The retrospective data analysis on the pedigree of nervous system diseases in children.

Nowadays, the development of diagnosis and treatment technology is constantly changing the pedigree and classification of nervous system diseases. Analyzing changes in earlier disease pedigrees can help us understand the changes involved in disease diagnosis from a macro perspective, as well as predict changes in later disease pedigrees and the direction of diagnosis and treatment. The inpatients of the neurology department from January 2012 to December 2020 in Hunan Children's Hospital were retrospectively analyzed. There were 36,777 patients enrolled in this study. The next analysis was based on factors like age, gender, length of stay (LoS), number of patients per month and per year (MNoP and ANoP, respectively), and average daily hospital cost (ADHE). To evaluate the characteristics of neurological diseases, we applied a series of statistical tools such as numerical characteristics, boxplots, density charts, one-way ANOVA, Kruskal-Wallis tests, time-series plots, and seasonally adjusted indices. The statistical analysis of neurological diseases led to the following conclusions: First, children having neurological illnesses are most likely to develop them between the ages of 4 and 8 years. Benign intracranial hypertension was the youngest mean age of onset among the various neurologic diseases, and most patients with bacterial intracranial infection were young children. Some diseases have a similar mean age of onset, such as seizures (gastroenteritis/diarrhea) and febrile convulsions. Second, women made up most patients with autoimmune diseases of the central nervous system. Treatment options for inherited metabolic encephalopathy and epilepsy are similar, but they differ significantly for viral intracranial infection. Some neurologic diseases were found to have seasonal variations; for example, infectious diseases of the central nervous system were shown to occur more commonly in the warm season, whereas, autoimmune diseases primarily appeared in the autumn and winter months. Additionally, the number of patients admitted to hospitals with intracranial infections and encephalopathy has dramatically dropped recently, but the number of patients with autoimmune diseases of the central nervous system and hereditary metabolic encephalopathy has been rising year over year. Finally, we discovered apparent polycentric distributions in various illnesses' density distributions. The study offered an epidemiological basis for common nervous system diseases, including evidence from age of onset, number of cases, and so on. The pedigree of nervous system diseases has significantly changed. The proportion of patients with neuroimmune diseases and genetic metabolic diseases is rising while the number of patients with infection-related diseases and uncertain diagnoses is decreasing. The existence of a disease multimodal model suggests that there is still a lack of understanding of many diseases' diagnosis and treatment, which needs to be improved further because accurate diagnosis aids in the formulation of individualized treatment plans and the allocation of medical resources; additionally, there is still a lack of effective treatment for most genetic diseases. The seasonal characteristics of nervous system diseases suggest the need for the improvement of sanitation, living conditions, and awareness of daily health care.

Modeling the effect of age on quantiles of the incubation period distribution of COVID-19.

BACKGROUND: The novel coronavirus SARS-CoV-2 (coronavirus disease 2019, COVID-19) has caused serious consequences on many aspects of social life throughout the world since the first case of pneumonia with unknown etiology was identified in Wuhan, Hubei province in China in December 2019. Note that the incubation period distribution is key to the prevention and control efforts of COVID-19. This study aimed to investigate the conditional distribution of the incubation period of COVID-19 given the age of infected cases and estimate its corresponding quantiles from the information of 2172 confirmed cases from 29 provinces outside Hubei in China. METHODS: We collected data on the infection dates, onset dates, and ages of the confirmed cases through February 16th, 2020. All the data were downloaded from the official websites of the health commission. As the epidemic was still ongoing at the time we collected data, the observations subject to biased sampling. To address this issue, we developed a new maximum likelihood method, which enables us to comprehensively study the effect of age on the incubation period. RESULTS: Based on the collected data, we found that the conditional quantiles of the incubation period distribution of COVID-19 vary by age. In detail, the high conditional quantiles of people in the middle age group are shorter than those of others while the low quantiles did not show the same differences. We estimated that the 0.95-th quantile related to people in the age group 23 ∼55 is less than 15 days. CONCLUSIONS: Observing that the conditional quantiles vary across age, we may take more precise measures for people of different ages. For example, we may consider carrying out an age-dependent quarantine duration in practice, rather than a uniform 14-days quarantine period. Remarkably, we may need to extend the current quarantine duration for people aged 0 ∼22 and over 55 because the related 0.95-th quantiles are much greater than 14 days.