The Differential Impact of Multimorbidity Patterns and Subsequent Accumulation on Longitudinal Trajectories of Physical Function Decline in a Population-based Cohort of Older People

Background Although both the patterns and accumulation of multimorbidity are important for predicting physical function, studies have not simultaneously examined their impact on functional decline. This study aimed to associate multimorbidity patterns and subsequently developed conditions with longitudinal trajectories of functional decline, and it tested whether the effects of newly developed conditions on functional decline varied across distinct multimorbidity patterns. Methods We included 6,634 participants aged at least 60 years from the China Health and Retirement Longitudinal Survey. Latent class analysis identified multimorbidity patterns from 14 chronic conditions. Mixed negative binomial models estimated the changes in physical function measured across four waves as a function of multimorbidity patterns, subsequently developed conditions and their interactions. Results Five distinct patterns were identified three years before wave 1: stomach/arthritis (15.7%), cardiometabolic (6.7%), arthritis/hypertension (47.9%), hepatorenal/multi-system (18.3%), and lung/asthma (11.4%). The hepatorenal/multi-system and the lung/asthma pattern were associated with worse baseline physical function, and the hypertension/arthritis pattern was associated with greater decline of physical function. The effect of developing new conditions on decline of physical function over time was most evident for individuals from the cardiometabolic pattern. Discussion Considering both the combinations and progressive nature of multimorbidity is important for identifying individuals at greater risk of disability. Future studies are warranted to differentiate the factors responsible for the progression of chronic conditions in distinct multimorbidity patterns and investigate the potential implications for improved prediction of functional decline.

Reduction in Backup Tool Requirements: Risks vs. Benefits, a Probability Analysis

Summary A review of the use of measurement while drilling (MWD), logging while drilling (LWD), and directional drilling (DD) tools mobilized to offshore drilling units in the North Sea highlighted an opportunity to lower operational cost for the operator and reduce capital used for the oilfield services company. An objective was set to develop a risk-based probability model that would assess the positive and negative financial impacts of reducing, or perhaps entirely removing, backup tools in this historically risk-averse basin. The scope of the initial analysis was a drilling campaign on a single rig contracted by the operator (Rig A). This analysis was then extended to review scenarios in which several operations in close proximity would share backup tools. The last 3 years of MWD/LWD/DD tool reliability data from North Sea operations, recorded by the oilfield services company, were used as an input. To assess the probability of failure, a binomial model was developed to create a binomial distribution for each tool to calculate the probability of having zero to X failures for a selected tool or bottomhole assembly (BHA) for a given number of runs. Three binomial models were developed to study the effect of “easy,” “moderate,” and “challenging” drilling environments on tool reliability. A financial risk model was designed to balance the probability-weighted cost of failure for the operator against the lower costs resulting from reduced tool provision by the oilfield services company. To better estimate risks and financial impacts on the project, a sensitivity analysis was performed on the financial risk model using the three binomial models. As a result of the analysis, it was demonstrated that recent improvements in tool reliability support a reduction in the provision of backup MWD/LWD/DD drilling tools for the majority of North Sea drilling scenarios.

Long-term Deadly Impacts of Redlining: An examination of people killed by police in American cities

How does structural racism influence where people are killed during encounters with police? We analyzed geo-located incidents of fatal encounters with police that occurred between 2000-2020 in Census tracts that received a classification by the Home Owners Loan Corporation (HOLC) during the 1930’s. After adjusting for population, 53 of the 100 most deadly Census tracts analyzed in this study were rated as “D” zones, contemporarily referred to as “redlined” areas. 38 are in “C” zones, 8 are “B” zones and only 1 is an “A” zone. Hierarchical Bayesian Negative Binomial models of all tracts estimate incidents of fatal encounters with police are highest in formerly redlined areas, and are 66% more likely than in zones that received the more favorable “A” rating. Contemporary demographic and economic conditions in Census tracts also predict the incidence of fatal encounters with the police, but the effect of historic HOLC classification remains after taking these factors into account. The estimates of fatal encounters converge across zone classifications only in areas with high proportions of Black residents or residents in in poverty (>60% or >30% respectively). These findings augment the literature on the lasting effect of redlined communities in the United States and provides evidence of structural biases in policing rooted in historical segregation policies.

Profile and patterns of dual use of e-cigarettes and combustible cigarettes among Mexican adults

Objective. To describe the profile and patterns of dual uses (n=954) and exclusive cigarette users (n=2 070) and determine the correlates of more frequent e-cigarette use among dual users and their reasons for e-cigarette use. Materials and methods. An online survey of Mexican adult smokers. Logistic models regressed dual-use (exclusive smoking vs. dual user) on sociodemographic, smoking varia­bles and substance use behaviors. We conducted censorial binomial models to estimate the correlates of frequency of e-cigarette use among dual users. Results. Dual users were younger had higher education (AOR=2.22) and higher levels of smoking dependence (AOR=1.31), preferred cigarettes with flavor capsules (AOR=1.58) and had recently attempted to quit smoking (AOR=1.38). Marijuana use and being daily smokers were correlates of higher frequency of use among dual users. Conclusion. Dual users had a higher risk profile than exclusive smokers, which was even more prominent in dual-users who used e-cigarettes frequently.

Long-Term Exposure to PM2.5, Facemask Mandates, Stay Home Orders and COVID-19 Incidence in the United States

Long-term PM2.5 exposure might predispose populations to SARS-CoV-2 infection and intervention policies might interrupt SARS-CoV-2 transmission and reduce the risk of COVID-19. We conducted an ecologic study across the United States, using county-level COVID-19 incidence up to 12 September 2020, to represent the first two surges in the U.S., annual average of PM2.5 between 2000 and 2016 and state-level facemask mandates and stay home orders. We fit negative binomial models to assess COVID-19 incidence in association with PM2.5 and policies. Stratified analyses by facemask policy and stay home policy were also performed. Each 1-µg/m3 increase in annual average concentration of PM2.5 exposure was associated with 7.56% (95% CI: 3.76%, 11.49%) increase in COVID-19 risk. Facemask mandates and stay home policies were inversely associated with COVID-19 with adjusted RRs of 0.8466 (95% CI: 0.7598, 0.9432) and 0.9193 (95% CI: 0.8021, 1.0537), respectively. The associations between PM2.5 and COVID-19 were consistent among counties with or without preventive policies. Our study added evidence that long-term PM2.5 exposure increased the risk of COVID-19 during each surge and cumulatively as of 12 September 2020, in the United States. Although both state-level implementation of facemask mandates and stay home orders were effective in preventing the spread of COVID-19, no clear effect modification was observed regarding long-term exposure to PM2.5 on the risk of COVID-19.

Features Associated With Contagiousness Among COVID-19 Cases in Shanghai, China

Background: Little is known about the characteristics of those who transmit SARS-CoV-2 infection vs those who do not, but this information could inform disease control policies. This study described the features of clusters in the first wave of COVID-19 in Shanghai and compared contagiousness by clinical and health care risk factors.Methods: In this retrospective cohort study of cases in Shanghai in January and February 2020, cases with successive generations were considered to be “contagious.” Characteristics of contagious and non-contagious cases were compared in log-binomial models that also adjusted for age and sex. Results: Between January 21 and February 17, 2020, 333 cases of COVID-19 were reported in Shanghai across 28 known infection chains. Contagiousness was higher among cases with a sore throat (risk ratio [RR]: 3.41, 95% CI: 1.59, 7.35, P=0.0051), and those with heart disease (RR: 2.06, 95% CI: 0.72, 5.90). Delays in diagnosis were also associated with higher risk of contagiousness. Having ≥2 medical visits before diagnosis was associated with 4.46 times higher risk of contagiousness (95% CI: 2.03, 9.83, P=0.0002), and there was a non-significant increase in risk with increasing numbers of days between disease onset and isolation (for each day, RR: 1.08, 95% CI: 1.01, 1.16, P=0.1734).Conclusions: Individuals with mild COVID-19 symptoms in the upper respiratory tract may still be contagious, and such individuals should be prioritized for early diagnosis and isolation to limit further chains of transmission.

Density or Connectivity: What Are the Main Causes of the Spatial Proliferation of COVID-19 in Korea?

COVID-19 has sparked a debate on the vulnerability of densely populated cities. Some studies argue that high-density urban centers are more vulnerable to infectious diseases due to a higher chance of infection in crowded urban environments. Other studies, however, argue that connectivity rather than population density plays a more significant role in the spread of COVID-19. While several studies have examined the role of urban density and connectivity in Europe and the U.S., few studies have been conducted in Asian countries. This study aims to investigate the role of urban spatial structure on COVID-19 by comparing different measures of urban density and connectivity during the first eight months of the outbreak in Korea. Two measures of density were derived from the Korean census, and four measures of connectivity were computed using social network analysis of the Origin-Destination data from the 2020 Korea Transport Database. We fitted both OLS and negative binomial models to the number of confirmed COVID-19 patients and its infection rates at the county level, collected individually from regional government websites in Korea. Results show that both density and connectivity play an important role in the proliferation of the COVID-19 outbreak in Korea. However, we found that the connectivity measure, particularly a measure of network centrality, was a better indicator of COVID-19 proliferation than the density measures. Our findings imply that policies that take into account different types of connectivity between cities might be necessary to contain the outbreak in the early phase.

A Case of Negative Externality: Use of Pesticide and Its Impact on Health Among Farmers in Salumbhu Village, Nepal

The widespread and uncontrolled use of harmful pesticide to facilitate mass-level agricultural production creates negative externalities ranging from environmental degradation to having adverse health implications to the users. This article explores a primary village-level data of farmers in the Salumbhu village of Nepal to investigate the health effects associated with pesticide use. A total of six health symptoms grouped into two categories such as serious health-related issues and irritants are analysed separately using ordered logit model. For the robustness of the results, Poisson and negative binomial models are also used. We found that farmers as compared to the non-farmers are significantly more prone to facing serious health issues. Furthermore, as farmers are the applicators of pesticide, the odds of them facing all the health symptoms increases significantly. The results are uniform across different measures of health and over multiple models, which advocates the need of appropiate regulations in the usage of pesticide in Nepal.

Neighborhood Disadvantage Measures and COVID-19 Cases in Boston, 2020

Objective Understanding the pattern of population risk for coronavirus disease 2019 (COVID-19) is critically important for health systems and policy makers. The objective of this study was to describe the association between neighborhood factors and number of COVID-19 cases. We hypothesized an association between disadvantaged neighborhoods and clusters of COVID-19 cases. Methods We analyzed data on patients presenting to a large health care system in Boston during February 5–May 4, 2020. We used a bivariate local join-count procedure to determine colocation between census tracts with high rates of neighborhood demographic characteristics (eg, Hispanic race/ethnicity) and measures of disadvantage (eg, health insurance status) and COVID-19 cases. We used negative binomial models to assess independent associations between neighborhood factors and the incidence of COVID-19. Results A total of 9898 COVID-19 patients were in the cohort. The overall crude incidence in the study area was 32 cases per 10 000 population, and the adjusted incidence per census tract ranged from 2 to 405 per 10 000 population. We found significant colocation of several neighborhood factors and the top quintile of cases: percentage of population that was Hispanic, non-Hispanic Black, without health insurance, receiving Supplemental Nutrition Assistance Program benefits, and living in poverty. Factors associated with increased incidence of COVID-19 included percentage of population that is Hispanic (incidence rate ratio [IRR] = 1.25; 95% CI, 1.23-1.28) and percentage of households living in poverty (IRR = 1.25; 95% CI, 1.19-1.32). Conclusions We found a significant association between neighborhoods with high rates of disadvantage and COVID-19. Policy makers need to consider these health inequities when responding to the pandemic and planning for subsequent health needs.