scholarly journals Do patients who die from an alcohol-related condition ‘drift’ into areas of greater deprivation? Alcohol-related mortality and health selection theory in Scotland

2017 ◽  
Vol 72 (2) ◽  
pp. 109-112 ◽  
Author(s):  
Andrew Pulford ◽  
Ruth Gordon ◽  
Lesley Graham ◽  
James Lewsey ◽  
Gerry McCartney ◽  
...  
Keyword(s):  
Hospital Admissions ◽  
Area Deprivation ◽  
Downward Mobility ◽  
Health Selection ◽  
Related Condition ◽  
Logistic Regression Models ◽  
Initial Area ◽  
The Difference ◽  
Index Of Multiple Deprivation ◽  
Related Mortality

BackgroundHealth selection has been proposed to explain the patterning of alcohol-related mortality by area deprivation. This study investigated whether persons who die from alcohol-related conditions are more likely to experience social drift than those who die from other causes.MethodsDeaths recorded in Scotland (2013, >21 years) were coded as ‘alcohol-related’ or ‘other’ and by deprivation decile of residence at death. Acute hospital admissions data from 1996 to 2012 were used to provide premortality deprivation data. χ² tests estimated the difference between observed and expected alcohol-related deaths by first Scottish Index of Multiple Deprivation (SIMD) decile and type of death. Logistic regression models were fitted using type of death as the outcome of interest and change in SIMD decile as the exposure of interest.ResultsOf 47 012 deaths, 1458 were alcohol-related. Upward and downward mobility was observed for both types of death. An estimated 31 more deaths than expected were classified ‘alcohol-related’ among cases whose deprivation score decreased, while 204 more deaths than expected were classified ‘alcohol-related’ among cases whose initial deprivation ranking was in the four most deprived deciles. Becoming more deprived and first deprivation category were both associated with increased odds of type of death being alcohol-related after adjusting for confounders.ConclusionThis study suggests that health selection appears to contribute less to the deprivation gradient in alcohol-related mortality in Scotland than an individual’s initial area deprivation category.

scholarly journals Sick leave absence and the relationship between intra-generational social mobility and mortality: health selection in Sweden

2020 ◽  
Vol 20 (1) ◽  
Author(s):  
Sunnee Billingsley
Keyword(s):  
Sickness Absence ◽  
Social Mobility ◽  
Event History Analysis ◽  
Upward Mobility ◽  
Poor Health ◽  
Downward Mobility ◽  
Health Selection ◽  
Average Marginal Effects ◽  
The Relationship ◽  
Related Mortality

Abstract Background Poor health could influence how individuals are sorted into occupational classes. Health selection has therefore been considered a potential modifier to the mortality class gradient through differences in social mobility. Direct health selection in particular may operate in the short-term as poor health may lead to reduced work hours or achievement, downward social mobility, unemployment or restricted upward mobility, and death. In this study, the relationship between social mobility and mortality (all-cause, cancer-related, cardiovascular disease-related (CVD), and suicide) is explored when the relationship is adjusted for poor health. Methods Using Swedish register data (1996–2012) and discrete time event-history analysis, odds ratios and average marginal effects (AME) of social mobility and unemployment on mortality are observed before and after accounting for sickness absence in the previous year. Results After adjusting for sickness absence, all-cause mortality remained lower for men after upward mobility in comparison to not being mobile (OR 0.82, AME -0.0003, CI − 0.0003 to − 0.0002). Similarly, upward mobility continued to be associated with lower cancer-related mortality for men (OR 0.85, AME -0.00008, CI − 0.00002 to − 0.0002), CVD-related mortality for men (OR 0.76, AME -0.0001, CI − 0.00006 to − 0.0002) and suicide for women (OR 0.67, AME -0.00002, CI − 0.000002 to − 0.00003). The relationship between unemployment and mortality also persisted across most causes of death for both men and women after controlling for previous sickness absence. In contrast, adjusting for sickness absence renders the relationship between downward mobility and cancer-related mortality not statistically different from the non-mobile. Conclusions Health selection plays a role in how downward mobility is linked to cancer related deaths. It additionally accounts for a portion of why upward mobility is associated with lower mortality. That health selection plays a role in how social mobility and mortality are related may be unexpected in a context with strong job protection. Job protection does not, however, equalize opportunities for upward mobility, which may be limited for those who have been ill. Because intra-generational upward mobility and mortality remained related after adjusting for sickness absence, other important mechanisms such as indirect selection or social causation should be explored.

scholarly journals Epidemiological features of influenza in Canadian adult intensive care unit patients

2015 ◽  
Vol 144 (4) ◽  
pp. 741-750 ◽  
Author(s):  
G. TAYLOR ◽  
K. ABDESSELAM ◽  
L. PELUDE ◽  
R. FERNANDES ◽  
R. MITCHELL ◽  
...  
Keyword(s):  
Intensive Care ◽  
Regression Models ◽  
Influenza A ◽  
Hospital Admissions ◽  
Admission Rate ◽  
Influenza B ◽  
Icu Patients ◽  
Adult Intensive Care Unit ◽  
Logistic Regression Models ◽  
Related Mortality

SUMMARYTo identify predictive factors and mortality of patients with influenza admitted to intensive care units (ICU) we carried out a prospective cohort study of patients hospitalized with laboratory-confirmed influenza in adult ICUs in a network of Canadian hospitals between 2006 and 2012. There were 626 influenza-positive patients admitted to ICUs over the six influenza seasons, representing 17·9% of hospitalized influenza patients, 3·1/10 000 hospital admissions. Variability occurred in admission rate and proportion of hospital influenza patients who were admitted to ICUs (proportion range by year: 11·7–29·4%; 21·3% in the 2009–2010 pandemic). In logistic regression models ICU patients were younger during the pandemic and post-pandemic period, and more likely to be obese than hospital non-ICU patients. Influenza B accounted for 14·2% of all ICU cases and had a similar ICU admission rate as influenza A. Influenza-related mortality was 17·8% in ICU patients compared to 2·0% in non-ICU patients.

Predictors of Success Using a mHealth Intervention in Adults with Prediabetes: Results of a Pilot Study (Preprint)

2019 ◽  
Author(s):  
Nestoras Mathioudakis ◽  
Estelle Everett ◽  
Noora Al-Hajri ◽  
Mohammed Abusamaan ◽  
Clare Lee ◽  
...  
Keyword(s):  
Pilot Study ◽  
Intervention Program ◽  
Area Deprivation ◽  
Inverse Association ◽  
Small Pilot Study ◽  
Mhealth Intervention ◽  
Increased Risk ◽  
Metabolic Equivalent Of Task ◽  
Mobile Intervention ◽  
The Difference

BACKGROUND About one-third of American adults have prediabetes and are at increased risk of type 2 diabetes. Mobile health (mHealth) technologies provide a scalable approach to diabetes prevention by encouraging physical activity (PA), weight loss, and adherence to a healthy diet in large numbers of patients. OBJECTIVE To identify factors associated with improvements in PA and glycated hemoglobin (A1c) measures among prediabetic adults who received a mobile intervention program (smartphone app in combination with a digital body weight scale) in a previously completed pilot study. METHODS We conducted a post hoc analysis of a 3-month prospective, single-arm, observational study using the Sweetch™ mHealth intervention among adults with prediabetes. Change in A1C was calculated as the difference between the 3-month and baseline A1C measurements and was categorized as decrease vs. no decrease. PA was evaluated using the total minutes and metabolic equivalent of task (MET)-hours per week. Change in MET-hours/week was categorized as increase vs. no increase. Age, sex, race, education, employment status, area deprivation, smartphone usage attitudes, and PA stage of change were compared between groups by outcomes of change in A1C and change in MET-hour/week. RESULTS A total of 37 adults received the final Sweetch mobile intervention and were included in the analysis. 62% were female and 81% were white, with average age of 57 years. The median [IQR] baseline A1C was 6.0% [5.8, 6.2]. A1C measure at 3-month was decreased in 24 (65%) participants when compared to baseline A1C. There was an inverse association between average MET-hours per week and change in A1C. Among participants whose A1C decreased vs. did not decrease, the MET-hours per week in last 2 weeks of study was 18.7 (8.4) and 15.0 (7.1), respectively (P=0.19), and the change in MET-hours per week was 2.1 (7.1) and 4.1(6.1), respectively (P=0.41). There were otherwise no statistically significant differences in participant factors by A1C and PA outcomes. CONCLUSIONS In this small pilot study, Sweetch mHealth intervention achieved comparable A1C response prediabetic adults with different individual, sociodemographic and anthropometric characteristics. CLINICALTRIAL ClincialTrials.gov NCT02896010; https://clinicaltrials.gov/ct2/show/NCT02896010 (Archived by WebCite at http://www.webcitation.org/6xJYxrgse)

scholarly journals Is area deprivation associated with greater impacts of COVID-19 in care homes across England? A preliminary analysis of COVID-19 outbreaks and deaths

2021 ◽  
pp. jech-2020-215039 ◽  
Author(s):  
Anders Malthe Bach-Mortensen ◽  
Michelle Degli Esposti
Keyword(s):  
Negative Binomial ◽  
Care Home ◽  
Negative Binomial Regression ◽  
Population Characteristics ◽  
Care Homes ◽  
Future Research ◽  
Area Deprivation ◽  
Binomial Regression ◽  
Index Of Multiple Deprivation

IntroductionThe COVID-19 pandemic has disproportionately impacted care homes and vulnerable populations, exacerbating existing health inequalities. However, the role of area deprivation in shaping the impacts of COVID-19 in care homes is poorly understood. We examine whether area deprivation is linked to higher rates of COVID-19 outbreaks and deaths among care home residents across upper tier local authorities in England (n=149).MethodsWe constructed a novel dataset from publicly available data. Using negative binomial regression models, we analysed the associations between area deprivation (Income Deprivation Affecting Older People Index (IDAOPI) and Index of Multiple Deprivation (IMD) extent) as the exposure and COVID-19 outbreaks, COVID-19-related deaths and all-cause deaths among care home residents as three separate outcomes—adjusting for population characteristics (size, age composition, ethnicity).ResultsCOVID-19 outbreaks in care homes did not vary by area deprivation. However, COVID-19-related deaths were more common in the most deprived quartiles of IDAOPI (incidence rate ratio (IRR): 1.23, 95% CI 1.04 to 1.47) and IMD extent (IRR: 1.16, 95% CI 1.00 to 1.34), compared with the least deprived quartiles.DiscussionThese findings suggest that area deprivation is a key risk factor in COVID-19 deaths among care home residents. Future research should look to replicate these results when more complete data become available.

Reliability of claims-based measures used to assess cancer care quality.

2021 ◽  
Vol 39 (28_suppl) ◽  
pp. 42-42
Author(s):  
Benjamin Urick ◽  
Sabree Burbage ◽  
Christopher Baggett ◽  
Jennifer Elston Lafata ◽  
Hanna Kelly Sanoff ◽  
...  
Keyword(s):  
Risk Adjustment ◽  
Cancer Care ◽  
Hospital Admissions ◽  
Practice Variation ◽  
Care Quality ◽  
Physician Practices ◽  
Provider Performance ◽  
Logistic Regression Models ◽  
Alternative Measures ◽  
Hospice Use

42 Background: As value-based payment models for cancer care expand, the need for measures which reliably assess the quality of care provided increases. This is especially true for models like the Oncology Care Model (OCM) that rely on quality rankings to determine potential shared savings. Under models like these, unreliable measures may result in arbitrary application of value-based payments. The goal of this project is to evaluate the extent to which measures used within the OCM are reliable indicators of provider performance. Methods: Data for this project came from North Carolina Medicare claims from 2015-2017. Episodes were attributed to physician practices at the tax identification number (TIN) level, lasted 6 months, and were divided into two performance years beginning 1/1/2016 and 7/1/2016. TINs with fewer than 20 attributed patients were excluded. Three claims-based OCM measures were used in this evaluation: 1) proportion of episodes with all-cause hospital admissions; 2) proportion of episodes with all-cause emergency department (ED) visits or observation stays; and 3) proportion of patients that died who were admitted to hospice for 3 days or more. Risk adjustment followed the method described by measure specifications from the OCM. Reliability was calculated as the ratio of between practice variation (e.g. signal) to the sum of between practice variation and within practice variation (e.g. noise). Variance estimates were derived from hierarchical logistic regression models used for risk adjustment. Results: For the hospitalization and ED visit measures, episode counts for years 1 and 2 were 30,746 and 28,430 and TIN counts were 86 and 84, respectively. Hospice use measures had fewer episodes (2,677 and 2,428) and TINs (36 and 33). Across all measures, median reliability scores failed to achieve the recommended 0.7 threshold and only hospice had a median reliability score above 0.5 (Table). Conclusions: These findings suggest claims-based measures included in the OCM may produce imprecise estimates of provider performance and are vulnerable to random variation. Consideration should be given to developing alternative measures which may be more reliable estimates of provider performance and to increasing minimum denominator requirements for existing measures.[Table: see text]

Impact of cancer on the risk of unplanned 30-day readmissions.

2021 ◽  
Vol 39 (15_suppl) ◽  
pp. 6581-6581
Author(s):  
Alexander Qian ◽  
Edmund Qiao ◽  
Vinit Nalawade ◽  
Nikhil V. Kotha ◽  
Rohith S. Voora ◽  
...  
Keyword(s):  
Cancer Patients ◽  
Cancer Diagnosis ◽  
Hospital Admissions ◽  
Reference Group ◽  
Cancer Site ◽  
Cancer Type ◽  
Logistic Regression Models ◽  
Increased Risk ◽  
Initial Hospitalization ◽  
The Impact

6581 Background: Hospital readmission are associated with unfavorable patient outcomes and increased costs to the healthcare system. Devising interventions to reduce risks of readmission requires understanding patients at highest risk. Cancer patients represent a unique population with distinct risk factors. The purpose of this study was to define the impact of a cancer diagnosis on the risks of unplanned 30-day readmissions. Methods: We identified non-procedural hospital admissions between January through November 2017 from the National Readmission Database (NRD). We included patients with and without a cancer diagnosis who were admitted for non-procedural causes. We evaluated the impact of cancer on the risk of 30-day unplanned readmissions using multivariable mixed-effects logistic regression models. Results: Out of 18,996,625 weighted admissions, 1,685,099 (8.9%) had record of a cancer diagnosis. A cancer diagnosis was associated with an increased risk of readmission compared to non-cancer patients (23.5% vs. 13.6%, p < 0.001). However, among readmissions, cancer patients were less likely to have a preventable readmission (6.5% vs. 12.1%, p < 0.001). When considering the 10 most common causes of initial hospitalization, cancer was associated with an increased risk of readmission for each of these 10 causes (OR range 1.1-2.7, all p < 0.05) compared to non-cancer patients admitted for the same causes. Compared to patients aged 45-64, a younger age was associated with increased risk for cancer patients (OR 1.29, 95%CI [1.24-1.34]) but decreased risk for non-cancer patients (OR 0.65, 95%CI [0.64-0.66]). Among cancer patients, cancer site was the most robust individual predictor for readmission with liver (OR 1.47, 95%CI [1.39-1.55]), pancreas (OR 1.36, 95%CI [1.29-1.44]), and non-Hodgkin’s lymphoma (OR 1.35, 95%CI [1.29-1.42]) having the highest risk compared to the reference group of prostate cancer patients. Conclusions: Cancer patients have a higher risk of 30-day readmission, with increased risks among younger cancer patients, and with individual risks varying by cancer type. Future risk stratification approaches should consider cancer patients as an independent group with unique risks of readmission.

scholarly journals Asymmetric Fixed Effects Models for Panel Data

2018 ◽  
Author(s):  
Paul D Allison
Keyword(s):  
Logistic Regression ◽  
Panel Data ◽  
Opposite Direction ◽  
Difference Method ◽  
Fixed Effects ◽  
Regression Models ◽  
Social Phenomena ◽  
Logistic Regression Models ◽  
Fixed Effects Models ◽  
The Difference

Standard fixed effects methods presume that effects of variables are symmetric: the effect of increasing a variable is the same as the effect of decreasing that variable but in the opposite direction. This is implausible for many social phenomena. York and Light (2017) showed how to estimate asymmetric models by estimating first-difference regressions in which the difference scores for the predictors are decomposed into positive and negative changes. In this paper, I show that there are several aspects of their method that need improvement. I also develop a data generating model that justifies the first-difference method but can be applied in more general settings. In particular, it can be used to construct asymmetric logistic regression models.

scholarly journals Adherence to healthy lifestyles in the Programa Academia da Saúde

2020 ◽  
Vol 25 ◽  
pp. 1-9
Author(s):  
Raquel de Deus Mendonça ◽  
Mariana Souza Lopes ◽  
Maria Cecília Ramos de Carvalho ◽  
Patrícia Pinheiro de Freitas ◽  
Aline Cristine Souza Lopes
Keyword(s):  
Healthy Lifestyle ◽  
Healthy Lifestyles ◽  
Logistic Regression Models ◽  
Drinking Habits ◽  
Randomized Controlled ◽  
Five Factors ◽  
The Difference ◽  
Belo Horizonte ◽  
Fourth Quartile ◽  
Community Trial

This study aims to measure healthy lifestyles according to the time of participation in the Programa Academia da Saúde (PAS). We used baseline data from a randomized controlled community trial with a representative sample of PAS users from Belo Horizonte, Brazil (n = 3,414). The data on healthy lifestyles collected were: daily fruit and vegetables intake (≥5 servings); physical activity engagement (≥180min/week); body mass index (18.5kg/m² ≥ BMI ≤ 24.9kg/m²), smoking and drinking habits. The time of participation in the PAS was calculated by the difference between the date of registration in the program and the date of the data collection. Logistic regression models were used to evaluate associations between healthy lifestyles and time of participation in the PAS. Almost half of the participants (43.3%) had three healthy lifestyle factors. The prevalence of having all five factors varied according to the time of participation in the service; the lowest rates were in the first quartile (4.9%) and the highest rates in the fourth quartile (8.1%). Those who have attended the service for the longest time (fourth quartile) were more likely to have a healthy BMI (OR = 1.43; 95%CI: 1.14-1.80; p = 0.002) and to avoid smoking (OR = 1.62; 95%CI: 1.06-4.49; p = 0.01), compared to those who have attended the program for less time (first quartile). The prevalence of healthy lifestyles in PAS users was low. However, a longer permanence in the program seems to favor positive changes on BMI and on smoking habits.

scholarly journals Biphasic pattern in the effect of severe measles infection; the difference between additive and multiplicative scale

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Nhat Thanh Hoang Le ◽  
Nhan Thi Ho ◽  
Bryan Grenfell ◽  
Stephen Baker ◽  
Ronald B. Geskus
Keyword(s):  
Infectious Diseases ◽  
Hospital Admission ◽  
Measles Virus ◽  
Hospital Admissions ◽  
Admission Rate ◽  
Second Phase ◽  
Before And After ◽  
The Difference ◽  
Two Phases ◽  
Additive Scale

Abstract Background Infection with measles virus (MeV) causes immunosuppression and increased susceptibility to other infectious diseases. Only few studies reported a duration of immunosuppression, with varying results. We investigated the effect of immunosuppression on the incidence of hospital admissions for infectious diseases in Vietnamese children. Methods We used retrospective data (2005 to 2015; N = 4419) from the two pediatric hospitals in Ho Chi Minh City, Vietnam. We compared the age-specific incidence of hospital admission for infectious diseases before and after hospitalization for measles. We fitted a Poisson regression model that included gender, current age, and time since measles to obtain a multiplicative effect measure. Estimates were transformed to the additive scale. Results We observed two phases in the incidence of hospital admission after measles. The first phase started with a fourfold increased rate of admissions during the first month after measles, dropping to a level quite comparable to children of the same age before measles. In the second phase, lasting until at least 6 years after measles, the admission rate decreased further, with values up to 20 times lower than in children of the same age before measles. However, on the additive scale the effect size in the second phase was much smaller than in the first phase. Conclusion The first phase highlights the public health benefits of measles vaccination by preventing measles and immune amnesia. The beneficial second phase is interesting, but its strength strongly depends on the scale. It suggests a complicated interaction between MeV infection and the host immunity.

scholarly journals Differences in Sickness Allowance Receipt between Swedish Speakers and Finnish Speakers in Finland

2017 ◽  
Vol 52 ◽  
pp. 43-58 ◽  
Author(s):  
Kaarina S. Reini ◽  
Jan Saarela
Keyword(s):  
Regression Models ◽  
Health Condition ◽  
Logistic Regression Models ◽  
Individual Level ◽  
Objective Health ◽  
Language Groups ◽  
Health Measure ◽  
All Cause Mortality ◽  
The Difference ◽  
The Individual

Previous research has documented lower disability retirement and mortality rates of Swedish speakers as compared with Finnish speakers in Finland. This paper is the first to compare the two language groups with regard to the receipt of sickness allowance, which is an objective health measure that reflects a less severe poor health condition. Register-based data covering the years 1988-2011 are used. We estimate logistic regression models with generalized estimating equations to account for repeated observations at the individual level. We find that Swedish-speaking men have approximately 30 percent lower odds of receiving sickness allowance than Finnish-speaking men, whereas the difference in women is about 15 percent. In correspondence with previous research on all-cause mortality at working ages, we find no language-group difference in sickness allowance receipt in the socially most successful subgroup of the population.

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