Coleção de Artigos Acadêmicos

URI permanente para esta coleçãohttps://repositorio.insper.edu.br/handle/11224/3227

Navegar

Filtros

2021 - 20232

Configurações

Palavra-chave: search.filters.subject.Primary care

Resultados da Pesquisa

Agora exibindo 1 - 2 de 2
  • N/D
    Artigo Científico
    Primary care coverage and individual health: evidence from a likelihood model using biomarkers in Brazil
    (2021) Postali, Fernando Antonio Slaibe; Diaz, Maria Dolores Montoya; Ferreira-Batista, Natalia Nunes; Teixeira, Adriano Dutra; Moreno-Serra, Rodrigo
    Background Although the use of biomarkers to assess health outcomes has recently gained momentum, literature is still scarce for low- to middle-income countries. This paper explores the relationship between primary care coverage and individual health in Brazil using a dataset of blood-based biomarkers collected by the Brazilian National Health Survey. Both survey data and laboratory results were crossed with coverage data from the Family Health Strategy (ESF) program, the most important primary care program in Brazil; the coverage measures aim to capture both direct (household) and indirect (spill-over) effects. Methods The empirical strategy used a probit model to estimate the relationship between ESF program coverage and the likelihood of abnormal biomarker levels while controlling for a rich set of individual and household characteristics based on data from the national survey. Results Household ESF coverage was associated with a lower likelihood of abnormal results for biomarkers related to anemia (marginal effect between − 2.16 and − 2.18 percentage points), kidney failure (between − 1.01 and − 1.19 p.p.), and arterial hypertension (between − 1.48 and − 1.64 p.p). The likelihood of abnormal levels of white blood cells and thrombocytes was negatively related to primary care coverage (marginal effect between − 1.8 and − 2 p.p.). The spillover effects were relevant for kidney failure and arterial hypertension, depending on the regional level. Although not sensitive to household coverage, diabetes mellitus was negatively associated with the state supply of primary care, and abnormal cholesterol levels did not present any relationship with ESF program coverage. Conclusions The presence of spillover effects of ESF program coverage regarding these conditions reveals that the strengthening of primary care by increasing the household registration and the regional density of ESF teams is an efficient strategy to address important comorbidities.
  • Imagem de Miniatura
    Artigo Científico
    Data-driven decision making for the screening of cognitive impairment in primary care: a machine learning approach using data from the ELSA-Brasil study
    (2023) Szlejf, C.; ANDRE FILIPE DE MORAES BATISTA; Bertola, L.; Lotufo, P.A.; Benseñor, I.M.; Chiavegatto Filho, A.D.P.; Suemoto, C.K.
    The systematic assessment of cognitive performance of older people without cognitive complaints is controversial and unfeasible. Identifying individuals at higher risk of cognitive impairment could optimize resource allocation. We aimed to develop and test machine learning models to predict cognitive impairment using variables obtainable in primary care settings. In this cross-sectional study, we included 8,291 participants of the baseline assessment of the ELSA-Brasil study, who were aged between 50 and 74 years and were free of dementia. Cognitive performance was assessed with a neuropsychological battery and cognitive impairment was defined as global cognitive z-score below 2 standard deviations. Variables used as input to the prediction models included demographics, social determinants, clinical conditions, family history, lifestyle, and laboratory tests. We developed machine learning models using logistic regression, neural networks, and gradient boosted trees. Participants’ mean age was 58.3±6.2 years, 55% were female. Cognitive impairment was present in 328 individuals (4%). Machine learning algorithms presented fair to good discrimination (areas under the ROC curve between 0.801 and 0.873). Extreme Gradient Boosting presented the highest discrimination, high specificity (97%), and negative predictive value (97%). Seventy-six percent of the individuals with cognitive impairment were included among the highest ranked individuals by this algorithm. In conclusion, we developed and tested a machine learning model to predict cognitive impairment based on primary care data that presented good discrimination and high specificity. These characteristics could support the detection of patients who would not benefit from cognitive assessment, facilitating the allocation of human and economic resources.