ResearchPad - census https://www.researchpad.co Default RSS Feed en-us © 2020 Newgen KnowledgeWorks <![CDATA[Medications for opioid use disorder among pregnant women referred by criminal justice agencies before and after Medicaid expansion: A retrospective study of admissions to treatment centers in the United States]]> https://www.researchpad.co/article/elastic_article_15710 There has been a 4-fold increase in the number of pregnant women with opioid use disorder (OUD). Medications such as methadone and buprenorphine are standard of care for OUD and are recommended during pregnancy, but only 50% of pregnant women receive such medication.Pregnant women with OUD who are involved in the criminal justice system are at high risk of poor outcomes, but data regarding the use of medications for OUD in this population are limited.What did the researchers find?From 1992 to 2017, pregnant women in the US who were referred to treatment for OUD by a criminal justice agency (versus other referral sources) were half as likely to receive medication as part of their treatment plan.After implementation of the Affordable Care Act’s Medicaid expansion, medication for OUD increased significantly more among pregnant women referred to treatment by criminal justice agencies in Medicaid expansion states compared with nonexpansion states.What do these findings mean?Pregnant women referred to treatment for OUD by criminal justice agencies were consistently less likely to receive evidence-based treatment, which increases their risk of overdose and poor maternal and neonatal outcomes.Improving access to Medicaid for justice-involved individuals may increase the rate at which pregnant women receive evidence-based treatment for OUD. ]]> <![CDATA[The association between cervical cancer screening participation and the deprivation index of the location of the family doctor’s office]]> https://www.researchpad.co/article/elastic_article_14737 Cervical cancer screening rates are known to be strongly associated with socioeconomic status. Our objective was to assess whether the rate is also associated with an aggregated deprivation marker, defined by the location of family doctors’ offices.MethodsTo access this association, we 1) collected data from the claim database of the French Health Insurance Fund about the registered family doctors and their enlisted female patients eligible for cervical screening; 2) carried out a telephone survey with all registered doctors to establish if they were carrying out Pap-smears in their practices; 3) geotracked all the doctors’ offices in the smallest existing blocks of socioeconomic homogenous populations (IRIS census units) that were assigned a census derived marker of deprivation, the European Deprivation Index (EDI), and a binary variable of urbanization; and 4) we used a multivariable linear mixed model with IRIS as a random effect.ResultsOf 348 eligible doctors, 343 responded to the telephone survey (98.6%) and were included in the analysis, encompassing 88,152 female enlisted patients aged 25–65 years old. In the multivariable analysis (adjusted by the gender of the family doctor, the practice of Pap-smears by the doctor and the urbanization of the office location), the EDI of the doctor’s office was strongly associated with the cervical cancer screening participation rate of eligible patients (p<0.001).ConclusionThe EDI linked to the location of the family doctor’s office seems to be a robust marker to predict female patients’ participation in cervical cancer screening. ]]> <![CDATA[Cross-comparative analysis of evacuation behavior after earthquakes using mobile phone data]]> https://www.researchpad.co/article/5c76fe4dd5eed0c484e5b867

Despite the importance of predicting evacuation mobility dynamics after large scale disasters for effective first response and disaster relief, our general understanding of evacuation behavior remains limited because of the lack of empirical evidence on the evacuation movement of individuals across multiple disaster instances. Here we investigate the GPS trajectories of a total of more than 1 million anonymized mobile phone users whose positions were tracked for a period of 2 months before and after four of the major earthquakes that occurred in Japan. Through a cross comparative analysis between the four disaster instances, we find that in contrast to the assumed complexity of evacuation decision making mechanisms in crisis situations, an individual’s evacuation probability is strongly dependent on the seismic intensity that they experience. In fact, we show that the evacuation probabilities in all earthquakes collapse into a similar pattern, with a critical threshold at around seismic intensity 5.5. This indicates that despite the diversity in the earthquakes profiles and urban characteristics, evacuation behavior is similarly dependent on seismic intensity. Moreover, we found that probability density functions of the distances that individuals evacuate are not dependent on seismic intensities that individuals experience. These insights from empirical analysis on evacuation from multiple earthquake instances using large scale mobility data contributes to a deeper understanding of how people react to earthquakes, and can potentially assist decision makers to simulate and predict the number of evacuees in urban areas with little computational time and cost. This can be achieved by utilizing only the information on population density distribution and seismic intensity distribution, which can be observed instantaneously after the shock.

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<![CDATA[An open source algorithm to detect natural gas leaks from mobile methane survey data]]> https://www.researchpad.co/article/5c6dc9e7d5eed0c48452a459

The data collected by mobile methane (CH4) sensors can be used to find natural gas (NG) leaks in urban distribution systems. Extracting actionable insights from the large volumes of data collected by these sensors requires several data processing steps. While these survey platforms are commercially available, the associated data processing software largely constitute a black box due to their proprietary nature. In this paper we describe a step-by-step algorithm for developing leak indications using data from mobile CH4 surveys, providing an under-the-hood look at the choices and challenges associated with data analysis. We also describe how our algorithm has evolved over time, and the data-driven insights that have prompted these changes. Applying our algorithm to data collected in 15 cities produced more than 6100 leak indications and estimates of the leaks’ size. We use these results to characterize the distribution of leak sizes in local NG distribution systems. Mobile surveys are already an effective and necessary tool for managing NG distribution systems, but improvements in the technology and software will continue to increase its value.

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<![CDATA[Examining individual and geographic factors associated with social isolation and loneliness using Canadian Longitudinal Study on Aging (CLSA) data]]> https://www.researchpad.co/article/5c5df369d5eed0c484581267

Background

A large body of research shows that social isolation and loneliness have detrimental health consequences. Identifying individuals at risk of social isolation or loneliness is, therefore, important. The objective of this study was to examine personal (e.g., sex, income) and geographic (rural/urban and sociodemographic) factors and their association with social isolation and loneliness in a national sample of Canadians aged 45 to 85 years.

Methods

The study involved cross-sectional analyses of baseline data from the Canadian Longitudinal Study on Aging that were linked to 2016 census data at the Forward Sortation Area (FSA) level. Multilevel logistic regression analyses were conducted to examine the association between personal factors and geographic factors and social isolation and loneliness for the total sample, and women and men, respectively.

Results

The prevalence of social isolation and loneliness was 5.1% and 10.2%, respectively, but varied substantially across personal characteristics. Personal characteristics (age, sex, education, income, functional impairment, chronic diseases) were significantly related to both social isolation and loneliness, although some differences emerged in the direction of the relationships for the two measures. Associations also differed somewhat for women versus men. Associations between some geographic factors emerged for social isolation, but not loneliness. Living in an urban core was related to increased odds of social isolation, an effect that was no longer significant when FSA-level factors were controlled for. FSAs with a higher percentage of 65+ year old residents with low income were consistently associated with higher odds of social isolation.

Conclusion

The findings indicate that socially isolated individuals are, to some extent, clustered into areas with a high proportion of low-income older adults, suggesting that support and resources could be targeted at these areas. For loneliness, the focus may be less on where people live, but rather on personal characteristics that place individuals at risk.

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<![CDATA[A census-based estimate of Earth's bacterial and archaeal diversity]]> https://www.researchpad.co/article/5c61e8f2d5eed0c48496f4a9

The global diversity of Bacteria and Archaea, the most ancient and most widespread forms of life on Earth, is a subject of intense controversy. This controversy stems largely from the fact that existing estimates are entirely based on theoretical models or extrapolations from small and biased data sets. Here, in an attempt to census the bulk of Earth's bacterial and archaeal ("prokaryotic") clades and to estimate their overall global richness, we analyzed over 1.7 billion 16S ribosomal RNA amplicon sequences in the V4 hypervariable region obtained from 492 studies worldwide, covering a multitude of environments and using multiple alternative primers. From this data set, we recovered 739,880 prokaryotic operational taxonomic units (OTUs, 16S-V4 gene clusters at 97% similarity), a commonly used measure of microbial richness. Using several statistical approaches, we estimate that there exist globally about 0.8–1.6 million prokaryotic OTUs, of which we recovered somewhere between 47%–96%, representing >99.98% of prokaryotic cells. Consistent with this conclusion, our data set independently "recaptured" 91%–93% of 16S sequences from multiple previous global surveys, including PCR-independent metagenomic surveys. The distribution of relative OTU abundances is consistent with a log-normal model commonly observed in larger organisms; the total number of OTUs predicted by this model is also consistent with our global richness estimates. By combining our estimates with the ratio of full-length versus partial-length (V4) sequence diversity in the SILVA sequence database, we further estimate that there exist about 2.2–4.3 million full-length OTUs worldwide. When restricting our analysis to the Americas, while controlling for the number of studies, we obtain similar richness estimates as for the global data set, suggesting that most OTUs are globally distributed. Qualitatively similar results are also obtained for other 16S similarity thresholds (90%, 95%, and 99%). Our estimates constrain the extent of a poorly quantified rare microbial biosphere and refute recent predictions that there exist trillions of prokaryotic OTUs.

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<![CDATA[District level estimates and mapping of prevalence of diarrhoea among under-five children in Bangladesh by combining survey and census data]]> https://www.researchpad.co/article/5c5df364d5eed0c4845811fe

The demand for district level statistics has increased tremendously in Bangladesh due to existence of decentralised approach to governance and service provision. The Bangladesh Demographic Health Surveys (BDHS) provide a wide range of invaluable data at the national and divisional level but they cannot be used directly to produce reliable district-level estimates due to insufficient sample sizes. The small area estimation (SAE) technique overcomes the sample size challenges and can produce reliable estimates at the district level. This paper uses SAE approach to generate model-based district-level estimates of diarrhoea prevalence among under-5 children in Bangladesh by linking data from the 2014 BDHS and the 2011 Population Census. The diagnostics measures show that the model-based estimates are precise and representative when compared to the direct survey estimates. Spatial distribution of the precise estimates of diarrhoea prevalence reveals significant inequality at district-level (ranged 1.1–13.4%) with particular emphasis in the coastal and north-eastern districts. Findings of the study might be useful for designing effective policies, interventions and strengthening local-level governance.

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<![CDATA[Correlated impulses: Using Facebook interests to improve predictions of crime rates in urban areas]]> https://www.researchpad.co/article/5c61e910d5eed0c48496f765

Much research has examined how crime rates vary across urban neighborhoods, focusing particularly on community-level demographic and social characteristics. A parallel line of work has treated crime at the individual level as an expression of certain behavioral patterns (e.g., impulsivity). Little work has considered, however, whether the prevalence of such behavioral patterns in a neighborhood might be predictive of local crime, in large part because such measures are hard to come by and often subjective. The Facebook Advertising API offers a special opportunity to examine this question as it provides an extensive list of “interests” that can be tabulated at various geographic scales. Here we conduct an analysis of the association between the prevalence of interests among the Facebook population of a ZIP code and the local rate of assaults, burglaries, and robberies across 9 highly populated cities in the US. We fit various regression models to predict crime rates as a function of the Facebook and census demographic variables. In general, models using the variables for the interests of the whole adult population on Facebook perform better than those using data on specific demographic groups (such as Males 18-34). In terms of predictive performance, models combining Facebook data with demographic data generally have lower error rates than models using only demographic data. We find that interests associated with media consumption and mating competition are predictive of crime rates above and beyond demographic factors. We discuss how this might integrate with existing criminological theory.

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<![CDATA[Changes in the spatial distribution of the under-five mortality rate: Small-area analysis of 122 DHS surveys in 262 subregions of 35 countries in Africa]]> https://www.researchpad.co/article/5c50c4a9d5eed0c4845e8baa

The under-five mortality rate (U5MR) is a critical and widely available population health indicator. Both the MDGs and SDGs define targets for improvement in the U5MR, and the SDGs require spatial disaggregation of indicators. We estimate trends in the U5MR for Admin-1 subnational areas using 122 DHS surveys in 35 countries in Africa and assess progress toward the MDG target reductions for each subnational region and each country as a whole. In each country, direct weighted estimates of the U5MR from each survey are calculated and combined into a single estimate for each Admin-1 region across five-year periods. Our method fully accounts for the sample design of each survey. The region-time-specific estimates are smoothed using a Bayesian, space-time model that produces more precise estimates (when compared to the direct estimates) at a one-year scale that are consistent with each other in both space and time. The resulting estimated distributions of the U5MR are summarized and used to assess subnational progress toward the MDG 4 target of two-thirds reduction in the U5MR during 1990–2015. Our space-time modeling approach is tractable and can be readily applied to a large collection of sample survey data. Subnational, regional spatial heterogeneity in the levels and trends in the U5MR vary considerably across Africa. There is no generalizable pattern between spatial heterogeneity and levels or trends in the U5MR. Subnational, small-area estimates of the U5MR: (i) identify subnational regions where interventions are still necessary and those where improvement is well under way; and (ii) countries where there is very little spatial variation and others where there are important differences between subregions in both levels and trends. More work is necessary to improve both the data sources and methods necessary to adequately measure subnational progress toward the SDG child survival targets.

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<![CDATA[How happy are your neighbours? Variation in life satisfaction among 1200 Canadian neighbourhoods and communities]]> https://www.researchpad.co/article/5c5217fed5eed0c484795ed8

This paper presents a new public-use dataset for community-level life satisfaction in Canada, based on more than 500,000 observations from the Canadian Community Health Surveys and the General Social Surveys. The country is divided into 1216 similarly sampled geographic regions, using natural, built, and administrative boundaries. A cross-validation exercise suggests that our choice of minimum sampling thresholds approximately maximizes the predictive power of our estimates. The resulting dataset reveals robust differences in life satisfaction between and across urban and rural communities. We compare aggregated life satisfaction data with a range of key census variables to illustrate some of the ways in which lives differ in the most and least happy communities.

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<![CDATA[Differential Impacts of HIV status on short-term fertility desires among couples in Rakai, Uganda]]> https://www.researchpad.co/article/5c536b17d5eed0c484a48024

Background

Fertility desires of female and male partners in current relationships are often correlated. We examined the influence of HIV seropositive status of female and male partners on short-term fertility desires in Rakai, Uganda, a setting with high fertility and HIV infection rates.

Methods

Participants were couples (15–49 years old) enrolled in the Rakai Community Cohort Study, from 2011 to 2013 (n = 2,291). Cohen’s kappa coefficient was used to measure the correlation of female and male partners’ short-term fertility desires (measured as ‘wanting a child in the next 12 months’), in both total sample and stratified serostatus groups. HIV serostatus and additional characteristics of female and male partners were included in Poisson regression models to estimate the rate ratios (RR) for each partner’s short-term fertility desires. Individual and partner characteristics included HIV status, partner HIV status, age in years, partner age in years, educational attainment, number of living children, community of residence, and socioeconomic status (SES).

Results

Short-term fertility desires among female and male partners were moderately associated (Kappa = 0.37, p-value<0.001). The association was weakest among female sero-positive and male sero-negative couples (Kappa = 0.29, p-value<0.001). When adjusting for parity and other covariates in the model, women’s short-term fertility desires were significantly associated with their positive sero-status regardless of male partners’ sero-status (adjRR = 1.58, p<0.001 for F+M-; adjRR = 1.33, p = 0.001 for F+M+; in comparison with F-M-). Men’s short-term fertility desires were significantly associated with their positive sero-status, in addition to their female partners’ positive sero-status (adjRR = 1.23 with p-value = 0.022 for F-M+; adjRR = 1.42 with p-value<0.001 for F+M-; adjRR = 1.26 with p-value<0.001 for F+M+; in comparison with F-M-). When the differential effect of parity was included in the model, similar associations remained for both female and male partners when the number of living children was small, but largely reduced when the number of living children was large (3 or more).

Conclusion

Female and male partners in couple dyads demonstrated moderate agreements about short-term fertility desires. The HIV seropositive status of female partners was most strongly associated with short-term fertility desires of both genders, and this association was even stronger for women who had few or no living children.

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<![CDATA[Estimating recent migration and population-size surfaces]]> https://www.researchpad.co/article/5c466533d5eed0c484517f56

In many species a fundamental feature of genetic diversity is that genetic similarity decays with geographic distance; however, this relationship is often complex, and may vary across space and time. Methods to uncover and visualize such relationships have widespread use for analyses in molecular ecology, conservation genetics, evolutionary genetics, and human genetics. While several frameworks exist, a promising approach is to infer maps of how migration rates vary across geographic space. Such maps could, in principle, be estimated across time to reveal the full complexity of population histories. Here, we take a step in this direction: we present a method to infer maps of population sizes and migration rates associated with different time periods from a matrix of genetic similarity between every pair of individuals. Specifically, genetic similarity is measured by counting the number of long segments of haplotype sharing (also known as identity-by-descent tracts). By varying the length of these segments we obtain parameter estimates associated with different time periods. Using simulations, we show that the method can reveal time-varying migration rates and population sizes, including changes that are not detectable when using a similar method that ignores haplotypic structure. We apply the method to a dataset of contemporary European individuals (POPRES), and provide an integrated analysis of recent population structure and growth over the last ∼3,000 years in Europe.

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<![CDATA[Winter behavior of Saimaa ringed seals: Non-overlapping core areas as indicators of avoidance in breeding females]]> https://www.researchpad.co/article/5c390bc0d5eed0c48491e22c

Climate change, together with increasing human activity, poses a threat to the breeding success of endangered landlocked ringed seals (Phoca hispida saimensis). In this study, we estimated the spatial ecology of Saimaa ringed seals during the breeding season in the ice-covered period of December-April. The telemetry data on tagged seals (n = 20), with a total of 25 separate tracking periods and birth lair locations (n = 59) of non-tagged seals, were studied to estimate the movement ecology and breeding density. The movements of the ringed seals were more restricted during the ice-covered season; the total home range size (average 7.4 km2) in winter was 13 times smaller than that in summer. Individual tagged seals occupied an average of 5 ± 3 SD subnivean haul outs (snow lairs or ice cavities), and the mean distance between the haul outs was 1.6 ± 1.1 SD km (range 0.2–5.9 km). Moreover, our data indicated that ringed seal females likely exhibited breeding time avoidance of each other’s core areas, which may indicate some degree of territoriality. This was supported by the findings that the core areas (mean 1.2 km2) of tagged adult females (n = 9), did not overlap with each other. Also data on non-tagged seals showed that females did not give birth to pups within the core area radius of other parturient females. This study, together with earlier findings on the home ranges of nursed pups and perinatal mortality rates, has implications into land usage planning in Lake Saimaa by highlighting the need of undisturbed area between seal lairs and anthropogenic disturbances.

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<![CDATA[Modeling the effects of atmospheric pressure on suicide rates in the USA using geographically weighted regression]]> https://www.researchpad.co/article/5c117b58d5eed0c484698c77

Low atmospheric pressure may increase depression and suicide through inducing hypoxia. Previous studies have not evaluated the geographic variation of this relationship across the United States. Analyses were based on three groupings of age-adjusted completed suicide rates (all suicide, firearm-related suicide, non-firearm-related suicide) from 2286 counties in the United States. Multiple regression was used to determine the overall relationship between atmospheric pressure and completed suicide rates. Geographically weighted regression (GWR) models were used to obtain local coefficient estimates. A negative correlation between atmospheric pressure and completed suicide rates was observed for all three suicide groupings (p-value <0.0001). Significant, negative GWR coefficient estimates were located in the West and Northeast for the all suicides and firearm-related suicides, and in the Midwest for non-firearm-related suicides.

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<![CDATA[The Portuguese version of the European Deprivation Index: Development and association with all-cause mortality]]> https://www.researchpad.co/article/5c117bd3d5eed0c48469a944

Socioeconomic inequalities are major health determinants. To monitor and understand them at local level, ecological indexes of socioeconomic deprivation constitute essential tools. In this study, we describe the development of the updated version of the European Deprivation Index for Portuguese small-areas (EDI-PT), describe its spatial distribution and evaluate its association with a general health indicator–all-cause mortality in the period 2009–2012. Using data from the 2011 European Union–Statistics on Income and Living Conditions Survey (EU-SILC), we obtained an indicator of individual deprivation. After identifying variables that were common to both the EU-SILC and the census, we used the indicator of individual deprivation to test if these variables were associated with individual-level deprivation, and to compute weights. Accordingly, eight variables were included. The EDI-PT was produced for the smallest area unit possible (n = 18084 census block groups, mean/area = 584 inhabitants) and resulted from the weighted sum of the eight selected variables. It was then categorized into quintiles (Q1-least deprived to Q5-most deprived). To estimate the association with mortality we fitted Bayesian spatial models. The EDI-PT was unevenly distributed across Portugal–most deprived areas concentrated in the South and in the inner North and Centre of the country, and the least deprived in the coastal North and Centre. The EDI-PT was positively and significantly associated with overall mortality, and this relation followed a rather clear dose-response relation of increasing mortality as deprivation increases (Relative Risk Q2 = 1.012, 95% Credible Interval 0.991–1.033; Q3 = 1.026, 1.004–1.048; Q4 = 1.053, 1.029–1.077; Q5 = 1.068, 1.042–1.095). Summing up, we updated the index of socioeconomic deprivation for Portuguese small-areas, and we showed that the EDI-PT constitutes a sensitive measure to capture health inequalities, since it was consistently associated with a key measure of population health/development, all-cause mortality. We strongly believe this updated version will be widely employed by social and medical researchers and regional planners.

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<![CDATA[S-maup: Statistical test to measure the sensitivity to the modifiable areal unit problem]]> https://www.researchpad.co/article/5c06f026d5eed0c484c6d1d3

This work presents a nonparametric statistical test, S-maup, to measure the sensitivity of a spatially intensive variable to the effects of the Modifiable Areal Unit Problem (MAUP). To the best of our knowledge, S-maup is the first statistic of its type and focuses on determining how much the distribution of the variable, at its highest level of spatial disaggregation, will change when it is spatially aggregated. Through a computational experiment, we obtain the basis for the design of the statistical test under the null hypothesis of non-sensitivity to MAUP. We performed an exhaustive simulation study for approaching the empirical distribution of the statistical test, obtaining its critical values, and computing its power and size. The results indicate that, in general, both the statistical size and power improve with increasing sample size. Finally, for illustrative purposes, an empirical application is made using the Mincer equation in South Africa, where starting from 206 municipalities, the S-maup statistic is used to find the maximum level of spatial aggregation that avoids the negative consequences of the MAUP.

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<![CDATA[Commentary on “The number of undocumented immigrants in the United States: Estimates based on demographic modeling with data from 1990-2016”]]> https://www.researchpad.co/article/5bae98d740307c0c23a1c146

“The number of undocumented immigrants in the United States: Estimates based on demographic modeling with data from 1990–2016” by Fazel-Zarandi, Feinstein and Kaplan presents strikingly higher estimates of the unauthorized immigrant population than established estimates using the residual method. Fazel-Zarandi et. al.’s estimates range from a low or “conservative” number of 16.7 million unauthorized immigrants, to an “average” of 22.1 million, and to a high of 27.5 million. The Pew Hispanic Center estimated the population at 11.3 million in 2016, and the Department of Homeland Security (DHS) estimated it at 12.3 million. The new method shows much more rapid growth in unauthorized immigration during the 1990s and a substantially higher population in 2000 (13.3 million according to their “conservative” model) than Pew (8.6 million) and DHS (8.5 million). In this commentary, we explain that such an estimate for 2000 is implausible, as it suggests that the 2000 Census undercounted the unauthorized immigrant population by at least 42% in the 2000 Census, and it is misaligned with other demographic data. Fazel-Zarandi, Feinstein and Kaplan’s model produces estimates that have a 10 million-person range in 2016, far too wide to be useful for public policy purposes; their estimates are not benchmarked against any external data sources; and their model appears to be driven by assumptions about return migration of unauthorized immigrants during the 1990s. Using emigration rates from the binational Mexican Migration Project survey for the illegal border-crosser portion of the unauthorized population, we generate a 2000 unauthorized population estimate of 8.2 million—slightly below Pew and DHS’s estimates—without changing other assumptions in the model. We conclude that this new model’s estimates are highly sensitive to assumptions about emigration, and moreover, that the knowledge base about emigration in the unauthorized population during the 1990s is not well enough developed to support the model underlying their estimates.

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<![CDATA[Why do ambulance services have different non-transport rates? A national cross sectional study]]> https://www.researchpad.co/article/5bae98e440307c0c23a1c14b

Background

Some patients calling ambulance services (known as Emergency Medical Services internationally) are not transported to hospital. In England, national ambulance quality indicators show considerable variation in non-transport rates between the ten large regional ambulance services. The aim of this study was to explain variation between ambulance services in two types of non-transport: discharge at scene and telephone advice.

Methods

Mixed model logistic regressions using one month of data (November 2014) from the Computer Aided Despatch systems of the ten large regional ambulance services in England.

Results

41% (251 677/615 815) of patients calling ambulance services were not transported to hospital. Most were discharged at scene after attendance by an ambulance (29% n = 182 479) and a small percentage were given telephone advice (7% n = 40 679). Discharge at scene rates varied by patient-level factors e.g. they were higher for elderly patients, where the reason for calling was falls, and for patients attended by paramedics with extended skills. These patient-level factors did not explain variation between ambulance services. After adjustment for patient-level factors, the following ambulance service level factors explained variation in discharge at scene rates: proportion of patients attended by paramedics with extended skills (odds ratio 1.05 (95% CI 1.04, 1.07)), the perception of ambulance service staff that paramedics with extended skills were established and valued within the workforce (odds ratio 1.84 (1.45, 2.33), and the perception of ambulance service staff that senior management viewed non-transport as risky (odds ratio 0.78 (0.63, 0.98)). Variation in telephone advice rates could not be explained.

Conclusions

Variation in discharge at scene rates was explained by differences in workforce configuration and managerial motivation, factors that are largely modifiable by ambulance services.

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<![CDATA[Ethnicity estimation using family naming practices]]> https://www.researchpad.co/article/5c0c047dd5eed0c48481c067

This paper examines the association between given and family names and self-ascribed ethnicity as classified by the 2011 Census of Population for England and Wales. Using Census data in an innovative way under the new Office for National Statistics (ONS) Secure Research Service (SRS; previously the ONS Virtual Microdata Laboratory, VML), we investigate how bearers of a full range of given and family names assigned themselves to 2011 Census categories, using a names classification tool previously described in this journal. Based on these results, we develop a follow-up ethnicity estimation tool and describe how the tool may be used to observe changing relations between naming practices and ethnic identities as a facet of social integration and cosmopolitanism in an increasingly diverse society.

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<![CDATA[Urbanization as a risk factor for aortic stiffness in a cohort in India]]> https://www.researchpad.co/article/5b6d94b0463d7e2f79286cbb

Urbanization is associated with higher prevalence of cardiovascular disease worldwide. Aortic stiffness, as measured by carotid-femoral pulse wave velocity is a validated predictor of cardiovascular disease. Our objective was to determine the association between urbanization and carotid-femoral pulse wave velocity. The analysis included 6166 participants enrolled in an ongoing population-based study (mean age 42 years; 58% female) who live in an 80 × 80 km region of southern India. Multiple measures of urbanization were used and compared: 1) census designations, 2) satellite derived land cover (crops, grass, shrubs or trees as rural; built-up areas as urban), and 3) distance categories based on proximity to an urban center. The association between urbanization and carotid-femoral pulse wave velocity was tested in sex-stratified linear regression models. People residing in urban areas had significantly (p < 0.05) elevated mean carotid-femoral pulse wave velocity compared to non-urban populations after adjustment for other risk factors. There was also an inverse association between distance from the urban center and mean carotid-femoral pulse wave velocity: each 10 km increase in distance was associated with a decrease in mean carotid-femoral pulse wave velocity of 0.07 m/s (95% CI: -0.09, -0.06 m/s). The association was stronger among older participants, among smokers, and among those with other cardiovascular risk factors. Further research is needed to determine which components in the urban environment are associated with higher carotid-femoral pulse wave velocity.

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