PPGMDS https://repositorio.ufpb.br/jspui/handle/tede/5442 2026-03-15T23:32:57Z 2026-03-15T23:32:57Z Nogueira, Rafaela Raulino https://repositorio.ufpb.br/jspui/handle/123456789/37855 2026-03-12T06:07:03Z 2025-08-28T00:00:00Z

Título: A oferta de cuidados à saúde das pessoas com deficiência em municipios brasileiros de pequeno e medio porte Autor(es): Nogueira, Rafaela Raulino Orientador: Ribeiro, Kátia Suely Queiroz Silva Abstract: This thesis aimed to analyze the provision of health care for persons with disabilities in smalland medium-sized Brazilian municipalities, addressing the following guiding question: how have these municipalities organized care for persons with disabilities to meet their health needs? It is a mixed-method study with a descriptive, exploratory, and cross-sectional design, developed using data from the national project “Evaluation of the Comprehensive Care Network for Persons with Disabilities in the SUS – Redecin Brasil”, which covered 50 municipalities distributed across the five geographic regions of the country. In the quantitative stage, 1,555 Primary Health Care (PHC) professionals participated through stratified sampling, responding to a structured questionnaire on health actions aimed at persons with disabilities. The internal consistency of the questions related to specific actions was assessed using Cronbach’s Alpha (α = 0.809), resulting in the Primary Health Care Action Score for Persons with Disabilities (EAS/PHC-PwD). Statistical analysis included comparative tests and multiple linear regressions, stratified according to the population size of the municipalities. The results revealed that knowledge about the Care Network for Persons with Disabilities (RCPD), perception of the adequacy of care provided to persons with disabilities, team composition, and the number of available services showed statistically significant evidence of being associated with an increase in the EAS/PHC-PwD. Additionally, small and medium-sized municipalities demonstrated better performance in implementing actions compared to large municipalities, highlighting the importance of differentiated approaches according to local specificities. The qualitative stage, conducted with 16 managers (PHC coordinators and municipal health secretaries) from municipalities with lower densities of health services, analyzed perceptions and experiences related to the organization of the RCPD and care for persons with disabilities through interviews and content analysis, systematized using the SWOT Matrix. Identified Strengths included physical accessibility, management strategies, and specific PHC actions; Weaknesses involved the absence of clinical protocols, inadequate infrastructure, and limited professional training; Opportunities were represented by state-level support and strengthened communication in small municipalities; while Threats referred to underfunding, political instability, pandemic impacts, and limited access to specialized services. The findings demonstrate that team qualification, knowledge of the RCPD, and the strengthening of PHC structure and actions are central elements for ensuring comprehensive care for persons with disabilities. The final proposal of the thesis included decision-support models sensitive to territorial particularities, with the potential to guide managers and professionals in developing more effective and equitable practices within the PHC context. Editor: Universidade Federal da Paraíba Tipo: Tese

2025-08-28T00:00:00Z Bandeira, Rafael Nóbrega https://repositorio.ufpb.br/jspui/handle/123456789/37645 2026-02-18T06:05:55Z 2020-08-27T00:00:00Z

Título: Avaliação multidimensional da voz como método de tomada de decisão na predição de transtornos mentais comuns em pacientes disfônicos Autor(es): Bandeira, Rafael Nóbrega Orientador: Almeida, Anna Alice Figueiredo de Abstract: Introduction: The scientific literature has pointed out, for years, to the relationship between common mental disorders (CMD) and behavioral dysphonia. However, little has been discussed about the effectiveness of each of the multidimensional voice assessment methods to differentiate the presence of these disorders. Objectives: To develop a decision model to identify the variables that have higher prediction power of the presence of CMD in patients with behavioral dysphonia, as well as present and compare the findings of each stage of multidimensional voice assessment between groups of dysphonic patients with and without CMD. Methodology: Field study carried out at the Speech-Language Therapy School-Clinic of a higher education institution, with 68 patients of both sexes. The volunteers responded to instruments for obtaining information regarding vocal self-assessment and CMD, using the Voice Symptoms Scale, Vocal Handicap Index, Trait-State Anxiety Inventory, BORG-CR10- BR scale and Self-Reporting Questionnaire, which was used to classify them into behavioral dysphonic groups with CMD (CCMD, n = 36) and without CMD (SCMD, n = 32). They were also sent for recording of voice emissions and subsequent auditory perceptual and acoustic analysis, as well as for laryngoscopic and electromyographic exams. Data analysis was performed using descriptive and inferential statistics, using group comparison tests and Weight of Evidence. Results: Dysphonic CCMD show more alterations in the multidimensional evaluation of the voice compared to Dysphonic SCMD, with statistically higher frequency in vocal symptoms of a strained voice, fatigue, shortness of breath and pain when speaking, exposure to a stressful environment, limitation, physical and total domains of VoiSS, BORGCR10- BR scale, electromyographic indices and relative fundamental frequency. The most explanatory variables for the presence of CMD in dysphonic behavioral patients are: BORGCR10- BR scale, Symptoms of itchy throat, fatigue and pain to talk and strained voice, Number of sensorial and total symptoms, Perceptual auditory assessment parameters, Acoustic measures of Jitter, CPPS, GNE, Intensity and fundamental frequency, Electromyographic indices, Female sex, Strait and Trait of Anxiety, Number of personal and total risk factors, Domains of the Voice symptoms scale and Voice Handicap Index. Conclusion: Patients with behavioral dysphonia and common mental disorders have more altered results in all stages of multidimensional voice assessment, compared to dysphonic patients without CMD, with emphasis on the higher prevalence of measures related to phonatory effort. Editor: Universidade Federal da Paraíba Tipo: Tese

2020-08-27T00:00:00Z Silva, Niedja Dias da https://repositorio.ufpb.br/jspui/handle/123456789/37351 2026-01-17T06:07:20Z 2025-08-27T00:00:00Z

Título: Análise da taxa de hospitalização devido a doenças de transmissão hídrica e alimentar no Brasil em 2021 Autor(es): Silva, Niedja Dias da Orientador: Silva, Ana Hermínia Andrade e Abstract: Foodborne and Waterborne Diseases (FWBD) represent a persistent challenge to public health, with significant impacts on morbidity, mortality, and the burden on healthcare systems. The ingestion of contaminated food or water—by biological, chemical, or physical agents—can trigger outbreaks of varying clinical severity, often requiring hospitalization. In this context, the present study aimed to analyze the factors associated with hospitalization rates resulting from WFBD outbreaks in Brazil, focusing on epidemiological data from the year 2021. This is a descriptive study with a quantitative approach, based on secondary data extracted from the Notifiable Diseases Information System (SINAN), accessed via Tabwin/DATASUS. Statistical analysis was performed using R software, applying a zero-and-one inflated beta regression model, suitable for proportional variables with a concentration of extreme values (0 and 1), such as hospitalization rates. The results showed that bacterial outbreaks were the most prevalent, with Escherichia coli and Salmonella spp. being the most frequent pathogens. Most outbreaks occurred in households, followed by restaurants and events. Açaí was the main food associated with protozoan outbreaks, while water was strongly linked to bacterial and viral outbreaks. Statistical modeling indicated that outbreaks classified as “dispersed cases” had lower average hospitalization rates, suggesting lower clinical severity. Conversely, symptoms such as diarrhea and vomiting were significantly associated with hospitalization, while fever showed an inverse association. The absence of information regarding improper food handling was linked to a higher likelihood of outbreaks without hospitalization, possibly indicating underreporting or lower severity. The analysis of model parameters (μ, σ, ν, and τ) allowed the identification of factors associated with the mean hospitalization rate, its dispersion, the absence of hospitalizations, and total hospitalization of cases, respectively. The model demonstrated good statistical fit, with a higher log-likelihood than the null model, a pseudo-R² of 28.4%, and favorable AIC and deviance values. These findings reinforce the importance of epidemiological surveillance, data quality, and the implementation of rigorous preventive measures—especially in domestic environments—to reduce the severity of outbreaks and the need for hospitalization. This study contributes to the understanding of the determinants of hospitalization due to FWBD and provides insights for improving public health policies related to food safety and collective health in Brazil. Editor: Universidade Federal da Paraíba Tipo: Dissertação

2025-08-27T00:00:00Z Soares, Maria Júlia Galindo https://repositorio.ufpb.br/jspui/handle/123456789/37347 2026-01-16T06:05:19Z 2025-08-25T00:00:00Z

Título: Modelos de aprendizado de máquina para a predição do estágio de prontidão para terapia de voz Autor(es): Soares, Maria Júlia Galindo Orientador: Almeida, Anna Alice Figueirêdo de Abstract: Voice assessment is multidimensional as it addresses various aspects, including the main evaluation methods: perceptual-auditory judgment (PAJ), acoustic assessment, laryngological evaluation, and self-assessment. The latter encompasses the patient’s perspective and provides information on the experience of symptoms, impacts on quality of life, and cognitive-behavioral aspects related to voice. The stage of readiness for voice therapy emerges in this context and reflects the patient’s willingness to adopt changes that contribute to their vocal behavior. Identifying the individual’s stage allows for a more targeted and effective intervention. Moreover, the use of machine learning (ML) models to predict stages of readiness represents an innovation to support clinical decision-making in the evaluation and treatment of dysphonia. The objective of this research is to evaluate and compare the performance of ML models for predicting the stage of readiness for speech-language voice therapy. This is an observational, analytical, and quantitative study with a cross-sectional design, conducted through the retrospective analysis of clinical data, applying predictive ML models to predict the stage of readiness. The data sources for the models were obtained from the initial speech-language and laryngological assessments of patients of both sexes seeking care at the Integrated Voice Studies Laboratory of the Federal University of Paraíba. Data were extracted from the Anamnesis and Vocal Assessment Protocol, the University of Rhode Island Change Assessment – Voice Validated Scale, the Vocal Handicap Index, the Voice-Related Quality of Life Scales, Vocal Symptoms and Vocal Tract Discomfort Scales, and from PAJ, acoustic measures, and laryngological diagnosis. The sample comprised 236 individuals, divided into two readiness stage groups: contemplation and maintenance. Descriptive statistical analysis was performed for quantitative and qualitative variables, and supervised ML algorithms were tested, including logistic regression, k-nearest neighbors (KNN), naive Bayes, decision tree, and random forest. Model performance was evaluated through accuracy, sensitivity, specificity, among other measures. Results indicate that the KNN and Random Forest models performed best with the anamnesis data. For PAJ data, laryngological diagnosis, and dysphonia classification, the Logistic Regression and Naive Bayes models demonstrated balanced performance between sensitivity and specificity. For self-assessment instruments and acoustic measures, the models showed lower performance. In models combining data from different dimensions of voice assessment, KNN stood out, presenting high accuracy. Although still limited in predictive power, these findings suggest that ML models have the potential to contribute to identifying the stage of readiness, enabling more targeted intervention strategies in voice therapy. Editor: Universidade Federal da Paraíba Tipo: Dissertação

2025-08-25T00:00:00Z