Level of human development is associated with cervical cancer stage at diagnosis.
Diama Bhadra ValeCatherine SauvagetRichard MuwongeLuiz Claudio Santos ThulerPartha BasuLuiz Carlos ZeferinoRengaswamy SankaranarayananPublished in: Journal of obstetrics and gynaecology : the journal of the Institute of Obstetrics and Gynaecology (2018)
The objective was to describe the cervical cancer cases in Brazil by the age-group and stage at diagnosis, and to associate them with the human development index (HDI), where the women live. This was a retrospective study that used data from the Brazilian hospital-based cancer registry from 2005 to 2014. The data were accessed by 5-year age/groups and the federal units. The association between the proportion of cases at Stage I and HDI was estimated in an adjusted linear regression analysis. Among the staged cases, the proportions of cases diagnosed at FIGO Stage I, II, III and IV were 21.2%, 30.7%, 39.9% and 8.2%, respectively. The cases were diagnosed mostly in women aged 45-49 years. There was a significant increase in the proportion of Stage I cases with an increasing HDI (coefficient, 0.46; 95% confidence interval, 0.17-0.76). In conclusion, most of the cases were diagnosed at late stages. The stage at the diagnosis was associated with the human development level. Impact Statement What is already known on this subject? The stage at diagnosis varies according to the level of organisation of the cancer control programme. It is expected that in well-developed programmes there will be a shift to an early stage diagnosis. What the results of this study add? The stage at a diagnosis was associated with the human development level where the women live in Brazil, where most cases were diagnosed at the late stages. What the implications are of these findings for clinical practice and/or further research? This analysis can help with better planning strategies for cancer control. Regional strategies would improve the efficiency of cancer care interventions in countries with large socioeconomic disparities.
Keyphrases
- endothelial cells
- early stage
- papillary thyroid
- pluripotent stem cells
- clinical practice
- clinical trial
- squamous cell carcinoma
- healthcare
- type diabetes
- physical activity
- radiation therapy
- electronic health record
- adipose tissue
- pregnancy outcomes
- computed tomography
- deep learning
- lymph node metastasis
- artificial intelligence
- data analysis
- study protocol
- locally advanced
- neural network