Critical evaluation of national vital statistics: the case of preterm birth trends in Portugal

Identification of Diseases Based on the Use of Inertial Sensors: A Systematic Review

Inertial sensors are commonly embedded in several devices, including smartphones, and other specific devices. This type of sensors may be used for different purposes, including the recognition of different diseases. Several studies are focused on the use of accelerometer signals for the automatic recognition of different diseases, and it may empower the different treatments with the use of less invasive and painful techniques for patients. This paper aims to provide a systematic review of the studies available in the literature for the automatic recognition of different diseases by exploiting accelerometer sensors. The most reliably detectable disease using accelerometer sensors, available in 54% of the analyzed studies, is the Parkinson’s disease. The machine learning methods implemented for the automatic recognition of Parkinson’s disease reported an accuracy of 94%. The recognition of other diseases is investigated in a few other papers, and it appears to be the target of further analysis in the future.

Air Quality Monitoring Using Assistive Robots for Ambient Assisted Living and Enhanced Living Environments through Internet of Things

This paper presents iAirBot, an assistive robot for indoor air quality monitoring based on Internet of Things. The system can communicate with occupants and triggers alerts automatically using social networks. The information can be accessed by the caregiver to plan interventions for enhanced living environments in a timely manner. The results are promising, as the proposed architecture presents a cost-effective assistive robot for indoor quality monitoring. It connects several technological fields and knowledge areas, such as ambient assisted living, Internet of Things, wireless sensor networks, social networks, and indoor air quality. When compared to other systems, iAirBot stands out for the modularity and scalability of its sensors network, as well as the use of social networks for information sharing. Therefore, iAirBot is a significant system for enhanced living environments, occupational health, and well-being.

Trends in sociodemographic and health care factors in Portuguese and non-Portuguese mothers giving birth in Portugal, 1995-2014

BACKGROUND

Time-trend disparity in adverse pregnancy outcomes according to maternal nationality or immigration status has been well documented. In this study, we investigated time changes in individual-level risk factors for adverse pregnancy outcomes that have not been given the needed attention.

OBJECTIVE

To compare 20-year trends in sociodemographic, obstetric, health care factors, and adverse pregnancy outcomes in Portuguese and non-Portuguese women.

METHODS

We studied 2 105 497 livebirths from Portuguese national birth registry (1995-2014). We compared maternal sociodemographic characteristics (age, education, employment, and marital status), obstetric, and health care factors (parity, number of foetuses, place and type of delivery) in Portuguese and non-Portuguese at four periods (1995-1999, 2000-2004, 2005-09, and 2010-2014). Time-trend analysis using joinpoint regression method was performed to identify trends (joinpoints) and compare time changes in the prevalence of sociodemographic, obstetric, and health care factors expressed as annual percentage change (APC).

RESULTS

The proportion of livebirths in non-Portuguese mothers increased between 1995 and 1999 (2.9%), 2000-2004 (6.3%), 2005-2009 (9.5%), and 2010-2014 (9.8%). The proportion of women aged ≥ 35 years among Portuguese mothers varied from 11.1% (1995-1999), 14.4% (2000-2004), 18.6% (2005-2009) to 25.5% (2010-2014); among non-Portuguese women, the corresponding proportions were 15.7% (1995-1999), 14.6% (2000-2004), 16.1% (2005-2009), and 19.0% (2010-2014), respectively. The rate of change in maternal age ≥ 35 years had 2 joinpoints in both Portuguese (APC = 6.5%, 95% confidence interval [CI] 6.2, 6.9; 2005-2014) and non-Portuguese (3.3%, 95% CI 2.5, 4.0; 2002-2014). Increase in caesarean rate was higher for non-Portuguese (24.0%-36.1%) than the Portuguese (30.6%-31.6%) between 1995 and 2014.

CONCLUSIONS

Sociodemographic, obstetric, and health care factors have divergent time trends and rate of change for Portuguese and non-Portuguese.

Adverse Pregnancy Outcomes: A Comparison of Risk Factors and Prevalence in Native and Migrant Mothers of Portuguese Generation XXI Birth Cohort

Epidemiological studies report conflicting findings regarding association between maternal immigration status and pregnancy outcomes. In this study we compared risk factors and prevalence of adverse pregnancy outcomes in native Portuguese and migrants. Cross-sectional analysis was conducted using information collected at delivery from the participants of Generation XXI birth cohort. Logistic regression models were fitted to assess the association between migrant status and adverse pregnancy outcomes. Prevalence of risk factors for adverse pregnancy outcomes varied between native Portuguese and migrants: teenage mothers (5.6 and 2.0%), primiparae (57.1 and 63.9%), smoking during pregnancy (23.0 and 19.1%), twins (3.2 and 8.0%), and caesarean section (35.2 and 45.7%). Among singleton births, prevalence of low birthweight, preterm birth and small for gestational age were 7.3 and 3.9%, 7.5 and 6.2%, and 15.1 and 7.6%, respectively for native Portuguese and migrants. The native Portuguese had an adjusted significantly higher risk of low birthweight (OR 2.67, 95% CI 1.30, 5.48) and small for gestational age (OR 2.01, 95% CI 1.26, 3.21), but a similar risk for preterm birth (OR 1.38, 95% CI 0.81, 2, 34). Migrant mothers presented a lower risk of low birthweight and small for gestation and data suggest a healthy immigrant effect.