Background Immunization is one of modern medicine’s greatest achievements in the last three decades. Annually it can prevent nearly 2 to 3 million deaths. Understanding the determinants of effective immunization coverage is a critical undertaking. Accordingly, we set out to check the best available evidence of outstanding predictors of immunization coverage among children aged 12–23 months in Ethiopia. Method Electronic databases including PubMed, Google Scholar, HINARI, and SCOPUS, Web of Science, African Journals Online, Ethiopian Medical Journals were searched. The search process, study selection, critical appraisal, and data extraction were done independently by two reviewers using Joanna Briggs Institute Meta-analysis for Review Instrument (JBI-MAStARI). The difference between reviewers was resolved with a third person. The risk of bias was assessed by the Newcastle Ottawa Tool for observational studies. Data were extracted using the Microsoft Excel checklist and exported to STATA 13. Heterogeneity was assessed using I2, Funnel plot and Egger’s test was used to check for publication bias. Results We identified 26 studies with 15,042 children with mothers/caretakers to assess factors associated with immunization coverage and significant factors were: maternal formal education, (OR = 2.45; 95% CI: 1.62–3.72), paternal formal education, (OR = 1.01; 95% CI: 0.27–3.77), residence, (OR = 2.11; 95% CI: 1.00–4.45), birth at health facility (OR = 1.86; 95% CI: 0.99–3.49), family size less than four, (OR = 1.81; 95% CI: 1.16–2.84), knowledge on age of immunization to be completed (OR = 6.18;95% CI: 3.07–12.43), knowledge on immunization schedule (OR = 2.49; 95% CI: 1.35–4.59), time to travel to health faculties, (OR = 1.74; 95% CI: 0.62–4.89), antennal care, (OR = 3.11; 95% CI: 1.64–5.88), and tetanus toxoid vaccination, (OR = 4.82; 95% CI: 2.99–7.75). Conclusion Our findings showed that literacy, residence, awareness, family size, maternal health services use, and proximity of the health facilities were factors associated with full immunization. This implies that there is a need for primary health service expansion and health education to “hard to reach areas” to improve immunization coverage for children aged 12–23 months.

Background: Immunization is a cost-effective public health strategy. Immunization averts nearly three million deaths annually but immunization coverage is low in some countries and some regions within countries. The aim of this systematic review and meta-analysis is to assess pooled immunization coverage in Ethiopia. Method: A systematic search was done from PubMed, Google Scholar, EMBASE, HINARI, and SCOPUS, WHO's Institutional Repository for Information Sharing (IRIS), African Journals Online databases, grey literature and reviewing reference lists of already identified articles. A checklist from the Joanna Briggs Institute was used for appraisal. The I 2 was used to assess heterogeneity among studies. Funnel plot were used to assess publication bias. A random effect model was used to estimate the pooled prevalence of immunization among 12-23 month old children using STATA 13 software. Result: Twenty eight articles were included in the meta-analysis with a total sample size of 20,048 children (12-23 months old). The pooled prevalence of immunization among 12-23 month old children in Ethiopia was found to be 47% (95%, CI: 46.0, 47.0). A subgroup analysis by region indicated the lowest proportion of immunized children in the Afar region, 21% (95%, CI: 18.0, 24.0) and the highest in the Amhara region, 89% (95%, CI: 85.0, 92.0). Conclusion: Nearly 50% of 12-23 month old children in Ethiopia were fully vaccinated according to this systematic review and meta-analysis this indicates that the coverage, is still low with a clear disparity among regions. Our finding suggests the need for mobile and outreach immunization services for hard to reach areas, especially pastoral and semi-pastoral regions. In addition, more research may be needed to get more representative data for all regions.

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This study showed that the presence of green space may not itself encourage the necessary preventative health behaviours to tackle physical inactivity in urban populations. Development of more appropriate green spaces may be required. Further research is needed to shed light on the types green spaces that are most effective.

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Introduction: Primary CNS lymphomas (PCNSL) are heterogeneous, aggressive, extra-nodal non-Hodgkin lymphomas limited to the neuraxis. Published response rates to high-dose methotrexate (MTX) based induction regimens for PCNSL range from 35-78%. However, >50% of patients relapse and have a median survival of 2 months without additional treatment. Our ability to prognosticate outcomes is limited to clinical models like the International Extranodal Lymphoma Study Group (IELSG) score and Memorial Sloan-Kettering Cancer Center (MSKCC) classifier. There is an urgent need to develop improved biologic and radiologic predictive models for PCNSL to facilitate therapeutic advances. We hypothesize that a machine learning model using advanced magnetic resonance imaging (MRI) tumor characteristics will improve the accuracy of clinical models to predict response to MTX and survival outcomes. Methods: Data from patients with PCNSL treated at UT Southwestern and Parkland Health and Hospital System hospitals from 2008-2020 (n=95) were collected. An analytical dataset of 61 patients was selected based on the availability of T1 postcontrast (T1c) and T2w FLAIR MR images. A subset of 47 patients was used to evaluate MTX treatment response. Expert neuroradiologists drew regions of interest (ROIs) on the multiparametric MR images including whole tumor (consisting of edema + enhancing tumor + necrosis), enhancing tumor and necrosis (Figure 1). Response to methotrexate-based induction was defined per the International Primary CNS Lymphoma Collaborative Group (IPCG) criteria. For overall- and progression-free survival (OS and PFS) analysis, short (≤1 year) and long-term (>1 year) survivor groups were defined. A support vector machine (SVM) network was used for predicting treatment response to MTX and for predicting the OS groups. A Multinomial Naive Bayes (MNB) network was used for predicting the PFS groups. PyRadiomics package was used to extract 106 texture-based features from the combination of each MR image and tumor ROI. A total of 642 features were extracted from the imaging parameters. Clinical features including age, race, performance status, MSKCC class, IELSG score, histology, delay from 1st MRI to start of treatment, induction and consolidation treatments used were included in the analysis. Feature reduction methodology based on the feature importance derived from the gradient boost model was applied to reduce the number of features. 17 features (imaging = 14, clinical = 3) were used for predicting OS/PFS and 7 features (imaging = 5, clinical = 2) were used for predicting treatment response to MTX. Networks utilizing only clinical features were analyzed for comparison. The sklearn package in python was used for the machine learning analysis. 5-Fold cross validation was performed to generalize the network performance. Results: Baseline wclinical characteristics of the study population is shown in Table 1. Table 2 lists the accuracy, F1 score, sensitivity, specificity, positive predictive value, negative predictive value and area under the curve (AUC) values averaged for the 5-fold cross validation. The SVM network achieved a mean testing accuracy of 81.1 ± 12.3% for predicting the treatment response to MTX-based induction. Sensitivity, specificity and AUC values were 90.5 ± 13.1%, 63.3 ± 22.1% and 0.81 ± 0.14 respectively. The SVM and the MNB network achieved mean testing accuracies of 80.3 ± 11.4% and 83.3 ± 11.8% for predicting the long and short survival groups in OS and PFS respectively. Sensitivity, specificity and AUC values for the SVM and MNB networks were 79.3 ± 6.5%, 80.5 ± 16.5% and 0.86 ± 0.12 and 85.3 ± 12.9%, 81.9 ± 11.8% and 0.86 ± 0.13 respectively. The accuracy values for predicting treatment response to MTX, OS and PFS using only the clinical features were 61.6 ± 9.2%, 59.1 ± 16.4% and 62.1 ± 17.5% respectively. Conclusion: This machine learning model boosted the accuracy (≥20%) over currently validated clinical models alone in predicting response to methotrexate-based therapies and survival outcomes in PCNSL. The current analysis is limited by the small sample size, and we plan to statistically test this model across a larger dataset and report results at the meeting. Our preliminary results suggest that machine learning based radiomic analysis may predict biologic aggressiveness in PCNSL and has the potential to be integrated in clinical predictive tools and design of clinical trials. Disclosures Awan: Blueprint medicines: Consultancy; Celgene: Consultancy; Sunesis: Consultancy; Karyopharm: Consultancy; MEI Pharma: Consultancy; Astrazeneca: Consultancy; Genentech: Consultancy; Dava Oncology: Consultancy; Kite Pharma: Consultancy; Gilead Sciences: Consultancy; Pharmacyclics: Consultancy; Janssen: Consultancy; Abbvie: Consultancy. Desai:Boston Scientific: Consultancy, Other: Trial Finding.

Background This study analyzes sociodemographic barriers for primary CNS lymphoma (PCNSL) treatment and outcomes at a public safety-net hospital versus a private tertiary academic institution. We hypothesized that these barriers would lead to access disparities and poorer outcomes in the safety-net population. Methods We reviewed records of PCNSL patients from 2007-2020 (n = 95) at a public safety-net hospital (n = 33) and a private academic center (n = 62) staffed by the same university. Demographics, treatment patterns, and outcomes were analyzed. Results Patients at the safety-net hospital were significantly younger, more commonly Black or Hispanic, and had a higher prevalence of HIV/AIDS. They were significantly less likely to receive induction chemotherapy (67% vs 86%, p = 0.003) or consolidation autologous stem cell transplantation (0% vs. 44%, p = 0.001), but received more whole-brain radiation therapy (35% vs 15%, p = 0.001). Younger age and receiving any consolidation therapy were associated with improved progression-free (PFS, p = 0.001) and overall survival (OS, p = 0.001). Hospital location had no statistical impact on PFS (p = 0.725) or OS (p = 0.226) on an age-adjusted analysis. Conclusions Our study shows significant differences in treatment patterns for PCNSL between a public safety-net hospital and an academic cancer center. A significant survival difference was not demonstrated, which is likely multifactorial, but likely was positively impacted by the shared multidisciplinary care delivery between the institutions. As personalized therapies for PCNSL are being developed, equitable access including clinical trials should be advocated for resource-limited settings.