Facial fracture is one of the most common injuries globally. Some types of facial fractures may cause irreversible damage and can be life-threatening. This study aimed to investigate the health burden of facial fractures at the global, regional, and national levels from 1990 to 2017.Facial fracture data, including the incidence, prevalence, and years lived with disability (YLDs) from 1990 to 2017, were obtained from the Global Burden of Disease study. We calculated the estimated annual percentage changes (EAPCs) to assess the changes of facial fractures in 195 countries or territories and 21 regions.From 1990 to 2017, the change in cases of facial fracture incidence was 39% globally, while the age-standardized incidence rate showed a downtrend with an EAPC of 0.00. Syria experienced a ten-fold increase in incidence cases with an EAPC of 9.2, and this condition is largely responsible for the global health burden of facial fractures. The prevalence and YLDs showed a similar trend worldwide as the incidence. Additionally, we found that the incidence, prevalence, and YLDs showed a discrepancy among various age groups with a gradual change of proportion over the past 28 years. The age-standardized rates (ASRs) of facial fractures were nearly twice for male than those for female from 1990 to 2017.EAPC showed a correlation with the ASRs of facial fractures and had no relationship with socio-demographic index. The proportion of children and elderly suffering from facial fractures slightly changed with time. The ratio of facial fractures between males and females was 2:1. These findings suggest that more targeted and specific strategies based on age and gender should be established in various countries and regions. Injuries place huge burden on all populations worldwide, leading to high morbidity or mortality, regardless of age, gender, or geographical region . ApproxiThe GBD study contains the data of 354 diseases and injuries in 195 countries or territories around the world including information about facial fractures, providing an opportunity for the exhaustive estimation of the distribution, burden, and trends of facial fractures in various countries and regions. GBD study produces the incidence, prevalence and years lived with disability to quantify the health loss for non-fatal disease such as facial fractures, thus contributing to the comparison of facial fractures with other health-damaging injuries or diseases. Moreover, GBD covers the distribution of facial fractures among gender, age, SDI, region and country across a range of years, showing a changing trend of facial fractures. Indeed, many injuries or diseases showed a specific pattern with age or gender and may correlate with the social development. Therefore, GBD study which includes global data and cross-country comparisons might prompt current understanding of complex and multifactorial diseases such as facial fractures. In this study, we analyzed the GBD 2017 data of facial fractures and provided empirical evidence for policy makers in various countries or territories to adequately utilize the limited medical resources and formulate more targeted policies according to corresponding social development level, age distribution and gender difference in order to protect people from suffering facial fractures.http://ghdx.healthdata.org/gbd-results-tool). Various indexes like “location”, “year”, “sex”, “cause” and “age” can be selected in the query tool according to specific study objectives. The official GBD website provides a detailed instruction on the general methods applied for GBD 2017 . In this study, we obtained data on the annual number of cases for the incidence, prevalence, and YLDs of facial fractures at all-age levels and their corresponding ASRs including the 95% uncertainty interval  from 1990 to 2017. The three measurements including incidence, prevalence and YLDs collectively indicate the global burden of facial fractures. YLDs were assessed by multiplying the number of people living with facial fractures and disability weight that qualifies the magnitude of health loss owning to facial fractures  online data source query tool  was 0.00 (−0.20 to 0.10), showing a downtrend, and the ASIR of facial fractures decreased from 100.47 per 100,000 persons to 98.47 per 100,000 persons over 28 years . In addiIn 2017, a high number of facial fractures incident cases was recorded in India , China , and USA , whereas a few incident cases were recorded in Northern Mariana Islands (36.68), Dominica (41.64), and American Samoa (43.22). Syria recorded the highest  ASIR in 2017, whereas Indonesia had the lowest ASIR  among the 195 countries or territories . Moreoveρ= –0.3842, P < 0.0001, ρ = 0.0036, P = 0.9603, From 1990 to 2017, the incident cases of facial fractures increased in 159 countries or territories, whereas the EAPC was negative in 48 countries. As shown in During the last 28 years, global prevalence of facial fractures increased by 54.39% from 1,819,732 in 1990 to 1,178,636 in 2017. By contrast, the age-standardized prevalence rate (ASPR) decreased worldwide, with an EAPC of −0.10 (−0.20 to 0.10). Similar to incidence, the fracture of face bones predisposed to occur in males , which was nearly twice more than that in females , and it was consistent with the prevalence rate after age standardization.ρ= −0.4418, P < 0.0001, ρ = 0.0150, P = 0.8352, Among the countries with various prevalence cases of facial fractures, the lowest ASPRs were recorded in Indonesia  and Mauritius , Northern Mariana Islands (0.60), and American Samoa 0.62, . SimilarFrom 1990 to 2017, in all regions, the incident, prevalent cases, and YLDs in connection with facial fractures were mainly assembled between the age of 15 and 49 years, followed by the age group of 50–69 years. In addition, in both sexes, these indices showed upward trends over time in people aged over 50 years and downward trends in people under 14 years. Notably, a dynamic equilibrium was observed in people aged 15–49 years across 28 years .Both in 1990 and 2017, the incidence rate of facial fractures showed double peaks at the age of 20–35 years and over 80 years in males  and 9B. In both sexes, the age-standardized incidence rate, prevalence rate and years lived with facial fractures showed a relatively stable trend globally from 1990 to 2017 .InterestOur analysis based on the GBD study displayed the latest worldwide patterns and the trends in the incidence, prevalence, and YLDs of facial fractures. From 1990 to 2006, the ASIR, ASPR, and age-standardized YLDs rate of facial fractures showed a slow downward trend globally. However, since 2007, among both females and males, the facial fracture burden increased slightly. The facial fracture burden was more skewed towards males than females worldwide. These findings will provide basis for policy makers to allocate medical resources reasonably and determine the underlying causes of facial fractures, thus decreasing the incidence rate of facial fractures. In addition, considering the various developing trends of facial fractures in different SDI quintiles, more targeted policy should be applied.As shown in The EAPC of ASIR, ASPR, and age-standardized YLDs had no correlation with SDI, because the fracture mechanisms and risk factors of facial fractures are irrelevant with the SDI or regions. Conversely, EAPC showed negative correlations with the ASRs of incidence, prevalence, and YLDs. Facial fractures aggravated the health and financial burden globally. The total hospitalization charges for facial fracture in the United States were $1.06 billion in 2008 . AccordiAs shown in Interestingly, unlike females, the incidence rate of facial fractures in males also peaked at people aged between 25–29 years both in 1990 and 2017. Moreover, as shown in In conclusion, the incident cases, prevalent cases, and YLDs of facial fractures increased worldwide. By contrast, the ASRs showed downtrends globally. EAPC showed a correlation with the ASRs of facial fractures and was hardly associated with SDI. The ratio between males and females approached 2:1. Besides, the proportion of children and elderly suffering from facial fractures slightly changed with time. Facial fractures occurred more in young men aged between 25–29 years and in the elderly aged over 80 years in both sexes.To our best knowledge, this study was the first to systematically investigate the changing trends of incidence, prevalence, and YLDs in facial fractures from 1990 to 2017 at the global, national, and regional levels. However, this study has some limitations. The accuracy of results depended on the quality and quantity of GBD data. For instance, the method utilized in the GBD study cannot provide access to cover all districts worldwide, and the opportunity to be diagnosed with facial fractures was not equal between developed and less developed countries because of differences in specialized medical care and imaging resources. Besides, in this text, we focused solely on the changing tendency of facial fractures in incidence, prevalence, and YLDs and did not analyze the trends of causes leading to facial fractures at the global, national, and regional levels from 1990 to 2017, which could be discussed in future research.Totally, government in various countries or regions should formulate more targeted policies to reduce the incidence of facial fractures. For example, attention should be paid more on traffic control in regions with high SDI whilst in regions with low SDI, improvement of diagnosis ability of facial fractures should be more concerned. It is also essential to promote relevant protection awareness on entertainments with high risk in public, especially among young males. Moreover, the enormous health burden of facial fractures caused by warfare should not be neglected. With the increasing pattern of life expectancy globally, elderly people should be more focused on.10.7717/peerj.10693/supp-1Supplemental Information 1Click here for additional data file.10.7717/peerj.10693/supp-2Supplemental Information 2Click here for additional data file.10.7717/peerj.10693/supp-3Supplemental Information 3Click here for additional data file.10.7717/peerj.10693/supp-4Supplemental Information 4Click here for additional data file.10.7717/peerj.10693/supp-5Supplemental Information 5Click here for additional data file.10.7717/peerj.10693/supp-6Supplemental Information 6Click here for additional data file.


The optimal range of serum uric acid (urate) associated with the lowest risk for developing cardiometabolic diseases is unknown in a generally healthy population. This 5-year cohort study is designed to identify the optimal range of serum urate. The data were collected from 13,070 Japanese between ages 30 and 85 at the baseline (2004) from the Center for Preventive Medicine, St. Luke’s International Hospital, Tokyo. We evaluated the number of subjects  of those free of the following conditions: hypertension, diabetes, dyslipidemia, and chronic kidney disease (CKD) over 5 years for each 1 mg/dL of serum urate stratified by sex. Furthermore, the odds ratios (ORs) for remaining free of these conditions were calculated with multiple adjustments. Except for truly hypouricemic subjects, having lower serum urate was an independent factor for predicting the absence of hypertension, dyslipidemia, and CKD, but not diabetes. The OR of each 1 mg/dL serum urate decrease as a protective factor for hypertension, dyslipidemia, and CKD was 1.153 , 1.164 (1.077–1.258), and 1.226 (1.152–1.306) in men; 1.306 (1.169–1.459), 1.121 (1.022–1.230), and 1.424 (1.311–1.547) in women, respectively. Moreover, comparing serum urate of 3–5 mg/dL in men and 2–4 mg/dL in women, hypouricemia could be a higher risk for developing hypertension  and CKD  in women, but not in men. The optimal serum urate range associated with the lowest development of cardiometabolic diseases was less than 5 mg/dL for men and 2–4 mg/dL for women, respectively. Both epidemiologically and in animal models, hyperuricemia is strongly associated with the development and progression of cardiovascular disease ,2. In thTherefore, we aimed to evaluate whether a J-shaped curve exists for serum urate with cardiometabolic diseases ) and to identify the optimal range of serum urate that is associated with the lowest risk for developing these conditions by a longitudinal design.This study included a single-center, large-scale, cross-sectional study and a 5-year longitudinal cohort study in Japan. We reviewed and used the database of health records from the Center for Preventive Medicine, St. Luke’s International Hospital, Tokyo, Japan between 2004 and 2009. The study subjects came to the center to have annual regular health check-up by themselves, and also provided a general history for comorbidities. Every subject and/or their companies paid for the examinations and each subject had identical physical and laboratory examinations including blood pressure. In general, the patients with any symptoms go to their hospital or clinic with Japanese government insurance, and therefore this study population was defined as a ‘generally healthy population’. Some of the findings from this database have already been published ,13,14,152. We calculated eGFR using the Japanese GFR equation: eGFR (mL/min/1.73m2) = 194 × serum creatinine −1.094 × age −0.287 (×0.739 if woman) [Hypertension is defined as a condition when subjects are on current antihypertensive medication and/or systolic blood pressure of more than or equal to 140 mmHg and/or diastolic blood pressure of more than or equal to 90 mmHg ,17. Bloof woman) p < 0.05 (two-tailed). Data are expressed as mean ± standard deviation or as percent frequency unless otherwise specified. Comparisons between two groups were performed with student t-tests for normally distributed variables, and χ2 analyses for categorical data. The maintaining factors for lacking hypertension, diabetes, dyslipidemia, and CKD in the period of over five years were evaluated both by crude models and by multivariable logistic regression models with adjustments of the age, BMI, smoking and drinking habits, serum urate, and the other cardiometabolic diseases. We also calculated odds ratios (ORs) in each group. When we analyzed logistic regression analyses in the longitudinal study, we excluded hypouricemic subjects because there was not a linear association between serum urate levels and the maintaining rate of lacking prevalence of these cardiometabolic diseases only in hypouricemic subjects. Moreover, we compared cumulative incidence of each cardiometabolic disease between hypouricemia and normouricemia to clarify whether J. curve phenomenon exists or not. In this analysis, we used propensity score matching to combine the other factors  into one parameter because the number of hypouricemic subjects were small (45 hypouricemic subjects).All the statistical analyses were performed using the SPSS Statistics software . The statistically significant level was set at probability We adhered to the principles of the Declaration of Helsinki. All data were collected and compiled in a protected computer database. Individual data were anonymous without identifiable personal information. Informed consent was obtained from all subjects by a comprehensive agreement method provided by St. Luke’s International Hospital. St. Luke’s International Hospital Ethics Committee approved the protocol for this study .The number of subjects with the new development of hypertension, diabetes, dyslipidemia, and CKD over 5 years was 1108/10,471 (10.6%), 318/12,495 (2.5%), 1454/7952 (18.3%), and 1961/12,578 (15.6%), respectively. We conducted additional analyses using four categories of serum urate levels; 2 or less (hypouricemia), from 2 to 4 mg/dL, from 4 to 6 mg/dL, and more than 6 mg/dL (hyperuricemia). We compared the relative risk for being free of cardiometabolic disease  among these four serum urate categories over a five-year period. J. curve phenomenon.To determine the optimal range of serum urate to prevent the development of cardiometabolic disease, we conducted a multivariable logistic regression analysis and calculated ORs for maintaining conditions without hypertension, diabetes, dyslipidemia, and CKD after excluding 45 hypouricemic subjects (30 men and 15 women) since we intended to exclude the effects of To evaluate factors that predict continued normotension, we analyzed 10,471 subjects after excluding 2599 subjects with hypertensions at baseline. After multivariable adjustments for age, BMI, smoking and drinking habits, diabetes, dyslipidemia, and CKD, lower serum urate was an independent factor that protects against the development of hypertension both in men  and women  (p = 0.093). In contrast, lower serum urate was not an independent protective factor for the development of diabetes in men (p = 0.24) , but it did not reach the significant  and women   and women   (We compared the cumulative incidence of cardiometabolic diseases between hypouricemic subjects (30 men and 15 women) and normouricemic subjects (3–5 mg/dL for 958 men and 2–4 mg/dL for 2192 women). The number of hypouricemic subjects were small, and we could not analyze ORs of diabetes both in men and women and dyslipidemia in women. After multiple adjustments age, BMI, smoking and drinking habits, diabetes, dyslipidemia, and CKD, hypouricemia tends to be higher risk for the development of CKD in women , but not reach significance , our study indicates that lower serum urate level is an independent protective factor for the development of cardiometabolic disease. We show that in heathy subjects, for each 1 mg/dL decrease of serum urate in men, there was an 18% increment in the protection from developing hypertension, a 16% increment against dyslipidemia, and a 23% increment against CKD. Compared to men, lower serum urate in women conferred greater odds for preventing the appearance of cardiometabolic diseases. Specifically, for each 1 mg/dL decrease of serum urate in women, there was a 31% increment in the protection from developing hypertension, a 12% increment against dyslipidemia, and a 42% increment against CKD.J. curve phenomenon only in women, and the optimal serum urate range associated with the less development of cardiometabolic diseases could be less than 5 mg/dL for men and 2–4 mg/dL for women in a generally healthy population.We also compared the cumulative incidence of cardiometabolic diseases over 5 years between hypouricemic subjects and normouricemic subjects (3–5 mg/dL for men and 2–4 mg/dL for women). The number of hypouricemic subjects was small (30 men and 15 women), and it might be difficult to apply the results to every population because of less power to analyze. However, our results suggest that hypouricemia could be a risk for development of hypertension and CKD in women, but not in men. Accounting for these results, we could see the Other studies have also showed an inverse correlation between serum urate and the incidence of cardiovascular diseases in subjects with serum urate levels lower than 4.5 mg/dL in men and 3.2 mg/dL in women ,21,22. TOur study also showed that hypouricemic subjects demonstrated greater risk for developing hypertension and CKD than normouricemic subjects in women. We postulate that the higher cumulative incidence of cardiometabolic diseases in hypouricemic women could well relate to the relatively frequent genetic loss of the urate transporter (URAT) in the Japanese population. Potential mechanisms for why this increases the risk for these conditions might relate to the marked uricosuria that may increase the risk for kidney disease, or potentially the possibility that a low serum urate may reduce antioxidant activity in the patients ,24. ImpoOur study points out the necessity of addressing the risk of hypouricemia in addition to hyperuricemia in the pathogenesis of cardiovascular disease. Our published data demonstrated that hypouricemia is associated with endothelium dysfunction . ConsistThis study showed a positive association between serum urate and cardiometabolic diseases, but most Mendelian randomization studies or meta-analyses suggested that elevated serum urate was only associated with gout ,27,28,29Our study has several limitations. First, this study is a retrospective single center study, which may have introduced selection bias. However, single center studies had some advantages of the similarity of the methodology. Second, we could not check the additional and withdrawal medication or gouty attacks over the periods. Some hyperuricemic subjects with gouty attacks might have medication especially non-steroidal anti-inflammatory drugs (NSAIDs), which might cause CKD or hypertension. However, our definition of each disease included medication use. Moreover, we did not exclude the subjects on medication for hyperuricemia or gout intentionally, because the serum urate levels on medication could be useful to evaluate the effects of serum urate on cardiometabolic diseases. However, there is a possibility of the influence of urate-lowering medications on the development (or prevention) of other cardiometabolic diseases, which thus may bias the present results. We additionally conducted the sensitivity analyses that excluded 373 (2.9%) subjects with urate-lowering medications . The resEven in the normal range, having higher serum urate could be a risk for hypertension, dyslipidemia, and CKD. The optimal serum urate range, which conferred the lowest risk for developing cardiometabolic diseases, could be less than 5 mg/dL for men and 2–4 mg/dL for women in a generally healthy population. These findings suggest that routine screening of serum urate is useful as a predictor for cardiometabolic diseases in primary care settings.


Sixteen patients (39.0%) presented with acute nephritic syndrome, of whom five (31.25%) developed rapidly progressive glomerulonephritis. In a mean follow-up time of 36.1 (±28.3) months, nine (21.95%) patients ended up with end-stage kidney disease, and eight (19.5%) died. Conclusion: We found that 3.2% of patients who underwent a native kidney biopsy in our institution during the past decade, for any reason, were identified as having some type of GD associated with a malignancy. Serology indicated a significant presence of ANA or MPO-ANCA antibodies in patients with nephritic syndrome and the absence of PLA2R antibodies in patients with membranous nephropathy.Aim: Glomerular diseases (GD) associated with malignancies  have unique features, which are important to recognize, in the light of the progress made in cancer therapy. We aimed to describe the clinical and histopathological characteristics of patients with GDAM in relation to the presence of circulating autoantibodies, pointing to potential immune pathogenic pathways connecting cancer to GD. Materials and Methods: The included patients were studied retrospectively on the basis of a kidney biopsy proving GD and a related biopsy to establish the diagnosis of AM. We recorded patients’ demographics, serological and laboratory parameters, histopathological findings, and the type of malignancy, GD, and therapy. Results: In total, 41 patients with GDAM, with a mean age of 63.1 (±10.7) years, were studied. In 28 (68.3%) cases, GD was associated with a solid tumor, and in 13 (31.7%) patients with a lymphoid malignancy. The most frequent histopathological pattern was membranous nephropathy (43.9%). Overall, at the time of GD diagnosis, 17% of the patients were positive for antinuclear antibodies (ANA), and 12.2% for antineutrophil cytoplasmic autoantibodies (ANCA), all against myeloperoxidase (MPO). In addition, 93.3% of the patients who had membranous nephropathy were negative for transmembrane glycoprotein M-type phospholipase A Glomerular diseases (GD) represent certain patterns of injury of the glomeruli, which may be associated with an inherited or acquired disorder and manifest with various clinical pictures and grades of severity, from asymptomatic urinary abnormalities to acute kidney insufficiency. Since the glomerulus and its surrounding Bowman’s capsule constitute the basic filtration unit of the kidney, long-standing or aggressive disease causing glomerular changes may result in irreversible kidney damage, chronic renal failure, and end-stage kidney disease. Genetic and environmental factors have been implicated in the pathogenesis of GDs, including infections, medications, and malignancies. GD associated with malignancies (GDAM) represent a rare, secondary form of glomerular lesion and a complication of cancer, which remains a challenge for both nephrologists and oncologists. They are not directly related to the tumor burden, invasion, or metastasis but are assumed to be caused by tumor cell products, such as hormones, growth factors, cytokines, and tumor antigens . RecogniEstimations on the frequency of GDAM are confounded by several factors, including the more aggressive screening for cancer in patients with nephrotic syndrome than in those with nephritic syndrome and the fact that a significant proportion of patients may not present malignancy-related symptoms at the discovery of GD . BesidesThe aim of this study was to describe the patterns of renal histopathology in patients with GDAM in relation to serological and clinical features and the type of malignancy. The detection of circulating autoantibodies in serum might also indicate potential immunopathogenic pathways connecting these cancer and GD.We retrospectively reviewed the medical records of all patients who were diagnosed with any type of GDAM in the period 2008–2018 in our hospital. GDAM might have been identified simultaneously with cancer, i.e., during the same admission workup, before, or after the diagnosis of cancer. Participants had to meet the following criteria: (i) histopathological diagnosis of GD in a native kidney biopsy, (ii) GD associated with active and biopsy-proven malignancy , (iii) diagnosis of the malignancy preceding or following the diagnosis of GD, typically within the same year, with the perquisite that, if the diagnosis of the malignancy followed the diagnosis of the GD, the patient should have not received immunosuppressive therapy for GD. Conversely, if the diagnosis of the malignancy preceded the diagnosis of GD, the patient should not have received chemotherapy or immunotherapy. Exclusion criteria: (i) patients with multiple myeloma and other plasma cell dyscrasia-induced GD, (ii) patients with a history of GD, who were diagnosed with cancer after treatment with immunosuppressants, (iii) patients who received the diagnosis of GD after the diagnosis of malignancy and had received chemotherapy and/or immunotherapy.2) with pulses of glucocorticoids, and/or plasma exchange. When used, plasma exchange was initiated for a total of 7 times for 2 weeks, with approximately 1 time the predicted plasma volume (estimated by the following formula: [0.065 × body weight (kg)] × [1 − hematocrit]) per session, using freshly frozen plasma combined with human albumin as the replacement solution. Likewise, for patients with severe nephrotic syndrome resistant to diuretics, who might receive cyclosporine, in a dose of 3 mg/kg of body weight, C0 and C2 levels were determined every three months.Patients’ demographics, biochemistry indexes including serum creatinine and the corresponding estimated glomerular filtration rate (eGFR), 24 h urine protein excretion, findings provided by microscopic urine analysis, hematological and serological tests (antinuclear antibodies (ANA), anti-double-stranded DNA (dsDNA), complement measurements , antineutrophil cytoplasmic autoantibodies (ANCA), anti-glomerular basement membrane autoantibodies, rheumatic factor at the time of kidney biopsy and thereafter every 3 months or by clinical indication were recorded. Histopathology findings were recorded, along with the extra-renal manifestations, therapies given for GDAM, and related responses. The main target of GD management was tumor resection and/or chemotherapy. When GDAM was manifesting as rapidly progressive glomerulonephritis (RPGN), with or without life-threatening symptoms , a short course of immunosuppression was administered, including intravenous cyclophosphamide , and death. Remission of GDAM was defined (i) for patients with nephritic syndrome and/or RPGN, as the improvement or stabilization of renal function combined with the resolution of glomerular hematuria and the cessation of hemodialysis and (ii) for patients with nephrotic syndrome, as the decrease in 24 h proteinuria, sustained and >50% of the initial measurement, which remained below the nephrotic range, combined with the disappearance of signs or symptoms of edema. The need of chronic dialysis was defined as ESKD. Estimation of GFR was done using the Modification of Diet in Renal Disease formula .Formalin-fixed and paraffin-embedded tissue sections were prepared for evaluation. Thirteen sections per paraffin block were obtained for light microscopy examination. Three eosin/hematoxylin-stained slides, as well as Periodic Acid Schiff (PAS), Silver, Masson and Congo-Red histochemical stains were evaluated in each case. Immunofluorescence examination on frozen tissue for the detection of immunoglobulins, such as IgG, IgA, and IgM, complement components C3 and C1q, and k and l light chains , was performed in each case. Slides were examined under a NIKON ECLIPSE 80i Immunofluorescence Microscope with a digital camera. In addition, a small part of the renal cortex was kept in glutaraldehyde solution 2.5%. In case there was not a clear-cut diagnosis from the other two aforementioned methods or clinical indications required electron microscopy studies, this specimen was proceeded adequately for electron microscopy examination with a FEI Morgagni 268 Electron Microscope equipped with a digital camera.A total of 41 cases with biopsy-proven GDAM were studied, including 28 (68.3%) patients with solid tumors and 13 (31.7%) with lymphoid malignancies. All included patients were Caucasians, with a mean age of 63.3 (±10.7) years at the time of the diagnostic kidney biopsy. There were 25 (60.9%) males. Patients were initially admitted in the internal medicine department or the hematology section, and during the workup, nephrology consultation was requested, due to the discovery of abnormal renal indexes.Forty-one consecutive patients were identified as having GDAM, accounting for 3.2% of the native kidney biopsies performed in the period 2008–2018. Twenty-four cases (58.5%) presented with new onset of nephrotic syndrome, while eight (33.3%) of them showed also some degree of glomerular hematuria. Sixteen (39.1%) additional patients had newly diagnosed nephritic syndrome, of whom six (37.5%) presented acute glomerulonephritis, six (37.5%) RPGN , and theHistopathological evaluation of the kidney biopsy specimens revealed MN in 18 cases (43.9%), membranoproliferative glomerulonephritis (MPGN) in 6 (14.6%), PI-GN in 7 (17.0%), minimal change disease (MCD) in 5 (12.2%), IgA nephropathy with crescent formation or IgA angiitis in 2 (4.9%), lupus-like (WHO class III) glomerulonephritis in 1 (2.4%), mesangial proliferative glomerulonephritis in 1 (2.4%), and focal segmental glomerulosclerosis (FSGS) in 1 (2.4%) .The diagnosis of GDAM was established concomitantly with the diagnosis of the malignancy in 14 (34.1%) cases, while in 13 (31.7%), the diagnosis of GDAM preceded the diagnosis of the malignancy, and in 14 (34.1%), it followed the diagnosis of cancer. Among patients with solid tumors , the mosPatient survival: During a mean follow-up time of 36.1 (±28.3) months, eight patients (19.5%) died. For four of them, the cause of death was related to metastatic disease, for three to complications linked to therapy, and for one to a cardiovascular event. Two patients were lost in the follow-up. In this series of patient, survival did not differ if GD was documented at the same time as the malignancy, prior, or after it.Outcome of GD: Nine (21.95%) patients ended up in chronic dialysis. Five of them had initially presented with severe nephrotic syndrome, which was associated with non-Hodgkin lymphomas in three occasions, acute lymphoblastic leukemia in one occasion, and ovary cancer in the remaining one. Nephrotic syndrome was caused by MN in three  of them 2 receptor (PLA2R) antibody [2R antibody. Thus, employment of this test, combined with an overall clinical assessment, is proven very helpful in providing a first filter to identify patients who need a more thorough screening for malignancies.Among patients with GDAM, in our institution, MN was the most prevalent histopathological diagnosis 43.9%). The proportion of patients with GDAM who had MN accounted for the 14.9% of the total cohort of patients with MN diagnosed during the same period. Most of the patients with nephrotic syndrome due to MN had also a solid tumor. The association of MN with solid tumors is well known ,8,9,10. %. The prantibody , which iFurthermore, as seen in our study, 12 out of 41 patients had positive serological findings, among which, 5 with lymphoid malignancies and membranoproliferative glomerulonephritis presented ANA, whereas, considering the patients with solid tumors, 5 presented PI-GN, histopathologically associated with MPO-ANCA, 1 PI-GN and ANA, and finally 1 lupus-like histopathologic findings and ANA. PI-GN was exclusively associated with solid tumors, while MPGN was mostly found in patients with lymphoid malignancies. The association of PI-GN with solid tumors has been repeatedly reported ,15,16,17Limitations of this study include the small number of patients, which is related to the fact that these disorders are particularly rare and their diagnosis can be very difficult, due to delayed onset of symptoms for certain malignancies and the presence of other secondary causes of kidney disease. We included only patients who received both diagnoses either concomitantly or within the same year, and thus, occasional patients, who manifested the GDAM out of this period, may have been missed. The probability of random incidence of two independent events cannot be excluded, but the closer the diagnosis between the two, the lower the probability of concurrency by chance. Yet, the lack of expected remission of GD after tumor resection or treatment was probably related to the fact that malignancies represent systemic diseases with obvious and occasionally not evident metastases.2R antibodies in the vast majority of patients with MN indicates the secondary cause of nephrotic syndrome, i.e., the malignancy, and thus may be very helpful in terms of clinical management to identify those patients with MN who need a more extensive workup for malignancies. Prompt diagnosis and individualized management, in accordance with the specific characteristics of the patient, the malignancy, and GD histopathology, are critical for these patients.In conclusion, in this series of patients with GDAM, serological findings such as ANA positivity and detection of ANCA antibodies were relatively frequent, implying a potential connection with immunopathogenesis, especially in the case of PI-GN. In contrast, the absence of anti-PLAThis is a retrospective, descriptive study, which was performed in compliance with the declaration of Helsinki for published research. No other ethics committee approval was required for this study. This article does not contain any studies with human participants or animals performed by any of the authors


This paper reviews the usefulness, current status, and potential of primary human hepatocytes (PHHs) in three-dimensional (3D) cultures, also known as spheroids, in the field of pharmacokinetics (PK). Predicting PK and toxicity means pharmaceutical research can be conducted more efficiently. Various in vitro test systems using human hepatocytes have been proposed as tools to detect hepatic toxicity at an early stage in the drug development process. However, such evaluation requires long-term, low-level exposure to the test compound, and conventional screening systems such as PHHs in planar (2D) culture, in which the cells can only survive for a few days, are unsuitable for this purpose. In contrast, spheroids consisting of PHH are reported to retain the functional characteristics of human liver for at least 35 days. Here, we introduce a fundamental PK and toxicity assessment model of PHH spheroids and describe their applications for assessing species-specific metabolism, enzyme induction, and toxicity, focusing on our own work in these areas. The studies outlined in this paper may provide important information for pharmaceutical companies to reduce termination of development of drug candidates. This review addresses in vitro evaluation of pharmacokinetics (PK) and associated toxicity, a crucial step in pharmaceutical research. In particular, this paper introduces a fundamental pharmacokinetic and toxicity assessment model of three-dimensional (3D) culture of primary human hepatocytes (PHHs), i.e., spheroids. Since predicting PK and toxicity means pharmaceutical research can be conducted more efficiently, these findings may prove advantageous to pharmaceutical companies.In drug development, ratios of progression/termination calculated from 812 oral small molecule drug candidates at AstraZeneca, Eli Lilly and Company, GlaxoSmithKline, and Pfizer were 0.17, 0.45, and 0.61 for candidate nomination, Phase I, and Phase II, respectively . During Experimental animals or animal-derived cells are usually used in toxicity studies, but unfortunately the metabolic enzymes and metabolic pathways of drugs differ fundamentally between humans and experimental animals . In factTherefore, various in vitro test systems using human hepatocytes have been proposed as tools to detect hepatic toxicity early in the drug screening process ,23. In pTo overcome these drawbacks, 3D-cultured hepatocyte systems, which can maintain functions specific to the human liver for a long period of time, have been developed . VariousAn initial search of papers containing the keywords “spheroid” and “3D model” garnered more than 2000 candidate results. Adding “liver”, “in vitro” and “human” to the search reduced this number to 140 papers. Further refinement via the keyword “primary” reduced that number to approximately 50. Furthermore, once the cell line was removed from these results, we found just 15 papers focusing on pharmacokinetic and toxicity studies using PHHs. In the following sections, we review the current status of applications of spheroid cultures of PHHs to PK see , includiHepatic drug metabolism plays a key role in drug PK in humans, and various pharmacokinetic models have been developed for evaluating candidate drugs. For example, Vorrink et al. reported that the metabolic activity of CYP1A2, CYP2C8, CYP2C9, CYP2D6, and CYP3A4 is more stable in 3D spheroid cultures than in 2D monolayer cultures . Specifi50 values of hepatotoxic drugs such as acetaminophen, bosentan, diclofenac, fialuridine, and troglitazone also indicated that spheroid culture is more sensitive to long-term exposure. In this context, it has been reported that the hepatic phenotype of 3D-cultured spheroids is well maintained during long-term culture (for at least 35 days), as evidenced by well-sustained ATP levels, albumin secretion, and the overall stability of CYP enzyme activities [PHH spheroids are useful as hepatotoxicity detection models because of their long-term viability compared with 2D cultures. Vorrink et al. reported an assessment of DILI for 123 compounds using PHH spheroids . With ATtivities . Moreovetivities . In additivities . Transcrtivities . They fotivities . Marked ®, Toyo Gosei, Tokyo, Japan). First, supporter (feeder) cells to promote hepatocyte adhesion were seeded and cultured for 4 days, and then PHHs were added. The hepatocytes formed spheroids, which maintained the cellular morphology for at least 21 days after seeding. The mRNA expression levels of multiple drug-metabolizing enzymes  at 2 and 7 days after seeding were similar to those of uncultured fresh hepatocytes. The spheroids were exposed to acetaminophen, diclofenac, lamotrigine, midazolam, propranolol, and salbutamol from 2 days after seeding, and metabolites were analyzed after 2 and 7 days of drug exposure by means of triple quadrupole liquid chromatography mass spectrometry (LC-MS/MS), with reference to our earlier report [N-glucuronate and salbutamol 4-O-sulfate conjugate, respectively. These metabolites, which are human-specific, were not observed in a conventional liver culture system [In 2014, we reported the usefulness of PHH spheroids for metabolite analysis , using 9r report . Continur report . Althougr report ,64,65. Oe system . Such me1.2 value of AST) as a criterion of “increased AST”. For albumin secretion, the 50% inhibitory concentration  of the test compound was used as an index. When the above compounds were evaluated under these conditions, the IC50 value of albumin and the F1.2 value of AST changed between day 7 and day 14, but remained stable between day 14 and day 21, except in the case of diclofenac. Therefore, a 14-day drug exposure period was considered to be sufficient to assess hepatotoxicity. For many of the compounds tested, there was a positive correlation between the IC50 value for albumin secretion and the F1.2 value for AST leakage, supporting their availability as indicators of drug-induced hepatotoxicity  [50 value for albumin secretion from Sph(f−) and the F1.2 value for AST leakage were equal to or less than those from Sph(f+) for all tested drugs. These results confirm that feeder cells have a protective effect on hepatocytes, suggesting that DILI can be more accurately assessed using by means of the Sph(f−) system.In 3D-culture methods, feeder cells are often used to improve hepatocyte adhesion, but these feeder cells also have a protective effect on the hepatocytes. In addition, toxic compounds can alter the enzyme activities of feeder cells, so only albumin secretion is available as a parameter for tracing toxicity. Therefore, we devised a system without feeder cells using MatrigelNY, USA) . For 14 In general, metabolic reactions are thought to contribute to the detoxification of drugs. However, since DILI can be induced by metabolites, liver toxicity might be increased in the enzyme-induced state in some cases. The Organisation for Economic Co-operation and Development (OECD), an international organization that aims to provide internationally agreed methodology for assessing the safety of chemicals, has recently proposed a draft test guideline for assessing CYP-inducing activity . This guAHR), constitutive androstane receptor , and pregnane X receptor , respectively [CYP induction is activated by the associated xenobiotic receptors and transcription factors; e.g., CYP1A2, CYP2B6, and CYP3A4 are induced by the activation of aryl hydrocarbon receptor , and it was found to be notably increased in an OPZ concentration-dependent manner. Eleven and 16 days after seeding (two and seven days after DTIC addition), amounts of unchanged DTIC and produced metabolite (AIC) in the supernatant were investigated. Without the addition of OPZ, AIC was detected in the supernatants, and approximately 50% of DTIC was metabolized after two days and about 75% after seven days. The amount of produced AIC was significantly increased and the amount of unchanged DTIC was significantly decreased when 30 μM OPZ was added. Moreover, hepatic function markers were measured on day 16 after seeding. These hepatic function markers were significantly decreased by the addition of OPZ and DTIC . HoweverDevices that maintain mechanical adhesion without the use of feeder cells are commercially available; an example is the NanoCulture Plates , a novel microstructural plate designed as a base for the 3D culture of cells/tissues. If spheroids become too large, sufficient oxygen does not reach the cells in the interior, and these cells may die. We have shown that PHH spheroids constructed on NCPs retain an acceptable size with sufficient gaps to remain viable .Post-seeding, the hepatocytes formed aggregates and maintained consistent ATP content for their 21 days of culture. Throughout the 21-day culture period, expression of CYP1A2, CYP2B6, CYP2C9, CYP2C19, CYP2D6, CYP2E1, and CYP3A4 mRNAs was detected. The addition of acetaminophen, diclofenac, lamotrigine, and midazolam (CYP substrate drugs) led to the formation of numerous metabolites and a corresponding decrease in the quantity of unchanged compounds. 1′-hydroxymidazolam, which was observed on day 2, was notably not detected on day 7. 4′-Hydroxydiclofenac acyl glucuronide was formed by hepatocytes on an NCP, whereas in previous studies, diclofenac acyl glucuronide had not been detected in human hepatocyte suspensions ,66. ThesThe supply of PHHs is limited, so it is necessary to use cultured PHHs, even though differences among lots in terms of metabolic enzyme activity and survival rate will inevitably occur. In order to overcome this issue, the use of hepatocytes isolated from liver-humanized mice, mature hepatocytes differentiated from liver progenitor cells, or hepatocytes derived from human iPS cells instead of PHHs has been investigated.® ) have been suggested to be useful for assessing human liver function. For example, Watari et al. cultured hepatocytes isolated from liver-humanized mice using collagen vitrigel membrane and evaluated their CYP activity, albumin secretion, and urea synthesis [In the case of hepatocytes isolated from liver-humanized mice, human hepatocytes are transplanted into mice with both immunodeficiency and hepatic failure. This generates a mouse model having a liver with the characteristics of human hepatocytes. When the liver is isolated and cultured, the original cells of the mouse disappear, and cells with characteristics similar to those of human hepatocytes are formed. These cells can be obtained in larger quantities than conventional cultured PHHs, and the engraftment success rate is high, so the number of samples that can be processed in the same lot is greatly increased . In addiynthesis . Similarynthesis . In the ynthesis . Althoug50 values in 3D cultures, suggesting the spheroids are more sensitive for detecting toxicity.On the other hand, it has been suggested that if hepatocytes with functions equivalent to those of PHHs could be created from human iPS cells, which can grow indefinitely, they could be a valuable alternative to PHHs . YamashiThus, there is a range of possible approaches to increase the versatility of hepatocyte spheroids, and further developments can be anticipated.PHH spheroids have some drawbacks in HTS application see  and may 1.2 values of AST leakage” in DILI studies (see The usefulness of the 3D-cultured PHH spheroid model for assessing species-specific metabolism, enzyme induction, and associated toxicity is already well established. Based on our long-term exposure studies with PHH spheroids, we speculate that in vitro assessment of liver function involves three stages, as shown in dies see . ThirdlyWe believe spheroids are powerful in vitro tools for predicting the pharmacokinetic and toxicological properties of human drug candidates and can contribute greatly to drug development. Spheroids are not restricted to PHHs and can be formed using various cells, devices, or methods. Therefore, they should be available for a wide range of research applications, and they offer significant advantages over 2D culture models. We hope that this review of the current status of PHH spheroids will encourage their broader application.


The purpose of this meta-analysis was to compare the efficacy of the modified Stoppa approach (MSA) and ilioinguinal approach (IA) in the treatment of anterior pelvic ring and acetabular fractures.A literature search was conducted using PubMed, Embase, and Cochrane database for articles that compared MSA and IA in the treatment of anterior pelvic ring and acetabular fractures. All the included articles were evaluated by 2 trained reviewers in accordance with the Cochrane Collaboration Handbook for potential risk. The Jadad decision algorithm and Downs and Black scores were also used to assess the quality of the included studies. The extracted data included operative time, intraoperative blood loss, reduction quality, clinical outcome, and complications.P = .0002), decreased intraoperative blood loss (P = .002), and provided better reduction quality (P = .03). Meanwhile, this meta-analysis suggests no significant difference between MSA and IA regarding clinical outcomes (P = .63) and complications (P = .34). The subgroup analysis of complications also showed no statistically significant difference between the 2 groups .Five articles were included in this meta-analysis, with 186 patients in the MSA group and 219 patients in the IA group. Compared with IA, MSA significantly shortened the operative time (According to this meta-analysis, the currently available evidence suggests that MSA can significantly shorten operative time, decrease intraoperative blood loss, and provide better reduction quality than IA in the treatment of anterior pelvic ring and acetabular fractures. In addition, in terms of clinical outcomes and complications, no significant differences were found between the 2 groups.Level of Evidence: Level IV, meta-analysis. Choosing the appropriate surgical approach for the treatment of anterior pelvic ring and acetabular fractures is the key to achieving an anatomical reduction of fractures and reducing complications.,3Acetabular fractures are one of the most difficult fractures to treat because of the complexity of anatomical surgery, and is considered one of the most challenging operations for orthopedic surgeons. proposed the ilioinguinal approach (IA), it has been widely used to treat pelvic ring and acetabular fractures. IA approach  to treat pelvic ring or acetabular fractures. MSA  OR acetabulum fracture [Title/Abstract])) AND ((Stoppa approach [Title/Abstract]) OR ilioinguinal approach [Title/Abstract]). The latest article search of this meta-analysis was up to May 10, 2019. At the same time, reference lists of the published studies were checked to identify any suitable references for inclusion.A literature search was conducted using PubMed, Embase, and Cochrane databases to perform a meta-analysis according to the Preferred Reporting Items for Systematic Reviews and Meta-Analysis statement.2.2The inclusion criteria were as follows:1.articles that compared the outcomes of MSA with those of IA in the treatment of anterior pelvic ring and acetabular fractures;2.articles that included at least one of the following measurements: operative time, intraoperative blood loss, reduction quality, clinical outcomes, and complications; and3.articles written in English.The exclusion criteria were as follows:1.articles that did not compare MSA and IA;2.articles from which data could not be extracted;3.articles not written in English; and4.articles that did not include at least one of the following measurements: operative time, intraoperative blood loss, reduction quality, clinical outcomes, and complications.2.3 was used to assess the quality of randomized controlled trials, and the Downs and Black scores was used to evaluate the quality of nonrandomized controlled trials. If differences exist, they will be discussed or resolved by the third author after reviewing the article.In accordance with the inclusion and exclusion criteria, 2 authors read the titles, abstracts, and full texts of the initial examination documents and screened out the documents that met the evaluation independently. Each included article was evaluated by 2 trained authors in accordance with the Cochrane Collaboration Handbook for potential risk, including selection, performance, detection, attrition, reporting, or other biases. Meanwhile, the Jadad decision algorithm2.4Two trained authors extracted the data from the articles, including the authors, year of publication, basic patient information, number of participants, follow-up time, operative time, intraoperative blood loss, reduction quality, clinical outcomes, and complications. If the 2 authors had a disagreement, the third senior professor made the final decision after reviewing the article carefully.P ≤ .05. Q and I2 statistics were performed to assess the heterogeneity among the included studies. Articles were considered as having no heterogeneity when the P value was >.10 and I2 was <50%. We used a random-effects model to calculate the combined effect size to obtain a more conservative result.The collected data were analyzed by using the RevMan 5  for meta-analysis. The weighted mean difference was used to assess the continuous variables, and the dichotomous variables were evaluated as odds ratios. The associated 95% confidence intervals (CIs) were calculated for each included study, and the statistically significant difference was set at 33.1,14–17 were included in the meta-analysis after full texts were reviewed. The Preferred Reporting Items for Systematic Reviews and Meta-Analysis flow diagram is illustrated in Figure According to the search strategy, 379 articles were identified from the PubMed, Embase, and Cochrane databases, and reference lists of the published studies after duplicate articles were removed; 42 articles remained after the titles and abstracts were screened. Only 5 studies3.2 and Downs and Black scores were also used to assess the quality of the included studies. Ma et al scored 4 points (maximum: 5), and other articles,15–17 scored 19 to 22 points (maximum: 30). This means that among these studies, 1 study was of higher quality, and the other articles were of lower quality .Four articles3.3.2,15,17 with 62 patients in the MSA group and 64 patients in the IA group. The meta-analysis revealed a significant difference between the 2 groups in intraoperative blood loss  and satisfactory outcomes (2–3 mm) were considered an excellent quality of reduction. Figure P = .03; 95% CI: 1.08–3.39).Three studies3.3.4 with 65 patients in MSA group and 72 in the IA group. Excellent (17–18 points) and good results (15–16 points) were considered better clinical outcomes. This meta-analysis revealed no statistically significant differences between the 2 groups , decrease intraoperative blood loss , and provide better reduction quality  as compared with IA in the treatment of anterior pelvic ring and acetabular fractures. In addition, in terms of clinical outcomes and complications, we found no significant differences between the 2 groups (P > .05).On the basis of the currently available evidence, this meta-analysis suggests that MSA can significantly shorten operative time . However, the total complication rate in IA group was 24.20%, which is significantly higher than that in the MSA group (17.74%). This may be caused by insufficient sample size and low quality of the included literature. Meanwhile, we assessed the reduction quality between the 2 groups. Anatomical and satisfactory outcomes were considered excellent qualities of reduction. We found that MSA had a significantly higher rate of reduction quality (84.46%) than IA (73.62%), but better reduction quality did not translate to better clinical results. No significant difference was found in clinical outcome between the 2 groups (P = .63). This may be related to the insufficient follow-up time.In this meta-analysis, we compared complication rates between MSA and IA groups in the treatment of anterior pelvic ring and acetabular fracture, and found no statistically significant differences between the 2 groups in terms of total complication rate, infection, and vascular and nerve injuries ( has reviewed and analyzed the difference between the 2 groups, which included 4 studies. Meena et al suggested that MSA can provide better reduction quality and lower operative time, which were verified in our research. However, in terms of complication rates, Meena et al suggested that MSA had a lower complication rate than IA, which was different from our results. This may be related to the number of articles included and the method of statistical calculations. We used a random-effects model to calculate the combined effect size to obtain a more conservative result.Few scholars have conducted systematic reviews or meta-analysis to compare the efficacy between the 2 approaches in the treatment of anterior pelvic ring and acetabular fractures. To the best of our knowledge, only 1 articleThis study has several limitations. First, only 5 studies with 405 patients were included in this meta-analysis and the sample sizes of the articles were not enough, which may be a potential source of bias. Second, the details of the operative techniques and preoperative combined injury in each patient were different. Third, although we searched the 3 most commonly used medical literature databases in strict accordance with the eligibility criteria, this meta-analysis included only 1 randomized controlled trial. Most of the included articles were retrospective studies, which may be a potential source of bias.Therefore, more high-quality randomized controlled trials are needed to compare the clinical outcomes, radiographic outcomes, and complication rates between MSA and IA in the treatment of anterior pelvic ring and acetabular fractures. Meanwhile, studies with long-term follow-up periods should also be conducted.5This meta-analysis suggests that for anterior pelvic ring and acetabular fractures, MSA can significantly shorten the operative time, reduce the intraoperative blood loss, and provide better reduction quality than IA. In addition, in terms of clinical outcomes and complications, we found no significant differences between the 2 groups. High-quality randomized controlled trials with long-term follow-up are needed to verify our results.Conceptualization: Hao Wu.Data curation: Hao Wu, LiJun Zhang, XiaoJun Jiang.Formal analysis: Hao Wu, LiJun Zhang, XiaoJun Jiang.Investigation: Hao Wu, LiJun Zhang, XiaoJun Jiang.Methodology: XiaoJun Jiang.Project administration: XiaoMin Guo.Resources: XiaoMin Guo.Software: XiaoMin Guo.Supervision: XiaoJun Jiang.Validation: XiaoMin Guo.Visualization: LiJun Zhang.Writing – original draft: Hao Wu, LiJun Zhang, XiaoJun Jiang.Writing – review & editing: LiJun Zhang, XiaoJun Jiang.


It suggested that (1) the COVID-19 epidemics at both levels in Hubei province had evident characteristics of significant global spatial autocorrelations and significant centralized high-risk outbreaks. (2) The COVID-19 epidemics were significantly associated with the natural factors, such as LA, MAXE, MNE, and RAE, -only at the county level, not at the prefecture level, from 2 February to 18 February 2020. (3) The COVID-19 epidemics were significantly related to the socioeconomic factors, such as RGP, RSP, TRS, and GDP, at both levels from 26 January to 18 February 2020. It is desired that this study enrich our understanding of the spatiotemporal patterns and influencing factors of the COVID-19 epidemic and benefit classified prevention and control of the COVID-19 epidemic for policymakers. The coronavirus disease 2019 (COVID-19) epidemic has had a crucial influence on people’s lives and socio-economic development. An understanding of the spatiotemporal patterns and influencing factors of the COVID-19 epidemic on multiple scales could benefit the control of the outbreak. Therefore, we used spatial autocorrelation and Spearman’s rank correlation methods to investigate these two topics, respectively. The COVID-19 epidemic data reported publicly and relevant open data in Hubei province were analyzed. The results showed that (1) at both prefecture and county levels, the global spatial autocorrelation was extremely significant for the cumulative confirmed COVID-19 cases (CCC) in Hubei province from 30 January to 18 February 2020. Further, (2) at both levels, the significant hotspots and cluster/outlier areas were observed solely in Wuhan city and most of its districts/sub-cities from 30 January to 18 February 2020. (3) At the prefecture level in Hubei province, the number of CCC had a positive and extremely significant correlation ( Since December 2019, coronavirus disease 2019 (COVID-19) emerged in Wuhan city, then rapidly spread throughout China [A cumulative total of 82,802 confirmed COVID-19 cases, including 3331 deaths, was reported in China as of 2 April 2020 . As of 1Shortly after the COVID-19 outbreak, some scholars carried out extensive research on COVID-19 in terms of pathogenesis, epidemiology, etiology, biology, virology, molecular biology, genomics, imaging, and clinical medicine ,18,19,20Time-series spatial statistics and influencing factor analyses can help unveil the spatiotemporal characteristics and evolution mechanism of the epidemic, consequently assisting scientific decision making for the prevention and control of the epidemic. Previous studies investigated the transmission network , spatialAs for the influencing factors of the COVID-19 epidemic, previous literature showed that meteorological variables such as temperature and humidity on the large spatial scales were mainly investigated. Sun et al. (2020)  proposedThe COVID-19 epidemic has been so far a pandemic, posing a severe threat to public health and social and economic development. Most recently, Liu et al. (2020)  investigTherefore, this study attempts to investigate the time-series spatial autocorrelation patterns and natural, social, and economic factors influencing the COVID-19 epidemic in Hubei province systematically from both prefecture and county levels using ArcGIS  spatial statistics and Spearman’s rank correlation methods. The aims of the study are to (i) enrich research on the epidemic temporal and spatial evolution and (ii) provide beneficial information for scientific prevention and control of COVID-19 epidemics.To the best of our knowledge, this study was one of the first to investigate the spatial statistics and influencing factors of the COVID-19 epidemic on multiple scales. Four scientific contributions of this paper can be condensed as follows. First, a new, multiple-scale, and time-series geodatabase of Hubei province was constructed for the COVID-19 epidemic study, offering a finer spatial scale than that in the previous studies. Second, the global and local spatial autocorrelation methods were combinedly adopted in the detection of the areas of hotspots, clusters, and outliers for identification of the high-risk areas of the COVID-19 epidemic on multiple scales. Third, natural, social, and economic indicators were comprehensively considered to study the potential influencing factors at the prefecture and county levels of the COVID-19 epidemic. Lastly, except for the regular population indicators, the Baidu migration index (BMI) was used in the COVID-19 epidemic study as an alternative indicator signifying a population mobility factor.4 km2, accounting for 1.94% of the country’s total land area. The terrain is mountainous in the east, west, and north and low in the central region, with an incomplete basin opening slightly to the south. The most area within the province has a subtropical monsoon humid climate, with an average annual temperature of 15 °C to 17 °C and average annual precipitation of 800 mm to 1600 mm. At present, Hubei province has 12 prefecture cities , one autonomous prefecture (Enshi Tujia and Miao Autonomous Prefecture), one forest prefecture (Shennongjia forest prefecture), and three county level cities  directly under the provincial government, collectively referred to as 17 prefecture cities in the following analyses. The county level administrative units in Hubei province include 39 municipal districts, 22 county level cities , 36 counties, and two autonomous counties  . The procounties . As of t3 km2. As of the end of 2019, the registered population and resident population in Wuhan city was 9.08 × 106 and 14.18 × 106, respectively. As of the end of 2018, there were 6.34 × 103 health institutions, 9.59 × 104 beds, 7.48 hospital beds per thousand people, 10.67 × 104 health care personnel, and 3.42 doctors per thousand people in Wuhan city. The reported incidence rate of class A and B infectious diseases was 18 per ten thousand people for the year 2018.Wuhan city, the capital of Hubei province, is located in the eastern part of the Jianghan Plain and the middle reach of the Yangtze River. It is known as “the thoroughfare of nine provinces” due to its national comprehensive transportation hub. The land area of Wuhan city is 8.57 × 10Five kinds of datasets from different sources were used in this study, which included COVID-19 data, Baidu migration index data, social and economic statistical data, terrain (DEM), and geographic base map data as well.The Paper. This dataset from The Paper was compiled to two sub-datasets, i.e., the provincial level and prefecture level datasets of cumulative and daily new confirmed COVID-19 cases nationwide in China. As a result, four kinds of COVID-19 epidemic datasets from different sources were collected and preprocessed according to three spatial statistical scales, i.e., the provincial, prefecture, and county levels.First, the cumulative and daily new confirmed COVID-19 cases (23 January to 18 February 2020) for the 17 prefecture cities in Hubei province were obtained manually from the official website of the Hubei Provincial Health Committee. They were compiled to a dataset of prefecture level confirmed COVID-19 cases in Hubei province. Except for the districts and cities under the jurisdiction of Wuhan city, whose county level COVID-19 epidemic data were not reported publicly during the study period, the COVID-19 epidemic data (23 January to 18 February 2020) for the other counties  in Hubei province were obtained by hand from the official websites/newspapers/social media accounts of the prefecture governments or their affiliated health committees, and were compiled to a dataset of the county level confirmed COVID-19 cases. Additionally, a dataset for the daily new confirmed COVID-19 cases  at the prefecture level in China was collected and provided by the Meishu Class news agency, Second, the prefecture level data of the Baidu migration index (BMI) were obtained from the Baidu Smart Eye Map . BMI represents a relative flow of population migration from a city to another in percentage, which can be applied in comparison to population migration at the same level of prefecture cities or provinces at present. We collected the data from Wuhan city and the other cities in Hubei province except for Shennongjia forest prefecture for the 16 days from 17 January to 1 February 2020, a period of massively large population movements and gathering for the Chinese Lunar New Year holidays traditionally. The Baidu Smart Eye Map does not provide the county level data of BMI, resulting in a lack of county level Spearman’s rank correlation between BMI and the number of CCC in this paper.Third, the statistical data for the land area (LA), population density (PD), registered population (RGP), resident population (RSP), regional gross domestic product (GDP), and total retail sales of consumer goods (TRS) at the prefecture and county levels in Hubei province were obtained from the 2019 Statistical Yearbook of Hubei and the 2018 statistical yearbooks of the relevant prefecture cities in Hubei province, respectively. These data include only the yearly statistical figures in 2018 (a few in 2017).http://www.resdc.cn). The DEM data were derived from the SRTM (Shuttle Radar Topography Mission) data of the US space shuttle Endeavour in 2003. Zonal statistics in ArcGIS 10.7 were adopted to achieve four parameters, i.e., minimum elevation (MINE), maximum elevation (MAXE), mean elevation (MNE), and range of elevation (RAE) for each prefecture city and each county (or district) in Hubei province.Forth, the terrain  raster data of Hubei province with 90 m spatial resolution were downloaded from the Resource and Environmental Science Data Center of the Chinese Academy of Sciences  in the ArcGIS shapefile format were acquired from the Resource and Environmental Science Data Center of the Chinese Academy of Sciences. We updated them according to the Tianditu website, a national online platform for common geospatial information services in China. The county level geographic base map of China (1999) in the ArcGIS shapefile format was acquired from the website of the former National Bureau of Surveying and Mapping. The prefecture level and county level administrative maps of Hubei province were clipped from the relevant maps of China mentioned above with ArcGIS 10.7. Some county level borders in Hubei province were edited and updated with ArcGIS 10.7 according to the 2018 standard maps of the related prefecture cities in Hubei province, downloaded from the Hubei Natural Resources Department.All of these data were double-checked and integrated carefully into an ArcGIS file Geodatabase for the COVID-19 epidemic study with a focus on Hubei province, of which the spatial feature data were linked with the attribute data by a common field, the geocode of an administrative area. It is worth noting that the time of the first confirmed case reported publicly varied from place to place, particularly for the county level data. Consequently, the periods of our studies for different scales were also various.Two kinds of statistical approaches were used in the current study, of which the significant confidence levels were both 95% for the statistical tests.n is the total number of spatial units, x is an attribute of interest from a spatial unit, x, ijw is an element of the spatial weight matrix used to quantify the spatial relationship between spatial unit i and j (I ≠ j), and Firstly, spatial autocorrelation technology was used to test whether the confirmed COVID-19 cases at the provincial, prefecture and county levels had significant global or local spatial autocorrelation characteristics, which was achieved by using Spatial Statistics and ModelBuilder Tools of ArcGIS 10.7. It has been widely used to study the spatial distribution of population ,39, regip-value must be applied to determine statistical significance together with Moran’s I index. In this study, the global spatial autocorrelation was used to detect spatial characteristics of the confirmed COVID-19 cases and to analyze the overall spatial correlation within the entire study area.If Moran’s I index is statistically positive, there is a positive correlation in the spatial distribution, indicating a spatial clustering effect; otherwise, a negative spatial correlation exists. If Moran’s I index is zero, there is a random distribution. Statistical Z-score and i*. Local Moran’s I is the decomposition of global Moran’s I into various sub-regional units, also known as Local Indicators of Spatial Association (LISA) [i*) of ArcGIS was used to identify the spatial association between hotspots and cold spots of COVID-19 cases with a statistical significance [Local spatial autocorrelation is commonly characterized by local Moran’s I and Getis-Ord Gn (LISA) . In thisn (LISA)  of ArcGIificance  on diffeρ, which is formulated as Equation (2).n is the total number of samples, ix and iy is the rank of the variable iX and iY, respectively, X and Y, respectively, cov is the covariance of x and y, xS and yS is the product of their standard deviations, respectively, and ρ denotes the Spearman’s rank correlation coefficient.Secondly, Spearman’s rank correlation of SPSS 22 was used to examine relationships between the confirmed COVID-19 cases and the natural, population, and economic factors at the prefecture and county levels. The descriptive statistical results showed that the COVID-19 epidemic and relevant data do not meet the prerequisites of Pearson correlation analysis due to their non-Gaussian normal distributions, spatial autocorrelations, and possible nonlinear relationships. Generally, Spearman’s rank correlation is a suitable nonparametric estimator to estimate the correlation between two variables whose statistical distributions are unknown or non-Gaussian, and the relationship between these variables does not need to be linear . It is cρ is, the stronger the relationship between the two variables is. Like Pearson’s coefficient, the absolute value of Spearman’s ρ ranging 0.8~1 indicates a correlation is extremely strong, 0.6~0.8, strong, 0.4~0.6, moderate, 0.2~0.4 weak, and 0~0.2, no correlation. The Spearman’s rank correlation has been used in recent studies on hemorrhagic fever with renal syndrome [This coefficient varies between −1 and +1, with +1 (−1) signifying a perfect positive (negative) correlation between the two variables. The greater the absolute value of Spearman’s syndrome , urban hsyndrome , ozone psyndrome , COVID-1syndrome , and so i*) tools of ArcGIS 10.7. Primarily, this study used 34 spatial units nationwide for the provincial level spatial statistics analysis from 19 January to 18 February 2020, 359 spatial units nationwide for the prefecture level spatial statistics analysis from 19 January to 18 February 2020, 103 spatial units province-wide, including 38 counties, 39 districts, 22 county level cities, three prefecture cities, and Shennongjia forest prefecture for the county level spatial statistics analysis mainly from 30 January to 18 February 2020.This section analyzed the time-series variation characteristics of global spatial autocorrelation and local spatial autocorrelation of the COVID-19 epidemic in Hubei province from three scales: the provincial, prefecture, and county levels by using Global Moran’s I, cluster and outlier analysis, and hot spot analysis  for the number of CCC at the provincial  level nationwide.The results indicated that from 19 January to 18 February 2020, there was no significant global spatial autocorrelation  analysis results  showed that from 19 January to 18 February 2020, Hubei province was the only significant high-low outlier area (the 95% confidence) of the number of CCC in China nationwide during these 31 days while some surrounding provinces  were the significant low-high outlier areas.i*) results  showed that Hubei province was the only extremely significant hotspot area (the 99% confidence) for the number of CCC at the provincial level in China nationwide during these 31 days from 19 January to 18 February 2020.The inverse distance-based hot spot analysis  for the prefecture level CCC nationwide, which may be due to the insufficient amount of the prefecture cities reporting the number of CCC in China during this period. On 23 January 2020, there was a significant global spatial autocorrelation  for the prefecture level CCC nationwide. From 24 January to 18 February 2020, the prefecture level cumulative COVID-19 confirmed cases demonstrated an extremely significant global spatial autocorrelation , indicating a very significant concentrated distribution for the nationwide prefecture level COVID-19 cases. The trend of Moran’s I index in The Anselin Local Moran’s I (ALMI) results revealed that from 19 January to 18 February 2020, most prefecture cities in Hubei province were the significant high-high cluster areas (the 95% confidence) for the prefecture level CCC while some prefecture cities neighboring Hubei province were low-high outlier areas (the 95% confidence) nationwide. i*) results  on 11 February 2020. For the same reason, after 12 February 2020, Xiaogan city became a nonsignificant area compared to Wuhan city. Although the other prefecture cities in Hubei province also had a large number of CCC compared to the other domestic cities in China, they were not significant hotspots due to the oddly high CCC in Wuhan city. The results suggested that Wuhan city was the prefecture city with the most severe and intensive COVID-19 epidemic in Hubei province over the study period and that Huanggang city and Xiaogan city were also among the prefecture cities with the most severe and intensive COVID-19 epidemic in Hubei province for some time. In all, it was found from the prefecture level that Wuhan city had been the most high-risk area, followed by Huanggang city and Xiaogan city, in Hubei province during the initial outbreak and spread of the epidemic.Because the county level COVID-19 epidemic data were not reported publicly in Wuhan city during the study period, we collected the county level COVID-19 epidemic data solely for the other prefecture cities except Wuhan city in Hubei province. To exclude the statistical instability due to the exceptionally high cumulative cases as a whole in Wuhan city, the weighted average method by the county level RSP was used to divided CCC into the corresponding districts/cities in Wuhan city to perform spatial autocorrelation analysis. Due to data availability, we selected the county level COVID-19 data from 30 January to 18 February 2020, for the study.p < 0.0001, Z > 2.58) and that the global Moran’s I index and Z-score increased since 31 January. It indicated that the county level COVID-19 epidemic in Hubei province had a very significant concentrated characteristic and that the spatial autocorrelation became increasingly intensive from 31 January to 18 February 2020. It is noted that the values of Global Moran’s I index are all positive and greater than 0.59, and the Z-score values are all greater than 9.0 during the study period, indicating a significant and strong global spatial autocorrelation. In other words, the county level epidemic in Hubei province had a significant and strong spatial dependence between the neighboring counties during the study period.The global spatial autocorrelation results  illustrap < 0.05) regarding the number of CCC at the county level in Hubei province. Wuhan’s 12 districts (excluding Hannan district) and its neighboring Xiaonan district and Hanchuan city in Xiaogan city were the significant high-high cluster areas (p < 0.05). The Hannan district in Wuhan city and its neighborhood Hong’an county in Huanggang city, as well as Huarong district and Liangzihu district in Ezhou city were the significant low-high outlier areas (p < 0.05), and most county level units in southwest Hubei were the significant low-low cluster areas (p < 0.05). It indicated that the county level COVID-19 epidemic in Wuhan city was significantly severe (p < 0.05) during this period. Notably, Xiling district in Yichang city showed a significant high-low outlier (p < 0.05), from 30 January to 9 February 2020 (p < 0.05) than its surroundings and causing Xiling district a significant high-risk area (p < 0.05) of the COVID-19 epidemic compared to its surrounding areas. From 10 February to 18 February 2020, this district was not a significant abnormal area, which indicated that the COVID-19 epidemic in the district had relatively alleviated during this period.The Anselin Local Moran’s I (ALMI) results  results , LA, four population indicators , and two economic indicators (GDP and TRS) for the prefecture level Spearman’s rank correlation analysis and ten indicators for the county level Spearman’s rank correlation analysis except for BMI due to no access to the relevant data temporarily. Particularly, this study used 17 spatial units province-wide for the prefecture level correlation analysis from 23 January to 18 February 2020. For the county level correlation analysis, this study used 88 spatial units province-wide, excluding 13 districts/cities in Wuhan city, one district in Jingzhou city, and Shennongjia forest prefecture from 26 January to 18 February 2020.p < 0.01) with RGP from 23 January to 18 February 2020; had a positive, and extremely significant correlation (p < 0.01) with RSP on 23 January 2020, and from 25 January to 18 February 2020, respectively, of which the correlations were strong except for the extremely strong ones on 2 February and 4 February 2020; had a positive and extremely significant correlation (p < 0.01) with TRS from 25 January to 18 February 2020, respectively, of which the correlations were strong except for the extremely strong ones from 31 January to 4 February 2020, and 8 February 2020; had a positive and extremely significant correlation (p < 0.01) with GDP on 23 January 2020, and from 25 January to 18 February 2020, respectively, of which the correlations were strong except for the extremely strong ones from 29 January to 9 February 2020; had a strong, positive, and extremely significant correlation (p < 0.01) with BMI on 27 January 2020, and from 29 January to 18 February 2020, respectively, while had a nearly strong, positive and significant correlation (p < 0.05) with BMI on 23, 25, 26, and 28 January 2020, respectively; had a moderate, positive, and significant correlation (p < 0.05) with PD only from 13–18 February 2020; had a moderate, negative and significant correlation (p < 0.05) with MINE on 22 January 2020, from 25–27 January 2020, and from 2–18 February 2020, respectively; nevertheless, the correlation with MAXE, MNE, RAE, LA, and PD was not significant (p > 0.05) from 23 January to 18 February 2020, respectively.The Spearman’s rank correlation results  demonstrp < 0.01) with RGP, RSP, TRS, GDP, and BMI, respectively; had a moderate, negative, and significant correlation (p < 0.05) with MINE; in contrast, the correlation with MAXE, MNE, RAE, LA, and PD was not significant (p > 0.05), respectively.The average number of CCC at the prefecture level in Hubei province from 23 January to 18 February 2020, was calculated for the correlation with the eleven indicators mentioned above in order to eliminate the instability of data and to validate the analysis results of time series. The results  showed tThe results suggested that social and economic development and population movements could have strong impacts on the COVID-19 epidemic spread. The result showed that the Spearman’s rank correlation coefficient between the number of CCC and RSP was all greater than that for RGP from 26 January to 18 February 2020, respectively. It suggested that the more population and the greater PD at the early outbreak stage, the greater the risk of infection of SARS-CoV-2 could be, and the severer the COVID-19 epidemic thus could be. Interestingly, the variation trend of the Spearman’s rank correlation coefficient for the number of CCC and GDP was entirely in agreement with that and TRS during all the study period, of which the coefficients both were around 0.70 or above, a strong correlation level, with the latter all greater than the former. This suggested that economic development and trade exchanges could positively contribute to the epidemic spread. It is worth pointing out that this does not mean that the more developed the economy is, the stronger the epidemic will be, but the bigger the infectious risk will be. To sum up, from the point of view of CCC at the prefecture level in Hubei province, population , economic development (GDP), and trade exchanges (TRS) could be the high-risk factors to the COVID-19 epidemic spread.p > 0.05) for LA, MAXE, MNE, and RAE. The number of DCC at the prefecture level in Hubei province had a strong, positive, and extremely significant correlation (p < 0.01) with TRS and GDP, respectively, from 25 January to 8 February 2020; that correlation with TRS was moderate, positive, and significant (p < 0.05) on 9 and 10 February 2020, and not significant (p > 0.05) from 11–18 February 2020; that correlation with GDP was moderate, positive, and significant (p < 0.05) from 9–11 February 2020, on 14 and 18 February 2020, and was nevertheless not significant (p > 0.05) on any other day. The number of DCC at the prefecture level in Hubei province had a strong, positive, and extremely significant correlation (p < 0.01) with RGP and RSP, respectively, from 25 January to 6 February 2020, and on 8 February 2020; had a moderate, positive, and significant correlation (p < 0.05) on 7 February 2020, and from 9–11 February 2020; had no significant correlation (p > 0.05) on any other day nevertheless.In addition to the correlation analysis for the number of CCC, we also analyzed the number of DCC. The Spearman’s rank correlation analysis results  showed tp < 0.05) with PD, BMI, and MINE from 24 January to 18 February 2020 (p < 0.01 and p < 0.05) only for 12 discontinuous days. The correlation between the number of DCC and BMI at the prefecture level in Hubei province was complex. It was strong, positive, and extremely significant (p < 0.01) from 27–30 January, on 2 and 4 February, from 6–8 February, and on 14 February 2020; was positive and significant (p < 0.05) on 31 January and 1, 3, 5, 9, and 11 February 2020, and was not significant on any other day. The correlation between the number of DCC and MINE at the prefecture level in Hubei province was also complex, with a significance only for 11 discontinuous days.However, the number of DCC at the prefecture level in Hubei province had no steadily significant correlation (ary 2020 . The corp < 0.01) with RGP, RSP, TRS, GDP, and BMI, respectively. The correlation between this average value and PD was moderate, positive, and significant (p < 0.05). The correlation between this average value and MINE was moderate, negative, and significant (p < 0.05). However, the correlations with MAXE, MNE, RAE, and LA were not significant (p > 0.05) at the prefecture level in Hubei province.The 26-days average number of DCC at the prefecture level in Hubei province from 24 January to 18 February 2020 had a strong, positive, and extremely significant correlations (p < 0.01) with PD from 26–30 January 2020, and had a strong, positive, and extremely significant correlation (p < 0.01) with PD on from 31 January to 18 February 2020, respectively; that had a moderate, positive, and extremely significant correlation (p < 0.01) with TRS from 26–28 January 2020, and had a strong, positive, and extremely significant correlation (p < 0.01) from 29 January to 18 February 2020, respectively; that had a weak, positive, and significant correlation (p < 0.05) on 26 January 2020, had a weak, positive, and extremely significant correlation (p < 0.01) with RGP on 27 January 2020, and had a moderate, positive, and extremely significant correlation from 28 January to 18 February 2020, respectively; that had a weak, positive and significant correlation (p < 0.05) with RSP on 26 January 2020, had a moderate, positive, and extremely significant correlation (p < 0.01) with RSP from 28 January to 1 February 2020, and had a strong, positive, and extremely significant correlation (p < 0.01) with RSP from 2 February to 18 February 2020, respectively; that had a weak, positive and significant correlation (p < 0.05) with GDP on 26 January 2020, had a weak, positive and extremely significant correlation (p < 0.01) with GDP on 27 January 2020, had a moderate, positive, and extremely significant correlation (p < 0.01) with GDP from 28–29 January, on 2 February 2020, and from 4 February to 18 February 2020, and had a strong, positive, and extremely significant correlation (p < 0.01) with GDP from 30 January to 1 February 2020 and on 3 February 2020, respectively; and that had a weak, negative and significant correlation (p < 0.05) with LA on 26 January 2020, and from 30 January to 1 February had no significant correlation from 27–29 January 2020, and had a weak, negative, and extremely significant correlation (p < 0.01) with LA from 2–18 February 2020, respectively.The Spearman’s rank correlation analysis results  indicatep < 0.05) with MINE from 26–27 January 2020; had a weak and extremely significant correlation (p < 0.01) with MINE from 28–31 January 2020; had a moderate and extremely significant correlation (p < 0.01) with MINE from 2–18 February 2020. It had a moderate and extremely significant correlation (p < 0.01) with MAXE from 26 January to 3 February 2020; it had a strong and extremely significant correlation (p < 0.01) with MINE from 4–18 February 2020. It had a moderate and extremely significant correlation (p < 0.01) with MNE from 26 January to 1 February 2020; it had a strong and extremely significant correlation (p < 0.01) with MNE from 2–18 February 2020. Furthermore, it had a moderate and extremely significant correlation (p < 0.01) with RAE from 26 January to 5 February 2020; it had a strong and extremely significant correlation (p < 0.01) with RAE from 6–18 February 2020.Interestingly, the results  showed tp < 0.01) with PD, RSP, TRS, respectively; it had a moderate, positive, and extremely significant correlation (p < 0.01) with RGP and GDP, respectively; it had a weak, negative, and extremely significant correlation (p < 0.01) with LA; it had a moderate, negative, and extremely significant correlation (p < 0.01) with MINE; it had a strong, negative, and extremely significant correlation (p < 0.01) with MAXE, MNE, and RAE, respectively. It should be noted that the results were almost the same as those of time series.In order to ensure the reliability of data and to validate the analysis results of time series, the average of the number of CCC at the county level in Hubei province from 26 January to 18 February 2020, was calculated for the correlation with the ten indicators mentioned above. p < 0.01) with PD, RSP, RSP, and TRS, respectively; it had a moderate, positive, and extremely significant correlation (p < 0.01) with RGP and GDP, respectively; it had nevertheless a weak, negative, and extremely significant correlation (p < 0.01) with LA; it had a moderate, negative, and extremely significant correlation (p < 0.01) with MINE; it had a strong, negative, and extremely significant correlation (p < 0.01) with MAXE, MNE, and RAE, respectively.The correlation analysis of DCC at the county level in Hubei province  did not Knowledge mining of geospatial information can not only reveal the spatiotemporal spread and aggregation characteristics of infectious diseases, but also discover spatial risk factors that have an important influence on the spread of infectious diseases and identify hot spots with high transmission risks . It is oIn general, the factors that affect an epidemic outbreak and spread mainly occur through the influences on the source of infection, the route of transmission, and the susceptible population. These factors have close relationships with individuals microcosmically while with the natural environment and socio-economic development macroscopically. On the whole, it is believed that the COVID-19 epidemic spread could be closely related to certain natural, social, and economic factors as well as to prevention and control policies controlling sources of the outbreak and cutting off routes of transmission. Recently, researchers proposed that some meteorological parameters such as temperature and humidity were potential environmental factors influencing the outbreak of COVID-19 . Most laHowever, this study expounded the influencing factors of CCC from all three aspects of nature, society, and economy at both prefecture and county levels in Hubei province. Furthermore, this study analyzed different indicators from previous works for potential risk factors of the COVID-19 spread. In this paper, land area and four terrain parameters  were taken as natural indicators, while GDP and TRS were two economic indicators, and PD, RGP, RSP, and BMI were four key social indicators. More specifically, BMI signified the population mobility. Our results showed that most of these three kinds of indicators were highly related to the COVID-19 epidemic to some extent. This was in line with the study of You et al. (2020) . In natuIn particular, BMI can be taken as a quantitative indicator for the population migration and mobility from one place to another and possible for the social gathering and holiday travel. As expected, the number of CCC had an extremely strong dependence on BMI, indicating that population mobility would significantly increase the risk of the COVID-19 spread at the early epidemic stage. Consequently, there could be huge population movements and commercial communications between Wuhan and these cities in Hubei province before the implementation of strict quarantine policy, increasing the infectious chance of the COVID-19 spread threat. This might be the reason that the number of CCC at the prefecture level in Hubei province had an extremely significant positive correlation with BMI. Therefore, BMI could be a potential risk indicator for the COVID-19 spread, like the other social and economic indicators.Moreover, comparing Because the COVID-19 data analyzed were collected from the different public sources of the 17 prefecture cities and more than 100 county level administrative districts in Hubei province and had various kinds of unstandardized and unstructured formats, these data had to be manually processed and integrated in a time-consuming and labor-intensive manner, especially for the county level data in Hubei province. Moreover, it was recently reported that the official COVID-19 epidemic data might not include asymptomatic cases, which would impact the accuracy of data analyzed. Furthermore, multicollinearity, spatial heterogeneity, and temporal inconsistency between the socioeconomic data and the epidemic data very likely existed . These uFirst, the mechanism controlling the spatiotemporal patterns and the COVID-19 spread on different scales has not been resolved. It is an interdisciplinary and complex problem in theory. Our results showed that CCC at the provincial, prefecture and county levels had significant local spatial autocorrelation and clustering characteristics, but there was no global spatial autocorrelation at the provincial level. The global spatial autocorrelation of CCC at the county level in Hubei province was significantly higher than that at the prefecture level. This paper presented only a case study of Hubei province, which could help to improve our understanding of spatiotemporal dynamic characteristics of the COVID-19 epidemic on the local scale. As the spread of the pandemic has been rapidly growing across the global, newer, bigger, and longer time series data might be collected and used to analyze the spatial statistics of the COVID-19 pandemic and to further expound its controlling mechanism from local to global scales.Second, the Spearman’s rank correlation analysis of the influencing factors of the COVID-19 epidemic in Hubei province is only based on the relationship between different variable data, but not their cause and effect because neither individual infected data in detail nor contemporaneous data were collected for analysis. The spread of the COVID-19 epidemic is not only related to the natural environment, population mobility, and socioeconomic activities but also closely related to the policies of the epidemic prevention and control. This study explored the correlations between the number of confirmed COVID-19 cases and the natural, social, and economic indicators. Any study of the COVID-19 epidemic influencing factors requires further consideration of more detailed data of infectious cases, such as gender, age, job, etc., and more indicators of the natural environment, population, society, economy, medical conditions, and policies. Therefore, it could be beneficial to further study other potential influencing factors and in-depth epidemiology in order to reveal the causes of the COVID-19 epidemic spread.Third, limited by the accessibility and availability of data, the results of the spatial statistics and influencing factors of the COVID-19 epidemic at the county scale in this study might have some uncertainties because of the lack of the county level data in Wuhan city and asymptomatic cases of COVID-19 not reported before . OpennesIn this paper, we established a new geodatabase for the COVID-19 epidemic study based on the COVID-19 epidemic data available publicly on multiple scales in Hubei province. Furthermore, we used ArcGIS spatial statistics methods to analyze spatiotemporal patterns of the COVID-19 epidemic and explored the influencing factors using Spearman’s rank correlation analysis from the prefectural and county levels in Hubei province, China during the early stage of the epidemic outbreak and spread from January to February 2020. The following conclusions were achieved.(1) At both prefecture and county levels, extremely significant global spatial autocorrelations were observed for the number of CCC in Hubei from 30 January to 18 February 2020. The global spatial autocorrelation of the number of CCC at the county level was much stronger than that at the prefecture level in Hubei province, indicating a scale effect of this autocorrelation.(2) Wuhan city and most of the districts/sub-cities in Wuhan city were significant high-risk epidemic areas at the prefecture, and county levels, respectively, during the study period.(3) The numbers of CCC at both prefecture and county levels had local spatial autocorrelations at the 99% confidence and had positive and extremely significant correlations with main social and economic indicators  from 30 January to 18 February 2020.(4) The number of CCC had a moderate–strong, negative, and extremely significant correlation with major terrain parameters  at the county level, but no significant correlation at the prefecture level in Hubei province from 26 January to 18 February 2020.In summary, the COVID-19 epidemics at both levels in Hubei province had the evident characteristics of significant global spatial autocorrelations, significant centralized outbreaks, and had an extremely significant association with social and economic factors during the certain study period. The natural factors, such as LA, MAXE, MNE, and RAE, had significant relationship with the COVID-19 epidemics at the county level, not at the prefecture level, from 2 February to 18 February 2020. In the meantime, the socioeconomic factors, such as RGP, RSP, TRS, and GDP, were significantly related to the COVID-19 epidemics at both levels from 26 January to 18 February 2020. Like repetitive experiments, the consistent time-series results of spatial autocorrelations and influencing factors should validate the reliability of our conclusions. It is expected that this study could help to improve our understanding of spatiotemporal patterns and influencing factors of the COVID-19 epidemic and benefit the scientific prevention and control of the epidemic for policymakers at multiple levels of government.