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Background Long-term symptoms following severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection are a major concern, yet their prevalence is poorly understood. Methods We conducted a prospective cohort study comparing adults with SARS-CoV-2 infection (coronavirus disease-positive [COVID+]) with adults who tested negative (COVID−), enrolled within 28 days of a Food and Drug Administration (FDA)-approved SARS-CoV-2 test result for active symptoms. Sociodemographic characteristics, symptoms of SARS-CoV-2 infection (assessed with the Centers for Disease Control and Prevention [CDC] Person Under Investigation Symptom List), and symptoms of post-infectious syndromes (ie, fatigue, sleep quality, muscle/joint pains, unrefreshing sleep, and dizziness/fainting, assessed with CDC Short Symptom Screener for myalgic encephalomyelitis/chronic fatigue syndrome) were assessed at baseline and 3 months via electronic surveys sent via text or email. Results Among the first 1000 participants, 722 were COVID+ and 278 were COVID−. Mean age was 41.5 (SD 15.2); 66.3% were female, 13.4% were Black, and 15.3% were Hispanic. At baseline, SARS-CoV-2 symptoms were more common in the COVID+ group than the COVID− group. At 3 months, SARS-CoV-2 symptoms declined in both groups, although were more prevalent in the COVID+ group: upper respiratory symptoms/head/eyes/ears/nose/throat (HEENT; 37.3% vs 20.9%), constitutional (28.8% vs 19.4%), musculoskeletal (19.5% vs 14.7%), pulmonary (17.6% vs 12.2%), cardiovascular (10.0% vs 7.2%), and gastrointestinal (8.7% vs 8.3%); only 50.2% and 73.3% reported no symptoms at all. Symptoms of post-infectious syndromes were similarly prevalent among the COVID+ and COVID− groups at 3 months. Conclusions Approximately half of COVID+ participants, as compared with one-quarter of COVID− participants, had at least 1 SARS-CoV-2 symptom at 3 months, highlighting the need for future work to distinguish long COVID. Clinical Trials Registration NCT04610515.

The net effect of the pandemic mitigation strategies on childhood mortality is not known. During the first year of the COVID-19 pandemic, mitigation policies and behaviors were widespread, and although vaccinations and effective treatments were not yet widely available, the risk of death from SARS-CoV-2 infection was low. In that first year, there was a 7% decrease in medical (“natural causes”) mortality among children ages 0-9 during the first pandemic year (5% among infants 1 year was absent. However, smaller increases in external (“non-natural causes”) mortality were also observed during the study period, which decreased the overall number of pediatric deaths averted during both years and the pandemic period. In total, 1,468 fewer all-cause pediatric deaths than expected occurred in the United States during the first 24 months of the COVID-19 pandemic.

IntroductionThe US continued to record all-cause excess mortality after the rollout of vaccines. We sought to quantify excess mortality by state and compare these rates to primary series vaccination completion levels.MethodsObservational cohort, US and state-level data. Expected monthly deaths were modeled using pre-pandemic US and state-level data (2015-2020). Mortality data was accessed from CDC public reporting.ResultsWe find that in a two-year period since the rollout of vaccines, the US recorded >874,000 excess deaths. Vaccination rates and excess mortality were most strongly correlated in first two periods before the Omicron variant.ConclusionThe association between vaccination and lower excess mortality rates was strongest in 2021 and early 2022, prior to high population rates of infection-acquired immunity. The findings underscore the benefits of the rapid vaccination rollout campaign and the continued need to boost at-risk populations.

IntroductionThe US has continued to see excess mortality through the Delta and Omicron periods. We sought to quantify excess mortality on a state level and calculate potential deaths averted if all states matched the excess mortality rates of those with the 10 lowest excess mortality rates.MethodsObservational cohort, US and state-level data. Expected monthly deaths were modeled using pre-pandemic US and state-level data (2015-2020). Mortality data was accessed from CDC public reporting.ResultsWe find that during the Delta and Omicron waves, the US recorded over 596,000 excess deaths. 60% of the nation’s total excess mortality during these periods could have been averted if all states had excess mortality rates equal to those with the 10 lowest excess mortality rates.ConclusionWith large differences in excess mortality across US states in this 15-month study period, we note that a significant portion of deaths could have been averted with higher vaccination rates, improved mitigation policies, and adherence to other behaviors.

IntroductionThere were concerns that suicide deaths might increase due to Covid-19 pandemic-related stressors. Previous research demonstrated that suicide deaths actually decreased in 2020 in the US. An update covering 2021-2022 with regional data is warranted.MethodsObservational cohort, US and regional data. Expected monthly deaths were modeled using pre-pandemic US and regional data (2015-2020). Mortality data was accessed from CDC public reporting.ResultsWe find that suicide deaths in the United States were below expected levels throughout the pandemic period (March 1, 2020-June 30,2022) with >4,100 fewer suicide deaths than would have been expected to occur during the study period. Stratifying suicide mortality by US Census Bureau region yielded statistically significant decreases from expected suicide deaths in all regions except the Midwest, (which recorded no significant change in suicide deaths during the overall pandemic period).ConclusionSuicide mortality is down in the US since the pandemic began, through June 30, 2022. Possible explanations include an early “coming together” effect; Later, increased access to mental health resources and a greater focus on mental health in the media may have reduced stigma and barriers in seeking necessary psychiatric care.

The COVID-19 pandemic has disproportionately affected Hispanic or Latino, non-Hispanic Black (Black), non-Hispanic American Indian or Alaska Native (AI/AN), and non-Hispanic Native Hawaiian or Other Pacific Islander (NH/PI) populations in the United States. These populations have experienced higher rates of infection and mortality compared with the non-Hispanic White (White) population (1-5) and greater excess mortality (i.e., the percentage increase in the number of persons who have died relative to the expected number of deaths for a given place and time) (6). A limitation of existing research on excess mortality among racial/ethnic minority groups has been the lack of adjustment for age and population change over time. This study assessed excess mortality incidence rates (IRs) (e.g., the number of excess deaths per 100,000 person-years) in the United States during December 29, 2019-January 2, 2021, by race/ethnicity and age group using data from the National Vital Statistics System. Among all assessed racial/ethnic groups (non-Hispanic Asian [Asian], AI/AN, Black, Hispanic, NH/PI, and White populations), excess mortality IRs were higher among persons aged ≥65 years (426.4 to 1033.5 excess deaths per 100,000 person-years) than among those aged 25-64 years (30.2 to 221.1) and those aged25 years (-2.9 to 14.1). Among persons aged65 years, Black and AI/AN populations had the highest excess mortality IRs. Among adults aged ≥65 years, Black and Hispanic persons experienced the highest excess mortality IRs of1,000 excess deaths per 100,000 person-years. These findings could help guide more tailored public health messaging and mitigation efforts to reduce disparities in mortality associated with the COVID-19 pandemic in the United States,* by identifying the racial/ethnic groups and age groups with the highest excess mortality rates.

Objective Although timely administration of antibiotics has an established benefit in serious bacterial infection, the majority of studies evaluating antibiotic delay focus only on the first dose. Recent evidence suggests that delays in redosing may also be associated with worse clinical outcome. In light of the increasing burden of boarding in Emergency Departments (ED) and subsequent need to redose antibiotic in the ED, we examined the association between delayed second antibiotic dose administration and mortality among patients admitted from the ED with a broad array of infections and characterized risk factors associated with delayed second dose administration. Methods We performed a retrospective cohort study of patients admitted through five EDs in a single healthcare system from 1/2018 through 12/2018. Our study included all patients, aged 18 years or older, who received two intravenous antibiotic doses within a 30-h period, with the first dose administered in the ED. Patients with end stage renal disease, cirrhosis and extremes of weight were excluded due to a lack of consensus on antibiotic dosing intervals for these populations. Delay was defined as administration of the second dose at a time-point greater than 125% of the recommended interval. The primary outcome was in-hospital mortality. Results A total of 5605 second antibiotic doses, occurring during 4904 visits, met study criteria. Delayed administration of the second dose occurred during 21.1% of visits. After adjustment for patient characteristics, delayed second dose administration was associated with increased odds of in-hospital mortality (OR 1.50, 95%CI 1.05–2.13). Regarding risk factors for delay, every one-hour increase in allowable compliance time was associated with a 18% decrease in odds of delay (OR 0.82 95%CI 0.75–0.88). Other risk factors for delay included ED boarding more than 4 h (OR 1.47, 95%CI 1.27–1.71) or a high acuity presentation as defined by emergency severity index (ESI) (OR 1.54, 95%CI 1.30–1.81 for ESI 1–2 versus 3–5). Conclusions Delays in second antibiotic dose administration were frequent in the ED and early hospital course, and were associated with increased odds of in-hospital mortality. Several risk factors associated with delays in second dose administration, including ED boarding, were identified.

IntroductionExcess mortality does not depend on labeling the cause of death and is an accurate representation of the pandemic population-level effects. A comprehensive evaluation of all-cause excess mortality in the United States during the first two years of the COVID-19 pandemic, stratified by age, sex, region, and race/ethnicity can provide insight into the extent and variation in harm.MethodsWith Centers for Disease Control and Prevention (CDC)/National Center for Health Statistics (NCHS) data from 2014-2022, we use seasonal autoregressive integrated moving averages (sARIMA) to estimate excess mortality during the pandemic, defined as the difference between the number of observed and expected deaths. We continuously correct monthly expected deaths to reflect the decreased population owing to cumulative pandemic-associated excess deaths recorded. We calculate excess mortality for the total US population, and by age, sex, US census division, and race/ethnicity.ResultsFrom March 1, 2020, through February 28, 2022, there were 1.17 million excess deaths in the United States. Overall, mortality was 20% higher than expected during the study period. Of the excess deaths, 799,477 (68%) were among residents aged 65 and older. The largest relative increase in all-cause mortality was 27% among adults ages 18-49 years. Males comprised most of the excess mortality (57%), but this predominance declined with age. A higher relative mortality occurred among non-Hispanic American Indian/Alaskan Native, non-Hispanic Black, non-Hispanic Native Hawaiian and Other Pacific Islander, Hispanic people. Excess mortality differed by region; the highest rates were in the South, including in the population ages ≥65 years. Excess mortality rose and fell contemporaneously with COVID-19 waves.ConclusionIn the first two years of the pandemic, the US experienced 1.17 million excess deaths, with greater relative increases in all-cause mortality among men, in American Indian/Alaskan Native, Black and Hispanic people, and the South.

Background To estimate, prior to finalization of claims, the national monthly numbers of admissions and rates of 30-day readmissions and post-discharge observation-stays for Medicare fee-for-service beneficiaries hospitalized with acute myocardial infarction (AMI), heart failure (HF), or pneumonia. Methods The centers for Medicare & Medicaid Services (CMS) Integrated Data Repository, including the Medicare beneficiary enrollment database, was accessed in June 2015, February 2017, and February 2018. We evaluated patterns of delay in Medicare claims accrual, and used incomplete, non-final claims data to develop and validate models for real-time estimation of admissions, readmissions, and observation stays. Results These real-time reporting models accurately estimate, within 2 months from admission, the monthly numbers of admissions, 30-day readmission and observation-stay rates for patients with AMI, HF, or pneumonia. Conclusions This work will allow CMS to track the impact of policy decisions in real time and enable hospitals to better monitor their performance nationally.

This cohort study examines mortality data from Texas, a racially and ethnically diverse state, to better understand excess mortality among adults aged 25 to 44 years during early months of the COVID-19 pandemic.

BackgroundAll-cause excess mortality (the number of deaths that exceed projections in any period) has been widely reported during the Covid-19 pandemic. Whether excess mortality has occurred during the Delta wave is less well understood.MethodsWe performed an observational study using data from the Massachusetts Department of Health. Five years of US Census population data and CDC mortality statistics were applied to a seasonal autoregressive integrated moving average (sARIMA) model to project the number of expected deaths for each week of the pandemic period, including the Delta period (starting in June 2021, extending through August 28th 2021, for which mortality data are >99% complete). Weekly Covid-19 cases, Covid-19-attributed deaths, and all-cause deaths are reported. County-level excess mortality during the vaccine campaign are also reported, with weekly rates of vaccination in each county that reported 100 or more all-cause deaths during any week included in the study period.ResultsAll-cause mortality was not observed after March 2021, by which time over 75% of persons over 65 years of age in Massachusetts had received a vaccination. Fewer deaths than expected (which we term ‘deficit mortality’) occurred both during the summer of 2020, the spring of 2021 and during the Delta wave (beginning June 13, 2021 when Delta isolates represented >10% of sequenced cases). After the initial wave in the spring of 2020, more Covid-19-attributed deaths were recorded that all-cause excess deaths, implying that Covid-19 was misattributed as the underlying cause, rather than a contributing cause of death in some cases.ConclusionIn a state with high vaccination rates, excess mortality has not been recorded during the Delta period. Deficit mortality has been recorded during this period.

Moisture and salt content of soil are the two predominant factors influencing its shear strength. This study aims to investigate the effects of these two factors on shear strength behavior of loess in the Xining Basin of Northeast Qinghai-Tibet Plateau, where such geological hazards as soil erosion, landslides and debris flows are widespread due to the highly erodible loess. Salinized loess soil collected from the test site was desalinized through saltleaching in the laboratory. The desalinized and ovendried loess samples were also artificially moisturized and salinized in order to examine how soil salinity affects its shear strength at different moisture levels. Soil samples prepared in different ways (moisturizing, salt-leaching, and salinized) were measured to determine soil cohesion and internal friction angle. The results show that salt-leaching up to 18 rounds almost completely removed the salt content and considerably changed the physical components of loess, but the soil type remained unchanged. As salt content increases from 0.00% to 12.00%, both the cohesion and internal friction angle exhibit an initial decrease and then increase with salt content. As moisture content is 12.00%, the salt content threshold value for both cohesion and internal friction angle is identified as 3.00%. As the moisture content rises to 16.0% and 20.00%, the salt content threshold value for cohesion is still 6.00%, but 3.00% for internal friction angle. At these thresholds soil shear strength is the lowest, below which it is inversely related to soil salinity. Beyond the thresholds, however, the relationship is positive. Dissimilar to salinity, soil moisture content exerts an adverse effect on shear strength of loess. The findings of this study can provide a valuable guidance on stabilizing the engineering properties of salinized loess to prevent slope failures during heavy rainfall events.

This study uses national death certificate data to characterize trends in death and excess mortality from drug overdoses, homicides, unintentional injuries, motor vehicle crashes, and suicide during the first 6 months of the pandemic in the US.

Introduction The COVID-19 pandemic has been associated with substantial rates of all-cause excess mortality. The contribution of external causes of death to excess mortality including drug overdose, homicide, suicide, and unintentional injuries during the initial outbreak in the United States is less well documented. Methods Using public data published by the National Center for Health Statistics on February 10, 2021, we measured monthly excess mortality (the gap between observed and expected deaths) from five external causes using national-level data published by National Center for Health Statistics; assault (homicide); intentional self-harm (suicide); accidents (unintentional injuries); and motor vehicle accidents. We used seasonal autoregressive integrated moving average (sARIMA) models developed with cause-specific monthly mortality counts and US population data from 2015-2019 and estimated the contribution of individual cause-specific mortality to all-cause excess mortality from March-July 2020. Results From March-July, 2020, 212,825 (95% CI 136,236-290,776) all-cause excess deaths occurred in the US). There were 8,540 excess drug overdoses (all intents) (95% CI 5,106 to 11,975), accounting for 4% of all excess mortality; 1,455 excess homicide deaths (95% CI 708 to 2202, accounting for 0.7% of excess mortality; 5,492 excess deaths due to unintentional accidents occurred (95% CI 85 to 10,899, accounting for 2.6% of excess mortality. Though a non-significantly 135 (95% CI -1361 to 1,630) more MVA deaths were recorded during the study period, a significant decrease in April (525; 95% CI -817 to -233) and significant increases in June-July (965; 95% CI 348 to 1,587) were observed. Suicide deaths were statistically lower than projected by 2,067 (95% CI 941-3,193 fewer deaths). Meaning Excess deaths from drug overdoses, homicide, and addicents occurred during the pandemic but represented a small fraction of all-cause excess mortality. The excess external causes of death, however, still represent thousands of lives lost. Notably, deaths from suicide were lower than expected and therefore did not contribute to excess mortality.

ObjectivesWe identify a correction for excess mortality that takes the sudden unexpected changes in the size of the United States population into account.DesignThis is a weekly cross-sectional analysis of all-cause mortality since week 5, 2020. We describe and apply a simple correction that takes population changes into account in order to provide corrected weekly estimates of expected deaths for 2020 and 2021.SettingThe United States.ParticipantsAll United States residents.InterventionsThe covid-19 pandemic.Main outcome measuresExpected and excess mortality for the United States during the covid-19 period.ResultsAs of week 53, 2020 (ending January 2, 2021), approximately >10,200 more excess deaths have occurred in the United States than could be detected if expected deaths projections were not amended to reflect population decreases during 2020. The figure is projected to rise to >12,600 (>600 weekly) by week 5, 2021. Assuming recent excess mortality and pandemic-associated visa reductions continue until the earliest time herd immunity could be approached resulting from a combination of infections and vaccinations (week 17, 2021), if point estimates of expected deaths are not corrected, expected deaths will be overestimated (and therefore potential excess mortality underestimated) by ∼43,000 during 2021, or >53,300 since the outbreak of the pandemic measurement period (beginning week 5, 2020). By late December 2021, weekly expected death differences are projected to approach 1,000 per week.ConclusionsCurrent models measuring excess mortality should be revised immediately so that public health officials do not lose the ability to detect ongoing excess mortality as the population changes continue to compound, lowering the number of weekly expected deaths. A similar approach should be used in the middle and late phases of all future pandemics.

ImportanceCOVID-19 case fatality and hospitalization rates, calculated using the number of confirmed cases of COVID-19, have been described widely in the literature. However, the number of infections confirmed by testing underestimates the total infections as it is biased based on the availability of testing and because asymptomatic individuals may remain untested. The infection fatality rate (IFR) and infection hospitalization rate (IHR), calculated using the estimated total infections based on a representative sample of a population, is a better metric to assess the actual toll of the disease.ObjectiveTo determine the IHR and IFR for COVID-19 using the statewide SARS-CoV-2 seroprevalence estimates for the non-congregate population in Connecticut.DesignCross-sectional.SettingAdults residing in a non-congregate setting in Connecticut between March 1 and June 1, 2020.ParticipantsIndividuals aged 18 years or above.ExposureEstimated number of adults with SARS-CoV-2 antibodies.Main Outcome and MeasuresCOVID-19-related hospitalizations and deaths among adults residing in a non-congregate setting in Connecticut between March 1 and June 1, 2020.ResultsOf the 2.8 million individuals residing in the non-congregate settings in Connecticut through June 2020, 113,515 (90% CI 56,758–170,273) individuals had SARS-CoV-2 antibodies. There were a total of 9425 COVID-19-related hospitalizations and 4071 COVID-19-related deaths in Connecticut between March 1 and June 1, 2020, of which 7792 hospitalizations and 1079 deaths occurred among the non-congregate population. The overall COVID-19 IHR and IFR was 6.86% (90% CI, 4.58%–13.72%) and 0.95% (90% CI, 0.63%–1.90%) among the non-congregate population. Older individuals, men, non-Hispanic Black individuals and those belonging to New Haven and Litchfield counties had a higher burden of hospitalization and deaths, compared with younger individuals, women, non-Hispanic White or Hispanic individuals, and those belonging to New London county, respectively.Conclusion and RelevanceUsing representative seroprevalence estimates, the overall COVID-19 IHR and IFR were estimated to be 6.86% and 0.95% among the non-congregate population in Connecticut. Accurate estimation of IHR and IFR among community residents is important to guide public health strategies during an infectious disease outbreak.

An increasing proportion of people in the US hospitalized for acute myocardial infarction (AMI) are younger than 55 years, with the largest increase in young women. Effective prevention requires an understanding of risk factors associated with risk of AMI in young women compared with men.To assess the sex-specific associations of demographic, clinical, and psychosocial risk factors with first AMI among adults younger than 55 years, overall, and by AMI subtype.This study used a case-control design with 2264 patients with AMI, aged 18 to 55 years, from the VIRGO (Variation in Recovery: Role of Gender on Outcomes of Young AMI Patients) study and 2264 population-based controls matched for age, sex, and race and ethnicity from the National Health and Nutrition Examination Survey from 2008 to 2012. Data were analyzed from April 2020 to November 2021.A wide range of demographic, clinical, and psychosocial risk factors.Odds ratios (ORs) and population attributable fractions (PAF) for first AMI associated with demographic, clinical, and psychosocial risk factors.Of the 4528 case patients and matched controls, 3122 (68.9%) were women, and the median (IQR) age was 48 (44-52) years. Seven risk factors (diabetes [OR, 3.59 (95% CI, 2.72-4.74) in women vs 1.76 (1.19-2.60) in men], depression [OR, 3.09 (95% CI, 2.37-4.04) in women vs 1.77 (1.15-2.73) in men], hypertension [OR, 2.87 (95% CI, 2.31-3.57) in women vs 2.19 (1.65-2.90) in men], current smoking [OR, 3.28 (95% CI, 2.65-4.07) in women vs 3.28 (2.65-4.07) in men], family history of premature myocardial infarction [OR, 1.48 (95% CI, 1.17-1.88) in women vs 2.42 (1.71-3.41) in men], low household income [OR, 1.79 (95% CI, 1.28-2.50) in women vs 1.35 (0.82-2.23) in men], hypercholesterolemia [OR, 1.02 (95% CI, 0.81-1.29) in women vs 2.16 (1.49-3.15) in men]) collectively accounted for the majority of the total risk of AMI in women (83.9%) and men (85.1%). There were significant sex differences in risk factor associations: hypertension, depression, diabetes, current smoking, and family history of diabetes had stronger associations with AMI in young women, whereas hypercholesterolemia had a stronger association in young men. Risk factor profiles varied by AMI subtype, and traditional cardiovascular risk factors had higher prevalence and stronger ORs for type 1 AMI compared with other AMI subtypes.In this case-control study, 7 risk factors, many potentially modifiable, accounted for 85% of the risk of first AMI in young women and men. Significant differences in risk factor profiles and risk factor associations existed by sex and by AMI subtype. These findings suggest the need for sex-specific strategies in risk factor modification and prevention of AMI in young adults. Further research is needed to improve risk assessment of AMI subtypes.

Importance: A seroprevalence study can estimate the percentage of people with SARS-CoV-2 antibodies in the general population. Most existing reports have used a convenience sample, which may bias their estimates. Objective: To estimate the seroprevalence of antibodies against SARS-CoV-2 based on a random sample of adults living in Connecticut between March 1 and June 1, 2020. Design: Cross-sectional. Setting: We sought a representative sample of Connecticut residents who completed a survey between June 4 and June 23, 2020 and underwent serology testing for SARS-CoV-2-specific IgG antibodies between June 10 and July 6, 2020. Participants: 505 respondents, aged ≥18 years, residing in non-congregate settings who completed both the survey and the serology test. Main outcomes and measures: We estimated the seroprevalence of SARS-CoV-2-specific IgG antibodies among the overall population and across pre-specified subgroups. We also assessed the prevalence of symptomatic illness, risk factors for virus exposure, and self-reported adherence to risk mitigation behaviors among this population. Results: Of the 505 respondents (mean age 50 [±17] years; 54% women; 76% non-Hispanic White individuals) included, 32% reported having at least 1 symptom suggestive of COVID-19 since March 1, 2020. Overall, 18 respondents had SARS-CoV-2-specific antibodies, resulting in the state-level weighted seroprevalence of 3.1 (90% CI 1.4-4.8). Individuals who were asymptomatic had significantly lower seroprevalence (0.6% [90% CI 0.0-1.5]) compared with the overall state estimate, while those who reported having had ≥1 and ≥2 symptoms had a seroprevalence of 8.0% (90% CI 3.1-12.9) and 13.0% (90% CI 3.5-22.5), respectively. All 9 of the respondents who reported previously having a positive coronavirus test were positive for SARS-CoV-2-specific IgG antibodies. Nearly two-third of respondents reported having avoided public places (74%) and small gatherings of family or friends (75%), and 97% reported wearing a mask outside their home, at least part of the time. Conclusions and relevance: These estimates indicate that most people in Connecticut do not have detectable levels of antibodies against SARS-CoV-2. There is a need for continued adherence to risk mitigation behaviors among Connecticut residents, to prevent resurgence of COVID-19 in this region.

Background: Although increased age is associated with higher systolic blood pressure (SBP) in general, there may be variation across individuals in how SBP changes over time. The goal of this paper is to identify heterogeneity in SBP trajectories among young adults with similar initial values and identify personal characteristics associated with different trajectory patterns. This may have important implications for prevention and prognosis. Methods: A cohort of 12,468 individuals aged 18–35 years in the Qingdao Port Cardiovascular Health Study in China was followed yearly during 2000–2011. Individuals were categorized into three strata according to their baseline SBP: ≤110 mmHg, 111–130 mmHg, and >130 mmHg. Within each stratum, group-based trajectory analyses were conducted to identify distinct SBP trajectory patterns, and their association with sociodemographic and baseline health characteristics was assessed by ordinal logistic regression. Results: Five distinct groups of individuals exhibiting divergent patterns of increasing, stable or decreasing SBP trends were identified within each stratum. This is a first report to identify a subgroup with decreasing trend in SBP. Individuals with more advanced age, having less than high school education, family history of cardiovascular diseases, greater body mass index, greater waist circumference, and hyperlipidemia at baseline were more likely to experience trajectories of higher SBP within each stratum. Conclusions: The diverging trajectories among young adults with similar initial SBP highlight the need for prevention and feasibility of effective blood pressure control, while the identified risk factors may inform targeted interventions.

Background Measurement of cancer-related fatigue and nutrition in the same colorectal cancer patient group using fast-track surgery has never been examined previously. The association between fatigue and nutritional status in the same patient group is thus worthwhile to be investigated. Objective The aim of this study was to evaluate the relationship between fatigue and nutrition risk factors in colorectal cancer patients with fast-track surgery. Methods This is a single-arm, observational study. Seventy eligible postoperative patients with colorectal cancer fast-track surgery were enrolled in this study. Patients completed the Cancer Fatigue Scale and the Patient-Generated Subjective Global Assessment (PG-SGA) besides routine perioperative laboratory examination. Results In this study, all patients were found to have cancer-related fatigue; 20% of the patients had severe fatigue. Furthermore, 94.29% of the patients were malnourished according to the PG-SGA score; the average was 15.585.18. Fatigue severity was significantly, positively correlated with nutrition status. White blood cells and serum calcium were significantly, positively related to both Cancer Fatigue Scale and PG-SGA scores. Conclusion Fatigue and malnutrition commonly exist in patients with colorectal cancer experiencing fast-track surgery. Fatigue may reflect the nutritional status in this group of patients. Implications for practice Clinical nursing staff need to evaluate patients' fatigue status and nutritional status to provide the suitable clinical intervention when needed.

This study aims to assess the hydrological effects of four herbs and four shrubs planted in a selfestablished test area in Xining Basin of northeastern Qinghai-Tibet Plateau, China. The Rainfall-Intercepting Capability (RIC) of the herbs and shrubs was evaluated in rainfall interception experiment at the end of the third, fourth and fifth month of the growth period in 2007. The leaf transpiration rate and the effects of roots on promoting soil moisture evaporation in these plants were also assessed in transpiration experiment and root-soil composite system evaporation experiment in the five month’s growth period. It is found that the RIC of the four studied herbs follows the order of E. repens, E. dahuricus, A. trachycaulum and L. secalinus; the RIC of the four shrubs follows the order of A. canescens, Z. xanthoxylon, C. korshinskii and N. tangutorum. The RIC of all the herbs is related linearly to their mean height and canopy area (R2 ≥ 0.9160). The RIC of all the shrubs bears a logarithmic relationship with their mean height (R2 ≥ 0.9164), but a linear one with their canopy area (R2 ≥ 0.9356). Moreover, different species show different transpiration rates. Of the four herbs, E. repens has the highest transpiration rate of 1.07 mg/(m2·s), and of the four shrubs, A. canescens has the highest transpiration rate (0.74 mg/(m2·s)). The roots of all the herbs and shrubs can promote soil moisture evaporation. Of the four herbs, the evaporation rate of E. repens root-soil composite system is the highest (2.14%), and of the four shrubs, the root-soil composite system of A. canescens has the highest evaporation rate (1.41%). The evaporation rate of the root-soil composite system of E. dahuricus and Z. xanthoxylon bears a second-power linear relationship with evaporation time (R2 ≥ 0.9924). The moisture content of all the eight root-soil composite systems decreases exponentially with evaporation time (R2 ≥ 0.8434). The evaporation rate and moisture content of all the plants’ root-soil composite systems increases logarithmically (R2 ≥ 0.9606) and linearly (R2 ≥ 0.9777) with root volume density. The findings of this study indicate that among the four herbs and four shrubs, E. repens and A. canescens possess the most effective hydrological effects in reducing the soil erosion and shallow landslide in this region.

In order to improve convergence rate and optimization precision of the cuckoo search (CS) algorithm, an improved dynamic self-adaption cuckoo search algorithm based on collaboration between subpopulations (SC-DSCS, where ‘SC’ represents ‘Subpopulation Collaboration,’ ‘DS’ represents ‘dynamic self-adaption’) is proposed. In SC-DSCS, the population of cuckoos is divided into two subgroups. The nest locations of birds belonging to the first subgroup are updated according to the traditional CS algorithm so as to retain the global search ability, and the second subgroup produces the corresponding nest locations for next generation by flying from the better nest locations to enhance the local search ability of the CS algorithm. Through collaboration between two subgroups, the problem that the local search ability of CS algorithm is not strong can be effectively solved. In order to reduce the probability of the algorithm falling into local optimum and improve the optimization precision, the SC-DSCS algorithm creates a new bird’s nest under the comprehensive assessment of the first three best bird’s nests. The new nest is added to the optimal bird’s nest sequence. In order to improve the adaptability of the SC-DSCS, adaptive step length control is adopted in the bird’s nest position updating process. Finally, nine benchmark functions are adopted to carry out the simulation experiments. The proposed algorithm is compared with particle swarm optimization algorithm, artificial colony algorithm and differential evolution algorithm. Simulation results show that the proposed SC-DSCS algorithm has better convergence speed and optimization precision.

[Image: see text] CENTRAL MESSAGE Patient and procedure-specific risk models likely outperform ‘universal’ aggregate perioperative risk models and should be sought out whenever possible to accurately predict individual patient risks.

This comparative effectiveness research study assesses whether using present on admission codes and single, rather than grouped, diagnostic codes can enhance Centers for Medicare & Medicaid (CMS) models to predict payment for hospitalization for acute myocardial infarction, heart failure, and pneumonia., Key Points Question Does leveraging present on admission codes and using single, rather than grouped, diagnostic codes enhance risk models for acute myocardial infarction, heart failure, and pneumonia payment measures? Findings In this comparative effectiveness research study of risk models on 1 667 983 patients with 1 943 049 Medicare fee-for-service hospitalizations, use of present on admission codes and single diagnosis codes and separation of index admission codes from codes in the previous year improved models predicting payment that were compared with models based on Centers for Medicare & Medicaid Services grouped codes. The patient-level pseudo R2 improved from 0.077 to 0.129 for acute myocardial infarction, from 0.042 to 0.129 for heart failure, and from 0.114 to 0.237 for pneumonia. Meaning Changing candidate variables from the current standard improved models predicting payments, which has implications for research, benchmarking, public reporting, and calculations for population-based programs., Importance Predicting payments for particular conditions or populations is essential for research, benchmarking, public reporting, and calculations for population-based programs. Centers for Medicare & Medicaid Services (CMS) models often group codes into disease categories, but using single, rather than grouped, diagnostic codes and leveraging present on admission (POA) codes may enhance these models. Objective To determine whether changes to the candidate variables in CMS models would improve risk models predicting patient total payment within 30 days of hospitalization for acute myocardial infarction (AMI), heart failure (HF), and pneumonia. Design, Setting, and Participants This comparative effectiveness research study used data from Medicare fee-for-service hospitalizations for AMI, HF, and pneumonia at acute care hospitals from July 1, 2013, through September 30, 2015. Payments across multiple care settings, services, and supplies were included and adjusted for geographic and policy variations, corrected for inflation, and winsorized. The same data source was used but varied for the candidate variables and their selection, and the method used by CMS for public reporting that used grouped codes was compared with variations that used POA codes and single diagnostic codes. Combinations of use of POA codes, separation of index admission diagnoses from those in the previous 12 months, and use of individual International Classification of Diseases, Ninth Revision, Clinical Modification codes instead of grouped diagnostic categories were tested. Data analysis was performed from December 4, 2017, to June 10, 2019. Main Outcomes and Measures The models’ goodness of fit was compared using root mean square error (RMSE) and the McFadden pseudo R2. Results Among the 1 943 049 total hospitalizations of the study participants, 343 116 admissions were for AMI (52.5% male; 37.4% aged ≤74 years), 677 044 for HF (45.5% male; 25.9% aged ≤74 years), and 922 889 for pneumonia (46.4% male; 28.2% aged ≤74 years). The mean (SD) 30-day payment was $23 103 ($18 221) for AMI, $16 365 ($12 527) for HF, and $17 097 ($12 087) for pneumonia. Each incremental model change improved the pseudo R2 and RMSE. Incorporating all 3 changes improved the pseudo R2 of the patient-level models from 0.077 to 0.129 for AMI, from 0.042 to 0.129 for HF, and from 0.114 to 0.237 for pneumonia. Parallel improvements in RMSE were found for all 3 conditions. Conclusions and Relevance Leveraging POA codes, separating index from previous diagnoses, and using single diagnostic codes improved payment models. Better models can potentially improve research, benchmarking, public reporting, and calculations for population-based programs.