Global burden of acute viral hepatitis and its association with socioeconomic development status, 1990-2019

BACKGROUND & AIMS

Acute viral hepatitis (AVH) represents an important global health problem; however, the progress in understanding AVH is limited because of the priority of combating persistent HBV and HCV infections. Therefore, an improved understanding of the burden of AVH is required to help design strategies for global intervention.

METHODS

Data on 4 major AVH types, including acute hepatitis A, B, C, and E, excluding D, were collected by the Global Burden of Disease (GBD) 2019 database. Age-standardized incidence rates and disability-adjusted life year (DALY) rates for AVH were extracted from GBD 2019 and stratified by sex, level of socio-demographic index (SDI), country, and territory. The association between the burden of AVH and socioeconomic development status, as represented by the SDI, was described.

RESULTS

In 2019, there was an age-standardized incidence rate of 3,615.9 (95% CI 3,360.5-3,888.3) and an age-standardized DALY rate of 58.0 (47.3-70.0) per 100,000 person-years for the 4 major types of AVH. Among the major AVH types, acute hepatitis A caused the heaviest burden. There was a significant downward trend in age-standardized DALY rates caused by major incidences of AVH between 1990 and 2019. In 2019, regions or countries located in West and East Africa exhibited the highest age-standardized incidence rates of the 4 major AVH types. These rates were stratified by SDI: high SDI and high-middle SDI locations recorded the lowest incidence and DALY rates of AVH, whereas the low-middle SDI and low SDI locations showed the highest burden of AVH.

CONCLUSIONS

The socioeconomic development status and burden of AVH are associated. Therefore, the GBD 2019 data should be used by policymakers to guide cost-effective interventions for AVH.

LAY SUMMARY

We identified a negative association between socioeconomic development status and the burden of acute viral hepatitis. The lowest burden of acute viral hepatitis was noted for rich countries, whereas the highest burden of acute viral hepatitis was noted for poor countries.

Socioeconomics and attributable etiology of primary liver cancer, 1990-2019

BACKGROUND

Primary liver cancer (PLC) is a major contributor to cancer-related deaths. Data on global and country-specific levels and trends of PLC are essential for understanding the effects of this disease and helping policymakers to allocate resources.

AIM

To investigate the association between the burden of PLC and socioeconomic development status.

METHODS

Cancer mortality and incidence rates were obtained from the Global Burden of Disease (GBD) 2019, and the data were stratified by country and territory, sex, and the Socio-demographic Index (SDI) level. The association between the attributable etiology of PLC and socioeconomic development status, represented using the SDI, was described. The attributable etiology of PLC included hepatitis B, hepatitis C, alcohol use, and nonalcoholic steatohepatitis. The association between the attributable etiology of PLC and SDI was further stratified by sex and geographical location. A confidence analysis was also performed based on bootstrap draw.

RESULTS

The age-standardized incidence rate of PLC was 6.5 [95% confidence intervals (CI): 5.9-7.2] per 100000 person-years, which decreased by -27.5% (-37.0 to -16.6) from 1990 to 2019. Several countries located in East Asia, South Asia, West Africa, and North Africa shouldered the heaviest burden of PLC in 2019. In terms of incidence rates, the first leading underlying cause of PLC identified was hepatitis B, followed by hepatitis C, alcohol use, and nonalcoholic steatohepatitis. Regarding stratification using the SDI, the incidence rate of PLC was the highest for high and middle SDI locations. Further, the leading attributable etiologies of PLC were hepatitis B for the middle and high middle SDI locations while hepatitis C and nonalcoholic steatohepatitis for the high SDI locations.

CONCLUSION

The pronounced association between socioeconomic development status and PLC burden indicates socioeconomic development status affects attributable etiologies for PLC. GBD 2019 data are valuable for policymakers implementing PLC cost-effective interventions.

Liver Chemistries in Patients With COVID-19 Who Were Discharged Alive or Died: A Meta-analysis

Although abnormal liver chemistries are linked to a higher risk of coronavirus disease 2019 (COVID-19)-related death, liver manifestations may be diverse and even confusing. Thus, we performed a meta-analysis of published liver manifestations and described the liver damage in patients with COVID-19 who died or discharged alive. We searched PubMed, Google Scholar, medRxiv, bioRxiv, the Cochrane Library, Embase, and three Chinese electronic databases through April 22, 2020. We analyzed pooled data on liver chemistries stratified by the main clinical outcome of COVID-19, using a fixed or random-effects model. In our meta-analysis of 19 studies, which included a total of 4,103 patients, the pooled mean alanine aminotransferase and aspartate aminotransferase levels were, respectively, 31.7 IU/L and 51.0 IU/L in the patients with COVID-19 who died and 27.7 IU/L and 32.9 IU/L in those discharged alive (both P < 0.0001). Compared with the patients discharged alive, those who died tended to have lower albumin levels but longer prothrombin time and higher international normalized ratio. Conclusion: In this meta-analysis, according to the main clinical outcome of COVID-19, we comprehensively describe three patterns of liver impairment related to COVID-19: hepatocellular injury, cholestasis, and hepatocellular disfunction. The patients who died from COVID-19 tended to have different liver chemistries from those discharged alive. Special caution should be given to the patients with a relatively higher index of liver chemistries.

Global burden of common cancers attributable to metabolic risks from 1990 to 2019

BACKGROUND

Non-alcoholic fatty liver disease (NAFLD) is usually accompanied by metabolic syndrome, which is associated with increased risk of cancer. To inform a tailored cancer screen in patients at higher risks, we estimated the global burden of cancer attributable to metabolic risks.

METHODS

Data of common metabolism-related neoplasms (MRNs) were derived from the Global Burden of Disease (GBD) 2019 database. Age-standardized, disability-adjusted life year (DALY) rates and death rates of patients with MRNs were extracted from the GBD 2019 database and stratified by metabolic risk, sex, age, and level of socio-demographic index (SDI). The annual percentage changes of age-standardized DALYs and death rates were calculated.

FINDINGS

Metabolic risks, consisting of high body mass index and fasting plasma glucose, contributed substantially to the burden of neoplasms, including colorectal cancer (CRC), tracheal, bronchus, and lung cancer (TBLC), etc. Globally, in 2019, there was an estimated age-standardized DALY rate (ASDR) of 234 (95% confidence interval [CI] 124-376) per 100,000 person years for neoplasms attributable to metabolic risks. ASDRs of MRNs were higher for CRC, TBLC, men, patients aged ≥50 years, and patients with high or high-middle SDI.

CONCLUSIONS

The findings of this study further underpin the correlation between NAFLD and intrahepatic and extrahepatic cancers and highlight the possibility of tailored cancer screening for the NAFLD population at higher risks.

FUNDING

This work was supported by the National Natural Science Foundation of China and Natural Science Foundation of Fujian Province of China.

Deep Learning-Based Universal Expert-Level Recognizing Pathological Images of Hepatocellular Carcinoma and Beyond

Background and Aims

We aim to develop a diagnostic tool for pathological-image classification using transfer learning that can be applied to diverse tumor types.

Methods

Microscopic images of liver tissue with and without hepatocellular carcinoma (HCC) were used to train and validate the classification framework based on a convolutional neural network. To evaluate the universal classification performance of the artificial intelligence (AI) framework, histological images from colorectal tissue and the breast were collected. Images for the training and validation sets were obtained from the Xiamen Hospital of Traditional Chinese Medicine, and those for the test set were collected from Zhongshan Hospital Xiamen University. The accuracy, sensitivity, and specificity values for the proposed framework were reported and compared with those of human image interpretation.

Results

In the human–machine comparisons, the sensitivity, and specificity for the AI algorithm were 98.0, and 99.0%, whereas for the human experts, the sensitivity ranged between 86.0 and 97.0%, while the specificity ranged between 91.0 and 100%. Based on transfer learning, the accuracies of the AI framework in classifying colorectal carcinoma and breast invasive ductal carcinoma were 96.8 and 96.0%, respectively.

Conclusion

The performance of the proposed AI framework in classifying histological images with HCC was comparable to the classification performance achieved by human experts, indicating that extending the proposed AI’s application to diagnoses and treatment recommendations is a promising area for future investigation.