from their medical records and the HEAL Scale was administered to their parents via phone call at enrolment and within 1-2 weeks of the initial call. Adherence to HU was 43% using the HEAL scale and 18% using the MCV values. Internal consistency of the HEAL scale by Cronbach alpha was 0.88 and test-retest reliability correlation was 0.68(p-value:0.001). No statistically significant difference was also found between HEAL scores from the initial test and retest timepoints. Correlation of the HEAL score with MCV was weak (ρ = 0.29, p-value = 0.136). The HEAL scale correctly predicted 60% of adherence on the MCV scale. HU adherence in our sample was low and the HEAL scale was found to be reliable though it did not correlate strongly with the MCV values. Comprehensive action is required to improve HU adherence and by extension benefit of HU. Further studies are recommended to confirm the validity of the HEAL scale in sub-Saharan Africa. Figures Figure 1 Background Sickle Cell Disease (SCD) is a genetic disorder in which two abnormal haemoglobin genes including the sickle haemoglobin are inherited from both parents. It is a monogenetic inheritable condition due to a mutation of the HBB gene, leading to the production of abnormal haemoglobins in affected red blood cells. The prevalence of SCD was estimated in 2021 to be 7.7 million people 1 . The major drivers of the increase from previous prevalence estimates of 5.5 million people in 2000, included early diagnosis, improvement in management, increased survival and the general population growth 1 . SCD as a chronic disease causes the body to produce abnormal sickle shaped red blood cells which can carry less oxygen to the body tissues, have a tendency of blocking blood vessels and also have a shorter lifespan of red blood cells. Appropriate SCD management targeting an overall healthy lifestyle through regular comprehensive acute and chronic care enhances improved quality of life. Proven interventions include early detection of the disease, infection prevention, screening and treatment of acute and chronic complications. 2 Additionally, there are several emerging disease-modifying therapies and curative therapies such as bone marrow transplants and gene editing. 3 Persons with SCD require daily prophylactic medications to help the body prevent infections. They also require regular screening tests to promptly identify impending complications for attention and also to help improve their quality of life. One of the most effective and affordable disease modifying medications is hydroxyurea (HU), 4 which is gradually becoming a drug of choice in low- and middle-income countries (LMICs). HU works by improving haematological indices, leading to decreasing number of complications. 5 – 7 A holistic benefit of this medication is hinged on strict adherence to prescriptions for its use 8 – 11 . Adherence has been described by the World Health Organisation as a measure of the level of correlation between mutually agreed health maintenance practices and the patient's actual behaviour. 8 General adherence to medication is estimated to be low (50%) reaching even lower levels in developing countries 10 . Non-adherence to medication has been attributed to individual, societal and health systems challenges. Non-adherence to HU can lead to poor health outcomes which will require increased health services utilization which leads to increased healthcare costs which are mostly borne by the patient and their families. The high healthcare costs then feed into the vicious cycle of non-adherence. In the US, general poor medication adherence costs the government USD1 million annually in hospital admissions and an estimated 125,000 die. 12 , 13 In Ghana, where health expenditure per Gross Domestic Product is lower than required for Lower Middle-Income countries, 14 avoiding increases in healthcare costs due to modifiable factors like drug non-adherence is of prime importance. A first step is regular assessment of HU adherence for early detection of non-adherence. With this in hand, patient education plans can be targeted to ensure better compliance and in the long term wholistic benefit from HU. In non-clinical trial settings, there is paucity of data on HU adherence and its barriers and facilitators in sub-Saharan Africa. Studies reporting adherence in Africa are mostly in controlled clinical trial settings 15 which do not paint an accurate picture of adherence in real world situations. In Ghana, aside from one study in the largest treatment centre in Ghana, Korle-Bu Teaching Hospital in Accra 16 , no study has been done in the Komfo Anokye Teaching Hospital (KATH) in Kumasi, the second largest treatment centre, or anywhere else in Ghana. There is also no specific, quick, simple, validated and cheap tool for assessing HU adherence aside other non-specific tools and the use of laboratory tests which are expensive and fail to assess barriers and facilitators to HU adherence. The study assessed HU adherence and the reliability and validity of the Hydroxyurea Evaluation of Adherence for Life (HEAL) scale, a 24-item HU treatment adherence questionnaire, in a Ghanaian population of patients with Sickle Cell Disease. Study design The study was an analytical cross-sectional study to assess the adherence to hydroxyurea therapy using the HEAL scale and its reliability and validity. Retrospective data on MCV values pre-initiation of HU were also extracted from patient medical records. Profile of study area The study was conducted in Kumasi Centre for Sickle Cell Disease at the Komfo Anokye Teaching Hospital (KCSCD-KATH) which hosts the largest Sickle Cell Disease Clinic in the Ashanti Region of Ghana and the second largest Sickle Cell Disease Clinic in Ghana after the Korle Bu Teaching Hospital from July to August 2024. KATH is located in Kumasi, the regional capital of the Ashanti Region of Ghana, which is the second most populous in Ghana, with a little over 5.4 million inhabitants according to the Ghana Statistical Service 2021 census figures. KATH is a tertiary teaching hospital affiliated to KNUST, with an estimated bed capacity of 1200, which serves almost 13 out of the 16 regions of Ghana. The KATH Sickle Cell Clinic is the second largest in the country of 16 acknowledged treatment sites offering hydroxyurea therapy for SCD. The KSCD-KATH also holds the accolades of the first newborn screened baby in Ghana in February 1995 and the most babies screened for SCD than any other site in Africa 17 with approximately 8,000 presumptive SCD patients identified as at 2016 18 . The Sickle Cell Disease Registry at KCSCD-KATH had 4523 patients registered as at 3rd July 2024. Study population The study population consisted of paediatric SCD patients of SCD-SS phenotype who are currently receiving hydroxyurea therapy at KATH and their caregivers. Parents were eligible if their wards were 1. paediatric HbSS patients, 2. had been on HU therapy for at least 6 months at the time of interview (assuming the maximum tolerated dose would have been reached by then) and 3. had at least two full/complete blood count results available, one of which must be before they started taking hydroxyurea with the latest not older than 1 year available. The list of patients meeting the inclusion criteria was obtained from the Sickle Cell Unit of the Komfo Anokye Teaching Hospital. A census sampling technique was employed in this study to enrol parents/caregivers of these patients. Data collection techniques and tools 1.1.1 Data collection technique Demographic information, clinical history, laboratory values of the MCV were extracted from patient records available to the KCSCD-KATH using a structured questionnaire. The MCV values were used as an objective reference proxy for adherence in line with existing literature and availability at site. 16 , 19 .The study employed Research Electronic Data Capture (REDCap) tool, which is a secure, web-based software designed to support data capture for research studies with linkage with an offline mobile app to make it easy to collect data anyway and data quality checks are instantaneous. 20 – 22 Caregivers who consented completed the HEAL at enrolment via phone call. Caregivers were called 1–2 weeks following the first administration to retake the HEAL scale to evaluate test-retest reliability of the HEAL scale. The HEAL scale contains 24 items which are measured on a Likert Scale. The scale is divided into eight (8) sub-scales covering factors that affect HU adherence. The factors include the dosage, forgetfulness, planning, cost, effectiveness, understanding of the therapy and challenges with laboratory and pharmacy access. Permission to use the HEAL scale was obtained from the developers. Additionally, two haem-oncologists at KATH reviewed the scale and agreed for its use as provided by the developers. Data analysis Categorical variables are summarised as frequencies and percentages while numeric variables are presented as either mean and standard deviation or median and interquartile range, depending on the normality of the observations. The HEAL scores were summarised at overall, subscale and individual item levels. The change in MCV from pre initiation was calculated as the difference between the pre initiation MCV value and the post initiation MCV value divided by the pre initiation MCV value and converted into percentages. Parametric tests have been found to have similar power as non-parametric tests for Likert scales 23 and hence, we adopted both parametric and non-parametric tests for the HEAL scale. Subscale scores were calculated as an average of the score in their component items. The total HEAL score was calculated as an average of all the individual items as described by Janson 19 . The study assessed reliability at 2-levels: internal consistency reliability in the questionnaire items and test-retest reliability for consistency of responses in phone-based interviews 1–2 weeks apart. Individual and subscale composition were evaluated using internal consistency reliability (Cronbach’s alpha, α) in the study sample: an α ≥ 0.8 was considered to reflect excellent internal consistency; 0.8 > α ≥ 0.7, very good; 0.7 > α ≥ 0.6, good; 0.6 > α ≥ 0.5, minimally acceptable and α < 0.5 considered unacceptable based on the levels used in the development of the tool as described by Janson et al 19 . Test-retest reliability of HEAL subscales and aggregate scores were evaluated with Pearson product-moment correlations (r) and t-tests comparing HEAL scores at initial and retest completion times. The study evaluated the criterion validity of the HEAL Scale using the MCV classification as the reference. Criteria validity was therefore assessed using correlational analyses of HEAL scores with MCV values. The overall HEAL scores less than 5 were categorised as non-adherent and more than 4 categorised as adherent while for MCV values, a cut off of ≥ 100fL was classified as adherent as described in previous studies 24 . The categorisation from the MCV laboratory data was used as the reference value for testing the sensitivity, specificity, positive predictive value and negative predictive value of the HEAL screening assessment test. The sensitivity was calculated as the ratio of participants classified as adherent by both the HEAL and MCV values to the participants classified as adherent by MCV. Specificity was also calculated as the proportion of participants classified as non- adherent on both the MCV and HEAL scale to those who were also correctly classified as non-adherent by the MCV. The positive predictive value was calculated as the truly adherent (adherent on both MCV and HEAL) divided by the adherents by the HEAL scale while the negative predictive value will be calculated as the truly non-adherent (non-adherent on both MCV and HEAL) divided by the non-adherents by the HEAL scale. The percentage of correct classification will also be calculated as the proportion of the sum of the truly adherent and non-adherent to the total sample size. Receiver Operating Characteristic Curve was used to assess the level of agreement overall of the HEAL scale with the MCV values. Ethical considerations Ethical approval for the study was obtained from the Committee of Human Research Publication and Ethics of the KNUST (CHRPE/AP/423/24) and the KATH Institutional Review Board (KATH-IRB) - KATHIRB/AP/092/24. Caregivers of eligible participants were called and consented to participate in the study. Verbal consent was obtained from caregivers of the paediatric patients (3 months-16 years). Participant information was only accessible to study personnel and regulatory authorities. As data was captured directly into REDCap, access to the system was user-password protected and stored securely on REDCap. Results Socio-demographic characteristics The study therefore contacted all 38 eligible patients using the two phone contact numbers obtained from the KCSCD-KATH. Out of these 38 persons, ten (10) could not be reached on any of their phone contacts to participate after 9 tries over 3 days. All the remaining twenty-eight (28) agreed to participate. Majority of the patients were male 19(67.86%) and were not screened at birth for SCD 22(78.57%). A large proportion were however, screened by 1 year of age 16(57.14%). On the average, the enrolled patients were started on hydroxyurea therapy by 9 years of age (Range: 2–16). The mean duration of Hydroxyurea therapy was 18.3 months ranging from 8.6 months to 29.4 months. (Table 1 ) Table 1 Socio-demographic characteristics of study participants Characteristic Frequency (n = 28) Percentage Patient Sex Female 9 32.14 Male 19 67.86 Patient Educational Level (Completed/ current) Basic 21 75 JHS 5 17.86 SHS/Vocational 2 7.14 Diagnostic Pathway Non-Newborn screened 22 78.57 Newborn screened 6 21.43 Age at diagnosis Infant (up to 1 year) 16 57.14 Toddler (1–3 years) 6 21.43 Child (4–12 years) 6 21.43 Patient age (years) at initiation of HU Mean ± SD (min, max) 8.57 ± 4.50 (2, 15) Duration on HU (months) Mean ± SD (min, max) 18.30 ± 5.69 (8.60, 29.44) Laboratory indices and medication adherence 1.1.2 MCV values The mean MCV values pre-initiation of hydroxyurea was 78.6 ± 7.17 compared to mean post-initiation values of 88.0 ± 10.38. A statistically significant difference was found between pre-initiation and post initiation MCV values. The average percentage change in MCV from baseline value was 12%±12.4. Table 3 Paired-sample test comparing MCV values before and after HU Mean (SD a ) 95%CI b Change in MCV values (pre-post) Percentage change p-value Pre initiation MCV values (Baseline) 78.65 (7.16) 75.88–81.42 9.38 (9.45) 12.28 (12.39) < 0.001 Post initiation MCV values 88.03 (10.38) 84 -92.05 a Standard Deviation; b Confidence interval 1.1.3 HEAL Scale An item-by-item analysis revealed that for all reverse-scored items, the mean scores were slightly less than 5(Disagree) and the median scores were 5, for 12 out of the 15 items. For the positively scored items, all the individual items, except for items on safety of HU, had a mean score of at least 5.56 (Agree). The mean score was highest (6.43 ± 1.07) for the item on “I believe that HU helps my child” and lowest (3.71 ± 1.74) for the item on “Lab work and appointments for HU are very demanding or stressful”. The median score for 3 out of the 24 items was 7 indicating a strong positive adherence leaning. On the subscale average scorecard, the subscale on effectiveness showed the highest mean score (6.12 ± 0.89) with the lowest being the subscale on cost (4.12 ± 1.78). Only 3 out of the 8 subscales ( Dose, Effectiveness, and Understanding) crossed the mean score of 5 leaning towards positive adherence. If focus is shifted however to the median score, then only 3 out of the 8 subscales ( Cost, Lab, Pharmacy) fall below the positive adherence leaning score of 5. Both mean (4.83 ± 0.65) and median (4.75) total scores on the HEAL scale show a tendency for poor adherence in the sample studied. Table 3 HEAL scale items descriptive statistics Percentiles Mean (SD) Min/Max 25 50 75 Dose 5.4 (0.8) 3 /6.33 5 5.67 6 1. Give recommended amount 5.64 (1.1) 3 /7 5 5 7 2. Know exact amount 5.86 (1.27) 3 /7 5 6 7 3. Sometimes give incorrect amount* 4.71 (0.9) 3 /7 5 5 5 Remember 4.71 (1.1) 3 /7 4 5 5 4. Forget to give* 4.57 (1.37) 3 /7 3 5 5 5. Skip doses* 4.71 (1.41) 3 /7 3 5 5 6. Miss scheduled doses* 4.86 (1.21) 3 /7 5 5 5 Plan 4.76 (1.21) 3 /7 3.67 5 5 7. Run out of HU because no prescription* 4.86 (1.43) 1 /7 5 5 5 8. Irregular dose because run out* 4.71 (1.18) 3 /7 4 5 5 9. Miss dose because run out* 4.71 (1.41) 3 /7 3 5 5 Cost 4.12 (1.78) 1 /7 3 4 5 10. Too expensive to take regularly* 4.29 (1.9) 1 /7 3 5 5 11. Hard to afford for family* 4 (2) 1 /7 3 4 5 12. Cost is stressful* 4.07 (2) 1 /7 3 3 5 Effectiveness 6.12 (0.89) 4.33 /7 5.67 6.33 7 13. Believe HU helps 6.43 (1.07) 3 /7 6 7 7 14. HU is effective 6.36 (0.95) 5 /7 5 7 7 15. HU is safe 5.57 (1.32) 3 /7 5 5 7 Understand 5.05 (0.94) 3 /7 4.33 5 5.33 16. Understand how HU works 5 (1.81) 1 /7 4 5 7 17. Could explain how HU works 6 (1.15) 3 /7 5 7 7 18. Know how HU works 4.14 (1.48) 1 /7 3 5 5 Lab 4.24 (1.38) 1.67 /7 3.33 4.33 5 19. Labs difficult to do* 4.14 (1.76) 1 /7 3 5 5 20. Lab demanding to do* 3.71 (1.74) 1 /7 3 3 5 21. Getting labs done is hard* 4.86 (1.63) 1 /7 5 5 5 Pharmacy 4.26 (1.03) 1.67 /6.33 3.67 4.33 5 22. Difficulty getting HU from pharmacy* 4.14 (1.38) 1 /7 3 5 5 23. Hard to find nearby pharmacy with HU* 4.14 (1.58) 1 /7 3 5 5 24. Working with pharmacy is stressful* 4.5 (1.17) 1 /7 4 5 5 Total 4.83 (0.65) 3.83 /6.33 4.38 4.75 5.17 *Reverse scored item 1.1.4 Adherence levels Using the MCV cut-off of 100fL values, approximately 18% (5) of the patients were found to be adherent to HU therapy whilst the HEAL scale revealed 43% (12) adherence. No statistically significant difference was found between the HEAL classification and the MCV classification of adherence. Table 5 HEAL Scale and Mean Corpuscular Volume (MCV) indicated level of adherence to hydroxyurea medication Mean Corpuscular Volume (MCV) HEAL Scale Non adherent Adherent Total p-value* Non adherent, n (%) 14(87.5) 2(12.5) 16(100) 0.624 Adherent, n (%) 9(75) 3(25) 12(100) Total, n (%) 23(82.14) 5(17.86) 28(100) *2-sided Fisher exact p-value Reliability of HEAL Scale 1.1.4.1 Internal consistency Each of the items on the HEAL scale scored excellent internal consistency ranging from 0.86–0.88. Categorisation of the items into subscales also revealed a similar excellent internal consistency. The total score also shows excellent internal consistency of 0.88. 1.1.4.2 Test -retest Test-retest reliability using the Pearson Correlation showed positive correlation in all the subscales. A strong correlation between test and retest for the Plan, Lab and Dose subscales was found. Moderate correlation was found between the Cost, Pharmacy and Total Score. A negligible correlation was found in the Remember subscale. The correlation was significant for the Dose, Plan, Cost, Lab, Pharmacy subscales as well as the total score. However, a student t-test testing the mean difference found only a significant difference in the lab subscale. (Table 5 ) The validity of the HEAL scale subscale and total score was evaluated using the MCV values as the comparator. There was positive negligible correlation with the Lab and Effectiveness subscales whereas only the Remember subscale showed positive moderate correlation which was significant. A weak negative correlation was found with the Understand scale. (Table 5 ) Table 5 Test- Retest Correlation of HEAL subscales and Correlation with MCV values Test- Retest Correlation Pearson correlation coefficient Subscale r p-value r* p-value Dose 0.89 < 0.001 0.137 0.487 Remember 0.1 0.679 0.417 0.027 Plan 0.74 < 0.001 0.358 0.062 Cost 0.52 0.018 0.251 0.199 Effectiveness 0.38 0.102 0.040 0.839 Understand 0.16 0.488 -0.084 0.672 Lab 0.75 < 0.001 0.006 0.978 Pharmacy 0.67 0.001 0.095 0.630 Total 0.68 0.001 0.289 0.136 Diagnostic accuracy Sensitivity and sensitivity of the HEAL scale in reference to the MCV cut-off of 100fl was approximately 60% (Table 6 ). A high negative predictive value was revealed. Table 6 Diagnostic accuracy of HEAL scale Parameter Value 95%CI Fisher exact test of association 0.624 Sensitivity 60.00% 41.85%- 78.15% Specificity 60.87% 42.79%- 78.95% Positive predictive value 25.00% 8.96%- 41.04% Negative predictive value 87.50% 75.25%- 99.75% Prevalence 17.86% 3.67%- 32.04% ROC AUC(SE) 0.6043(0.1331) 0.34354–0.86515 CI- Confidence interval; ROC- Receiver-Operating characteristic curve; AUC- Area Under Curve; SE- Standard Error The Receiver-Operating Characteristic curve estimated a 60% chance of the HEAL scale correctly identifying adherence (Fig. 8). Discussion The larger male proportion among study participants is consistent with data from studies among patients with SCD using hydroxyurea 6 , 16 , 24 – 26 . This male dominance is also noted from available SCD registries in Africa 17 , 27 , which could be attributed to sociocultural practices which seemingly favour the male child over the female. Over 75% of the study patients were not screened at birth but following acute complications of SCD. This is indicative of the slow upscaling of the newborn screening for SCD policy of Ghana. The proportion of participants screened by 1 year of age is however higher than reported in a study in Brazil reporting 46.7% 28 . However, the little under 50% not diagnosed by age one (1) could have proven detrimental to the patients as they would have developed severe complications by the time of their diagnosis. Hydroxyurea (HU) therapy has been initiated to quite a substantial proportion of patients in the KCSCD-KATH who are regular clinic attendants. This is a good step in trying to reap the proven benefits of HU in preventing complications in the patients. This also serves as a step geared towards reversing the trend of underutilization as described by Ally and Balandya 29 . Per policy, persons being initiated on HU must have had a laboratory test completed to ascertain their baseline laboratory indices prior but the study revealed the lack of documentation of the pre-initiation laboratory indices in patient medical records. This leaves little to be desired as it makes it difficult to objectively provide data from Ghana on the efficacy and effectiveness of HU using laboratory indices. Compared with other cohorts in literature, our study participants were not initiated on hydroxyurea early enough, maybe linked to the beginning of free HU treatment in 2018 through efforts on Novartis and the Sickle Cell Foundation of Ghana and continued by the government of Ghana on 2022 25,30–32 . The late start is detrimental to obtaining optimal benefit from HU, as initiation of HU at a young age by 2 years has been proven to provide overall haematological efficacy 33 . Adherence to HU 1.1.5 Adherence level by Mean Corpuscular Volume (MCV) The mean baseline MCV values in the study participants was similar to a multi-site study in Africa 15 , a study in Nigeria 34 , and a study in the United Arab Emirates (UAE), but lower by 13% compared to a study in Brazil 6 . This however is within limits of normal MCV levels of 80-100fl. This difference could be due to the population level differences in haematological differences between these different continents of the world. The statistically significant difference between the pre-initiation and post-initiation MCV values confirms the assertion of HU significantly increasing the MCV values in several studies 6 , 15 , 24 , 35 . There were however a few decliners some of whom witnessed a drop in MCV of almost 10% from baseline. However, based on the cut-off of 100fl, less than one-fifths were found to be adherent to HU, far lower than the one-thirds reported in a study in USA 24 . The low proportion based on this MCV cut-off could be also due to non-responders and slow responders. 1.1.6 HU adherence level using the HEAL scale Most of the subscale scores for the HEAL scale in our study were lower than the cut-off of 5 pointing to a higher tendency for non-adherence to HU therapy. This is lower than as reported by Janson who validated the scale in patients in the USA. The most comparable subscale score was of “Effectiveness” which explored perception of the efficacy, effectiveness and safety of HU therapy 19 . The similar perception of efficacy, effectiveness and safety could be due to the public knowledge and health education on the benefits of HU in the population. In our study, we found that cost and health system related factors (lab, appointments and pharmacy), were the most probable barriers to adherence as they had scores less than 4.5. Cost has been identified as a major barrier to drug adherence in several studies. This finding however is surprising for our context, as since July 2022, HU therapy is fully sponsored by the government on the National Health Insurance Scheme (NHIS). Other factors related to the cost of treatment could therefore be the reason for cost still leading as a probable barrier to adherence. Our study also reported a median total score of less than 5 further elucidating that more than 50% of the sample were likely to be non-adherent. In absolute proportions, our study found lower adherence than the 75% reported by Janson using the same scale, as testament of the effect of geographical location of measures of adherence. Looking past the difference in scales used, the level of adherence in our study was also found to be lower than the adherence level reported in other studies worldwide and even in Ghana though in another population. Assessment of adherence to penicillin V in our population by Odoom et al revealed adherence of 30% by urine assay and 68% by direct questioning 36 which is far higher than adherence in our study which was focused on hydroxyurea. Another study looking at general adherence to all routine medications in our study population revealed adherence of 13.5% 37 , which is close to findings in our study by MCV cut-off. Level of adherence reported was higher by almost 25% in the HEAL scale (subjective method) compared with the MCV values (objective method). This difference is comparable to other studies suggesting a possible level of over or underreporting with subjective scales. The lack of significant association between the adherence level by the MCV score and the HEAL score could be explained by the smaller sample size in our population due to a lack of documentation of preinitiation lab values. Reliability of the HEAL scale 1.1.7 Reliability of HEAL Scale on the internal consistency domain The total score of the HEAL scale showed excellent internal consistency comparable to that in the initial study by Janson. This points to how closely related the scale items are to each other in measuring the underlying factor of adherence. Internal consistency values were lower in our study for the Remember, Effectiveness, Understand, Pharmacy subscales but higher for the Dose, Plan, Cost, and Lab subscales compared to the initial validation study. In comparison with other scales, our study showed higher internal consistency than the 8 -item Morisky Medication Adherence Scale from a systematic review and meta-analysis 38 and another study in China 39 . This points to a better correlation between the items in the HEAL scale and its reliability for use in our population. 1.1.8 Reliability of HEAL Scale on the test-retest domains Test-retest reliability correlations showed strong significant correlation for three (3) out of the eight (8) subscales (Dose, Plan, Pharmacy and Lab) contrary to findings in the initial validation study where 7 out of the eight (except cost). The difference could be attributed to the mean duration between tests which is 3 times in the initial study compared with our current study. Variations in correlation more than 0.2 were noted for Remember, Cost, Effectiveness, and Understand subscales. The lack of moderate to strong correlation could be due to the heightened consciousness from the initial test which could have modified responses. It is envisaged that a longer period between test and retest of three (3) months would provide a better correlation as heightened consciousness would have waned. Except for the subscale of the lab, a student t-test at 95% confidence level found no significant difference between the test and retest scores at the subscale levels. This adds to the credibility of the HEAL scale in measuring reliably in repeat tests. Validity 1.1.9 Correlation of the HEAL scale scores with MCV values The HEAL scale did not seem to correlate with the MCV values as all the correlation coefficients were less than 0.5 and went as low as 0.006 for the Lab subscale. The most correlated subscales were the Remember and Plan. These findings were similar to the initial study validation with the foetal haemoglobin levels and ANC levels. This points to the Remember and Plan subscales being able to more predict what the MCV levels and hence adherence to HU could possibly be. Other subscales seem to only help in assessing barriers and not necessarily point to adherence. 1.1.10 Diagnostic accuracy The HEAL classification was found to be independent of the MCV classification. The total HEAL score was also found to correctly predict adherence and non-adherence by similar proportion compared to MCV values. This is below the recommended proportion for a tool to be recommended for use. The ROC AUC less than 0.7 is below the acceptable value and the HEAL scale cannot be expected to measure adherence like the MCV. Limitation of the Study 1. The sample size for the study was fairly small and generalizations of the level of adherence must be done cautiously. 2. The laboratory tests yielding MCV values were not collected at the same time as the HEAL initial test and therefore might not be the most current picture of adherence. 3. The subjective nature of the HEAL scale is prone to recall bias and answers of convenience which could mar the picture of the level of adherence. 4. We could not determine whether study participants had reached the maximum tolerated dose of HU. 5. We did not rule out any other comorbidities like vitamin B12 deficiency that could affect the MCV in this study Conclusion HU adherence in the sample from the KCSCD-KATH is generally low: lower by the MCV values than the HEAL Scale. The HEAL scale was found to have a good reliability based on internal consistency and test-retest domains. It was however little correlated with MCV values though showing sensitivity and specificity of 60%. It is good enough as a screening tool for HU adherence in our population as it helps identify the barriers to adherence. Declarations Author Contribution EXA, conceptualised the study with support from LOT, YGOM, VP and EKN. EXA, and LOT, participated in data curation. EXA analysed and interpreted the data and provided the initial draft of the manuscript. EKN supervised all aspects of the study. All authors read and approved the final manuscript and agree to be responsible for every aspect of the study. Acknowledgement We acknowledge all study participants for their time during the study. Heartfelt gratitude goes to Miss Adwoa Agyeiwaa Owusu-Ansah for the kind support in data collection. Data Availability Data that supports the findings of this study are available on request to the corresponding author. References Thomson AM, McHugh TA, Oron AP, Teply C, Lonberg N, Vilchis Tella V, et al. Global, regional, and national prevalence and mortality burden of sickle cell disease, 2000–2021: a systematic analysis from the Global Burden of Disease Study 2021. Lancet Haematol [Internet]. 2023 Aug [cited 2024 Mar 24];10(8):e585–99. Available from: https://linkinghub.elsevier.com/retrieve/pii/S2352302623001187 Ansong D, Osei-Akoto A, Ocloo D, Ohene-Frempong KOF. Sickle Cell Disease: Management options and challenges in developing countries. Mediterr J Hematol Infect Dis [Internet]. 2013;5(1):e2013062. Available from: http://www.mjhid.org/index.php/mjhid/article/view/2013.062 Makani J, Cell S. Curative options for sickle cell disease in Africa : Approach in Tanzania. Hematol Oncol Stem Cell Ther [Internet]. 2019;(xxxx). Available from: https://doi.org/10.1016/j.hemonc.2019.12.012 Voskaridou E, Christoulas D, Bilalis A, Plata E, Varvagiannis K, Stamatopoulos G, et al. The effect of prolonged administration of hydroxyurea on morbidity and mortality in adult patients with sickle cell syndromes: Results of a 17-year, single-center trial (LaSHS). Blood. 2010;115(12):2354–63. Al Sabbah MA, Radaideh M, Saleh SM, Al-Doory SA, Abdalqader AM, Mir FF, et al. Is Hydroxyurea Treatment Changing the Life of Children with Sickle Cell Disease? Dubai Medical Journal [Internet]. 2023 Dec 6 [cited 2024 Sep 8];6(4):301–5. Available from: https://dx.doi.org/10.1159/000531257 Silva-Pinto AC, Angulo IL, Brunetta DM, Neves FIR, Bassi SC, Santis GC De, et al. Clinical and hematological effects of hydroxyurea therapy in sickle cell patients: a single-center experience in Brazil. Sao Paulo Med J [Internet]. 2013;131(4):238–43. Available from: http://www.ncbi.nlm.nih.gov/pubmed/24141294 Keikhaei B, Yousefi H, Bahadoram M. Hydroxyurea: Clinical and Hematological Effects in Patients With Sickle Cell Anemia. Glob J Health Sci [Internet]. 2015;8(3):252–6. Available from: http://www.ncbi.nlm.nih.gov/pubmed/26493428 Sabaté E. Adherence to long-term therapies: Evidence for action [Internet]. 2003 [cited 2023 Oct 11]. Available from: https://iris.who.int/bitstream/handle/10665/42682/9?sequence=1 Strouse JJ, Heeney MM. Hydroxyurea for the treatment of sickle cell disease: Efficacy, barriers, toxicity, and management in children. Vol. 59, Pediatric Blood and Cancer. 2012. p. 365–71. Brown MT, Bussell JK. Medication adherence: WHO cares? Vol. 86, Mayo Clinic Proceedings. Elsevier Ltd; 2011. p. 304–14. Jimmy B, Jose J. Patient Medication Adherence: Measures in Daily Practice. Oman Med J [Internet]. 2011 [cited 2024 Sep 11];26(3):155. Available from: /pmc/articles/PMC3191684/ Senst BL, Achusim LE, Genest RP, Cosentino LA, Ford CC, Little JA, et al. Practical approach to determining costs and frequency of adverse drug events in a healthcare network. American Journal of Health-System Pharmacy [Internet]. 2001 Jun 15 [cited 2024 Sep 12];58(12):1126–32. Available from: https://dx.doi.org/10.1093/ajhp/58.12.1126 McDonnell PJ, Jacobs MR, Monsanto HA, Kaiser JM. Hospital admissions resulting from preventable adverse drug reactions. Annals of Pharmacotherapy. 2002;36(9):1331–6. UNICEF. Health Budget Brief 2023 [Internet]. 2023 [cited 2025 Feb 12]. Available from: https://www.unicef.org/ghana/media/5001/file/2023%20Health%20Budget%20Brief.pdf Tshilolo L, Tomlinson G, Williams TN, Santos B, Olupot-Olupot P, Lane A, et al. Hydroxyurea for Children with Sickle Cell Anemia in Sub-Saharan Africa. New England Journal of Medicine. 2019;380(2):121–31. Dwuma-Badu D, Segbefia CI, Druye AA, Dei-Adomakoh YA. Adherence to hydroxyurea therapy and health-related quality of life in children with sickle cell anaemia at Korle Bu Teaching Hospital in Ghana. Health Sciences Investigations Journal. 2022;3(2):352–9. Paintsil V, Amuzu EX, Nyanor I, Asafo-Adjei E, Mohammed AR, Yawnumah SA, et al. Establishing a Sickle Cell Disease Registry in Africa: Experience From the Sickle Pan-African Research Consortium, Kumasi-Ghana. Front Genet [Internet]. 2022 Feb 24 [cited 2022 Dec 26];13. Available from: /pmc/articles/PMC8908904/ Ohene-Frempong K. Kumasi area Newborn Screening for Sickle Cell Disease data 1995–2016 (unpublished). 2017. Janson IA, Bloom EM, Hampton KC, Meier ER, Rampersad AG, Kronenberger WG. Development and Validation of the Patient/Caregiver Reported Hydroxyurea Evaluation of Adherence for Life (HEAL) Scale. Patient Prefer Adherence. 2022;16:3229–39. Harris PA, Taylor R, Minor BL, Elliott V, Fernandez M, O’Neal L, et al. The REDCap consortium: Building an international community of software platform partners. J Biomed Inform [Internet]. 2019 Jul [cited 2024 Mar 24];95:103208. Available from: https://linkinghub.elsevier.com/retrieve/pii/S1532046419301261 Harris PA, Delacqua G, Taylor R, Pearson S, Fernandez M, Duda SN. The REDCap Mobile Application: a data collection platform for research in regions or situations with internet scarcity. JAMIA Open [Internet]. 2021 Jul 31 [cited 2024 Mar 24];4(3):1–7. Available from: https://academic.oup.com/jamiaopen/article/doi/ 10.1093/jamiaopen/ooab078/6369217 Harris PA, Taylor R, Thielke R, Payne J, Gonzalez N, Conde JG. Research electronic data capture (REDCap)—A metadata-driven methodology and workflow process for providing translational research informatics support. J Biomed Inform [Internet]. 2009 Apr [cited 2024 Mar 24];42(2):377–81. Available from: https://linkinghub.elsevier.com/retrieve/pii/S1532046408001226 De Winter JCF, Dodou D. Five-Point Likert Items: t test versus Mann-Whitney-Wilcoxon. 2010;15(11). Creary S, Chisolm D, Stanek J, Neville K, Garg U, Hankins JS, et al. Measuring hydroxyurea adherence by pharmacy and laboratory data compared with video observation in children with sickle cell disease. Pediatr Blood Cancer. 2020;67(8). Ohene-Frempong K, Segbefia C, Spector J, Amoah E, Asubonteng A, Hammond-Addo R, et al. S129: IMPLEMENTATION OF HYDROXYUREA THERAPY FOR SICKLE CELL DISEASE ON A LARGE SCALE IN GHANA. Hemasphere. 2022;6:16–16. Madkhali MA, Abusageah F, Hakami F, Zogel B, Hakami KM, Alfaifi S, et al. Adherence to Hydroxyurea and Patients’ Perceptions of Sickle Cell Disease and Hydroxyurea: A Cross-Sectional Study. Medicina (Lithuania). 2024;60(1). Kandonga D, Zozimus Sangeda R, Masamu U, Kazumali E, Jonathan A, Msangawale M, et al. Development of the sickle Pan-African research consortium registry in Tanzania: opportunity to harness data science for sickle cell disease. Frontiers in Hematology. 2023;2:1040720. Sant’Ana PG dos S, Araujo AM, Pimenta CT, Bezerra MLPK, Junior SPB, Neto VM, et al. Clinical and laboratory profile of patients with sickle cell anemia. Rev Bras Hematol Hemoter [Internet]. 2017 Jan 1 [cited 2019 Aug 22];39(1):40–5. Available from: https://linkinghub.elsevier.com/retrieve/pii/S1516848416301050 Ally M, Balandya E. Current challenges and new approaches to implementing optimal management of sickle cell disease in sub-Saharan Africa. Semin Hematol. 2023;60(4):192–9. Novartis. Government of Ghana Makes Hydroxyurea Available to People With Sickle Cell Disease Through First of Its Kind Public-private Partnership With Global Medicines Company Novartis – Press Releases on CSRwire.com [Internet]. Cooporate Social Responsibility Newswire. 2019 [cited 2020 May 11]. Available from: https://www.csrwire.com/press_releases/43038 -Government-of-Ghana-Makes-Hydroxyurea-Available-to-People-With-Sickle-Cell-Disease-Through-First-of-Its-Kind-Public-private-Partnership-With-Global-Medicines-Company-Novartis Ministry of Health G. Press Release - Government of Ghana partners with Novartis - Ministry Of Health [Internet]. MOH Press Statement. 2019 [cited 2020 May 11]. Available from: https://www.moh.gov.gh/press-release-government-of-ghana-partner-with-novartis/ Odame I. Public health and programme development in sickle cell disease in Ghana: the legacy of Kwaku Ohene-Frempong. Lancet Haematol [Internet]. 2023 Aug 1 [cited 2024 Sep 9];10(8):e579–80. Available from: http://www.thelancet.com/article/S2352302623001588/fulltext Willen S, Shah N, Thornburg C, Rothman J. Timing of the Initiation of Hydroxyurea and Hematologic Outcomes in Patients with Sickle Cell Disease (SCD). Blood. 2012;120(21):1004–1004. Aliu R, Iliya J, Quadri OR, Ibrahim OR, Daniel E. Haematological Profile of Children With Sickle Cell Anaemia in Steady State. Cureus [Internet]. 2020 Oct 18 [cited 2024 Sep 14];12(10). Available from: /pmc/articles/PMC7671082/ Reeves SL, Dombkowski KJ, Peng HK, Phan H, Kolenic G, Creary SE, et al. Adherence to hydroxyurea and clinical outcomes among children with sickle cell anemia. Pediatr Blood Cancer. 2023;70(7):e30332. Odoom SF, Boahen KG, Newton SK, Nakua EK, Nguah SB, Ansong D, et al. Penicillin V prophylaxis uptake among children living with sickle cell disease in a specialist sickle cell clinic in Ghana: A cross-sectional study. Health Sci Rep [Internet]. 2022 Nov 1 [cited 2022 Dec 26];5(6):e953. Available from: https://onlinelibrary.wiley.com/doi/full/ 10.1002/hsr2.953 Agyekum MA, Nguah SB, Attakorah J, Nettey GK, Oppong KG, Paintsil V, et al. Adherence to routinely prescribed medications among paediatric sickle cell disease patients in Kumasi, Ghana. Ghana Med J. 2024;58(2):117–23. Moon SJ, Lee WY, Hwang JS, Hong YP, Morisky DE. Accuracy of a screening tool for medication adherence: A systematic review and meta-analysis of the Morisky Medication Adherence Scale-8. PLoS One [Internet]. 2017 Nov 1 [cited 2024 Sep 11];12(11):e0187139. Available from: https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0187139 Zhang Y, Wang R, Chen Q, Dong S, Guo X, Feng Z, et al. Reliability and validity of a modified 8-item morisky medication adherence scale in patients with chronic pain. Ann Palliat Med. 2021;10(8):9088–95. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7221221","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":491974006,"identity":"a4d2e30b-22f0-404a-be82-dc0d67551270","order_by":0,"name":"Evans Xorse Amuzu","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA40lEQVRIie3PIQvCQBjG8Y2DW3lxdcf0OxwMzuY+iHFg8pIgimFBMA2zfotZLk8GrhxYBYtTsBsU19xslptrgvcPT3p/cGcYOt2PZ57KgVYTgmhFcBOCnffWndlOcM2LRa9P95JN78NeGxsoPx8UhKwGXQ9kwONEsmNHBOXDsOcNFYTKhLnGBPF4G7EjEagkgF0V8WX2IE8a8jgFNiIirCfUipgDk5THO2DmTaT1xMmisQsy42uJR64pMsCo5i/23BKkWMz4cp9uboWY+bY1zy8q8hGC9357XmU+m1zrdDrd3/QCGXpDoAxWlkoAAAAASUVORK5CYII=","orcid":"","institution":"Komfo Anokye Teaching Hospital","correspondingAuthor":true,"prefix":"","firstName":"Evans","middleName":"Xorse","lastName":"Amuzu","suffix":""},{"id":491974007,"identity":"66861195-7b21-4182-b368-1d8230db6f57","order_by":1,"name":"Lawrence Osei-Tutu","email":"","orcid":"","institution":"Komfo Anokye Teaching Hospital","correspondingAuthor":false,"prefix":"","firstName":"Lawrence","middleName":"","lastName":"Osei-Tutu","suffix":""},{"id":491974008,"identity":"be0806bd-0db9-4d32-a2b9-790605b2c238","order_by":2,"name":"Yaa Gyamfua Oppong-Mensah","email":"","orcid":"","institution":"Komfo Anokye Teaching Hospital","correspondingAuthor":false,"prefix":"","firstName":"Yaa","middleName":"Gyamfua","lastName":"Oppong-Mensah","suffix":""},{"id":491974009,"identity":"10c0a91d-a9d7-419a-ba97-8378f5b0dff1","order_by":3,"name":"Vivian Paintsil","email":"","orcid":"","institution":"Kwame Nkrumah University of Science and Technology","correspondingAuthor":false,"prefix":"","firstName":"Vivian","middleName":"","lastName":"Paintsil","suffix":""},{"id":491974010,"identity":"3fdb0808-b2ef-4cac-bb19-5ac2e36f9e24","order_by":4,"name":"Emmanuel Nakua","email":"","orcid":"","institution":"Kwame Nkrumah University of Science and Technology","correspondingAuthor":false,"prefix":"","firstName":"Emmanuel","middleName":"","lastName":"Nakua","suffix":""}],"badges":[],"createdAt":"2025-07-26 12:38:12","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7221221/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7221221/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":87832852,"identity":"48ec37fd-9d66-46aa-a9e2-3179708f3cfe","added_by":"auto","created_at":"2025-07-29 12:42:53","extension":"jpeg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":17539,"visible":true,"origin":"","legend":"\u003cp\u003eReceiver-operating characteristic curve (ROC) of HEAL and MCV\u003c/p\u003e","description":"","filename":"floatimage1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7221221/v1/cf248141e12ee5c01fdb001b.jpeg"},{"id":87834762,"identity":"3f0ac38e-8e2f-431d-80e0-afec3f5bcb2a","added_by":"auto","created_at":"2025-07-29 13:06:56","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1144036,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7221221/v1/8a0ff9da-4920-48ad-87ff-076c8307537c.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Reliability and Validity of Parent/Caregiver Reported Hydroxyurea Evaluation of Adherence for Life Scale in Patients with Sickle Cell Disease in Ghana","fulltext":[{"header":"Background","content":"\u003cp\u003eSickle Cell Disease (SCD) is a genetic disorder in which two abnormal haemoglobin genes including the sickle haemoglobin are inherited from both parents. It is a monogenetic inheritable condition due to a mutation of the HBB gene, leading to the production of abnormal haemoglobins in affected red blood cells. The prevalence of SCD was estimated in 2021 to be 7.7\u0026nbsp;million people\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e. The major drivers of the increase from previous prevalence estimates of 5.5\u0026nbsp;million people in 2000, included early diagnosis, improvement in management, increased survival and the general population growth\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e. SCD as a chronic disease causes the body to produce abnormal sickle shaped red blood cells which can carry less oxygen to the body tissues, have a tendency of blocking blood vessels and also have a shorter lifespan of red blood cells.\u003c/p\u003e\u003cp\u003eAppropriate SCD management targeting an overall healthy lifestyle through regular comprehensive acute and chronic care enhances improved quality of life. Proven interventions include early detection of the disease, infection prevention, screening and treatment of acute and chronic complications.\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e Additionally, there are several emerging disease-modifying therapies and curative therapies such as bone marrow transplants and gene editing.\u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e Persons with SCD require daily prophylactic medications to help the body prevent infections. They also require regular screening tests to promptly identify impending complications for attention and also to help improve their quality of life. One of the most effective and affordable disease modifying medications is hydroxyurea (HU),\u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e which is gradually becoming a drug of choice in low- and middle-income countries (LMICs). HU works by improving haematological indices, leading to decreasing number of complications.\u003csup\u003e\u003cspan additionalcitationids=\"CR6\" citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e–\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e A holistic benefit of this medication is hinged on strict adherence to prescriptions for its use \u003csup\u003e\u003cspan additionalcitationids=\"CR9 CR10\" citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e–\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eAdherence has been described by the World Health Organisation as a measure of the level of correlation between mutually agreed health maintenance practices and the patient's actual behaviour.\u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e General adherence to medication is estimated to be low (50%) reaching even lower levels in developing countries\u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e. Non-adherence to medication has been attributed to individual, societal and health systems challenges. Non-adherence to HU can lead to poor health outcomes which will require increased health services utilization which leads to increased healthcare costs which are mostly borne by the patient and their families. The high healthcare costs then feed into the vicious cycle of non-adherence. In the US, general poor medication adherence costs the government USD1\u0026nbsp;million annually in hospital admissions and an estimated 125,000 die.\u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e,\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e In Ghana, where health expenditure per Gross Domestic Product is lower than required for Lower Middle-Income countries,\u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e avoiding increases in healthcare costs due to modifiable factors like drug non-adherence is of prime importance.\u003c/p\u003e\u003cp\u003eA first step is regular assessment of HU adherence for early detection of non-adherence. With this in hand, patient education plans can be targeted to ensure better compliance and in the long term wholistic benefit from HU. In non-clinical trial settings, there is paucity of data on HU adherence and its barriers and facilitators in sub-Saharan Africa. Studies reporting adherence in Africa are mostly in controlled clinical trial settings\u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e which do not paint an accurate picture of adherence in real world situations. In Ghana, aside from one study in the largest treatment centre in Ghana, Korle-Bu Teaching Hospital in Accra\u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e, no study has been done in the Komfo Anokye Teaching Hospital (KATH) in Kumasi, the second largest treatment centre, or anywhere else in Ghana. There is also no specific, quick, simple, validated and cheap tool for assessing HU adherence aside other non-specific tools and the use of laboratory tests which are expensive and fail to assess barriers and facilitators to HU adherence.\u003c/p\u003e\u003cp\u003eThe study assessed HU adherence and the reliability and validity of the Hydroxyurea Evaluation of Adherence for Life (HEAL) scale, a 24-item HU treatment adherence questionnaire, in a Ghanaian population of patients with Sickle Cell Disease.\u003c/p\u003e"},{"header":"Study design","content":"\u003cp\u003eThe study was an analytical cross-sectional study to assess the adherence to hydroxyurea therapy using the HEAL scale and its reliability and validity. Retrospective data on MCV values pre-initiation of HU were also extracted from patient medical records.\u003c/p\u003e\u003cp\u003e\u003cspan type=\"BoldUnderline\" class=\"BoldUnderline\" name=\"Emphasis\"\u003eProfile of study area\u003c/span\u003e\u003c/p\u003e\u003cp\u003eThe study was conducted in Kumasi Centre for Sickle Cell Disease at the Komfo Anokye Teaching Hospital (KCSCD-KATH) which hosts the largest Sickle Cell Disease Clinic in the Ashanti Region of Ghana and the second largest Sickle Cell Disease Clinic in Ghana after the Korle Bu Teaching Hospital from July to August 2024. KATH is located in Kumasi, the regional capital of the Ashanti Region of Ghana, which is the second most populous in Ghana, with a little over 5.4\u0026nbsp;million inhabitants according to the Ghana Statistical Service 2021 census figures.\u003c/p\u003e\u003cp\u003eKATH is a tertiary teaching hospital affiliated to KNUST, with an estimated bed capacity of 1200, which serves almost 13 out of the 16 regions of Ghana. The KATH Sickle Cell Clinic is the second largest in the country of 16 acknowledged treatment sites offering hydroxyurea therapy for SCD.\u003c/p\u003e\u003cp\u003eThe KSCD-KATH also holds the accolades of the first newborn screened baby in Ghana in February 1995 and the most babies screened for SCD than any other site in Africa\u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e with approximately 8,000 presumptive SCD patients identified as at 2016\u003csup\u003e18\u003c/sup\u003e. The Sickle Cell Disease Registry at KCSCD-KATH had 4523 patients registered as at 3rd July 2024.\u003c/p\u003e\u003cp\u003e\u003cspan type=\"BoldUnderline\" class=\"BoldUnderline\" name=\"Emphasis\"\u003eStudy population\u003c/span\u003e\u003c/p\u003e\u003cp\u003eThe study population consisted of paediatric SCD patients of SCD-SS phenotype who are currently receiving hydroxyurea therapy at KATH and their caregivers. Parents were eligible if their wards were\u003c/p\u003e\u003cp\u003e1. paediatric HbSS patients,\u003c/p\u003e\u003cp\u003e2. had been on HU therapy for at least 6 months at the time of interview (assuming the maximum tolerated dose would have been reached by then) and\u003c/p\u003e\u003cp\u003e3. had at least two full/complete blood count results available, one of which must be before they started taking hydroxyurea with the latest not older than 1 year available.\u003c/p\u003e\u003cp\u003eThe list of patients meeting the inclusion criteria was obtained from the Sickle Cell Unit of the Komfo Anokye Teaching Hospital. A census sampling technique was employed in this study to enrol parents/caregivers of these patients.\u003c/p\u003e\u003cp\u003e\u003cspan type=\"BoldUnderline\" class=\"BoldUnderline\" name=\"Emphasis\"\u003eData collection techniques and tools\u003c/span\u003e\u003c/p\u003e\u003cp\u003e1.1.1 \u003cb\u003eData collection technique\u003c/b\u003e\u003c/p\u003e\u003cp\u003eDemographic information, clinical history, laboratory values of the MCV were extracted from patient records available to the KCSCD-KATH using a structured questionnaire. The MCV values were used as an objective reference proxy for adherence in line with existing literature and availability at site.\u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e,\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e.The study employed Research Electronic Data Capture (REDCap) tool, which is a secure, web-based software designed to support data capture for research studies with linkage with an offline mobile app to make it easy to collect data anyway and data quality checks are instantaneous.\u003csup\u003e\u003cspan additionalcitationids=\"CR21\" citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e–\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e\u003cp\u003eCaregivers who consented completed the HEAL at enrolment via phone call. Caregivers were called 1–2 weeks following the first administration to retake the HEAL scale to evaluate test-retest reliability of the HEAL scale.\u003c/p\u003e\u003cp\u003eThe HEAL scale contains 24 items which are measured on a Likert Scale. The scale is divided into eight (8) sub-scales covering factors that affect HU adherence. The factors include the dosage, forgetfulness, planning, cost, effectiveness, understanding of the therapy and challenges with laboratory and pharmacy access. Permission to use the HEAL scale was obtained from the developers. Additionally, two haem-oncologists at KATH reviewed the scale and agreed for its use as provided by the developers.\u003c/p\u003e\u003ch2\u003eData analysis\u003c/h2\u003e\u003cp\u003eCategorical variables are summarised as frequencies and percentages while numeric variables are presented as either mean and standard deviation or median and interquartile range, depending on the normality of the observations. The HEAL scores were summarised at overall, subscale and individual item levels. The change in MCV from pre initiation was calculated as the difference between the pre initiation MCV value and the post initiation MCV value divided by the pre initiation MCV value and converted into percentages.\u003c/p\u003e\u003cp\u003eParametric tests have been found to have similar power as non-parametric tests for Likert scales \u003csup\u003e\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e and hence, we adopted both parametric and non-parametric tests for the HEAL scale. Subscale scores were calculated as an average of the score in their component items. The total HEAL score was calculated as an average of all the individual items as described by Janson\u003csup\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eThe study assessed reliability at 2-levels: internal consistency reliability in the questionnaire items and test-retest reliability for consistency of responses in phone-based interviews 1–2 weeks apart. Individual and subscale composition were evaluated using internal consistency reliability (Cronbach’s alpha, α) in the study sample: an α ≥ 0.8 was considered to reflect excellent internal consistency; 0.8 \u0026gt; α ≥ 0.7, very good; 0.7 \u0026gt; α ≥ 0.6, good; 0.6 \u0026gt; α ≥ 0.5, minimally acceptable and α \u0026lt; 0.5 considered unacceptable based on the levels used in the development of the tool as described by Janson et al\u003csup\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e. Test-retest reliability of HEAL subscales and aggregate scores were evaluated with Pearson product-moment correlations (r) and t-tests comparing HEAL scores at initial and retest completion times. The study evaluated the criterion validity of the HEAL Scale using the MCV classification as the reference. Criteria validity was therefore assessed using correlational analyses of HEAL scores with MCV values.\u003c/p\u003e\u003cp\u003eThe overall HEAL scores less than 5 were categorised as non-adherent and more than 4 categorised as adherent while for MCV values, a cut off of ≥ 100fL was classified as adherent as described in previous studies\u003csup\u003e\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e. The categorisation from the MCV laboratory data was used as the reference value for testing the sensitivity, specificity, positive predictive value and negative predictive value of the HEAL screening assessment test.\u003c/p\u003e\u003cp\u003eThe sensitivity was calculated as the ratio of participants classified as adherent by both the HEAL and MCV values to the participants classified as adherent by MCV. Specificity was also calculated as the proportion of participants classified as non- adherent on both the MCV and HEAL scale to those who were also correctly classified as non-adherent by the MCV. The positive predictive value was calculated as the truly adherent (adherent on both MCV and HEAL) divided by the adherents by the HEAL scale while the negative predictive value will be calculated as the truly non-adherent (non-adherent on both MCV and HEAL) divided by the non-adherents by the HEAL scale. The percentage of correct classification will also be calculated as the proportion of the sum of the truly adherent and non-adherent to the total sample size. Receiver Operating Characteristic Curve was used to assess the level of agreement overall of the HEAL scale with the MCV values.\u003c/p\u003e\u003cp\u003e\u003cspan type=\"BoldUnderline\" class=\"BoldUnderline\" name=\"Emphasis\"\u003eEthical considerations\u003c/span\u003e\u003c/p\u003e\u003cp\u003eEthical approval\u003c/strong\u003e for the study was obtained from the Committee of Human Research Publication and Ethics of the KNUST (CHRPE/AP/423/24) and the KATH Institutional Review Board (KATH-IRB) - KATHIRB/AP/092/24.\u003c/p\u003e\u003cp\u003eCaregivers of eligible participants were called and consented to participate in the study. Verbal consent was obtained from caregivers of the paediatric patients (3 months-16 years).\u003c/p\u003e\u003cp\u003eParticipant information was only accessible to study personnel and regulatory authorities. As data was captured directly into REDCap, access to the system was user-password protected and stored securely on REDCap.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cspan type=\"BoldUnderline\" class=\"BoldUnderline\" name=\"Emphasis\"\u003eSocio-demographic characteristics\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003eThe study therefore contacted all 38 eligible patients using the two phone contact numbers obtained from the KCSCD-KATH. Out of these 38 persons, ten (10) could not be reached on any of their phone contacts to participate after 9 tries over 3 days. All the remaining twenty-eight (28) agreed to participate.\u003c/p\u003e\n\u003cp\u003eMajority of the patients were male 19(67.86%) and were not screened at birth for SCD 22(78.57%). A large proportion were however, screened by 1 year of age 16(57.14%). On the average, the enrolled patients were started on hydroxyurea therapy by 9 years of age (Range: 2\u0026ndash;16). The mean duration of Hydroxyurea therapy was 18.3 months ranging from 8.6 months to 29.4 months. (Table \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e)\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\u003cbr\u003e\n \u003ctable id=\"Tab1\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eSocio-demographic characteristics of study participants\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCharacteristic\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eFrequency (n\u0026thinsp;=\u0026thinsp;28)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ePercentage\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003ePatient Sex\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e32.14\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e67.86\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003e\u003cstrong\u003ePatient Educational Level (Completed/ current)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBasic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e75\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eJHS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e17.86\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSHS/Vocational\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.14\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003e\u003cstrong\u003eDiagnostic Pathway\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNon-Newborn screened\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e78.57\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNewborn screened\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e21.43\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003e\u003cstrong\u003eAge at diagnosis\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eInfant (up to 1 year)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e57.14\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eToddler (1\u0026ndash;3 years)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e21.43\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eChild (4\u0026ndash;12 years)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e21.43\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003ePatient age (years) at initiation of HU\u003c/strong\u003e \u003cem\u003eMean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD (min, max)\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e8.57\u0026thinsp;\u0026plusmn;\u0026thinsp;4.50 (2, 15)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eDuration on HU (months)\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cem\u003eMean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD (min, max)\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e18.30\u0026thinsp;\u0026plusmn;\u0026thinsp;5.69 (8.60, 29.44)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\u003c/tfoot\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u003cspan type=\"BoldUnderline\" class=\"BoldUnderline\" name=\"Emphasis\"\u003eLaboratory indices and medication adherence\u003c/span\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e1.1.2 \u003cstrong\u003eMCV values\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe mean MCV values pre-initiation of hydroxyurea was 78.6\u0026thinsp;\u0026plusmn;\u0026thinsp;7.17 compared to mean post-initiation values of 88.0\u0026thinsp;\u0026plusmn;\u0026thinsp;10.38. A statistically significant difference was found between pre-initiation and post initiation MCV values. The average percentage change in MCV from baseline value was 12%\u0026plusmn;12.4.\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n \u003cdiv align=\"left\" class=\"colspec\"\u003e\u003cbr\u003e\u003c/div\u003e\n \u003ctable id=\"Tab2\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003ePaired-sample test comparing MCV values before and after HU\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eMean (SD\u003csup\u003ea\u003c/sup\u003e)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e95%CI\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eChange in MCV values (pre-post)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ePercentage change\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ep-value\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003ePre initiation MCV values (Baseline)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e78.65 (7.16)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e75.88\u0026ndash;81.42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003e9.38 (9.45)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003e12.28 (12.39)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003ePost initiation MCV values\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e88.03 (10.38)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e84 -92.05\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"6\"\u003e\n \u003cp\u003e\u003csup\u003ea\u003c/sup\u003e Standard Deviation; \u003csup\u003eb\u003c/sup\u003e Confidence interval\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cdiv id=\"Sec4\" class=\"Section3\"\u003e\n \u003cdiv class=\"Heading\"\u003e1.1.3 HEAL Scale\u003c/div\u003e\n \u003cp\u003eAn item-by-item analysis revealed that for all reverse-scored items, the mean scores were slightly less than 5(Disagree) and the median scores were 5, for 12 out of the 15 items. For the positively scored items, all the individual items, except for items on safety of HU, had a mean score of at least 5.56 (Agree). The mean score was highest (6.43\u0026thinsp;\u0026plusmn;\u0026thinsp;1.07) for the item \u003cem\u003eon \u0026ldquo;I believe that HU helps my child\u0026rdquo;\u003c/em\u003e and lowest (3.71\u0026thinsp;\u0026plusmn;\u0026thinsp;1.74) for the item on \u003cem\u003e\u0026ldquo;Lab work and appointments for HU are very demanding or stressful\u0026rdquo;.\u003c/em\u003e The median score for 3 out of the 24 items was 7 indicating a strong positive adherence leaning.\u003c/p\u003e\n \u003cp\u003eOn the subscale average scorecard, the subscale on effectiveness showed the highest mean score (6.12\u0026thinsp;\u0026plusmn;\u0026thinsp;0.89) with the lowest being the subscale on cost (4.12\u0026thinsp;\u0026plusmn;\u0026thinsp;1.78). Only 3 out of the 8 subscales (\u003cem\u003eDose, Effectiveness, and Understanding)\u003c/em\u003e crossed the mean score of 5 leaning towards positive adherence. If focus is shifted however to the median score, then only 3 out of the 8 subscales (\u003cem\u003eCost, Lab, Pharmacy)\u003c/em\u003e fall below the positive adherence leaning score of 5.\u003c/p\u003e\n \u003cp\u003eBoth mean (4.83\u0026thinsp;\u0026plusmn;\u0026thinsp;0.65) and median (4.75) total scores on the HEAL scale show a tendency for poor adherence in the sample studied.\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003ctable id=\"Tab3\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eHEAL scale items descriptive statistics\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003ePercentiles\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eMean (SD)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eMin/Max\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e25\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e50\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e75\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eDose\u003c/em\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003e5.4 (0.8)\u003c/em\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003e3 /6.33\u003c/em\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003e5\u003c/em\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003e5.67\u003c/em\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003e6\u003c/em\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1. Give recommended amount\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.64 (1.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3 /7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2. Know exact amount\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.86 (1.27)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3 /7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3. Sometimes give incorrect amount*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.71 (0.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3 /7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eRemember\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e4.71 (1.1)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e3 /7\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e4\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e5\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e5\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4. Forget to give*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.57 (1.37)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3 /7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5. Skip doses*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.71 (1.41)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3 /7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6. Miss scheduled doses*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.86 (1.21)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3 /7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003ePlan\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e4.76 (1.21)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e3 /7\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e3.67\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e5\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e5\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7. Run out of HU because no prescription*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.86 (1.43)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1 /7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8. Irregular dose because run out*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.71 (1.18)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3 /7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9. Miss dose because run out*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.71 (1.41)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3 /7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eCost\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e4.12 (1.78)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e1 /7\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e3\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e4\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e5\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10. Too expensive to take regularly*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.29 (1.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1 /7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11. Hard to afford for family*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4 (2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1 /7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e12. Cost is stressful*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.07 (2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1 /7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eEffectiveness\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e6.12 (0.89)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e4.33 /7\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e5.67\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e6.33\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e7\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e13. Believe HU helps\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.43 (1.07)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3 /7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e14. HU is effective\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.36 (0.95)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5 /7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e15. HU is safe\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.57 (1.32)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3 /7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eUnderstand\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e5.05 (0.94)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e3 /7\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e4.33\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e5\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e5.33\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e16. Understand how HU works\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5 (1.81)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1 /7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e17. Could explain how HU works\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6 (1.15)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3 /7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e18. Know how HU works\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.14 (1.48)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1 /7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eLab\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e4.24 (1.38)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e1.67 /7\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e3.33\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e4.33\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e5\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e19. Labs difficult to do*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.14 (1.76)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1 /7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e20. Lab demanding to do*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.71 (1.74)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1 /7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e21. Getting labs done is hard*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.86 (1.63)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1 /7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003ePharmacy\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e4.26 (1.03)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e1.67 /6.33\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e3.67\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e4.33\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e5\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e22. Difficulty getting HU from pharmacy*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.14 (1.38)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1 /7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e23. Hard to find nearby pharmacy with HU*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.14 (1.58)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1 /7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e24. Working with pharmacy is stressful*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.5 (1.17)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1 /7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eTotal\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e4.83 (0.65)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e3.83 /6.33\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e4.38\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e4.75\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e5.17\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"6\"\u003e\n \u003cp\u003e\u003cem\u003e*Reverse scored item\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec5\" class=\"Section3\"\u003e\n \u003cdiv class=\"Heading\"\u003e1.1.4 Adherence levels\u003c/div\u003e\n \u003cp\u003eUsing the MCV cut-off of 100fL values, approximately 18% (5) of the patients were found to be adherent to HU therapy whilst the HEAL scale revealed 43% (12) adherence. No statistically significant difference was found between the HEAL classification and the MCV classification of adherence.\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003cdiv align=\"left\" class=\"colspec\"\u003e\u003cbr\u003e\u003c/div\u003e\n \u003ctable id=\"Tab4\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eHEAL Scale and Mean Corpuscular Volume (MCV) indicated level of adherence to hydroxyurea medication\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eMean Corpuscular Volume (MCV)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eHEAL Scale\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eNon adherent\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eAdherent\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eTotal\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ep-value*\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNon adherent, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e14(87.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2(12.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e16(100)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.624\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAdherent, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9(75)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3(25)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e12(100)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTotal, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e23(82.14)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5(17.86)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e28(100)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"5\"\u003e\n \u003cp\u003e\u003cem\u003e*2-sided Fisher exact p-value\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003e\u003cspan type=\"BoldUnderline\" name=\"Emphasis\"\u003eReliability of HEAL Scale\u003c/span\u003e\u003c/p\u003e\n \u003cp\u003e1.1.4.1 \u003cstrong\u003eInternal consistency\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eEach of the items on the HEAL scale scored excellent internal consistency ranging from 0.86\u0026ndash;0.88. Categorisation of the items into subscales also revealed a similar excellent internal consistency. The total score also shows excellent internal consistency of 0.88.\u003c/p\u003e\n \u003cdiv id=\"Sec6\" class=\"Section4\"\u003e\n \u003cdiv class=\"Heading\"\u003e1.1.4.2 Test -retest\u003c/div\u003e\n \u003cp\u003eTest-retest reliability using the Pearson Correlation showed positive correlation in all the subscales. A strong correlation between test and retest for the Plan, Lab and Dose subscales was found. Moderate correlation was found between the Cost, Pharmacy and Total Score. A negligible correlation was found in the Remember subscale. The correlation was significant for the Dose, Plan, Cost, Lab, Pharmacy subscales as well as the total score. However, a student t-test testing the mean difference found only a significant difference in the lab subscale. (Table \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e)\u003c/p\u003e\n \u003cp\u003eThe validity of the HEAL scale subscale and total score was evaluated using the MCV values as the comparator. There was positive negligible correlation with the Lab and Effectiveness subscales whereas only the Remember subscale showed positive moderate correlation which was significant. A weak negative correlation was found with the Understand scale. (Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e)\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003ctable id=\"Tab5\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eTest- Retest Correlation of HEAL subscales and Correlation with MCV values\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eTest- Retest Correlation\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003ePearson correlation coefficient\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSubscale\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003er\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ep-value\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003er*\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ep-value\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDose\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.89\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;\u0026thinsp;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.137\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.487\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRemember\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.679\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.417\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.027\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePlan\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;\u0026thinsp;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.358\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.062\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCost\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.018\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.251\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.199\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEffectiveness\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.102\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.040\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.839\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUnderstand\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.488\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.084\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.672\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLab\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;\u0026thinsp;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.006\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.978\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePharmacy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.095\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.630\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTotal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.68\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.289\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.136\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003e\u003cspan type=\"BoldUnderline\" name=\"Emphasis\"\u003eDiagnostic accuracy\u003c/span\u003e\u003c/p\u003e\n \u003cp\u003eSensitivity and sensitivity of the HEAL scale in reference to the MCV cut-off of 100fl was approximately 60% (Table \u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003e). A high negative predictive value was revealed.\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003cdiv align=\"left\" class=\"colspec\"\u003e\u003cbr\u003e\u003c/div\u003e\n \u003ctable id=\"Tab6\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eDiagnostic accuracy of HEAL scale\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eParameter\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eValue\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e95%CI\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFisher exact test of association\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.624\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSensitivity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e60.00%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e41.85%- 78.15%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSpecificity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e60.87%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e42.79%- 78.95%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePositive predictive value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e25.00%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8.96%- 41.04%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNegative predictive value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e87.50%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e75.25%- 99.75%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePrevalence\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e17.86%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.67%- 32.04%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eROC AUC(SE)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.6043(0.1331)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.34354\u0026ndash;0.86515\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003e\u003cem\u003eCI- Confidence interval; ROC- Receiver-Operating characteristic curve; AUC- Area Under Curve; SE- Standard Error\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003eThe Receiver-Operating Characteristic curve estimated a 60% chance of the HEAL scale correctly identifying adherence (Fig. 8).\u003c/p\u003e\n \u003c/div\u003e\n\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe larger male proportion among study participants is consistent with data from studies among patients with SCD using hydroxyurea \u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e,\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e,\u003cspan additionalcitationids=\"CR25\" citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/sup\u003e. This male dominance is also noted from available SCD registries in Africa \u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e,\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e, which could be attributed to sociocultural practices which seemingly favour the male child over the female. Over 75% of the study patients were not screened at birth but following acute complications of SCD. This is indicative of the slow upscaling of the newborn screening for SCD policy of Ghana. The proportion of participants screened by 1 year of age is however higher than reported in a study in Brazil reporting 46.7%\u003csup\u003e28\u003c/sup\u003e. However, the little under 50% not diagnosed by age one (1) could have proven detrimental to the patients as they would have developed severe complications by the time of their diagnosis.\u003c/p\u003e\u003cp\u003eHydroxyurea (HU) therapy has been initiated to quite a substantial proportion of patients in the KCSCD-KATH who are regular clinic attendants. This is a good step in trying to reap the proven benefits of HU in preventing complications in the patients. This also serves as a step geared towards reversing the trend of underutilization as described by Ally and Balandya\u003csup\u003e\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u003c/sup\u003e. Per policy, persons being initiated on HU must have had a laboratory test completed to ascertain their baseline laboratory indices prior but the study revealed the lack of documentation of the pre-initiation laboratory indices in patient medical records. This leaves little to be desired as it makes it difficult to objectively provide data from Ghana on the efficacy and effectiveness of HU using laboratory indices. Compared with other cohorts in literature, our study participants were not initiated on hydroxyurea early enough, maybe linked to the beginning of free HU treatment in 2018 through efforts on Novartis and the Sickle Cell Foundation of Ghana and continued by the government of Ghana on 2022\u003csup\u003e25,30\u0026ndash;32\u003c/sup\u003e. The late start is detrimental to obtaining optimal benefit from HU, as initiation of HU at a young age by 2 years has been proven to provide overall haematological efficacy\u003csup\u003e\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003e\u003cspan type=\"BoldUnderline\" class=\"BoldUnderline\" name=\"Emphasis\"\u003eAdherence to HU\u003c/span\u003e\u003c/p\u003e\u003cp\u003e1.1.5 \u003cb\u003eAdherence level by Mean Corpuscular Volume (MCV)\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThe mean baseline MCV values in the study participants was similar to a multi-site study in Africa\u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e, a study in Nigeria\u003csup\u003e\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u003c/sup\u003e, and a study in the United Arab Emirates (UAE), but lower by 13% compared to a study in Brazil\u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e. This however is within limits of normal MCV levels of 80-100fl. This difference could be due to the population level differences in haematological differences between these different continents of the world. The statistically significant difference between the pre-initiation and post-initiation MCV values confirms the assertion of HU significantly increasing the MCV values in several studies \u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e,\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e,\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e,\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e\u003c/sup\u003e. There were however a few decliners some of whom witnessed a drop in MCV of almost 10% from baseline. However, based on the cut-off of 100fl, less than one-fifths were found to be adherent to HU, far lower than the one-thirds reported in a study in USA\u003csup\u003e\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e. The low proportion based on this MCV cut-off could be also due to non-responders and slow responders.\u003c/p\u003e\u003cdiv id=\"Sec8\" class=\"Section3\"\u003e\u003cdiv class=\"Heading\"\u003e1.1.6 HU adherence level using the HEAL scale\u003c/div\u003e\u003cp\u003eMost of the subscale scores for the HEAL scale in our study were lower than the cut-off of 5 pointing to a higher tendency for non-adherence to HU therapy. This is lower than as reported by Janson who validated the scale in patients in the USA. The most comparable subscale score was of \u0026ldquo;Effectiveness\u0026rdquo; which explored perception of the efficacy, effectiveness and safety of HU therapy \u003csup\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e. The similar perception of efficacy, effectiveness and safety could be due to the public knowledge and health education on the benefits of HU in the population. In our study, we found that cost and health system related factors (lab, appointments and pharmacy), were the most probable barriers to adherence as they had scores less than 4.5. Cost has been identified as a major barrier to drug adherence in several studies. This finding however is surprising for our context, as since July 2022, HU therapy is fully sponsored by the government on the National Health Insurance Scheme (NHIS). Other factors related to the cost of treatment could therefore be the reason for cost still leading as a probable barrier to adherence.\u003c/p\u003e\u003cp\u003eOur study also reported a median total score of less than 5 further elucidating that more than 50% of the sample were likely to be non-adherent. In absolute proportions, our study found lower adherence than the 75% reported by Janson using the same scale, as testament of the effect of geographical location of measures of adherence.\u003c/p\u003e\u003cp\u003eLooking past the difference in scales used, the level of adherence in our study was also found to be lower than the adherence level reported in other studies worldwide and even in Ghana though in another population.\u003c/p\u003e\u003cp\u003eAssessment of adherence to penicillin V in our population by Odoom et al revealed adherence of 30% by urine assay and 68% by direct questioning\u003csup\u003e\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e\u003c/sup\u003e which is far higher than adherence in our study which was focused on hydroxyurea. Another study looking at general adherence to all routine medications in our study population revealed adherence of 13.5%\u003csup\u003e37\u003c/sup\u003e, which is close to findings in our study by MCV cut-off.\u003c/p\u003e\u003cp\u003eLevel of adherence reported was higher by almost 25% in the HEAL scale (subjective method) compared with the MCV values (objective method). This difference is comparable to other studies suggesting a possible level of over or underreporting with subjective scales. The lack of significant association between the adherence level by the MCV score and the HEAL score could be explained by the smaller sample size in our population due to a lack of documentation of preinitiation lab values.\u003c/p\u003e\u003cp\u003e\u003cspan type=\"BoldUnderline\" class=\"BoldUnderline\" name=\"Emphasis\"\u003eReliability of the HEAL scale\u003c/span\u003e\u003c/p\u003e\u003cp\u003e1.1.7 \u003cb\u003eReliability of HEAL Scale on the internal consistency domain\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThe total score of the HEAL scale showed excellent internal consistency comparable to that in the initial study by Janson. This points to how closely related the scale items are to each other in measuring the underlying factor of adherence. Internal consistency values were lower in our study for the Remember, Effectiveness, Understand, Pharmacy subscales but higher for the Dose, Plan, Cost, and Lab subscales compared to the initial validation study.\u003c/p\u003e\u003cp\u003eIn comparison with other scales, our study showed higher internal consistency than the 8 -item Morisky Medication Adherence Scale from a systematic review and meta-analysis\u003csup\u003e\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e\u003c/sup\u003e and another study in China\u003csup\u003e\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e\u003c/sup\u003e. This points to a better correlation between the items in the HEAL scale and its reliability for use in our population.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec9\" class=\"Section3\"\u003e\u003cdiv class=\"Heading\"\u003e1.1.8 Reliability of HEAL Scale on the test-retest domains\u003c/div\u003e\u003cp\u003eTest-retest reliability correlations showed strong significant correlation for three (3) out of the eight (8) subscales (Dose, Plan, Pharmacy and Lab) contrary to findings in the initial validation study where 7 out of the eight (except cost). The difference could be attributed to the mean duration between tests which is 3 times in the initial study compared with our current study. Variations in correlation more than 0.2 were noted for Remember, Cost, Effectiveness, and Understand subscales. The lack of moderate to strong correlation could be due to the heightened consciousness from the initial test which could have modified responses. It is envisaged that a longer period between test and retest of three (3) months would provide a better correlation as heightened consciousness would have waned.\u003c/p\u003e\u003cp\u003eExcept for the subscale of the lab, a student t-test at 95% confidence level found no significant difference between the test and retest scores at the subscale levels. This adds to the credibility of the HEAL scale in measuring reliably in repeat tests.\u003c/p\u003e\u003cp\u003e\u003cspan type=\"BoldUnderline\" class=\"BoldUnderline\" name=\"Emphasis\"\u003eValidity\u003c/span\u003e\u003c/p\u003e\u003cp\u003e1.1.9 \u003cb\u003eCorrelation of the HEAL scale scores with MCV values\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThe HEAL scale did not seem to correlate with the MCV values as all the correlation coefficients were less than 0.5 and went as low as 0.006 for the Lab subscale. The most correlated subscales were the Remember and Plan. These findings were similar to the initial study validation with the foetal haemoglobin levels and ANC levels. This points to the Remember and Plan subscales being able to more predict what the MCV levels and hence adherence to HU could possibly be. Other subscales seem to only help in assessing barriers and not necessarily point to adherence.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec10\" class=\"Section3\"\u003e\u003cdiv class=\"Heading\"\u003e1.1.10 Diagnostic accuracy\u003c/div\u003e\u003cp\u003eThe HEAL classification was found to be independent of the MCV classification. The total HEAL score was also found to correctly predict adherence and non-adherence by similar proportion compared to MCV values. This is below the recommended proportion for a tool to be recommended for use. The ROC AUC less than 0.7 is below the acceptable value and the HEAL scale cannot be expected to measure adherence like the MCV.\u003c/p\u003e\u003cp\u003e\u003cspan type=\"BoldUnderline\" class=\"BoldUnderline\" name=\"Emphasis\"\u003eLimitation of the Study\u003c/span\u003e\u003c/p\u003e\u003cp\u003e1. The sample size for the study was fairly small and generalizations of the level of adherence must be done cautiously.\u003c/p\u003e\u003cp\u003e2. The laboratory tests yielding MCV values were not collected at the same time as the HEAL initial test and therefore might not be the most current picture of adherence.\u003c/p\u003e\u003cp\u003e3. The subjective nature of the HEAL scale is prone to recall bias and answers of convenience which could mar the picture of the level of adherence.\u003c/p\u003e\u003cp\u003e4. We could not determine whether study participants had reached the maximum tolerated dose of HU.\u003c/p\u003e\u003cp\u003e5. We did not rule out any other comorbidities like vitamin B12 deficiency that could affect the MCV in this study\u003c/p\u003e\u003c/div\u003e"},{"header":"Conclusion","content":"\u003cp\u003eHU adherence in the sample from the KCSCD-KATH is generally low: lower by the MCV values than the HEAL Scale. The HEAL scale was found to have a good reliability based on internal consistency and test-retest domains. It was however little correlated with MCV values though showing sensitivity and specificity of 60%. It is good enough as a screening tool for HU adherence in our population as it helps identify the barriers to adherence.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eEXA, conceptualised the study with support from LOT, YGOM, VP and EKN. EXA, and LOT, participated in data curation. EXA analysed and interpreted the data and provided the initial draft of the manuscript. EKN supervised all aspects of the study. All authors read and approved the final manuscript and agree to be responsible for every aspect of the study.\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e\u003cp\u003eWe acknowledge all study participants for their time during the study. Heartfelt gratitude goes to Miss Adwoa Agyeiwaa Owusu-Ansah for the kind support in data collection.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eData that supports the findings of this study are available on request to the corresponding author.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eThomson AM, McHugh TA, Oron AP, Teply C, Lonberg N, Vilchis Tella V, et al. Global, regional, and national prevalence and mortality burden of sickle cell disease, 2000\u0026ndash;2021: a systematic analysis from the Global Burden of Disease Study 2021. Lancet Haematol [Internet]. 2023 Aug [cited 2024 Mar 24];10(8):e585\u0026ndash;99. 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Ann Palliat Med. 2021;10(8):9088\u0026ndash;95.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-7221221/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7221221/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"Sickle Cell Disease (SCD) is an inherited disorder of the red blood cells with highest burden in sub-Saharan Africa. The burden of SCD pain episodes can be reduced by the appropriate use of hydroxyurea (HU). Non-adherence to medication leads to increased health service utilization and costs and further impairs adherence and prognosis. Adherence assessment methods mostly involve laboratory tests which are expensive, and fraught with delays in low resource settings. Other assessment methods like psychometric tools, which are quicker and cheaper, are hardly used in these settings. In Ghana, HU adherence has only been reported in Accra but no study has focused on evaluating a psychometric tool specifically developed for HU adherence assessment. The study evaluated the adherence to HU using the Hydroxyurea Evaluation for Adherence for Life (HEAL) tool in paediatric SCD patients in Kumasi and assessed the tool’s reliability and validity compared to the Mean Corpuscular Volume (MCV) values.\nWe enrolled twenty-eight (28) SCD-SS patients, who had been on HU for at least six (6) months at the Komfo Anokye Teaching Hospital (KCSCD-KATH). Their MCV values were obtained from their medical records and the HEAL Scale was administered to their parents via phone call at enrolment and within 1-2 weeks of the initial call.\nAdherence to HU was 43% using the HEAL scale and 18% using the MCV values. Internal consistency of the HEAL scale by Cronbach alpha was 0.88 and test-retest reliability correlation was 0.68(p-value:0.001). No statistically significant difference was also found between HEAL scores from the initial test and retest timepoints. Correlation of the HEAL score with MCV was weak (ρ = 0.29, p-value = 0.136). The HEAL scale correctly predicted 60% of adherence on the MCV scale.\nHU adherence in our sample was low and the HEAL scale was found to be reliable though it did not correlate strongly with the MCV values. Comprehensive action is required to improve HU adherence and by extension benefit of HU. Further studies are recommended to confirm the validity of the HEAL scale in sub-Saharan Africa.","manuscriptTitle":"Reliability and Validity of Parent/Caregiver Reported Hydroxyurea Evaluation of Adherence for Life Scale in Patients with Sickle Cell Disease in Ghana","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-07-29 12:42:49","doi":"10.21203/rs.3.rs-7221221/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"a251870a-852d-4f9c-af34-68e3b01dd978","owner":[],"postedDate":"July 29th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-07-29T12:42:51+00:00","versionOfRecord":[],"versionCreatedAt":"2025-07-29 12:42:49","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7221221","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7221221","identity":"rs-7221221","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}