Search

Janinne Ortega-Montiel,1 Luis A Martinez-Juarez,1,2 Alejandra Montoya,1 Linda Morales-Juárez,3 Héctor Gallardo-Rincón,1,4 Victoria Galicia-Hernández,1 Rodrigo Garcia-Cerde,1 María Jesus Ríos-Blancas,1 Diego-Abelardo Álvarez-Hernández,1 Julieta Lomelin-Gascon,1 Gisela Martínez-Silva,1 Lucía M Illescas-Correa,5 Daniel A Diaz Martinez,6 Francisco Javier Magos Vázquez,6 Edwin Vargas Ávila,6 Ma Concepción Carmona-Ramos,7 Ricardo Mújica‐Rosales,1 Enrique Reyes-Muñoz,8 Roberto Tapia-Conyer9 1Carlos Slim Foundation, Mexico City, Mexico; 2Johns Hopkins Center for Humanitarian Health, Bloomberg School of Public Health, Baltimore, MD, USA; 3Institute for Obesity Research, Tecnológico de Monterrey, Mexico City, Mexico; 4Health Sciences University Center, University of Guadalajara, Guadalajara, Jalisco, Mexico; 5Maternal and Childhood Research Center (CIMIGEN), Mexico City, Mexico; 6Ministry of Health of the State of Guanajuato, Tamazuca, Guanajuato, Mexico; 7Ministry of Health of the State of Hidalgo, Pachuca, Hidalgo, Mexico; 8Coordinatión of Gynecological and Perinatal Endocrinology, National Institute of Perinatology Isidro Espinosa de los Reyes, Mexico City, Mexico; 9National Autonomous University of Mexico, School of Medicine, Mexico City, MexicoCorrespondence: Alejandra Montoya, Carlos Slim Foundation, Lago Zurich 245, Presa Falcon Building (Floor 20), Col. Ampliación Granada, Miguel, Hidalgo, Mexico City, 11529, Mexico, Tel +52 55 4976 0344, Email airainmr@fundacioncarlosslim.orgPurpose: This study explores the impact of gestational diabetes mellitus (GDM) subtypes classified by oral glucose tolerance test (OGTT) values on maternal and perinatal outcomes.Patients and Methods: This multicenter prospective cohort study (May 2019–December 2022) included participants from the Mexican multicenter cohort study Cuido mi Embarazo (CME). Women were classified into four groups per 75-g 2-h OGTT: 1) normal glucose tolerance (normal OGTT), 2) GDM-Sensitivity (isolated abnormal fasting or abnormal fasting in combination with 1-h or 2-h abnormal results), 3) GDM-Secretion (isolated abnormal values at 1-h or 2-h or their combination), and 4) GDM-Mixed (three abnormal values). Cesarean delivery, neonates large for gestational age (LGA), and pre-term birth rates were among the outcomes compared. Between-group comparisons were analyzed using either the t-test, chi-square test, or Fisher’s exact test.Results: Of 2,056 Mexican pregnant women in the CME cohort, 294 (14.3%) had GDM; 53.7%, 34.4%, and 11.9% were classified as GDM-Sensitivity, GDM-Secretion, and GDM-Mixed subtypes, respectively. Women with GDM were older (p = 0.0001) and more often multiparous (p = 0.119) vs without GDM. Cesarean delivery (63.3%; p = 0.02) and neonate LGA (10.7%; p = 0.078) were higher in the GDM-Mixed group than the overall GDM group (55.6% and 8.4%, respectively). Pre-term birth was more common in the GDM-Sensitivity group than in the overall GDM group (10.2% vs 8.5%, respectively; p=0.022). At 6 months postpartum, prediabetes was more frequent in the GDM-Sensitivity group than in the overall GDM group (31.6% vs 25.5%). Type 2 diabetes was more common in the GDM-Mixed group than in the overall GDM group (10.0% vs 3.3%).Conclusion: GDM subtypes effectively stratified maternal and perinatal risks. GDM-Mixed subtype increased the risk of cesarean delivery, LGA, and type 2 diabetes postpartum. GDM subtypes may help personalize clinical interventions and optimize maternal and perinatal outcomes.Keywords: Gestational Diabetes, GDM-Mixed, GDM-Secretion, GDM-Sensitivity, macrosomia, cesarean delivery, pre-term delivery

Background: Post-term pregnancy, defined as reaching or exceeding 42 + 0 weeks of gestation, is known to be associated with unfavourable birth outcomes. High-income countries have responded to this risk by widely adopting labour induction protocols in late-term, but many low- and middle-income countries have not. However, understanding underlying mechanisms linking post-term births to adverse newborn and infant outcomes remains limited., Objective: To investigate the (a) prevalence of post-term, (b) the risk factors associated with post-term (c) the association between post-term births and the risk of small-for-gestational-age (SGA) neonates and of infant mortality in middle-income settings., Methods: We used existing electronic datasets from the general population of Brazil, Mexico, and Palestinian refugees. Regression models were used to explore the associations between post-term birth and SGA and infant mortality., Results: We analysed 21,335,033 live births in Brazil (2011-2018), 23,416,126 in Mexico (2008-2019), and 966,102 in Palestinian refugees (2010-2020) (N = 45,717,261). Post-term deliveries accounted for 3.1% of births in Brazil, 1.2% in Mexico, and 2.1% in Palestinian refugees. Post-term births had approximately three times the risk of resulting in SGA neonates compared to term births. Additionally, post-term neonates exhibited a 15% to 40% increased risk of infant mortality compared to term infants. Notably, post-term SGA neonates faced a significantly increased risk of infant mortality compared to term appropriate for gestational age neonates., Conclusions: These findings emphasise the critical significance of implementing induction strategies to prevent post-term pregnancies and mitigate the associated risks of SGA neonates and subsequent infant mortality. Moreover, the study highlights the importance of accurately determining gestational age and using INTERGROWTH-21st charts to improve the identification of SGA cases, enabling targeted interventions. This is especially relevant because post-term SGA neonates may not exhibit low birthweight (a commonly used risk marker) and, therefore, may miss out on required specialised attention., (© 2024 John Wiley & Sons Ltd.)

Background: Protecting the health of migrants and refugees during the pandemic was a significant challenge in the Latin American region. We aimed to describe and contrast the response of the health systems of Mexico, Colombia and Perú to migrants' and refugees' health needs during the COVID-19 pandemic, and to situate the response in the context of the migration and health policies of each country., Methods: We conducted case studies of the three countries. The data collected for each case included: 1) policy documents; 2) peer-reviewed and grey literature; 3) qualitative interviews with key informants., Findings: The three countries issued policies to detect COVID-19 cases among migrants and refugees, and granted them access to COVID-19 care regardless of their migration status or facilitated regularization as a way to improve access to care. However, other health care needs were not considered, some groups of migrants remained invisible in the policies, and policies rarely considered the increased social and economic vulnerability of migrants and refugees during this period. Pre-existing barriers to care were made worse during the pandemic. All three countries' response to migrants was reliant on non-governmental organizations, with international cooperation being especially active in Colombia, and local civil society organizations in Mexico., Interpretation: The response of each country was a continuation of their previous approach to migration and health. Developing plans for pandemic response that consider migrants and refugees' particular needs will be important to improve response capacities in the future., Funding: Funding was provided by USA for the International Organization for Migration (IOM) under an agreement with the Johns Hopkins Center for Humanitarian Health., Competing Interests: Ietza Bojorquez, Andrés Cubillos and Juan Arroyo received funding for the conduction of this study from the Johns Hopkins Center for Humanitarian Health (provided under an agreement with USA for the International Organization for Migration (IOM). The funding source had no role in the study design, data collection, analysis or interpretation, the writing of this article or the decision to submit for publication. All other authors declare no competing interests., (© 2024 The Author(s).)

Purpose: A variable number of tandem repeats (VNTR) in the insulin gene (INS) control region may be involved in type 2 diabetes (T2D). The TH01 microsatellite is near INS and may regulate it. We investigated whether the TH01 microsatellite and INS VNTR, assessed via the surrogate marker single nucleotide polymorphism rs689, are associated with T2D and serum insulin levels in a Mexican population., Methods: We analyzed a main case-control study (n = 1986) that used univariate and multivariate logistic regression models to calculate the risk conferred by TH01 and rs689 loci for T2D development; rs689 results were replicated in other case-control (n = 1188) and cross-sectional (n = 1914) studies., Results: TH01 alleles 6, 8, 9, and 9.3 and allele A of rs689 were independently associated with T2D, with differences between sex and age at diagnosis. TH01 alleles with ≥ 8 repeats conferred an increased risk for T2D in males compared with ≤ 7 repeats (odds ratio, ≥ 1.46; 95% confidence interval, 1.1-1.95). In females, larger alleles conferred a 1.5-fold higher risk for T2D when diagnosed ≥ 46 years but conferred protection when diagnosed ≤ 45 years. Similarly, rs689 allele A was associated with T2D in these groups. In males, larger TH01 alleles and the rs689 A allele were associated with a significant decrease in median fasting plasma insulin concentration with age in T2D cases; the reverse occurred in controls., Conclusion: Larger TH01 alleles and rs689 A allele may potentiate insulin synthesis in males without T2D, a process disabled in those with T2D., (© 2023. The Author(s).)

Given the barriers to early detection of gestational diabetes mellitus (GDM), this study aimed to develop an artificial intelligence (AI)-based prediction model for GDM in pregnant Mexican women. Data were retrieved from 1709 pregnant women who participated in the multicenter prospective cohort study 'Cuido mi embarazo'. A machine-learning-driven method was used to select the best predictive variables for GDM risk: age, family history of type 2 diabetes, previous diagnosis of hypertension, pregestational body mass index, gestational week, parity, birth weight of last child, and random capillary glucose. An artificial neural network approach was then used to build the model, which achieved a high level of accuracy (70.3%) and sensitivity (83.3%) for identifying women at high risk of developing GDM. This AI-based model will be applied throughout Mexico to improve the timing and quality of GDM interventions. Given the ease of obtaining the model variables, this model is expected to be clinically strategic, allowing prioritization of preventative treatment and promising a paradigm shift in prevention and primary healthcare during pregnancy. This AI model uses variables that are easily collected to identify pregnant women at risk of developing GDM with a high level of accuracy and precision., (© 2023. The Author(s).)

Introduction: Gestational diabetes mellitus (GDM) is underdiagnosed in Mexico. Early GDM risk stratification through prediction modeling is expected to improve preventative care. We developed a GDM risk assessment model that integrates both genetic and clinical variables., Research Design and Methods: Data from pregnant Mexican women enrolled in the 'Cuido mi Embarazo' (CME) cohort were used for development (107 cases, 469 controls) and data from the 'Mónica Pretelini Sáenz' Maternal Perinatal Hospital (HMPMPS) cohort were used for external validation (32 cases, 199 controls). A 2-hour oral glucose tolerance test (OGTT) with 75 g glucose performed at 24-28 gestational weeks was used to diagnose GDM. A total of 114 single-nucleotide polymorphisms (SNPs) with reported predictive power were selected for evaluation. Blood samples collected during the OGTT were used for SNP analysis. The CME cohort was randomly divided into training (70% of the cohort) and testing datasets (30% of the cohort). The training dataset was divided into 10 groups, 9 to build the predictive model and 1 for validation. The model was further validated using the testing dataset and the HMPMPS cohort., Results: Nineteen attributes (14 SNPs and 5 clinical variables) were significantly associated with the outcome; 11 SNPs and 4 clinical variables were included in the GDM prediction regression model and applied to the training dataset. The algorithm was highly predictive, with an area under the curve (AUC) of 0.7507, 79% sensitivity, and 71% specificity and adequately powered to discriminate between cases and controls. On further validation, the training dataset and HMPMPS cohort had AUCs of 0.8256 and 0.8001, respectively., Conclusions: We developed a predictive model using both genetic and clinical factors to identify Mexican women at risk of developing GDM. These findings may contribute to a greater understanding of metabolic functions that underlie elevated GDM risk and support personalized patient recommendations., Competing Interests: Competing interests: MZ, LM, MA, and LS are employees of Patia Europe. HG-R, LAM-J, JL-G, JO-M, and AM are employees of the Carlos Slim Foundation. MdlAM-M, EdSCR, HM-Z, JdJGG, and RT-C have no conflicts of interest to declare., (© Author(s) (or their employer(s)) 2023. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ.)

Purpose: Few pregnant women in low-resource settings are screened for gestational diabetes mellitus (GDM) using the gold standard oral glucose tolerance test (OGTT). This study compared capillary blood glucose testing with 2-h plasma glucose measurements obtained using the 75-g OGTT to screen for GDM at primary healthcare clinics in Mexico., Patients and Methods: Pregnant women who participated in a previous prospective multicenter longitudinal cohort study and who had not been previously diagnosed with diabetes were included. Participants were evaluated using the plasmatic 2-h 75-g OGTT with simultaneous capillary blood glucose measurements using a glucometer. The study endpoint was the comparability of the glucometer results to the gold standard OGTT when collected simultaneously. Sensitivity, specificity, and area under the curve of the glucose measurements obtained for capillary blood compared with venous plasma (gold standard) were calculated to determine diagnostic accuracy., Results: The study included 947 pregnant women who had simultaneous glucose measurements available (blood capillary [glucometer] and venous blood OGTT). Overall, capillary blood glucose testing was very sensitive (89.47%); the specificity was 66.58% and the area under the curve (95% confidence interval) was 0.78 (0.74-0.81). The sensitivity, specificity and area under the curve of each capillary measurement were: 89.47%, 66.58% and 0.78 (0.74-0.82) for the fasting measurement, 91.53%, 93.24% and 0.92 (0.88-0.96) for the one-hour measurement, and 89.80%, 93.32%, 0.91 (0.87-0.95) for the second-hour measurement, respectively. No adverse events were reported., Conclusion: Capillary OGTT is a valid alternative to the gold standard OGTT for screening of GDM in low-resource situations or in situations where there are other limitations to performing the OGTT as part of primary healthcare services., Competing Interests: HGR, JLG, LAMJ, AM, JOM, VGH, DAAH, RAD and RMR, are employees of the Carlos Slim Foundation. The authors report no other conflicts of interest in this work., (© 2022 Gallardo-Rincón et al.)

Introduction: High-flow nasal cannula (HFNC) therapy in patients with hypoxemic respiratory failure due to COVID-19 is poorly understood and remains controversial., Methods: We evaluated a large cohort of patients with COVID-19-related hypoxemic respiratory failure at the temporary COVID-19 hospital in Mexico City. The primary outcome was the success rate of HFNC to prevent the progression to invasive mechanical ventilation (IMV). We also evaluated the risk factors associated with HFNC success or failure., Results: HFNC use effectively prevented IMV in 71.4% of patients [270 of 378 patients; 95% confidence interval (CI) 66.6-75.8%]. Factors that were significantly different at admission included age, the presence of hypertension, and the Charlson comorbidity index. Predictors of therapy failure (adjusted hazard ratio, 95% CI) included the comorbidity-age-lymphocyte count-lactate dehydrogenase (CALL) score at admission (1.27, 1.09-1.47; p  < 0.01), Rox index at 1 hour (0.82, 0.7-0.96; p  = 0.02), and no prior steroid treatment (0.34, 95% CI 0.19-0.62; p  < 0.0001). Patients with HFNC success rarely required admission to the intensive care unit and had shorter lengths of hospital stay [19/270 (7.0%) and 15.0 (interquartile range, 11-20) days, respectively] than those who required IMV [104/108 (96.3%) and 26.5 (20-36) days, respectively]., Conclusion: Treating patients with HFNC at admission led to improvement in respiratory parameters in many patients with COVID-19., Competing Interests: Conflict of interest statement: The Carlos Slim Foundation funded this study. APC, ESL, MGN, RRVV, HHB, LMJ, MAA, LERG, LMC, RVB, RVAW, BSO, and MLRC are full-time employees of the Temporary COVID-19 Hospital. HGR, JLG, LAMJ, and RTC are full-time employees of the Carlos Slim Foundation in Mexico. The authors declare no other conflicts of interest or outside funding from any other organizations., (© The Author(s), 2021.)

Introduction: In response to the evolution of the coronavirus disease 2019 (COVID-19) pandemic, the admission protocol for the temporary COVID-19 hospital in Mexico City has been updated to hospitalize patients preemptively with an oxygen saturation (SpO 2 ) of >90%., Methods: This prospective, observational, single-center study compared the progression and outcomes of patients who were preemptively hospitalized versus those who were hospitalized based on an SpO 2 ⩽90%. We recorded patient demographics, clinical characteristics, COVID-19 symptoms, and oxygen requirement at admission. We calculated the risk of disease progression and the benefit of preemptive hospitalization, stratified by CALL Score: age, lymphocyte count, and lactate dehydrogenase (<8 and ⩾8) at admission., Results: Preemptive hospitalization significantly reduced the requirement for oxygen therapy (odds ratio 0.45, 95% confidence interval 0.31-0.66), admission to the intensive care unit (ICU) (0.37, 0.23-0.60), requirement for invasive mechanical ventilation (IMV) (0.40, 0.25-0.64), and mortality (0.22, 0.10-0.50). Stratification by CALL score at admission showed that the benefit of preemptive hospitalization remained significant for patients requiring oxygen therapy (0.51, 0.31-0.83), admission to the ICU (0.48, 0.27-0.86), and IMV (0.51, 0.28-0.92). Mortality risk remained significantly reduced (0.19, 0.07-0.48)., Conclusion: Preemptive hospitalization reduced the rate of disease progression and may be beneficial for improving COVID-19 patient outcomes., Competing Interests: Conflict of interest statement: The Carlos Slim Foundation funded this study. The funding source was involved in the design of the study, analysis, interpretation of data, and in writing the manuscript. The authors HGR, JLG, RSM, LAMJ, and RTC are employees of the Carlos Slim Foundation in Mexico. RRVV, RVB, LMJ, MAA, STB, RVAW, ESL, MDNO, AGR, LMBR, PEC, LERG, ACS, JBM, AGR, APC, HHB, LVA, and RARL are employed full-time by the temporary COVID-19 hospital. The authors declare no other outside funding from any other organizations and declare no further conflicts., (© The Author(s), 2021.)

Objectives: Diabetes is the leading cause of disability-adjusted life years in Mexico, and cost-effective care models are needed to address the epidemic. We sought to evaluate the cost and cost-effectiveness of a novel community-based model of diabetes care in rural Mexico, compared with usual care., Design: We performed time-driven activity-based costing to estimate annualised costs associated with typical diabetes care in Chiapas, Mexico, as well as a novel diabetes care model known as Compañeros En Salud Programa de Enfermedades Crónicas (CESPEC). We conducted Markov chain analysis to estimate the cost-effectiveness of CESPEC compared with usual care from a societal perspective. We used patient outcomes from CESPEC in 2016, as well as secondary data from existing literature., Setting: Rural primary care clinics in Chiapas, Mexico., Participants: Adults with diabetes., Interventions: CESPEC is a novel, comprehensive, diabetes care model that integrates community health workers, provider education, supply chain management and active case finding., Outcome Measure: The primary outcome was the incremental cost-effectiveness of CESPEC compared with care as usual, per quality-adjusted life year (QALY) gained, expressed in 2016 US dollars., Results: The economic cost of the CESPEC diabetes model was US$144 per patient per year, compared with US$125 for diabetes care as usual. However, CESPEC care was associated with 0.13 additional years of health-adjusted life expectancy compared with usual care and 0.02 additional years in the first 5 years of treatment. This translated to an incremental cost-effectiveness ratio (ICER) of US$2981 per QALY gained over a patient's lifetime and an ICER of US$10 444 over the first 5 years. Findings were robust to multiple sensitivity analyses., Conclusions: CESPEC is a cost-effective, community-based model of diabetes care for patients in rural Mexico. Given the high prevalence and significant morbidity associated with diabetes in Mexico and other countries in Central America, this model should be considered for broader scale up and evaluation., Competing Interests: Competing interests: DP receives funding from Last Mile Health as a consultant to the Community Health Academy., (© Author(s) (or their employer(s)) 2021. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ.)

Competing Interests: Drs. Martinez, Sedas, Orcutt, and Bhopal have no conflicts of interest.