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Monocyte chemoattractant protein-1 (MCP-1, also known as chemokine CCL2) is a vital chemokine that mediates inflammation in Alzheimer’s disease (AD). We analyzed the associations between the baseline plasma MCP-1 level, longitudinal cognitive changes, and genetic effects of CCL2 rs1024611 and its receptor, CC-chemokine receptor 2 (CCR2) rs1799864, in AD. In total, 310 AD patients and 66 mild cognitive impairment (MCI) patients were followed for 2 years, and 120 controls were recruited at baseline for comparison. After adjusting for covariates using one-way analysis of covariance, AD patients had higher plasma MCP-1 levels compared with MCI patients and controls, and severe AD patients had the highest levels. After adjusting for covariates using generalized estimating equation analysis, the results showed that the baseline MCP-1 level was significantly correlated with changes in the two-year Mini-Mental Status Examination (p = 0.046). The A allele of CCR2 rs1799864 was associated with a higher MCP-1 level in AD and MCI patients. In conclusion, plasma MCP-1 might reflect the risk and disease course of AD. A higher plasma MCP-1 level is associated with greater severity and faster cognitive decline. Additionally, the CCR2 polymorphism may play a role in the regulation of MCP-1/CCR2 signaling in AD.

Objectives: We investigated the impact of COVID-19 on tuberculosis (TB) case notification and treatment outcomes in Eswatini. Methods: A comparative retrospective cohort study was conducted using TB data from eight facilities. An interrupted time series analysis, using segmented Poisson regression was done to assess the impact of COVID-19 on TB case notification comparing period before (December 2018-February 2020, n = 1,560) and during the pandemic (March 2020–May 2021, n = 840). Case notification was defined as number of TB cases registered in the TB treatment register. Treatment outcomes was result assigned to patients at the end of treatment according to WHO rules. Results: There was a significant decrease in TB case notification (IRR 0.71, 95% CI: 0.60–0.83) and a significant increase in death rate among registrants during the pandemic (21.3%) compared to pre-pandemic (10.8%, p < 0.01). Logistic regression indicated higher odds of unfavorable outcomes (death, lost-to-follow-up, and not evaluated) during the pandemic than pre-pandemic (aOR 2.91, 95% CI: 2.17–3.89). Conclusion: COVID-19 negatively impacted TB services in Eswatini. Eswatini should invest in strategies to safe-guard the health system against similar pandemics.

Changeful seasonal influenza activity in subtropical areas such as Taiwan causes problems in epidemic preparedness. The Taiwan Centers for Disease Control has maintained real-time national influenza surveillance systems since 2004. Except for timely monitoring, epidemic forecasting using the national influenza surveillance data can provide pivotal information for public health response. We aimed to develop predictive models using machine learning to provide real-time influenza-like illness forecasts. Using surveillance data of influenza-like illness visits from emergency departments (from the Real-Time Outbreak and Disease Surveillance System), outpatient departments (from the National Health Insurance database), and the records of patients with severe influenza with complications (from the National Notifiable Disease Surveillance System), we developed 4 machine learning models (autoregressive integrated moving average, random forest, support vector regression, and extreme gradient boosting) to produce weekly influenza-like illness predictions for a given week and 3 subsequent weeks. We established a framework of the machine learning models and used an ensemble approach called stacking to integrate these predictions. We trained the models using historical data from 2008-2014. We evaluated their predictive ability during 2015-2017 for each of the 4-week time periods using Pearson correlation, mean absolute percentage error (MAPE), and hit rate of trend prediction. A dashboard website was built to visualize the forecasts, and the results of real-world implementation of this forecasting framework in 2018 were evaluated using the same metrics. All models could accurately predict the timing and magnitudes of the seasonal peaks in the then-current week (nowcast) (ρ=0.802-0.965; MAPE: 5.2%-9.2%; hit rate: 0.577-0.756), 1-week (ρ=0.803-0.918; MAPE: 8.3%-11.8%; hit rate: 0.643-0.747), 2-week (ρ=0.783-0.867; MAPE: 10.1%-15.3%; hit rate: 0.669-0.734), and 3-week forecasts (ρ=0.676-0.801; MAPE: 12.0%-18.9%; hit rate: 0.643-0.786), especially the ensemble model. In real-world implementation in 2018, the forecasting performance was still accurate in nowcasts (ρ=0.875-0.969; MAPE: 5.3%-8.0%; hit rate: 0.582-0.782) and remained satisfactory in 3-week forecasts (ρ=0.721-0.908; MAPE: 7.6%-13.5%; hit rate: 0.596-0.904). This machine learning and ensemble approach can make accurate, real-time influenza-like illness forecasts for a 4-week period, and thus, facilitate decision making.

In observational studies, how the magnitude of potential selection bias in a sensitivity analysis can be quantified is rarely discussed. The purpose of this study was to develop a sensitivity analysis strategy by using the bias-correction index (BCI) approach for quantifying the influence and direction of selection bias. We used a BCI, a function of selection probabilities conditional on outcome and covariates, with different selection bias scenarios in a logistic regression setting. A bias-correction sensitivity plot was illustrated to analyze the associations between proctoscopy examination and sociodemographic variables obtained using the data from the Taiwan National Health Interview Survey (NHIS) and of a subset of individuals who consented to having their health insurance data further linked. We included 15,247 people aged ≥20 years, and 87.74% of whom signed the informed consent. When the entire sample was considered, smokers were less likely to undergo proctoscopic examination (odds ratio (OR): 0.69, 95% CI [0.57-0.84]), than nonsmokers were. When the data of only the people who provided consent were considered, the OR was 0.76 (95% CI [0.62-0.94]). The bias-correction sensitivity plot indicated varying ORs under different degrees of selection bias. When data are only available in a subsample of a population, a bias-correction sensitivity plot can be used to easily visualize varying ORs under different selection bias scenarios. The similar strategy can be applied to models other than logistic regression if an appropriate BCI is derived.

In COPD, pulmonary hyperinflation causes decreased stroke volume thereby decreased oxygen pulse (O 2 P). While O 2 P flattening is related to myocardial ischemia in cardiac patients, O 2 P patterns have seldom been explored in COPD. The aims of the study were to investigate O 2 P-curve patterns and associated factors in COPD. Seventy-five patients with stable COPD were enrolled. The demographics, cardiac size, physiological measurements and stress EKG were compared among O 2 P-curve pattern groups. An algorithm to identify O 2 P-curve patterns was developed in 28 patients. In the remaining 45 patients after excluding two with poor effort, this algorithm revealed 20 (44%) flattening, 16 (36%) increasing, and nine (20%) decreasing patterns. The flattening-type group had lower body mass, cardiac size, and diffusing capacity, and larger lung volumes ( p  = 0.05–<0.0001) compared to the increasing-type group. During exercise, the flattening-type group had a lower operable O 2 P and more hyperventilation and dyspnea ( p  = 0.02–<0.01). None had ST-T changes. Most differences were related to body mass and mildly to inspiratory fraction. The decreasing-type group performed higher effort than the increasing-type group ( p  < 0.05). In conclusion, O 2 P flattening was common and was associated with reduced body mass and pulmonary hyperinflation rather than with myocardial ischemia. The decreasing-type may be caused by motivation to exercise.

Infection with hepatitis C virus (HCV) does not induce protective immunity, and re-exposure to HCV can reinfect the population engaging in high-risk behavior. An increasing incidence of acute hepatitis C infection in people living with HIV (PLWH) has been described in recent years. This retrospective cohort study was conducted in PLWH who completed HCV therapy between June 2009 and June 2020 at an HIV care hospital, to analyze their basic characteristics and risky behavior. Of 2419 patients, 639 were diagnosed with HCV infection and 516 completed the HCV therapy with a sustained virologic response. In total, 59 patients (11.4%) were reinfected with acute hepatitis C, and the median time to reinfection was 85.3 weeks (IQR: 57–150). The incidence of reinfection was 6.7 cases/100 person-years. The factors associated with reinfection were being male (AHR, 8.02; 95% CI 1.08–59.49), DAA (direct-acting antiviral) treatment (AHR, 2.23; 95% CI 1.04–4.79), liver cirrhosis (AHR, 3.94; 95% CI 1.09–14.22), heroin dependency (AHR: 7.41; 95% CI 3.37–14.3), and HIV viral loads <50 copies/mL at the follow-up (AHR: 0.47, 95% CI 0.24–0.93) in the subgroup of people who inject drugs (PWID). Amphetamine abuse (AHR: 20.17; 95% CI 2.36–172.52) was the dominant factor in the subgroup of men who have sex with men (MSM). Our study suggests that education and behavioral interventions are needed in this population to prevent reinfection.

Most reports regarding unplanned extubation (UE) are case-control studies with matching age and disease severity. To avoid diminishing differences in matched factors, this study with only matching duration of mechanical ventilation aimed to re-examine the risk factors and the factors governing outcomes of UE in intensive care units (ICUs). This case-control study was conducted on 1,775 subjects intubated for mechanical ventilation. Thirty-seven (2.1%) subjects with UE were identified, and 156 non-UE subjects were randomly selected as the control group. Demographic data, acute Physiological and Chronic Health Evaluation II (APACHE II) scores, and outcomes of UE were compared between the two groups. Logistic regression analysis was used to identify the risk factors of UE. Milder disease, younger age, and higher Glasgow Coma Scale (GCS) scores with more frequently being physically restrained (all p<0.05) were related to UE. Logistic regression revealed that APACHE II score (odds ratio (OR) 0.91, p<0.01), respiratory infection (OR 0.24, p<0.01), physical restraint (OR 5.36, p<0.001), and certain specific diseases (OR 3.79-5.62, p<0.05) were related to UE. The UE patients had a lower ICU mortality rate (p<0.01) and a trend of lower in-hospital mortality rate (p = 0.08). Cox regression analysis revealed that in-hospital mortality was associated with APACHE II score, age, shock, and oxygen used, all of which were co-linear, but not UE. The results showed that milder disease with higher GCS scores thereby requiring a higher use of physical restraints were related to UE. Disease severity but not UE was associated with in-hospital mortality.

In patients with chronic obstructive pulmonary disease (COPD), the independent contributions of individual lung function variables to outcomes may be lower when they are modelled together if they are collinear. In addition, lung volume measurements may not be necessary after spirometry data have been obtained. However, these hypotheses depend on whether forced vital capacity (FVC) can predict total lung capacity (TLC). Moreover, the definitions of hyperinflation and air trapping according to lung function variables overlap and need be clarified. Therefore, the aim of this study was to evaluate the relationships among various lung function parameters to elucidate these issues. Demographic data and 26 parameters of full lung function were measured in 94 men with COPD and analyzed using factor and correlation analyses. Factor analysis revealed five latent factors. Inspiratory capacity (IC)/TLC and residual volume (RV)/TLC were most strongly correlated with all other lung volumes. IC/TLC, RV/TLC, and functional residual capacity (FRC)/TLC were collinear and were potential markers of air trapping, whereas TLC%, FRC%, and RV% were collinear and were potential markers of hyperinflation. RV/TLC >0.4 (or IC/TLC <0.4) was comparable with the ratio of forced expiratory volume in one second (FEV ) and FVC <0.7. FVC% and FEV % were poorly correlated with TLC%. The correlation study showed that TLC%, RV/TLC, and FEV % could be used to represent individual latent factors for hyperinflation, air trapping, inspiration, expiration, and obstruction. Combined with diffusion capacity%, these four factors could be used to represent comprehensive lung function. This study identified collinear relationships among individual lung function variables and thus selecting variables with close relationships for correlation studies should be performed with caution. This study also differentiated variables for air trapping and lung hyperinflation. Lung volume measurements are still required even when spirometry data are available. Four out of 26 lung function variables from individual latent factors could be used to concisely represent lung function.

Invasive aspergillosis (IA) has become the emerging life-threatening disease in recent years. Influenza has been identified as an independent risk factor for IA. Vaccination is the most effective way to prevent influenza, while whether it can reduce IA in high-risk population still uncertain. We aimed to investigate the association between influenza vaccination and the risk of IA in high-risk population. We performed a population-based cohort study of people who qualified for government-funded influenza vaccination and were at high risk for IA at the start of the influenza season each year between 2016 and 2019. We utilized Taiwan's National Health Insurance Research Database to identify the influenza vaccination status and IA diagnosis during the follow-up period. We compared the risk of IA between people with and without vaccination using multivariable logistic regression analysis. Out of total 8,544,451 people who were eligible during the 3 influenza seasons, 3,136,477 (36.7%) were vaccinated. A total of 1179 IA cases with the incidence of 13.8 cases per 100,000 high-risk individuals were identified during the follow-up. Compared to non-vaccinated group, vaccinated individuals had a 21% risk reduction of IA (adjusted odds ratio 0.79, 95% confidence interval 0.70-0.90). Influenza vaccination was associated with a lower risk of IA among males, immunosuppressive conditions, malignancy, diabetes, and those having host factors according to the European Organization for Research and Treatment of Cancer and the Mycoses Study Group Education and Research Consortium. Influenza vaccination is recommended for high-risk population to reduce the risk of IA.

Mortality due to chronic obstructive pulmonary disease (COPD) is increasing. However, dead space fractions at rest (V /V ) and peak exercise (V /V ) and variables affecting survival have not been evaluated. This study aimed to investigate these issues. This retrospective observational cohort study was conducted from 2010-2020. Patients with COPD who smoked, met the Global Initiatives for Chronic Lung Diseases (GOLD) criteria, had available demographic, complete lung function test (CLFT), medication, acute exacerbation of COPD (AECOPD), Charlson Comorbidity Index, and survival data were enrolled. V /V and V /V were estimated (estV /V and estV /V ). Univariate and multivariable Cox regression with stepwise variable selection were performed to estimate hazard ratios of all-cause mortality. Overall, 14,910 patients with COPD were obtained from the hospital database, and 456 were analyzed after excluding those without CLFT or meeting the lung function criteria during the follow-up period (median (IQR) 597 (331-934.5) days). Of the 456 subjects, 81% had GOLD stages 2 and 3, highly elevated dead space fractions, mild air-trapping and diffusion impairment. The hospitalized AECOPD rate was 0.60 ± 2.84/person/year. Forty-eight subjects (10.5%) died, including 30 with advanced cancer. The incidence density of death was 6.03 per 100 person-years. The crude risk factors for mortality were elevated estV /V , estV /V , ≥2 hospitalizations for AECOPD, advanced age, body mass index (BMI) <18.5 kg/m , and cancer (hazard ratios (95% C.I.) from 1.03 [1.00-1.06] to 5.45 [3.04-9.79]). The protective factors were high peak expiratory flow%, adjusted diffusing capacity%, alveolar volume%, and BMI 24-26.9 kg/m . In stepwise Cox regression analysis, after adjusting for all selected factors except cancer, estV /V and BMI <18.5 kg/m were risk factors, whereas BMI 24-26.9 kg/m was protective. Cancer was the main cause of all-cause mortality in this study; however, estV /V and BMI were independent prognostic factors for COPD after excluding cancer. The predictive formula for dead space fraction enables the estimation of V /V , and the mortality probability formula facilitates the estimation of COPD mortality. However, the clinical implications should be approached with caution until these formulas have been validated.