Detalhes bibliográficos
| Ano de defesa: |
2016 |
| Autor(a) principal: |
Amorim, Silvia Maria Costa
 |
| Orientador(a): |
THOMAZ, Erika Bárbara Abreu Fonseca |
| Banca de defesa: |
Não Informado pela instituição |
| Tipo de documento: |
Dissertação
|
| Tipo de acesso: |
Acesso aberto |
| Idioma: |
por |
| Instituição de defesa: |
Universidade Federal do Maranhão
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| Programa de Pós-Graduação: |
PROGRAMA DE PÓS-GRADUAÇÃO EM REDE - REDE NORDESTE DE FORMAÇÃO EM SAÚDE DA FAMÍLIA/CCBS
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| Departamento: |
DEPARTAMENTO DE ENFERMAGEM/CCBS
|
| País: |
Brasil
|
| Palavras-chave em Português: |
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| Palavras-chave em Inglês: |
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| Área do conhecimento CNPq: |
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| Link de acesso: |
http://tedebc.ufma.br:8080/jspui/handle/tede/1448
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Resumo: |
Introduction: The More Health Program (Programa Mais Médicos – PMM) for Brazil was created in order to reduce professional shortage in the regions with the greatest need and vulnerability and invest in training and qualification of all the professionals involved. In Maranhão, the program included 558 professionals until the 8th cycle in nineteen regions of health. Objective: analyze the evolution of health indicators with the implementation of PMM to Brazil in Maranhão municipalities. Methods: This was an ecological study, temporal, descriptive and analytical series. Secondary data will be analyzed, aggregated to the municipal level, through means (± standard deviations) if the variables have normal distribution, or median (± interquartile deviations) for variables with asymmetric distribution. To assess the normality of the distribution will be considered histograms, box-plots, skewness coefficient, kurtosis and the Kolmogorov-Smirnov test. Correlations between the n° of PMM physicians and the study variables were estimated by Spearman correlation coefficient (R). To test differences in health indicators with the implementation of PMM were estimated regression coefficients (β) in linear regression analysis of mixed effects, with hierarchical modeling (alpha = 5%). Results: 214 municipalities have received at least one doctor from PMM until the eighth cycle. Of these, seven in Special Indigenous Health District. The majority received from 1-4 physicians. Maranhão went from 0.58 to 0.67 physicians / 100 inhabitants. Most benefited municipalities had poverty profile (74.67%) and were between 10,000 and 50,000 inhabitants. There was a significant correlation between the number of PMM doctors deployed in municipalities with the following structure variables: Numbers of Basic Health Units (BHU) in construction (R = 0.115), average doctors / staff (R = 0.475), doctors in Primary Health Care (PHC) / 3000 inhabitants (R = 0.194), % BHU opening in minimum time (R = 0.127), % BHU that supply ≥75% of vaccines of the basic calendar (R = 0298), % BHU to offer rapid tests (R = 0.137) and % BHU that has minimal structure for Telehealth (R = 0491). There was no correlation with the working process variables (P> 0.05). There was also correlation with three variables expressing outcome – prenatal exam in pregnant women (R = 0.134). After adjustment of the models, remained associated with the number of implanted in PMM only one structure variable (number of BHU under construction: β = 0.188, P = 0.035) and one indicator of work process (% of family health team with access to telehealth in the city (β = 0.175, P = 0.008). Conclusion. Despite advances harmonized by the program, such as increased physician / inhabitant ratio and distribution of physicians to locations with greater vulnerability, remain the shortage of professionals and care empty. It is noticeable impact on rehabilitation of BHU and improving access to telehealth. |
