Cervical cancer prevention program in Mexico disrupted due to COVID-19 pandemic: Challenges and opportunities.

Introduction: The COVID-19 pandemic disrupted the preventive services for cervical cancer (CC) control programs in Mexico, which will result in increased mortality. This study aims to assess the impact of the pandemic on the interruption of three preventive actions in the CC prevention program in Mexico. Methods: This study is a retrospective time series analysis based on administrative records for the uninsured population served by the Mexican Ministry of Health. Patient data were retrieved from the outpatient service information system and the hospital discharge database for the period 2017-2021. Data were aggregated by month, distinguishing a pre-pandemic and a pandemic period, considering April 2020 as the start date of the pandemic. A Poisson time series analysis was used to model seasonal and secular trends. Five process indicators were selected to assess the disruption of the CC program, these were analyzed as monthly data (N=39 pre-pandemic, N=21 during the pandemic). HPV vaccination indicators (number of doses and coverage) and diagnostic characteristics of CC cases were analyzed descriptively. The time elapsed between diagnosis and treatment initiation in CC cases was modeled using restricted cubic splines from robust regression. Results: Annual HPV vaccination coverage declined dramatically after 2019 and was almost null in 2021. The number of positive Papanicolaou smears decreased by 67.8% (90%CI: -72.3, -61.7) in April-December 2020, compared to their expected values without the pandemic. The immediate pandemic shock (April 2020) in the number of first-time and recurrent colposcopies was -80.5% (95%CI:-83.5, -77.0) and -77.9% (95%CI: -81.0, -74.4), respectively. An increasing trend was observed in the proportion of advanced stage and metastatic CC cases. The fraction of CC cases that did not receive medical treatment or surgery increased, as well as CC cases that received late treatment after diagnosis. Conclusions: Our analyses show significant impact of the COVID-19 pandemic with declines at all levels of CC prevention and increasing inequalities. The restarting of the preventive programs against CC in Mexico offers an opportunity to put in place actions to reduce the disparities in the burden of disease between socioeconomic levels.

Lethality risk markers by sex and age-group for COVID-19 in Mexico: a cross-sectional study based on machine learning approach.

BACKGROUND: Mexico ranks fifth worldwide in the number of deaths due to COVID-19. Identifying risk markers through easily accessible clinical data could help in the initial triage of COVID-19 patients and anticipate a fatal outcome, especially in the most socioeconomically disadvantaged regions. This study aims to identify markers that increase lethality risk in patients diagnosed with COVID-19, based on machine learning (ML) methods. Markers were differentiated by sex and age-group. METHODS: A total of 11,564 cases of COVID-19 in Mexico were extracted from the Epidemiological Surveillance System for Viral Respiratory Disease. Four ML classification methods were trained to predict lethality, and an interpretability approach was used to identify those markers. RESULTS: Models based on Extreme Gradient Boosting (XGBoost) yielded the best performance in a test set. This model achieved a sensitivity of 0.91, a specificity of 0.69, a positive predictive value of 0.344, and a negative predictive value of 0.965. For female patients, the leading markers are diabetes and arthralgia. For males, the main markers are chronic kidney disease (CKD) and chest pain. Dyspnea, hypertension, and polypnea increased the risk of death in both sexes. CONCLUSIONS: ML-based models using an interpretability approach successfully identified risk markers for lethality by sex and age. Our results indicate that age is the strongest demographic factor for a fatal outcome, while all other markers were consistent with previous clinical trials conducted in a Mexican population. The markers identified here could be used as an initial triage, especially in geographic areas with limited resources.

Application of Data Science for Cluster Analysis of COVID-19 Mortality According to Sociodemographic Factors at Municipal Level in Mexico

Mexico is among the five countries with the largest number of reported deaths from COVID-19 disease, and the mortality rates associated to infections are heterogeneous in the country due to structural factors concerning population. This study aims at the analysis of clusters related to mortality rate from COVID-19 at the municipal level in Mexico from the perspective of Data Science. In this sense, a new application is presented that uses a machine learning hybrid algorithm for generating clusters of municipalities with similar values of sociodemographic indicators and mortality rates. To provide a systematic framework, we applied an extension of the International Business Machines Corporation (IBM) methodology called Batch Foundation Methodology for Data Science (FMDS). For the study, 1,086,743 death certificates corresponding to the year 2020 were used, among other official data. As a result of the analysis, two key indicators related to mortality from COVID-19 at the municipal level were identified: one is population density and the other is percentage of population in poverty. Based on these indicators, 16 municipality clusters were determined. Among the main results of this research, it was found that clusters with high values of mortality rate had high values of population density and low poverty levels. In contrast, clusters with low density values and high poverty levels had low mortality rates. Finally, we think that the patterns found, expressed as municipality clusters with similar characteristics, can be useful for decision making by health authorities regarding disease prevention and control for reinforcing public health measures and optimizing resource distribution for reducing hospitalizations and mortality.

Correlation between mobility in mass transport and mortality due to COVID-19: A comparison of Mexico City, New York, and Madrid from a data science perspective.

In most big cities, public transports are enclosed and crowded spaces. Therefore, they are considered as one of the most important triggers of COVID-19 spread. Most of the existing research related to the mobility of people and COVID-19 spread is focused on investigating highly frequented paths by analyzing data collected from mobile devices, which mainly refer to geo-positioning records. In contrast, this paper tackles the problem by studying mass mobility. The relations between daily mobility on public transport (subway or metro) in three big cities and mortality due to COVID-19 are investigated. Data collected for these purposes come from official sources, such as the web pages of the cities' local governments. To provide a systematic framework, we applied the IBM Foundational Methodology for Data Science to the epidemiological domain of this paper. Our analysis consists of moving averages with a moving window equal to seven days so as to avoid bias due to weekly tendencies. Among the main findings of this work are: a) New York City and Madrid show similar distribution on studied variables, which resemble a Gauss bell, in contrast to Mexico City, and b) Non-pharmaceutical interventions don't bring immediate results, and reductions to the number of deaths due to COVID are observed after a certain number of days. This paper yields partial evidence for assessing the effectiveness of public policies in mitigating the COVID-19 pandemic.

Health status and gerontological evaluation in Mexican older adults in the face of the COVID-19 pandemic.

INTRODUCTION: Older adults constitute the most vulnerable population group to the COVID-19 pandemic. In Mexico, their biopsychosocial conditions might intensify their vulnerability. METHOD: Affiliation to health systems, health conditions and gerontological evaluation of 3,218 older adults were analyzed following the methodology of the PAHO-Mexico Health, Well-being and Aging Survey. RESULTS: 88.6 % of older adults referred being affiliated to health systems; 30.2 %, 52.4 %, 10.3 %, 4.1 % and 5.6 % referred suffering from diabetes mellitus, high blood pressure, chronic obstructive pulmonary disease, heart disease and cerebrovascular disease, respectively; 15.6 % reported urinary incontinence, and 11.3%, fecal incontinence; 12.1 % of the women referred having suffered from breast cancer at some point, and 6.3 %, cervical cancer. The habit of smoking tobacco was observed in 11.1 %, risk of malnutrition in 32.8 %, established malnutrition in 4.1 %, functional dependence for basic and instrumental activities of daily life in 16.3 % and 17.6 %, respectively. CONCLUSION: Comprehensive gerontological evaluation is essential for efficient care of older adults who suffer from COVID-19, and for adequate care of the effects or health conditions at the conclusion of the confinement imposed by the pandemic.

INTRODUCCIÓN: Los adultos mayores constituyen el grupo más vulnerable ante la pandemia por COVID-19; en México, sus condiciones biopsicosociales podrían potenciar su vulnerabilidad. MÉTODO: Se analizó afiliación a sistemas de salud, condiciones de salud y evaluación gerontológica de 3218 adultos mayores conforme a la metodología de la Encuesta Salud, Bienestar y Envejecimiento OPS-México. RESULTADOS: 88.6 % de los adultos mayores refirió afiliación a un sistema de salud; 30.2, 52.4, 10.3, 4.1 y 5.6 % indicaron padecer diabetes mellitus, hipertensión arterial, enfermedad pulmonar obstructiva crónica, enfermedad cardiaca y evento vascular cerebral, respectivamente; 15.6 % reportó incontinencia urinaria y 11.3 %, fecal; 12.1 % de las mujeres indicó haber padecido en algún momento cáncer de mama y 6.3 %, cáncer cervicouterino. Se observó hábito de fumar tabaco en 11.1 %, riesgo de malnutrición en 32.8 %, malnutrición establecida en 4.1 %, dependencia funcional para las actividades básicas en 16.3 % e instrumentales de la vida diaria en 17.6 %. CONCLUSIÓN: La evaluación gerontológica integral es fundamental para la atención eficiente de los adultos mayores que padecen COVID-19 y para la adecuada atención por los efectos o condiciones de salud al terminar el confinamiento por la pandemia.