Social inequalities impacts of care management and survival in patients with non-hodgkin lymphomas (ISO-LYMPH)

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1 Session 3 : Epidemiology and public health Social inequalities impacts of care management and survival in patients with non-hodgkin lymphomas (ISO-LYMPH) Le Guyader-Peyrou Sandra Bergonie Institut

2 Context: factors associated with place of care & survival: Socio economic status Age, sex Educational level Occupation Income Place of residence Level of Deprivation Rural/urban areas Distance house/reference center (teaching H) Density of GP s Marital status Level of specialization s centers Nursing Home Clinical features Performance status Comorbidities Score of prognosis Disease stage Care management Delays of care management Type of treatment Clinical trials Expertise meetings 2

3 Context (2): Non Hodgkin Lymphoma (NHL) Malignant transformation of normal lymphoïd cells new cases in 2012 in France* Two most frequent NHL subtypes : Diffuse Large B-cell (DLBCL) and follicular lymphoma (FL) DLBCL 4096 new cases / year in France / median age 69 y/ 5 y net survival : 57%* FL 2530 new cases / year in France /median age 65 y/ 5 y net survival : 87 %* Two histological subtypes with well standardized care..management Relatively rare disease / healthcare facilities Large number of health players-> variability of care management and a possible impact of survival of these patients * National estimation of incidence and survival from french network Francim net survival calculated on more recent period

4 Objective ISO-LYMPH study: To describe the care pathway of diffuse large B-cell lymphoma (DLBCL) and follicular lymphoma in the general population and identify the factors predicting the place of care and survival in a population-based cohort of Non Hodgkin Lymphoma (NHL) patients from three counties of France between 2002 and

5 Collaboration of 5 teams Material and method: Three haematological malignancies registries in France (REPIH network ) ERI 3 Inserm, Cancers et population, Caen Department of sociology of Bordeaux University Case collection of all incident DLBCL and FL diagnosed in Calvados, Manche, Côte d Or and Gironde between 2002 and 2008 Data Collection Individual medical data (e.g., patient characteristics, management and treatment), distance to the nearest reference center, vital status at 30/06/2013) Aggregates variables: rural/urban area, deprivation index (EDI), GP s medical density Statistical analysis: MuItiple imputation by Chained Equation (MICE) Logistic regression - Cox regression model - Relative survival (Esteve ) 5

6 Flowchart Extraction of 3 registries databases n=2015 Other NHL, patients out county (n=18) Death J0, histological record only (n=20) CNS NHL (n=83), skin only (n=64) and AIDS (n=25) Early death without any treatment (n=57) Study cohort N=1977 Analysis «Place of care and Survival (without treatment data)» N= DLBCL & 633 FL Analysis «Survival (with treatment data)» N= DLBCL & 630 FL Surgery only (n=7) 6

7 Baseline characteristics. DBCL, by place of care Characteristics Private or community hospitals Teaching hospitals Total N= 475 (%) N= 690 (%) N= 1165 (%) p value Median age, range 74 (21-99) 70 (9-99) 72 (9-99) p<10-3 Age group p< , , , , Sex 0.69 Male , Marital Status 0.74 Alone (single. widower or Comorbidity (ACE-27) 0.08 Moderate & severe Performance status (PS) , B symptoms 0.23 Present , Ann Arbor stage at diagnosis III ou IV (dissimined) Extranodal sites 0.09 Yes Serum Lactate dehydrogenase Elevated Risk group (IPI*) 0.58 Low Intermédiate high , *IPI=International Prognostic Index (Age, PS, LDH, stage, extranodal sites)

8 Baseline characteristics. DLBCL, by place of care Characteristics Private or community hospitals Teaching hospitals Total N= 475 (%) N= 690 (%) N= 1165 (%) p value Year of diagnosis Healthcare facilities Registry area p<10-3 Registry A Registry B Registry C first medical contact p<10-3 Spécialist , Medical specialities p<10-3 Hémato/ oncology Travel time to nearest reference center (in minutes) Urban and rural areas 0.15 Urban , Socioeconomic status (EDI national score) 0.26 deprived (last two quintile) Care management Inclusion on clinical trial p<10-3 Yes Multidisciplinary meeting (within 90 days) 0.84 Yes Anthracycline Based-chemotherapy with immunotherapy 0.27 Yes

9 Associated factors with excess hazard ratios (EHR) of death during the five years since diagnosis, DLBCL, Characteristics EHR* p value Year of diagnosis [ ] Comorbidity (ACE-27) p<10-3 None 1 Mild 1.53 [ ] Moderate & severe 1.68 [ ] Marital Status p<10-3 Married 1 Alone (single, widower or divorced) 1.45 [ ] Registry area p<10-3 Registry A 1 Registry B 0.83 [ ] Registry C 1.48 [ ] Place of treatment 0.07 Private or community 1 Teaching hospitals 0.82 [ ] Medical specialities p<10-3 Other specialities 1 Hémato/ oncology 0.35 [ ] Travel time to nearest reference center [ ] [ ] *adjusted on age, sex and IPI 95% CI Borderline association with place of treatment Role of medical speciality and marital status Registry area effect No contextual data associated with relative survival (EDI, rural area ) 9

10 Associated factors with excess hazard ratios of death during the five years since diagnosis, DLBCL, Adjusted on all other factors Various items related to treatment were also independently associated with excess mortality rates such as : Characteristics EHR* 95% CI p value Inclusion of trial No 1 p<10-3 Yes 0.44 [ ] Multidisciplinary meeting (within 90 days) No 1 Yes 0.71 [ ] Anthracycline Based-Chemotherapy with immunotherapy No 1 Yes 0.32 [ ] p<10-3 *adjusted on age, sex, marital status, comorbidity, risk group IPI, registry area, medical speciality and travel time 10

11 Conclusions Most of the results comparable to literature: Histology significantly related to survival (Sant, Eur J Cancer 2008) IPI (Shipp, N Engl J Med 1993, Solal-Celigny, Blood 2004) Significant association between ABC treatment, effect of immunotherapy as Rituximab on survival (Coiffier 2002, Pfreudschuch 2006) Effect of inclusion of clinical trial on survival (Armitage, J Clin Oncol 1998) Interesting findings No association between SES and NHL survival (Menvielle BEH 2008) Association with marital status (Osborne 2005) Association with medical speciality of care (Engelen 2006) Heterogeneity of survival according to place of diagnosis and treatment Residual confounding? Time of travel (Campbell 2002, Dejardin 2005) Borderline association between place of care and survival 11

12 Perspectives Identify the socioeconomic and medical factors predicting longest delays of care and to study the influence of longest delays on survival Analyse all these factors on a frailty population defined by a high age and/or other criteria of vulnerability (such as comorbidity). (65 y and more n=1165 ; 75 y and more n=641, 80 y and more n=371 ) Introduction of «CMU statut» (on going agreement with SNIRAM/IDS) 12

13 Acknowledgments Thank you for your attention 13

14 DBCL net survival Cutpoint 30/06/ ,5 % DC at cutpoint Median follow up 5,5 y Loss of follow up <2% 1 y net survival 73 % 3 y net survival 63% 5y net survival 59% DBCL : Net Survival analysis time 14

15 DBCL: Net survival by marital status DBCL Net survival by speciality analysis time solitude = non solitude = oui DBCL net survival by age group analysis time spemcancero = 0 spemcancero = Onco/hémato analysis time

16 Characteristics Multiple imputation for missing values EHR 95% CI p value Age group p< [ ] [ ] [ ] Sex Male 1 Female 0.73 [ ] Marital Status Married 1 Alone (single [ ] Comorbidity(ACE-27) p<10-3 None 1 Mild 1.54 [ ] Moderate & severe 1.67 [ ] Risk group (IPI) p<10-3 Low 1 Intermediate 2.72 [ ] high 6.43 [ ] Registry area Registry A 1 Registry B 0.95 [ ] Registry C 1.27 [ ] Medical specialities p<10-3 Other specialities 1 Hémato/ oncology 0.46 [ ] Inclusion of trial p<10-3 No 1 Yes 0.44 [ ] Multidisciplanary meeting(90 days) No 1 Yes 0.71 [ ] Anthra based-chemotherapy & immuno p<10-3 No 1 Yes 0.32 [ ] Travel time to nearest reference (in mns) [ ] [ ]

17 . tab site Site Freq. Percent Cum. bn co gi Total 1,

18 R. GIORGI : simulation of dataset 18

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