Effectiveness and cost-effectiveness of a pragmatic clinical trial of the New Medicine Service intervention in Spanish community pharmacies

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Community pharmacies are shown to be key elements in improving adherence to prescribed medications, optimizing patient outcomes and increasing care efficiency. Objective Examine whether the New Medicine Service intervention improved medication adherence among patients with new prescriptions for four diseases, compared with usual pharmacy care. Another aim was to estimate the cost-effectiveness of this intervention. Methods pragmatic cluster randomised clinical trial at community-pharmacy level in 23 community pharmacies (12 in control and 11 intervention arm) in Andalusia (Spain). Patients were invited to join the study who were identified as starting treatment for chronic obstructive pulmonary disease, hypertension or diabetes mellitus, or were on an anticoagulant/antiplatelet agent. The NMS intervention consisted of three steps: 1) establishing the commitment to the patient; 2) assessing adherence to the new medication and identifying problems with the medication and coming up with strategies to minimize such problems; 3) conducting follow-up visits to check adherence and evaluate intervention effects. Results At ten weeks and six months, outcomes showed slightly better medication adherence resulting from the intervention, however, this outcome was not statistically significant. At six months follow-up, reduced costs were exhibited in the intervention group relative to the control group, whilst the intervention group demonstrated greater utility. All of the above reveals the NMS intervention to be a dominant strategy when compared with typical treatment. Conclusion The NMS intervention that is based on communication between patients and pharmacists managed to achieve slight improvements in adherence to new prescriptions in patients with one of four pathologies. Cost-effectiveness analysis supports further use of the examined intervention, as it improves quality of life and reduces costs in comparison with typical practice ClinicalTrials.gov Identifier: NCT04195191 (Date: 2020-02-05). Figures Figure 1 1. Introduction Lack of adherence to medication is an important public health issue that has implications for healthcare systems, due to its high impact on morbidity, mortality and healthcare costs. According to the World Health Organisation, it is estimated that 50% of patients do not adhere to treatment for chronic illnesses[ 1 ]. Reasons for this lack of adherence are complex and diverse. Some of the most frequently described factors in existing literature include adverse effects, lack of effectiveness, and the complexity of and lack of trust in the drug[ 2 , 3 ]. Given this landscape, community pharmacists could help improve system efficiency and address chronicity through cognitive services attached to pharmaceutical care[ 4 , 5 ]. Cognitive services are defined as patient-centred services delivered by pharmacists who call on specific knowledge in an attempt to improve procedures related with medication use or outcomes[ 6 ]. The new medicine service (NMS) is a cognitive service delivered via community pharmacies[ 7 – 11 ] with the aim of increasing adherence amongst patients who are new to treatment. The NMS consists of counselling that is based on the communication between pharmacists and patients in order to provide patients with information and advice about any potential issues, concerns and false beliefs or expectations. Evidence demonstrates that NMS interventions are proven to be cost-effective[ 12 , 13 ], well accepted by patients [ 14 ] and effective at increasing treatment adherence[ 13 , 15 – 18 ]. The primary aim of the present study was to examine whether the NMS intervention improved medication adherence among patients with a new prescription for four diseases when compared with usual pharmacy care provision. Another aim was to estimate the cost-effectiveness of this intervention. 2. Methods 2.1 Study design The present study is a pragmatic randomised clinical trial that was delivered at community-pharmacy level (clusters). The study population comprised 224 participants intentionally recruited by participating pharmaceutical staff coming from 23 Andalusian CPs which were randomly assigned to the control (n = 12) or intervention group (n = 11) (Response rate: 84%)[ 19 , 20 ]. Randomisation was blinded performed using computer-generated random numbers (Epidat_software: https://www.sergas.es/Saude-publica/EPIDAT?idioma=es ). Patients in the intervention group received the pharmacist-patient communication-based NMS intervention. The control group received normal practice (NP) which consisted of usual advice and no follow-up[ 21 ]. The modifications to the protocol can be seen in the Appendices [ 21 ]. The study adhered to 2022 Consolidated Health Economic Evaluation Reporting Standards (CHEERS)[ 22 ], Consolidated Standards of Reporting Trials (CONSORT) criteria[ 23 ] (Appendices) and recommendations for economic evaluations in the health technology setting in Spain[ 24 ]. The study was approved by the Andalusian regional ethics committee (code 1202-N-18). Following receipt of expression of interest from a potentially eligible patient about joining the scheme, they were asked a number of questions to confirm eligibility and were asked to provide written informed consent and the information sheet was given to each patient. In compliance with general data protection laws ( Ley Orgánica 3/2018 ), no information was gathered which would enable patient identification. The study was in compliance with the Helsinki Declaration Study participants were adult patients receiving a new prescription for hypertension, chronic obstructive pulmonary disease (COPD), diabetes, or an anticoagulant/antiplatelet agent. Patients suffering from a physical or mental disorder that prevented them from complying with the intervention and patients with seasonal or acute treatment were excluded. 2.2 Intervention The aim of the intervention was to monitor and improve medication adherence in patients. It consisted of three steps. Firstly, step 1 focused on establishing verbal patient commitment. Next, step 2 focused on both assessing adherence to the new prescribed medication and identifying issues with the medication and potential strategies for minimising such issues. Finally, step 3 entailed a follow-up visit to monitor adherence and evaluate intervention outcomes[ 25 ]. Full details regarding project design and methodology are described elsewhere[ 21 ]. In order to ensure standardisation of intervention delivery, staff from participating pharmacies were given a programme (Axon-Pharma, available at: https://axonfarma.es/ ) which provided guidance on the steps to follow and approach to recording information; furthermore, all patient´s information relevant to the study was here recorded. To support intervention delivery, participating pharmacists also had access to explanatory videos and other information and dissemination materials (leaflets, etc.) to give out to patients in order to improve their adherence. Two training sessions were provided by researchers in order to introduce the project, outline its objectives, methods and target population, and encourage homogenisation and standardisation of the intervention (length, procedures, emphasis on the use of communication skills and empathy, amongst other aspects). Training was also provided to assist use of the Moodle-platform. This platform was used to help resolve any of the issues or incidents that might arise throughout the study process. It also contained a user guide and summary report including answers to frequently asked questions, whose utility was continually discussed so that it could be periodically updated as the intervention progressed. In addition, every six months a telephone meeting was held with researchers and pharmacist in order to address potential problems, monitor study progress and ensure adherence to the study protocol. 2.3 Variables : The primary dependent variable was “adherence to new prescription medication” which was estimated according to two items: 1) forgetting to take medication at six months (yes/no), and 2) treatment completion rate at six months (calculated as the n of medicines taken divided by the n of prescribed medicines multiplied by 100). Independent variables were categorised as a) sociodemographic, such as age, sex, number of children and social support, b) clinical, such as treatment indication, number of other treatments and total number of pills taken per day and, health-related quality of life (HRQoL) measured at baseline, ten weeks and six months, c) satisfaction with both service provision and medication, and d) healthcare resource use (primary care appointments, specialty care appointments, emergency provision in both primary and hospital care, hospital admissions, and NMS intervention) by each patient during the study period gathered directly from patients at the end of the study (annexs). Social support (high/low) was measured according to the single item measure conceived by Blake[ 26 ], whilst the SATMED-Q was used to evaluate treatment satisfaction[ 27 ]. Data on adherence to treatment were collected at ten weeks and six months. HRQoL was measured using the EuroQol-5D-5L (Spanish version)[ 28 ], with this questionnaire being administered at the beginning of the study, after ten weeks and at the end of the study (six-month follow-up). Utility was estimated using the published tariff and QALYs were calculated according to the area under the curve. 2.4 Cost variables The costs of healthcare resource use and hospitalisations were taken from the Official Gazette of the Autonomous Community of Andalusia[ 29 – 32 ]. Intervention cost included cost related to provision of the service, cost of initial investment and annual maintenance, general expenses and marketing, training cost for each pharmacist and cost of the pharmacist’s time spent during the intervention. These values were taken from Valverde-Merino (2021)[ 33 ]. All unit costs were updated according to 2024 figures[ 34 ] (Annexes, Supplementary table 1 ). 2.5 Effectiveness analysis Descriptive analysis of the sample was performed according to intervention and control groups. Comparative analysis according to Chi 2 was performed for qualitative variables, whilst Mann-Whitney or t-test analyses were used for quantitative variables. Statistical analyses were performed with blinding. Whether it was relevant to perform further multilevel analysis (establishing six-month treatment completion rate as the dependent variable and including patients and pharmacies at level one and two, respectively) was decided based on intraclass correlation coefficient (ICC) outcomes. As the ICC was equal to zero, indicating no difference in treatment completion rate at six months between pharmacies), further multilevel analysis was deemed not to be necessary. In order to assess adherence as a function of treatment group, odd ratios were calculated alongside their 95% confidence intervals, with three models being constructed: Model 1 (null-model): simple logistic regression analysis to assess the raw effect of NMS on adherence. Model 2 (main-analysis): logistic regression analysis, adjusting for factors which, at the bivariate level, were expected to influence associations (age, sex, treatment indication, social support and baseline utility). Model 3 (missing data on outcomes): multiple imputation using chained equations calculated from model 2. Missing data were analysed in accordance with Faria (2014)[ 35 ]. Missing EQ-5D-5L baseline data were imputed using the group mean. Missing EQ-5D-5L index scores at ten weeks and six months were imputed using multiple imputation with chained equations under the missing at random assumption (base-case). Third, sensitivity analysis was performed to test the robustness of outcomes. A complete case analysis was performed only of patients who completed all follow-ups in order to represent all patients who initially agreed to participate. This approach is based on the assumption that missing data are omitted completely at random. Bivariate regression was performed ( sureg command in Stata) to calculate incremental cost and QALYs. QALYs were adjusted for baseline HRQoL to account for differences between groups at baseline. Rubin’s rules were used to pool outcomes from all imputed datasets[ 35 ]. Data were assumed to follow a bivariate normal distribution when estimating the probability that the intervention was cost-effective[ 36 ]. Analyses were performed using STATA/SEv16.1. The patients were analysed according to the groups to which they were originally assigned (intention-to-treat analysis). 2.6 Economic Analysis This analysis was carried out from the healthcare system perspective, with a six-month time horizon[ 24 ]. Outcomes results were expressed as cost-utility of NMS intervention vs normal practice (NP), which was calculated as an incremental cost-effectiveness ratio (ICER) = (NMS_cost – NP_cost) / (NMS_QALY – NP_QALY). Probabilities pertaining to different decision alternatives were obtained from gathered data. In order to assess the robustness of results, sensitivity analysis was performed removing all patients who did not return the HRQoL questionnaire or for whom cost data were missing. Additionally, both multiple imputation and complete case analysis were repeated after removing hospitalisations. 3. Results A total of 214 participants were recruited of which 105 (47%) were assigned to the control group and 119 (53%) to the intervention group. No significant baseline differences were found between groups (Table 1 ). Figure 1 (appendices) presents study flow via a Consort flow diagram. General satisfaction was rated by patients as being high with a score of 9.07 out of 10 (the program received a ten rating from 54% of participants) (Supplementary Table 2). With regards to SATMED-Q outcomes, statistically significant differences (p < 0.05) were found between the intervention and control group for medical follow-up of the patient’s condition and overall opinion of medication and patient health (appendices, Supplementary Table 3). Table 1 Sample baseline characteristics according to group (intervention or control) NMS (n = 119) N (%) NP (n = 105) N (%) p-value * Sex Male 57 (47.90) 48 (52.10) 0.744 Female 62 (52.10) 57 (47.90) Number of children 0 24 (45.45) 20 (54.55) 0.173 1–2 58 (60.42) 38 (39.58) 3–4 31 (44.93) 38 (55.07) > 4 6 (40) 9 (60) Social support Low (0–10 friends) 98 (51.04) 94 (48.96) 0.126 High (> 10 friends) 21 (65.63) 11 (34.48) Treatment indication Anticoagulant 11 (50) 11 (50) 0.697 Diabetes 22 (51.16) 21 (48.84) COPD 9 (42.86) 12 (57.14) Hypertension 77 (55.80) 61 (44.20) Intervention mean (SD) Control mean (SD) p-value † Age (years) 66.37 (1.22) 64.78 (1.44) 0.494 HRQoL 0.762 (0.017) 0.757 (0.022) 0.837 HRQoL (Visual Analogical Score) 69.08 (1.60) 67.06 (2.36) 0.902 * Pearson's chi 2 test; † Mann-Whitney U-test NMS: new medicine service; NP: normal practice; SD: standard deviation; HRQoL: health-related quality of life; COPD: chronic obstructive pulmonary disease; HRQoL: health-related quality of life. 3.1 Efficacy of the intervention regarding adherence At ten weeks and six months, outcomes revealed slightly better medication adherence in the intervention group, however, this outcome was not statistically significant (Table 2 ). Multivariate analysis was performed via logistic and linear regression, with no significant differences being found between intervention and control groups or as a function of whether case analysis or data imputation was performed (Table 3 , Supplementary Tables 4–5 in appendices). Table 2 Change in adherence from ten weeks to six months as a function of group Time Forgot to take medication Intervention N (%) Control N (%) p-value + 10 weeks No 51 (83.61) 38 (73.08) 0.173 a Yes 10 (16.39) 14 (26.92) 6 months No 32 (71.11) 16 (57.14) 0.221 a Yes 13 (28.89) 12 (42.86) p-value ++b 0.0522 0.2059 6 months* 1.78 c 0.106 d p-value ++e 0.989 0.896 Note: + p-value refers to proportional comparison of yes/no responses as a function of group at the same given time-point, ++ p-value refers to proportional change in yes/no responses from 10 weeks to 6 months within each group, a chi-2 test, b McNemar test, * multiple imputation model, c odds ratio, d logistic regression, e bootstrap estimation Table 3 Multivariate models for adherence at six months (n = 224) Logistic regression model for forgot to take medication OR (95% CI) p-value Model 1: Null model Normal practice New medicine service 1.052 (0.457–2.419) 0.905 Model 2: Main effects* Normal practice 0.699 New medicine service 1.188 (0.494–2.855) Model 3: missing outcome data† Normal practice New medicine service 1.880 (0.889–3.977) 0.098 Linear regression model for treatment completion rate β (95% CI) p-value Model 1: Null model Normal practice New medicine service 1.691 (-1.841; 5.224) 0.343 Model 2: Main effects* Normal practice 0.445 New medicine service 1.426 (-2.281; 5.134) Model 3: missing outcome data† Normal practice New medicine service 1.859 (-0.846; 4.564) 0.175 *: adjusted for age, baseline health-related quality of life, treatment indication, sex and social support; †: adjusted for age, baseline health-related quality of life, treatment indication and sex. 3.2 Missing data analysis The proportion of missing data differed between groups and across different time points (appendices, Supplementary Table 6). Supplementary table 7 (appendix) presents odds ratios produced from logistic regression models of factors associated with missing costs and QALYs pertaining to the intervention whilst adjusting for selected baseline covariates. Anticoagulant patients were found to be less likely to have missing data for both the EQ-5D and cost data. However, HRQoL measured at baseline, ten weeks and study end did not statistically differ between disease conditions (results not shown). Logistic regression also revealed that missing HRQoL at six months was associated with HRQoL at ten weeks. It was, therefore, assumed that data were MAR. 3.3 Economic analysis Average total cost per participant was higher in the control group than in the intervention group (€493 and €107, respectively; difference: €-285; p < 0.001, 95% confidence interval [95%CI]: €-386 to €-184). The cost of the intervention represented 26% of the total cost in the intervention group, however, this was partly offset by the lower use of healthcare services (e.g., fewer emergency hospital visits (ten in the control group vs. one in the intervention group) and fewer hospital admissions (four in the control group vs. none in the intervention group). Information on both resource use and costs in the intervention and control group is available in Supplementary Table 9. Unadjusted utility was slightly higher in the intervention group than in the control group at the end of the study (difference: 0.0275; p = 0.022, 95%CI: 0.0040–0.0510). However, between-group differences are also seen in baseline utility (intervention group = 0.7624 vs. control group = 0.7570), which, even when small, can distort QALYs calculations and, resultantly, impact cost-effectiveness outcomes. For this reason, baseline differences between groups were adjusted for[ 37 ] and missing data was imputed. This produced a difference in QALYs of 0.0269 (p = 0.007, 95%CI: 0.0075–0.0463). Cost-effectiveness outcomes favour the intervention programme in the sense that they indicate that the intervention improves quality of life and saves costs in comparison with typical care. Similar results were obtained from complete case analysis and when removing hospitalisations from the control group (Table 4 ). Table 4 Economic assessment of NMS versus normal practice at six months. Alternative Cost Incremental cost QALYs Incremental effectiveness† ICER ‡ Multiple imputation (base-case) Normal practice €393 0.29100 New medicine service €108 €-285 0.31805 0.0269 dominant § Complete case analysis Normal practice €537 0.3903 New medicine service €171 €-366 0.0900 0.0187 dominant § Multiple imputation (hospitalizations removed from the control group) Normal practice €342 0.2975 New medicine service €108 €-235 0.3243 0.0268 dominant § Complete case analysis (removing hospitalizations in the control group) Normal practice €377 0.3956 New medicine service €171 €-206 0.4141 0.0185 dominant § QALYs: quality adjusted life year; ICER: incremental cost-effectiveness ratio. †Incremental effectiveness: difference in effectiveness between treatment alternatives; ‡ICER: additional cost in euros per quality adjusted life year (QALYs); §dominant: the new medicine service is both clinically superior and cost saving. Total costs were imputed using multiple imputation. 4. Discussion An intervention is a dominant alternative with respect to another intervention if it is better and cheaper. The present study sought to examine whether the NMS intervention was dominant when compared with standard care by examining both effectiveness and cost-effectiveness. At six-month follow-up, the NMS intervention led to a 1.69% increase in the proportion of patients reporting adherence to their new medication (96.67% NMS vs 94.48% adherence in the group receiving standard treatment, p = 0.343). This outcome may be relevant, given that a prior cost-consequence analysis found that a sustained increase in adherence by as little as 1% was sufficient to make the intervention cost-effective for new statin users[ 15 ]. Turning attention towards satisfaction with medication (SATMED-Q) outcomes, generally speaking, it can be observed that the intervention group reported higher scores in response to a number of the aforementioned questionnaire items. This finding, in consideration of the nature and makeup of the NMS intervention, may be particularly important given that greater satisfaction could explain the greater adherence and lower impact of different events seen in the NMS group. Subsequently, this could improve the way in which medication is taken and its resultant impact on effectiveness and safety[ 38 , 39 ]. Outcomes of the economic assessment reveal that the NMS intervention is preferable to standard practice, given that it led to cost savings (€285) and a slight increase in QALY (0.0269). The difference observed in costs may be explained by the fact that more emergency hospital visits took place in the control group than in the intervention group (six vs. one), alongside the occurrence of four hospitalisations in the control group. At six months, with a spending availability being set at €20,000/QALY[ 40 ], the NMS intervention had a 99.99% likelihood of being effective compared with normal care. This was the case despite the fact that the reduced sample size could have had an impact on effect size. Further, removing hospitalised cases from the control group was also found to have little to no effect with the NMS retaining the same cost-effectiveness likelihood. The present work presents a number of limitations which must be considered when interpreting findings. Firstly, a greater than expected loss to follow-up was observed, unfortunately, it was not possible to gather data on the reason for non-completion. This impedes any potential assessment of attrition bias and missing data bias[ 20 , 41 ]. In order to minimise this potential bias, missing data were imputed via the technique known as multiple imputation. In this sense, it was observed that a high degree of heterogeneity exists in the proportion of missing cases seen from different participating community pharmacies. This finding highlights the need to perform a more in-depth examination and consider internal and external factors that might act as barriers to pharmacists proposing and discussing lifestyle factors and, subsequently, implementing community pharmacy interventions. Factors proposed by relevant literature in the field as potential barriers to the implementation of cognitive service include lack of communication between pharmacists and primary care physicians, management culture, inadequate training, lack of time, low confidence, lack of skills, absence of demand, and pharmacist belief that patients may react negatively to unwanted advice[ 14 , 18 , 38 , 42 – 45 ]. Anecdotal comments suggest that the mean reason was the lack of time for both patients and pharmacists. Finally, as a means of gathering data on study variables related with costs, patient-reported information was used, with this then providing the basis of the economic assessment of the intervention. This data collection method is broadly used in studies conducted with community pharmacies[ 33 ], given that patient medical records cannot be accessed. It is important to keep in mind that two types of measures were employed to gather data on adherence. Specifically, a direct measure was taken in accordance with reports of forgetting to take medication alongside an indirect measure which pertained to possession rate. A high agreement rate was found between the outcomes produced by both measures. One of the strengths of the present work is that it was performed in a real-world setting, in that it was conducted during the typical working day and formed an integral part of participating pharmacists’ tasks. In addition, community pharmacists were invited to participate which reflected, to a large extent, all of the settings in which healthcare duties are performed in pharmacies in Andalusia. Two relevant systematic reviews of systematic evaluations of interventions implemented by pharmaceutical services were retrieved by the present research team, which highlight that the number of published studies on this topic have clearly increased over the last decade[ 33 , 46 , 47 ]. The majority of published studies carried out in the context of community pharmacies are related with improving medication use, for instance, through the revision of medication use, provision of funded minor ailment services, and management and screening of long-term conditions, amongst other services. One of the papers included in this aforementioned review concluded that services provided in community and primary care settings were found to be either dominant or cost effective when setting a €20,000 per quality-adjusted life-year threshold[ 47 ]. Community pharmacists are often recognised as the most accessible part of the health system and the public’s first point of contact[ 48 ]. This places pharmacists in a privileged position from which they can play an important role in improving the health and wellbeing of the population, whilst also offering the potential for bringing about meaningful financial savings. However, present findings also shed light on the urgent need to encourage greater engagement of pharmacy staff when it comes to tackling issues related with patient medication. Declarations Funding: This study is funded by the Andalusian Council of Official Pharmaceutical Associations ( Consejo Andaluz de Colegios Oficiales de Farmacéuticos : CACOF). The Andalusian School of Public Health ( Escuela Andaluza de Salud Pública ), the institution responsible for the management and development of the project, received funding from CACOF who had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Trial registration number : ClinicalTrials.gov Identifier: NCT04195191 Date: 2020-02-05). Competing interests: The authors declare that no competing interests exist. Ethics approval: The study was approved by the Andalusian regional ethics committee (code 1202-N-18). Data availability : Data used and codes used in the development of this article is publicly available upon inquiry to the corresponding author and authorizations of the funder. Acknowledgements: The authors would like to thank Rosario Caceres Fernández-Bolaños and Emily Caitlin Lily Knox (PhD) for their assistance. Authorship : All authors have made substantial contributions to all of the following: (1) the conception and design of the study, data acquisition, data analysis and interpretation, (2) drafting of the article and critical review, and (3) final approval of the version to be submitted. References Sabaté E, Sabaté E. Adherence to long-term therapies: evidence for action. World Health Organization; 2003. George J, Elliott RA, Stewart DC. 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Development and Validation of the Treatment Satisfaction with Medicines Questionnaire(SATMED-Q)©. Value Health. 2008;11:913–26. Herdman M, Badia X, Berra S. El EuroQol-5D: una alternativa sencilla para la medición de la calidad de vida relacionada con la salud en atención primaria. Aten Primaria. 2001;28:425. Boletín Oficial de la Junta de Andalucía (BOJA) Orden de 18 de noviembre de 2015 [Internet]. Boja. 2015. Available from: https://www.juntadeandalucia.es/boja/buscador/ Boletín Oficial de la Junta de Andalucía (BOJA). Orden de 8 de mayo de 2018 [Internet]. 2018. Available from: http://www.juntadeandalucia.es/eboja Boletín Oficial de la Junta de Andalucía (BOJA). Orden de 8 de noviembre de 2016. [Internet]. 2016. Available from: http://www.juntadeandalucia.es/eboja Boletín Oficial de la Junta de Andalucía (BOJA). Orden de 18 de noviembre de 2015 [Internet]. 2015. Available from: https://www.juntadeandalucia.es/eboja.html Valverde-Merino M-I, Martinez-Martinez F, Garcia-Mochon L, Benrimoj SI, Malet-Larrea A, Perez-Escamilla B et al. Cost–Utility Analysis of a Medication Adherence Management Service Alongside a Cluster Randomized Control Trial in Community Pharmacy. Patient Prefer Adherence. 2021;2363–76. Instituto Nacional de Estadistica (INE). Update a personal income or spending with the overall CPI (CPI system base 2023) for complete annual periods. [cited 2024 May 15]; Available from: https://www.ine.es/calcula/index.do?L=1 Faria R, Gomes M, Epstein D, White IR. A guide to handling missing data in cost-effectiveness analysis conducted within randomised controlled trials. PharmacoEconomics. 2014;32:1157–70. O’Brien BJ, Briggs AH. Analysis of uncertainty in health care cost-effectiveness studies: an introduction to statistical issues and methods. Stat Methods Med Res. 2002;11:455–68. Manca A, Hawkins N, Sculpher MJ. Estimating mean QALYs in trial-based cost‐effectiveness analysis: the importance of controlling for baseline utility. Health Econ. 2005;14:487–96. Stewart D, Whittlesea C, Dhital R, Newbould L, McCambridge J. Community pharmacist led medication reviews in the UK: A scoping review of the medicines use review and the new medicine service literatures. Res Social Adm Pharm. 2020;16:111–22. Piwowar A, Czwojdziński E, Marchewka Z, Adamczuk A, Świątoniowska-Lonc N, Jankowska-Polańska B. Compliance and satisfaction with treatment as a success of therapy effectiveness in the group of patients with ischemic heart disease: a cross-sectional study. Int J Occup Med Environ Health. 2023;36:465. Vallejo-Torres L, García-Lorenzo B, Castilla I, Valcárcel Nazco C, García-Pérez L, Linertová R, et al. Valor Monetario de un Año de Vida Ajustado por Calidad: Estimación empírica del coste de oportunidad en el Sistema Nacional de Salud. Madrid: Ministerio de Sanidad SS e IgualdadSC de TSC de la S, editor.; 2015. Delgado-Rodríguez M, Llorca J, Bias. J Epidemiol Community Health. 2004;58:635–41. Al-Arkee S, Al-Ani O. Community pharmacist-led interventions to improve medication adherence in patients with cardiovascular disease: a systematic review of randomised controlled trials. Int J Pharm Pract. 2023;31:269–75. Hossain LN, Fernandez-Llimos F, Luckett T, Moullin JC, Durks D, Franco-Trigo L, et al. Qualitative meta-synthesis of barriers and facilitators that influence the implementation of community pharmacy services: perspectives of patients, nurses and general medical practitioners. BMJ Open. 2017;7:e015471. Westerholm A, Leiman K, Kiiski A, Pohjanoksa-Mäntylä M, Mistry A, Airaksinen M. Developing medication review competency in undergraduate pharmacy training: a self-assessment by third-year students. Int J Environ Res Public Health. 2023;20:5079. Croke A, Cardwell K, Clyne B, Moriarty F, McCullagh L, Smith SM. The effectiveness and cost of integrating pharmacists within general practice to optimize prescribing and health outcomes in primary care patients with polypharmacy: a systematic review. BMC Prim Care. 2023;24:41. Price E, Shirtcliffe A, Fisher T, Chadwick M, Marra CA. A systematic review of economic evaluations of pharmacist services. Int J Pharm Pract. 2023;31:459–71. Phimarn W, Saramunee K, Leelathanalerk A, Srimongkon P, Chanasopon S, Phumart P, et al. Economic evaluation of pharmacy services: a systematic review of the literature (2016–2020). Int J Clin Pharm. 2023;45:1326–48. Koster ES, Philbert D, Bouvy ML. Impact of the COVID-19 epidemic on the provision of pharmaceutical care in community pharmacies. Res Social Adm Pharm. 2021;17:2002–4. Additional Declarations No competing interests reported. Supplementary Files 1CUESTIONARIONIVELBASAL.pdf 2CUESTIONARIOMEDIDAINTERMEDIA.pdf 3CUESTIONARIOMEDIDAFINALv2.pdf Appendices.docx Cite Share Download PDF Status: Under Review Version 1 posted Reviews received at journal 18 May, 2026 Reviewers agreed at journal 11 May, 2026 Reviews received at journal 10 Sep, 2025 Reviewers agreed at journal 17 Jul, 2025 Reviewers invited by journal 17 Jul, 2025 Editor assigned by journal 09 Jul, 2025 Editor invited by journal 18 Jun, 2025 Submission checks completed at journal 17 Jun, 2025 First submitted to journal 17 Jun, 2025 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-6871385","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":486799791,"identity":"df0d6484-017f-4766-9ca8-792899b05572","order_by":0,"name":"María de los Ángeles González Vera","email":"","orcid":"","institution":"Programa de Doctorado en Farmacia Social de la Universidad de Granada","correspondingAuthor":false,"prefix":"","firstName":"María","middleName":"de los Ángeles González","lastName":"Vera","suffix":""},{"id":486799792,"identity":"aa7e6d4c-05fe-4003-a8cb-5d6e8b68b36f","order_by":1,"name":"Zuzana Špacírová","email":"data:image/png;base64,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","orcid":"","institution":"Escuela Andaluza de Salud Pública (Andalusian School of Public Health)","correspondingAuthor":true,"prefix":"","firstName":"Zuzana","middleName":"","lastName":"Špacírová","suffix":""},{"id":486799793,"identity":"a2e99b55-1390-4ff1-8ee8-146065d2affc","order_by":2,"name":"Antonio Olry-de-Labry-Lima","email":"","orcid":"","institution":"centro andaluz de información del medicamento (CADIME)","correspondingAuthor":false,"prefix":"","firstName":"Antonio","middleName":"","lastName":"Olry-de-Labry-Lima","suffix":""}],"badges":[],"createdAt":"2025-06-11 11:38:10","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6871385/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6871385/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":87382351,"identity":"a7e267e7-acec-46e5-8b7b-73c0a971fb6b","added_by":"auto","created_at":"2025-07-23 08:38:45","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":38377,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eConsort flow diagram\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFor interpretation of the flow diagram, n refers to the number of participants providing at least one response from which data was drawn.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-6871385/v1/69987729ed743f0cc18c8784.png"},{"id":87384106,"identity":"6d5159a7-66bc-4393-a74c-1938680da2ee","added_by":"auto","created_at":"2025-07-23 08:46:50","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":840378,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6871385/v1/e566bf4e-03d4-4a14-b6bc-c69c7c82f18a.pdf"},{"id":87382334,"identity":"5e89166a-02c2-410c-a592-a7f54ce911e1","added_by":"auto","created_at":"2025-07-23 08:38:45","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":902876,"visible":true,"origin":"","legend":"","description":"","filename":"1CUESTIONARIONIVELBASAL.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6871385/v1/beb68a169f788ef0fc6ff54b.pdf"},{"id":87382346,"identity":"ff1b70e0-c423-4f77-b52f-95af22487e05","added_by":"auto","created_at":"2025-07-23 08:38:45","extension":"pdf","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":1079049,"visible":true,"origin":"","legend":"","description":"","filename":"2CUESTIONARIOMEDIDAINTERMEDIA.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6871385/v1/91341fc46314706c43f9084e.pdf"},{"id":87382217,"identity":"6f71579b-fe18-449d-b7f7-b9effd498685","added_by":"auto","created_at":"2025-07-23 08:38:41","extension":"pdf","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":1122549,"visible":true,"origin":"","legend":"","description":"","filename":"3CUESTIONARIOMEDIDAFINALv2.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6871385/v1/b8981d6613362097b53fdf58.pdf"},{"id":87382299,"identity":"fdb4bd48-f9e7-498f-bb67-1b20b46164fb","added_by":"auto","created_at":"2025-07-23 08:38:43","extension":"docx","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":79099,"visible":true,"origin":"","legend":"","description":"","filename":"Appendices.docx","url":"https://assets-eu.researchsquare.com/files/rs-6871385/v1/1298735666468e2fc22b8419.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Effectiveness and cost-effectiveness of a pragmatic clinical trial of the New Medicine Service intervention in Spanish community pharmacies","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eLack of adherence to medication is an important public health issue that has implications for healthcare systems, due to its high impact on morbidity, mortality and healthcare costs. According to the World Health Organisation, it is estimated that 50% of patients do not adhere to treatment for chronic illnesses[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Reasons for this lack of adherence are complex and diverse. Some of the most frequently described factors in existing literature include adverse effects, lack of effectiveness, and the complexity of and lack of trust in the drug[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eGiven this landscape, community pharmacists could help improve system efficiency and address chronicity through cognitive services attached to pharmaceutical care[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Cognitive services are defined as patient-centred services delivered by pharmacists who call on specific knowledge in an attempt to improve procedures related with medication use or outcomes[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eThe new medicine service (NMS) is a cognitive service delivered via community pharmacies[\u003cspan additionalcitationids=\"CR8 CR9 CR10\" citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e] with the aim of increasing adherence amongst patients who are new to treatment. The NMS consists of counselling that is based on the communication between pharmacists and patients in order to provide patients with information and advice about any potential issues, concerns and false beliefs or expectations. Evidence demonstrates that NMS interventions are proven to be cost-effective[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e], well accepted by patients [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e] and effective at increasing treatment adherence[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan additionalcitationids=\"CR16 CR17\" citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eThe primary aim of the present study was to examine whether the NMS intervention improved medication adherence among patients with a new prescription for four diseases when compared with usual pharmacy care provision. Another aim was to estimate the cost-effectiveness of this intervention.\u003c/p\u003e"},{"header":"2. Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003e2.1 Study design\u003c/h2\u003e\u003cp\u003eThe present study is a pragmatic randomised clinical trial that was delivered at community-pharmacy level (clusters). The study population comprised 224 participants intentionally recruited by participating pharmaceutical staff coming from 23 Andalusian CPs which were randomly assigned to the control (n\u0026thinsp;=\u0026thinsp;12) or intervention group (n\u0026thinsp;=\u0026thinsp;11) (Response rate: 84%)[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. Randomisation was blinded performed using computer-generated random numbers (Epidat_software: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.sergas.es/Saude-publica/EPIDAT?idioma=es\u003c/span\u003e\u003cspan address=\"https://www.sergas.es/Saude-publica/EPIDAT?idioma=es\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). Patients in the intervention group received the pharmacist-patient communication-based NMS intervention. The control group received normal practice (NP) which consisted of usual advice and no follow-up[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. The modifications to the protocol can be seen in the Appendices [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eThe study adhered to 2022 Consolidated Health Economic Evaluation Reporting Standards (CHEERS)[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e], Consolidated Standards of Reporting Trials (CONSORT) criteria[\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e] (Appendices) and recommendations for economic evaluations in the health technology setting in Spain[\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eThe study was approved by the Andalusian regional ethics committee (code 1202-N-18). Following receipt of expression of interest from a potentially eligible patient about joining the scheme, they were asked a number of questions to confirm eligibility and were asked to provide written informed consent and the information sheet was given to each patient. In compliance with general data protection laws (\u003cem\u003eLey Org\u0026aacute;nica 3/2018\u003c/em\u003e), no information was gathered which would enable patient identification. The study was in compliance with the Helsinki Declaration\u003c/p\u003e\u003cp\u003eStudy participants were adult patients receiving a new prescription for hypertension, chronic obstructive pulmonary disease (COPD), diabetes, or an anticoagulant/antiplatelet agent. Patients suffering from a physical or mental disorder that prevented them from complying with the intervention and patients with seasonal or acute treatment were excluded.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\u003ch2\u003e2.2 Intervention\u003c/h2\u003e\u003cp\u003eThe aim of the intervention was to monitor and improve medication adherence in patients. It consisted of three steps. Firstly, step 1 focused on establishing verbal patient commitment. Next, step 2 focused on both assessing adherence to the new prescribed medication and identifying issues with the medication and potential strategies for minimising such issues. Finally, step 3 entailed a follow-up visit to monitor adherence and evaluate intervention outcomes[\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. Full details regarding project design and methodology are described elsewhere[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eIn order to ensure standardisation of intervention delivery, staff from participating pharmacies were given a programme (Axon-Pharma, available at: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://axonfarma.es/\u003c/span\u003e\u003cspan address=\"https://axonfarma.es/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) which provided guidance on the steps to follow and approach to recording information; furthermore, all patient\u0026acute;s information relevant to the study was here recorded. To support intervention delivery, participating pharmacists also had access to explanatory videos and other information and dissemination materials (leaflets, etc.) to give out to patients in order to improve their adherence.\u003c/p\u003e\u003cp\u003e Two training sessions were provided by researchers in order to introduce the project, outline its objectives, methods and target population, and encourage homogenisation and standardisation of the intervention (length, procedures, emphasis on the use of communication skills and empathy, amongst other aspects). Training was also provided to assist use of the Moodle-platform. This platform was used to help resolve any of the issues or incidents that might arise throughout the study process. It also contained a user guide and summary report including answers to frequently asked questions, whose utility was continually discussed so that it could be periodically updated as the intervention progressed. In addition, every six months a telephone meeting was held with researchers and pharmacist in order to address potential problems, monitor study progress and ensure adherence to the study protocol.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\u003ch2\u003e\u003cb\u003e2.3 Variables\u003c/b\u003e:\u003c/h2\u003e\u003cp\u003eThe primary dependent variable was \u0026ldquo;adherence to new prescription medication\u0026rdquo; which was estimated according to two items: 1) forgetting to take medication at six months (yes/no), and 2) treatment completion rate at six months (calculated as the n of medicines taken divided by the n of prescribed medicines multiplied by 100). Independent variables were categorised as a) sociodemographic, such as age, sex, number of children and social support, b) clinical, such as treatment indication, number of other treatments and total number of pills taken per day and, health-related quality of life (HRQoL) measured at baseline, ten weeks and six months, c) satisfaction with both service provision and medication, and d) healthcare resource use (primary care appointments, specialty care appointments, emergency provision in both primary and hospital care, hospital admissions, and NMS intervention) by each patient during the study period gathered directly from patients at the end of the study (annexs).\u003c/p\u003e\u003cp\u003eSocial support (high/low) was measured according to the single item measure conceived by Blake[\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e], whilst the SATMED-Q was used to evaluate treatment satisfaction[\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. Data on adherence to treatment were collected at ten weeks and six months. HRQoL was measured using the EuroQol-5D-5L (Spanish version)[\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e], with this questionnaire being administered at the beginning of the study, after ten weeks and at the end of the study (six-month follow-up). Utility was estimated using the published tariff and QALYs were calculated according to the area under the curve.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\u003ch2\u003e2.4 Cost variables\u003c/h2\u003e\u003cp\u003eThe costs of healthcare resource use and hospitalisations were taken from the Official Gazette of the Autonomous Community of Andalusia[\u003cspan additionalcitationids=\"CR30 CR31\" citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. Intervention cost included cost related to provision of the service, cost of initial investment and annual maintenance, general expenses and marketing, training cost for each pharmacist and cost of the pharmacist\u0026rsquo;s time spent during the intervention. These values were taken from Valverde-Merino (2021)[\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. All unit costs were updated according to 2024 figures[\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e] (Annexes, Supplementary table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\u003ch2\u003e2.5 Effectiveness analysis\u003c/h2\u003e\u003cp\u003eDescriptive analysis of the sample was performed according to intervention and control groups. Comparative analysis according to Chi\u003csup\u003e2\u003c/sup\u003e was performed for qualitative variables, whilst Mann-Whitney or t-test analyses were used for quantitative variables. Statistical analyses were performed with blinding. Whether it was relevant to perform further multilevel analysis (establishing six-month treatment completion rate as the dependent variable and including patients and pharmacies at level one and two, respectively) was decided based on intraclass correlation coefficient (ICC) outcomes. As the ICC was equal to zero, indicating no difference in treatment completion rate at six months between pharmacies), further multilevel analysis was deemed not to be necessary. In order to assess adherence as a function of treatment group, odd ratios were calculated alongside their 95% confidence intervals, with three models being constructed:\u003c/p\u003e\u003cp\u003e\u003col\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003eModel 1 (null-model): simple logistic regression analysis to assess the raw effect of NMS on adherence.\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003eModel 2 (main-analysis): logistic regression analysis, adjusting for factors which, at the bivariate level, were expected to influence associations (age, sex, treatment indication, social support and baseline utility).\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003eModel 3 (missing data on outcomes): multiple imputation using chained equations calculated from model 2.\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003c/ol\u003e\u003c/p\u003e\u003cp\u003eMissing data were analysed in accordance with Faria (2014)[\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. Missing EQ-5D-5L baseline data were imputed using the group mean. Missing EQ-5D-5L index scores at ten weeks and six months were imputed using multiple imputation with chained equations under the missing at random assumption (base-case). Third, sensitivity analysis was performed to test the robustness of outcomes. A complete case analysis was performed only of patients who completed all follow-ups in order to represent all patients who initially agreed to participate. This approach is based on the assumption that missing data are omitted completely at random.\u003c/p\u003e\u003cp\u003eBivariate regression was performed (\u003cem\u003esureg command\u003c/em\u003e in Stata) to calculate incremental cost and QALYs. QALYs were adjusted for baseline HRQoL to account for differences between groups at baseline. Rubin\u0026rsquo;s rules were used to pool outcomes from all imputed datasets[\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. Data were assumed to follow a bivariate normal distribution when estimating the probability that the intervention was cost-effective[\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. Analyses were performed using STATA/SEv16.1. The patients were analysed according to the groups to which they were originally assigned (intention-to-treat analysis).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003e2.6 Economic Analysis\u003c/h2\u003e\u003cp\u003eThis analysis was carried out from the healthcare system perspective, with a six-month time horizon[\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. Outcomes results were expressed as cost-utility of NMS intervention vs normal practice (NP), which was calculated as an incremental cost-effectiveness ratio (ICER) = (NMS_cost \u0026ndash; NP_cost) / (NMS_QALY \u0026ndash; NP_QALY). Probabilities pertaining to different decision alternatives were obtained from gathered data. In order to assess the robustness of results, sensitivity analysis was performed removing all patients who did not return the HRQoL questionnaire or for whom cost data were missing. Additionally, both multiple imputation and complete case analysis were repeated after removing hospitalisations.\u003c/p\u003e\u003c/div\u003e"},{"header":"3. Results","content":"\u003cp\u003eA total of 214 participants were recruited of which 105 (47%) were assigned to the control group and 119 (53%) to the intervention group. No significant baseline differences were found between groups (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Figure\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e (appendices) presents study flow via a Consort flow diagram. General satisfaction was rated by patients as being high with a score of 9.07 out of 10 (the program received a ten rating from 54% of participants) (Supplementary Table\u0026nbsp;2). With regards to SATMED-Q outcomes, statistically significant differences (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) were found between the intervention and control group for medical follow-up of the patient\u0026rsquo;s condition and overall opinion of medication and patient health (appendices, Supplementary Table\u0026nbsp;3).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eSample baseline characteristics according to group (intervention or control)\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eNMS (n\u0026thinsp;=\u0026thinsp;119)\u003c/p\u003e\u003cp\u003eN (%)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eNP (n\u0026thinsp;=\u0026thinsp;105)\u003c/p\u003e\u003cp\u003eN (%)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003ep-value \u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eSex\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e57 (47.90)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e48 (52.10)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e0.744\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eFemale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e62 (52.10)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e57 (47.90)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e\u003cp\u003eNumber of children\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e24 (45.45)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e20 (54.55)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\" morerows=\"3\" rowspan=\"4\"\u003e\u003cp\u003e0.173\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1\u0026ndash;2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e58 (60.42)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e38 (39.58)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3\u0026ndash;4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e31 (44.93)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e38 (55.07)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026gt;\u0026thinsp;4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e6 (40)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e9 (60)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eSocial support\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eLow (0\u0026ndash;10 friends)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e98 (51.04)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e94 (48.96)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e0.126\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eHigh (\u0026gt;\u0026thinsp;10 friends)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e21 (65.63)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e11 (34.48)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e\u003cp\u003eTreatment indication\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAnticoagulant\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e11 (50)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e11 (50)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\" morerows=\"3\" rowspan=\"4\"\u003e\u003cp\u003e0.697\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eDiabetes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e22 (51.16)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e21 (48.84)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCOPD\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e9 (42.86)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e12 (57.14)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eHypertension\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e77 (55.80)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e61 (44.20)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eIntervention\u003c/p\u003e\u003cp\u003emean (SD)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eControl\u003c/p\u003e\u003cp\u003emean (SD)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003ep-value\u003csup\u003e\u0026dagger;\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAge (years)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e66.37 (1.22)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e64.78 (1.44)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.494\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eHRQoL\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.762 (0.017)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.757 (0.022)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.837\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eHRQoL (Visual Analogical Score)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e69.08 (1.60)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e67.06 (2.36)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.902\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e\u003cp\u003e\u003csup\u003e*\u003c/sup\u003ePearson's chi\u003csup\u003e2\u003c/sup\u003e test; \u003csup\u003e\u0026dagger;\u003c/sup\u003eMann-Whitney U-test\u003c/p\u003e\u003cp\u003eNMS: new medicine service; NP: normal practice; SD: standard deviation; HRQoL: health-related quality of life; COPD: chronic obstructive pulmonary disease; HRQoL: health-related quality of life.\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\u003ch2\u003e3.1 Efficacy of the intervention regarding adherence\u003c/h2\u003e\u003cp\u003eAt ten weeks and six months, outcomes revealed slightly better medication adherence in the intervention group, however, this outcome was not statistically significant (Table \u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Multivariate analysis was performed via logistic and linear regression, with no significant differences being found between intervention and control groups or as a function of whether case analysis or data imputation was performed (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, Supplementary Tables\u0026nbsp;4\u0026ndash;5 in appendices).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eChange in adherence from ten weeks to six months as a function of group\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTime\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eForgot to take medication\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eIntervention\u003c/p\u003e\u003cp\u003eN (%)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eControl N (%)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003ep-value\u003csup\u003e+\u003c/sup\u003e\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e10 weeks\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e51 (83.61)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e38 (73.08)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e0.173\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e10 (16.39)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e14 (26.92)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e6 months\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e32 (71.11)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e16 (57.14)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e0.221\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e13 (28.89)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e12 (42.86)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003ep-value\u003csup\u003e++b\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.0522\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.2059\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e6 months*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u003cp\u003e1.78\u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.106\u003csup\u003ed\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003ep-value\u003csup\u003e++e\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.989\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.896\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"5\"\u003eNote: \u003csup\u003e+\u003c/sup\u003ep-value refers to proportional comparison of yes/no responses as a function of group at the same given time-point, \u003csup\u003e++\u003c/sup\u003ep-value refers to proportional change in yes/no responses from 10 weeks to 6 months within each group, \u003csup\u003ea\u003c/sup\u003echi-2 test, \u003csup\u003eb\u003c/sup\u003eMcNemar test, \u003csup\u003e*\u003c/sup\u003emultiple imputation model, \u003csup\u003ec\u003c/sup\u003eodds ratio, \u003csup\u003ed\u003c/sup\u003elogistic regression, \u003csup\u003ee\u003c/sup\u003ebootstrap estimation\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eMultivariate models for adherence at six months (n\u0026thinsp;=\u0026thinsp;224)\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"3\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLogistic regression model for forgot to take medication\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eOR (95% CI)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003ep-value\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e\u003cp\u003eModel 1: Null model\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNormal practice\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNew medicine service\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.052 (0.457\u0026ndash;2.419)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.905\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e\u003cp\u003eModel 2: Main effects*\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNormal practice\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e0.699\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNew medicine service\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.188 (0.494\u0026ndash;2.855)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e\u003cp\u003eModel 3: missing outcome data\u0026dagger;\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNormal practice\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNew medicine service\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.880 (0.889\u0026ndash;3.977)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.098\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLinear regression model for treatment completion rate\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eβ (95% CI)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003ep-value\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e\u003cp\u003eModel 1: Null model\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNormal practice\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNew medicine service\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.691 (-1.841; 5.224)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.343\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e\u003cp\u003eModel 2: Main effects*\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNormal practice\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e0.445\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNew medicine service\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.426 (-2.281; 5.134)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e\u003cp\u003eModel 3: missing outcome data\u0026dagger;\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNormal practice\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNew medicine service\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.859 (-0.846; 4.564)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.175\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"3\"\u003e*: adjusted for age, baseline health-related quality of life, treatment indication, sex and social support; \u0026dagger;: adjusted for age, baseline health-related quality of life, treatment indication and sex.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003e3.2 Missing data analysis\u003c/h2\u003e\u003cp\u003eThe proportion of missing data differed between groups and across different time points (appendices, Supplementary Table\u0026nbsp;6). Supplementary table 7 (appendix) presents odds ratios produced from logistic regression models of factors associated with missing costs and QALYs pertaining to the intervention whilst adjusting for selected baseline covariates. Anticoagulant patients were found to be less likely to have missing data for both the EQ-5D and cost data. However, HRQoL measured at baseline, ten weeks and study end did not statistically differ between disease conditions (results not shown). Logistic regression also revealed that missing HRQoL at six months was associated with HRQoL at ten weeks. It was, therefore, assumed that data were MAR.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\u003ch2\u003e3.3 Economic analysis\u003c/h2\u003e\u003cp\u003eAverage total cost per participant was higher in the control group than in the intervention group (\u0026euro;493 and \u0026euro;107, respectively; difference: \u0026euro;-285; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001, 95% confidence interval [95%CI]: \u0026euro;-386 to \u0026euro;-184). The cost of the intervention represented 26% of the total cost in the intervention group, however, this was partly offset by the lower use of healthcare services (e.g., fewer emergency hospital visits (ten in the control group vs. one in the intervention group) and fewer hospital admissions (four in the control group vs. none in the intervention group). Information on both resource use and costs in the intervention and control group is available in Supplementary Table\u0026nbsp;9.\u003c/p\u003e\u003cp\u003eUnadjusted utility was slightly higher in the intervention group than in the control group at the end of the study (difference: 0.0275; p\u0026thinsp;=\u0026thinsp;0.022, 95%CI: 0.0040\u0026ndash;0.0510). However, between-group differences are also seen in baseline utility (intervention group\u0026thinsp;=\u0026thinsp;0.7624 vs. control group\u0026thinsp;=\u0026thinsp;0.7570), which, even when small, can distort QALYs calculations and, resultantly, impact cost-effectiveness outcomes. For this reason, baseline differences between groups were adjusted for[\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e] and missing data was imputed. This produced a difference in QALYs of 0.0269 (p\u0026thinsp;=\u0026thinsp;0.007, 95%CI: 0.0075\u0026ndash;0.0463). Cost-effectiveness outcomes favour the intervention programme in the sense that they indicate that the intervention improves quality of life and saves costs in comparison with typical care. Similar results were obtained from complete case analysis and when removing hospitalisations from the control group (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eEconomic assessment of NMS versus normal practice at six months.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"6\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAlternative\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCost\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eIncremental cost\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eQALYs\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eIncremental effectiveness\u0026dagger;\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eICER\u003csup\u003e\u0026Dagger;\u003c/sup\u003e\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"6\" nameend=\"c6\" namest=\"c1\"\u003e\u003cp\u003eMultiple imputation (base-case)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNormal practice\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026euro;393\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.29100\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNew medicine service\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026euro;108\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026euro;-285\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.31805\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.0269\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003edominant\u003csup\u003e\u0026sect;\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"6\" nameend=\"c6\" namest=\"c1\"\u003e\u003cp\u003eComplete case analysis\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNormal practice\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026euro;537\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.3903\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNew medicine service\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026euro;171\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026euro;-366\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.0900\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.0187\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003edominant\u003csup\u003e\u0026sect;\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"6\" nameend=\"c6\" namest=\"c1\"\u003e\u003cp\u003eMultiple imputation (hospitalizations removed from the control group)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNormal practice\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026euro;342\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.2975\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNew medicine service\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026euro;108\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026euro;-235\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.3243\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.0268\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003edominant\u003csup\u003e\u0026sect;\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"6\" nameend=\"c6\" namest=\"c1\"\u003e\u003cp\u003eComplete case analysis (removing hospitalizations in the control group)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNormal practice\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026euro;377\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.3956\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNew medicine service\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026euro;171\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026euro;-206\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.4141\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.0185\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003edominant\u003csup\u003e\u0026sect;\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"6\"\u003eQALYs: quality adjusted life year; ICER: incremental cost-effectiveness ratio.\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd colspan=\"6\"\u003e\u0026dagger;Incremental effectiveness: difference in effectiveness between treatment alternatives; \u0026Dagger;ICER: additional cost in euros per quality adjusted life year (QALYs); \u0026sect;dominant: the new medicine service is both clinically superior and cost saving.\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd colspan=\"6\"\u003eTotal costs were imputed using multiple imputation.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003e\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003eAn intervention is a dominant alternative with respect to another intervention if it is better and cheaper. The present study sought to examine whether the NMS intervention was dominant when compared with standard care by examining both effectiveness and cost-effectiveness. At six-month follow-up, the NMS intervention led to a 1.69% increase in the proportion of patients reporting adherence to their new medication (96.67% NMS vs 94.48% adherence in the group receiving standard treatment, p\u0026thinsp;=\u0026thinsp;0.343). This outcome may be relevant, given that a prior cost-consequence analysis found that a sustained increase in adherence by as little as 1% was sufficient to make the intervention cost-effective for new statin users[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eTurning attention towards satisfaction with medication (SATMED-Q) outcomes, generally speaking, it can be observed that the intervention group reported higher scores in response to a number of the aforementioned questionnaire items. This finding, in consideration of the nature and makeup of the NMS intervention, may be particularly important given that greater satisfaction could explain the greater adherence and lower impact of different events seen in the NMS group. Subsequently, this could improve the way in which medication is taken and its resultant impact on effectiveness and safety[\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eOutcomes of the economic assessment reveal that the NMS intervention is preferable to standard practice, given that it led to cost savings (\u0026euro;285) and a slight increase in QALY (0.0269). The difference observed in costs may be explained by the fact that more emergency hospital visits took place in the control group than in the intervention group (six vs. one), alongside the occurrence of four hospitalisations in the control group. At six months, with a spending availability being set at \u0026euro;20,000/QALY[\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e], the NMS intervention had a 99.99% likelihood of being effective compared with normal care. This was the case despite the fact that the reduced sample size could have had an impact on effect size. Further, removing hospitalised cases from the control group was also found to have little to no effect with the NMS retaining the same cost-effectiveness likelihood.\u003c/p\u003e\u003cp\u003eThe present work presents a number of limitations which must be considered when interpreting findings. Firstly, a greater than expected loss to follow-up was observed, unfortunately, it was not possible to gather data on the reason for non-completion. This impedes any potential assessment of attrition bias and missing data bias[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]. In order to minimise this potential bias, missing data were imputed via the technique known as multiple imputation. In this sense, it was observed that a high degree of heterogeneity exists in the proportion of missing cases seen from different participating community pharmacies. This finding highlights the need to perform a more in-depth examination and consider internal and external factors that might act as barriers to pharmacists proposing and discussing lifestyle factors and, subsequently, implementing community pharmacy interventions. Factors proposed by relevant literature in the field as potential barriers to the implementation of cognitive service include lack of communication between pharmacists and primary care physicians, management culture, inadequate training, lack of time, low confidence, lack of skills, absence of demand, and pharmacist belief that patients may react negatively to unwanted advice[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e, \u003cspan additionalcitationids=\"CR43 CR44\" citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e]. Anecdotal comments suggest that the mean reason was the lack of time for both patients and pharmacists. Finally, as a means of gathering data on study variables related with costs, patient-reported information was used, with this then providing the basis of the economic assessment of the intervention. This data collection method is broadly used in studies conducted with community pharmacies[\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e], given that patient medical records cannot be accessed. It is important to keep in mind that two types of measures were employed to gather data on adherence. Specifically, a direct measure was taken in accordance with reports of forgetting to take medication alongside an indirect measure which pertained to possession rate. A high agreement rate was found between the outcomes produced by both measures.\u003c/p\u003e\u003cp\u003eOne of the strengths of the present work is that it was performed in a real-world setting, in that it was conducted during the typical working day and formed an integral part of participating pharmacists\u0026rsquo; tasks. In addition, community pharmacists were invited to participate which reflected, to a large extent, all of the settings in which healthcare duties are performed in pharmacies in Andalusia.\u003c/p\u003e\u003cp\u003eTwo relevant systematic reviews of systematic evaluations of interventions implemented by pharmaceutical services were retrieved by the present research team, which highlight that the number of published studies on this topic have clearly increased over the last decade[\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e, \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e, \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e]. The majority of published studies carried out in the context of community pharmacies are related with improving medication use, for instance, through the revision of medication use, provision of funded minor ailment services, and management and screening of long-term conditions, amongst other services. One of the papers included in this aforementioned review concluded that services provided in community and primary care settings were found to be either dominant or cost effective when setting a \u0026euro;20,000 per quality-adjusted life-year threshold[\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e].\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eCommunity pharmacists are often recognised as the most accessible part of the health system and the public\u0026rsquo;s first point of contact[\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e]. This places pharmacists in a privileged position from which they can play an important role in improving the health and wellbeing of the population, whilst also offering the potential for bringing about meaningful financial savings. However, present findings also shed light on the urgent need to encourage greater engagement of pharmacy staff when it comes to tackling issues related with patient medication.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eFunding:\u003c/strong\u003e This study is funded by the Andalusian Council of Official Pharmaceutical Associations (\u003cem\u003eConsejo Andaluz de Colegios Oficiales de Farmac\u0026eacute;uticos\u003c/em\u003e: CACOF). The Andalusian School of Public Health (\u003cem\u003eEscuela Andaluza de Salud P\u0026uacute;blica\u003c/em\u003e), the institution responsible for the management and development of the project, received funding from CACOF who had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTrial registration number\u003c/strong\u003e: ClinicalTrials.gov Identifier: NCT04195191 Date: 2020-02-05).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests:\u0026nbsp;\u003c/strong\u003eThe authors declare that no competing interests exist.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval:\u0026nbsp;\u003c/strong\u003eThe study was approved by the Andalusian regional ethics committee (code 1202-N-18).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e: Data used and codes used in the development of this article is publicly available upon inquiry to the corresponding author and authorizations of the funder.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements:\u0026nbsp;\u003c/strong\u003eThe authors would like to thank Rosario Caceres Fern\u0026aacute;ndez-Bola\u0026ntilde;os and Emily Caitlin Lily Knox (PhD) for their assistance.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthorship\u003c/strong\u003e: All authors have made substantial contributions to all of the following: (1) the conception and design of the study, data acquisition, data analysis and interpretation, (2) drafting of the article and critical review, and (3) final approval of the version to be submitted.\u0026nbsp;\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eSabat\u0026eacute; E, Sabat\u0026eacute; E. 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The effectiveness and cost of integrating pharmacists within general practice to optimize prescribing and health outcomes in primary care patients with polypharmacy: a systematic review. BMC Prim Care. 2023;24:41.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003ePrice E, Shirtcliffe A, Fisher T, Chadwick M, Marra CA. A systematic review of economic evaluations of pharmacist services. Int J Pharm Pract. 2023;31:459\u0026ndash;71.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003ePhimarn W, Saramunee K, Leelathanalerk A, Srimongkon P, Chanasopon S, Phumart P, et al. Economic evaluation of pharmacy services: a systematic review of the literature (2016\u0026ndash;2020). Int J Clin Pharm. 2023;45:1326\u0026ndash;48.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKoster ES, Philbert D, Bouvy ML. Impact of the COVID-19 epidemic on the provision of pharmaceutical care in community pharmacies. Res Social Adm Pharm. 2021;17:2002\u0026ndash;4.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"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":"bmc-health-services-research","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bhsr","sideBox":"Learn more about [BMC Health Services Research](http://bmchealthservres.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/BHSR/default.aspx","title":"BMC Health Services Research","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-6871385/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6871385/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNon-adherence is associated with reduced quality of life, poorer outcomes, increased hospitalisations and deaths, and, consequently, higher healthcare costs. Community pharmacies are shown to be key elements in improving adherence to prescribed medications, optimizing patient outcomes and increasing care efficiency.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eObjective\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eExamine whether the New Medicine Service intervention improved medication adherence among patients with new prescriptions for four diseases, compared with usual pharmacy care. Another aim was to estimate the cost-effectiveness of this intervention.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003epragmatic cluster randomised clinical trial at community-pharmacy level in 23 community pharmacies (12 in control and 11 intervention arm) in Andalusia (Spain). Patients were invited to join the study who were identified as starting treatment for chronic obstructive pulmonary disease, hypertension or diabetes mellitus, or were on an anticoagulant/antiplatelet agent. The NMS intervention consisted of three steps: 1) establishing the commitment to the patient; 2) assessing adherence to the new medication and identifying problems with the medication and coming up with strategies to minimize such problems; 3) conducting follow-up visits to check adherence and evaluate intervention effects.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAt ten weeks and six months, outcomes showed slightly better medication adherence resulting from the intervention, however, this outcome was not statistically significant. At six months follow-up, reduced costs were exhibited in the intervention group relative to the control group, whilst the intervention group demonstrated greater utility. All of the above reveals the NMS intervention to be a dominant strategy when compared with typical treatment.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe NMS intervention that is based on communication between patients and pharmacists managed to achieve slight improvements in adherence to new prescriptions in patients with one of four pathologies. Cost-effectiveness analysis supports further use of the examined intervention, as it improves quality of life and reduces costs in comparison with typical practice\u003c/p\u003e\n\u003cp\u003eClinicalTrials.gov Identifier: NCT04195191 (Date: 2020-02-05).\u003c/p\u003e","manuscriptTitle":"Effectiveness and cost-effectiveness of a pragmatic clinical trial of the New Medicine Service intervention in Spanish community pharmacies","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-07-23 08:38:10","doi":"10.21203/rs.3.rs-6871385/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"editorInvitedReview","content":"","date":"2026-05-18T18:26:40+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"211975739937625931178996889460608507855","date":"2026-05-11T15:06:30+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-09-10T16:02:54+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"128206362582881140412674584904975346777","date":"2025-07-17T09:25:17+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-07-17T04:17:49+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-07-09T07:21:33+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-06-18T11:33:58+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-06-17T12:11:33+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Health Services Research","date":"2025-06-17T12:06:48+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-health-services-research","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bhsr","sideBox":"Learn more about [BMC Health Services Research](http://bmchealthservres.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/BHSR/default.aspx","title":"BMC Health Services Research","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"b2a6d643-4e1d-4792-9f65-0ea9872fa882","owner":[],"postedDate":"July 23rd, 2025","published":true,"recentEditorialEvents":[{"type":"editorInvitedReview","content":"","date":"2026-05-18T18:26:40+00:00","index":104,"fulltext":""},{"type":"reviewerAgreed","content":"211975739937625931178996889460608507855","date":"2026-05-11T15:06:30+00:00","index":101,"fulltext":""}],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2025-07-23T08:38:10+00:00","versionOfRecord":[],"versionCreatedAt":"2025-07-23 08:38:10","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6871385","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6871385","identity":"rs-6871385","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}