Coleção de Artigos Acadêmicos
URI permanente para esta coleçãohttps://repositorio.insper.edu.br/handle/11224/3227
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14 resultados
Resultados da Pesquisa
Artigo Científico Private ownership of water and wastewater systems: Assessing health impacts(2025) Chaves, Rodrigo França; ADRIANO BORGES FERREIRA DA COSTAThis study examines the impact of private ownership of water and wastewater systems on disease reduction linked to sanitation in Brazil from 1998 to 2021. It updates Saiani and de Azevedo (2018), which analyzed the period 1995–2008, by incorporating over a decade of additional data, key policy changes such as the 2020 Sanitation Law, and employing the Callaway–Sant’Anna Staggered DID methodology to address heterogeneity in treatment effects. Our findings reveal mixed results: while some municipalities achieved reductions in morbidity rates, others showed no change or increases, underscoring the context-dependent nature of privatization outcomes. A notable example is the case of Tocantins, where transitioning from a hybrid private-state model to full private ownership led to a significant decrease in disease morbidity, particularly among the most affected age groups. These advancements provide a robust, updated perspective on the privatization debate, offering valuable implications for policy and practice.Artigo Científico The role of primary healthcare amid the COVID-19 pandemic: Evidence from the Family Health Strategy in Brazil(2024) Teixeira, Adriano Dutra; Postali, Fernando Antonio Slaibe; Ferreira-Batista, Natalia Nunes; Diaz, Maria Dolores Montoya; Moreno-Serra, RodrigoThis paper investigates the role of primary healthcare in mitigating the consequences of the COVID-19 pandemic, focusing on the Brazilian Family Health Strategy (ESF) as a case study. ESF is Brazil’s major primary care initiative, with prior evidence indicating its effectiveness in improving various health outcomes. The COVID-19 pandemic submitted the Brazilian healthcare system to a rigorous and unprecedented stress test, whose repercussions are still under study. Using comprehensive administrative microdata from 2016 to 2022 encompassing dimensions related to mortality, healthcare service, supply of family health teams, and vaccination coverage, our empirical strategy accounts for heterogeneous effects based on program intensity and pandemic evolution of the 5570 Brazilian municipalities. Our findings reveal that municipalities with high-intensity of ESF coverage (i.e. stronger primary care) experienced 347.93 (95% CI: 289.04, 406.81) fewer COVID-19 and cardiorespiratory deaths per million inhabitants throughout the pandemic period, compared to those in low-intensity ESF areas, despite sharing similar profiles of deaths from respiratory and cardiovascular causes. Among the channels contributing to this relative performance, high-intensity ESF municipalities were found to engage in more home-based primary care visits and health promotion activities while maintaining a similar supply of community health workers. Additionally, they achieved higher vaccination coverage, and these effects were more pronounced in areas with greater ESF presence, emphasising the importance of primary care coverage. In conclusion, our findings underscore the relevance of strong primary care in mitigating the consequences of the pandemic and addressing post-pandemic health challenges.Artigo Científico Is primary health care worth it in the long run? Evidence from Brazil(2023) Ferreira-Batista, Natalia Nunes; Teixeira, Adriano Dutra; Diaz, Maria Dolores Montoya; Postali, Fernando Antonio Slaibe; Moreno-Serra, Rodrigo; Love-Koh, JamesThis paper assesses whether Brazilian primary health care is worth it in the long-run by estimating the accumulated costs and benefits of its flagship, the Family Health Strategy program (ESF). We employ an alternative strategy centered on years of exposure to the program to incorporate its dynamics. We also account for the program's heterogeneity with respect to the remuneration of ESF health teams and the intensity of coverage across Brazilian municipalities, measure by the number of people assisted by each ESF team, on average. To address heterogeneity in professional earnings, this paper employs, for the first time, a dataset containing the remuneration of professionals allocated to all ESF teams nationwide. The benefits are measured by the avoided deaths and hospitalizations due to causes sensitive to primary care. Results suggest that the net monetary benefit of the program is positive on average, with an optimum time of exposure of approximately 16 years. Significant heterogeneities in cost-benefit results were found since costs outweigh benefits in localities where the coverage is low intensive. On the other hand, the benefits outweigh the costs by 22.5% on average in municipalities with high intensive coverage.Artigo Científico The Brazilian Family Health Strategy and adult health: Evidence from individual and local data for metropolitan areas(2022) Natalia N. Ferreira-Batista; Fernando Antonio Slaibe Postali; Maria Dolores Montoya DiazPrevious studies have found that the expansion of primary health care in Brazil following the country-wide family health strategy (ESF), one of the largest primary care programs in the world, has improved health out comes. However, these studies have relied either on aggregate data or on limited individual data, with no fine grained information available concerning household participation in the ESF or local supply of ESF services, which represent crucial aspects for analytical and policy purposes. This study analyzes the relationship between the ESF and health outcomes for the adult population in metropolitan areas in Brazil. We investigate this rela tionship through two linked dimensions of the ESF: the program’s local supply of health teams and ESF household registration. In contrast with previous studies focusing on comparisons between certain definitions of "treated" versus "nontreated" populations, our results indicate that the local density of health teams is important to the observed effects of the ESF on adult health. We also find evidence consistent with the presence of positive primary health care spillovers to people not registered with the ESF. However, current ESF coverage levels in metropolitan areas have limited ability to address prevailing health inequalities. Our analysis suggests that the local intensity of ESF coverage should be a key consideration for evaluations and policy efforts related to future ESF expansion.Artigo Científico Assessment of the association between the Brazilian family health strategy and adult mortality(2022) Diaz, Maria Dolores Montoya; Teixeira, Adriano Dutra; Postali, Fernando Antonio Slaibe; Ferreira-Batista, Natalia Nunes; Moreno-Serra, RodrigoThis study aimed to analyse a wide range of related health problems that respond favourably to efficient primary care treatment among adults. We evaluate the direct impact of the Family Health Strategy (ESF) in Brazil on mortality of adults aged 25–64 years related to conditions for which access to effective primary care can reduce the likelihood of more severe outcomes. Additionally, we discuss heterogeneous effects associated with different intensities of the programme. To address these issues, we estimated a model with variation at the municipal level of the ESF expansion, including municipal-fixed effects, municipal specific trends and year-fixed effects. Our results show that a higher intensity of ESF is associated with reduced mortality by all conditions sensitive to primary care and for some diseases, especially after some years: avoidable conditions, asthma, heart failure, cerebrovascular diseases and gastrointestinal ulcer, infectious gastroenteritis and complications, diseases of the lower airways, hypertension and diabetes. As a public policy view, these results help understand how a nationwide primary care strategy can help mitigate mortality and emphasize the role of having sufficient health teams to attend to the population.Artigo Científico Neonatal mortality prediction with routinely collected data: a machine learning approach(2021) ANDRE FILIPE DE MORAES BATISTA; Diniz, Carmen S. G.; Bonilha, Eliana A.; Kawachi, Ichiro; Chiavegatto Filho, Alexandre D. P.Background: Recent decreases in neonatal mortality have been slower than expected for most countries. This study aims to predict the risk of neonatal mortality using only data routinely available from birth records in the largest city of the Americas. Methods: A probabilistic linkage of every birth record occurring in the municipality of São Paulo, Brazil, between 2012 e 2017 was performed with the death records from 2012 to 2018 (1,202,843 births and 447,687 deaths), and a total of 7282 neonatal deaths were identified (a neonatal mortality rate of 6.46 per 1000 live births). Births from 2012 and 2016 (N = 941,308; or 83.44% of the total) were used to train five different machine learning algorithms, while births occurring in 2017 (N = 186,854; or 16.56% of the total) were used to test their predictive performance on new unseen data. Results: The best performance was obtained by the extreme gradient boosting trees (XGBoost) algorithm, with a very high AUC of 0.97 and F1-score of 0.55. The 5% births with the highest predicted risk of neonatal death included more than 90% of the actual neonatal deaths. On the other hand, there were no deaths among the 5% births with the lowest predicted risk. There were no significant differences in predictive performance for vulnerable subgroups. The use of a smaller number of variables (WHO’s five minimum perinatal indicators) decreased overall performance but the results still remained high (AUC of 0.91). With the addition of only three more variables, we achieved the same predictive performance (AUC of 0.97) as using all the 23 variables originally available from the Brazilian birth records. Conclusion: Machine learning algorithms were able to identify with very high predictive performance the neonatal mortality risk of newborns using only routinely collected data.Artigo Científico Predictors of tooth loss: A machine learning approach(2021) Elani, Hawazin W.; ANDRE FILIPE DE MORAES BATISTA; W. Murray Thomson; Kawachi, Ichiro; Chiavegatto Filho, Alexandre D. P.Introduction Little is understood about the socioeconomic predictors of tooth loss, a condition that can negatively impact individual’s quality of life. The goal of this study is to develop a machine-learning algorithm to predict complete and incremental tooth loss among adults and to compare the predictive performance of these models. Methods We used data from the National Health and Nutrition Examination Survey from 2011 to 2014. We developed multiple machine-learning algorithms and assessed their predictive performances by examining the area under the receiver operating characteristic curve (AUC), accuracy, sensitivity, specificity, and positive and negative predictive values. Results The extreme gradient boosting trees presented the highest performance in the prediction of edentulism (AUC = 88.7%; 95%CI: 87.1, 90.2), the absence of a functional dentition (AUC = 88.3% 95%CI: 87.3,89.3) and for predicting missing any tooth (AUC = 83.2%; 95%CI, 82.0, 84.4). Although, as expected, age and routine dental care emerged as strong predictors of tooth loss, the machine learning approach identified additional predictors, including socioeconomic conditions. Indeed, the performance of models incorporating socioeconomic characteristics was better at predicting tooth loss than those relying on clinical dental indicators alone. Conclusions Future application of machine-learning algorithm, with longitudinal cohorts, for identification of individuals at risk for tooth loss could assist clinicians to prioritize interventions directed toward the prevention of tooth loss.Artigo Científico Cause-specific mortality prediction in older residents of São Paulo, Brazil: a machine learning approach(2021) Nascimento, Carla Ferreira do; Hellen Geremias dos Santos; ANDRE FILIPE DE MORAES BATISTA; Lay, Alejandra Andrea Roman; Duarte, Yeda Aparecida OliveiraBackground: Populational ageing has been increasing in a remarkable rate in developing countries. In this scenario, preventive strategies could help to decrease the burden of higher demands for healthcare services. Machine learning algorithms have been increasingly applied for identifying priority candidates for preventive actions, presenting a better predictive performance than traditional parsimonious models. Methods: Data were collected from the Health, Well Being and Aging (SABE) Study, a representative sample of older residents of São Paulo, Brazil. Machine learning algorithms were applied to predict death by diseases of respiratory system (DRS), diseases of circulatory system (DCS), neoplasms and other specific causes within 5 years, using socioeconomic, demographic and health features. The algorithms were trained in a random sample of 70% of subjects, and then tested in the other 30% unseen data. Results: The outcome with highest predictive performance was death by DRS (AUC−ROC = 0.89), followed by the other specific causes (AUC−ROC = 0.87), DCS (AUC−ROC = 0.67) and neoplasms (AUC−ROC = 0.52). Among only the 25% of individuals with the highest predicted risk of mortality from DRS were included 100% of the actual cases. The machine learning algorithms with the highest predictive performance were light gradient boosted machine and extreme gradient boosting. Conclusion: The algorithms had a high predictive performance for DRS, but lower for DCS and neoplasms. Mortality prediction with machine learning can improve clinical decisions especially regarding targeted preventive measures for older individuals.Artigo Científico Data Leakage in Health Outcomes Prediction With Machine Learning. Comment on “Prediction of Incident Hypertension Within the Next Year: Prospective Study Using Statewide Electronic Health Records and Machine Learning"(2021) Chiavegatto Filho, Alexandre; ANDRE FILIPE DE MORAES BATISTA; Santos, Hellen Geremias dosArtigo Científico A multipurpose machine learning approach to predict COVID-19 negative prognosis in São Paulo, Brazil(2021) Fernandes, Fernando Timoteo; Oliveira, Tiago Almeida de; Teixeira, Cristiane Esteves; ANDRE FILIPE DE MORAES BATISTA; Costa, Gabriel Dalla; Chiavegatto Filho, Alexandre Dias PortoThe new coronavirus disease (COVID-19) is a challenge for clinical decision-making and the effective allocation of healthcare resources. An accurate prognostic assessment is necessary to improve survival of patients, especially in developing countries. This study proposes to predict the risk of developing critical conditions in COVID-19 patients by training multipurpose algorithms. We followed a total of 1040 patients with a positive RT-PCR diagnosis for COVID-19 from a large hospital from São Paulo, Brazil, from March to June 2020, of which 288 (28%) presented a severe prognosis, i.e. Intensive Care Unit (ICU) admission, use of mechanical ventilation or death. We used routinely-collected laboratory, clinical and demographic data to train five machine learning algorithms (artificial neural networks, extra trees, random forests, catboost, and extreme gradient boosting). We used a random sample of 70% of patients to train the algorithms and 30% were left for performance assessment, simulating new unseen data. In order to assess if the algorithms could capture general severe prognostic patterns, each model was trained by combining two out of three outcomes to predict the other. All algorithms presented very high predictive performance (average AUROC of 0.92, sensitivity of 0.92, and specificity of 0.82). The three most important variables for the multipurpose algorithms were ratio of lymphocyte per C-reactive protein, C-reactive protein and Braden Scale. The results highlight the possibility that machine learning algorithms are able to predict unspecific negative COVID-19 outcomes from routinely-collected data.