Associations between soluble urokinase plasminogen activator receptor (suPAR) concentration and psychiatric disorders - A systematic review and meta-analysis.

BACKGROUND: There is some evidence of an association between inflammation in the pathogenesis of mental disorders. Soluble urokinase plasminogen activator receptor (suPAR) is a biomarker of chronic inflammation, which provides a more stable index of systemic inflammation than more widely used biomarkers. This review aims to synthesise studies that measured suPAR concentrations in individuals with a psychiatric disorder, to determine if these concentrations are altered in comparison to healthy participants. METHOD: Comprehensive literature searches from inception to October 2023 were conducted of five relevant databases (PubMed, Web of Science, Embase, Scopus, APA PsychInfo). Random-effects meta-analyses were performed to compare the standardised mean difference of blood suPAR levels (i.e. plasma or serum) for individuals with any psychiatric disorder relative to controls. Separate meta-analyses of suPAR levels were conducted for individuals with schizophrenia or other psychotic disorder and depressive disorder. Risk of bias was assessed using the Newcastle Ottawa Scale. Post-hoc sensitivity analyses included excluding studies at high risk of bias, and analyses of studies that measured suPAR concentrations either in serum or in plasma separately. RESULTS: The literature search identified 149 records. Ten full-text studies were screened for eligibility and 9 studies were included for review. Primary analyses revealed no significant difference in suPAR levels between individuals with any psychiatric disorder compared to controls (k = 7, SMD = 0.42, 95 % CI [-0.20, 1.04]). However, those with depressive disorder had elevated suPAR levels relative to controls (k = 3, SMD = 0.61, 95 % CI [0.34, 0.87]). Similarly, secondary analyses showed no evidence of a significant difference in suPAR levels in individuals with any psychiatric disorder when studies at high risk of bias were excluded (k = 6, SMD = 0.54, 95 % CI [-0.14, 1.22]), but elevated suPAR concentrations for those with schizophrenia or other psychotic disorder were found (k = 3, SMD = 0.98, 95 % CI [0.39, 1.58]). Furthermore, studies that analysed plasma suPAR concentrations found elevated plasma suPAR levels in individuals with any psychiatric disorder relative to controls (k = 5, SMD = 0.84, 95 % CI [0.38, 1.29]), while studies measuring serum suPAR levels in any psychiatric disorder did not find a difference (k = 2, SMD = -0.61, 95 % CI [-1.27, 0.04]). For plasma, elevated suPAR concentrations were also identified for those with schizophrenia or other psychotic disorder (k = 3, SMD = 0.98, 95 % CI [0.39, 1.58]). DISCUSSION: When studies measuring either only serum or only plasma suPAR were considered, no significant difference in suPAR levels were observed between psychiatric disorder groups, although significantly elevated suPAR levels were detected in those with moderate to severe depressive disorder. However, plasma suPAR levels were significantly elevated in those with any psychiatric disorder relative to controls, while no difference in serum samples was found. A similar finding was reported for schizophrenia or other psychotic disorder. The plasma findings suggest that chronic inflammatory dysregulation may contribute to the pathology of schizophrenia and depressive disorder. Future longitudinal studies are required to fully elucidate the role of this marker in the psychopathology of these disorders.

Pregnancy and birth complications and long-term maternal mental health outcomes: A systematic review and meta-analysis.

BACKGROUND: Few studies have examined the associations between pregnancy and birth complications and long-term (>12 months) maternal mental health outcomes. OBJECTIVES: To review the published literature on pregnancy and birth complications and long-term maternal mental health outcomes. SEARCH STRATEGY: Systematic search of Cumulative Index to Nursing and Allied Health Literature (CINAHL), Excerpta Medica Database (Embase), PsycInfo®, PubMed® and Web of Science from inception until August 2022. SELECTION CRITERIA: Three reviewers independently reviewed titles, abstracts and full texts. DATA COLLECTION AND ANALYSIS: Two reviewers independently extracted data and appraised study quality. Random-effects meta-analyses were used to calculate pooled estimates. The Meta-analyses of Observational Studies in Epidemiology (MOOSE) guidelines were followed. The protocol was prospectively registered on the International Prospective Register of Systematic Reviews (PROSPERO: CRD42022359017). MAIN RESULTS: Of the 16 310 articles identified, 33 studies were included (3 973 631 participants). Termination of pregnancy was associated with depression (pooled adjusted odds ratio, aOR 1.49, 95% CI 1.20-1.83) and anxiety disorder (pooled aOR 1.43, 95% CI 1.20-1.71). Miscarriage was associated with depression (pooled aOR 1.97, 95% CI 1.38-2.82) and anxiety disorder (pooled aOR 1.24, 95% CI 1.11-1.39). Sensitivity analyses excluding early pregnancy loss and termination reported similar results. Preterm birth was associated with depression (pooled aOR 1.37, 95% CI 1.32-1.42), anxiety disorder (pooled aOR 0.97, 95% CI 0.41-2.27) and post-traumatic stress disorder (PTSD) (pooled aOR 1.75, 95% CI 0.52-5.89). Caesarean section was not significantly associated with PTSD (pooled aOR 2.51, 95% CI 0.75-8.37). There were few studies on other mental disorders and therefore it was not possible to perform meta-analyses. CONCLUSIONS: Exposure to complications during pregnancy and birth increases the odds of long-term depression, anxiety disorder and PTSD.

Detecting goals of care conversations in clinical notes with active learning.

OBJECTIVE: Goals of care (GOC) discussions are an increasingly used quality metric in serious illness care and research. Wide variation in documentation practices within the Electronic Health Record (EHR) presents challenges for reliable measurement of GOC discussions. Novel natural language processing approaches are needed to capture GOC discussions documented in real-world samples of seriously ill hospitalized patients' EHR notes, a corpus with a very low event prevalence. METHODS: To automatically detect sentences documenting GOC discussions outside of dedicated GOC note types, we proposed an ensemble of classifiers aggregating the predictions of rule-based, feature-based, and three transformers-based classifiers. We trained our classifier on 600 manually annotated EHR notes among patients with serious illnesses. Our corpus exhibited an extremely imbalanced ratio between sentences discussing GOC and sentences that do not. This ratio challenges standard supervision methods to train a classifier. Therefore, we trained our classifier with active learning. RESULTS: Using active learning, we reduced the annotation cost to fine-tune our ensemble by 70% while improving its performance in our test set of 176 EHR notes, with 0.557 F1-score for sentence classification and 0.629 for note classification. CONCLUSION: When classifying notes, with a true positive rate of 72% (13/18) and false positive rate of 8% (13/158), our performance may be sufficient for deploying our classifier in the EHR to facilitate bedside clinicians' access to GOC conversations documented outside of dedicated notes types, without overburdening clinicians with false positives. Improvements are needed before using it to enrich trial populations or as an outcome measure.

Methods and Annotated Data Sets Used to Predict the Gender and Age of Twitter Users: Scoping Review.

BACKGROUND: Patient health data collected from a variety of nontraditional resources, commonly referred to as real-world data, can be a key information source for health and social science research. Social media platforms, such as Twitter (Twitter, Inc), offer vast amounts of real-world data. An important aspect of incorporating social media data in scientific research is identifying the demographic characteristics of the users who posted those data. Age and gender are considered key demographics for assessing the representativeness of the sample and enable researchers to study subgroups and disparities effectively. However, deciphering the age and gender of social media users poses challenges. OBJECTIVE: This scoping review aims to summarize the existing literature on the prediction of the age and gender of Twitter users and provide an overview of the methods used. METHODS: We searched 15 electronic databases and carried out reference checking to identify relevant studies that met our inclusion criteria: studies that predicted the age or gender of Twitter users using computational methods. The screening process was performed independently by 2 researchers to ensure the accuracy and reliability of the included studies. RESULTS: Of the initial 684 studies retrieved, 74 (10.8%) studies met our inclusion criteria. Among these 74 studies, 42 (57%) focused on predicting gender, 8 (11%) focused on predicting age, and 24 (32%) predicted a combination of both age and gender. Gender prediction was predominantly approached as a binary classification task, with the reported performance of the methods ranging from 0.58 to 0.96 F1-score or 0.51 to 0.97 accuracy. Age prediction approaches varied in terms of classification groups, with a higher range of reported performance, ranging from 0.31 to 0.94 F1-score or 0.43 to 0.86 accuracy. The heterogeneous nature of the studies and the reporting of dissimilar performance metrics made it challenging to quantitatively synthesize results and draw definitive conclusions. CONCLUSIONS: Our review found that although automated methods for predicting the age and gender of Twitter users have evolved to incorporate techniques such as deep neural networks, a significant proportion of the attempts rely on traditional machine learning methods, suggesting that there is potential to improve the performance of these tasks by using more advanced methods. Gender prediction has generally achieved a higher reported performance than age prediction. However, the lack of standardized reporting of performance metrics or standard annotated corpora to evaluate the methods used hinders any meaningful comparison of the approaches. Potential biases stemming from the collection and labeling of data used in the studies was identified as a problem, emphasizing the need for careful consideration and mitigation of biases in future studies. This scoping review provides valuable insights into the methods used for predicting the age and gender of Twitter users, along with the challenges and considerations associated with these methods.

Methods to Establish Race or Ethnicity of Twitter Users: Scoping Review.

BACKGROUND: A growing amount of health research uses social media data. Those critical of social media research often cite that it may be unrepresentative of the population; however, the suitability of social media data in digital epidemiology is more nuanced. Identifying the demographics of social media users can help establish representativeness. OBJECTIVE: This study aims to identify the different approaches or combination of approaches to extract race or ethnicity from social media and report on the challenges of using these methods. METHODS: We present a scoping review to identify methods used to extract the race or ethnicity of Twitter users from Twitter data sets. We searched 17 electronic databases from the date of inception to May 15, 2021, and carried out reference checking and hand searching to identify relevant studies. Sifting of each record was performed independently by at least two researchers, with any disagreement discussed. Studies were required to extract the race or ethnicity of Twitter users using either manual or computational methods or a combination of both. RESULTS: Of the 1249 records sifted, we identified 67 (5.36%) that met our inclusion criteria. Most studies (51/67, 76%) have focused on US-based users and English language tweets (52/67, 78%). A range of data was used, including Twitter profile metadata, such as names, pictures, information from bios (including self-declarations), or location or content of the tweets. A range of methodologies was used, including manual inference, linkage to census data, commercial software, language or dialect recognition, or machine learning or natural language processing. However, not all studies have evaluated these methods. Those that evaluated these methods found accuracy to vary from 45% to 93% with significantly lower accuracy in identifying categories of people of color. The inference of race or ethnicity raises important ethical questions, which can be exacerbated by the data and methods used. The comparative accuracies of the different methods are also largely unknown. CONCLUSIONS: There is no standard accepted approach or current guidelines for extracting or inferring the race or ethnicity of Twitter users. Social media researchers must carefully interpret race or ethnicity and not overpromise what can be achieved, as even manual screening is a subjective, imperfect method. Future research should establish the accuracy of methods to inform evidence-based best practice guidelines for social media researchers and be guided by concerns of equity and social justice.

GeoBoost2: a natural languageprocessing pipeline for GenBank metadata enrichment for virus phylogeography.

SUMMARY: We present GeoBoost2, a natural language-processing pipeline for extracting the location of infected hosts for enriching metadata in nucleotide sequences repositories like National Center of Biotechnology Information's GenBank for downstream analysis including phylogeography and genomic epidemiology. The increasing number of pathogen sequences requires complementary information extraction methods for focused research, including surveillance within countries and between borders. In this article, we describe the enhancements from our earlier release including improvement in end-to-end extraction performance and speed, availability of a fully functional web-interface and state-of-the-art methods for location extraction using deep learning. AVAILABILITY AND IMPLEMENTATION: Application is freely available on the web at https://zodo.asu.edu/geoboost2. Source code, usage examples and annotated data for GeoBoost2 is freely available at https://github.com/ZooPhy/geoboost2. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Targeting the Toll-like receptor pathway as a therapeutic strategy for neonatal infection.

Toll-like receptors (TLRs) are crucial transmembrane receptors that form part of the innate immune response. They play a role in the recognition of various microorganisms and their elimination from the host. TLRs have been proposed as vital immunomodulators in the regulation of multiple neonatal stressors that extend beyond infection such as oxidative stress and pain. The immune system is immature at birth and takes some time to become fully established. As such, babies are especially vulnerable to sepsis at this early stage of life. Findings suggest a gestational age-dependent increase in TLR expression. TLRs engage with accessory and adaptor proteins to facilitate recognition of pathogens and their activation of the receptor. TLRs are generally upregulated during infection and promote the transcription and release of proinflammatory cytokines. Several studies report that TLRs are epigenetically modulated by chromatin changes and promoter methylation upon bacterial infection that have long-term influences on immune responses. TLR activation is reported to modulate cardiorespiratory responses during infection and may play a key role in driving homeostatic instability observed during sepsis. Although complex, TLR signaling and downstream pathways are potential therapeutic targets in the treatment of neonatal diseases. By reviewing the expression and function of key Toll-like receptors, we aim to provide an important framework to understand the functional role of these receptors in response to stress and infection in premature infants.

Toward Using Twitter for Tracking COVID-19: A Natural Language Processing Pipeline and Exploratory Data Set.

BACKGROUND: In the United States, the rapidly evolving COVID-19 outbreak, the shortage of available testing, and the delay of test results present challenges for actively monitoring its spread based on testing alone. OBJECTIVE: The objective of this study was to develop, evaluate, and deploy an automatic natural language processing pipeline to collect user-generated Twitter data as a complementary resource for identifying potential cases of COVID-19 in the United States that are not based on testing and, thus, may not have been reported to the Centers for Disease Control and Prevention. METHODS: Beginning January 23, 2020, we collected English tweets from the Twitter Streaming application programming interface that mention keywords related to COVID-19. We applied handwritten regular expressions to identify tweets indicating that the user potentially has been exposed to COVID-19. We automatically filtered out "reported speech" (eg, quotations, news headlines) from the tweets that matched the regular expressions, and two annotators annotated a random sample of 8976 tweets that are geo-tagged or have profile location metadata, distinguishing tweets that self-report potential cases of COVID-19 from those that do not. We used the annotated tweets to train and evaluate deep neural network classifiers based on bidirectional encoder representations from transformers (BERT). Finally, we deployed the automatic pipeline on more than 85 million unlabeled tweets that were continuously collected between March 1 and August 21, 2020. RESULTS: Interannotator agreement, based on dual annotations for 3644 (41%) of the 8976 tweets, was 0.77 (Cohen κ). A deep neural network classifier, based on a BERT model that was pretrained on tweets related to COVID-19, achieved an F1-score of 0.76 (precision=0.76, recall=0.76) for detecting tweets that self-report potential cases of COVID-19. Upon deploying our automatic pipeline, we identified 13,714 tweets that self-report potential cases of COVID-19 and have US state-level geolocations. CONCLUSIONS: We have made the 13,714 tweets identified in this study, along with each tweet's time stamp and US state-level geolocation, publicly available to download. This data set presents the opportunity for future work to assess the utility of Twitter data as a complementary resource for tracking the spread of COVID-19.

Text classification models for the automatic detection of nonmedical prescription medication use from social media.

BACKGROUND: Prescription medication (PM) misuse/abuse has emerged as a national crisis in the United States, and social media has been suggested as a potential resource for performing active monitoring. However, automating a social media-based monitoring system is challenging-requiring advanced natural language processing (NLP) and machine learning methods. In this paper, we describe the development and evaluation of automatic text classification models for detecting self-reports of PM abuse from Twitter. METHODS: We experimented with state-of-the-art bi-directional transformer-based language models, which utilize tweet-level representations that enable transfer learning (e.g., BERT, RoBERTa, XLNet, AlBERT, and DistilBERT), proposed fusion-based approaches, and compared the developed models with several traditional machine learning, including deep learning, approaches. Using a public dataset, we evaluated the performances of the classifiers on their abilities to classify the non-majority "abuse/misuse" class. RESULTS: Our proposed fusion-based model performs significantly better than the best traditional model (F1-score [95% CI]: 0.67 [0.64-0.69] vs. 0.45 [0.42-0.48]). We illustrate, via experimentation using varying training set sizes, that the transformer-based models are more stable and require less annotated data compared to the other models. The significant improvements achieved by our best-performing classification model over past approaches makes it suitable for automated continuous monitoring of nonmedical PM use from Twitter. CONCLUSIONS: BERT, BERT-like and fusion-based models outperform traditional machine learning and deep learning models, achieving substantial improvements over many years of past research on the topic of prescription medication misuse/abuse classification from social media, which had been shown to be a complex task due to the unique ways in which information about nonmedical use is presented. Several challenges associated with the lack of context and the nature of social media language need to be overcome to further improve BERT and BERT-like models. These experimental driven challenges are represented as potential future research directions.

Bugs, breathing and blood pressure: microbiota-gut-brain axis signalling in cardiorespiratory control in health and disease.

There is clear evidence of physiological effects of the gut microbiota on whole-body function in health and disease. Microbiota-gut-brain axis signalling is recognised as a key player in behavioural disorders such as depression and anxiety. Recent evidence suggests that the gut microbiota affects neurocontrol networks responsible for homeostatic functions that are essential for life. We consider the evidence suggesting the potential for the gut microbiota to shape cardiorespiratory homeostasis. In various animal models of disease, there is an association between cardiorespiratory morbidity and perturbed gut microbiota, with strong evidence in support of a role of the gut microbiota in the control of blood pressure. Interventions that target the gut microbiota or manipulate the gut-brain axis, such as short-chain fatty acid supplementation, prevent hypertension in models of obstructive sleep apnoea. Emerging evidence points to a role for the microbiota-gut-brain axis in the control of breathing and ventilatory responsiveness, relevant to cardiorespiratory disease. There is also evidence for an association between the gut microbiota and disease severity in people with asthma and cystic fibrosis. There are many gaps in the knowledge base and an urgent need to better understand the mechanisms by which gut health and dysbiosis contribute to cardiorespiratory control. Nevertheless, there is a growing consensus that manipulation of the gut microbiota could prove an efficacious adjunctive strategy in the treatment of common cardiorespiratory diseases, which are the leading causes of morbidity and mortality.

Promoting Reproducible Research for Characterizing Nonmedical Use of Medications Through Data Annotation: Description of a Twitter Corpus and Guidelines.

BACKGROUND: Social media data are being increasingly used for population-level health research because it provides near real-time access to large volumes of consumer-generated data. Recently, a number of studies have explored the possibility of using social media data, such as from Twitter, for monitoring prescription medication abuse. However, there is a paucity of annotated data or guidelines for data characterization that discuss how information related to abuse-prone medications is presented on Twitter. OBJECTIVE: This study discusses the creation of an annotated corpus suitable for training supervised classification algorithms for the automatic classification of medication abuse-related chatter. The annotation strategies used for improving interannotator agreement (IAA), a detailed annotation guideline, and machine learning experiments that illustrate the utility of the annotated corpus are also described. METHODS: We employed an iterative annotation strategy, with interannotator discussions held and updates made to the annotation guidelines at each iteration to improve IAA for the manual annotation task. Using the grounded theory approach, we first characterized tweets into fine-grained categories and then grouped them into 4 broad classes-abuse or misuse, personal consumption, mention, and unrelated. After the completion of manual annotations, we experimented with several machine learning algorithms to illustrate the utility of the corpus and generate baseline performance metrics for automatic classification on these data. RESULTS: Our final annotated set consisted of 16,443 tweets mentioning at least 20 abuse-prone medications including opioids, benzodiazepines, atypical antipsychotics, central nervous system stimulants, and gamma-aminobutyric acid analogs. Our final overall IAA was 0.86 (Cohen kappa), which represents high agreement. The manual annotation process revealed the variety of ways in which prescription medication misuse or abuse is discussed on Twitter, including expressions indicating coingestion, nonmedical use, nonstandard route of intake, and consumption above the prescribed doses. Among machine learning classifiers, support vector machines obtained the highest automatic classification accuracy of 73.00% (95% CI 71.4-74.5) over the test set (n=3271). CONCLUSIONS: Our manual analysis and annotations of a large number of tweets have revealed types of information posted on Twitter about a set of abuse-prone prescription medications and their distributions. In the interests of reproducible and community-driven research, we have made our detailed annotation guidelines and the training data for the classification experiments publicly available, and the test data will be used in future shared tasks.

GeoBoost: accelerating research involving the geospatial metadata of virus GenBank records.

Summary: GeoBoost is a command-line software package developed to address sparse or incomplete metadata in GenBank sequence records that relate to the location of the infected host (LOIH) of viruses. Given a set of GenBank accession numbers corresponding to virus GenBank records, GeoBoost extracts, integrates and normalizes geographic information reflecting the LOIH of the viruses using integrated information from GenBank metadata and related full-text publications. In addition, to facilitate probabilistic geospatial modeling, GeoBoost assigns probability scores for each possible LOIH. Availability and implementation: Binaries and resources required for running GeoBoost are packed into a single zipped file and freely available for download at https://tinyurl.com/geoboost. A video tutorial is included to help users quickly and easily install and run the software. The software is implemented in Java 1.8, and supported on MS Windows and Linux platforms. Contact: gragon@upenn.edu. Supplementary information: Supplementary data are available at Bioinformatics online.

Sexual and Relationship Interest, Knowledge, and Experiences Among Adolescents and Young Adults with Autism Spectrum Disorder.

Little research exists to describe sexual and romantic relationships among adolescents and young adults with autism spectrum disorder (ASD) from their perspectives. Sexuality and intimacy are developmentally important and influence health and quality of life for all adolescents and young adults, including those with ASD. This study explored and compared the sex and relationship experiences of 27 adolescents and young adults with ASD (males = 20). Adolescents and young adults participated in semi-structured interviews to explore this topic. Using theme analysis, we uncovered four thematic categories: (1) interest in relationships, (2) ideal partners, (3) realities of adolescent and young adult relationships, and (4) advice about sex and relationships. Although many adolescents and young adults expressed wanting a relationship, few reported having partners. Among those that did, their actual relationships rarely met ideals. Most adolescents and young adults talked with parents and friends but not healthcare providers about sex and relationships. All adolescents and young adults described the need for additional education. Adolescents and young adults express the need for education that covers basic safety and sexual health topics as well as social/relationship skills building and courtship modeling. These findings can inform the design of tailored sexual health intervention. Future research should examine specific issues related to sexuality from the adolescents' and young adults' perspectives.

Cholesterol Screening and Treatment Practices and Preferences: A Survey of United States Pediatricians.

OBJECTIVES: To determine pediatricians' practices, attitudes, and barriers regarding screening for and treatment of pediatric dyslipidemias in 9- to 11-year-olds and 17- to 21-year-olds. STUDY DESIGN: American Academy of Pediatrics (AAP) 2013-2014 Periodic Survey of a national, randomly selected sample of 1627 practicing AAP physicians. Pediatricians' responses were described and modeled. RESULTS: Of 614 (38%) respondents who met eligibility criteria, less than half (46%) were moderately/very knowledgeable about the 2008 AAP cholesterol statement; fewer were well-informed about 2011 National Heart, Lung, and Blood Institute Guidelines or 2007 US Preventive Service Task Force review (both 26%). Despite published recommendations, universal screening was not routine: 68% reported they never/rarely/sometimes screened healthy 9- to 11-year-olds. In contrast, more providers usually/most/all of the time screened based on family cardiovascular history (61%) and obesity (82%). Screening 17- to 21-year-olds was more common in all categories (P?

Pediatricians Working Part-Time Has Plateaued.

OBJECTIVE: To examine trends in pediatricians working part-time and residents seeking part-time work and to examine associated characteristics. STUDY DESIGN: The American Academy of Pediatrics (AAP) Periodic Survey of Fellows and the AAP Annual Survey of Graduating Residents were used to examine part-time employment. Fourteen periodic surveys were combined with an overall response rate of 57%. Part-time percentages were compared for surveys conducted from 2006-2009 and 2010-2013. The AAP Annual Surveys of Graduating Residents (combined response rate = 60%) from 2006-2009 were compared with 2010-2013 surveys for residents seeking and obtaining part-time positions following training. Multivariable logistic regression models identified characteristics associated with part-time work. RESULTS: Comparable percentages of pediatricians worked part-time in 2006-2009 (23%) and 2010-2013 (23%). There was similarly no statistically significant difference in residents seeking part-time work (30%-28%), and there was a slight decline in residents accepting part-time work (16%-13%, aOR .75, 95% CI .56-.96). Increases in working part-time were not found for any subgroups examined. Women consistently were more likely than men to work part-time (35% vs 9%), but they showed different patterns of part-time work across age. Women in their 40s (40%) were more likely than other women (33%) and men in their 60s (20%) were more likely than other men (5%) to work part-time. CONCLUSIONS: There has been a levelling off in the number of pediatricians working part-time and residents seeking part-time work. Overall, women remain more likely to work part-time, although 1 in 5 men over 60 work part-time.