4. MARCO DE REFERENCIA
4.3 LA GESTIÓN COMUNITARIA EN SISTEMAS AGROALIMENTARIOS,
Scientific Justification:
The impact of early and late occurring complications that are associated with significant morbidity and mortality among HSCT recipients is well documented. Regular follow-up with careful assessment and amelioration of early and late occurring complications among HSCT recipients is vital to ensure optimal HSCT outcomes. Despite the importance of ensuring follow-up of all HSCT recipients at transplant centers HSCT recipients may be lost to follow-up. To date, no estimates of HSCT-related follow-up care have been made. In order to fill this gap, the proposed analyses will provide a characterization of the problem of HSCT recipients being lost to follow-up as well as the identification of risk factors for being lost to follow-up. The results of these analyses will have the potential to inform the development of interventions to improve the retention of identified subgroups of HSCT recipients.
Patient Eligibility Population:
Eligibility criteria will include patients who underwent autologous or allogeneic HSCT between 1980 and 2010 for a malignant or non-malignant disorder and were reported to the CIBMTR. We have chosen this time period to be able to characterize lost to follow-up rates among sequential cohorts of HSCT
recipients prior to and following the establishment of programs and guidelines dedicated to the care of HSCT recipients.
Data Requirements:
No additional data collection will be required. We will utilize Transplant Essential Data (TED) level data. TED data will include the following variable such as disease type, age, sex, pre-transplant, date of diagnosis, transplant type (autologous/allogeneic), graft type (bone marrow/peripheral blood stem cells/cord blood), conditioning regimen, post-transplant disease progression and survival, development of a new malignancy, and cause of death. TED level data, collected pre-transplant, 100 days, and six months post transplant and annually thereafter or until death will be utilized.
Outcomes: being lost to follow-up will be defined as a transplant center being unable to obtain follow- up data from the referring physician or a lack of contact with the recipient for the reporting period resulting in the filing of the Form 2802 for the HSCT recipient.
Exploratory variables: recipient age, recipient sex, diagnosis (malignant / non-malignant), recipient age at HSCT, time period of HSCT, transplant team region, transplant type (autologous / allogeneic),
transplant number, graft source (bone marrow, peripheral blood stem cells, cord blood), degree of HLA match, conditioning, acute graft-versus-host disease, and chronic graft-versus-host disease.
Study Design:
Descriptive statistics will be calculated for the CIBMTR sample including sociodemographic and HSCT related characteristics such as recipient age, recipient sex, diagnosis, recipient age at HSCT, time period of HSCT, transplant team region, transplant type, transplant number, graft source, degree of HLA match, conditioning, acute graft-versus-host disease, and chronic graft-versus-host disease.
The cumulative incidence of being lost to follow-up will be calculated using the Kaplan Meier method with death treated as a competing event. We will calculate cumulative incidence rates of being lost to follow-up for various time points (e.g. 5 years, 10 years, etc.). We acknowledge that we will be unable to calculate certain cumulative incidence rates (e.g. 10 years) for being lost to follow-up among the most recent cohorts of HSCT recipients. We plan to calculate and describe cumulative incidence rates of being lost to follow-up by time period in order to characterize lost to follow-up rates among sequential cohorts of HSCT recipients to better understand changes the impact of trends in HSCT care that have
occurred over time (e.g., greater interest in establishing follow-up care away from the transplant center). We also plan to calculate and describe incidence rates of being lost to follow-up by transplant team region to better understand these trends on a international level. Patients who were not lost to follow-up will be censored at the last research-level follow-up. Cumulative incidence estimates will be provided with 95% confidence intervals. We acknowledge that the process by which a HSCT recipient is considered lost to follow-up will differ among transplant centers. For those HSCT recipients that have a Form 2802 filed we will describe the time interval between the last date of contact and the date at which the declaration of lost to follow-up was filed. We will also describe the frequency of unsuccessful attempts to locate the recipient (e.g., called home and/or work numbers, sent letter, etc.) as reported on the Form 2802.
Statistical analysis to assess differences among HSCT recipients that were lost to follow-up and those that were not lost to follow-up will be assessed using chi square analysis and Fisher’s exact test for categorical and binary variables; respectively. Adjusted logistic regression models will be used to identify predictors for being lost to follow-up. Each candidate factor described above will be analyzed in a separate model adjusted for recipient sex, age, and time since HSCT. Predictors that are significant at the p < 0.10 level will be included in multivariable modeling. For multivariable modeling we will start with a full model and variables will be eliminated at the p > 0.05 level until all remaining variables are statistically significant. Adjusted odds ratios accompanied by 95% confidence intervals will be reported.
References:
1. Pasquini MC, Wang Z. Current use and outcome of hematopoietic stem cell transplantation: CIBMTR Summary Slides, 2013. Available at: http://www.cibmtr.org.
2. Upton A, Kirby KA, Carpenter P, et al. Invasive Aspergillosis following Hematopoietic Cell
Transplantation: Outcomes and Prognostic Factors Associated with Mortality. Clin Infect Dis. 2007; 44(4): 531-40.
3. Pasquini MC. Impact of graft-versus-host disease on survival. Best Pract Res Clin Haematol. 2008; 21(2): 193-204.
4. Sun CL, Francisco L, Kawashima T, et al. Prevalence and predictors of chronic health conditions after hematopoietic cell transplantation: a report from the Bone Marrow Transplant Survivor Study. Blood 2010; 116(17): 3129-39.
5. Sun CL, Kersey JH, Francisco L, et al. Burden of morbidity in 10+ year survivors of hematopoietic cell transplantation: report from the bone marrow transplantation survivor study. Biol Blood Marrow Transplant. 2013; 19(7): 1073-80.
6. Rizzo JD, Wingard JR, Tichelli A, et al. Recommended screening and preventive practices for long- term survivors after hematopoietic cell transplantation: joint recommendations of the European Group for Blood and Marrow Transplantation, the Center for International Blood and Marrow Transplant Research, and the American Society of Blood and Marrow Transplantation. Biol Blood Marrow Transplant. 2006; 12(2): 138-51.
7. Nathan PC, Greenberg ML, Ness KK, et al. Medical care in long-term survivors of childhood cancer: a report from the childhood cancer survivor study. J Clin Oncol. 2008; 26(27): 4401-9.
Table 1. Characteristics of US patients underwent a first allogeneic transplant between 2000 and 2010 registered with the CIBMTR
If the patient being lost to follow up
Variable No Yes Number of patients 42183 3756 Number of centers 195 143 Age, median 44 (<1-83) 23 (<1-75) Age, in years <18 8740 (21) 1571 (42) 18 - 29 4974 (12) 605 (16) 30 - 39 4651 (11) 490 (13) 40 - 49 7773 (18) 538 (14) 50 - 59 9959 (24) 420 (11) 60 - 69 5648 (13) 125 (3) 70+ 438 (1) 7 (<1) Gender Male 24636 (58) 2181 (58) Female 17547 (42) 1575 (42) Disease
Acute myelogenous leukemia 14676 (35) 936 (25)
Acute lymphoblastic leukemia 6947 (16) 611 (16)
Other leukemia 2066 (5) 88 (2)
Chronic myelogenous leukemia 2768 (7) 382 (10)
Myelodysplastic syndrome/myeloproliferative disease 5230 (12) 336 (9)
Other acute leukemia 437 (1) 38 (1)
Non-Hodgkin lymphoma 4694 (11) 362 (10)
Hodgkin lymphoma 349 (<1) 38 (1)
Multiple Myeloma 450 (1) 30 (<1)
Other Malignancies 393 (<1) 11 (<1)
Breast Cancer 51 (<1) 1 (<1)
Severe aplastic anemia 1338 (3) 362 (10)
Inherited abnormality of erythrocyte differentiation or function
884 (2) 230 (6)
SCID and other immune system disorders 809 (2) 157 (4)
Inherited abnormalities of platelets 45 (<1) 11 (<1)
Inherited disorders of metabolism 561 (1) 97 (3)
Histiocytic disorder 379 (<1) 55 (1)
Autoimmune Diseases 37 (<1) 4 (<1)
If the patient being lost to follow up Variable No Yes Graft Source Bone Marrow 11361 (27) 1715 (46) Peripheral Blood 26327 (62) 1467 (39) Cord Blood 4495 (11) 574 (15) Donor HLA-identical sibling 16929 (40) 1848 (49)
Other Related donor 2773 (7) 235 (6)
Unrelated donor 22481 (53) 1673 (45) Race Caucasian 34965 (83) 2821 (75) African-American 3132 (7) 342 (9) Asian 1369 (3) 142 (4) Pacific islander 4 (<1) 2 (<1) Native American 166 (<1) 20 (<1) Other 1754 (4) 352 (9) Unknown 793 (2) 77 (2)
Kanofsky score prior to transplant
>=80 30476 (72) 2662 (71) <80 3351 (8) 145 (4) Missing 8356 (20) 949 (25) Year of transplant 2000 2747 (7) 490 (13) 2001 2814 (7) 434 (12) 2002 3044 (7) 453 (12) 2003 3135 (7) 482 (13) 2004 3452 (8) 435 (12) 2005 3769 (9) 411 (11) 2006 4010 (10) 405 (11) 2007 4131 (10) 289 (8) 2008 4512 (11) 178 (5) 2009 5136 (12) 127 (3) 2010 5433 (13) 52 (1)
Table 2. Characteristics of US patients underwent a first autologous transplant between 2000 and 2010 registered with the CIBMTR
If the patient being lost to follow up Variable No Yes Number of patients 58572 4652 Number of centers 256 147 Age, median 55 (<1-86) 47 (<1-82) Age, in years <18 3769 (6) 380 (8) 18 - 29 3850 (7) 687 (15) 30 - 39 4709 (8) 716 (15) 40 - 49 9474 (16) 883 (19) 50 - 59 17642 (30) 1140 (25) 60 - 69 16027 (27) 749 (16) 70+ 3101 (5) 97 (2) Gender Male 34365 (59) 2721 (58) Female 24207 (41) 1931 (42) Disease
Acute myelogenous leukemia 2254 (4) 162 (3)
Acute lymphoblastic leukemia 166 (<1) 8 (<1)
Other leukemia 161 (<1) 3 (<1)
Chronic myelogenous leukemia 21 (<1) 2 (<1)
Myelodysplastic syndrome/myeloproliferative disease 49 (<1) 5 (<1)
Other acute leukemia 45 (<1) 2 (<1)
Non-Hodgkin lymphoma 17562 (30) 1576 (34)
Hodgkin lymphoma 6205 (11) 1148 (25)
Multiple Myeloma 26000 (44) 1260 (27)
Other Malignancies 4830 (8) 342 (7)
Breast Cancer 987 (2) 103 (2)
Severe aplastic anemia 3 (<1) 0
Inherited abnormality of erythrocyte differentiation or function
1 (<1) 0
SCID and other immune system disorders 4 (<1) 0
If the patient being lost to follow up Variable No Yes Histiocytic disorder 1 (<1) 2 (<1) Autoimmune Diseases 197 (<1) 28 (<1) Other, specify 85 (<1) 11 (<1) Graft Source Bone Marrow 874 (1) 77 (2) Peripheral Blood 57698 (99) 4575 (98) Race Caucasian 47556 (81) 3572 (77) African-American 6396 (11) 555 (12) Asian 1103 (2) 76 (2) Pacific islander 12 (<1) 2 (<1) Native American 136 (<1) 12 (<1) Other 1868 (3) 262 (6) Unknown 1501 (3) 173 (4)
Kanofsky score prior to transplant
>=80 39430 (67) 3020 (65) <80 3112 (5) 201 (4) Missing 16030 (27) 1431 (31) Year of transplant 2000 4301 (7) 433 (9) 2001 4329 (7) 425 (9) 2002 4406 (8) 475 (10) 2003 4508 (8) 484 (10) 2004 5268 (9) 452 (10) 2005 5518 (9) 462 (10) 2006 5663 (10) 540 (12) 2007 5256 (9) 420 (9) 2008 5359 (9) 379 (8) 2009 6565 (11) 367 (8) 2010 7399 (13) 215 (5)
Applied selection criteria N HCT between 2000 to 2010 in US 112066
Research Consent 109548
Exclude identical twin for Allo 109173 Exclude cord blood for Auto 109163
Study Proposal 1411-58 Study Title:
Impact of Socioeconomic Status on Pediatric Stem Cell Transplant Outcomes
Kira Bona, MD, MPH, Dana-Farber Cancer Institute/Boston Children’s Hospital, Boston, MA, [email protected]
Joanne Wolfe, MD, MPH, Dana-Farber Cancer Institute/Boston Children’s Hospital, Boston, MA [email protected]
Christine Duncan, MD, Dana-Farber Cancer Institute/Boston Children’s Hospital, Boston, MA [email protected]
Leslie Lehmann, MD, Dana-Farber Cancer Institute/Boston Children’s Hospital, Boston, MA [email protected]
Hypothesis:
Lower socioeconomic status is correlated with increased morbidity (including graft-versus-host-disease and infection) and mortality in pediatric stem cell transplant.
Specific Aims:
1. To determine the relationship between family socioeconomic status and rates of graft-versus- host disease in pediatric stem cell transplant.
2. To determine the relationship between family socioeconomic status and rates of post-transplant infection in pediatric stem cell transplant.
3. To determine the relationship between family socioeconomic status and mortality in pediatric stem cell transplant.
Scientific Justification:
Pediatric allogeneic hematopoietic cell transplant (HCT) can provide life-saving treatment for children with malignant and non-malignant diseases. Childhood cancer remains the leading non-accidental cause of death for children in the United States, and HCT remains a cornerstone of treatment for many of these children. Steady advances in HCT donor selection, stem cell source, conditioning regimens and supportive care have led to improved medical outcomes, and the frequency of HCT in pediatrics is increasing annually.1 Despite these advances, pediatric HCT remains a physically and emotionally demanding treatment for children and their families, and transplant-related morbidity and mortality remain substantial.1 It is widely recognized that poverty is correlated with negative health outcomes, including mortality, in pediatric primary care and chronic illness. It is not known how poverty impacts pediatric HCT outcomes.2,4-11 Understanding the contribution of social determinants of health to pediatric HCT outcomes may provide targetable factors involved in residual morbidity and mortality. Though disease relapse represents the leading cause of post-transplant death in pediatric HCT, transplant-related complications including graft-versus-host disease (GVHD) and infection continue to account for significant morbidity and mortality.1 Identification of children at increased risk for these complications based on donor and recipient biologic characteristics has allowed for more individualized preventive measures.12 While little is known about the role of socioeconomic status in mediating HCT complications in children, recent studies in adults have provided provocative evidence that
socioeconomic status may impact HCT outcomes.13,14 In a comprehensive analysis of CIBMTR data, Baker et al found median household income to be independently associated with worse OS and higher
treatment-related mortality in unrelated donor HCT. 14 While pediatric HCT recipients were included in this analysis, they were not analyzed as a separate subgroup.
Recent publications in both the adult and pediatric HCT literature have demonstrated significant levels of financial hardship in the post-HCT population.15,16 In a recent single institution study by our research group, nearly 40% of pediatric HCT families reported food, housing, or energy insecurity post-transplant due to financial hardship, and 25% reported household income losses of >40% due to transplant. Exploratory analyses identified a univariate relationship between GVHD and both concrete hardship (food, housing, energy insecurity) and family income poverty (household income <200% Federal Poverty Level).16 Needless to say, such a finding is preliminary in that we were unable to control for known risk factors for GVHD due to an underpowered sample size. The relationship remained significant when controlling for stem cell source (unpublished). It is possible that the relationship between measures of poverty and GVHD is confounded by child or donor characteristics, or by unmeasured variables such as drug compliance. Our findings suggest an area worthy of further investigation in a large sample which can control for known risk factors.
Plausible mechanistic relationships between poverty and pediatric GVHD exist. Data in general pediatrics demonstrate higher rates infectious disease for children living in homes with concrete hardship.4,5,10,17,18 These healthcare consequences likely extrapolate to the HCT setting. Increased exposure to viral or fungal sources of infection in children living in overcrowded or substandard housing could plausibly increase the risk for GVHD. Similarly, prior publications have documented a relationship between medication adherence and socioeconomic status in children and adults with chronic disease which may also increase risk for GVHD in children.19,20
Whether poverty in fact impacts child clinical outcomes in HCT deserves further investigation. This study will investigate the relationship between family income poverty and childhood HCT outcomes—
specifically rates of GVHD, infection, and mortality. Patient Eligibility Population:
Inclusion criteria:
Children age 0-18 (inclusive)
Allogeneic stem cell transplant for any disease indication All stem cell sources (excluding DLI)
All myeloablative conditioning regimens
Follow-up data available regarding graft-versus-host-disease, infection, disease status, survival Socioeconomic data available including zip code, household gross annual income, insurance
status at
Time of transplant
Exclusion criteria:
Prior allogeneic or autologous hematopoietic cell transplant T-cell depleted transplant
Data Requirements:
Patient-related:
Age in years at time of transplant Gender
Race Ethnicity
Survival status
Cause of death (if applicable): relapse/progression/persistent disease, HCT related, new malignancy
Other, unknown
Disease-related:
Diagnosis
Functional status prior to preparative regimen (Karnofsky/Lansky) Disease status at transplant (early, intermediate, advanced, unknown)
Transplant-related:
Year of transplant
Donor type: matched sibling, other related donor, unrelated donor
Stem cell source: bone marrow, peripheral blood stem cells, umbilical cord Donor age
Donor gender Degree of HLA match
Conditioning regimen: Cy/TBI, Bu/Cy, Cy/other, other
GVHD prophylaxis: calcineurin inhibitor/methotrexate, calcineurin
inhibitor/methotrexate/corticosteroid, calcineurin inhibitor/mycophenolate mofetil Acute GVHD grades 2-4 prior to one year: yes or no
Chronic GVHD prior to one year: yes or no Clinically significant infection prior to one year
Total number inpatient days (day 0 to day 100) in first 100 days post-HCT
Socioeconomic-related:
Patient zip code
Health insurance coverage (Medicaid, private, Medicaid + private, self-pay, other) Combined household gross annual income
Study Design:
The primary objectives of this study are to identify the relationship between family socioeconomic status and rates of graft-versus-host-disease, infection, and survival in pediatric HCT recipients. This study will be performed as a retrospective cohort analysis utilizing data from the CIBMTR database. Patient demographic information for those with and without zip code/household income information will be compared to evaluate the representativeness of the cohort available for analysis. Categorical variables will be compared between groups using a Fisher exact test and continuous variables will be compared between groups using a Wilcoxon rank-sum test.
The primary predictor of interest is family income. Two variables are available in the CIBMTR database for potential use as predictor variable: (1) zip code which can be linked to median household income by U.S. census tract and (2) family reported combined household gross income. Choice of predictor variable will depend in part on completeness of data available in database. If both variables available for a significant proportion of the cohort of interest, a two-step analysis utilizing each variable as a predictor will be performed. If only one available for a significant proportion of the cohort, that variable will be chosen as the proxy for family income and utilized in all analyses.
Predictor variable 1: Aggregate data will obtained from the U.S. Census Bureau 2007-2011 American Community Survey which provides information on household income by zip code census tract. The
quartiles for the median household income by zip code for the sample will be calculated as a proxy for family SES.
Predictor variable 2: Family reported combined household gross income. Household income will be stratified in quartiles for analysis.
Aim 1: The proportion of patients with acute GVHD grades 2-4 and chronic GVHD prior to one year will