Assessing the cost-effectiveness of Smartinhaler technology in primary care: a health economic decision Model in school-Aged children (SMART-MOD)

Study type

• Study topic:
  Disease /health condition
• Study type:
  Non-interventional study
• Scope of the study:
  Effectiveness study (incl. comparative)
• Data collection methods:
  Secondary use of data

Non-interventional study

• Non-interventional study design:
  Other
• Non-interventional study design, other:
  Health economic evaluation (based on secondary data)

Study drug and medical condition

• Medical condition to be studied:
  Asthma

Population studied

• Short description of the study population:
  School-aged children (5 to 16 years) with a clinical diagnosis of asthma who have been prescribed inhaled corticosteroids (ICS) as maintenance therapy. The study population is derived from routinely collected UK primary care electronic health records.
• Age groups:
  Children (2 to < 12 years)

  Adolescents (12 to < 18 years)
• Estimated number of subjects:
  112949

Study design details

• Study design:
  Study Design: Retrospective secondary analysis using cohort of school-aged children with ; asthma aged 5-16 from 2022-2024.
• Main study objective:
  Research Question; What interventions are effective, and cost-effective towards achieving optimal adherence to inhaled medication in school-aged children with asthma?; ; Aim; To evaluate the effectiveness, and cost-effectiveness of interventions aimed at achieving optimal ; adherence to inhaled medication among school-aged children with asthma.; ; Objectives; i) To describe and characterise the extent of medication adherence among school-aged children with ; asthma.; ii) To extract and define unscheduled medical contacts from NHS primary healthcare records; iii) To analyse the relationship between asthma medication adherence and unscheduled medical ; contacts by: assessing (1) the association between adherence levels and the frequency of ; unscheduled medical contacts, (2) the causal impact of adherence on reducing asthma-related ; exacerbations and healthcare utilisation, and (3) the influence of seasonal variations on the ; adherence-outcome relationship.; iv) To develop a health economic decision model to represent the relationship between asthma ; medication adherence and unscheduled medical contacts.; v) To simulate the effects of one or more interventions that improve medication adherence using ; this decision model, and identify those that are cost-effective towards achieving optimal levels
• Setting:
  The study is a secondary analysis of longitudinal United Kingdom primary care data collected between 2022 and 2024, using routinely recorded electronic health records that include information on prescriptions, clinical diagnoses, and healthcare use.; ; The study population includes school-aged children aged 5 to 16 years with a coded diagnosis of asthma who have received at least one prescription for inhaled corticosteroids as maintenance therapy during the study period. Children are included if they have sufficient follow-up data to calculate medication adherence and to identify unscheduled medical contacts.; ; Medication adherence is measured quarterly using the medication possession ratio and categorised into three levels:; ; Optimal adherence: 80% or higher; ; Intermediate adherence: 50 to 79%; ; Poor adherence: below 50%; ; Patients are stratified by adherence level to examine differences in outcomes. Model-based comparisons are conducted between usual care and intervention scenarios, such as the implementation of digital adherence technologies like Smartinhalers. These scenarios are evaluated within a individual-level simulation model to estimate the clinical and economic impact of improved adherence.; ; The model includes the following covariates:; ; Baseline covariates: age, sex, socioeconomic status, baseline asthma severity; Time-varying covariates: medication adherence as Medicines Possessions Ratio, asthma control, asthma symptom severity, previous unscheduled medical contacts, healthcare use
• Comparators:
  The study uses an individual-level simulation model to compare the clinical and economic outcomes of different adherence strategies in school-aged children with asthma.; ; The model will compare:; Standard care, where children are prescribed inhaled corticosteroids without additional adherence support; ; Digital adherence interventions, such as Smartinhaler technology, which monitor inhaler use and aim to improve adherence through feedback or reminders; ; Comparisons will also be made across adherence levels defined by quarterly medication possession ratio:; ; Optimal adherence (80% or higher); ; Intermediate adherence (50 to 79%); ; Poor adherence (below 50%); ; The simulation will estimate the impact of these comparators on unscheduled medical contacts and routine asthma management costs over time. The analysis will assess which strategies are cost-effective from the perspective of the UK National Health Service and Personal Social Services.
• Outcomes:
  The adherence outcome variable will be defined by the Medicines Possession Ratio (MPR). MPR is ; calculated as the ratio of the number of days' supply of medication obtained by a patient over a ; specific period, to the total number of days in that period. The total days covered will be sourced through the summation of asthma medication prescriptions within the dosage table of the patient’s EHR. It is anticipated that prescriptions for first-line, low dose ICS maintenance therapy in children, such as 100-200 mcg/daily of Beclomethasone dipropionate and Budesonide, will be used to calculate the MPR initially.; The dependent variable of this analysis are unscheduled medical contacts. Within the NHS, ; unscheduled medical contacts are those that occur without a pre-arranged appointment. These ; types of contacts cater to immediate or urgent healthcare needs. ; ; Annual Unscheduled Care Frequency; The unscheduled medical contacts that form this variable include asthma-related Emergency ; Department (A&E), inpatient and outpatient attendances including urgent care centres, walk-in ; centres, out-of-hours GP services, and home visits by GPs. The total frequency of unscheduled ; medical contacts will be calculated by totalling each individual event for each patient over the year, thereby providing a single measure of the frequency of unscheduled care utilisation for each patient. ;
• Data analysis plan:
  A longitudinal person-period dataset will be constructed using quarterly intervals, capturing changes in adherence and healthcare use over time. Medication adherence will be measured using the Medication Possession Ratio and categorised into good, intermediate, or poor. These adherence profiles will be tracked across time and linked to unscheduled medical contacts and healthcare costs.; ; To account for time-varying confounding, inverse probability of treatment weighting (IPTW) will be used to estimate causal effects. Stabilised weights will be applied at each time point to create a pseudo-population where adherence is independent of prior covariates. Weighted regression models will estimate the average treatment effect of adherence level on both unscheduled contact rates and healthcare costs, providing a measure of relative and absolute risk over time.; ; These results will be used as inputs in an individual-level simulation model. The model will project patient-level outcomes over time and simulate the effects of different adherence scenarios, including standard care and adherence-enhancing interventions such as Smartinhalers. Comparative analysis will estimate the incremental costs and outcomes of each strategy.; ; Internal validity will be strengthened through the use of IPTW and sensitivity analyses testing alternative assumptions. While external validity is limited to the UK primary care context, real-world data enhances generalisability. Cost-effectiveness will be assessed from the NHS and Personal Social Services perspective using standard decision modelling techniques.
• Summary results:
  The model will produce estimates of the impact of adherence-improving interventions on unscheduled medical contacts and healthcare costs in children with asthma. Results will include comparative outcomes between standard care and Smartinhaler-based intervention scenarios. Primary results will include incremental costs, reductions in unscheduled contacts, and incremental cost-effectiveness ratios (ICERs) from the UK NHS and Personal Social Services perspective. ; Where applicable, quality-adjusted life years (QALYs) and cost-effectiveness acceptability curves will also be reported to support decision-making.