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Abstract Background Satellite-based PM2.5 predictions are being used to advance exposure science and air-pollution epidemiology in developed countries; including emerging evidence about the impacts of PM2.5 on acute health outcomes beyond the cardiovascular and respiratory systems, and the potential modifying effects from individual-level factors in these associations. Research on these topics is lacking in low and middle income countries. We aimed to explore the association between short-term exposure to PM2.5 with broad-category and cause-specific mortality outcomes in the Mexico City Metropolitan Area (MCMA), and potential effect modification by age, sex, and SES characteristics in such associations. Methods We used a time-stratified case-crossover study design with 1,479,950 non-accidental deaths from the MCMA for the period of 2004–2019. Daily 1 × 1 km PM2.5 (median = 23.4 μg/m3; IQR = 13.6 μg/m3) estimates from our satellite-based regional model were employed for exposure assessment at the sub-municipality level. Associations between PM2.5 with broad-category (organ-system) and cause-specific mortality outcomes were estimated with distributed lag conditional logistic models. We also fit models stratifying by potential individual-level effect modifiers including; age, sex, and individual SES-related characteristics namely: education, health insurance coverage, and job categories. Odds ratios were converted into percent increase for ease of interpretation. Results PM2.5 exposure was associated with broad-category mortality outcomes, including all non-accidental, cardiovascular, cerebrovascular, respiratory, and digestive mortality. A 10-μg/m3 PM2.5 higher cumulative exposure over one week (lag06) was associated with higher cause-specific mortality outcomes including hypertensive disease [2.28% (95%CI: 0.26%–4.33%)], acute ischemic heart disease [1.61% (95%CI: 0.59%–2.64%)], other forms of heart disease [2.39% (95%CI: -0.35%–5.20%)], hemorrhagic stroke [3.63% (95%CI: 0.79%–6.55%)], influenza and pneumonia [4.91% (95%CI: 2.84%–7.02%)], chronic respiratory disease [2.49% (95%CI: 0.71%–4.31%)], diseases of the liver [1.85% (95%CI: 0.31%–3.41%)], and renal failure [3.48% (95%CI: 0.79%–6.24%)]. No differences in effect size of associations were observed between age, sex and SES strata. Conclusions Exposure to PM2.5 was associated with non-accidental, broad-category and cause-specific mortality outcomes beyond the cardiovascular and respiratory systems, including specific death-causes from the digestive and genitourinary systems, with no indication of effect modification by individual-level characteristics.

Abstract Background The study aimed to assess the performance of a multidisciplinary-team diabetes care program called DIABETIMSS on glycemic control of type 2 diabetes (T2D) patients, by using available observational patient data and machine-learning-based targeted learning methods. Methods We analyzed electronic health records and laboratory databases from the year 2012 to 2016 of T2D patients from six family medicine clinics (FMCs) delivering the DIABETIMSS program, and five FMCs providing routine care. All FMCs belong to the Mexican Institute of Social Security and are in Mexico City and the State of Mexico. The primary outcome was glycemic control. The study covariates included: patient sex, age, anthropometric data, history of glycemic control, diabetic complications and comorbidity. We measured the effects of DIABETIMSS program through 1) simple unadjusted mean differences; 2) adjusted via standard logistic regression and 3) adjusted via targeted machine learning. We treated the data as a serial cross-sectional study, conducted a standard principal components analysis to explore the distribution of covariates among clinics, and performed regression tree on data transformed to use the prediction model to identify patient sub-groups in whom the program was most successful. To explore the robustness of the machine learning approaches, we conducted a set of simulations and the sensitivity analysis with process-of-care indicators as possible confounders. Results The study included 78,894 T2D patients, from which 37,767patients received care through DIABETIMSS. The impact of DIABETIMSS ranged, among clinics, from 2 to 8% improvement in glycemic control, with an overall (pooled) estimate of 5% improvement. T2D patients with fewer complications have more significant benefit from DIABETIMSS than those with more complications. At the FMC’s delivering the conventional model the predicted impacts were like what was observed empirically in the DIABETIMSS clinics. The sensitivity analysis did not change the overall estimate average across clinics. Conclusions DIABETIMSS program had a small, but significant increase in glycemic control. The use of machine learning methods yields both population-level effects and pinpoints the sub-groups of patients the program benefits the most. These methods exploit the potential of routine observational patient data within complex healthcare systems to inform decision-makers.

Abstract Background The Mexican health system segments access and right to healthcare according to worker position in the labour market. In this contribution we analyse how access and continuity of healthcare gets interrupted by employment turnover in the labour market, including its formal and informal sectors, as experienced by affiliates to the Mexican Institute of Social Security (IMSS) at national level, and of workers with type 2 diabetes (T2DM) in Mexico City. Methods Using data from the National Employment and Occupation Survey, 2014, and from IMSS electronic medical records for workers in Mexico City, we estimated annual employment turnover rates to measure the loss of healthcare access due to labour market dynamics. We fitted a binary logistic regression model to analyse the association between sociodemographic variables and employment turnover. Lastly we analysed job-related access to health care in relation to employment turnover events. Results At national level, 38.3% of IMSS affiliates experienced employment turnover at least once, thus losing the right to access to healthcare. The turnover rate for T2DM patients was 22.5%. Employment turnover was more frequent at ages 20–39 (38.6% national level; 28% T2DM) and among the elderly (62.4% national level; 26% T2DM). At the national level, higher educational levels (upper-middle, OR = 0.761; upper, OR = 0.835) and income (5 minimum wages or more, OR = 0.726) were associated with lower turnover. Being single and younger were associated with higher turnover (OR = 1.413). T2DM patients aged 40–59 (OR = 0.655) and with 5 minimum wages or more (OR = 0.401) experienced less turnover. Being a T2DM male patient increased the risk of experiencing turnover (OR = 1.166). Up to 89% of workers losing IMSS affiliation and moving on to other jobs failed to gain job-related access to health services. Only 9% gained access to the federal workers social security institute (ISSSTE). Conclusions Turnover across labour market sectors is frequently experienced by the workforce in Mexico, worsening among the elderly and the young, and affecting patients with chronic diseases. This situation needs to be prospectively addressed by health system policies that aim to expand the financial health protection during an employment turnover event.

Abstract Background Permanent occupational disability is one of the most severe consequences of diabetes that impedes the performance of usual working activities among economically active individuals. Survival rates and worker compensation expenses have not previously been examined among Mexican workers. We aimed to describe the worker compensation expenses derived from pension payments and also to examine the survival rates and characteristics associated with all-cause mortality, in a cohort of 34,014 Mexican workers with permanent occupational disability caused by diabetes during the years 2000–2013 at the Mexican Institute of Social Security. Methods A cross-sectional analysis study was conducted using national administrative records data from the entire country, regarding permanent occupational disability medical certification, pension payment and vital status. Survival rates were estimated using the Kaplan–Meier method. Multivariate Cox proportional hazard model was used to estimate adjusted hazard ratios (HR) and 95 % confidence intervals (95 % CI) in order to assess the cohort characteristics and all-cause mortality risk. Total expenses derived from pension payments for the period were accounted for in U.S. dollars (USD, 2013). Results There were 12,917 deaths in 142,725.1 person-years. Median survival time was 7.26 years. After multivariate adjusted analysis, males (HR, 1.39; 95 % CI, 1.29–1.50), agricultural, forestry, and fishery workers (HR, 1.41; 95 % CI, 1.15–1.73) and renal complications (HR, 3.49; 95 % CI, 3.18–3.83) had the highest association with all-cause mortality. The all-period expenses derived from pension payments amounted to $777.78 million USD (2013), and showed a sustained increment: from $58.28 million USD in 2000 to $111.62 million USD in 2013 (percentage increase of 91.5 %). Conclusions Mexican workers with permanent occupational disability caused by diabetes had a median survival of 7.26 years, and those with renal complications showed the lowest survival in the cohort. Expenses derived from pension payments amounted to $ 777 million USD and showed an important increase from 2000 to 2013.

Abstract Background The Mexican Institute of Social Security (IMSS) is the largest health care provider in Mexico, covering about 48% of the Mexican population. In this report, we describe the epidemiological patterns related to confirmed cases, hospitalizations, intubations, and in-hospital mortality due to COVID-19 and associated factors, during five epidemic waves recorded in the IMSS surveillance system. Methods We analyzed COVID-19 laboratory-confirmed cases from the Online Epidemiological Surveillance System (SINOLAVE) from March 29th, 2020, to August 27th, 2022. We constructed weekly epidemic curves describing temporal patterns of confirmed cases and hospitalizations by age, gender, and wave. We also estimated hospitalization, intubation, and hospital case fatality rates. The mean days of in-hospital stay and hospital admission delay were calculated across five pandemic waves. Logistic regression models were employed to assess the association between demographic factors, comorbidities, wave, and vaccination and the risk of severe disease and in-hospital death. Results A total of 3,396,375 laboratory-confirmed COVID-19 cases were recorded across the five waves. The introduction of rapid antigen testing at the end of 2020 increased detection and modified epidemiological estimates. Overall, 11% (95% CI 10.9, 11.1) of confirmed cases were hospitalized, 20.6% (95% CI 20.5, 20.7) of the hospitalized cases were intubated, and the hospital case fatality rate was 45.1% (95% CI 44.9, 45.3). The mean in-hospital stay was 9.11 days, and patients were admitted on average 5.07 days after symptoms onset. The most recent waves dominated by the Omicron variant had the highest incidence. Hospitalization, intubation, and mean hospitalization days decreased during subsequent waves. The in-hospital case fatality rate fluctuated across waves, reaching its highest value during the second wave in winter 2020. A notable decrease in hospitalization was observed primarily among individuals ≥ 60 years. The risk of severe disease and death was positively associated with comorbidities, age, and male gender; and declined with later waves and vaccination status. Conclusion During the five pandemic waves, we observed an increase in the number of cases and a reduction in severity metrics. During the first three waves, the high in-hospital fatality rate was associated with hospitalization practices for critical patients with comorbidities.