- Proceedings of the National Academy of Sciences of the United States of America
- Published over 5 years ago
Both social isolation and loneliness are associated with increased mortality, but it is uncertain whether their effects are independent or whether loneliness represents the emotional pathway through which social isolation impairs health. We therefore assessed the extent to which the association between social isolation and mortality is mediated by loneliness. We assessed social isolation in terms of contact with family and friends and participation in civic organizations in 6,500 men and women aged 52 and older who took part in the English Longitudinal Study of Ageing in 2004-2005. A standard questionnaire measure of loneliness was administered also. We monitored all-cause mortality up to March 2012 (mean follow-up 7.25 y) and analyzed results using Cox proportional hazards regression. We found that mortality was higher among more socially isolated and more lonely participants. However, after adjusting statistically for demographic factors and baseline health, social isolation remained significantly associated with mortality (hazard ratio 1.26, 95% confidence interval, 1.08-1.48 for the top quintile of isolation), but loneliness did not (hazard ratio 0.92, 95% confidence interval, 0.78-1.09). The association of social isolation with mortality was unchanged when loneliness was included in the model. Both social isolation and loneliness were associated with increased mortality. However, the effect of loneliness was not independent of demographic characteristics or health problems and did not contribute to the risk associated with social isolation. Although both isolation and loneliness impair quality of life and well-being, efforts to reduce isolation are likely to be more relevant to mortality.
Using deep learning and Google Street View to estimate the demographic makeup of neighborhoods across the United States
- Proceedings of the National Academy of Sciences of the United States of America
- Published 10 months ago
The United States spends more than $250 million each year on the American Community Survey (ACS), a labor-intensive door-to-door study that measures statistics relating to race, gender, education, occupation, unemployment, and other demographic factors. Although a comprehensive source of data, the lag between demographic changes and their appearance in the ACS can exceed several years. As digital imagery becomes ubiquitous and machine vision techniques improve, automated data analysis may become an increasingly practical supplement to the ACS. Here, we present a method that estimates socioeconomic characteristics of regions spanning 200 US cities by using 50 million images of street scenes gathered with Google Street View cars. Using deep learning-based computer vision techniques, we determined the make, model, and year of all motor vehicles encountered in particular neighborhoods. Data from this census of motor vehicles, which enumerated 22 million automobiles in total (8% of all automobiles in the United States), were used to accurately estimate income, race, education, and voting patterns at the zip code and precinct level. (The average US precinct contains [Formula: see text]1,000 people.) The resulting associations are surprisingly simple and powerful. For instance, if the number of sedans encountered during a drive through a city is higher than the number of pickup trucks, the city is likely to vote for a Democrat during the next presidential election (88% chance); otherwise, it is likely to vote Republican (82%). Our results suggest that automated systems for monitoring demographics may effectively complement labor-intensive approaches, with the potential to measure demographics with fine spatial resolution, in close to real time.
We conduct a detailed investigation of correlations between real-time expressions of individuals made across the United States and a wide range of emotional, geographic, demographic, and health characteristics. We do so by combining (1) a massive, geo-tagged data set comprising over 80 million words generated in 2011 on the social network service Twitter and (2) annually-surveyed characteristics of all 50 states and close to 400 urban populations. Among many results, we generate taxonomies of states and cities based on their similarities in word use; estimate the happiness levels of states and cities; correlate highly-resolved demographic characteristics with happiness levels; and connect word choice and message length with urban characteristics such as education levels and obesity rates. Our results show how social media may potentially be used to estimate real-time levels and changes in population-scale measures such as obesity rates.
INTRODUCTION: The elastic ligature is the most used method for the out-patient treatment of haemorrhoids, with excellent results in control of bleeding. However, the recurrences in prolapse vary between 15 and 40%. We propose a new method for applying the elastic ligatures. PATIENTS: A total of 17 patients with grade iii haemorrhoids were chosen for the vertical ligatures (VL). The first elastic band was placed 3 to 4cm from the pectineal line and 1 or 2 more in the root of the haemorrhoid group. Another 34 randomly selected patients were used as a control group. Data collected included, demographic details, number of bands and sessions, pain scale, complications and results. The patients were followed up at week one, week 3, and 3 months and one year after the intervention. RESULTS: A total of 12 males and 5 females, with a median age of 46 years, were treated with VL. The median follow-up was 10 (from 8 to 19) months. A median of 3 sessions and 7 elastic bands were used, with 6 patients having moderate pain that required analgesic treatment. None of the patients needed urgent treatment for pain or bleeding. There was a complete response to bleeding in 15 patients (88.2%) and to prolapse in 14 (82.2%). Two patients required haemorrhoidectomy due to treatment failure. The measurements of therapeutic effect after one year were: number needed to treat (NNT) of 4 (95% CI, 2 to 22), for prolapse, and NNT of 4 (95% CI, 2 a 15), for bleeding. CONCLUSIONS: Elastic ligatures could become a new treatment option for Grade iii haemorrhoids, improving control of bleeding and prolapse.
Anthropogenic changes in land use and the extirpation of apex predators have facilitated explosive growth of mesopredator populations. Consequently, many species have been subjected to extensive control throughout portions of their range due to their integral role as generalist predators and reservoirs of zoonotic disease. Yet, few studies have monitored the effects of landscape composition or configuration on the demographic or behavioral response of mesopredators to population manipulation. During 2007 we removed 382 raccoons (Procyon lotor) from 30 forest patches throughout a fragmented agricultural ecosystem to test hypotheses regarding the effects of habitat isolation on population recovery and role of range expansion and dispersal in patch colonization of mesopredators in heterogeneous landscapes. Patches were allowed to recolonize naturally and demographic restructuring of patches was monitored from 2008-2010 using mark-recapture. An additional 25 control patches were monitored as a baseline measure of demography. After 3 years only 40% of experimental patches had returned to pre-removal densities. This stagnant recovery was driven by low colonization rates of females, resulting in little to no within-patch recruitment. Colonizing raccoons were predominantly young males, suggesting that dispersal, rather than range expansion, was the primary mechanism driving population recovery. Contrary to our prediction, neither landscape connectivity nor measured local habitat attributes influenced colonization rates, likely due to the high dispersal capability of raccoons and limited role of range expansion in patch colonization. Although culling is commonly used to control local populations of many mesopredators, we demonstrate that such practices create severe disruptions in population demography that may be counterproductive to disease management in fragmented landscapes due to an influx of dispersing males into depopulated areas. However, given the slow repopulation rates observed in our study, localized depopulation may be effective at reducing negative ecological impacts of mesopredators in fragmented landscapes at limited spatial and temporal scales.
In four large, nationally representative surveys (N = 11.2 million), American adolescents and emerging adults in the 2010s (Millennials) were significantly less religious than previous generations (Boomers, Generation X) at the same age. The data are from the Monitoring the Future studies of 12th graders (1976-2013), 8th and 10th graders (1991-2013), and the American Freshman survey of entering college students (1966-2014). Although the majority of adolescents and emerging adults are still religiously involved, twice as many 12th graders and college students, and 20%-40% more 8th and 10th graders, never attend religious services. Twice as many 12th graders and entering college students in the 2010s (vs. the 1960s-70s) give their religious affiliation as “none,” as do 40%-50% more 8th and 10th graders. Recent birth cohorts report less approval of religious organizations, are less likely to say that religion is important in their lives, report being less spiritual, and spend less time praying or meditating. Thus, declines in religious orientation reach beyond affiliation to religious participation and religiosity, suggesting a movement toward secularism among a growing minority. The declines are larger among girls, Whites, lower-SES individuals, and in the Northeastern U.S., very small among Blacks, and non-existent among political conservatives. Religious affiliation is lower in years with more income inequality, higher median family income, higher materialism, more positive self-views, and lower social support. Overall, these results suggest that the lower religious orientation of Millennials is due to time period or generation, and not to age.
Examine fatal and nonfatal firearm injuries among children aged 0 to 17 in the United States, including intent, demographic characteristics, trends, state-level patterns, and circumstances.
This paper specifies, designs and critically evaluates two tools for the automated identification of demographic data (age, occupation and social class) from the profile descriptions of Twitter users in the United Kingdom (UK). Meta-data data routinely collected through the Collaborative Social Media Observatory (COSMOS: http://www.cosmosproject.net/) relating to UK Twitter users is matched with the occupational lookup tables between job and social class provided by the Office for National Statistics (ONS) using SOC2010. Using expert human validation, the validity and reliability of the automated matching process is critically assessed and a prospective class distribution of UK Twitter users is offered with 2011 Census baseline comparisons. The pattern matching rules for identifying age are explained and enacted following a discussion on how to minimise false positives. The age distribution of Twitter users, as identified using the tool, is presented alongside the age distribution of the UK population from the 2011 Census. The automated occupation detection tool reliably identifies certain occupational groups, such as professionals, for which job titles cannot be confused with hobbies or are used in common parlance within alternative contexts. An alternative explanation on the prevalence of hobbies is that the creative sector is overrepresented on Twitter compared to 2011 Census data. The age detection tool illustrates the youthfulness of Twitter users compared to the general UK population as of the 2011 Census according to proportions, but projections demonstrate that there is still potentially a large number of older platform users. It is possible to detect “signatures” of both occupation and age from Twitter meta-data with varying degrees of accuracy (particularly dependent on occupational groups) but further confirmatory work is needed.
The effects of climate change on high latitude regions are becoming increasingly evident, particularly in the rapid decline of sea ice cover in the Arctic. Many high latitude species dependent on sea ice are being forced to adapt to changing habitats. Harp seals (Pagophilus groenlandicus) are an indicator species for changing high-latitude ecosystems. This study analyzed multiple factors including ice cover, demographics, and genetic diversity, which could affect harp seal stranding rates along the eastern coast of the United States. Ice cover assessments were conducted for the month of February in the Gulf of St. Lawrence whelping region from 1991-2010 using remote sensing data, and harp seal stranding data were collected over the same time period. Genetic diversity, which may affect how quickly species can adapt to changing climates, was assessed using ten microsatellite markers to determine mean d (2) in a subset of stranded and by-caught (presumably healthy) seals sampled along the northeast U.S. coast. Our study found a strong negative correlation (R (2) = 0.49) between ice cover in the Gulf of St. Lawrence and yearling harp seal strandings, but found no relationship between sea ice conditions and adult strandings. Our analysis revealed that male seals stranded more frequently than females during the study period and that this relationship was strongest during light ice years. In contrast, we found no significant difference in mean d (2) between stranded and by-caught harp seals. The results demonstrate that sea ice cover and demographic factors have a greater influence on harp seal stranding rates than genetic diversity, with only a little of the variance in mean d (2) among stranded seals explained by ice cover. Any changes in these factors could have major implications for harp seals, and these findings should be considered in the development of future management plans for the Arctic that incorporate climate variability.
We explored the characteristics and motivations of people who, having obtained their genetic or genomic data from Direct-To-Consumer genetic testing (DTC-GT) companies, voluntarily decide to share them on the publicly accessible web platform openSNP. The study is the first attempt to describe open data sharing activities undertaken by individuals without institutional oversight. In the paper we provide a detailed overview of the distribution of the demographic characteristics and motivations of people engaged in genetic or genomic open data sharing. The geographical distribution of the respondents showed the USA as dominant. There was no significant gender divide, the age distribution was broad, educational background varied and respondents with and without children were equally represented. Health, even though prominent, was not the respondents' primary or only motivation to be tested. As to their motivations to openly share their data, 86.05% indicated wanting to learn about themselves as relevant, followed by contributing to the advancement of medical research (80.30%), improving the predictability of genetic testing (76.02%) and considering it fun to explore genotype and phenotype data (75.51%). Whereas most respondents were well aware of the privacy risks of their involvement in open genetic data sharing and considered the possibility of direct, personal repercussions troubling, they estimated the risk of this happening to be negligible. Our findings highlight the diversity of DTC-GT consumers who decide to openly share their data. Instead of focusing exclusively on health-related aspects of genetic testing and data sharing, our study emphasizes the importance of taking into account benefits and risks that stretch beyond the health spectrum. Our results thus lend further support to the call for a broader and multi-faceted conceptualization of genomic utility.