Postpartum Depression: Is it a condition affecting the mother-infant interaction and the development of the child across the first year of life?

Noting that maternal depression is common during a baby's first year, this study examined the interaction of depressed and non-depressed mother-child dyads. A sample of 26 first-time mothers with postpartum depression at the third month after birth and their 3-month-old infants was compared to a sample of 25 first-time mothers with no postpartum depression at the third month after birth and their 3-month-old infants. The observations were repeated at 6 months and again at 12 months postpartum. The samples were compared for differences in mother interaction behavior, mother's infant care, mother's concern with the baby, infant behavioral difficulties, infant mental and motor development, and infant behavior with the observer. Among the findings are the following: (1) depressed mothers' interaction behavior and care of their infants are less adequate than the non-depressed mothers' interaction behavior and care of their infants at 3, 6, and 12 months postpartum; (2) infants' interaction behaviors during feeding and face-to-face interaction with depressed mothers are less adequate than infants' interactions with non-depressed mothers at 3, 6, and 12 months postpartum; (3) mother-infant interactions are less adequate in the depressed mother dyads than the non-depressed dyads at 3, 6, and 12 months postpartum; (4) depressed mothers are less concerned about their infants than non-depressed mothers at 3, 6, and 12 months postpartum; (5) infants of depressed mothers have more behavioral difficulties at 3, 6, and 12 months postpartum than infants of non-depressed mothers; (6) infants of depressed mothers had lower mental and motor development rates at 6 and 12 months postpartum than infants of non-depressed mothers; and (7) infants of non-depressed mothers behaved in a more positive way with the observer than the infants of depressed mothers. (AS)

Aggregating community resources of care and assistance services for the elderly population

Changes in population age structure are a major concern and represent a priority in the agendas and policies of the developed world, which are demanding for renewed models of social and healthcare as well as assistance services to the elderly population. Studies indicate that as far as possible these types of services should desirably be provided at the user’s home, and that ICT-based solutions can have tremendous impact on the delivery of new services. This paper highlight and discusses some of the main results of a project undertaken in a Portuguese Municipality that demonstrates the potential contribution of an e-Marketplace of care and assistance services to the well-being of elderly people. Studies undertaken allowed identifying the main services that should be provided by such e-Marketplace (termed GuiMarket), the relevance that the population grant to this platform and, conversely, the fact that the Digital Divide phenomena influences the potential utilization of this project (and alike projects). The findings support that there is a strong relation between age and qualifications, and between access to ICT and the intended use of GuiMarket.

Child abuse monitor system model: a health care critical knowledge monitor system

The Childhood protection is a subject with high value for the society, but, the Child Abuse cases are difficult to identify. The process from suspicious to accusation is very difficult to achieve. It must configure very strong evidences. Typically, Health Care services deal with these cases from the beginning where there are evidences based on the diagnosis, but they aren’t enough to promote the accusation. Besides that, this subject it’s highly sensitive because there are legal aspects to deal with such as: the patient privacy, paternity issues, medical confidentiality, among others. We propose a Child Abuses critical knowledge monitor system model that addresses this problem. This decision support system is implemented with a multiple scientific domains: to capture of tokens from clinical documents from multiple sources; a topic model approach to identify the topics of the documents; knowledge management through the use of ontologies to support the critical knowledge sensibility concepts and relations such as: symptoms, behaviors, among other evidences in order to match with the topics inferred from the clinical documents and then alert and log when clinical evidences are present. Based on these alerts clinical personnel could analyze the situation and take the appropriate procedures.

Clustering-based approach for categorizing pregnant women in obstetrics and maternity care

When a pregnant woman is guided to a hospital for obstetrics purposes, many outcomes are possible, depending on her current conditions. An improved understanding of these conditions could provide a more direct medical approach by categorizing the different types of patients, enabling a faster response to risk situations, and therefore increasing the quality of services. In this case study, the characteristics of the patients admitted in the maternity care unit of Centro Hospitalar of Porto are acknowledged, allowing categorizing the patient women through clustering techniques. The main goal is to predict the patients’ route through the maternity care, adapting the services according to their conditions, providing the best clinical decisions and a cost-effective treatment to patients. The models developed presented very interesting results, being the best clustering evaluation index: 0.65. The evaluation of the clustering algorithms proved the viability of using clustering based data mining models to characterize pregnant patients, identifying which conditions can be used as an alert to prevent the occurrence of medical complications.

Predicting preterm birth in maternity care by means of data mining

Worldwide, around 9% of the children are born with less than 37 weeks of labour, causing risk to the premature child, whom it is not prepared to develop a number of basic functions that begin soon after the birth. In order to ensure that those risk pregnancies are being properly monitored by the obstetricians in time to avoid those problems, Data Mining (DM) models were induced in this study to predict preterm births in a real environment using data from 3376 patients (women) admitted in the maternal and perinatal care unit of Centro Hospitalar of Oporto. A sensitive metric to predict preterm deliveries was developed, assisting physicians in the decision-making process regarding the patients’ observation. It was possible to obtain promising results, achieving sensitivity and specificity values of 96% and 98%, respectively.

Step towards multiplatform framework for supporting pediatric and neonatology care unit decision process

Children are an especially vulnerable population, particularly in respect to drug administration. It is estimated that neonatal and pediatric patients are at least three times more vulnerable to damage due to adverse events and medication errors than adults are. With the development of this framework, it is intended the provision of a Clinical Decision Support System based on a prototype already tested in a real environment. The framework will include features such as preparation of Total Parenteral Nutrition prescriptions, table pediatric and neonatal emergency drugs, medical scales of morbidity and mortality, anthropometry percentiles (weight, length/height, head circumference and BMI), utilities for supporting medical decision on the treatment of neonatal jaundice and anemia and support for technical procedures and other calculators and widespread use tools. The solution in development means an extension of INTCare project. The main goal is to provide an approach to get the functionality at all times of clinical practice and outside the hospital environment for dissemination, education and simulation of hypothetical situations. The aim is also to develop an area for the study and analysis of information and extraction of knowledge from the data collected by the use of the system. This paper presents the architecture, their requirements and functionalities and a SWOT analysis of the solution proposed.

Step towards a patient timeline in intensive care units

In Intensive Medicine, the presentation of medical information is done in many ways, depending on the type of data collected and stored. The way in which the information is presented can make it difficult for intensivists to quickly understand the patient's condition. When there is the need to cross between several types of clinical data sources the situation is even worse. This research seeks to explore a new way of presenting information about patients, based on the timeframe in which events occur. By developing an interactive Patient Timeline, intensivists will have access to a new environment in real-time where they can consult the patient clinical history and the data collected until the moment. The medical history will be available from the moment in which patients is admitted in the ICU until discharge, allowing intensivist to examine data regarding vital signs, medication, exams, among others. This timeline also intends to, through the use of information and models produced by the INTCare system, combine several clinical data in order to help diagnose the future patients’ conditions. This platform will help intensivists to make more accurate decision. This paper presents the first approach of the solution designed

Intelligent decision support to predict patient barotrauma risk in intensive care units

The occurrence of Barotrauma is identified as a major concern for health professionals, since it can be fatal for patients. In order to support the decision process and to predict the risk of occurring barotrauma Data Mining models were induced. Based on this principle, the present study addresses the Data Mining process aiming to provide hourly probability of a patient has Barotrauma. The process of discovering implicit knowledge in data collected from Intensive Care Units patientswas achieved through the standard process Cross Industry Standard Process for Data Mining. With the goal of making predictions according to the classification approach they several DM techniques were selected: Decision Trees, Naive Bayes and Support Vector Machine. The study was focused on identifying the validity and viability to predict a composite variable. To predict the Barotrauma two classes were created: “risk” and “no risk”. Such target come from combining two variables: Plateau Pressure and PCO2. The best models presented a sensitivity between 96.19% and 100%. In terms of accuracy the values varied between 87.5% and 100%. This study and the achieved results demonstrated the feasibility of predicting the risk of a patient having Barotrauma by presenting the probability associated.

Quality of institutional care and early childhood development

Institutional rearing adversely affects children’s development, but the extent to which specific characteristics of the institutional context and the quality of care provided contribute to problematic development remains unclear. In this study, 72 preschoolers institutionalised for at least 6 months were evaluated by their caregiver using the Child Behavior Checklist and the Disturbances of Attachment Interview. Distal and proximate indices of institutional caregiving quality were assessed using both staff reports and direct observation. Results revealed that greater caregiver sensitivity predicted reduced indiscriminate behaviour and secure-base distortions. A closer relationship with the caregiver predicted reduced inhibited attachment behaviour. Emotional and behavioural problems proved unrelated to caregiving quality. Results are discussed in terms of (non)-shared caregiving factors that influence institutionalised children’s development.

Real-time monitoring of progression towards renal failure in primary care patients

First published online: December 16, 2014.

First- and second-time parents’ couple relationship: from pregnancy to second year postpartum

First- and second-time parents’ couple relationships were studied from early pregnancy to the second year postpartum. The Relationship Questionnaire (RQ) was administered to Portuguese couples (N = 82), first- or second-time parents, at the first, second and third pregnancy trimester, childbirth, 3 and 18 months postpartum. Adverse changes in positive and negative partner relationship dimensions were reported from early pregnancy to the second year postpartum by all participants; in the same way by mothers and fathers and by first- and second-time parents. Second-time parents reported a worse couple relationship (lower RQ-positive scores) than first-time parents, but only during pregnancy. Results from the present study suggest a decline in partner relationship quality during the transition to parenthood both in mothers and fathers, as well as in first- and second-time parents.

24-h urinary free cortisol from mid-pregnancy to 3-months postpartum: gender and parity differences and effects

Background: Pregnancy and postpartum have been associated to several physiological changes;however, empirical evidence was almost exclusively obtained in primiparous women and few studies focus on hormonal changes in men and second-time parents. The main aim of this study is to examine 24-h urinary free cortisol from mid-pregnancy to 3-months postpartum, comparing women/men and first/second-time parents.Methods: Twenty-six women and 22 men (N = 48) were recruited from an antenatal obstetric unit in Porto, Portugal. 24-h urinary free cortisol was measured at the 2nd and 3rd trimester and at 3-months postpartum. Repeated measures analyses of variance were conducted, in order to analyze 24-h urinary free cortisol patterns of change over this period. Gender and parity were included in the analyses as potential modifiers, in order to compare women and men, and first-and second-time parents.Results: An increase from the 2nd to the 3rd trimester (p = .006) and a decrease from the 3rd trimester to 3-months postpartum (p = .005) were reported in all parents’ 24-h urinary free cortisol. The interaction effects for Time * Gender (p = .03) and Time * Parity (p = .02) were found. Women and first-time parents revealed higher levels, while men and second-time parents showed lower 24-h urinary free cortisol levels at the 2nd trimester than at 3-months postpartum.Conclusions: Findings appear to clarify the direction, as well as, the timing, gender and parity extension of 24-h urinary free cortisol changes from mid-pregnancy to 3-months postpartum.The same pattern of change in all parents’ 24-h urinary free cortisol from mid-pregnancy to 3-months postpartum is consistent with the proposed role of hormones in preparation to parenting.

Breastfeeding is negatively affected by prenatal depression and reduces postpartum depression

Background. This prospective cohort study explored the effects of prenatal and postpartum depression on breastfeeding and the effect of breastfeeding on postpartum depression. Method. The Edinburgh Postpartum Depression Scale (EPDS) was administered to 145 women at the first, second and third trimester, and at the neonatal period and 3 months postpartum. Self-report exclusive breastfeeding since birth was collected at birth and at 3, 6 and 12 months postpartum. Data analyses were performed using repeated-measures ANOVAs and logistic and multiple linear regressions. Results. Depression scores at the third trimester, but not at 3 months postpartum, were the best predictors of exclusive breastfeeding duration (β =−0.30, t=−2.08, p<0.05). A significant decrease in depression scores was seen from childbirth to 3 months postpartum in women who maintained exclusive breastfeeding for53 months (F1,65 =3.73, p<0.10, ηp 2 =0.05). Conclusions. These findings suggest that screening for depression symptoms during pregnancy can help to identify women at risk for early cessation of exclusive breastfeeding, and that exclusive breastfeeding may help to reduce symptoms of depression from childbirth to 3 months postpartum.

The state of art of biological processes in paternal care

This review of the state of art aimed to present the most recent data on neuronal, neurochemical, hormonal and genetic bases of paternal care using MEDLINE and PsycInfo databases (1970-2013). An integrated model of biological substrates that assist men in the transition to fatherhood is presented. Guided by a genetic background, hypothalamic-midbrain-limbic-paralimbic-cortical circuits were found to be activated in fathers when infant stimuli are presented. A set of specifi c neuropeptides and steroid hormones are produced and seem to be related to brain activation, potentiating the paternal phenotype. Together, genetic, brain and hormonal processes suggest the existence of biological bases of paternal care in humans, activated and enhanced by infant stimuli and responsive to variations in the father-infant relationship.

Screening for depression and anxiety disorders from pregnancy to postpartum with the EPDS and STAI

The Edinburgh Postnatal Depression Scale (EPDS) and the State Anxiety Inventory (STAI-S) are widely used self-report measures that still need to be further validated for the perinatal period. The aim of this study was to examine the screening performance of the EPDS and the STAI-S in detecting depressive and anxiety disorders at pregnancy and postpartum. Women screening positive on EPDS (EPDS ≥ 9) or STAI-S (STAI-S ≥ 45) during pregnancy (n = 90), as well as matched controls (n = 58) were selected from a larger study. At 3 months postpartum, 99 of these women were reassessed. At a second stage, women were administered a clinical interview to establish a DSM-IV-TR diagnosis. Receiver operator characteristics (ROC) analysis yielded areas under the curve higher than .80 and .70 for EPDS and STAI-S, respectively. EPDS and STAI-S optimal cut-offs were found to be lower at postpartum (EDPS = 7; STAI-S = 34) than during pregnancy (EPDS = 9; STAI-S = 40). EPDS and STAI-S are reasonably valid screening tools during pregnancy and the postpartum.