Abstract
Background Median survival for cystic fibrosis (CF) patients in Europe is unknown and is likely to be influenced by socioeconomic factors. Using the European CF Society Patient Registry (ECFSPR), median survival estimates were obtained for CF patients across Europe and the impact of socioeconomic status on survival was examined.
Methods CF subjects known to be alive and in the ECFSPR between 2010 and 2014 were included. Survival curves were estimated using the Kaplan–Meier method. Differences in the survival curves were assessed using the log-rank test. Cox regression was used to estimate the association between socioeconomic factors and the age-specific hazard of death, with adjustment for sex, age at diagnosis, CF transmembrane conductance regulator (CFTR) genotype and transplant status.
Results The final analysis included 13 countries with 31 987 subjects (135 833 person-years of follow-up) and 1435 deaths. Median survival age for these patients in the ECFSPR was 51.7 (95% CI 50.0–53.4) years. After adjusting for potential confounders age at diagnosis, sex, CFTR genotype and transplant status, there remained strong evidence of an association between socioeconomic factors and mortality (p<0.001). Countries in the highest third of healthcare spending had a 46% lower hazard of mortality (HR 0.54, 95% CI 0.45–0.64) than countries in the lowest third of healthcare spending.
Conclusions Median survival for patients with CF in Europe is comparable to that reported in other jurisdictions and differs by socioeconomic factors.
Abstract
Median survival for patients with CF in Europe is similar to that reported in other jurisdictions and differs depending on socioeconomic status, with measures of higher healthcare spending associated with improved survival. https://bit.ly/3jYF37q
Introduction
Cystic fibrosis (CF) is one of the most common autosomal recessive genetic conditions in Europe that causes progressive lung disease and premature death. Median survival age for patients with CF is estimated to be in the mid-40s although estimates can vary across countries [1]. Reasons for this variation in survival outcomes include genetic and environmental factors [2]. A recent comparison of CF survival between US and Canadian CF registries [3] identified differences in median survival that were attributed in part to differences in nutrition, access to lung transplantation and socioeconomic factors [4]. To date, median survival estimates for European CF patients as a whole are not known, although disparities in outcomes across Europe have been identified [5, 6].
In 2003, the European CF Society (ECFS) developed a patient registry to collect clinical and demographic data on CF patients attending specialised CF centres throughout Europe [7, 8]. The ECFS Patient Registry (ECFSPR) now contains longitudinal data on almost 50 000 CF patients attending CF centres in 38 European countries [9]. The primary aim of the study was to estimate median survival for CF patients throughout Europe, and the secondary aim was to examine the association between country-level socioeconomic factors and survival.
Methods
The study design is a retrospective cohort study using the ECFSPR during the observation period from 2010 to 2014. All procedures were approved by the Research and Ethics Committee of St Vincent's University Hospital (Dublin, Ireland) and by the ECFSPR Steering Committee.
Patient population
Once a year, annual summary data for each CF patient enrolled in the ECFSPR are uploaded to the registry [9]. Demographic and clinical characteristics of the patient population were extracted from the ECFSPR for all patients in the registry between 2010 and 2014. These characteristics were: sex, age, vital status during year (alive/dead), transplant status, age at diagnosis, CF transmembrane conductance regulator (CFTR) genotype, highest annual forced expiratory volume in 1 s, forced vital capacity, height and weight.
Due to concerns relating to incomplete data, only countries with national registries and high enrolment (>80% of estimated CF patient population enrolled) with annual data for the 5-year period from 2010 to 2014 were included. Belgium, a national registry with high enrolment, only had annual data from 2010 to 2013 and was also included. The survival outcome of interest was all-cause mortality, including deaths post-transplant.
Socioeconomic factors
Three validated country-level socioeconomic factors were used [10]. Two were measures of country healthcare spending (proportion of Gross Domestic Product (GDP) spent on healthcare and average number of physicians per 1000 people) and one was a measure of country wealth (Gross National Income (GNI) as estimated by the World Bank [11]). For the analysis, GDP spent on healthcare and average number of physicians per 1000 people were divided into thirds using terciles as the cut-off points. GNI was also initially analysed in thirds using terciles as the cut-off points, but as the highest and middle income thirds were similar, this was dichotomised into highest/middle versus lowest income.
Statistical analysis
Descriptive statistics were used to present the demographics and clinical data of the CF cohort. Definitions of clinical variables are as determined by the ECFSPR [12]. Overall survival curves were estimated using the Kaplan–Meier method and Cox proportional hazards modelling was used to estimate hazard ratios (HRs) in the cohort. The timescale was age. Patients were considered to be at risk from age of entry into the cohort until the earliest of: age of exit, death or end of follow-up period on 31 December 2014. Death was defined as all-cause mortality either before or after transplant. Loss to follow-up was defined as present if ≥2 years of observation were missing before the end date for the cohort (31 December 2014 for all countries except Belgium, whose end date was 31 December 2013) [3]. Due to incomplete follow-up of CF patients post-transplant in many countries, analysis was repeated using the composite outcome of death or transplant as well as censoring at time of transplant.
Univariable Cox regression analysis was carried out examining the association between age at diagnosis, sex, CFTR genotype as well as transplant status and survival. CFTR genotype was characterised by the presence or absence of F508del mutations and by the presence of compound heterozygosity for two CFTR class I–III mutations using the classification system proposed by Welsh and Smith [13] and Tsui [14]. Transplant status was defined as a transplant of any type (primarily lung and/or liver) and was used as a time-dependent variable. Measures associated with survival in univariable analysis were included in a multivariable model for the adjusted association between socioeconomic factors and survival. Due to the difference in CFTR genotypes across Europe and the known association of CFTR genotypes with survival, sensitivity analyses were also carried out limiting the population to CF patients homozygous for F508del and to CF patients compound heterozygous for two CFTR class I–III mutations. The proportional hazards assumption was assessed using graphical methods (log-log plot of survival) and methods based on Schoenfeld residuals with no significant deviations found. All statistical analysis was carried out using Stata version 14.0 (StataCorp, College Station, TX, USA).
Results
There were 31 987 CF patients in the ECFSPR between 2010 and 2014 from 13 countries that met all of the inclusion criteria. The ECFSPR patient population included in the study is outlined in table 1. There were 1435 deaths with an average patient follow-up of 4.2 years and 135 833 person-years at risk. 983 (3.0%) patients were lost to follow-up. Demographics of ECFSPR countries excluded from the study are shown in supplementary table S1. Demographics and clinical characteristics for patients during their first year of entry into the cohort are summarised in table 2. Standardised all-population survival rates for each county and country classification of socioeconomic measures are shown in table 3. As would be expected, variation was seen across European countries for the three different socioeconomic measures.
Study population in cohort from 2010 to 2014
Baseline demographics and clinical data at time of entry into the European Cystic Fibrosis (CF) Society Patient Registry
Standardised death rates and socioeconomic measures by country
Survival analysis
Median age of survival for all European patients included in the study was 51.7 (95% CI 50.0–53.4) years (p<0.001). The Kaplan–Meier curve for the study cohort all-cause mortality is shown in figure 1. Results including median survival for CF genetic subgroups and when transplant is considered as a death are shown in table 4. Median survival with the composite outcome of death or transplant was 38.5 (95% CI 37.5–39.4) years (p<0.001). The Kaplan–Meier curve for the study cohort with the composite outcome of death or transplant is shown in figure 2. Median survival censoring at transplant was 56.8 (95% CI 54.0–60.2) years (p<0.001). The Kaplan–Meier curve for the study cohort censored at transplant is shown in figure 3.
Estimated survival (95% CI) for European cystic fibrosis patients: all-cause mortality.
Summary of time-to-event data and median survival estimate for the 2010–2014 cohort
Estimated survival (95% CI) for European cystic fibrosis patients: composite outcome of all-cause mortality or transplant.
Estimated survival (95% CI) for European cystic fibrosis patients: censoring at transplant.
In univariable analyses, age at diagnosis, sex, CFTR genotype and transplant status were all strongly associated with differences in survival (table 5). Female sex was associated with a 28% increased hazard of death compared with male sex.
Univariable predictors of survival
Socioeconomic factors and survival
All measures of country-level socioeconomic factors were associated with increased hazard for death in univariable analyses. After adjusting for age at diagnosis, sex, CFTR genotype and transplant status, the proportion of GDP spent on healthcare and number of physicians per capita were each independently associated with survival. Countries in the highest third of GDP spend on healthcare had a 45% lower hazard than those in the lowest third (HR 0.544 (95% CI 0.448–0.641)). Similarly, countries in the highest third of physicians per capita had a 47% lower hazard than those with the lowest third of physicians per capita ratio (HR 0.523 (95% CI 0.385–0.661)). These results are shown in table 6. The Kaplan–Meier curves for GDP spend on healthcare and physicians per capita are shown in figure 4a and b. After multivariable adjustment, high GNI was associated with a lower hazard; however, this finding was not statistically significant (HR for high versus low GNI 0.859 (95% CI 0.667–1.051)).
Country-level socioeconomic predictors of survival
Estimated survival (95% CI) for European cystic fibrosis patients grouped by different socioeconomic factors: a) terciles of healthcare expenditure (percentage of Gross Domestic Product) and b) terciles of number of physicians per capita.
Discussion
We have shown that median survival in patients with CF across Europe is comparable to that of Canada and the USA, and that there is variation across Europe that is associated with socioeconomic factors.
Survival for patients with CF is variable and is influenced by factors including background CFTR genetics and environmental exposures [2]. CFTR genotypes with at least one class IV–V CFTR mutation have a milder phenotype and better survival [15, 16]. Likewise, environmental factors such as acquisition of Pseudomonas aeruginosa [17], Staphylococcus aureus and Burkholderia cepacia complex [18] also influence mortality. In the USA, there is a clear association between individual-level socioeconomic status and CF outcomes, with absence of private medical insurance and lower median income independently associated with higher mortality [19, 20]. This relationship between socioeconomic status and survival in CF is multifactorial, with access to healthcare, education, adherence and expectations all contributing to differences in outcomes [21]. In Europe, McCormick et al. [6], using the European CF Demographics Registry dataset (a precursor of ECFSPR), demonstrated differences in demographics across Europe with a median patient age of 17.0 years in European Union (EU) countries compared with a median patient age of 12.1 years in non-EU countries. The proportion of patients aged >40 years was twice as high in EU countries than non-EU countries, raising concerns about underdiagnosis of CF and increased childhood mortality as a result of unequal access to specialist CF care and CF medicines. This was consistent with the earlier work of Fogarty et al. [5] who also found differences in median age of death for CF patients across countries, which they attributed to possible underdiagnosis and diagnostic misclassification of CF as well as socioeconomic factors.
One of the challenges of comparing differences in survival across countries has been differences in statistical methodology in single-country registry annual reports [22, 23]. In a recent study looking at survival in the US and Canadian CF patient registries, using the same methodology for survival analysis [24], there was an almost 10-year difference in median survival that has been increasing since 2005 [3]. Socioeconomic factors, nutrition and access to lung transplantation were all considered to influence this difference in survival [4]. Median survival in CF patients was 40.6 years in the USA compared with 50.9 years in Canada. The median survival estimated in our ECFSPR study, using a similar statistical methodology, was 51.7 years. However, a limitation of our study is that many European patients in the ECFSPR have limited data after lung transplant as many transplant centres are not enrolled in the ECFSPR. This results in individuals tending to be lost to follow-up at the time of transplant, which is likely to increase the survival estimates. In the USA–Canada study, censoring at time of transplant resulted in increased median survival in the USA to 44.0 years and to 57.1 years in Canada [3]. Our median survival censoring at transplant of 56.8 years lies between these estimates for the USA and Canada, which is likely to be a more accurate comparison.
The difference between median survival including post-transplantation follow-up (51.7 years) and using the composite outcome of death or transplant (38.5 years) highlights the impact of transplantation and the improved survival after transplantation [25]. This difference may be due to uncounted deaths in patients lost to follow-up post-transplant, as well as differences in access to transplantation in some countries reflected by a highly different percentage of transplanted patients among those seen each year, which varied between >12% in the UK and 0% in some Eastern European countries [9]. There were also differences in median survival when we limited the cohort to those homozygous for F508del, which is similar to other reports [15]. The distribution of F508del differs across European countries [9] and because of this, the influence of socioeconomic factors on survival was adjusted for CFTR genotype to account for differences in genotype frequencies in countries with lower socioeconomic measures.
Our study also demonstrates that survival outcomes vary depending on different socioeconomic factors. Studies of socioeconomic status and CF outcomes in the USA have shown that medical insurance status [20] and median household income [19] are both independently associated with difference in CF survival outcomes, even within a country with a high GNI. In the UK, a validated deprivation score was associated with poorer outcomes, including increased infection with P. aeruginosa and decreased access to and use of CF medications, all of which are associated with reduced CF survival [26]. This is the first study in Europe to quantify the association between national socioeconomic factors and survival, and shows that countries with the lowest measures of healthcare spending have hazard rates for death that are almost twice that of countries with higher measures of healthcare spending. This increase in hazard with lower socioeconomic spending was consistent across three separate country-level socioeconomic metrics. Despite common European Standards of Care for CF and a national health insurance system in almost all European countries, access to care and medication varies widely across Europe, especially in Eastern Europe [27]. The association between socioeconomic factors and CF survival is not unexpected as standardised mortality from all causes differs across Europe (as shown in table 3), although the magnitude of effect in CF is greater than that seen for the general population and demonstrates the need for further research in this area within Europe.
There are a number of limitations to our study. Missing data and data quality are always challenging in studies using registry data. The analysis was limited to countries with a national registry and coverage of >80% of their CF population. It was assumed that missing data on covariates within countries were missing completely at random. By restricting our cohort to countries with a national registry, we assumed that the combined population is representative of that in Europe as a whole. The overall median survival age could be subject to bias if this is not the case. However, the findings relating to association between socioeconomic factors and survival would only be biased if the association between socioeconomic factors and survival differed in countries that were not a part of this study. All of the included national registries have rigorous approaches to data quality. This, in addition to the data quality requirements of the ECFSPR [28], increases the likelihood that the results are reliable. At the time of study completion, data from two large European countries (Germany and Spain) were not available and it is possible that the survival estimates may change with the inclusion of these large countries. This will require a further follow-up study. Likewise, it is important to note that these survival estimates for Europe cannot be extrapolated to individual patients with CF or to all European countries as the median estimates are influenced by survivorship in the larger European countries. The three largest countries (UK, France and Italy) contributed 23 849 patients (75%) and 1093 deaths (76%), indicating that the median survival largely reflects the median survival of these three countries. We chose not to weight the survival estimates by country population as we were studying regional differences and used a similar methodology to that used for Canada and the USA. Also, to ensure as accurate a survival estimate as possible, we restricted the cohort to include countries with the highest coverage and the most complete data. Future analysis including more countries, especially Eastern European countries, will be planned once the ECFSPR has sufficient data to do so. It is also worth noting that these estimates reflect a cohort of CF patients followed before the widespread availability of CFTR modulator therapy and future survival estimates may change as these highly effective novel CF therapies become more widely used across Europe.
Finally, a number of deaths may have been missing. It is anticipated that this number is low as most CF patients were attending CF centres who would generally know each patient's vital status, although we acknowledge that outcomes post-transplant may be incomplete. The absence of follow-up post-transplant in some countries limits the interpretation of the overall survival estimates. The median survival estimate may be biased due to the exclusion of post-transplant deaths. As seen in table 1, the proportion of deaths when censored at transplant compared with total deaths is highly variable across European countries. This is likely to be due to differences in access to transplant, post-transplant loss to follow-up in the registry and transplant centre survival rates in the different European countries. As all of these factors may influence the median survival estimate, attempts are underway to audit data quality and number of deaths as well as include post-transplant centre data in the ECFSPR. We also included transplant status as a time-dependent covariate in multivariable Cox regression analyses. However, this may be a mediator of the association between socioeconomic factors and mortality (e.g. if access to transplant is affected by socioeconomic factors). We also did not allow the hazard ratio for transplant to depend on time since transplant. Another potential source of bias is noninformative censoring. The models used assume censoring (due to loss to follow-up) is uninformative for the event of interest. Loss to follow-up rates were generally low, but it is possible that covariates not included in our model may have influenced differences in each country's loss to follow-up. Unfortunately, there is no way to formally test this. In the analysis in which we censor patients at transplant, the focus is on cause-specific hazards for pre-transplant mortality and so censoring at transplant is not considered a form of informative censoring.
In conclusion, this study demonstrates that median survival for patients with CF in Europe is comparable to that reported in the USA and Canada, and that survival across Europe is highly influenced by socioeconomic factors. A more detailed understanding of how these differences in socioeconomic factors lead to poorer survival is critical to improving outcomes for CF patients from European countries with lower healthcare spending.
Supplementary material
Supplementary Material
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Supplementary table 1 ERJ-02288-2020.SUPPLEMENT
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Acknowledgement
We thank the people with CF, and their families, for consenting to their data being included in the ECFSPR. We thank the centres and individual country representatives for allowing the use of the data, and the ECFSPR for providing access to pseudonymised patient data.
The ECFSPR contributors list consists of the representatives of the countries whose data is used in this article, and the members of the Scientific Committee who reviewed the initial data application and the final manuscript. Collaborating authors: E. Aleksejeva (Dept of Pneumology, Children's Clinical University Hospital, Rīga Stradinš University, Riga, Latvia); E. Bardin (Cystic Fibrosis Europe, Brussels, Belgium); P.R. Burgel (Respiratory Medicine and National Cystic Fibrosis Reference Centre, Cochin Hospital, AP-HP, Université de Paris, Institut Cochin, INSERM U1016, Paris, France); R. Cosgriff (Cystic Fibrosis Trust, London, UK); G. Daneau (Sciensano, Epidemiology and Public Health, Health Services Research, Brussels, Belgium); I. de Monestrol (Stockholm Cystic Fibrosis Centre, Karolinska University Hospital, Stockholm, Sweden); P. Drevinek (Dept of Medical Microbiology, Second Faculty of Medicine, Charles University and Motol University Hospital, Prague, Czech Republic); G. Fletcher (The Cystic Fibrosis Registry of Ireland, Dublin, Ireland); S. Fustik (Centre for Cystic Fibrosis, University Children's Hospital, Skopje, North Macedonia); V. Gulmans (Dutch Cystic Fibrosis Foundation (NCFS), Baarn, The Netherlands); E. Hatziagorou (Cystic Fibrosis Unit, Hippokration General Hospital, Aristotle University of Thessaloniki, Thessaloniki, Greece); A. Jung (Paediatric Pulmonology, University Children's Hospital Zurich, Zurich, Switzerland); N. Kashirskaya (Dept of Genetic Epidemiology (Cystic Fibrosis Group), Research Centre for Medical Genetics, Moscow, Russia); H. Kayserova (Cystic Fibrosis Centre, University Hospital of Bratislava, Bratislava, Slovakia); U. Krivec (Dept of Paediatric Pulmonology, University Children's Hospital, Ljubljana University Medical Centre, Ljubljana, Slovenia); A. Lindblad (Gothenburg Cystic Fibrosis Centre, Queen Silvia Children's Hospital, The Sahlgrenska Academy at the University of Gothenburg, Gothenburg, Sweden); H. Makukh (Institute of Hereditary Pathology, Ukrainian National Academy of Medical Sciences, Lviv, Ukraine); K. Malakauskas (Dept of Pulmonology, Hospital of Lithuanian University of Health Sciences, Kaunas, Lithuania); M. Mei-Zahev (Pulmonary Institute, Schneider Children's Medical Center of Israel, Petah Tikva and Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel); H.V. Olesen (Dept of Paediatrics and Adolescent Medicine, Cystic Fibrosis Centre, Aarhus University Hospital, Aarhus, Denmark); R. Padoan (Cystic Fibrosis Support Centre, Dept of Paediatrics, University of Brescia, Brescia, Italy); A. Párniczky (Heim Pál National Paediatric Institute, Budapest, Hungary); M.D. Pastor-Vivero (Paediatric Pulmonology Dept, Cruces University Hospital, Biscay, Spain); L. Pereira (Centre for Cystic Fibrosis, Hospital de Santa Maria, Lisbon, Portugal); A. Pfleger (Dept of Paediatrics and Adolescent Medicine, Division of Paediatric Pulmonology and Allergology, Medical University of Graz, Graz, Austria); L. Pop (National Cystic Fibrosis Centre, Timișoare, Romania); M. Rodic (National Centre for Cystic Fibrosis, Mother and Child Health Institute of Serbia “Dr Vukan Cupic”, Belgrade, Serbia); O. Turcu (Ambulatory Cystic Fibrosis and Other Rare Diseases Centre, Institute for Maternal and Child Healthcare, State University of Medicine and Pharmacy “Nicolae Testemitanu”, Dept of Paediatrics, Chisinau, Republic of Moldova).
Footnotes
This article has supplementary material available from erj.ersjournals.com
This article has an editorial commentary: https://doi.org/10.1183/13993003.01487-2021
Author contributions: E.F. McKone had full access to all the data and the final responsibility for the decision to publish. All authors were involved in data collection or the study design as well as manuscript preparation and review.
Conflict of interest: E.F. McKone reports grants and personal fees from Vertex Pharmaceuticals, personal fees from Novartis, nonfinancial support from A Menarini, and grants from Gilead, outside the submitted work.
Conflict of interest: C. Ariti has nothing to disclose.
Conflict of interest: A. Jackson has nothing to disclose.
Conflict of interest: A. Zolin has nothing to disclose.
Conflict of interest: S.B. Carr reports nonfinancial support and other from Chiesi Pharmaceuticals (Advisory Board fee), nonfinancial support and other from Vertex Pharmaceuticals (Advisory Board, lecture fee, travel, Steering Committee), other from Zambon Pharmaceuticals (Advisory Board fee), other from Insmed (Advisory Board fee), outside the submitted work.
Conflict of interest: A. Orenti has nothing to disclose.
Conflict of interest: J.G. van Rens has nothing to disclose.
Conflict of interest: L. Lemonnier has nothing to disclose.
Conflict of interest: M. Macek Jr has nothing to disclose.
Conflict of interest: R.H. Keogh has nothing to disclose.
Conflict of interest: L. Naehrlich reports that he has received institutional fees for site participation in clinical trials from Vertex Pharmaceuticals.
Support statement: There was no external funding to the ECFSPR for this study. Statistical analysis was funded through a grant from the ECFSPR to London School of Hygiene and Tropical Medicine. Funding information for this article has been deposited with the Crossref Funder Registry.
- Received June 12, 2020.
- Accepted February 12, 2021.
- Copyright ©The authors 2021. For reproduction rights and permissions contact permissions{at}ersnet.org