Prepartum, childbirth, and immediate puerperium: Nursing diagnoses of mothers of extremely preterm infants.

BACKGROUND: Maternal mortality remains high, despite all the advances and efforts that have occurred in recent years and is directly related to the quality of care provided during pregnancy, childbirth, and in the puerperium. PURPOSE: Identify the possible nursing diagnoses of mothers of newborns admitted to neonatal intensive care in the prepartum period, childbirth, and the puerperium. METHODS: Observational, cross-sectional study carried out by analyzing the medical records of mothers of newborns who required hospitalization in neonatal intensive care and determining the diagnoses through a process of diagnostic inference, based on NANDA-I Taxonomy, during the period from 2007 to 2016. FINDINGS: After reviewing the medical records of 272 mothers, a total of 3,843 observations were identified, distributed in 42 diagnoses, with Excessive fluid volume presenting the greatest predictive power over the outcome variable. The second group of more predictive variables comprised the Risk of vascular trauma, Risk of infection, Impaired parenthood, and Risk of body temperature imbalance. The third group, in turn, consisted of the following diagnoses: Insomnia, Pain during labor, Risk of impaired bonding, Acute pain, and Ineffective Breastfeeding. CONCLUSIONS: The present study enabled the identification of the most frequent NANDA-I nursing diagnoses occurring during the prepartum period, childbirth, and the immediate puerperium of mothers of newborns admitted to neonatal intensive care, as well as those of greatest importance. Based on the encountered diagnoses, it can be concluded that future studies should be carried out in order for validation. IMPLICATIONS: The knowledge of these diagnoses can contribute to the instrumentalization of nursing professionals, with consequent improvements in the nursing process and the provided assistance, aiming to assist in the reduction of maternal morbidity and mortality.

Suicide mortality among youth in southern Brazil: a spatiotemporal evaluation of socioeconomic vulnerability

Objective: To conduct a geospatial analysis of suicide deaths among young people in the state of Paraná, southern Brazil, and evaluate their association with socioeconomic and spatial determinants. Methods: Data were obtained from the Mortality Information System and the Brazilian Institute of Geography and Statistics. Data on suicide mortality rates (SMR) were extracted for three age groups (15-19, 20-24, and 25-29 years) from two 5-year periods (1998-2002 and 2008-2012). Geospatial data were analyzed through exploratory spatial data analysis. We applied Bayesian networks algorithms to explore the network structure of the socioeconomic predictors of SMR. Results: We observed spatial dependency in SMR in both periods, revealing geospatial clusters of high SMR. Our results show that socioeconomic deprivation at the municipality level was an important determinant of suicide in the youth population in Paraná, and significantly influenced the formation of high-risk SMR clusters. Conclusion: While youth suicide is multifactorial, there are predictable geospatial and sociodemographic factors associated with high SMR among municipalities in Paraná. Suicide among youth aged 15-29 occurs in geographic clusters which are associated with socioeconomic deprivation. Rural settings with poor infrastructure and development also correlate with increased SMR clusters.

Suicide mortality among youth in southern Brazil: a spatiotemporal evaluation of socioeconomic vulnerability.

OBJECTIVE: To conduct a geospatial analysis of suicide deaths among young people in the state of Paraná, southern Brazil, and evaluate their association with socioeconomic and spatial determinants. METHODS: Data were obtained from the Mortality Information System and the Brazilian Institute of Geography and Statistics. Data on suicide mortality rates (SMR) were extracted for three age groups (15-19, 20-24, and 25-29 years) from two 5-year periods (1998-2002 and 2008-2012). Geospatial data were analyzed through exploratory spatial data analysis. We applied Bayesian networks algorithms to explore the network structure of the socioeconomic predictors of SMR. RESULTS: We observed spatial dependency in SMR in both periods, revealing geospatial clusters of high SMR. Our results show that socioeconomic deprivation at the municipality level was an important determinant of suicide in the youth population in Paraná, and significantly influenced the formation of high-risk SMR clusters. CONCLUSION: While youth suicide is multifactorial, there are predictable geospatial and sociodemographic factors associated with high SMR among municipalities in Paraná. Suicide among youth aged 15-29 occurs in geographic clusters which are associated with socioeconomic deprivation. Rural settings with poor infrastructure and development also correlate with increased SMR clusters.