The impact of primary health care on AIDS incidence and mortality: A cohort study of 3.4 million Brazilians.

BACKGROUND: Primary Health Care (PHC) is essential for effective, efficient, and more equitable health systems for all people, including those living with HIV/AIDS. This study evaluated the impact of the exposure to one of the largest community-based PHC programs in the world, the Brazilian Family Health Strategy (FHS), on AIDS incidence and mortality. METHODS AND FINDINGS: A retrospective cohort study carried out in Brazil from January 1, 2007 to December 31, 2015. We conducted an impact evaluation using a cohort of 3,435,068 ≥13 years low-income individuals who were members of the 100 Million Brazilians Cohort, linked to AIDS diagnoses and deaths registries. We evaluated the impact of FHS on AIDS incidence and mortality and compared outcomes between residents of municipalities with low or no FHS coverage (unexposed) with those in municipalities with 100% FHS coverage (exposed). We used multivariable Poisson regressions adjusted for all relevant municipal and individual-level demographic, socioeconomic, and contextual variables, and weighted with inverse probability of treatment weighting (IPTW). We also estimated the FHS impact by sex and age and performed a wide range of sensitivity and triangulation analyses; 100% FHS coverage was associated with lower AIDS incidence (rate ratio [RR]: 0.76, 95% CI: 0.68 to 0.84) and mortality (RR: 0.68, 95%CI: 0.56 to 0.82). FHS impact was similar between men and women, but was larger in people aged ≥35 years old both for incidence (RR: 0.62, 95% CI: 0.53 to 0.72) and mortality (RR: 0.56, 95% CI: 0.43 to 0.72). The absence of important confounding variables (e.g., sexual behavior) is a key limitation of this study. CONCLUSIONS: AIDS should be an avoidable outcome for most people living with HIV today and our study shows that FHS coverage could significantly reduce AIDS incidence and mortality among low-income populations in Brazil. Universal access to comprehensive healthcare through community-based PHC programs should be promoted to achieve the Sustainable Development Goals of ending AIDS by 2030.

Intramuscular adipose tissue, muscle area, and power as predictors of performance in breast cancer survivors.

PURPOSE: The decline in physical performance, assessed by physical tests such as the timed up and go (TUG) test, is a consequence of reduced physiological reserves at higher levels of a hierarchical process. This occurs due to changes in muscle architecture, including atrophy and fat infiltration into the muscles, which in turn lead to changes in muscle function, resulting in reduced muscle strength and power and, consequently, affecting physical performance. This study investigated predictive factors for physical performance in breast cancer survivor (BCS), focusing on intramuscular adipose tissue (IMAT), quadríceps muscle area (QMA), and muscular power. METHODS: This observational, analytical, and cross-sectional study included 23 women without a history of cancer (age, 58.5 ± 8.3 years; BMI, 27.2 ± 5.1 kg/m2) and 56 BCS (age, 58.5 ± 8.3 years; BMI, 27.2 ± 5.1 kg/m2). QMA and IMAT were assessed using computed tomography images. Muscular power and physical performance were measured using the 5-repetition sit-to-stand and TUG tests, respectively. RESULTS: IMAT (r = 0.4, P < 0.01) and muscular power (r = - 0.4, P < 0.01) were associated with TUG performance in BCS, whereas QMA (r = - 0.22, P = 0.10) showed no significant association. QMA (r = 0.55, P < 0.01) was associated with muscular power, while no significant association was found between IMAT and muscular power (r = - 0.05, P = 0.73). Age explained 19% (P < 0.01) of TUG performance variability. Adding muscular power increased explanatory power by 12% (P < 0.01), and including IMAT further increased it by 7% (P = 0.02) for TUG performance. Collectively, age, muscular power, and IMAT accounted for 38% of the performance variance in the TUG test (age, B = 0.06, P = 0.043; muscular power, B = - 0.01, P = 0.002; IMAT, B = - 0.05, P = 0.020). CONCLUSIONS: Our findings suggest that IMAT and muscular power predict the physical performance of BCS, while QMA does not have the same predictive capability.

Vessel and tissue recognition during third-space endoscopy using a deep learning algorithm.

In this study, we aimed to develop an artificial intelligence clinical decision support solution to mitigate operator-dependent limitations during complex endoscopic procedures such as endoscopic submucosal dissection and peroral endoscopic myotomy, for example, bleeding and perforation. A DeepLabv3-based model was trained to delineate vessels, tissue structures and instruments on endoscopic still images from such procedures. The mean cross-validated Intersection over Union and Dice Score were 63% and 76%, respectively. Applied to standardised video clips from third-space endoscopic procedures, the algorithm showed a mean vessel detection rate of 85% with a false-positive rate of 0.75/min. These performance statistics suggest a potential clinical benefit for procedure safety, time and also training.

Endoscopic prediction of submucosal invasion in Barrett's cancer with the use of artificial intelligence: a pilot study.

BACKGROUND: The accurate differentiation between T1a and T1b Barrett's-related cancer has both therapeutic and prognostic implications but is challenging even for experienced physicians. We trained an artificial intelligence (AI) system on the basis of deep artificial neural networks (deep learning) to differentiate between T1a and T1b Barrett's cancer on white-light images. METHODS: Endoscopic images from three tertiary care centers in Germany were collected retrospectively. A deep learning system was trained and tested using the principles of cross validation. A total of 230 white-light endoscopic images (108 T1a and 122 T1b) were evaluated using the AI system. For comparison, the images were also classified by experts specialized in endoscopic diagnosis and treatment of Barrett's cancer. RESULTS: The sensitivity, specificity, F1 score, and accuracy of the AI system in the differentiation between T1a and T1b cancer lesions was 0.77, 0.64, 0.74, and 0.71, respectively. There was no statistically significant difference between the performance of the AI system and that of experts, who showed sensitivity, specificity, F1, and accuracy of 0.63, 0.78, 0.67, and 0.70, respectively. CONCLUSION: This pilot study demonstrates the first multicenter application of an AI-based system in the prediction of submucosal invasion in endoscopic images of Barrett's cancer. AI scored equally to international experts in the field, but more work is necessary to improve the system and apply it to video sequences and real-life settings. Nevertheless, the correct prediction of submucosal invasion in Barrett's cancer remains challenging for both experts and AI.

Health equity and COVID-19: global perspectives.

The COVID-19 is disproportionally affecting the poor, minorities and a broad range of vulnerable populations, due to its inequitable spread in areas of dense population and limited mitigation capacity due to high prevalence of chronic conditions or poor access to high quality public health and medical care. Moreover, the collateral effects of the pandemic due to the global economic downturn, and social isolation and movement restriction measures, are unequally affecting those in the lowest power strata of societies. To address the challenges to health equity and describe some of the approaches taken by governments and local organizations, we have compiled 13 country case studies from various regions around the world: China, Brazil, Thailand, Sub Saharan Africa, Nicaragua, Armenia, India, Guatemala, United States of America (USA), Israel, Australia, Colombia, and Belgium. This compilation is by no-means representative or all inclusive, and we encourage researchers to continue advancing global knowledge on COVID-19 health equity related issues, through rigorous research and generation of a strong evidence base of new empirical studies in this field.

Internet of Medical Things: An Effective and Fully Automatic IoT Approach Using Deep Learning and Fine-Tuning to Lung CT Segmentation.

Several pathologies have a direct impact on society, causing public health problems. Pulmonary diseases such as Chronic obstructive pulmonary disease (COPD) are already the third leading cause of death in the world, leaving tuberculosis at ninth with 1.7 million deaths and over 10.4 million new occurrences. The detection of lung regions in images is a classic medical challenge. Studies show that computational methods contribute significantly to the medical diagnosis of lung pathologies by Computerized Tomography (CT), as well as through Internet of Things (IoT) methods based in the context on the health of things. The present work proposes a new model based on IoT for classification and segmentation of pulmonary CT images, applying the transfer learning technique in deep learning methods combined with Parzen's probability density. The proposed model uses an Application Programming Interface (API) based on the Internet of Medical Things to classify lung images. The approach was very effective, with results above 98% accuracy for classification in pulmonary images. Then the model proceeds to the lung segmentation stage using the Mask R-CNN network to create a pulmonary map and use fine-tuning to find the pulmonary borders on the CT image. The experiment was a success, the proposed method performed better than other works in the literature, reaching high segmentation metrics values such as accuracy of 98.34%. Besides reaching 5.43 s in segmentation time and overcoming other transfer learning models, our methodology stands out among the others because it is fully automatic. The proposed approach has simplified the segmentation process using transfer learning. It has introduced a faster and more effective method for better-performing lung segmentation, making our model fully automatic and robust.

Mortality associated with alternative primary healthcare policies: a nationwide microsimulation modelling study in Brazil.

BACKGROUND: Brazil's Estratégia Saúde da Família (ESF) is one of the largest and most robustly evaluated primary healthcare programmes of the world, but it could be affected by fiscal austerity measures and by the possible end of the Mais Médicos programme (MMP)-a major intervention to increase primary care doctors in underserved areas. We forecast the impact of alternative scenarios of ESF coverage changes on under-70 mortality from ambulatory care-sensitive conditions (ACSCs) until 2030, the date for achievement of the Sustainable Development Goals (SDGs). METHOD: A synthetic cohort of 5507 Brazilian municipalities was created for the period 2017-2030. A municipal-level microsimulation model was developed and validated using longitudinal data and estimates from a previous retrospective study evaluating the effects of municipal ESF coverage on mortality rates. Reductions in ESF coverage, and its effects on ACSC mortality, were forecast based on two probable austerity scenarios, compared with the maintenance of the current coverage or the expansion to 100%. Fixed effects longitudinal regression models were employed to account for secular trends, demographic and socioeconomic changes, healthcare-related variables, and programme duration effects. RESULTS: Under austerity scenarios of decreasing ESF coverage with and without the MMP termination, mean ACSC mortality rates would be 8.60% (95% CI 7.03-10.21%; 48,546 excess premature/under-70 deaths along 2017-2030) and 5.80% (95% CI 4.23-7.35%; 27,685 excess premature deaths) higher respectively in 2030 compared to maintaining the current ESF coverage. Comparing decreasing ESF coverage and MMP termination with achieving 100% ESF coverage (Universal Health Coverage scenario) in 2030, mortality rates would be 11.12% higher (95% CI 9.47-12.76%; 83,937 premature deaths). Reductions in ESF coverage would have stronger effects on mortality from infectious diseases and nutritional deficiencies and would disproportionately impact poorer municipalities, with the concentration index for ACSC mortality 11.77% higher (95% CI 0.31-22.32%) and also ending historical declines in racial health inequalities between white and black/pardo Brazilians. CONCLUSIONS: Reductions in primary healthcare coverage due to austerity measures are likely to be responsible for many avoidable deaths and may preclude achievement of SDGs for health and inequality in Brazil and in other low- and middle-income countries.

Imaging findings in congenital Zika virus infection syndrome: an update.

BACKGROUND: Zika virus (ZIKV) is a neurotropic and neurotoxic RNA Flavivirus prompt to cause severe fetal brain dysmorphisms during pregnancy, a period of rapid and critical central nervous system development. A wide range of clinico-radiological findings of congenital ZIKV infections were reported in the literature, such as microcephaly, overlapping sutures, cortical migrational and corpus callosum abnormalities, intracranial calcifications, ventriculomegaly, brain stem and cerebellar malformations, spinal cord involvement, and joint contractures. ZIKV is also related to other severe neurological manifestations in grown-up individuals such as Guillain-Barré syndrome and encephalomyelitis. PURPOSE: Our purpose is to review the radiological central nervous system abnormalities of congenital ZIKV infection syndrome on different imaging modalities.

Ticks (Acari: Ixodidae) in the Serra da Canastra National Park in Minas Gerais, Brazil: species, abundance, ecological and seasonal aspects with notes on rickettsial infection.

The Cerrado Biome is the second largest in Brazil covering roughly 2 million km2, with varying features throughout its area. The Biome is endangered but it is also source of animal species for rural, green urban and degraded rainforest areas. Ticks are among Cerrado species that establish at anthropogenic sites and although information about them is steadily increasing, several features are unknown. We herein report tick species, abundance and some ecological relationships within natural areas of the Cerrado at higher altitudes (800-1500 m) within and around Serra da Canastra National Park, in Minas Gerais State Brazil. In total of 1196 ticks were collected in the environment along 10 campaigns held in 3 years (2007-2009). Amblyomma sculptum was the most numerous species followed by Amblyomma dubitatum and Amblyomma brasiliense. Distribution of these species was very uneven and an established population of A. brasiliense in the Cerrado is reported for the first time. Other tick species (Amblyomma ovale, Amblyomma nodosum, Amblyomma parvum, Ixodes schulzei and Haemaphysalis leporispalustris) were found in lesser numbers. Domestic animals displayed tick infestations of both rural and urban origin as well as from natural areas (Rhipicephalus sanguineus sensu lato, Rhipicephalus microplus, Dermacentor nitens, A. sculptum, A. ovale, Amblyomma tigrinum, Argas miniatus). Amblyomma sculptum had the widest domestic host spectrum among all tick species. DNA of only one Rickettsia species, R. bellii, was found in an A. dubitatum tick. Several biological and ecological features of ticks of the studied areas are discussed.

Acute modification in plasma lipid levels in ultramarathon runners.

We aimed to evaluate the effects of a 24-h ultramarathon, an aerobic test of high physical load, on lipid profile and apolipoproteins B (ApoB) and A1 (ApoA1) levels, minimally modified low-density lipoprotein (LDL), and oxidised LDL. Prospective evaluation of 16 male athletes who participated in an ultramarathon run, where the objective was to run the greatest distance possible in 24 h. Fourteen participants completed the run. The mean distance achieved was 133.1 km (maximum of 169.6 km). There was a trend in reduction of triglycerides and total cholesterol (P = 0.06 and 0.05, respectively), without significant modifications in high-density lipoprotein, LDL and ApoA1 levels (P = 0.16; 0.55 and 0.67). There was a marked reduction in ApoB levels (P < 0.001), correlated directly to the distance covered (Pearson R = 0.68). Accordingly, an increase in the LDL/ApoB ratio was observed. The stress of this physical activity was not associated to an increase in minimally modified LDL or oxidised LDL. Lipid profile levels were not acutely altered by prolonged physical activity. Similarly, there was no evidence of greater oxidation of LDL over a 24-h period of physical activity. The reduction in ApoB was directly proportional to the distance covered, suggesting an acute positive change in phenotype of LDL molecules.

Low-load resistance training promotes muscular adaptation regardless of vascular occlusion, load, or volume.

PURPOSE: This study investigates the impact of two different intensities and different volumes of low-load resistance training (LLRT) with and without blood flow restriction on the adaptation of muscle strength and size. METHODS: The sample was divided into five groups: one set of 20 % of one repetition maximum (1RM), three sets of 20 % of 1RM, one set of 50 % of 1RM, three sets of 50 % of 1RM, or control. LLRT was performed with (OC) or without (NOC) vascular occlusion, which was selected randomly for each subject. The maximal muscle strength (leg extension; 1RM) and the cross-sectional area (quadriceps; CSA) were assessed at baseline and after 8 weeks of LLRT. RESULTS: 1RM performance was increased in both groups after 8 weeks of training: OC (1 × 50 % = 20.6 %; 3 × 50 % = 20.9 %; 1 × 20 % = 26.6 %; 3 × 20 % = 21.6 %) and NOC (1 × 50 % = 18.6 %; 3 × 50 % = 26.8 %; 1 × 20 % = 18.5 %; 3 × 20 % = 21.6 %; 3 × 20 % = 24.7 %) compared with the control group (-1.7 %). Additionally, the CSA was increased in both groups: OC (1 × 50 % = 2.4 %; 3 × 50 % = 3.8 %; 1 × 20 % = 4.6 %; 3 × 20 % = 4.8 %) and NOC (1 × 50 % = 2.4 %; 3 × 50 % = 1.5 %; 1 × 20 % = 4.3 %; 3 × 20 % = 3.8 %) compared with the control group (-0.7 %). There were no significant differences between the OC and NOC groups. CONCLUSION: We conclude that 8 weeks of LLRT until failure in novice young lifters, regardless of occlusion, load or volume, produces similar magnitudes of muscular hypertrophy and strength.

[Evaluation of the impact of the implementation of the new primary health care information system on records of patient care and home visits in Brazil]. / Avaliação do impacto da implantação do novo sistema de informações da atenção primária à saúde nos registros de atendimentos e visitas domiciliares no Brasil.

The replacement of the Primary Care Information System (SIAB, 1998-2015), as of January 2016, by the new Health Information System for Primary Care (SISAB) determined new forms of collecting, processing, and using information, with a possible impact on the records of activities carried out in primary health care in Brazil. This study aimed to evaluate the implementation impact of the new information system on records of physicians' and nurses' patient care and home visits of community health workers (CHW) in Brazil from 2007 to 2019. To this end, a Bayesian structural time-series model approach was used, based on a diffuse state-space regression. From 2016 to 2019, 463.47 million physician care, 210.61 million nursing care, and 1.28 billion CHW visits were recorded. Following the trend recorded before the implementation, 598.86 million, 430.46 million, and 1.5 billion physician and nursing appointments and CHW visits would be expected, respectively. In relative terms, there was a decrease of 25% in physician care, 51% in nursing care, and 15% in CHW visits when compared to the value expected by the Bayesian method. The negative impact on the records of patient care and home visits identified in this study, whether due to difficulties in adapting to the new system or a reduction in improper records, must be investigated so that the challenge of improving the primary care information system can be understood and overcome in a planned way.

A substituição do Sistema de Informação da Atenção Básica (SIAB, 1998-2015), a partir de janeiro de 2016, pelo novo Sistema de Informação em Saúde para a Atenção Básica (SISAB) determinou novas formas de coleta, processamento e uso das informações, com possível impacto nos registros das atividades desenvolvidas na atenção primária à saúde no Brasil. O objetivo deste estudo foi avaliar o impacto da implantação do novo sistema de informação sobre registros de atendimentos de médicos e enfermeiros, e de visitas domiciliares de agentes comunitários de saúde (ACS) brasileiros entre 2007 e 2019. Para tal, utilizou-se uma abordagem bayesiana de modelo estrutural para séries temporais, com base em uma regressão difusa de espaço-estado. Ao longo do período de 2016 a 2019, foram registrados 463,47 milhões de atendimentos médicos, 210,61 milhões de atendimentos de enfermagem e 1,28 bilhão de visitas de ACS. Seguindo a tendência registrada antes da implantação, seriam esperados 598,86 milhões, 430,46 milhões e 1,5 bilhão de atendimentos de médicos, enfermeiros e visitas de ACS, respectivamente. Em termos relativos, houve um decréscimo de 25% nos atendimentos médicos, 51% nos atendimentos de enfermagem e 15% nas visitas de ACS quando comparado com o valor esperado pelo método bayesiano. O impacto negativo no registro de atendimentos e de visitas domiciliares identificado neste estudo, seja por dificuldade de adaptação ao novo sistema, seja por diminuição de registros indevidos, merece ser alvo de investigação para que se possa, de forma planejada, compreender e superar o desafio da melhoria do sistema de informação da atenção primária.

La sustitución del Sistema de Información de la Atención Básica (SIAB, 1998-2015), desde enero de 2016, por el nuevo Sistema de Información en Salud para la Atención Básica (SISAB) estableció nuevas maneras para recolectar, procesar y utilizar las informaciones, con posibles impactos en los registros de las actividades desarrolladas en la atención primaria de salud en Brasil. El objetivo de este estudio fue evaluar el impacto de la implantación del nuevo sistema de información sobre los registros de atención de médicos y enfermeros y de visitas domiciliarias de agentes comunitarios de salud (ACS) en Brasil entre 2007 y 2019. Para eso, se utilizó un enfoque bayesiano de modelo estructural para series temporales basadas en una regresión difusa de espacio de estado. Entre los años 2016 y 2019, se registraron 463,47 millones de consultas médicas, 210,61 millones de consultas de enfermería y 1,28 mil millones de visitas de ACS. Siguiendo la tendencia registrada antes de la implantación, se esperarían 598,86 millones, 430,46 millones y 1,5 mil millones de consultas médicas y de enfermería y visitas de ACS respectivamente. En términos relativos, hubo una disminución del 25% en las consultas médicas, del 51% en las consultas de enfermería y del 15% en las visitas de ACS en comparación con el valor esperado por el método bayesiano. El impacto negativo en el registro de consultas y visitas domiciliarias identificado en este estudio, ya sea por dificultades en la adaptación al nuevo sistema o por la disminución de los registros indebidos, merece ser objeto de investigación para que se pueda, de manera planificada, comprender y superar el desafío continuo de mejorar el sistema de información de la atención primaria.

Layer-selective deep representation to improve esophageal cancer classification.

Even though artificial intelligence and machine learning have demonstrated remarkable performances in medical image computing, their accountability and transparency level must be improved to transfer this success into clinical practice. The reliability of machine learning decisions must be explained and interpreted, especially for supporting the medical diagnosis. For this task, the deep learning techniques' black-box nature must somehow be lightened up to clarify its promising results. Hence, we aim to investigate the impact of the ResNet-50 deep convolutional design for Barrett's esophagus and adenocarcinoma classification. For such a task, and aiming at proposing a two-step learning technique, the output of each convolutional layer that composes the ResNet-50 architecture was trained and classified for further definition of layers that would provide more impact in the architecture. We showed that local information and high-dimensional features are essential to improve the classification for our task. Besides, we observed a significant improvement when the most discriminative layers expressed more impact in the training and classification of ResNet-50 for Barrett's esophagus and adenocarcinoma classification, demonstrating that both human knowledge and computational processing may influence the correct learning of such a problem.

Competencies for Transformational Leadership in Public Health-An International Delphi Consensus Study.

Objectives: This Delphi study intended to develop competencies for transformational leadership in public health, including behavioral descriptions (descriptors) tailored to individuals and their contexts. Methods: The study involved five rounds, including online "e-Delphi" consultations and real-time online workshops with experts from diverse sectors. Relevant competencies were identified through a literature review, and experts rated, ranked, rephrased, and proposed descriptors. The study followed the Guidance on Conducting and REporting DElphi Studies (CREDES) and the COmpeteNcy FramEwoRk Development in Health Professions (CONFERD-HP) reporting guidelines. Results: Our framework comprises ten competencies for transformational public health leadership (each with its descriptors) within four categories, and also describes a four-stage model for developing relevant competencies tailored to different contexts. Conclusion: Educators responsible for curriculum design, particularly those aiming to align curricula with local goals, making leadership education context-specific and -sensitive, may benefit from the proposed framework. Additionally, it can help strengthen links between education and workforce sectors, address competency gaps, and potentially reduce the out-migration of graduates in the health professions.

Current and Projected Mortality and Hospitalization Rates Associated With Conditional Cash Transfer, Social Pension, and Primary Health Care Programs in Brazil, 2000-2030.

Importance: The health outcomes of increased poverty and inequalities in low- and middle-income countries (LMICs) have been substantially amplified as a consequence of converging multiple crises. Brazil has some of the world's largest conditional cash transfer (Programa Bolsa Família [PBF]), social pension (Beneficio de Prestacão Continuada [BPC]), and primary health care (Estratégia de Saúde da Família [ESF]) programs that could act as mitigating interventions during the current polycrisis era of increasing poverty, slow or contracting economic growth, and conflicts. Objective: To evaluate the combined association of the Brazilian conditional cash transfer, social pension, and primary health care programs with the reduction of morbidity and mortality over the last 2 decades and forecast their potential mitigation of the current global polycrisis and beyond. Design, Setting, and Participants: This cohort study used a longitudinal ecological design with multivariable negative binomial regression models (adjusted for relevant socioeconomic, demographic, and health care variables) integrating the retrospective analysis from 2000 to 2019, with dynamic microsimulation models to forecast potential child mortality scenarios up to 2030. Participants included a cohort of 2548 Brazilian municipalities from 2004 to 2019, projected from 2020 to 2030. Data analysis was performed from September 2022 to February 2023. Exposure: PBF coverage of the target population (those who were poorest) was categorized into 4 levels: low (0%-29.9%), intermediate (30.0%-69.9%), high (70.0%-99.9%), and consolidated (≥100%). ESF coverage was categorized as null (0), low (0.1%-29.9%), intermediate (30.0%-69.9%), and consolidated (70.0%-100%). BPC coverage was categorized by terciles. Main outcomes and measures: Age-standardized, all-cause mortality and hospitalization rates calculated for the entire population and by age group (<5 years, 5-29 years, 30-69 years, and ≥70 years). Results: Among the 2548 Brazilian municipalities studied from 2004 to 2019, the mean (SD) age-standardized mortality rate decreased by 16.64% (from 6.73 [1.14] to 5.61 [0.94] deaths per 1000 population). Consolidated coverages of social welfare programs studied were all associated with reductions in overall mortality rates (PBF: rate ratio [RR], 0.95 [95% CI, 0.94-0.96]; ESF: RR, 0.93 [95% CI, 0.93-0.94]; BPC: RR, 0.91 [95% CI, 0.91-0.92]), having all together prevented an estimated 1 462 626 (95% CI, 1 332 128-1 596 924) deaths over the period 2004 to 2019. The results were higher on mortality for the group younger than age 5 years (PBF: RR, 0.87 [95% CI, 0.85-0.90]; ESF: RR, 0.89 [95% CI, 0.87-0.93]; BPC: RR, 0.84 [95% CI, 0.82-0.86]), on mortality for the group aged 70 years and older, and on hospitalizations. Considering a shorter scenario of economic crisis, a mitigation strategy that will increase the coverage of PBF, BPC, and ESF to proportionally cover the newly poor and at-risk individuals was projected to avert 1 305 359 (95% CI, 1 163 659-1 449 256) deaths and 6 593 224 (95% CI, 5 534 591-7 651 327) hospitalizations up to 2030, compared with fiscal austerity scenarios that would reduce the coverage of these interventions. Conclusions and relevance: This cohort study's results suggest that combined expansion of conditional cash transfers, social pensions, and primary health care should be considered a viable strategy to mitigate the adverse health outcomes of the current global polycrisis in LMICs, whereas the implementation of fiscal austerity measures could result in large numbers of preventable deaths.