Burden, Incidence, Mortality and Lethality of Maternal Disorders in Mexico 1990-2019: An Analysis for the Global Burden of Disease Study 2019

Montoya, Alejandra; Sánchez-Domínguez, Mario Salvador; Fritz, Jimena; Lamadrid‐Figueroa, Héctor

doi:10.1016/j.arcmed.2022.12.013

Cited by 5 publications

(3 citation statements)

References 14 publications

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“…Esto contribuyó al incremento de la carga de las ECNT, lo que a su vez creó un entorno que elevó la susceptibilidad, el riesgo de mortalidad, la gravedad de COVID-19 y sus consecuencias. De 2016 a la fecha, se han publicado múltiples análisis sobre la carga de la enfermedad en México en revistas científicas arbitradas, [3][4][5][6][7][8][9][10][11] además, se han generado informes de gobierno [12][13][14] para apoyar la toma de decisiones en salud. En esta ocasión, se presenta un resumen de los resultados de Global Burden of Disease (GBD) 2021, que incluye estimaciones de COVID-19, un análisis de las tendencias de 1990 a 2021 y la evaluación de la carga a nivel estatal de 371 enfermedades y lesiones, 3499 entidades clínicas (secuelas), así como la carga atribuible a 88 factores de riesgo por grupos de edad y sexo, para lo cual se empleó la mayoría de los indicadores de resumen para las comparaciones en el tiempo y entre los estados.…”

Section: Antecedentesunclassified

La carga de la enfermedad, lesiones y factores de riesgo en México. Actualización del período 1990-2021

Lozano,

Razo,

Montoya

et al. 2023

GMM

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Antecedentes: Desde hace treinta años, México ha estudiado la carga de la enfermedad para sustentar decisiones en salud. Objetivo: Analizar la carga de enfermedad, lesiones y factores de riesgo en México entre 1990 y 2021. Material y métodos: Se utilizaron las estimaciones del Global Burden of Disease 2021 para analizar la mortalidad, los años de vida perdidos por muerte prematura, los años vividos con discapacidad y los años de vida saludable perdidos, así como la carga atribuible a los factores de riesgos por sexo, edad y entidad federativa.

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Section: Antecedentesunclassified

La carga de la enfermedad, lesiones y factores de riesgo en México. Actualización del período 1990-2021

Lozano,

Razo,

Montoya

et al. 2023

GMM

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“…However, recent data indicate a slowing in the rate of decrease in maternal mortality. According to the Global Burden of Disease Study 2019, the MMR in Mexico decreased 15% from 1990 to 2000 (71.4 to 61.7 deaths per 100,000 live births); 14% from 2000 to 2010 (61.7 to 52.9) but only 8% from 2010 to 2019 (52.9 to 48.6), raising questions about potential contributing factors [ 1 ].…”

Section: Introductionmentioning

confidence: 99%

Obstetric risk profiles and causes of death: Estimating their association with cesarean sections among maternal deaths in Mexico

Martínez-Garrido,

Fritz,

Montoya

et al. 2024

PLoS ONE

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Background Maternal mortality is a critical indicator of healthcare quality, and in Mexico, this has become increasingly concerning due to the stagnation in its decline, alongside a concurrent increase in cesarean section (C-section) rates. This study characterizes maternal deaths in Mexico, focusing on estimating the association between obstetric risk profiles, cause of death, and mode of delivery. Methods Utilizing a retrospective observational design, 4,561 maternal deaths in Mexico from 2010–2014 were analyzed. Data were sourced from the Deliberate Search and Reclassification of Maternal Deaths database, alongside other national databases. An algorithm was developed to extract the Robson Ten Group Classification System from clinical summaries text, facilitating a nuanced analysis of C-section rates. Information on the reasons for the performance of a C-section was also obtained. Logistic regression and multinomial logistic regression models were used to estimate the relation between obstetric risk factors, mode of delivery and causes of maternal death, adjusting for covariates. Results Among maternal deaths in Mexico from 2010–2014, 47.1% underwent a C-section, with a significant history of previous C-sections observed in 31.4% of these cases, compared to 17.4% for vaginal deliveries (p<0.001). Early prenatal care in the first trimester was more common in C-section cases (46.8%) than in vaginal deliveries (38.3%, p<0.001). A stark contrast was noted in the place of death, with 82.4% of post-C-section deaths occurring in public institutions versus 69.1% following vaginal births. According to Robson’s classification, the highest C-section rates were in Group 4 (67.2%, p<0.001) and Group 8 (66.9%, p<0.001). Logistic regression analysis revealed no significant difference in the odds of receiving a C-section in private versus other settings after adjusting for Robson criteria (OR: 1.21; 95% CI: 0.92, 1.60). A prior C-section significantly increased the likelihood of another (OR: 2.38; CI 95%: 2.01, 2.81). The analysis also indicated C-sections were significantly tied to deaths from hypertensive disorders (RRR = 1.25, 95% CI [1.12, 1.40]). In terms of indications, 6.3% of C-sections were performed under inadequate indications, while the indication was not identifiable in 33.1% of all C-sections. Conclusions This study highlights a significant overuse of C-sections among maternal deaths in Mexico (2010–2014), revealed through the Robson classification and ana analysis of the reported indications for the procedure. It underscores the need for revising clinical decision-making to promote evidence-based guidelines and favor vaginal deliveries when possible. High C-section rates, especially noted disparities between private and public sectors, suggest economic and non-clinical factors may be at play. The importance of accurate data systems and further research with control groups to understand C-section practices’ impact on maternal health is emphasized.

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“…Spatial epidemiology serves as a valuable tool in unraveling the causes behind diseases, injuries, and mortality (Boudou et al, 2023;Dawood, 2023;Kazi et al, 2023;Montoya et al, 2023). In the first step of analysis, geographical data are visualized, particularly in geographical maps, to provide insights into the spatial distribution patterns of diseases, injuries, and deaths, and facilitate causal identification (Wu, Shen et al, 2023;Wu, Williams et al, 2023).…”

Section: Introductionmentioning

confidence: 99%

Territorial patterns of COVID-19 in Iran

Aryan Kya

2024

ASPAL

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Motives: Spatial analysis has become an essential tool in understanding the underlying factors that contribute to the distribution of viral pandemics, diseases, injuries, and mortality patterns. By visualizing geographical data in spatial maps, researchers can identify local distribution patterns and potential drivers behind these patterns. In health and medical sciences, there has been a growing recognition that spatial analysis and mapping techniques are helpful in addressing various challenges related to the allocation of healthcare resource in both urban and rural areas. Aim: The objective of this study was to analyze the spatial distribution pattern of the COVID-19 pandemic and the Index of Proximity Distribution (IPD) across 31 provinces of Iran between February 2019 and February 2023. A two-stage sampling method combining convenience and cluster sampling was used to examine COVID-19 distribution patterns in 31 provinces of Iran between 22 February 2020 and 22 February 2023. COVID-19 and IPD data were collected as part of this panel study. Data were analyzed using t-tests, chi-square tests, and analysis of variance (ANOVA) in SPSS version 28 (α = 0.05). Subsequently, daily COVID-19 infection data for each province in the analyzed period were processed in ArcGIS software, and the spatial distribution pattern of the pandemic in Iran were visualized by point density analysis. Standard distance and standard deviation ellipse techniques were employed to assess the density or dispersion of infected individuals and to determine the spatial distribution pattern of COVID-19 in Iran. A spatial autocorrelation (Moran’s I) analysis was conducted to identify the spatial distribution pattern of COVID-19 in Iran. Additionally, distance-based spatial autocorrelation was used to examine the prevalence of COVID-19 infection across Iranian provinces. In a grouping analysis, 31 Iranian provinces were classified into five groups based on the number of COVID-19 cases, and spatial statistics were used to examine the prevalence of COVID-19 within each group. A hot spot analysis and a standard distance (SD) analysis were conducted to explore spatial correlations in the number of individuals affected by COVID-19 in each province. Results: Based on the Moran index, a random spatial pattern with a Z-Score of 1.485 was identified in March 2019, whereas a clustered distribution of COVID-19 with a Z-Score of 3.039 was determined in February 2023. The distance-based spatial autocorrelation analysis revealed a positive value of the Moran index (0.136627) at a distance of 383.3 kilometers from Tehran, which points to positive spatial autocorrelation and a higher number of COVID-19 cases in nearby regions. Conversely, the Moran index assumed a negative value of 0.040246 at a distance of 726.6 kilometers from Tehran, which suggests that the number of pandemic cases decreased over distance from Tehran. Moreover, based on the results of the hot spot analysis, Tehran province was identified as a hot cluster with a higher prevalence of COVID-19 cases in that region. In contrast, Bushehr province was classified as a cold cluster with a lower prevalence of COVID-19 cases in comparison with the surrounding regions. These findings provide valuable insights into the spatial distribution and clustering of COVID-19 cases in Iran. The shift from a random spatial pattern in 2019 to clustered distribution in 2023 indicates that the pandemic spread rate increased over time. The positive spatial autocorrelation near Tehran highlights the role of proximity and population movement in the transmission of the virus. Furthermore, the identification of hot spots and cold spots in a country can inform targeted interventions and resource allocation to effectively manage and control the pandemic. Overall, this study demonstrates the value of spatial analysis in identifying the spatial distribution patterns and the dynamics of the COVID-19 pandemic in Iran. The integration of spatial analysis techniques with epidemiological data contributes to a better understanding of spatial-temporal patterns, facilitates effective public health responses and resource allocation strategies. These findings contribute to the growing body of knowledge on the spatial epidemiology of COVID-19 and can aid in informing future preparedness and response efforts in Iran and other regions that face similar challenges.

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Burden, Incidence, Mortality and Lethality of Maternal Disorders in Mexico 1990-2019: An Analysis for the Global Burden of Disease Study 2019

Cited by 5 publications

References 14 publications

La carga de la enfermedad, lesiones y factores de riesgo en México. Actualización del período 1990-2021

La carga de la enfermedad, lesiones y factores de riesgo en México. Actualización del período 1990-2021

Obstetric risk profiles and causes of death: Estimating their association with cesarean sections among maternal deaths in Mexico

Territorial patterns of COVID-19 in Iran

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