Association Between Dynamic Trends of Functional Disability and Poverty Among People Aged 45 and Over

BackgroundThe disability problem has become prominent with the acceleration of the global aging process. Individual disability is associated with economic conditions and contributes to family poverty. As disability will change over a long period of time and may even show distinct dynamic trends, we aimed to focus on activities of daily living (ADL) and classify functional disability trends. Moreover, we aimed to highlight and analyze the association between functional disability trends and economic conditions and explore the influencing factors.Materials and MethodsA total of 11,222 individuals who were 45 years old or older were included in four surveys conducted by the China Health and Retirement Longitudinal Study in 2011, 2013, 2015, and 2018. Samples were analyzed after excluding those with missing key variables. The latent class growth model was used to classify the ADL trends. Two binary logistic regressions were established to observe the association between the ADL trends and follow-up economic conditions or catastrophic health expenditure trends.ResultsADL trends of older adults were classified into improving (25.4%), stabilizing (57.0%), and weakening ADL (17.6%). ADL trend was associated with follow-up poverty (p = 0.002) and catastrophic health expenditure trends (p < 0.001). Compared with the improving ADL trend, the stabilizing ADL may have a negative influence on individuals' economic conditions (OR = 1.175, 95%CI = 1.060–1.303). However, a stabilizing ADL trend was less likely to bring about catastrophic health expenditures (OR = 0.746, 95%CI = 0.678–0.820) compared with an improving ADL trend.ConclusionThe improvement of functional disability would make the medical expense burden heavier but would still be beneficial for the prevention of poverty. A significant association was found between socioeconomic factors and poverty. Preventing the older adults from developing disability and illness, improving the compensation level of medical insurance, and optimizing the long-term care insurance and the primary healthcare system can potentially contribute to the prevention of poverty. Meanwhile, focusing on people who are poor at early stages, women, middle-aged, low-educated, and in rural areas is important.

Association of Night-Time Screen-Viewing with Adolescents’ Diet, Sleep, Weight Status, and Adiposity

Night-time screen-viewing (SV) contributes to inadequate sleep and poor diet, and subsequently excess weight. Adolescents may use many devices at night, which can provide additional night-time SV. Purpose: To identify night-time SV patterns, and describe differences in diet, sleep, weight status, and adiposity between patterns in a cross-sectional and longitudinal manner. Methods: Adolescents (10–16 y) reported devices they viewed at night and completed food recalls. Accelerometry, anthropometrics, and imaging were conducted to measure sleep, weight status, and adiposity, respectively. Latent class analysis was performed to identify night-time SV clusters. Linear regression analysis was used to examine associations between clusters with diet, sleep, weight status, and adiposity. Results: Amongst 273 adolescents (12.5 ± 1.9 y, 54% female, 59% White), four clusters were identified: no SV (36%), primarily cellphone (32%), TV and portable devices (TV+PDs, 17%), and multiple PDs (17%). Most differences in sleep and adiposity were attenuated after adjustment for covariates. The TV+PDs cluster had a higher waist circumference than the no SV cluster in cross-sectional analysis. In longitudinal analysis, the primarily cellphone cluster had less change in waist circumference compared to the no SV cluster. Conclusions: Directing efforts towards reducing night-time SV, especially TV and PDs, may promote healthy development.

What’s important to you? Socioeconomic inequalities in the perceived importance of health compared to other life domains

Background Pressing issues, like financial concerns, may outweigh the importance people attach to health. This study tested whether health, compared to other life domains, was considered more important by people in high versus low socioeconomic positions, with future focus and financial strain as potential explanatory factors. Methods A cross-sectional survey was conducted in 2019 among N=1,330 Dutch adults. Participants rated the importance of two health-related domains (not being ill, living a long life) and seven other life domains (e.g., work, family) on a five-point scale. A latent class analysis grouped participants in classes with similar patterns of importance ratings. Differences in class membership according to socioeconomic position (indicated by income and education) were examined using structural equation modelling, with future focus and financial strain as mediators. Results Three classes were identified, which were defined as: neutralists, who found all domains neutral or unimportant (3.5% of the sample); hedonists, who found most domains important except living a long life, work, and religion (36.2%); and maximalists, who found nearly all domains important, including both health domains (60.3%). Of the neutralists, 38% considered not being ill important, and 30% considered living a long life important. For hedonists, this was 92% and 39%, respectively, and for maximalists this was 99% and 87%, respectively. Compared to belonging to the maximalists class, a low income predicted belonging to the neutralists, and a higher educational level and unemployment predicted belonging to the hedonists. No mediation pathways via future focus or financial strain were found. Conclusions Lower income groups were less likely to consider not being ill important. Those without paid employment and those with a higher educational level were less likely to consider living a long life important. Neither future focus nor financial strain explained these inequalities. Future research should investigate socioeconomic differences in conceptualisations of health, and if inequalities in the perceived importance of health are associated with inequalities in health. To support individuals dealing with challenging circumstances in daily life, health-promoting interventions could align to the life domains perceived important to reach their target group and to prevent widening socioeconomic health inequalities.

Multicentre evaluation of two multiplex PCR platforms for the rapid microbiological investigation of nosocomial pneumonia in UK ICUs: the INHALE WP1 study

BackgroundCulture-based microbiological investigation of hospital-acquired or ventilator-associated pneumonia (HAP or VAP) is insensitive, with aetiological agents often unidentified. This can lead to excess antimicrobial treatment of patients with susceptible pathogens, while those with resistant bacteria are treated inadequately for prolonged periods. Using PCR to seek pathogens and their resistance genes directly from clinical samples may improve therapy and stewardship.MethodsSurplus routine lower respiratory tract samples were collected from intensive care unit patients about to receive new or changed antibiotics for hospital-onset lower respiratory tract infections at 15 UK hospitals. Testing was performed using the BioFire FilmArray Pneumonia Panel (bioMérieux) and Unyvero Pneumonia Panel (Curetis). Concordance analysis compared machine and routine microbiology results, while Bayesian latent class (BLC) analysis estimated the sensitivity and specificity of each test, incorporating information from both PCR panels and routine microbiology.FindingsIn 652 eligible samples; PCR identified pathogens in considerably more samples compared with routine microbiology: 60.4% and 74.2% for Unyvero and FilmArray respectively vs 44.2% by routine microbiology. PCR tests also detected more pathogens per sample than routine microbiology. For common HAP/VAP pathogens, FilmArray had sensitivity of 91.7%–100.0% and specificity of 87.5%–99.5%; Unyvero had sensitivity of 50.0%–100.0%%, and specificity of 89.4%–99.0%. BLC analysis indicated that, compared with PCR, routine microbiology had low sensitivity, ranging from 27.0% to 69.4%.InterpretationConventional and BLC analysis demonstrated that both platforms performed similarly and were considerably more sensitive than routine microbiology, detecting potential pathogens in patient samples reported as culture negative. The increased sensitivity of detection realised by PCR offers potential for improved antimicrobial prescribing.

Association between county-level risk groups and COVID-19 outcomes in the United States: a socioecological study

Background Geographic heterogeneity in COVID-19 outcomes in the United States is well-documented and has been linked with factors at the county level, including sociodemographic and health factors. Whether an integrated measure of place-based risk can classify counties at high risk for COVID-19 outcomes is not known. Methods We conducted an ecological nationwide analysis of 2,701 US counties from 1/21/20 to 2/17/21. County-level characteristics across multiple domains, including demographic, socioeconomic, healthcare access, physical environment, and health factor prevalence were harmonized and linked from a variety of sources. We performed latent class analysis to identify distinct groups of counties based on multiple sociodemographic, health, and environmental domains and examined the association with COVID-19 cases and deaths per 100,000 population. Results Analysis of 25.9 million COVID-19 cases and 481,238 COVID-19 deaths revealed large between-county differences with widespread geographic dispersion, with the gap in cumulative cases and death rates between counties in the 90th and 10th percentile of 6,581 and 291 per 100,000, respectively. Counties from rural areas tended to cluster together compared with urban areas and were further stratified by social determinants of health factors that reflected high and low social vulnerability. Highest rates of cumulative COVID-19 cases (9,557 [2,520]) and deaths (210 [97]) per 100,000 occurred in the cluster comprised of rural disadvantaged counties. Conclusions County-level COVID-19 cases and deaths had substantial disparities with heterogeneous geographic spread across the US. The approach to county-level risk characterization used in this study has the potential to provide novel insights into communicable disease patterns and disparities at the local level.