ArticleCancer informatics2020
Modeling the Spatial Distribution of Cancer and Determining the Associated Risk Factors.
Article in Cancer informatics, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper, 1 of them a synthesis that pooled it.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
1 citing paper in PubMed, 1 synthesis or guideline pooled it, 2 citations in OpenAlex.
- Lung cancer mortality and associated predictors: systematic review using 32 scientific research findings.Frontiers in oncology · 2023Pooled it
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Authors and funding
2 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundCancer is the second leading cause of death globally. Despite developing countries including Ethiopia continuing to shoulder the greatest burden, insufficient research has been conducted to determine geographical and other characteristic effects. The main objective of this study was to assess the distribution and risk of cancer and determine the effects of some common clinical patient characteristics on current patient status by taking into account the spatial effect.
methodsThe data for this study were obtained from the oncology ward of Felege Hiwot Referral Hospital, Bahir Dar, Ethiopia. About 415 cancer patients were included in the study. Spatial mixed ordinal logistic regression model was used to explore the geographical patterns of the incidence of cancer and identify the risk factors.
resultsThe findings of this study show that only 1.45% of patients were cured and 46.02% improved, whereas the rest have shown no change and even worse status after treatment. The estimated odds of patients who received chemotherapy was 4.284 times the estimated odds of patients who received palliative care. Prognostic factor (stage of cancer tumor), complication of cancer such as anemia during diagnosis, and treatment of patients given in the hospital had significant effect on the patient status.
conclusionPatients without anemia were more likely to be cured and improved than patients with anemia during diagnosis. Most of the patients had advanced stage (IV) of cancer tumor, which dismantles the capability of the treatment to be less effective. There was negative spatial effect on the incidence of cancer, indicating that districts with higher cancer incidence were usually surrounded by districts with lower incidence.
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