Analysis of death causes of residents in poverty-stricken Areas in 2020: take Liangshan Yi Autonomous Prefecture in China as an example

Background Continuous surveillance of death can measure health status of the population, reflect social development of a region, thus promote health service development in the region and improve the health level of local residents. Liangshan Yi Autonomous Prefecture was a poverty-stricken region in Sichuan province, China. While at the end of 2020, as the announcement of its last seven former severely impoverished counties had shaken off poverty, Liangshan declared victory against poverty. Since it is well known that the mortality and cause of death structure will undergo some undesirable changes as the economy develops, this study aimed to reveal the distribution of deaths, as well as analyze the latest mortality and death causes distribution characteristics in Liangshan in 2020, so as to provide references for the decision-making on health policies and the distribution of health resources in global poverty-stricken areas. Methods Liangshan carried out the investigation on underreporting deaths among population in its 11 counties in 2018, and combined with the partially available data from underreporting deaths investigation data in 2020 and the field experience, we have estimated the underreporting rates of death in 2020 using capture-recapture (CRC) method. The crude mortality rate, age-standardized mortality rate, proportion and rank of the death causes, potential years of life lost (PYLL), average years of life lost (AYLL), potential years of life lost rate (PYLLR), standardized potential years of life lost (SPYLL), premature mortality from non-communicable diseases (premature NCD mortality), life expectancy and cause-eliminated life expectancy were estimated and corrected. Results In 2020, Liangshan reported a total of 16,850 deaths, with a crude mortality rate of 608.75/100,000 and an age-standardized mortality rate of 633.50/100,000. Male mortality was higher than female mortality, while 0-year-old mortality of men was lower than women’s. The former severely impoverished counties’ age-standardized mortality and 0-year-old mortality were higher than those of the non-impoverished counties. The main cause of death spectrum was noncommunicable diseases (NCDs), and the premature NCD mortality of four major NCDs were 14.26% for the overall population, 19.16% for men and 9.27% for women. In the overall population, the top five death causes were heart diseases (112.07/100,000), respiratory diseases (105.85/100,000), cerebrovascular diseases (87.03/100,000), malignant tumors (73.92/100,000) and injury (43.89/100,000). Injury (64,216.78 person years), malignant tumors (41,478.33 person years) and heart diseases (29,647.83 person years) had the greatest burden on residents in Liangshan, and at the same time, the burden of most death causes on men were greater than those on women. The life expectancy was 76.25 years for overall population, 72.92 years for men and 80.17 years for women, respectively, all higher than the global level (73.3, 70.8 and 75.9 years). Conclusions Taking Liangshan in China as an example, this study analyzed the latest death situation in poverty-stricken areas, and proposed suggestions on the formulation of health policies in other poverty-stricken areas both at home and abroad.

Choosing the right COVID-19 indicator: crude mortality, case fatality, and infection fatality rates influence policy preferences, behaviour, and understanding

Individuals worldwide are overwhelmed with news about COVID-19. In times of pandemic, media alternate the usage of different COVID-19 indicators, ranging from the more typical crude mortality rate to the case fatality rate, and the infection fatality rate continuously. In this article, we used experimental methods to test whether and how the treatment of individuals with different types of information on COVID-19 is able to change policy preferences, individual and social behaviours, and the understanding of COVID-19 indicators. Results show that while the usage of the crude mortality rate proves to be more efficient in terms of supporting policy preferences and behaviours to contain the virus, all indicators suffer from a significant misunderstanding on behalf of the population.

COVID-19 in Solid Organ Transplant Recipient: Exploring Cumulative Incidence, Seroprevalence and Risk Factors for Disease Severity

Background: Solid organ transplant (SOT) recipients may be at increased risk for severe disease and mortality from COVID-19 because of immunosuppression and prolonged end-stage organ disease. As a transplant center serving a diverse patient population, we report the cumulative incidence and outcomes of SARS-CoV-2 infection in our cohort of SOT recipients. Methods: We prospectively included in this observational study SOT recipients with a functioning kidney (n = 201), pancreas ± kidney (n = 66) or islet transplant (n = 24), attending outpatient regular follow-up at the San Raffaele Hospital from February 2020 to April 2021. Antibodies to SARS-CoV-2 were tested in all patients by a luciferase immunoprecipitation system assay. Results: Of the 291 SOT recipients, 30 (10.3%) tested positive for SARS-CoV-2 during the study period and prevalence was not different among different transplants. The SARS-CoV-2 antibody frequency was around 2.6-fold higher than the incidence of cases who tested positive for SARS-CoV-2 RT-PCR. As for the WHO COVID-19 severity classification, 19 (63.3%) SOT recipients were mild, nine (30%) were moderate, and two were critical and died yielding a crude mortality rate in our patient population of 6.7%. Kidney transplant (OR 12.9 (1.1–150) p = 0.041) was associated with an increased risk for moderate/critical disease, while statin therapy (OR 0.116 (0.015–0.926) p = 0.042) and pancreas/islet transplant (OR 0.077 (0.007–0.906) p = 0.041) were protective. Conclusions: The incidence of SARS-CoV-2 infection in SOT recipients may be higher than previously described. Due to the relative high crude mortality, symptomatic SOT recipients must be considered at high risk in case of SARS-CoV-2 infection.

Clues To Mortality Trends And Their Related Factors In IgG4-Related Disease: A Japanese Single-Center Retrospective Study

Background: Few observations on the long-term prognosis have been conducted in immunoglobulin G4-related disease (IgG4-RD) patients with various organ involvement, not limited to autoimmune pancreatitis. Especially, mortality and its related factors in patients with IgG4-RD with various organ involvement are not well known. This study aimed to clarify mortality trends and its related factors in IgG4-RD with various organ involvement.Methods: We retrospectively reviewed the medical records of patients with IgG4-RD at a single center in Japan. We calculated the crude mortality rate and the standardized mortality ratio (SMR) using national Japan mortality statistics and investigated the cause of death. We performed Cox regression analyses to assess mortality-related factors.Results: A total of 179 patients with IgG4-RD were included and the median follow-up from diagnosis was 47 months (IQR 19-96). Ten patients (5.6%) in our cohort died during the follow-up period. The crude mortality rate was 11.1 per 1,000 person-years. According to national Japan mortality statistics, 11.6 age- and sex-matched deaths would have been expected to occur within the follow-up period, resulting in an SMR of 0.86 (95% confidence interval [CI] 0.41-1.59). Univariate Cox regression analyses indicated that the number of affected organs at diagnosis (hazard ratio [HR] 1.45, 95% CI 1.02-2.05), eGFR <45 mL/min/1.73m2 at diagnosis (vs. ≥45, HR 8.48, 95% CI 2.42-29.79), and the presence of malignancy during the clinical course (HR 3.93, 95% CI 1.10-14.02) had a significant impact on the time to death.Conclusions: Our findings suggested that IgG4-RD does not significantly affect long-term patient survival. On the other hand, multi-organ involvement and renal dysfunction as well as malignancy might be associated with higher mortality trends in IgG4-RD. Early detection and appropriate management of risk factors may improve the long-term prognosis of IgG4-RD.

Access to healthcare as an important moderating variable for understanding geography of immunity levels for COVID-19 - preliminary insights from Poland

Background. Underascertainment of COVID-19 burden and uncertainty in estimation of immunity levels is a known and common phenomenon in infectious diseases. We tested to what extent healthcare access (HCA) related supply/demand interfered with registered data on COVID-19 from Poland. Material and methods. We have run a multiple linear regressions model with interactions to explain geographical variability in seroprevalence, hospitalization (on voivodeship: NUTS-2 level) and current (beginning of the 4th wave: 15.09-21.11.2021) case notifications/crude mortality (on poviats: old NUTS-4 level) taking vaccination coverage and cumulative case notifications till so called 3rd wave as predictor variables and supply/demand (HCA) as moderating variables. Results. HCA with interacting terms (mainly demand) explained to the great extent the variance of current incidence and most variance in case of current mortality. HCA (mainly supply) is significantly moderating cumulative case notifications till the 3rd wave explaining the variance across seroprevalence. Conclusions. Seeking causal relations between vaccination or infection gained immunity level and current infection dynamics could be misleading without understanding socio-epidemiological context such as the moderating role of HCA (sensu lato). After quantification, HCA could be incorporated into epidemiological models for better prediction of real disease burden.

Clues to Mortality Trends and Their Related Factors in IgG4-related Disease: a Japanese Single-center Retrospective Study

Background: Few observations on the long-term prognosis have been conducted in immunoglobulin G4-related disease (IgG4-RD) patients with various organ involvement, not limited to autoimmune pancreatitis. Especially, mortality and its related factors in patients with IgG4-RD with various organ involvement are not well known. This study aimed to clarify mortality trends and its related factors in IgG4-RD with various organ involvement.Methods: We retrospectively reviewed the medical records of patients with IgG4-RD at a single center in Japan. We calculated the crude mortality rate and the standardized mortality ratio (SMR) using national Japan mortality statistics and investigated the cause of death. We performed Cox regression analyses to assess mortality-related factors.Results: A total of 179 patients with IgG4-RD were included and the median follow-up from diagnosis was 47 months (IQR 19-96). Ten patients (5.6%) in our cohort died during the follow-up period. The crude mortality rate was 11.1 per 1,000 person-years. According to national Japan mortality statistics, 11.6 age- and sex-matched deaths would have been expected to occur within the follow-up period, resulting in an SMR of 0.86 (95% confidence interval [CI] 0.41-1.59). Univariate Cox regression analyses indicated that the number of affected organs at diagnosis (hazard ratio [HR] 1.45, 95% CI 1.02-2.05), eGFR <45 mL/min/1.73m2 at diagnosis (vs. ≥45, HR 8.48, 95% CI 2.42-29.79), and the presence of malignancy during the clinical course (HR 3.93, 95% CI 1.10-14.02) had a significant impact on the time to death.Conclusions: Our findings suggested that IgG4-RD does not significantly affect long-term patient survival. On the other hand, multi-organ involvement and renal dysfunction as well as malignancy might be associated with higher mortality trends in IgG4-RD. Early detection and appropriate management of risk factors may improve the long-term prognosis of IgG4-RD.

The association between first and second wave COVID-19 mortality in Italy

Background The relation between the magnitude of successive waves of the COVID-19 outbreak within the same communities could be useful in predicting the scope of new outbreaks. Methods We investigated the extent to which COVID-19 mortality in Italy during the second wave was related to first wave mortality within the same provinces. We compared data on province-specific COVID-19 2020 mortality in two time periods, corresponding to the first wave (February 24–June 30, 2020) and to the second wave (September 1–December 31, 2020), using cubic spline regression. Results For provinces with the lowest crude mortality rate in the first wave (February–June), i.e. < 22 cases/100,000/month, mortality in the second wave (September–December) was positively associated with mortality during the first wave. In provinces with mortality greater than 22/100,000/month during the first wave, higher mortality in the first wave was associated with a lower second wave mortality. Results were similar when the analysis was censored at October 2020, before the implementation of region-specific measures against the outbreak. Neither vaccination nor variant spread had any role during the study period. Conclusions These findings indicate that provinces with the most severe initial COVID-19 outbreaks, as assessed through mortality data, faced milder second waves.

Using Satellite Images and Deep Learning to Identify Associations Between County-Level Mortality and Residential Neighborhood Features Proximal to Schools: A Cross-Sectional Study

What is the relationship between mortality and satellite images as elucidated through the use of Convolutional Neural Networks?Background: Following a century of increase, life expectancy in the United States has stagnated and begun to decline in recent decades. Using satellite images and street view images, prior work has demonstrated associations of the built environment with income, education, access to care, and health factors such as obesity. However, assessment of learned image feature relationships with variation in crude mortality rate across the United States has been lacking.Objective: We sought to investigate if county-level mortality rates in the U.S. could be predicted from satellite images.Methods: Satellite images of neighborhoods surrounding schools were extracted with the Google Static Maps application programming interface for 430 counties representing ~68.9% of the US population. A convolutional neural network was trained using crude mortality rates for each county in 2015 to predict mortality. Learned image features were interpreted using Shapley Additive Feature Explanations, clustered, and compared to mortality and its associated covariate predictors.Results: Predicted mortality from satellite images in a held-out test set of counties was strongly correlated to the true crude mortality rate (Pearson r = 0.72). Direct prediction of mortality using a deep learning model across a cross-section of 430 U.S. counties identified key features in the environment (e.g., sidewalks, driveways, and hiking trails) associated with lower mortality. Learned image features were clustered, and we identified 10 clusters that were associated with education, income, geographical region, race, and age.Conclusions: The application of deep learning techniques to remotely-sensed features of the built environment can serve as a useful predictor of mortality in the United States. Although we identified features that were largely associated with demographic information, future modeling approaches that directly identify image features associated with health-related outcomes have the potential to inform targeted public health interventions.

Invasive Fungal Infections Complicating COVID-19: A Narrative Review

Invasive fungal infections (IFIs) can complicate the clinical course of COVID-19 and are associated with a significant increase in mortality, especially in critically ill patients admitted to an intensive care unit (ICU). This narrative review concerns 4099 cases of IFIs in 58,784 COVID-19 patients involved in 168 studies. COVID-19-associated invasive pulmonary aspergillosis (CAPA) is a diagnostic challenge because its non-specific clinical/imaging features and the fact that the proposed clinically diagnostic algorithms do not really apply to COVID-19 patients. Forty-seven observational studies and 41 case reports have described a total of 478 CAPA cases that were mainly diagnosed on the basis of cultured respiratory specimens and/or biomarkers/molecular biology, usually without histopathological confirmation. Candidemia is a widely described secondary infection in critically ill patients undergoing prolonged hospitalisation, and the case reports and observational studies of 401 cases indicate high crude mortality rates of 56.1% and 74.8%, respectively. COVID-19 patients are often characterised by the presence of known risk factors for candidemia such as in-dwelling vascular catheters, mechanical ventilation, and broad-spectrum antibiotics. We also describe 3185 cases of mucormycosis (including 1549 cases of rhino-orbital mucormycosis (48.6%)), for which the main risk factor is a history of poorly controlled diabetes mellitus (>76%). Its diagnosis involves a histopathological examination of tissue biopsies, and its treatment requires anti-fungal therapy combined with aggressive surgical resection/debridement, but crude mortality rates are again high: 50.8% in case reports and 16% in observational studies. The presence of other secondary IFIs usually diagnosed in severely immunocompromised patients show that SARS-CoV-2 is capable of stunning the host immune system: 20 cases of Pneumocystis jirovecii pneumonia, 5 cases of cryptococcosis, 4 cases of histoplasmosis, 1 case of coccidioides infection, 1 case of pulmonary infection due to Fusarium spp., and 1 case of pulmonary infection due to Scedosporium.

Increased risk of death in COVID-19 hospital admissions during the second wave as compared to the first epidemic wave: a prospective, single-centre cohort study in London, UK

Background The second coronavirus disease (COVID-19) epidemic wave in the UK progressed aggressively and was characterised by the emergence and circulation of variant of concern alpha (VOC 202012/01). The impact of this variant on in-hospital COVID-19-specific mortality has not been widely studied. We aimed to compare mortality, clinical characteristics, and management of COVID-19 patients across epidemic waves to better understand the progression of the epidemic at a hospital level and support resource planning. Methods We conducted an analytical, dynamic cohort study in a large hospital in South London. We included all adults (≥ 18 years) with confirmed severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) who required hospital admission to COVID-19-specific wards between January 2020 and March 2021 (n = 2701). Outcome was COVID-19-specific in-hospital mortality ascertained through Medical Certificate Cause of Death. Results In the second wave, the number of COVID-19 admissions doubled, and the crude mortality rate dropped 25% (1.66 versus 2.23 per 100 person-days in second and first wave, respectively). After accounting for age, sex, dexamethasone, oxygen requirements, symptoms at admission and Charlson Comorbidity Index, mortality hazard ratio associated with COVID-19 admissions was 1.62 (95% CI 1.26, 2.08) times higher in the second wave. Conclusions Although crude mortality rates dropped during the second wave, the multivariable analysis suggests a higher underlying risk of death for COVID-19 admissions in the second wave. These findings are ecologically correlated with an increased circulation of SARS-CoV-2 variant of concern 202012/1 (alpha). Availability of improved management, particularly dexamethasone, was important in reducing risk of death.