Wei Wang

The prevalence of obesity or overweight has been increasing yearly, and there has been rapid development of effective, efficient, and low-cost mobile health (mHealth) apps for managing obesity. In 2016, more than 900 million adults worldwide were overweight, and more than 650 million adults were obese.¹ In 2019, 34.3% of Chinese adults were overweight and 16.4% were obese.² Approximately 250 000 mHealth apps were available in 2016,³ of which more than half involved weight, diet, and exercise.⁴ Usability, feasibility, and acceptability of mHealth apps for management of obesity have been shown in the experimental context.^5,6 One study showed that after a 12-week diet and exercise intervention, 30 individuals who used a weight loss app lost 5% of their body weight.⁷ In another study, after a 6-month diet intervention, the mHealth app group had a higher follow-up patient retention rate (100% vs 37%) and lower treatment costs ($855.15 vs $1428.00) than the weight management group that did not use an mHealth app.⁸

Purposes: This study aims to identify the comorbidity patterns of older men with lung cancer in China. Methods: We analyzed the electronic medical records (EMRs) of lung cancer patients over age 65 in the Jilin Province of China. The data studied were obtained from 20 hospitals of Jilin Province in 2018. In total, 1510 patients were identified. We conducted a rank–frequency analysis and social network analysis to identify the predominant comorbidities and comorbidity networks. We applied the association rules to mine the comorbidity combination with the values of confidence and lift. A heatmap was utilized to visualize the rules. Results: Our analyses discovered that (1) there were 31 additional medical conditions in older patients with lung cancer. The most frequent comorbidities were pneumonia, cerebral infarction, and hypertension. (2) The network-based analysis revealed seven subnetworks. (3) The association rules analysis provided 41 interesting rules. The results revealed that hypertension, ischemic cardiomyopathy, and pneumonia are the most frequent comorbid combinations. Heart failure may not have a strong implicating role in these comorbidity patterns. Cerebral infarction was rarely combined with other diseases. In addition, glycoprotein metabolism disorder comorbid with hyponatremia or hypokalemia increased the risk of anemia by more than eight times in older lung cancer patients. Conclusions: This study provides evidence on the comorbidity patterns of older men with lung cancer in China. Understanding the comorbidity patterns of older patients with lung cancer can assist clinicians in their diagnoses and contribute to developing healthcare policies, as well as allocating resources.

Patients with cancer often carry the dual burden of the cancer itself and other co-existing medical conditions. The problems associated with comorbidities among elderly cancer patients are more prominent compared with younger patients. This study aimed to identify common cancer-related comorbidities in elderly patients through routinely collected hospital discharge data and to use association rules to analyze the prevalence and patterns of these comorbidities in elderly cancer patients at different cancer sites. We collected the discharge data of 80,574 patients who were diagnosed with cancers of the esophagus, stomach, colorectum, liver, lung, female breast, cervix, and thyroid between 2016 and 2018. The same number of non-cancer patients were randomly selected as the control group and matched with the case group by age and gender. The results showed that cardiovascular diseases, metabolic diseases, digestive diseases, and anemia were the most common comorbidities in elderly patients with cancer. The comorbidity patterns differed based on the cancer site. Elderly patients with liver cancer had the highest risk of comorbidities, followed by lung cancer, gastrointestinal cancer, thyroid cancer, and reproductive cancer. For example, elderly patients with liver cancer had the higher risk of the comorbid infectious and digestive diseases, whereas patients with lung cancer had the higher risk of the comorbid respiratory system diseases. The findings can assist clinicians in diagnosing comorbidities and contribute to the allocation of medical resources.

Hepatocellular carcinoma (HCC) is a common and fatal cancer. People with HCC report higher odds of comorbidity compared with people without HCC. To explore the association between HCC and medical comorbidity, we used routinely collected clinical data and applied a network perspective. In the network perspective, we used correlation analysis and community detection tests that described direct relationships among comorbidities. We collected 14,891 patients with HCC living in Jilin Province, China, between 2016 and 2018. Cirrhosis was the most common comorbidity of HCC. Hypertension and renal cysts were more common in male patients, while chronic viral hepatitis C, hypersplenism, hypoproteinemia, anemia and coronary heart disease were more common in female patients. The proportion of chronic diseases in comorbidities increased with age. The main comorbidity patterns of HCC were: HCC, cirrhosis, chronic viral hepatitis B, portal hypertension, ascites and other common complications of cirrhosis; HCC, hypertension, diabetes mellitus, coronary heart disease and cerebral infarction; and HCC, hypoproteinemia, electrolyte disorders, gastrointestinal hemorrhage and hemorrhagic anemia. Our findings provide comprehensive information on comorbidity patterns of HCC, which may be used for the prevention and management of liver cancer.

Background: Medical informatics (MI) is a multidisciplinary field in which researchers pursue scientific exploration, problem-solving, and decision-making to facilitate the effective use of biomedical data, information and knowledge for the improvement of human health. The purpose of this study is to identify research fronts in the field of MI and ultimately elucidate research activities and trends in this field. Methods: This study used topic model to identify research topics in the field of MI based on the latent Dirichlet allocation method (LDA). And the topic cloud is utilized to visualize the research topics. For identifying the research front topics, we proposed the indicators of identifying research front topics. In addition, we investigated how front topics change over time, and divided them into five categories based on the life cycle theory. Results: The data were collected from 35981 published journal abstracts between 2007 and 2016. In the topic distribution of MI, we found that the scope of MI related research has become increasingly interdisciplinary, particular for medical data analysis. Also, in the analysis of research fronts of MI, we found that the use of natural language processing and medical text knowledge extraction play an essential role for systematic analysis and indexing of the underlying semantic contents. Conclusions: By categorizing the research fronts, the results shows that there are twelve growing, five stable and two declining research fronts. We hope that this work will facilitate greater exploration of the method of identifying the research fronts. Moreover, the findings of this study provide an insight on the research fronts and trends in MI.

Background: The safety of vaccines is a critical factor in maintaining public trust in national vaccination programs. This study aimed to evaluate the safety profiles of human papillomavirus (HPV) vaccines with regard to the distribution of adverse events (AE) across gender and age, and the correlations across various AEs using the Food and Drug Administration/Centers for Disease Control and Prevention Vaccine Adverse Event Reporting System (VAERS).Methods: For analyses, 27,348 patients aged between 9 and 25 years old with at least one AE reported in VAERS between the year of 2006 and 2017 were included. AEs were summarized into two levels: the lower level preferred term (PT) and higher level system organ classes (SOCs) based on the structure of Medical Dictionary for Regulatory Activities (MedDRA). A series of statistical analyses were applied on both levels of AEs. Zero-truncated Poisson regression and multivariate logistic regression models were first developed to assess the rate and risk of SOCs across age groups and genders. Pairwise Pearson correlation analyses and hierarchical clustering analyses were then conducted to explore the interrelationships and clustering pattern among AEs.Results: We identified 27,337 unique HPV vaccine reports between 2006 and 2017. Disproportional reporting of AEs was observed across age and gender in 21 SOCs (p< 0.05). The correlation analyses found most SOCs demonstrate weak positive correlations except for five pairs which were negatively correlated: skin and subcutaneous tissue disorders + injury poisoning and procedural complications; skin and subcutaneous tissue disorders + nervous system disorders; Skin and subcutaneous tissue disorders + pregnancy, puerperium and perinatal conditions; nervous system disorders + pregnancy, puerperium and perinatal conditions; pregnancy, puerperium and perinatal conditions + general disorders and administration site conditions. Nervous system disorders had the most AEs which contributed to 12,448 (46%) cases. In the further analyses of correlations between PT in nervous system disorders, the three most strongly correlated AEs were psychiatric disorders (r= 0.35), gastrointestinal disorders (r= 0.215), and musculoskeletal and connective tissue disorders (r= 0.261). We observed an inter-SOCs correlation of the PTs among AE pairs by nervous system disorders/psychiatric disorders/gastrointestinal disorders/musculoskeletal and connective tissue disorders.Conclusions: The analyses revealed a different distribution pattern of AEs across gender and age subgroups in 21 SOC level AEs. Correlation analyses and hierarchical clustering analyses further revealed several correlated patterns across various AEs. However, findings from this study should be interpreted with caution. Further clinical studies are needed to understand the heterogeneity of AEs reporting across subgroups and the biological pathways among the statistically correlated AEs.

Increasing evidence has shown that sex differences exist in Adverse Drug Events (ADEs). Identifying those sex differences in ADEs could reduce the experience of ADEs for patients and could be conducive to the development of personalized medicine. In this study, we analyzed a normalized US Food and Drug Administration Adverse Event Reporting System (FAERS). Chi-squared test was conducted to discover which treatment regimens or drugs had sex differences in adverse events. Moreover, reporting odds ratio (ROR) and P value were calculated to quantify the signals of sex differences for specific drug-event combinations. Logistic regression was applied to remove the confounding effect from the baseline sex difference of the events. We detected among 668 drugs of the most frequent 20 treatment regimens in the United States, 307 drugs have sex differences in ADEs. In addition, we identified 736 unique drug-event combinations with significant sex differences. After removing the confounding effect from the baseline sex difference of the events, there are 266 combinations remained. Drug labels or previous studies verified some of them while others warrant further investigation.