Predictors of cost-effectiveness of selected COPD treatments in primary care: UNLOCK study protocol

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Predictors of cost-effectiveness of selected COPD treatments in primary care: UNLOCK study protocol

Job FM van Boven1, Miguel Romn-Rodrguez2, Janwillem WH Kocks3, Joan B Soriano4, Maarten J Postma1,5 and Thys van der Molen3

npj Primary Care Respiratory Medicine (2015) 25, 15051; doi:http://dx.doi.org/10.1038/npjpcrm.2015.51

Web End =10.1038/npjpcrm.2015.51 ; published online 6 August 2015

BACKGROUNDChronic obstructive pulmonary disease (COPD) puts a high burden on patients and governmental health-care budgets.1,2 General practitioners (GPs) have a pivotal role in the treatment of COPD patients in primary care. However, the strategies of treatment may differ considerably between individual GPs, resulting in large intra-individual differences in health-care utilisation and quality of life of their patients.3,4 Recently, the Spanish AUDIPOC study showed great variability in hospital treatment patterns and patients outcomes.5 Moreover, the European COPD audit indicated marked differences in resources available across different hospitals in Europe.6 In Spain though, it is estimated that at least 61% of COPD patients are only treated in primary care,7 with an average of 6.6 visits per year. The estimated prevalence of COPD in the Balearic Islands is 12.8%.8 Regarding health-care costs for respiratory patients, several cost drivers, mostly related to patient characteristics, have been identied in previous studies including associated comorbidities (e.g., heart disease), forced expiratory volume in 1 s (FEV1), the physical component of quality of life, 6-min walking distance, increased dyspnoea, number of medical visits and hospitalisations.911 Although one study identied an effect of the individual physician on health-care costs,12 treatment strategies were never incorporated as a predicting variable for costs or outcomes. Besides inter-physician differences in treatments, country-specic regulations and difference in the extent of adherence to clinical guidelines may affect the cost-effectiveness of treating COPD patients in primary care settings.13 It was shown

that adherence to COPD treatment guidelines is suboptimal.14

Moreover, non-adherence to guidelines was associated with higher total health-care costs.15 In particular, in times of increasing health-care costs and scarcer resources, there is a need to identify the cost-effectiveness of different treatment strategies for COPD patients across various primary care settings. The UNLOCK project of the International Primary Care Respiratory Group (IPCRG) offers a promising possibility.16

AIMSThe primary aim of this study is to assess what makes one COPD treatment strategy more cost-effective than others, by taking into account factors related to patients, the physician, and specic follow up and treatment approaches. A secondary objective is to assess whether real-world cost-effectiveness of treatments is comparable between Spain and other countries that have comparable data sets available.

METHODS Study design

This is a cost-effectiveness analysis that is performed with a real-world database on respiratory patients.

SettingThis study comprises two phases, with the rst phase including all primary care centres in the Balearic Islands, Spain. In a second phase of the study, primary care centres from other parts of the world will be included.

Data sourceAll the data will be extracted from the MAJOrca Real-world Investigation in

COPD and Asthma database (MAJORICA). The MAJORICA database contains combined data from the primary care system (e-SIAP), the hospital claims system (FIC), and the pharmacy database (RELE) in the Balearics, Spain. Together, these databases cover all health-care utilisation of the permanent inhabitants of the Balearics (1.1 million subjects). In the Balearics, there are about 400 different GPs, and most of the COPD patients are treated by one of these GPs. The MAJORICA database contains data from all patients aged 18 years with a primary care diagnosis of asthma and/or COPD in 2012, regardless of health insurance. All demographics, clinical data, diagnostic tests, as well as resource use, pharmacy dispense data, work absence and patient-reported outcomes from almost 70,000 respiratory patients are available for the period 20112014. A specication of the database is provided in Table 1. The database characteristics were reported according to the checklists of the IPCRG16 and the Respiratory Effectiveness Group (http://www.effectivenessevaluation.org). The unique island setting of the Balearics allows us to provide an almost complete picture of the real-world health-care use of COPD patients.

Inclusion criteriaAll patients (18 years) with a clinical diagnosis of COPD (ICD-9 codes: 491, 492, 496 and/or primary codes R79, R95) in 2012, available in the

MAJORICA database, were included. In addition, patients needed to be a permanent resident of the Balearic Islands and to be alive in 2014.

Health-care resource utilisationHealth-care resource use in 2013 and 2014 will be calculated for all the

COPD patients identied in 2012. Health-care resource use that will be included in the study refers to the following: GP visits, primary care nurses visits, emergency department (ED) visits, specialist visits, specialist nurse visits, hospitalisations, medication and diagnostic tests (that is, spirometry, CT-scans, X-rays, bronchoscopy). To estimate indirect costs, data on work absence will be extracted. These data will be extracted from the e-SIAP system, as work absence in Spain is registered by GPs.

1Unit of PharmacoEpidemiology and PharmacoEconomics (PE2), Department of Pharmacy, University of Groningen, Groningen, The Netherlands; 2Institituto de Investigacon Sanitaria de Palma (IdISPa), Palma de Mallorca, Spain; 3Department of General Practice, University Medical Centre Groningen, University of Groningen, Groningen, The Netherlands; 4Instituto de Investigacin Hospital Universitario de la Princesa (IISP), Universidad Autnonoma de Madrid, Ctedra UAM-Linde, Madrid, Spain and 5Institute of Science in Healthy Aging and HealthcaRE (SHARE), University Medical Centre Groningen, University of Groningen, Groningen, The Netherlands.

Correspondence: M Romn-Rodrguez (mailto:[email protected]

Web End [email protected])

Received 27 March 2015; revised 25 May 2015; accepted 10 June 2015

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Table 1. Specication of the MAJORICA database

Variable Specication

Type of databaseElectronic Medical Record Yes Claims Yes

Country/countries of data origin Balearics, Spain

Number of patients 68,578 Patients with asthma diagnosis (ICD-9: 493) 45,800 Patients with COPD diagnosis (ICD-9: 491, 492, 496) 27,871 Patients with asthma and COPD diagnosis (ICD-9:493 and (491. 492, 496))

5,093

Data collection (period) 20112014 Unique identier/anonymisation Yes Ethical approval Yes

Coding system diseasesICD-9, ICD-10, read ICD-9

Patient demographicsGender Yes Age Yes BMI Yes

Physician demographicsGender Yes Age Yes Setting (urban/rural) Yes

DrugsCoding ATC-7 Prescribed, dispensed, both Dispensed Drugs available All R03 Dose/dosing Yes Device No OTC medications No Inhaler technique No

VaccinationsInuenza, Pneumococcal Yes

OutcomesExacerbations

Steroids Yes Antibiotics Yes SABA Yes Exacerbations (ICD-9 code) Yes Health resource utilisation

Primary care consultations Yes Secondary care consultations Yes Consultations coded by disease Yes Consultations coded by routine/emergency Yes Hospitalisations Yes Hospitalisations coded by disease Yes Hospitalisation duration Yes Emergency room Yes ICU Yes ICU coded by disease Yes ICU duration Yes Rehabilitation No Physiotherapy No Patient-reportedmMRC Yes Asthma (ACQ, ACT) ACT score COPD (CCQ, CAT) CAT score Side effects

Pneumonias Yes Work absence

All cause Yes Respiratory specic Yes

Table 1. (Continued )

Variable Specication

CovariatesComorbidities

Diabetes Yes Cardiovascular diseasesHypertension Yes Cardiac insufciencyAtrial brillationCor pulmonaleAllergic rhinitis Yes Cerebrovascular disease Yes Osteoporosis Yes Sleep apnoea Yes Nasal polyps No Depression/anxiety Yes Reux (GERD) Yes Chronic kidney disease Yes Lung Cancer Yes AIDS/HIV Yes Cognitive dysfunction No Risk score

Cardiovascular risk score Yes Lifestyle

Smoking status Yes Smoking years Yes Socioeconomic status

Post code No Education level No Employment status Yes Salary range No Spirometry

FEV1/FVC, FEV1%pred, reversibility Yes Laboratory tests

Full blood count, FeNO, IgE and so on No Imaging

CRX Performed Y/N HRCT Performed Y/N

Abbreviations: ACT, asthma control test; ATC, anatomical therapeutic chemical; BMI, body mass index; CAT, COPD Assessment Test; CRX, chest X-ray; FEV, forced expiratory volume; FVC, forced vital capacity; GERD, gastroesophageal reux disease; HRCT, high-resolution computed tomography; ICD, International Classication of Diseases; ICU, intensive care unit; mMRC, modied Medical Research Council; N, no; OTC, over the counter; SABA, short-acting beta agonists; Y, yes.

Calculation of health-care costs and indirect costsTotal costs will be calculated by multiplying each unit of resource use and lost workdays with standard cost-per-unit prices, which are obtained from the Health Care Administration Ofce of the Balearics.17

Predictors for cost-effectivenessPredictors for cost-effective treatment will be assessed, including variables related to patient, physician or treatment. Predictors related to patients may include age, gender, body mass index, smoking status, exacerbations (physician diagnosis and/or prescription of prednisone), COPD severity by spirometry, short-acting 2-agonist use, health-related quality of life and comorbidity. Examples of predictors related to the physician are age, gender and setting, number of patients per practice and number of COPD patients per practice. Predictors related to treatment may include prescription of medication and adherence based on rell of medication, inuenza vaccination in the past year, requests for diagnostic tests, referrals to hospital or specialists and the use of patient-reported outcomes (PROs).

ComparisonsSpecications of the comparisons that could potentially be made, depending on the exact data available, are listed in Table 2.

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Data analysisThe total patients sample will be split into two groups, depending on the treatment variables that will be compared (Table 2). For example, to assess the impact of using PROs, all patients who were treated by a GP who uses PROs will be selected as the treatment group. An equal group of control patients, not treated by a GP who uses PROs, will be selected using a matching procedure. The matching procedure (based on propensity scores) will use patient characteristics (age, gender, smoking status) and disease severity (FEV1, exacerbations, quality of life, comorbidities).

For both groups, the average total costs per patient (as well as minimum, maximum and standard deviation) will be calculated on the basis of the direct health-care costs, as listed above (hospitalisations, medication, ED visits), and indirect costs. The cost difference between the two groups will result in a C variable to obtain an estimate of the incremental costs. The differences in effect size (E) will be expressed as the difference in health effects between the two groups that are compared. The health effects depend on what variables will be consistently available in the database. Exacerbations avoided will be used, as well as changes in COPD-specic changes in the quality of life, as dened by the COPD Assessment Test (CAT) or modied Medical Research Council (mMRC) questionnaire.

Subsequently, the incremental cost-effectiveness ratio (ICER) can be calculated as follows: (Costsgroup1 Costsgroup2) (Effectsgroup1 Effectsgroup2) =

C/E, which provides the incremental costs per exacerbation avoided or incremental costs per CAT point gained. The ICER will be calculated using both the health-care payers and the societal perspective. The societal perspective includes work productivity costs. Sensitivity analyses will be performed using the minimal and maximal costs (scenario analyses), as well as a bootstrap procedure (as patient-level data will be available). Bootstrapping relies on random sampling with replacement, and it will allow estimating accuracy (such as 95% condence intervals) to sample estimates.

External validity using UNLOCKOnce the predictors have been identied, we will invite members of the

UNLOCK project in other countries (e.g., The Netherlands, Sweden and

others) to participate to test the external validity and inter-country variation of these predictors.

To assure consistency of the analytic process and consequent results, data will be compared with other data sets from different IPCRG countries, including the same variables and applying the same methods.

Ethical approvalEthical approval was granted by the local primary care research committee.

DISCUSSIONCurrent clinical treatment guidelines are mainly based on evidence from large clinical trials with a selective study population, which does not seem to reect the majority of patients treated in real-world primary care.18,19 Therefore, there is an urgent need to assess the validity of treatment recommendations when applied in real-world treatment. Results from this study are expected to provide useful insights in the cost-effectiveness of the broad range of strategies and factors related to the primary care treatment of COPD. The use of a real-world database that covers the complete Balearic population is considered a major strength, as a representative population is assessed in which the risk of pre-selection bias is limited. A second strength is that results will be compared with other international settings, thereby increasing generalisability. Here, the UNLOCK project of IPCRG offers a useful possibility.16

However, given the retrospective observational design, some limitations should be acknowledged. First, by the use of real-world data, missing data are common. In particular, registration of data regarding the use of spirometry, smoking status and patient-reported outcomes is expected to be limited. Pulmonary rehabilitation and physiotherapy data are not included in the effectiveness analysis because of the difculty in collecting such data and because of the limited availability of these services. In addition, miscoding or incomplete and invalid data collection may have occurred because of the real-word setting. Another limitation lies in the observational design, which usually increases the risk for bias. Although the database itself covers the complete population, the individual analyses are prone to selection bias. To minimise this risk of bias, a matching procedure will be used, but unobserved bias may still occur. Despite these limitations, the need for more real-world evidence and comparative effectiveness research is increasing, thereby strengthening the overall relevance of this study.20

COMPETING INTERESTS

JWHK and JBS are Associate Editors of npj Primary Care Respiratory Medicine, but were not involved in the editorial review of, nor the decision to publish, this article. None of the other authors declare any conict of interest.

FUNDING

This study is funded by an UNLOCK study grant of the International Primary Care Respiratory Group (IPCRG) and co-funded by an unrestricted educational grant from GlaxoSmithKline (GSK).

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Table 2. Comparison of cost-effectiveness to be potentially made between groups

Predictors related to patientAge o75 years 75 years or more Gender Male Female

BMI o25 25 or more Smoking status Current smoker Former smoker or non-smoker Exacerbations o2 2 or more

Hospitalisations 0 1 or more Severity by FEV1 o50% 50% or more

Use of SABA o2 dispenses per year

2 or more

Comorbidity o2 2 or more Cardiovascular No Yes HRQoL CATo10 10 or more

GP visits o2 2 or more Medication adherence o80% 80% or more

Predictors related to GP

Age o35 35 years or older Gender Male FemaleRegion Urban RuralUse of PROs Yes NoRequests for lab/tests Yes No

Predictors related to specic treatment

Inuenza vaccination Yes No LABA Yes No LAMA Yes No LABA-ICS Yes No

Abbreviations: BMI, body mass index; CAT, COPD Assessment Test; GP, general practitioner; HRQoL, health-related quality of life; ICS, inhaled corticosteroid; LABA, long-acting 2-agonist; LAMA, long-acting muscarinic antagonist; PROs, patient-reported outcomes; SABA, short-acting 2-agonist.

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