Medical Cost to Treat Cervical Cancer Patients at a Social Security Third Level Oncology Hospital in Mexico City

Background: Cervical Cancer (CC) is an important public health problem worldwide. In 2015, CC was the sixth leading cause of death for women aged 30-59 years in Mexico. Despite the importance of having high-quality and accurate estimates of CC treatment costs that can be used to effectively evaluate the impact of preventive programs, there is scarce information on this topic in Mexico. Objective: To estimate the treatment costs by stage diagnosis in patients with CC at a Mexican Social Security Institute (IMSS) oncology hospital in Mexico City. Methods: An observational retrospective study of the resources used to treat 346 women with CC was conducted. Medical charts were reviewed and relevant resource use information was extracted using a data collection instrument that was created based on treatment guidelines. Data were classified into nine cost categories to estimate the total cost per patient. Results: The mean age of patients in the study sample was 54.3 years (range: 41-67), and the average body mass index (BMI) was >26 kg/m2. Among the participants, 37% were smokers, 39% had diabetes, and 56% had hypertension. The medical cost for stages I-IV ranged from $4,738 to$6,058 USD, with an estimated average cost of $5,114 USD. Conclusion: Total treatment costs per patient are high, especially since they were estimated considering only 7.5 months of treatment. This is the first study to estimate the annual cost to treat CC in Mexico and to additionally document the resource pattern use, cost by stage of cancer, and the distribution by cost categories

Short-term effects of ambient temperature on non-external and cardiovascular mortality among older adults of metropolitan areas of Mexico

Multi-city studies assessing the association between acute exposure to temperature and mortality in Latin American are limited. To analyze the short-term effect of changes in temperature (increase and decrease) on daily non-external and cardiovascular mortality from 1998 to 2014, in people 65 years old and over living in 10 metropolitan areas of Mexico. Analyses were performed through Poisson regression models with distributed lag non-linear models. Statistical comparison of minimum mortality temperature (MMT) and city-specific cutoffs of 24-h temperature mean values (5th/95th and 1st/99th percentiles) were used to obtain the mortality relative Risk (RR) for cold/hot and extreme cold/extreme hot, respectively, for the same day and lags of 0–3, 0–7, and 0–21 days. A meta-analysis was conducted to synthesize the estimates (RRpooled). Significant non-linear associations of temperature-mortality relation were found in U or inverted J shape. The best predictors of mortality associations with cold and heat were daily temperatures at lag 0–7 and lag 0–3, respectively. RRpooled of non-external causes was 6.3% (95%CI 2.7, 10.0) for cold and 10.2% (95%CI 4.4, 16.2) for hot temperatures. The RRpooled for cardiovascular mortality was 7.1% (95%CI 0.01, 14.7) for cold and 7.1% (95%CI 0.6, 14.0) for hot temperatures. Results suggest that, starting from the MMT, the changes in temperature are associated with an increased risk of non-external and specific causes of mortality in elderly people. Generally, heat effects on non-external and specific causes of mortality occur immediately, while cold effects occur within a few days and last longer.The study was supported by the fund of the Secretary of Environment and Natural Resources (SEMARNAT) and the Mexican Council of Science and Technology (CONACYT), grant SEMARNAT-2014-1-249465.Peer reviewe

Clinical characterization of data-driven diabetes subgroups in Mexicans using a reproducible machine learning approach

Introduction Previous reports in European populations demonstrated the existence of five data-driven adult-onset diabetes subgroups. Here, we use self-normalizing neural networks (SNNN) to improve reproducibility of these data-driven diabetes subgroups in Mexican cohorts to extend its application to more diverse settings.Research design and methods We trained SNNN and compared it with k-means clustering to classify diabetes subgroups in a multiethnic and representative population-based National Health and Nutrition Examination Survey (NHANES) datasets with all available measures (training sample: NHANES-III, n=1132; validation sample: NHANES 1999–2006, n=626). SNNN models were then applied to four Mexican cohorts (SIGMA-UIEM, n=1521; Metabolic Syndrome cohort, n=6144; ENSANUT 2016, n=614 and CAIPaDi, n=1608) to characterize diabetes subgroups in Mexicans according to treatment response, risk for chronic complications and risk factors for the incidence of each subgroup.Results SNNN yielded four reproducible clinical profiles (obesity related, insulin deficient, insulin resistant, age related) in NHANES and Mexican cohorts even without C-peptide measurements. We observed in a population-based survey a high prevalence of the insulin-deficient form (41.25%, 95% CI 41.02% to 41.48%), followed by obesity-related (33.60%, 95% CI 33.40% to 33.79%), age-related (14.72%, 95% CI 14.63% to 14.82%) and severe insulin-resistant groups. A significant association was found between the SLC16A11 diabetes risk variant and the obesity-related subgroup (OR 1.42, 95% CI 1.10 to 1.83, p=0.008). Among incident cases, we observed a greater incidence of mild obesity-related diabetes (n=149, 45.0%). In a diabetes outpatient clinic cohort, we observed increased 1-year risk (HR 1.59, 95% CI 1.01 to 2.51) and 2-year risk (HR 1.94, 95% CI 1.13 to 3.31) for incident retinopathy in the insulin-deficient group and decreased 2-year diabetic retinopathy risk for the obesity-related subgroup (HR 0.49, 95% CI 0.27 to 0.89).Conclusions Diabetes subgroup phenotypes are reproducible using SNNN; our algorithm is available as web-based tool. Application of these models allowed for better characterization of diabetes subgroups and risk factors in Mexicans that could have clinical applications