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Paid Parental Leave Policies and Infant Mortality Rates in OECD Countries: Policy Implications for the United States: Paid Parental Leave and IMR
Abstract
Infant mortality is an important indicator of a nation's overall health and well-being because of its association with education, availability and accessibility of health services, and income inequality. In this paper, we examine the effect of job-protected paid parental leave on infant and post-neonatal mortality rates in 19 OECD countries from 1960 to 2012. We utilize a generalized least squares model controlling for a host of variables traditionally examined in studies of infant mortality rates, as well as year fixed effects, country fixed effects, and country time trends. We find a statistically significant association between job-protected paid parental leave and a reduction in both infant mortality rates and post-neonatal mortality rates. The findings are particularly relevant for policymakers in the United States, the only industrialized democracy in the world that does not provide job-protected paid parental leave to working women and men.
... Selection seemed to be most often conditioned on the European Union, or the European continent more generally, and the Organisation of Economic Co-operation and Development (OECD), though, typically, not all member nations-based on the instances where these were also explicitly listed-were included in a given study. Some of the stated reasons for omitting certain nations included data unavailability [30,45,54] or inconsistency [20,58], Gross Domestic Product (GDP) too low [40], differences in economic development and political stability with the rest of the sampled countries [59], and national population too small [24,40]. On the other hand, the rationales for selecting a group of countries included having similar above-average infant mortality [60], similar healthcare systems [23], and being randomly drawn from a social spending category [61]. ...
... It was often the case that research had a more particular focus. Among others, minimum wages [79], hospital payment schemes [23], cigarette prices [63], social expenditure [20], residents' dissatisfaction [56], income inequality [30,69], and work leave [41,58] took center stage. Whenever variables outside of these specific areas were also included, they were usually identified as confounders or controls, moderators or mediators. ...
... Determinants of health in high income countries: A scoping review sophistication: case-wise deletion, i.e., removal of countries from the sample [20,45,48,58,59], (linear) interpolation [28,30,34,58,59,63], and multiple imputation [26,41,52]. Correlations, Pearson, Spearman, or unspecified, were the only technique applied with respect to the health outcomes of interest in eight analyses [33, 42-44, 46, 53, 57, 61]. ...
Background:
Identifying determinants of health and understanding their role in health production constitutes an important research theme. We aimed to document the state of recent multi-country research on this theme in the literature.
Methods:
We followed the PRISMA-ScR guidelines to systematically identify, triage and review literature (January 2013-July 2019). We searched for studies that performed cross-national statistical analyses aiming to evaluate the impact of one or more aggregate level determinants on one or more general population health outcomes in high-income countries. To assess in which combinations and to what extent individual (or thematically linked) determinants had been studied together, we performed multidimensional scaling and cluster analysis.
Results:
Sixty studies were selected, out of an original yield of 3686. Life-expectancy and overall mortality were the most widely used population health indicators, while determinants came from the areas of healthcare, culture, politics, socio-economics, environment, labor, fertility, demographics, life-style, and psychology. The family of regression models was the predominant statistical approach. Results from our multidimensional scaling showed that a relatively tight core of determinants have received much attention, as main covariates of interest or controls, whereas the majority of other determinants were studied in very limited contexts. We consider findings from these studies regarding the importance of any given health determinant inconclusive at present. Across a multitude of model specifications, different country samples, and varying time periods, effects fluctuated between statistically significant and not significant, and between beneficial and detrimental to health.
Conclusions:
We conclude that efforts to understand the underlying mechanisms of population health are far from settled, and the present state of research on the topic leaves much to be desired. It is essential that future research considers multiple factors simultaneously and takes advantage of more sophisticated methodology with regards to quantifying health as well as analyzing determinants' influence.
... Policy makers have considered paying parental leave and increasing economic opportunities as ways to address infant mortality. Maternity leave has been linked to lower infant mortality in the United States (Andres et al. 2016) and in other wealthy OECD countries (Patton, Costich, and Listromer 2017), yet the United States is the only wealthy industrialized country without paid parental leave. Even unpaid parental leave may be associated with lower infant mortality, although the evidence for that association is limited to college-educated married mothers (Rossin 2011). ...
... These findings are in contrast with studies that have found significant associations of women's SES and reproductive rights with lower IMRs (Koenen, Lincoln, and Appleton 2006), and with other studies linking women's political participation (Homan 2017) and work and family leave policies (Andres et al. 2016;Rossin 2011) with lower infant mortality. Researchers have found associations between policies such as paid parental leave and lower IMRs in other wealthy countries (Patton, Costich, and Listromer 2017), and between maternal leave and lower IMRs in the United States (Andres et al. 2016), even in instances when the leave was unpaid (Rossin 2011). We did not find those associations, possibly because the measures of work and family leave used in other studies were correlated with other factors that were separated out by our principal components analysis. ...
Despite mounting evidence that social factors and public policies affect state infant mortality rates (IMRs), few researchers have examined variation in IMRs associated with those factors and policies. We quantified disparities in infant mortality by state social factors and public policy characteristics. We hypothesized that some social factors and public policies would be more strongly associated with infant mortality than others, and that states with similar factors and policies would form clusters with varying levels of infant mortality. We examined associations of women’s economic empowerment, health and well-being, political participation, reproductive rights, and work and family-related policies with state IMRs in 2012 and 2015, using indicators created by the Institute for Women’s Policy Research. Methods included generalized linear models, principal component analysis, and cluster analysis. Health and well-being predicted IMRs (2012, 2015, both p < .05), as did poverty and opportunity, and reproductive rights (2012, p < .10). Consistent with our hypothesis, states formed clusters, with the states in each cluster having similar social factors and public policies, and similar IMRs. Women’s health status and insurance coverage were more predictive of state IMRs than other social factors. Improving health and insurance coverage may be an effective way to reduce state IMRs.
... Both parents' working status is also a risk factor (Deb et al. 2017;Debowska and Boduszek 2017). Some other factors related to the under-five mortality previously analyzed in the context of women's job status are paid maternity leave (Fallon et al. 2017;Nandi et al. 2018;Patton et al. 2017) and the minimum wages (Lenhart 2017). ...
While greater female participation in the job market is seen as an effective way of achieving higher economic growth, some studies reveal that maternal employment could be a risk factor for child mortality. We analyze the association between under-five mortality and maternal employment in 26 developing countries from Sub-Saharan Africa, South Asia, and the Middle East using Demographic and Health Surveys. Logistic regression results suggest that maternal employment is associated with a 24.5% higher risk of child mortality than stay-at-home mothers. Compared to stay-at-home mothers, maternal engagement in agriculture is associated with 24% higher odds of under-5 mortality, while engagement in blue-collar jobs is associated with 29% higher odds of under-5 mortality. We also find that white-collar jobs do not give any advantage over the stay-at-home mothers with respect to this risk. Interaction of maternal employment types with breastfeeding confirms the increased risk of child mortality for agriculture and blue-collar jobs.
... The life-course importance of linked lives too often has been neglected by policy makers. For instance, generous and job-protected paid leave not only provides support to employees, but also protects mothers' mental health and predicts lower infant mortality rates (Chen et al. 2016;Patton et al. 2017;Montez 2020;Bullinger 2019). While correlation does not prove causality in such matters, it is telling to note that U.S. policies provide the most limited paid maternal leave among Organization of Economic Cooperation and Development (OECD) countries and are accompanied by higher infant mortality rates than European countries (Chen et al. 2016). ...
... The life-course importance of "linked lives" has been too often neglected by policymakers. For instance, generous and job-protected paid leave not only provide support to employees, but also protect mothers' mental health and predict lower infant mortality rates as well as mothers' mental health (Chen et al. 2016;Patton et al. 2017; Montez 2020; Bullinger 2019). While correlation does not prove causality in such matters, it is telling to note that US policies provide the most limited paid maternal leave of any OECD countries, and are accompanied by higher infant mortality rates than European countries (Chen et al. 2016). ...
... 20 Organization for Economic Cooperation and Development (OECD) countries and saw a statistically significant association between PFML and both infant mortality and post-neonatal mortality rates. 21 This study found that PFML of at least 12 weeks could prevent 600 infant or post-neonatal deaths each year in the U.S. 22 Ruhm (2011) found that implementing 40 weeks of job-protected PFML had the greatest overall reduction to morality. 23 , a systematic review of five studies, suggest that lower infant death rates may be the result of decreased stress during pregnancy, allowing for more time for prenatal health care visits, parental bonding, and increased breastfeeding and immunization rates. ...
As mandated in statute, this report identifies results of a third-party expedited literature review with data and recommendations from leading experts in the field of public, social and occupational health. The research comes from state and federal reports and leading academic journals and text in the aforementioned fields. Findings from this review are organized by five levels of impact as outlined by the social ecological model. Each level of impact (e.g., “Benefits to family”) is further organized by short- and long-term impacts.
Findings indicate that Paid Family and Medical Leave (PFML) provides both direct and indirect benefits and improves the well being for employees, family members, and new children. PFML benefits employers by reducing the cost of healthcare for stressed employees and increases worker productivity. PFML benefits communities by reducing community crime rates through increased social support and parental involvement and can lead to local economic growth. PFML promotes gender equality in the workplace, which promotes economic growth and national productivity. A lack of PFML may perpetuate economic inequality, the gender wage gap and the race-wealth gap. Experts recommend a minimum of 12 weeks of paid family leave and medical leave.
... Considering next the relevant neural activity research, scalp electrophysiology and neuroimaging methods have been used to examine the potential impact of maltreatment and other early adversities on children's developmental outcomes. In one such study (Hanson et al., 2010), children who had experienced physical abuse had smaller brain volumes in the right orbitofrontal cortex (a region of the prefrontal cortex that has been implicated in many aspects of emotion and 2017; Cooke & Baxter, 2010;Patton, Costich, & Lidströmer, 2017). The positive effects of such policies are seen in behavior and health (Hahn, 2015). ...
Parenting and children’s development function in a family system that involves many biological factors that interact with the environment and the parent–child relationship. There has been rapid growth in parenting research that incorporates indirect and direct measures of a variety of biological factors, in the hope of better articulating the mechanisms of child and family development, as well as improving the efficacy of interventions to improve developmental and parenting outcomes. In the current chapter, we review this literature, with particular emphasis on genetics, neural structure and function, and hormones. We provide an overview of the major methodological approaches used, and findings pertaining to key dimensions of parenting and parent–child relationships (e.g., warmth, conflict, attachment), and children’s cognitive and social-emotional development. The chapter also points to gaps in knowledge and new directions that will be fruitful for basic and applied research in future.
... To begin with, there might be health effects resulting, for example, from reduced maternal stress or prolonged breastfeeding. 7 Macro-level evidence from a number of OECD countries (e. g., Patton et al. 2017;Tanaka 2005) suggests that longer job-protected, paid parental leave substantially decreases mortality among infants born to eligible mothers (with additional smaller positive effects on birth weight). Whereas Tanaka (2005) did not identify any significant effects if leave was provided without job protection or adequate payment 8 , Rossin (2011) found that even the introduction of 12 weeks of unpaid maternity leave mandated by the 1993 Family and Medical Leave Act in the US led to small increases in birth weight and a significant decline in infant mortality. ...
This article provides an overview of families and their institutional contexts in Western societies, focusing on the role of family policies and legal regulations in union dynamics, fertility, children’s wellbeing, and intergenerational relations. We argue that family dynamics are driven by changing institutional opportunities and constraints, whereas at the same time, welfare state institutions constantly need to adapt to the changing needs of “new” family forms. The empirical studies covered here provide ample evidence of multiple institutional effects on family-related behaviors and outcomes in a variety of domains. Family policy regimes supporting greater gender equality are those under which favorable outcomes are most likely to occur. Importantly, though, specific effects are not always as large, sustainable, or robust as might have been intended or expected beforehand. Methodologically rigorous evaluations of the effectiveness and efficiency of family policy measures and legal regulations thus appear an important task for future research.
Gender wellness and equity, including conversations about transgender and gender-diverse populations, are needed to promote a healthy and equitable workforce. The challenges facing gender wellness in healthcare are multifactorial. Strides have been made to improve gender equity. However, further movement is necessary. Change must begin with education and span to policy updates that promote inclusivity.KeywordsGender equityGender equalityTransgender persons
Future demographic projections have indicated that the low fertility rate problem will put significant pressures on the long-term sustainability of public finance. Nevertheless, among the concerned sustainability of public finance, the depletion of future national pension assets has received little attention. This paper provides numerical projection data by forecasting change of national pension assets in some of OECD countries. Among OECD countries, G7 countries which are leading society of OECD countries and Republic of Korea that has the lowest total fertility rate in OECD countries are analyzed. By adopting demographic-based machine learning (ML) approach, the forecasted results have been demonstrated, and possible future scenarios have been analyzed as variables (future total fertility rate, age when people begin pension receiving) are to be changed in the future. In doing so, possible solutions regarding demographic approach and political approach are suggested to each country.KeywordsLow fertility rateNational pension assetsPension depletionMachine learningArtificial intelligenceAgingOECDG7
Purpose:
The authors aimed to chronicle the evolution of the medical community's study of physician and surgeon pregnancy by investigating thematic trends in the literature in the context of pertinent sociopolitical events.
Method:
A scoping review was conducted in Cochrane Library, Google Scholar, Ovid MEDLINE, Ovid Embase, Scopus, and Web of Science Core Collection from inception through August 11, 2020, using vocabulary and terms for physicians (including surgeons), pregnancy, and family leave. Study populations were categorized by all physician specialties or exclusively surgical specialties as well as by all career levels or exclusively trainees. Subthemes and themes were based on a priori assumptions of physician pregnancy and extrapolated from previously published reviews, respectively. Thematic trends were analyzed by plotting the total number of publications and the frequency of themes and subthemes by publication year.
Results:
After title and abstract and full text reviews, 407 manuscripts met inclusion criteria. Publications on physician pregnancy first emerged in the 1960s and surged from 1988 to 1996 and again from 2010 to 2019. The first known manuscript exclusively on surgeon pregnancy was published in 1991; subsequent publication frequency trends for surgeon pregnancy generally paralleled those for all physician pregnancy publications albeit in reduced quantities. Four major themes were found: impact of pregnancy on the physician and her colleagues, pregnant physician work productivity, physician maternity leave policies, and physician maternal-fetal health outcomes.
Conclusions:
As the number of women physicians increased and the sociopolitical environment progressed, the thematic focus of the literature on physician pregnancy evolved. Multi-institutional prospective observational studies are needed to develop definitive evidence-based recommendations that will positively impact physician pregnancy.
PurposeLittle is known about program directors’ knowledge, attitudes, and beliefs regarding parental leave policies in anesthesiology training. This study sought to understand program director perceptions about the effects of pregnancy and parental leave on resident training, skills, and productivity.Methods
An online 43-question survey was developed to evaluate United States anesthesiology program directors’ perceptions of parental leave policies. The survey included questions regarding demographics, anesthesiology program characteristics, parental leave policies, call coverage, and the perceived effects of parental leave on resident performance. Data were collected by Qualtrics (Qualtrics, Provo, UT, USA).ResultsFifty-six of 145 (39%) anesthesiology program directors completed the survey. Forty-eight of 54 (89%) program directors had a female resident take maternity leave in the past three years. When asked how parental leave affects residents’ futures, 24/50 (48%) program directors felt it delayed board certification and 28/50 (56%) thought it affected fellowship opportunities. Program directors were split on their perceived impact of becoming a parent on a trainee’s work. Yet, when compared with male trainees, program directors perceived that becoming a parent negatively affected female trainees’ timeliness, technical skills, scholarly activities, procedural volume, and standardized test scores and affected training experience of co-residents. Program directors perceived no difference in impact on female trainees’ dedication to patients and clinical performance.Conclusions
Program directors perceived that becoming a parent negatively affects the work performance of female but not male trainees. These negative perceptions could impact evaluations and future plans of female residents.
Although many studies have found that women legislators pursue policies important to women, we know relatively little concerning the effect of women’s descriptive representation (WDR) on women’s quality of life. We address this question by examining the relationship between the election of women state legislators and public health. Specifically, we estimate the effect of WDR on premature death rates from 1982 to 2010. We find that WDR serves to improve the well-being of both women and men. However, the effect of WDR on women’s health is contingent on several important factors. First, the health-improving effects of WDR are most strongly related to the descriptive representation of Democratic women. Second, the effect of Democratic WDR on women’s health, relative to men’s health, increases with the scale of the state’s Medicaid program. Finally, we estimate the mediating role of nine specific policies, finding that four policies mediate the effect of Democratic WDR on premature mortality. Overall, our results suggest that policy remedies aimed at improving the severe underrepresentation of women in government may help to improve the relatively low ranking of the United States on indicators of public health.
Purpose
The purpose of this paper is to synthesize findings on the effects of existing paid parental leave programs on infant mortality rates (IMR) in the USA as an attempt to aid in efforts for the development of a national paid parental leave policy.
Design/methodology/approach
Three articles were reviewed to analyze findings on the effects of existing parental leave programs on IMR in the USA.
Findings
The results from the three studies analyzed indicate that unpaid parental leave and parental leave with partial wage replacement can reduce IMR in households with college educated, working mothers.
Research limitations/implications
This review is limited due to only having three studies available to synthesize that pertained to the USA. Implications for future research are to examine the effects of fully paid parental leave programs offered by individual organizations on IMR in the USA.
Social implications
Providing a needs-based income replacement policy to mothers who wish to take parental leave after the birth of a child may be the best policy to decrease IMR for infants from all socioeconomic backgrounds.
Originality/value
The findings in this review will aid in the ongoing efforts to develop a national paid parental leave policy in the USA.
Background
The coronavirus disease 2019 (COVID-19) pandemic caused by the SARS-Cov2 virus has become the greatest health and controversial issue for worldwide nations. It is associated with different clinical manifestations and a high mortality rate. Predicting mortality and identifying outcome predictors are crucial for COVID patients who are critically ill. Multivariate and machine learning methods may be used for developing prediction models and reduce the complexity of clinical phenotypes.
Methods
Multivariate predictive analysis was applied to 108 out of 250 clinical features, comorbidities, and blood markers captured at the admission time from a hospitalized cohort of patients ( N = 250) with COVID-19. Inspired modification of partial least square (SIMPLS)-based model was developed to predict hospital mortality. Prediction accuracy was randomly assigned to training and validation sets. Predictive partition analysis was performed to obtain cutting value for either continuous or categorical variables. Latent class analysis (LCA) was carried to cluster the patients with COVID-19 to identify low- and high-risk patients. Principal component analysis and LCA were used to find a subgroup of survivors that tends to die.
Results
SIMPLS-based model was able to predict hospital mortality in patients with COVID-19 with moderate predictive power ( Q ² = 0.24) and high accuracy (AUC > 0.85) through separating non-survivors from survivors developed using training and validation sets. This model was obtained by the 18 clinical and comorbidities predictors and 3 blood biochemical markers. Coronary artery disease, diabetes, Altered Mental Status, age > 65, and dementia were the topmost differentiating mortality predictors. CRP, prothrombin, and lactate were the most differentiating biochemical markers in the mortality prediction model. Clustering analysis identified high- and low-risk patients among COVID-19 survivors.
Conclusions
An accurate COVID-19 mortality prediction model among hospitalized patients based on the clinical features and comorbidities may play a beneficial role in the clinical setting to better management of patients with COVID-19. The current study revealed the application of machine-learning-based approaches to predict hospital mortality in patients with COVID-19 and identification of most important predictors from clinical, comorbidities and blood biochemical variables as well as recognizing high- and low-risk COVID-19 survivors.
Objective
The spread of COVID-19 has led to severe strain on hospital capacity in many countries. We aim to develop a model helping planners assess expected COVID-19 hospital resource utilization based on individual patient characteristics.
Materials and Methods
We develop a model of patient clinical course based on an advanced multistate survival model. The model predicts the patient's disease course in terms of clinical states—critical, severe, or moderate. The model also predicts hospital utilization on the level of entire hospitals or healthcare systems. We cross-validated the model using a nationwide registry following the day-by-day clinical status of all hospitalized COVID-19 patients in Israel from March 1st to May 2nd, 2020 (n = 2,703).
Results
Per-day mean absolute errors for predicted total and critical-care hospital-bed utilization were 4.72 ± 1.07 and 1.68 ± 0.40 respectively, over cohorts of 330 hospitalized patients; AUCs for prediction of critical illness and in-hospital mortality were 0.88 ± 0.04 and 0.96 ± 0.04, respectively. We further present the impact of patient influx scenarios on day-by-day healthcare system utilization. We provide an accompanying R software package.
Discussion
The proposed model accurately predicts total and critical-care hospital utilization. The model enables evaluating impacts of patient influx scenarios on utilization, accounting for the state of currently hospitalized patients and characteristics of incoming patients. We show that accurate hospital-load predictions were possible using only a patient’s age, sex, and day-by-day clinical state (critical, severe or moderate).
Conclusion
The multistate model we develop is a powerful tool for predicting individual-level patient outcomes and hospital-level utilization.
The sudden deterioration of patients with novel coronavirus disease 2019 (COVID-19) into critical illness is of major concern. It is imperative to identify these patients early. We show that a deep learning-based survival model can predict the risk of COVID-19 patients developing critical illness based on clinical characteristics at admission. We develop this model using a cohort of 1590 patients from 575 medical centers, with internal validation performance of concordance index 0.894 We further validate the model on three separate cohorts from Wuhan, Hubei and Guangdong provinces consisting of 1393 patients with concordance indexes of 0.890, 0.852 and 0.967 respectively. This model is used to create an online calculation tool designed for patient triage at admission to identify patients at risk of severe illness, ensuring that patients at greatest risk of severe illness receive appropriate care as early as possible and allow for effective allocation of health resources. The sudden deterioration of patients with novel coronavirus disease 2019 (COVID-19) into critical illness is of major concern and early assessment would be vital. Here, the authors show that a deep learning-based survival model can predict the risk of COVID-19 patients developing critical illness based on clinical characteristics at admission.
The COVID-19 Severity Score combines multiplex biomarker measurements and risk factors in a statistical learning algorithm to predict mortality.
Background
Like other scientific fields, such as cosmology, high-energy physics, or even the life sciences, medicine and healthcare face the challenge of an extremely quick transformation into data-driven sciences. This challenge entails the daunting task of extracting usable knowledge from these data using algorithmic methods. In the medical context this may for instance realized through the design of medical decision support systems for diagnosis, prognosis and patient management. The intensive care unit (ICU), and by extension the whole area of critical care, is becoming one of the most data-driven clinical environments.
Results
The increasing availability of complex and heterogeneous data at the point of patient attention in critical care environments makes the development of fresh approaches to data analysis almost compulsory. Computational Intelligence (CI) and Machine Learning (ML) methods can provide such approaches and have already shown their usefulness in addressing problems in this context. The current study has a dual goal: it is first a review of the state-of-the-art on the use and application of such methods in the field of critical care. Such review is presented from the viewpoint of the different subfields of critical care, but also from the viewpoint of the different available ML and CI techniques. The second goal is presenting a collection of results that illustrate the breath of possibilities opened by ML and CI methods using a single problem, the investigation of septic shock at the ICU.
Conclusion
We have presented a structured state-of-the-art that illustrates the broad-ranging ways in which ML and CI methods can make a difference in problems affecting the manifold areas of critical care. The potential of ML and CI has been illustrated in detail through an example concerning the sepsis pathology. The new definitions of sepsis and the relevance of using the systemic inflammatory response syndrome (SIRS) in its diagnosis have been considered. Conditional independence models have been used to address this problem, showing that SIRS depends on both organ dysfunction measured through the Sequential Organ Failure (SOFA) score and the ICU outcome, thus concluding that SIRS should still be considered in the study of the pathophysiology of Sepsis. Current assessment of the risk of dead at the ICU lacks specificity. ML and CI techniques are shown to improve the assessment using both indicators already in place and other clinical variables that are routinely measured. Kernel methods in particular are shown to provide the best performance balance while being amenable to representation through graphical models, which increases their interpretability and, with it, their likelihood to be accepted in medical practice.
Objectives—This report presents final 2015 data on the 10 leading causes of death in the United States by age, sex, race, and Hispanic origin. Leading causes of infant, neonatal, and postneonatal death are also presented. This report supplements “Deaths: Final Data for 2015,” the National Center for Health Statistics’ annual report of final mortality statistics. Methods—Data in this report are based on information from all death certificates filed in the 50 states and the District of Columbia in 2015. Causes of death classified by the International Classification of Diseases, Tenth Revision (ICD–10) are ranked according to the number of deaths assigned to rankable causes. Cause-of-death statistics are based on the underlying cause of death. Results—In 2015, the 10 leading causes of death were, in rank order: Diseases of heart; Malignant neoplasms; Chronic lower respiratory diseases; Accidents (unintentional injuries); Cerebrovascular diseases; Alzheimer’s disease; diabetes mellitus; Influenza and pneumonia; Nephritis, nephrotic syndrome and nephrosis; and Intentional self-harm (suicide). They accounted for 74% of all deaths occurring in the United States. Differences in the rankings are evident by age, sex, race, and Hispanic origin. Leading causes of infant death for 2015 were, in rank order: Congenital malformations, deformations and chromosomal abnormalities; Disorders related to short gestation
and low birth weight, not elsewhere classified; Sudden infant death syndrome; Newborn affected by maternal complications of pregnancy; Accidents (unintentional injuries); Newborn affected by complications of placenta, cord and membranes; Bacterial sepsis of newborn; Respiratory distress of newborn; Diseases of the circulatory system; and Neonatal hemorrhage. Important variations in the leading causes of infant death are noted for the neonatal and postneonatal periods.
Objectives
Operating at a societal level, public policy is often one of our best approaches to addressing social determinants of health (SDH). Yet, limited data availability has constrained past research on how national social policy choices affect health outcomes. We developed a new data infrastructure to illustrate how globally comparative data on labor policy might be used to examine the impact of social policy on health.
Methods
We used multivariate ordinary least squares regression models to examine the relationship between the duration of paid maternal leave and neonatal, infant, and child mortality rates in 141 countries when controlling for overall resources available to meet basic needs measured by per capita gross domestic product, total and government health expenditures, female literacy, and basic health care and public health provision.
Results
An increase of 10 full-time-equivalent weeks of paid maternal leave was associated with a 10% lower neonatal and infant mortality rate ( p≤0.001) and a 9% lower rate of mortality in children younger than 5 years of age ( p≤0.001). Paid maternal leave is associated with significantly lower neonatal, infant, and child mortality in non-Organisation for Economic Co-operation and Development (OECD) countries and OECD countries.
Conclusions
This preliminary study, using newly available worldwide policy data, demonstrates the potential strength of using globally comparative data to examine SDH. Further data development to make multilevel modeling of the impact of labor conditions possible and to broaden which social policies can be examined is a critical next step.
Background
We aimed to evaluate the clinical usefulness of qSOFA as a risk stratification tool for patients admitted with infection compared to traditional SIRS criteria or our triage system; the Rapid Emergency Triage and Treatment System (RETTS).
Methods
The study was an observational cohort study performed at one Emergency Department (ED) in an urban university teaching hospital in Norway, with approximately 20,000 visits per year. All patients >16 years presenting with symptoms or clinical signs suggesting an infection (n = 1535) were prospectively included in the study from January 1 to December 31, 2012. At arrival in the ED, vital signs were recorded and all patients were triaged according to RETTS vital signs, presenting infection, and sepsis symptoms. These admission data were also used to calculate qSOFA and SIRS. Treatment outcome was later retrieved from the patients’ electronic records (EPR) and mortality data from the Norwegian population registry.
Results
Of the 1535 admitted patients, 108 (7.0%) fulfilled the Sepsis2 criteria for severe sepsis. The qSOFA score ≥2 identified only 33 (sensitivity 0.32, specificity 0.98) of the patients with severe sepsis, whilst the RETTS-alert ≥ orange identified 92 patients (sensitivity 0.85, specificity 0.55). Twenty-six patients died within 7 days of admission; four (15.4%) of them had a qSOFA ≥2, and 16 (61.5%) had RETTS ≥ orange alert. Of the 68 patients that died within 30 days, only eight (11.9%) scored ≥2 on the qSOFA, and 45 (66.1%) had a RETTS ≥ orange alert.
Discussion
In order to achieve timely treatment for sepsis, a sensitive screening tool is more important than a specific one. Our study is the fourth study were qSOFA finds few of the sepsis cases in prehospital or at arrival to the ED. We add information on the RETTS triage system, the two highest acuity levels together had a high sensitivity (85%) for identifying sepsis at arrival to the ED - and thus, RETTS should not be replaced by qSOFA as a screening and trigger tool for sepsis at arrival.
Conclusion
In this observational cohort study, qSOFA failed to identify two thirds of the patients admitted to an ED with severe sepsis. Further, qSOFA failed to be a risk stratification tool as the sensitivity to predict 7-day and 30-day mortality was low. The sensitivity was poorer than the other warning scores already in use at the study site, RETTS-triage and the SIRS criteria.
Electronic supplementary material
The online version of this article (doi:10.1186/s13049-017-0399-4) contains supplementary material, which is available to authorized users.
One of the arguments that is advanced in support of paid maternity leave policies is that the mother’s time away from work, around childbirth, is expected to improve child health and development. However the research evidence on these links is scarce and, until recently, little was known about the link, if any, between child health and parental leave in particular. Using an extended random effects estimator to control for selection bias and unobserved heterogeneity, we employ micro-level data from the Parental Leave in Australia Survey, which is a nested survey of the Longitudinal Study of Australian Children, to examine the effects of parental leave on measures of child health and the provision of health inputs to the child. We found that parental leave around childbirth was significantly associated with prolonged breastfeeding, up-to-date immunisation and other positive effects on some chronic health conditions such as asthma, bronchiolitis. For example, children of mothers who took an additional week of paid maternity leave have a lower probability of having asthma and bronchiolitis (1.1 and 0.5 percentage points less likely, respectively). They are also slightly more likely to be breastfed until one month and 6 months of age (2.1 and 0.6 percentage points, respectively).
This study examined the effects of family leave policy on five age-specific child mortality rates across 19 Organization for Economic Co-operation and Development countries from 1969 to 2010. I used the dataset developed by Ruhm and Tanaka and extended it with data from various institutions, including the Max Planck Institute for Demographic Research. I controlled for six relevant variables including GDP per capita and health expenditures, three child health indicators, as well as three social expenditure measures for families. I included in all models country and year fixed effects as well as country-time trend interactions. Throughout all model specifications, the results indicated that job-protected paid leave significantly reduces infant mortality (death at less than 1 year of age) and postneonatal mortality (death between 1 month and 1 year of age). Other leave (unpaid or nonjob protected) had no significant effects on any of the outcome indicators.
Objectives—This report presents final 2010 data on the 10 leading causes of death in the United States by age, sex, race, and Hispanic origin. Leading causes of infant, neonatal, and postneonatal death are also presented. This report supplements the Division of Vital Statistics’ annual report of final mortality statistics.
Methods—Data in this report are based on information from all death certificates filed in the 50 states and the District of Columbia in 2010. Causes of death classified by the International Classification of Diseases, Tenth Revision (ICD–10) are ranked according to the number of deaths assigned to rankable causes. Cause-of-death statistics are based on the underlying cause of death.
Results—In 2010, the 10 leading causes of death were, in rank order: Diseases of heart; Malignant neoplasms; Chronic lower respiratory diseases; Cerebrovascular diseases; Accidents (unintentional injuries); Alzheimer’s disease; Diabetes mellitus; Nephritis, nephrotic syndrome and nephrosis; Influenza and pneumonia; and Intentional self-harm (suicide). These 10 causes accounted for 75% of all deaths occurring in the United States. Differences in the rankings are evident by age, sex, race, and Hispanic origin. Leading causes of infant death for 2010 were, in rank order: Congenital malformations, deformations and chromosomal abnormalities; Disorders related to short gestation and low birth weight, not elsewhere classified; Sudden infant death syndrome; Newborn affected by maternal complications of pregnancy; Accidents (unintentional injuries); Newborn affected by complications of placenta, cord and membranes; Bacterial sepsis of newborn; Respiratory distress of newborn; Diseases of the circulatory system; and Necrotizing enterocolitis of newborn. Important variations in the leading causes of infant death are noted for the neonatal and post-neonatal periods.
We examine some issues in the estimation of time-series cross-section models, calling into question the conclusions of many published studies, particularly in the field of comparative political economy. We show that the generalized least squares approach of Parks produces standard errors that lead to extreme overconfidence, often underestimating variability by 50% or more. We also provide an alternative estimator of the standard errors that is correct when the error structures show complications found in this type of model. Monte Carlo analysis shows that these “panel-corrected standard errors” perform well. The utility of our approach is demonstrated via a reanalysis of one “social democratic corporatist” model.
This article compares the employmentpatterns of women after first and second birthin Finland, Norway and Sweden during 1972–1992,focusing on the impact of parental leave andchildcare programs on the transitions tofull-time and part-time work. The resultsunanimously point to the great importance ofthe programs. Women who are entitled to a paidleave have a much higher overall employmententry rate during the first three yearsfollowing birth than non-eligible women.But since mothers tend to use their fullentitlement, the higher entry rates are largelyconcentrated to the period after leave expiry,except in Sweden where entitled mothers havehigher entry rates also during the leaveperiod. This is probably a result of thegreater flexibility of the Swedish program. Inall countries, leave extensions delay thereturn to work among entitled mothers andreduce their excess entry rate. Moreover, theFinnish home-care allowance system is found toreduce employment entry. On the whole, thissuggests that very long leave entitlements andchild-minding benefit programs could havenegative consequences for women''s career andearnings potentials and may preserve an unequaldivision of labour in the family.
Employee turnover is costly. In addition to replacement fees, there are hidden costs such as productivity loss, workplace safety issues, and morale damage. an improved selection process that assesses candidates' turnover risk and motivational fit early in the hiring process helps reduce turnover that translates into organization profitability. [ABSTRACT FROM AUTHOR] Copyright of Industrial Management is the property of Institute of Industrial Engineers and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
The struggle to balance work responsibilities with family obligations may be most difficult for working parents of the youngest children, those five and under. Any policy changes designed to ease the difficulties for these families are likely to be controversial, requiring a careful effort to weigh both the costs and benefits of possible interventions while respecting diverse and at times conflicting American values. In this article, Christopher Ruhm looks at two potential interventions-parental leave and early childhood education and care (ECEC)-comparing differences in policies in the United States, Canada, and several European nations and assessing their consequences for important parent and child outcomes. By and large, Canadian and European policies are more generous than those in the United States, with most women eligible for paid maternity leave, which in a few countries can last for three years or more. Many of these countries also provide for paid leave that can be used by either the mother or the father. And in many European countries ECEC programs are nearly universal after the child reaches a certain age. In the United States, parental leave, if it is available, is usually short and unpaid, and ECEC is generally regarded as a private responsibility of parents, although some federal programs help defray costs of care and preschool education. Ruhm notes that research on the effects of differences in policies is not completely conclusive, in part because of the difficulty of isolating consequences of leave and ECEC policies from other influences on employment and children's outcomes. But, he says, the comparative evidence does suggest desirable directions for future policy in the United States. Policies establishing rights to short parental leaves increase time at home with infants and slightly improve the job continuity of mothers, with small, but positive, long-run consequences for mothers and children. Therefore, Ruhm indicates that moderate extensions of existing U.S. leave entitlements (up to several months in duration) make sense. He also suggests that some form of paid leave would facilitate its use, particularly among less advantaged parents, and that efforts to improve the quality of ECEC, while maintaining or enhancing affordability, are desirable.
Operating at a societal level, public policy is often one of our best approaches to addressing social determinants of health (SDH). Yet, limited data availability has constrained past research on how national social policy choices affect health outcomes. We developed a new data infrastructure to illustrate how globally comparative data on labor policy might be used to examine the impact of social policy on health.
We used multivariate ordinary least squares regression models to examine the relationship between the duration of paid maternal leave and neonatal, infant, and child mortality rates in 141 countries when controlling for overall resources available to meet basic needs measured by per capita gross domestic product, total and government health expenditures, female literacy, and basic health care and public health provision.
An increase of 10 full-time-equivalent weeks of paid maternal leave was associated with a 10% lower neonatal and infant mortality rate (p ≤ 0.001) and a 9% lower rate of mortality in children younger than 5 years of age (p ≤ 0.001). Paid maternal leave is associated with significantly lower neonatal, infant, and child mortality in non-Organisation for Economic Co-operation and Development (OECD) countries and OECD countries.
This preliminary study, using newly available worldwide policy data, demonstrates the potential strength of using globally comparative data to examine SDH. Further data development to make multilevel modeling of the impact of labor conditions possible and to broaden which social policies can be examined is a critical next step.
The infant mortality rate (IMR) has been criticised as a measure of population health because it is narrowly based and likely to focus the attention of health policy on a small part of the population to the exclusion of the rest. More comprehensive measures such as disability adjusted life expectancy (DALE) have come into favour as alternatives. These more comprehensive measures of population health, however, are more complex, and for resource poor countries, this added burden could mean diverting funds from much needed programmes. Unfortunately, the conjecture, that DALE is a better measure of population health than IMR, has not been empirically tested.
IMR and DALE data for 1997 were obtained from the World Bank and the World Health Organisation, respectively, for 180 countries.
There is a strong (generally) linear association between DALE and IMR (r=0.91). Countries with low DALE tend to have a high IMR. The countries with the lowest IMRs had DALEs above that predicted by the regression line.
There is little evidence that the use of IMR as a measure of population health has a negative impact on older groups in the population. IMR remains an important indicator of health for whole populations, reflecting the intuition that structural factors affecting the health of entire populations have an impact on the mortality rate of infants. For countries with limited resources that require an easily calculated, pithy measure of population health, IMR may remain a suitable choice.
The tissue microarray (TMA) technique enables researchers to extract small cylinders of tissue from histological sections and arrange them in a matrix configuration on a recipient paraffin block such that hundreds can be analyzed simultaneously. TMA offers several advantages over traditional specimen preparation by maximizing limited tissue resources and providing a highly efficient means for visualizing molecular targets. By enabling researchers to reliably determine the protein expression profile for specific types of cancer, it may be possible to elucidate the mechanism by which healthy tissues are transformed into malignancies. Currently, the primary methods used to evaluate arrays involve the interactive review of TMA samples while they are viewed under a microscope, subjectively evaluated, and scored by a technician. This process is extremely slow, tedious, and prone to error. In order to facilitate large-scale, multi-institutional studies, a more automated and reliable means for analyzing TMAs is needed. We report here a web-based prototype which features automated imaging, registration, and distributed archiving of TMAs in multiuser network environments. The system utilizes a principal color decomposition approach to identify and characterize the predominant staining signatures of specimens in color space. This strategy was shown to be reliable for detecting and quantifying the immunohistochemical expression levels for TMAs.
Background and objective
The most common tool for population-wide COVID-19 identification is the Reverse Transcription-Polymerase Chain Reaction test that detects the presence of the virus in the throat (or sputum) in swab samples. This test has a sensitivity between 59% and 71%. However, this test does not provide precise information regarding the extension of the pulmonary infection. Moreover, it has been proven that through the reading of a computed tomography (CT) scan, a clinician can provide a more complete perspective of the severity of the disease. Therefore, we propose a comprehensive system for fully-automated COVID-19 detection and lesion segmentation from CT scans, powered by deep learning strategies to support decision-making process for the diagnosis of COVID-19.
Methods
In the workflow proposed, the input CT image initially goes through lung delineation, then COVID-19 detection and finally lesion segmentation. The chosen neural network has a U-shaped architecture using a newly introduced Multiple Convolutional Layers structure, that produces a lung segmentation mask within a novel pipeline for direct COVID-19 detection and segmentation. In addition, we propose a customized loss function that guarantees an optimal balance on average between sensitivity and precision.
Results
Lungs’ segmentation results show a sensitivity near 99% and Dice-score of 97%. No false positives were observed in the detection network after 10 different runs with an average accuracy of 97.1%. The average accuracy for lesion segmentation was approximately 99%. Using UNet as a benchmark, we compared our results with several other techniques proposed in the literature, obtaining the largest improvement over the UNet outcomes.
Conclusions
The method proposed in this paper outperformed the state-of-the-art methods for COVID-19 lesion segmentation from CT images, and improved by 38.2% the results for F1-score of UNet. The high accuracy observed in this work opens up a wide range of possible applications of our algorithm in other fields related to medical image segmentation.
AI is the latest technologic trend that likely will have a huge impact in medicine. AI's potential lies in its ability to process large volumes of data and perform complex pattern analyses. The ICU is an area of medicine that is particularly conducive to AI applications. Much AI ICU research currently is focused on improving high volumes of data on high-risk patients and making clinical workflow more efficient. Emerging topics of AI medicine in the ICU include AI sensors, sepsis prediction, AI in the NICU or SICU, and the legal role of AI in medicine. This review will cover the current applications of AI medicine in the ICU, potential pitfalls, and other AI medicine-related topics relevant for the ICU.
Importance
Sepsis is a heterogeneous syndrome. Identification of distinct clinical phenotypes may allow more precise therapy and improve care.
Objective
To derive sepsis phenotypes from clinical data, determine their reproducibility and correlation with host-response biomarkers and clinical outcomes, and assess the potential causal relationship with results from randomized clinical trials (RCTs).
Design, Settings, and Participants
Retrospective analysis of data sets using statistical, machine learning, and simulation tools. Phenotypes were derived among 20 189 total patients (16 552 unique patients) who met Sepsis-3 criteria within 6 hours of hospital presentation at 12 Pennsylvania hospitals (2010-2012) using consensus k means clustering applied to 29 variables. Reproducibility and correlation with biological parameters and clinical outcomes were assessed in a second database (2013-2014; n = 43 086 total patients and n = 31 160 unique patients), in a prospective cohort study of sepsis due to pneumonia (n = 583), and in 3 sepsis RCTs (n = 4737).
Exposures
All clinical and laboratory variables in the electronic health record.
Main Outcomes and Measures
Derived phenotype (α, β, γ, and δ) frequency, host-response biomarkers, 28-day and 365-day mortality, and RCT simulation outputs.
Results
The derivation cohort included 20 189 patients with sepsis (mean age, 64 [SD, 17] years; 10 022 [50%] male; mean maximum 24-hour Sequential Organ Failure Assessment [SOFA] score, 3.9 [SD, 2.4]). The validation cohort included 43 086 patients (mean age, 67 [SD, 17] years; 21 993 [51%] male; mean maximum 24-hour SOFA score, 3.6 [SD, 2.0]). Of the 4 derived phenotypes, the α phenotype was the most common (n = 6625; 33%) and included patients with the lowest administration of a vasopressor; in the β phenotype (n = 5512; 27%), patients were older and had more chronic illness and renal dysfunction; in the γ phenotype (n = 5385; 27%), patients had more inflammation and pulmonary dysfunction; and in the δ phenotype (n = 2667; 13%), patients had more liver dysfunction and septic shock. Phenotype distributions were similar in the validation cohort. There were consistent differences in biomarker patterns by phenotype. In the derivation cohort, cumulative 28-day mortality was 287 deaths of 5691 unique patients (5%) for the α phenotype; 561 of 4420 (13%) for the β phenotype; 1031 of 4318 (24%) for the γ phenotype; and 897 of 2223 (40%) for the δ phenotype. Across all cohorts and trials, 28-day and 365-day mortality were highest among the δ phenotype vs the other 3 phenotypes (P < .001). In simulation models, the proportion of RCTs reporting benefit, harm, or no effect changed considerably (eg, varying the phenotype frequencies within an RCT of early goal-directed therapy changed the results from >33% chance of benefit to >60% chance of harm).
Conclusions and Relevance
In this retrospective analysis of data sets from patients with sepsis, 4 clinical phenotypes were identified that correlated with host-response patterns and clinical outcomes, and simulations suggested these phenotypes may help in understanding heterogeneity of treatment effects. Further research is needed to determine the utility of these phenotypes in clinical care and for informing trial design and interpretation.
This analysis draws upon data from the Organization for Economic Cooperation and Development and other cross-national analyses to compare health care spending, supply, utilization, prices, and health outcomes across 13 high-income countries: Australia, Canada, Denmark, France, Germany, Japan, Netherlands, New Zealand, Norway, Sweden, Switzerland, the United Kingdom, and the United States. These data predate the major insurance provisions of the Affordable Care Act. In 2013, the U.S. spent far more on health care than these other countries. Higher spending appeared to be largely driven by greater use of medical technology and higher health care prices, rather than more frequent doctor visits or hospital admissions. In contrast, U.S. spending on social services made up a relatively small share of the economy relative to other countries. Despite spending more on health care, Americans had poor health outcomes, including shorter life expectancy and greater prevalence of chronic conditions.
Objectives:
Sepsis-3 definitions generated controversies regarding their general applicability. The Sepsis-3 Task Force outscored the need of validation with emphasis on quick SOFA (qSOFA) score. This was done in a prospective cohort coming from a different health-care setting.
Methods:
Patients with infections and at least two signs of the systemic inflammatory response syndrome (SIRS) were analyzed. Sepsis was defined as total SOFA ≥2 outside the ICU or as increase of ICU admission SOFA ≥2. The primary endpoint was the sensitivity of qSOFA outside the ICU and sepsis definition both outside and in the ICU to predict mortality.
Results:
3,346 infections outside the ICU and 1,058 infections in the ICU were analyzed. Outside the ICU, respective mortality with ≥2 SIRS and qSOFA ≥2 was 25.3% and 41.2% (p<0.0001); the sensitivities of qSOFA and of sepsis definition to predict death were 60.8% and 87.2% respectively. This was 95.9% for sepsis definition in the ICU. The sensitivity of qSOFA and of ≥3 SIRS criteria for organ dysfunction outside the ICU was 48.7% and 72.5% respectively (p< 0.0001). Misclassification outside the ICU with the 1991 and sepsis-3 definitions into stages of lower severity was 21.4% and 3.7% respectively (p< 0.0001) and 14.9% and 3.7% respectively in the ICU (p< 0.0001). Adding arterial pH≤7.30 to qSOFA increased sensitivity for death to 67.5% (p: 0.004).
Conclusions:
Our analysis positively validated the use of SOFA score to predict unfavorable outcome and to limit misclassification into lower severity. However, qSOFA score had inadequate sensitivity for early risk assessment.
Although spending rates on health care and social services vary substantially across the states, little is known about the possible association between variation in state-level health outcomes and the allocation of state spending between health care and social services. To estimate that association, we used state-level repeated measures multivariable modeling for the period 2000-09, with region and time fixed effects adjusted for total spending and state demographic and economic characteristics and with one- and two-year lags. We found that states with a higher ratio of social to health spending (calculated as the sum of social service spending and public health spending divided by the sum of Medicare spending and Medicaid spending) had significantly better subsequent health outcomes for the following seven measures: adult obesity; asthma; mentally unhealthy days; days with activity limitations; and mortality rates for lung cancer, acute myocardial infarction, and type 2 diabetes. Our study suggests that broadening the debate beyond what should be spent on health care to include what should be invested in health-not only in health care but also in social services and public health-is warranted.
The US has higher infant mortality than peer countries. In this paper, we combine micro-data from the US with similar data from four European countries to investigate this US infant mortality disadvantage. The US disadvantage persists after adjusting for potential di erential reporting of births near the threshold of viability. While the importance of birth weight varies across comparison countries, relative to all comparison countries the US has similar neonatal (<1 month) mortality but higher postneonatal (1-12 months) mortality. We document similar patterns across Census divisions within the US. The postneonatal mortality disadvantage is driven by poor birth outcomes among lower socioeconomic status individuals.
Importance
Definitions of sepsis and septic shock were last revised in 2001. Considerable advances have since been made into the pathobiology (changes in organ function, morphology, cell biology, biochemistry, immunology, and circulation), management, and epidemiology of sepsis, suggesting the need for reexamination.Objective
To evaluate and, as needed, update definitions for sepsis and septic shock.Process
A task force (n = 19) with expertise in sepsis pathobiology, clinical trials, and epidemiology was convened by the Society of Critical Care Medicine and the European Society of Intensive Care Medicine. Definitions and clinical criteria were generated through meetings, Delphi processes, analysis of electronic health record databases, and voting, followed by circulation to international professional societies, requesting peer review and endorsement (by 31 societies listed in the Acknowledgment).Key Findings From Evidence Synthesis
Limitations of previous definitions included an excessive focus on inflammation, the misleading model that sepsis follows a continuum through severe sepsis to shock, and inadequate specificity and sensitivity of the systemic inflammatory response syndrome (SIRS) criteria. Multiple definitions and terminologies are currently in use for sepsis, septic shock, and organ dysfunction, leading to discrepancies in reported incidence and observed mortality. The task force concluded the term severe sepsis was redundant.Recommendations
Sepsis should be defined as life-threatening organ dysfunction caused by a dysregulated host response to infection. For clinical operationalization, organ dysfunction can be represented by an increase in the Sequential [Sepsis-related] Organ Failure Assessment (SOFA) score of 2 points or more, which is associated with an in-hospital mortality greater than 10%. Septic shock should be defined as a subset of sepsis in which particularly profound circulatory, cellular, and metabolic abnormalities are associated with a greater risk of mortality than with sepsis alone. Patients with septic shock can be clinically identified by a vasopressor requirement to maintain a mean arterial pressure of 65 mm Hg or greater and serum lactate level greater than 2 mmol/L (>18 mg/dL) in the absence of hypovolemia. This combination is associated with hospital mortality rates greater than 40%. In out-of-hospital, emergency department, or general hospital ward settings, adult patients with suspected infection can be rapidly identified as being more likely to have poor outcomes typical of sepsis if they have at least 2 of the following clinical criteria that together constitute a new bedside clinical score termed quickSOFA (qSOFA): respiratory rate of 22/min or greater, altered mentation, or systolic blood pressure of 100 mm Hg or less.Conclusions and Relevance
These updated definitions and clinical criteria should replace previous definitions, offer greater consistency for epidemiologic studies and clinical trials, and facilitate earlier recognition and more timely management of patients with sepsis or at risk of developing sepsis.
Objectives-This report investigates the reasons for the United States' high infant mortality rate when compared with European countries. Specifically, the report measures the impact on infant mortality differences of two major factors: the percentage of preterm births and gestational age-specific infant mortality rates. Methods-Infant mortality and preterm birth data are compared between the United States and European countries. The percent contribution of the two factors to infant mortality differences is computed using the Kitagawa method, with Sweden as the reference country. Results-In 2010, the U.S. infant mortality rate was 6.1 infant deaths per 1,000 live births, and the United States ranked 26th in infant mortality among Organisation for Economic Co-operation and Development countries. After excluding births at less than 24 weeks of gestation to ensure international comparability, the U.S. infant mortality rate was 4.2, still higher than for most European countries and about twice the rates for Finland, Sweden, and Denmark. U.S. infant mortality rates for very preterm infants (24-31 weeks of gestation) compared favorably with most European rates. However, the U.S. mortality rate for infants at 32-36 weeks was second-highest, and the rate for infants at 37 weeks of gestation or more was highest, among the countries studied. About 39% of the United States' higher infant mortality rate when compared with that of Sweden was due to a higher percentage of preterm births, while 47% was due to a higher infant mortality rate at 37 weeks of gestation or more. If the United States could reduce these two factors to Sweden's levels, the U.S. infant mortality rate would fall by 43%, with nearly 7,300 infant deaths averted annually.
This analysis extends the concept of role incompatibility to examine the potential incompatibilities between breastfeeding and maternal employment. I hypothesize that women may face both structural and attitudinal conflicts between these behaviors. To test this hypothesis, this analysis uses data from Cycle IV of the National Survey of Family Growth to examine the relationship between women's postpartum employment and breastfeeding behaviors in the U.S. from 1980 to 1986. Analyses find that significantly more women who are employed part-time are likely to breastfeed and for longer durations than women employed full-time, suggesting that conflicts between breastfeeding and working at a job vary by the intensity of the employment. Further, a discrete-time hazard model finds that women are more likely to stop breastfeeding in the month they enter employment, suggesting that these behaviors constrain each other. The policy implications of these constraints are examined.
Low birthweight is the primary cause of neonatal morbidity and mortality in the United States. The purpose of our study was to identify factors associated with the effectiveness and apparent ineffectiveness of comprehensive, multicomponent, prenatal care programs for preventing low birthweight.
We reviewed obstetric, pediatric, and public health program evaluations, research reports, and commentaries, published in the English language literature, over the last four decades that pertained to the efficacy of prenatal care for preventing low birthweight.
The heterogeneous nature of the services delivered and the lack of consistency in the definition of variables made it impossible to use rigorous, quantitative techniques to summarize this evaluation of the literature. Two general limitations of research design that emerged from our reviews were the focus on clusters of commonly associated risk factors, which has blurred the causal pathways linking specific risk factors to low birthweight, and the failure to examine process variables. These two methodologic problems have led investigators to erroneous conclusions that overstate the significance of negative intervention outcomes. The success and failure of low-birthweight prevention programs has rarely been examined in relation to evidence that the intervention actually modified the targeted risk factors.
Few rigorous evaluations of well-designed programs have been conducted. Without an improvement in intervention designs and evaluation studies, recommendations to support or curtail the funding of comprehensive, multicomponent prenatal care services are inappropriate. Rigorously obtained evidence of the costs and benefits of approaches to the prevention of low birthweight are sorely needed.
Sepsis is one of the main causes of death for non-coronary ICU (Intensive Care Unit) patients and has become the 10th most common cause of death in western societies. This is a transversal condition affecting immunocompromised patients, critically ill patients, post-surgery patients, patients with AIDS, and the elderly. In western countries, septic patients account for as much as 25% of ICU bed utilization and the pathology affects 1–2% of all hospitalizations. Its mortality rates range from 12.8% for sepsis to 45.7% for septic shock.The prediction of mortality caused by sepsis is, therefore, a relevant research challenge from a medical viewpoint. The clinical indicators currently in use for this type of prediction have been criticized for their poor prognostic significance. In this study, we redescribe sepsis indicators through latent model-based feature extraction, using factor analysis. These extracted indicators are then applied to the prediction of mortality caused by sepsis. The reported results show that the proposed method improves on the results obtained with the current standard mortality predictor, which is based on the APACHE II score.
Studies have shown for decades that certain subpopulations of infants, for example, those in poverty and in certain minority groups, are at substantially higher risk for illness and death than the national average. If mothers and infants of these “vulnerable populations” were as healthy as their “nonvulnerable” counterparts, as many as one third (approximately 12,000 deaths) of all infant deaths in the United States might be avoided each year. This paper is intended to document which infants are vulnerable, to quantitate the degree of risk where possible, and to outline potential changes in public policy that may lead to improvements in the health of these infants.
To be meaningful social indicators must be components of some social systems model so that changes in the values of these social statistics over time tell us something about the functioning of the social system. A necessary next step in developing social indicators is constructing models involving interrelated sets of social indicators in each major institutional area of society. This paper reports an initial effort to derive a set of social indicators for the area of health care. A structural equation model has been constructed for the health care system serving the state of New Mexico. The model includes a network that specifies the causal relationships hypothesized as existing among a set of social, demographic, and economic variables related to the availability and use of health services and to health status; a set of structural equations that indicate the direct effect of variables in the model on each endogenous variable; a set of reduced form equations that indicate the combined direct and indirect effect of each predetermined variable on each endogenous variable included in the model. The model can be used to provide monitoring information pertaining to the effect of a change in a particular variable on all other variables comprising the health care system. Also it provides explanatory information regarding the differences in the availability of health care services, their use, and the health-status of the population in various counties. Finally, predictions of the effects of alternative health care policies that would affect the supply, the organization of care, or patterns of use of health services can be made based on the model.
A number of studies have suggested that inequalities in the distribution of income may be an important cause of variations in the average level of population health among rich industrial nations. However, what is missing from the debate so far is any systematic review of evidence about the relationship between different measures of income distribution and indicators of population health. This paper aims to bridge that gap. First, it summarizes the recent English language literature on this topic and illustrates the methodological problems that weaken the inferences that can be derived from it. Secondly, it presents new empirical estimates of the relationship between different measures of income distribution, infant mortality and life expectancy based on the most authoritative data published to date. In contrast to most earlier studies, we find very little support for the view that income inequality is associated with variations in average levels of national health in rich industrial countries. Some possible explanations for these differences are outlined.
Theory suggests that the decision to return to employment after childbirth and the decision to breast-feed may be jointly determined. We estimate models of simultaneous equations for two different aspects of the relationship between maternal employment and breast-feeding using 1993-1994 data from the U.S. Food and Drug Administration's Infant Feeding Practices Study. We first explore the simultaneous duration of breast-feeding and work leave following childbirth. We find that the duration of leave from work significantly affects the duration of breast-feeding, but the effect of breast-feeding on work leave is insignificant. We also estimate models of the daily hours of work and breast-feedings at infant ages 3 months and 6 months postpartum. At both times, the intensity of work effort significantly affects the intensity of breast-feeding, but the reverse is generally not found. Competition clearly exists between work and breast-feeding for many women in our sample.
This study investigates whether rights to parental leave improve pediatric health. Aggregate data are used for 16 European countries over the 1969 through 1994 period. More generous paid leave is found to reduce deaths of infants and young children. The magnitudes of the estimated effects are substantial, especially where a causal effect of leave is most plausible. In particular, there is a much stronger negative relationship between leave durations and post-neonatal or child fatalities than for perinatal mortality, neonatal deaths, or low birth weight. The evidence further suggests that parental leave may be a cost-effective method of bettering child health.
The present paper tries to measure the effects of paid maternity-leave on three demographic variables: infant mortality, labor-force participation of women in the prime childbearing ages, and fertility rates. A simultaneous-equations model is constructed, using the individual fixed-effects method and a data set comprising 17 OECD countries and four time periods. The structural estimates provide substantial evidence in support of predictions that lengthening the allowed duration of paid leave reduces infant mortality, while increasing both the labor-force participation of young women and the general fertility rate. However, the reduced-form analysis casts doubt on the long-run fertility effect. Maternal-leave policies in industrial countries are surveyed Section III deals with the data and estimation methods. -from Authors
Public health agencies around the world have renewed efforts to increase the incidence and duration of breastfeeding. Maternity leave mandates present an economic policy that could help achieve these goals. We study their efficacy, focusing on a significant increase in maternity leave mandates in Canada. We find very large increases in mothers' time away from work post-birth and in the attainment of critical breastfeeding duration thresholds. We also look for impacts of the reform on self-reported indicators of maternal and child health captured in our data. For most indicators we find no effect.
To understand the relationship between parental leave and child health better, this study examines the aggregate effects of parental leave policies on child health outcomes using data from 18 OECD countries -super-1 from 1969-2000. The focus is investigating the effects of both job-protected paid leave and other leave - including non-job-protected paid leave and unpaid leave - on child health outcomes, more specifically, infant mortality rates, low birth weight and child immunisation coverage. This study explores the effects of other social policies related to families and young children, such as public expenditures on family cash benefits, family allowances, and family services per child, on child health outcomes. Copyright 2005 Royal Economic Society.
This paper uses data from the National Longitudinal Survey of Youth to explore links between mothers' returns to work within 12 weeks of giving birth and health and developmental outcomes for their children. OLS models and propensity score matching methods are utilised to account for selection bias. Considerable associations between early returns to work and children's outcomes are found suggesting causal relationships between early returns to work and reductions in breastfeeding and immunisations, as well as increases in externalising behaviour problems. These results are generally stronger for mothers who return to work full-time within 12 weeks of giving birth. Copyright 2005 Royal Economic Society.
California's Paid Family Leave Law: Lessons from the First Decade
- Ann Bartel
- Charles Baum
- Maya Rossin-Slater
- Christopher Ruhm
- Jane Waldfogel
Bartel, Ann, Charles Baum, Maya Rossin-Slater, Christopher Ruhm, and Jane Waldfogel. 2014.
"California's Paid Family Leave Law: Lessons from the First Decade." Report prepared for
the U.S. Department of Labor, contract number DOL-OPS-14-C-0003. http://www.dol.gov/asp/
evaluation/reports/paidleavedeliverable.pdf. Accessed January 17, 2016.
Family and Medical Leave in 2012: Technical Report.” Prepared for U.S. Department of Labor. <https://www.dol.gov/asp/evaluation/fmla/FMLA-2012-Technical-Report.pdf> Accessed on June 28, 2016.
- Jacob Alex Klerman
- Kelly Daley
- Alyssa Pozniak
Regionalization: Issues in Intensive Care for High Risk Newborns and Their Families
- Claire S Rudolph
- Susan R Borker
Rudolph, Claire S., and Susan R. Borker. 1987. Regionalization: Issues in Intensive Care for High Risk
Newborns and Their Families. Santa Barbara, CA: Praeger.
Comparative Family Database, Version 3
- Anne H Gauthier
Gauthier, Anne H. 2010. "Comparative Family Database, Version 3 [computer file]." Netherlands
Interdisciplinary Demographic Institute and Max Planck Institute for Demographic Research
(distributors). http://www.demogr.mpg.de/en/. Accessed January 23, 2016.
---. 2011. "Comparative Family Database, Version 3 [computer file]." Netherlands Interdisciplinary
Demographic Institute and Max Planck Institute for Demographic Research (distributors).
http://www.demogr.mpg.de/en/. Accessed January 23, 2016.
Family and Medical Leave
- Jacob Klerman
- Kelly Alex
- Alyssa Daley
- Pozniak
Prepared for U.S. Department of Labor
- Jacob Klerman
- Kelly Alex
- Alyssa Daley
- Pozniak
Klerman, Jacob Alex, Kelly Daley, and Alyssa Pozniak. 2014. "Family and Medical Leave in 2012:
Technical Report." Prepared for U.S. Department of Labor. https://www.dol.gov/asp/
evaluation/fmla/FMLA-2012-Technical-Report.pdf. Accessed on June 28, 2016.
Artificial intelligence in critical care: the path from promise to practice
- A Vellido
- V Ribas
Vellido A, Ribas V. Artificial intelligence in critical care: the path from promise to practice. In:
Lidstromer N, editor. Artificial intelligence in medicine. Springer; 2022.