Evidence map›Paper›PMID 38902624›Full record

ArticleBMC cancer2024

Survival status and predictors of mortality among cervical cancer patients at oncologic centers in Addis Ababa, Ethiopia: a follow up study.

Samuel Dessu Sifer

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In one paragraph

Article in BMC cancer, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed, 1 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

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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.

2 · The registry

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3 · Its place in the literature

Who cites it

3 citing papers in PubMed, 1 synthesis or guideline pooled it.

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4 · The record

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PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

1 author.

Samuel Dessu SiferDepartment of Public Health, Yekatit 12 Hospital Medical College, Addis Ababa, Ethiopia. dessusamuel@yahoo.com.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundCervical cancer (CC) ranks as the third most commonly diagnosed cancer and the fourth leading cause of cancer-related deaths among women globally. In Addis Ababa, there is a shortage of available evidence concerning the phenomenon of survival time and its predictors among women diagnosed with CC. Therefore, this study aimed to assess the survival status and predictors of mortality among CC patients at oncologic centers in Addis Ababa, Ethiopia.

methodsA facility-based retrospective cohort study was conducted among records of women with cervical cancer enrolled from the 1st of January 2017 to the 30th of December 2022 among 252 cervical cancer patients. Data were collected using a pretested, structured data collection checklist by trained data collectors. The Kaplan-Meier survival curve was used to estimate the survival time of the respondents. The Cox multivariable regression model was carried out to identify predictors of CC. Variables with P-value < 0.05 in multivariable analysis were declared as statistically significant.

resultsThe cumulative proportion of surviving at the end of the 10th and 20th month was 99.6% (95%CI: 97.02, 99.94) and 96.99% (95%CI: 93.41, 98.64), respectively. Similarly, it was 92.67% (95%CI: 87.65, 95.70), 85.9% (95%CI: 78.68, 90.94), 68.0% (95%CI: 57.14, 76.66) and 18.27% (8.38, 31.16) at the end of 30th, 40th, 50th and 60th monthly respectively. The overall median survival time was 54 months (95%CI: 52.6, 55.4). The incidence of death among a cohort of women with CC was 7.34 per 1000 person months. Being anemic (AHR: 4.77; 95%CI: 1.93, 11.77; P-value: 0.001), took a single cancer treatment (AHR: 1.92; 95%CI: 1.01, 3.64; P-value: 0.046) and HIV sero status positive (AHR: 2.05; 95%CI: 1.01, 4.19; P-value: 0.048) were statistically significant in multivariable cox proportional hazard model. CONCLUSION AND RECOMMENDATION: Anemia, treatment initiation and HIV-sero status were independent predictors of mortality among women admitted with CC. It is imperative to enhance early screening initiatives and treatment resources for CC, alongside fostering public awareness through collaboration with various media outlets concerning preventive measures, screening procedures, and treatment alternatives for CC.

Indexed as

Uterine Cervical NeoplasmsAdultAgedEthiopiaFemaleFollow-Up StudiesHumansKaplan-Meier EstimateMiddle AgedPrognosisRetrospective StudiesSurvival RateYoung AdultAddis AbabaCervical cancerOncologic centersPredictorsSurvival status

Identifiers

PMID38902624
PMCPMC11188214

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LicenceCC BY
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.