Estimate Mortality Risk Research Articles

Assessing the short-term effects of PM2.5 and O3 on cardiovascular mortality using high-resolution exposure: a time-stratified case cross-over study in Southwestern China.

Air pollution is a major risk factor of cardiovascular disease (CVD). To date, limited studies have estimated the effects of ambient air pollution on CVD mortality using high-resolution exposure assessment, which might fail to capture the spatial variation in exposure and introduce bias in results. Besides, the three-year action plan (TYAP, 2018-2020) was released; thus, the constitution and health effect of air pollutants may have changed. In this study, we estimated the short-term effect exposed to particulate matters with parameter less than 2.5µm (PM2.5) and ozone (O3) with 0.05° × 0.05° resolution on CVD mortality and measured the influence of TYAP in the associations. We used random forest models with spatial weight matrices to attain high-resolution pollutant concentrations and conditional Poisson regression to assess the relationship between air pollution and cardiovascular mortality. With an increase of 10µg/m3 in PM2.5 and O3 during 2018-2021 in the Sichuan Basin (SCB), CVD mortality increased 1.0134 (95% CI 1.0102, 1.0166) and 1.0083 (95% CI 1.0060, 1.0107), respectively, using high-resolution air pollutant concentration, comparing to 1.0070 (95% CI 1.0052, 1.0087) and 1.0057 (95% CI 1.0037, 1.0078) using data from air quality monitoring stations (AQMs). After TYAP, the relative risk (RR) due to PM2.5 rose up to 1.0149 (95% CI 1.0054, 1.0243), and the RR due to O3 rose up to 1.0089 (95% CI 1.0030, 1.0148) in Sichuan Province. We found significantly positive association of cardiovascular mortality and air pollution in Sichuan Province. And using high-resolution exposure would be more accurate to estimate the effect of air pollution on CVD. After TYAP, the cardiovascular mortality risk estimation due to PM2.5 decreased in elderly in SCB, and the risk due to O3 increased in Sichuan Province.

Impact of 8 lifestyle factors on mortality and life expectancy among United States veterans: The Million Veteran Program

BackgroundLifestyle medicine has been proposed as a way to address the root causes of chronic disease and their associated health care costs. ObjectiveThis study aimed to estimate mortality risk and longevity associated with individual lifestyle factors and comprehensive lifestyle therapy. MethodsAge- and sex-specific mortality rates were calculated on the basis of 719,147 veterans aged 40–99 y enrolled in the Veteran Affairs Million Veteran Program (2011–2019). Hazard ratios and estimated increase in life expectancy were examined among a subgroup of 276,132 veterans with complete data on 8 lifestyle factors at baseline. The 8 lifestyle factors included never smoking, physical activity, no excessive alcohol consumption, restorative sleep, nutrition, stress management, social connections, and no opioid use disorder. ResultsOn the basis of 1.12 million person-years of follow-up, 34,247 deaths were recorded. Among veterans who adopted 1, 2, 3, 4, 5, 6, 7, and 8 lifestyle factors, the adjusted hazard ratios for mortality were 0.74 (0.60–0.90), 0.60 (95% CI: 0.49, 0.73), 0.50 (95% CI: 0.41, 0.61), 0.43 (95% CI: 0.35, 0.52), 0.35 (95% CI: 0.29, 0.43), 0.27 (95% CI: 0.22, 0.33), 0.21 (95% CI: 0.17, 0.26), and 0.13 (95% CI: 0.10, 0.16), respectively, as compared with veterans with no adopted lifestyle factors. The estimated life expectancy at age 40 y was 23.0, 26.5, 28.8, 30.8, 32.7, 35.1, 38.3, 41.3, and 47.0 y among males and 27.0, 28.8, 33.1, 38.0, 39.2, 41.4, 43.8, 46.3, and 47.5 y for females who adopted 0, 1, 2, 3, 4, 5, 6, 7, and 8 lifestyle factors, respectively. The difference in life expectancy at age 40 y was 24.0 y for male veterans and 20.5 y for female veterans when comparing adoption of 8–9 lifestyle factors. ConclusionsA combination of 8 lifestyle factors is associated with a significantly lower risk of premature mortality and an estimated prolonged life expectancy.

Incidence and Prognosis of Primary Malignancies in Ataxia-Telangiectasia Patients: A Report from the Ataxia-Telangiectasia Clinical Center (ATCC)

Biallelic germline mutations in the ATM gene result in Ataxia-Telangiectasia (AT), a classic DNA repair disorder associated with a high incidence of primary malignancies. The lack of standardized cancer treatment approaches for patients with AT is believed to contribute to poor survival after diagnosis. Robust characterization of the incidence, outcomes, and ATM variant enrichment of large cohorts of individuals with AT who develop malignancies are needed to inform therapeutic decision-making. The AT Clinical Center (ATCC) at Johns Hopkins Hospital is a large, retrospective cohort that prospectively collects outcomes data on individuals diagnosed with AT in the US. Standardized incidence ratios (SIRs) were used to compare cancer incidence to that of age-, sex-, and year-matched US general population using the SEER database. Standardized mortality ratios (SMRs) and mortality rate ratios were used to estimate mortality risk compared to the general population. Among 508 total participants with AT, 84 developed cancers (16.5%): 39 non-Hodgkin lymphoma (NHL), 7 Hodgkin lymphoma, 16 leukemia (n=13 of T cell origin), and 22 carcinomas. SIRs were 355.9, 67.4, 45.6, and 18.0, respectively. Median (interquartile range) age at cancer diagnosis was 14.3 years (10.3-22.8), with NHL occurring in the youngest (12.4 years, IQR 7.3-16.7) and solid tumors in the oldest (26.3 years, IQR 18.7-29.0) individuals. The SMR was 24.6 (95% CI: 21.1-28.4) for the overall cohort of AT patients and 232.9 (95% CI: 178.1-299.2) among those with cancer. Median survival following a cancer diagnosis was 2.5 years (95% CI: 0.1-14.4). The post-cancer mortality rate ratio was 2.2 (95% CI: 1.1-4.3, p=0.025) comparing AT patients with cancer treated without dose reduction or for whom dosing was unknown (n=24) versus AT patients with cancer who received dose reduced chemotherapy (n= 38). Among those who received chemotherapy (n=62), 11/24 (45.8%, 95% CI: 25.6-67.2) treated with standard dosing and 24/38 (63%, 95% CI: 46.0-78.2) treated with reduced dosing experienced toxicity (p=0.20). ATM variant associations were detected by single variant analysis among 247 patients with AT who had confirmed biallelic ATM mutations via whole exome sequencing. Individuals with AT are at very high-risk of primary malignancies, of which NHL is most common. Prognosis is poor regardless of standard versus dose reduced chemotherapy regimens, which calls for novel therapies.

Association between body composition and the risk of mortality in the obese population in the United States.

Recent studies have presented the concept of the obesity paradox, suggesting that individuals with obesity have a lower risk of death than those without obesity. This paradox may arise because body mass index (BMI) alone is insufficient to understand body composition accurately. This study investigated the relationship between fat and muscle mass and the risk of mortality in individuals with overweight/obesity. We used data from the National Health and Nutrition Examination Survey (NHANES) from 1999 to 2006 and 2011 to 2018, which were linked to mortality information obtained from the National Death Index. Multiple Cox regression analyses were performed to estimate mortality risk. Subgroup analysis was conducted using propensity score-matched (PSM) data for age, sex, and race/ethnicity. This study included 16,555 participants who were overweight/obese (BMI≥25 kg/m2). An increase in appendicular skeletal muscle mass index was associated with a lower mortality risk (hazard ratio [HR]: 0.856; 95% confidence interval [CI]: 0.802-0.915). This finding was consistent with the subgroup analysis of the PSM data. Contrastingly, a high fat mass index was associated with an increased risk of mortality. Sarcopenic overweight/obesity was significantly associated with high mortality compared to obesity without sarcopenia (HR: 1.612, 95%CI: 1.328-1.957). This elevated risk was significant in both age- and sex-based subgroups. This finding was consistent with the subgroup analysis using PSM data. In contrast to the obesity paradox, a simple increase in BMI does not protect against mortality. Instead, low body fat and high muscle mass reduce mortality risk.

Cognitive performance and all-cause mortality in community dwellers of Amerindian ancestry living in rural Ecuador: A population-based, longitudinal prospective study

ObjectiveThere is limited information on mortality risk in individuals with cognitive impairment living in rural Latin America. In this study, we assess the association between cognitive impairment and all-cause mortality in adults of Amerindian ancestry living in rural Ecuador. Patients and methodsMiddle-aged and older adults enrolled in the population-based Three Villages Study cohort were followed prospectively in order to estimate mortality risk according to their baseline cognitive performance as determined by the Montreal Cognitive Assessment (MoCA). Results were adjusted for demographics, level of education, traditional cardiovascular risk factors, symptoms of depression, severe tooth loss, and oily fish intake (factors previously associated with mortality in the study population). ResultsAnalysis included 1022 individuals followed for an average of 7.8 ± 3.4 years. Mean MoCA score was 21.2 ± 5.4 points (median: 22 points), with 334 (32.7%) individuals showing cognitive impairment, as evidenced by a MoCA score ≤ 19 points (the cutoff for poor cognitive performance based on previous studies in the same population). A total of 150 (14.7%) individuals died during the follow-up. Crude mortality rate was 2.87 per 100 person-years (95% C.I.: 2.08 – 3.96). For individuals with normal cognition, the mortality rate was 1.21 (95% C.I.: 0.92 – 1.50) while for those with cognitive impairment the rate increased to 3.48 (95% C.I.: 2.73 – 4.23). A multivariate Cox-proportional hazards model, confirmed that individuals with cognitive impairment had a significantly higher mortality risk than those without cognitive impairment (HR: 1.52; 95% C.I.: 1.05 – 2.18). ConclusionsCognitive impairment is associated with mortality in the study population.

Counting disability in the National Health Interview Survey and its consequence: Comparing the American Community Survey to the Washington Group disability measures

BackgroundThe objective of the National Health Interview Survey (NHIS) is to provide data that can be used to monitor the health of the US population. ObjectiveIn this study, we evaluate whether the disability questions currently used in the NHIS – the Washington Group questions – threaten the ability of this survey to fulfil its stated objective for disabled people. MethodsData were from the 2011–2012 NHIS with linkage to mortality status through 2019. We examined whether people who reported a disability in the American Community Survey disability questions had their disability counted in the Washington Group questions. We then examined the consequence of use of the Washington Group as opposed to the American Community Survey questions, on estimates of disability prevalence and comparative mortality risk. ResultsWe find that when compared to their predecessor, the American Community Survey disability questions, the Washington Group questions accounted for less than half of disabled people, primarily counting disabled people with more than one disability status, but not counting many disabled people with only one disability status. As a result of this undercount, disability prevalence rates based on the Washington Group questions underestimate the size of the disabled population in the US, and overestimate the comparatively higher mortality risk associated with disability status. ConclusionsThese results underscore the need to re-evaluate the disability questions used in the NHIS, and invest in the development of improved and expanded disability questionnaires for use in national surveys.

A comprehensive analysis of 30 Biomarkers and their association with cardiovascular outcomes and mortality in the UK Biobank

Abstract Background The use of a comprehensive panel of circulating biomarkers in risk estimation of cardiovascular disease and mortality in a general healthy population remains uncertain. Methods The individual association of 30 clinically available biomarkers with risk of incident heart failure (HF), atrial fibrillation (AF), stroke, myocardial infarction (MI), venous thromboembolism (VTE), and mortality was estimated within 5-years using separate logistic regression models in nearly 500,000 individuals in the UK Biobank; a prospective, population-based cohort recruited between 2006-2010. Results A total of 496,415 individuals were included (median age 58 years, 54% female). Higher concentrations of C-reactive protein, gamma-glutamyl transferase, cystatin C, and urate were associated with significantly higher odds, and albumin with lower odds, of all outcomes (Figure 1). Higher levels of sex hormone binding globulin were associated with a higher risk of AF for both sexes (men: Odds ratio [OR] per standard deviation [SD] 1.26, 95% confidence intervals [CI] 1.21-1.31; premenopausal women: OR per SD 1.20, 95% CI 1.09-1.31; postmenopausal women: OR per SD 1.41, 95% CI 1.12-1.77). No significant associations were found among pre- or postmenopausal women between estradiol and cardiovascular outcomes. For men, having estradiol ≥40 pmol/L was associated with a higher risk of mortality, HF and AF. Conclusions Several associations were found between multiple biomarkers and cardiovascular disease, and mortality, especially novel associations of hormonal markers associated with both lower and higher odds of cardiovascular disease. Further research is needed to validate these associations and explore the potential clinical applications of biomarker data.

External validation of the M-CARS score for mortality risk in coronary intensive care units

Abstract Background The M-CARS (Mayo-CICU Admission Risk Score) score was recently developed and validated for coronary intensive care units (CICU) for the estimation of mortality risk at admission. Most existing scores were developed and validated for general intensive care unit patients with a distinct profile, and need a myriad of both clinical and laboratory data from the first 24 hours after admission and constant updates. The M-CARS is based on only 7 data points collected at admission: the presence of cardiac arrest, respiratory failure and/or any type of shock, the Branden score, blood urea nitrogen > 23mg/dL, anion gap > 14 and red blood cell distribution width (RDW) > 14.3. The scale varies from zero to 10 points and three risk categories were described: low (0 to 2 points), intermediate (from 2 to 4), and high (>4 points). Purpose To provide external validation of the M-CARS score. Methods Retrospective cohort of patients admitted to two CICUs in the same academic hospital. One is linked to the emergency ward and mainly admits acute cardiac care patients. The second one is linked to the cardiology clinical and surgical wards, the outpatient care center, and the interventional and surgical suites. Demographic, clinical, and laboratory data were collected during admission to obtain the M-CARS score. Our primary endpoint was death within 30 days of admission to CICU. Readmissions for the same time interval were also evaluated. Statistical analysis used parametric and non-parametric tests as needed to describe the data and for comparison between groups. P-value < 5% was established as significant. Results A total of 408 consecutive patients were included: 60% men, 81% white and the median age was 64 years. The mean stay duration was 6±6 days. The total mortality at 30 days was 9.8% (40 patients), with no age (62.4±14,6years - alive vs 66.4 ± 17.0years - dead; p=0.10), gender (p=0.31), and CICU location (10.9%- CICU linked to the emergency ward vs 8.3% - CICU linked to cardiology wards; p=0.38) differences. The M-CARS score was obtained in 363 patients, and the lack of blood chloride levels needed for anion gap calculation was the reason for the 45 patients not being included in this analysis. Within this group, 38 (10.5%) patients died. The M-CARS score was significantly higher in the group of patients who died within 30 days (4.4±2.6 vs 2.0±1,6; p<0.0001). Considering the risk categories, mortality was significantly distinct between all mortality risk categories (Low = 3.4%, Intermediate = 10.2%, and High =40.5%; p<0.0001, and P<0.0001 for all Bonferroni post-tests). Readmissions with 30 days had similar M-CARS score (P=0.64).CONCLUSIONS: The M-CARS score was adequate in identifying the mortality risk category in CICUs from outside the country it was developed. It could not predict readmissions. It may be incorporated into the admission proceedings and be used to guide medical care and instruct relatives about the risks of the patient.

Age-period-cohort analysis of mortality and its association with deprivation in Hungary, 2007-2021

Abstract Background Severe spatial inequalities in health and its determinants are a well-known and long-standing phenomenon in Central-Eastern European countries, including Hungary. The aim of our study was to perform an age-period-cohort (APC) analysis to provide information on mortality trends and their association with deprivation in Hungary using the latest available data for the period 2007-2021. Methods The extended APC model, allowing estimation of mortality risk considering deprivation, time, age, and cohort effects and their interactions, was used to analyse the trajectories of several outcome variables along complex dimensions. Model fitting and inference were carried out by using Bayesian computation with INLA methodology. Results Mortality risk was the lowest in the 5-9 year age group and increased with age. Between 2007-19, the mortality risk showed a decreasing trend, which returned to the 2007 level by 2020-21 due to the COVID-19 pandemic. Considering the birth cohort effect, the mortality risk peaked in the 1953-57 cohort, then decreased until the 1972-76 cohort, followed by a steady state, and then decreased from the 1988-92 cohort onwards. Deprivation was positively associated with higher mortality risk. The interaction of deprivation and period effect on mortality risk showed a steeper increasing trend for deprived groups for 2020-21. In terms of the interaction of deprivation and cohort effect, the risk of mortality in the most deprived group was higher than the national risk for all birth cohorts. The risk increased until the 1967-71 cohort, then decreased until the 1992-96 cohort, and then again showed an increasing trend. The risk of mortality for the least deprived population was lower than the national average risk, while the opposite difference could be observed for the most deprived group. Conclusions Deprivation was associated with higher mortality risk, COVID-related mortality and cohort effect during the study period. Key messages • The COVID epidemic-associated increase in mortality affected groups of different deprivation status to varying degree. • Different cohort effects were found behind the mortality risk in different deprivation groups.

Do migrant workers have a higher risk of death and injury than the host population in high-income settings?

Abstract Karen Lau will describe the current situation in European policy and practice with respect to migrant workers in precious, exploitative or hazardous occupations. She will presenting findings from a recent systematic review on the mortality risk of migrant workers and previous work exploring health and wellbeing of migrant workers in high-income settings and the link between exploitative work and health. Given that existing research on the mortality of migrant workers remains limited and inconclusive, the aim of this study was to review and synthesise available mortality data on international migrant workers. This presentation will focus on findings relevant to migrant workers at risk of labour exploitation in the European region, including migrant worker characteristics and mortality risk estimates. The presentation will define challenges and opportunities in the study of comparative mortality of migrant workers in Europe and beyond.

Ratio of waist circumference to body mass index: A novel predictor of clinical outcome in hypertension patients.

We aim to investigate the influence of waist circumference and body mass index (BMI) on all-cause death and cardiovascular-specific death in patients with hypertension. This prospective cohort study, based on waist circumference and body mass index measurements in patients with hypertension, provided risk estimates of all-cause mortality and cardiovascular events. The waist circumference-to-BMI ratio (WtBR) is an anthropometric measure integrating waist circumference and BMI. We utilized multivariable Cox regression analysis, restricted cubic spline model, Kaplan-Meier plot, random forest analysis, and sensitivity analysis to assess the relationship of WtBR with all-cause mortality. Subsequently, Fine-Gray competing risk regression models were applied to precisely evaluate the probability of cardiovascular-specific death attributed to high WtBR. The results indicate that thea deceased group showed significantly higher WtBR and lower BMI compared with the alive groups (P<.05), while no significant difference was observed in waist circumference (P=.373). When analyzed as continuous, the risk of all-cause death elevated with increasing WtBR in the adjusted model with an HR of 2.42 (95% CI, 2.06-2.85). The restricted cubic spline illustrated an elevated risk of all-cause mortality as WtBR increased (J-shaped curve). Nevertheless, WtBR showed no significant association with cardiovascular-specific death and the prediction model exhibited a reliable performance in the testing set. This study supported that WtBR, an anthropometric measure, is independently associated with all-cause death in hypertensive patients. It's advisable to routinely assess waist circumference in hypertensive patients regardless of BMI, in order to more effectively manage the risk of obesity-related health.

Use of Ilizarov technique for bilateral knees flexion contracture in Juvenile-onset ankylosing spondylitis: A case report

Time series analysis is a valuable tool in epidemiology that complements the classical epidemiological models in two different ways: Prediction and forecast. Prediction is related to explaining past and current data based on various internal and external influences that may or may not have a causative role. Forecasting is an exploration of the possible future values based on the predictive ability of the model and hypothesized future values of the external and/or internal influences. The time series analysis approach has the advantage of being easier to use (in the cases of more straightforward and linear models such as Auto-Regressive Integrated Moving Average). Still, it is limited in forecasting time, unlike the classical models such as Susceptible-Exposed-Infectious-Removed. Its applicability in forecasting comes from its better accuracy for short-term prediction. In its basic form, it does not assume much theoretical knowledge of the mechanisms of spreading and mutating pathogens or the reaction of people and regulatory structures (governments, companies, etc. ). Instead, it estimates from the data directly. Its predictive ability allows testing hypotheses for different factors that positively or negatively contribute to the pandemic spread; be it school closures, emerging variants, etc. It can be used in mortality or hospital risk estimation from new cases, seroprevalence studies, assessing properties of emerging variants, and estimating excess mortality and its relationship with a pandemic.

Acute cholangitis: Does malignant biliary obstruction vs choledocholithiasis etiology change the clinical presentation and outcomes?

Whether clinical outcomes of acute cholangitis (AC) vary by etiology is unclear. To compare outcomes in AC caused by malignant biliary obstruction (MBO) and common bile duct stones (CBDS). This retrospective study included 516 patients undergoing endoscopic retrograde cholangiopancreatography (ERCP) due to AC caused by MBO (MBO group, n = 56) and CBDS (CBDS group, n = 460). Clinical and laboratory parameters were compared between the groups. Propensity score matching (PSM) created 55 matched pairs. Confounders used in the PSM analysis were age, sex, time to ERCP, and technical success of ERCP. The primary outcome comparison was 30-d mortality. The secondary outcome comparisons were intensive care unit (ICU) admission rate, length of hospital stay (LOHS), and 30-d readmission rate. Compared with the CBDS group, the MBO group had significantly lower body temperature, percentage of abnormal white blood cell counts, and serum levels of aspartate aminotransferase, alanine aminotransferase, and creatinine. Body temperature, percent abnormal white blood cell count, and serum aspartate aminotransferase levels remained significantly lower in the MBO group in the PSM analysis. Platelet count, prothrombin time/international normalized ratio, and serum levels of alkaline phosphatase and total bilirubin were significantly higher in the MBO group. The MBO group had a significantly higher percentage of severe AC (33.9% vs 22.0%, P = 0.045) and received ERCP later (median, 92.5 h vs 47.4 h, P < 0.001). However, the two differences were not found in the PSM analysis. The 30-d mortality (5.4% vs 0.7%, P = 0.019), ICU admission rates (12.5% vs 4.8%, P = 0.028), 30-d readmission rates (23.2% vs 8.0%, P < 0.001), and LOHS (median, 16.5 d vs 7.0 d, P < 0.001) were significantly higher or longer in the MBO group. However, only LOHS remained significant in the PSM analysis. Multivariate analysis revealed that time to ERCP and multiple organ dysfunction were independent factors associated with 30-d mortality. MBO patients underwent ERCP later and thus had a worse prognosis than CBDS patients. Therefore, clinicians should remain vigilant in MBO patients with clinically suspected AC, and perform ERCP for biliary drainage as soon as possible.

Usefulness of analyzing endoscopic features in identifying the colorectal serrated sessile lesions with and without dysplasia.

Sessile serrated lesions (SSLs) are often missed on colonoscopy, and studies have shown this to be an essential cause of interstitial colorectal cancer. The SSLs with dysplasia (SSL-D+), in particular, have a faster rate of carcinogenesis than conventional tubular adenomas. Therefore, there is a clinical need for some endoscopic features with independent diagnostic value for SSL-D+s to assist endoscopists in making immediate diagnoses, thus improving the quality of endoscopic examination and treatment. To compare the characteristics of SSLs, including those with and without dysplasia (SSL-D+ and SSL-D-), based on white light and image-enhanced endoscopy, to achieve an immediate differential diagnosis for endoscopists. From January 2017 to February 2023, cases of colorectal SSLs confirmed by colonoscopy and histopathology at the Gastrointestinal Endoscopy Center of Beijing Tsinghua Changgung Hospital were collected. The general, endoscopic, and histopathological data were reviewed and analyzed to determine the diagnostic utility. Univariate analysis was used to find potential diagnostic factors, and then multivariate regression analysis was performed to derive endoscopic features with independent diagnostic values for the SSL-D+. A total of 228 patients with 253 lesions were collected as a result. There were 225 cases of colorectal SSL-D-s and 28 cases of SSL-D+s. Compared to the colorectal SSL-D-, the SSL-D+ was more common in the right colon (P = 0.027) with complex patterns of depression, nodule, and elevation based on cloud-like surfaces (P = 0.003), reddish (P < 0.001), microvascular varicose (P < 0.001), and mixed type (Pit II, II-O, IIIL, IV) of crypt opening based on Pit II-O (P < 0.001). Multifactorial logistic regression analysis indicated that lesions had a reddish color [odds ratio (OR) = 18.705, 95% confidence interval (CI): 3.684-94.974], microvascular varicose (OR = 6.768, 95%CI: 1.717-26.677), and mixed pattern of crypt opening (OR = 20.704, 95%CI: 2.955-145.086) as the independent predictors for SSL-D+s. The endoscopic feature that has independent diagnostic value for SSL-D+ is a reddish color, microvascular varicose, and mixed pattern of crypt openings.

Unusual presentation of penile giant condyloma acuminatum with spontaneous prepuce perforation: A case report

Time series analysis is a valuable tool in epidemiology that complements the classical epidemiological models in two different ways: Prediction and forecast. Prediction is related to explaining past and current data based on various internal and external influences that may or may not have a causative role. Forecasting is an exploration of the possible future values based on the predictive ability of the model and hypothesized future values of the external and/or internal influences. The time series analysis approach has the advantage of being easier to use (in the cases of more straightforward and linear models such as Auto-Regressive Integrated Moving Average). Still, it is limited in forecasting time, unlike the classical models such as Susceptible-Exposed-Infectious-Removed. Its applicability in forecasting comes from its better accuracy for short-term prediction. In its basic form, it does not assume much theoretical knowledge of the mechanisms of spreading and mutating pathogens or the reaction of people and regulatory structures (governments, companies, etc. ). Instead, it estimates from the data directly. Its predictive ability allows testing hypotheses for different factors that positively or negatively contribute to the pandemic spread; be it school closures, emerging variants, etc. It can be used in mortality or hospital risk estimation from new cases, seroprevalence studies, assessing properties of emerging variants, and estimating excess mortality and its relationship with a pandemic.

Roles of biochemistry data, lifestyle, and inflammation in identifying abnormal renal function in old Chinese

Time series analysis is a valuable tool in epidemiology that complements the classical epidemiological models in two different ways: Prediction and forecast. Prediction is related to explaining past and current data based on various internal and external influences that may or may not have a causative role. Forecasting is an exploration of the possible future values based on the predictive ability of the model and hypothesized future values of the external and/or internal influences. The time series analysis approach has the advantage of being easier to use (in the cases of more straightforward and linear models such as Auto-Regressive Integrated Moving Average). Still, it is limited in forecasting time, unlike the classical models such as Susceptible-Exposed-Infectious-Removed. Its applicability in forecasting comes from its better accuracy for short-term prediction. In its basic form, it does not assume much theoretical knowledge of the mechanisms of spreading and mutating pathogens or the reaction of people and regulatory structures (governments, companies, etc. ). Instead, it estimates from the data directly. Its predictive ability allows testing hypotheses for different factors that positively or negatively contribute to the pandemic spread; be it school closures, emerging variants, etc. It can be used in mortality or hospital risk estimation from new cases, seroprevalence studies, assessing properties of emerging variants, and estimating excess mortality and its relationship with a pandemic.

Applications of time series analysis in epidemiology: Literature review and our experience during COVID-19 pandemic

Time series analysis is a valuable tool in epidemiology that complements the classical epidemiological models in two different ways: Prediction and forecast. Prediction is related to explaining past and current data based on various internal and external influences that may or may not have a causative role. Forecasting is an exploration of the possible future values based on the predictive ability of the model and hypothesized future values of the external and/or internal influences. The time series analysis approach has the advantage of being easier to use (in the cases of more straightforward and linear models such as Auto-Regressive Integrated Moving Average). Still, it is limited in forecasting time, unlike the classical models such as Susceptible-Exposed-Infectious-Removed. Its applicability in forecasting comes from its better accuracy for short-term prediction. In its basic form, it does not assume much theoretical knowledge of the mechanisms of spreading and mutating pathogens or the reaction of people and regulatory structures (governments, companies, etc. ). Instead, it estimates from the data directly. Its predictive ability allows testing hypotheses for different factors that positively or negatively contribute to the pandemic spread; be it school closures, emerging variants, etc. It can be used in mortality or hospital risk estimation from new cases, seroprevalence studies, assessing properties of emerging variants, and estimating excess mortality and its relationship with a pandemic.

"In situ bone flap" combined with vascular pedicled mucous flap to reconstruction of skull base defect.

At present, neuroendoscopy technology has made rapid development, and great progress has been made in the operation of lesions in the saddle area of the skull base. However, the complications of cerebrospinal fluid and intracranial infection after the operation are still important and life-threatening complications, which may lead to poor prognosis. To investigate the method of in situ bone flap combined with nasal septum mucosal flap for reconstruction of enlarged skull base defect by endonasal sphenoidal approach and to discuss its application effect. Clinical data of 24 patients undergoing transnasal sphenoidal endoscopic approach in the Department of Neurosurgery, Affiliated 2 Hospital of Nantong University from January 2019 to December 2022 were retrospectively analyzed. All patients underwent multi-layer reconstruction of skull base using in situ bone flap combined with nasal septum mucosa flap. The incidence of intraoperative and postoperative cerebrospinal fluid leakage and intracranial infection were analyzed, and the application effect and technical key points of in situ bone flap combined with nasal septum mucosa flap for skull base bone reconstruction were analyzed. There were 5 cases of high flow cerebrospinal fluid (CSF) leakage and 7 cases of low flow CSF leakage. Postoperative cerebrospinal fluid leakage occurred in 2 patients (8.3%) and intracranial infection in 2 patients (8.3%), which were cured after strict bed rest, continuous drainage of lumbar cistern combined with antibiotic treatment, and no secondary surgical repair was required. The patients were followed up for 8 to 36 months after the operation, and no delayed cerebrospinal fluid leakage or intracranial infection occurred during the follow-up. Computed tomography reconstruction of skull base showed satisfactory reconstruction after surgery. The use of in situ bone flap combined with vascular pedicled mucous flap to reconstruction of skull base defect after endonasal sphenoidal approach under neuroendoscopy has a lower incidence of cerebrospinal fluid leakage and lower complications, which has certain advantages and is worthy of clinical promotion.

Meta-analysis of the efficacy and safety of daratumumab in the treatment of multiple myeloma.

The treatment of multiple myeloma has significantly progressed over the past half-century. The purpose of this study was to perform a systematic review and meta-analysis in order to explore the efficacy and safety of daratumumab in treating multiple myeloma. To explore the efficacy and safety of daratumumab in treating multiple myeloma. A systematic literature search was performed using Chinese and English databases, including the China National Knowledge Infrastructure, Wanfang, China Biology Medicine, VIP, the Cochrane Library, Embase, and PubMed. The search encompassed studies in treating multiple myeloma with daratumumab, spanning from the inception of the database to June 2023. Revman 5.1 software was used for analysis. Our analysis included eight English articles and one Chinese article of high quality. The meta-analysis results indicated that compared to other therapies, daratumumab could improve the overall response rate (ORR) [odds ratio (OR) = 2.67, 95% confidence interval (CI) = 2.01, 3.53, Z = 6.85, P < 0.00001], complete remission (CR) (OR = 2.87, 95%CI = 2.16, 3.83, Z = 7.23, P < 0.00001) and progression-free survival (PFS) time (hazard ratio = 0.48, 95%CI = 0.38,0.60, Z = 6.54, P < 0.00001) in patients with multiple myeloma. These differences were statistically significant. Additionally, these results suggested that daratumumab increases the risk of neutropenia and thrombocytopenia with minimal effect on the incidences of anemia and upper respiratory tract infections. Daratumumab can improve ORR, CR rate, and PFS in patients with multiple myeloma. It also increases the risk of neutropenia and thrombocytopenia, necessitating careful monitoring during its clinical application.

First platelet transfusion refractoriness in a patient with acute myelocytic leukemia: A case report

Time series analysis is a valuable tool in epidemiology that complements the classical epidemiological models in two different ways: Prediction and forecast. Prediction is related to explaining past and current data based on various internal and external influences that may or may not have a causative role. Forecasting is an exploration of the possible future values based on the predictive ability of the model and hypothesized future values of the external and/or internal influences. The time series analysis approach has the advantage of being easier to use (in the cases of more straightforward and linear models such as Auto-Regressive Integrated Moving Average). Still, it is limited in forecasting time, unlike the classical models such as Susceptible-Exposed-Infectious-Removed. Its applicability in forecasting comes from its better accuracy for short-term prediction. In its basic form, it does not assume much theoretical knowledge of the mechanisms of spreading and mutating pathogens or the reaction of people and regulatory structures (governments, companies, etc. ). Instead, it estimates from the data directly. Its predictive ability allows testing hypotheses for different factors that positively or negatively contribute to the pandemic spread; be it school closures, emerging variants, etc. It can be used in mortality or hospital risk estimation from new cases, seroprevalence studies, assessing properties of emerging variants, and estimating excess mortality and its relationship with a pandemic.