Retinoblastoma

GBD 2019 estimated each epidemiological quantity of interest—incidence, prevalence, mortality, years lived with disability (YLDs), years of life lost (YLLs), and disability-adjusted life-years (DALYs)—for 23 age groups; males, females, and both sexes combined; and 204 countries and territories that were grouped into 21 regions and seven super-regions. For GBD 2019, nine countries and territories (Cook Islands, Monaco, San Marino, Nauru, Niue, Palau, Saint Kitts and Nevis, Tokelau, and Tuvalu) were added, such that the GBD location hierarchy now includes all WHO member states. GBD 2019 includes subnational analyses for Italy, Nigeria, Pakistan, the Philippines, and Poland, and 16 countries previously estimated at subnational levels (Brazil, China, Ethiopia, India, Indonesia, Iran, Japan, Kenya, Mexico, New Zealand, Norway, Russia, South Africa, Sweden, the UK, and the USA). All subnational analyses are at the first level of administrative organisation within each country except for New Zealand (by Māori ethnicity), Sweden (by Stockholm and non-Stockholm), the UK (by local government authorities), and the Philippines (by province). In this publication, we present subnational estimates for Brazil, India, Indonesia, Japan, Kenya, Mexico, Sweden, the UK, and the USA; given space constraints, these results are presented in appendix 2. At the most detailed spatial resolution, we generated estimates for 990 locations. The GBD diseases and injuries analytical framework generated estimates for every year from 1990 to 2019.
Diseases and injuries were organised into a levelled cause hierarchy from the three broadest causes of death and disability at Level 1 to the most specific causes at Level 4. Within the three Level 1 causes—communicable, maternal, neonatal, and nutritional diseases; non-communicable diseases; and injuries—there are 22 Level 2 causes, 174 Level 3 causes, and 301 Level 4 causes (including 131 Level 3 causes that are not further disaggregated at Level 4; see appendix 1 sections 3.4 and 4.12 for the full list of causes). 364 total causes are non-fatal and 286 are fatal. For GBD 2019, 12 new causes were added to the modelling framework: pulmonary arterial hypertension, eye cancer, soft tissue and other extraosseous sarcomas, malignant neoplasm of bone and articular cartilage, and neuroblastoma and other peripheral nervous cell tumours at Level 3, and hepatoblastoma, Burkitt lymphoma, other non-Hodgkin lymphoma, retinoblastoma, other eye cancers, and two sites of osteoarthritis (hand and other joints) at Level 4.

We retrospectively reviewed the medical records of consecutive patients with RB between 2006 and 2015 at 29 Chinese treatment centers over a 10-year period. Patients who had experienced trauma before the diagnosis of RB were selected and studied in further detail. This study adhered to the principles of the Declaration of Helsinki. Ethics Committee approval was obtained from Beijing Children’s Hospital. Signed informed consent was obtained from all of the children’s parents.
The collected patient data included age at initial trauma occurrence, sex (male, female), RB laterality (unilateral, bilateral), and heredity (sporadic, familial). The primary clinical presentation, diagnosis, and management before RB was suspected were also analyzed. Additionally, the site of trauma was noted. Intraocular RB staging at the time of detection was applied based on the International Classification of Intraocular Retinoblastoma (IIRC).[11 ] The interval between trauma occurrence and RB diagnosis was also calculated. Histopathological details of the enucleated eye were noted. Treatment outcomes (alive/dead) were recorded. The development of orbital recurrence or distant metastasis was recorded during the follow-up period.
For the statistical analyses, we used a statistical software package (SPSS version 22.0 IBM-SPSS, Chicago, IL). We first described the distribution of the main parameters by calculating their median or mean and standard deviation. We compared the assessed parameters between the two groups using the Student’s t-test or Mann–Whitney U test. Frequencies were compared using the chi-square test. The enucleation-free survival was determined using the Kaplan–Meier method and compared using the log-rank test. A P-value of < 0.05 was considered to be statistically significant.

A Weighted Least Squares method was used in order to estimate the age‐adjusted retinoblastoma cases in the age groups from 1 to 19 years old. This method allows for the computation of linear and non‐linear regression models.

The Signal Amplification and Risk‐Specific Stratification (SARSS) modeling technique was used to assess mortality risk by multivariable modeling of social determinants of health. This SARSS model allows for the assessment of the magnitude of confounding prior to adjustment in a multivariable model, magnitude of confounding MAC (=crude‐adjusted/crude × 100). In addition, this model provides the opportunity to assess for the effect measure modification implying clinically and biologically differences in the point estimate by stratum.¹²To determine the independence of the variables utilized in this study by sex, a chi‐squared statistic and Fischer's exact were used to compensate for the small, expected cell count in the data. The hypothesis‐driven analyses implying the mortality risk modeling by sex was performed using the binomial regression model. This model allows for a risk prediction of the probability of dying given the sex as male as well as female. To determine the risk ratio (RR), the risk in the exposed namely, male was divided by the risk in the unexposed namely, female. This quantification was restricted to univariable modeling. The survival analyses utilized the Cox proportional hazard modeling and met the survival of assumption that the hazard rate remains constant over time. Both the univariable and multivariable Cox proportional model were utilized in assessing the risk of dying as a function of time, given sex of children with retinoblastoma.
Since a single risk factor does not entirely predict the risk, the Cox proportional hazard model at a multivariable level was utilized to control for the confounding. To enter any potentially confounding variable into the model building, such variable was assessed for the MAC. If the MAC was greater than 10% based on MAC, such a variable was entered into the model.¹²The life table, Kaplan–Meier and Nelson–Helen cumulative hazards were used to illustrate the proportion of survival and cumulative hazard, respectively. Additionally, the log‐rank test was used to examine the quality of survival by sex. Furthermore, the margins plots were used to assess the predictive mean of dying, given the sex of children with retinoblastoma.
All tests were two‐tailed, and the type‐1 error tolerance were set at 5% (95% CI) and 1% (99% CI) for univariable and multivariable models, respectively. The entire analyses were performed using STATA, version 16 (STATA Corporation).