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INTRODUCTION: Surgical, anaesthesia and obstetric (SAO) care are essential, life-saving components of universal healthcare. In Chiapas, Mexico's southernmost state, the capacity of SAO care is unknown. This study aims to assess the surgical capacity in Chiapas, Mexico, as it relates to access, infrastructure, service delivery, surgical volume, quality, workforce and financial risk protection. METHODS: A cross-sectional study of Ministry of Health public hospitals and private hospitals in Chiapas was performed. The translated Surgical Assessment Tool (SAT) was implemented in sampled hospitals. Surgical volume was collected retrospectively from hospital logbooks. Fisher's exact test and Mann-Whitney U test were used to compare public and private hospitals. Catastrophic expenditure from surgical care was calculated. RESULTS: Data were collected from 17 public hospitals and 20 private hospitals in Chiapas. Private hospitals were smaller than public hospitals and public hospitals performed more surgeries per operating room. Not all hospitals reported consistent electricity, running water or oxygen, but private hospitals were more likely to have these basic infrastructure components compared with public hospitals (84% vs 95%; 60% vs 100%; 94.1% vs 100%, respectively). Bellwether surgical procedures performed in private hospitals cost significantly more, and posed a higher risk of catastrophic expenditure, than those performed in public hospitals. CONCLUSION: Capacity limitations are greater in public hospitals compared with private hospitals. However, the cost of care in the private sector is significantly higher than the public sector and may result in catastrophic expenditures. Targeted interventions to improve the infrastructure, workforce availability and data collection are needed.

BACKGROUND: Diagnostic services are an essential component of high-quality surgical, anesthesia and obstetric (SAO) care. Efforts to scale up SAO care in Latin America have often overlooked diagnostics capacity. This study aims to analyze the capacity of diagnostic services, including radiology, pathology, and laboratory medicine, in hospitals providing SAO care in the states of Chiapas, Mexico and Amazonas, Brazil. METHODS: A stratified cross-sectional evaluation of diagnostic capacity in hospitals performing surgery in Chiapas and Amazonas was performed using the Surgical Assessment Tool (SAT). National data sources were queried for indicators of diagnostics capacity in terms of workforce, infrastructure and diagnosis utilization. Fisher's exact tests and chi-square tests were used to compare categorical variables between the private and public sector in Chiapas while descriptive statistics are used to compare Amazonas and Chiapas. FINDINGS: In Chiapas, 53% (n = 17) of public and 34% (n = 20) of private hospitals providing SAO care were assessed. More private hospitals than public hospitals could always provide x-rays (35% vs 23.5%) and ultrasound (85% vs 47.1%). However neither sector could consistently perform basic laboratory testing such as complete blood counts (70.6% public, 65% private). In Amazonas, 30% (n = 18) of rural hospitals were surveyed. Most had functioning x-ray machine (77.8%) and ultrasound (55.6%). The majority of hospitals could provide complete blood count (66.7%) but only one hospital (5.6%) could always perform an infectious panel. Both Chiapas and Amazonas had dramatically fewer diagnostic practitioners per capita in each state compared to the national average capacity. INTERPRETATION: Facilities providing SAO care in low-resource states in Mexico and Brazil often lack functioning diagnostics services and workforce. Scale-up of diagnostic services is essential to improve SAO care and should occur with emphasis on equitable and adequate re

Evidence-based models may assist Mexican government officials and health authorities in determining the safest plans to respond to the coronavirus disease 2019 (COVID-19) pandemic in the most-affected region of the country, the Mexico City Metropolitan Area. This study aims to present the potential impacts of COVID-19 in this region and to model possible benefits of mitigation efforts. The COVID-19 Hospital Impact Model for Epidemics was used to estimate the probable evolution of COVID-19 in three scenarios: (i) no social distancing, (ii) social distancing in place at 50% effectiveness, and (iii) social distancing in place at 60% effectiveness. Projections of the number of inpatient hospitalizations, intensive care unit admissions, and patients requiring ventilators were made for each scenario. Using the model described, it was predicted that peak case volume at 0% mitigation was to occur on April 30, 2020 at 11,553,566 infected individuals. Peak case volume at 50% mitigation was predicted to occur on June 1, 2020 with 5,970,093 infected individuals and on June 21, 2020 for 60% mitigation with 4,128,574 infected individuals. Occupancy rates in hospitals during peak periods at 0%, 50%, and 60% mitigation would be 875.9%, 322.8%, and 203.5%, respectively, when all inpatient beds are included. Under these scenarios, peak daily hospital admissions would be 40,438, 13,820, and 8,650. Additionally, 60% mitigation would result in a decrease in peak intensive care beds from 94,706 to 23,116 beds and a decrease in peak ventilator need from 67,889 to 17,087 units. Mitigating the spread of COVID-19 through social distancing could have a dramatic impact on reducing the number of infected people and minimize hospital overcrowding. These evidence-based models may enable careful resource utilization and encourage targeted public health responses.