Nomogram Predicting Cancer-Specific Death in Parotid Carcinoma: a Competing Risk Analysis

PurposeMultiple factors have been shown to be tied to the prognosis of individuals with parotid cancer (PC); however, there are limited numbers of reliable as well as straightforward tools available for clinical estimation of individualized mortality. Here, a competing risk nomogram was established to assess the risk of cancer-specific deaths (CSD) in individuals with PC.MethodsData of PC patients analyzed in this work were retrieved from the Surveillance, Epidemiology, and End Results (SEER) data repository and the First Affiliated Hospital of Nanchang University (China). Univariate Lasso regression coupled with multivariate Cox assessments were adopted to explore the predictive factors influencing CSD. The cumulative incidence function (CIF) coupled with the Fine-Gray proportional hazards model was employed to determine the risk indicators tied to CSD as per the univariate, as well as multivariate analyses conducted in the R software. Finally, we created and validated a nomogram to forecast the 3- and 5-year CSD likelihood.ResultsOverall, 1,467 PC patients were identified from the SEER data repository, with the 3- and 5-year CSD CIF after diagnosis being 21.4% and 24.1%, respectively. The univariate along with the Lasso regression data revealed that nine independent risk factors were tied to CSD in the test dataset (n = 1,035) retrieved from the SEER data repository. Additionally, multivariate data of Fine-Gray proportional subdistribution hazards model illustrated that N stage, Age, T stage, Histologic, M stage, grade, surgery, and radiation were independent risk factors influencing CSD in an individual with PC in the test dataset (p < 0.05). Based on optimization performed using the Bayesian information criterion (BIC), six variables were incorporated in the prognostic nomogram. In the internal SEER data repository verification dataset (n = 432) and the external medical center verification dataset (n = 473), our nomogram was well calibrated and exhibited considerable estimation efficiency.ConclusionThe competing risk nomogram presented here can be used for assessing cancer-specific mortality in PC patients.

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Prognostic significance of advanced lung cancer inflammation index (ALI) in multiple myeloma patients – a retrospective study

10.21203/rs.3.rs-288915/v1 ◽

2021 ◽

Author(s):

Junxia Huang ◽

Juanjuan Hu ◽

Yan Gao ◽

Fanjun Meng ◽

Tianlan Li ◽

...

Keyword(s):

Risk Factors ◽

Lung Cancer ◽

Multiple Myeloma ◽

Overall Survival ◽

Progression Free Survival ◽

Advanced Lung Cancer ◽

Free Survival ◽

Factors Influencing ◽

Independent Risk Factors ◽

Cancer Inflammation

Abstract Background: Advanced lung cancer inflammation index (ALI) is known to predict the overall survival of patients having some solid tumors or B-cell lymphoma. The study investigates the predictive value of ALI in multiple myeloma (MM) patients and the correlation between ALI and prognosis.Methods: A database of 269 MM consecutive patients who underwent chemotherapy between December 2011 and June 2019 in the Affiliated Hospital of Qingdao University was reviewed. ALI cut-off value calculated before the initial chemotherapy and post 4 courses treatment were identified according to the receiver operating characteristic (ROC) curve, and its association with clinical characteristics, treatment response, overall survival (OS), and progression-free survival (PFS) were assessed.Results: Patients in the low ALI group (n=147) had higher risk of β2 microglobulin elevation, more advanced ISS (International Classification System stage), and TP53 gene mutation, with significantly lower median overall survival (OS; 36.29 vs. 57.92 months, P = 0.010) and progression-free survival (PFS; 30.94 vs. 35.67 months, P = 0.013). Independent risk factors influencing the OS of MM patients were ALI (P = 0.007), extramedullary infiltration (P = 0.001), TP53 (P = 0.020), Plt (P = 0.005), and bone destruction (P = 0.024). ALI (P = 0.005), extramedullary infiltration (P = 0.004), TP53 (P = <0.001), Plt (P = 0.017), and complex chromosome karyotype (P = 0.010) were independent risk factors influencing the PFS of MM patients.Conclusions: ALI is a potential independent risk factor predicting the prognosis of newly diagnosed MM patients.

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Nomogram for Predicting COVID-19 Disease Progression Based on Single-Center Data: Observational Study and Model Development (Preprint)

10.2196/preprints.19588 ◽

2020 ◽

Author(s):

Tao Fan ◽

Bo Hao ◽

Shuo Yang ◽

Bo Shen ◽

Zhixin Huang ◽

...

Keyword(s):

Risk Factors ◽

Regression Analysis ◽

Clinical Characteristics ◽

Cox Regression ◽

Disease Onset ◽

Lasso Regression ◽

Prediction Ability ◽

Calibration Curves ◽

Independent Risk Factors ◽

Critical Patients

BACKGROUND In late December 2019, a pneumonia caused by SARS-CoV-2 was first reported in Wuhan and spread worldwide rapidly. Currently, no specific medicine is available to treat infection with COVID-19. OBJECTIVE The aims of this study were to summarize the epidemiological and clinical characteristics of 175 patients with SARS-CoV-2 infection who were hospitalized in Renmin Hospital of Wuhan University from January 1 to January 31, 2020, and to establish a tool to identify potential critical patients with COVID-19 and help clinical physicians prevent progression of this disease. METHODS In this retrospective study, clinical characteristics of 175 confirmed COVID-19 cases were collected and analyzed. Univariate analysis and least absolute shrinkage and selection operator (LASSO) regression were used to select variables. Multivariate analysis was applied to identify independent risk factors in COVID-19 progression. We established a nomogram to evaluate the probability of progression of the condition of a patient with COVID-19 to severe within three weeks of disease onset. The nomogram was verified using calibration curves and receiver operating characteristic curves. RESULTS A total of 18 variables were considered to be risk factors after the univariate regression analysis of the laboratory parameters (P<.05), and LASSO regression analysis screened out 10 risk factors for further study. The six independent risk factors revealed by multivariate Cox regression were age (OR 1.035, 95% CI 1.017-1.054; P<.001), CK level (OR 1.002, 95% CI 1.0003-1.0039; P=.02), CD4 count (OR 0.995, 95% CI 0.992-0.998; P=.002), CD8 % (OR 1.007, 95% CI 1.004-1.012, P<.001), CD8 count (OR 0.881, 95% CI 0.835-0.931; P<.001), and C3 count (OR 6.93, 95% CI 1.945-24.691; P=.003). The areas under the curve of the prediction model for 0.5-week, 1-week, 2-week and 3-week nonsevere probability were 0.721, 0.742, 0.87, and 0.832, respectively. The calibration curves showed that the model had good prediction ability within three weeks of disease onset. CONCLUSIONS This study presents a predictive nomogram of critical patients with COVID-19 based on LASSO and Cox regression analysis. Clinical use of the nomogram may enable timely detection of potential critical patients with COVID-19 and instruct clinicians to administer early intervention to these patients to prevent the disease from worsening.

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Effect of bronchial artery embolisation on the management of tuberculosis-related haemoptysis

The International Journal of Tuberculosis and Lung Disease ◽

10.5588/ijtld.19.0135 ◽

2019 ◽

Vol 23 (12) ◽

pp. 1269-1276

Author(s):

Y. Peng ◽

Y. Zhu ◽

G. Ao ◽

Z. Chen ◽

X. Yuan ◽

...

Keyword(s):

Risk Factors ◽

Risk Factor ◽

Cox Regression ◽

Bronchial Artery ◽

Cox Regression Analysis ◽

Pleural Thickening ◽

Factors Influencing ◽

Arterial Embolisation ◽

Independent Risk Factors ◽

Bronchial Artery Embolisation

OBJECTIVE: To identify risk factors influencing outcomes of bronchial arterial embolisation (BAE) in tuberculosis (TB) related haemoptysis.METHODS: A cohort of 207 patients underwent BAE for TB-related haemoptysis between March 2014 and March 2018. The clinical data were reviewed. Follow-up ranged from 24 to 1749 days.RESULTS: Immediate haemostasis rate was 94.2%; aggressive pleural thickening (PT) was found to be a risk factor for haemoptysis (P = 0.000, OR 22.52). Cumulative recurrence-free rates were respectively 98.5%, 94.8%, 88.7%, 79.9%, 68.5%, 65.7% and 62.7% for 1, 3, 6, 12, 24, 36 and 48 months. Respectively 8 and 15 patients recovered from pneumonectomy and re-BAE. However, five patients required a third BAE. The Cox regression analysis indicated that aggressive PT (P = 0.000), diabetes mellitus (DM) (P = 0.018) and pulmonary fungal infection (PFI) (P = 0.001) were independent risk factors for recurrence. The death rate following BAE was 9.2%; aggressive PT was a risk factor (P = 0.000, OR 8.14).CONCLUSION: BAE is effective for TB-related haemoptysis in most cases. Aggressive PT, DM and PFI are independent risk factors influencing the prognosis following BAE. PFI and DM should be well managed, while proper surgery should be considered for aggressive PT.

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Identification and Validation of lncRNA-Related Prognostic Signatures in Cholangiocarcinoma

10.21203/rs.3.rs-705273/v1 ◽

2021 ◽

Author(s):

JiaNing Zhang ◽

Fengwei Li ◽

Yihua Huang ◽

Hui Xue ◽

Qifei Tao ◽

...

Keyword(s):

Risk Factors ◽

Vascular Invasion ◽

Disease Free Survival ◽

Lasso Regression ◽

Cox Models ◽

Hepatobiliary Cancer ◽

Tumorigenesis And Progression ◽

Independent Risk Factors

Abstract Background and Aims: Cholangiocarcinoma (CCA), the second most common hepatobiliary cancer, is associated with poor prognosis. Therefore, there is a need to elucidate on the pathogenic mechanisms of CCA. In this study, we aimed at identifying lncRNA-related prognostic signatures for CCA through bioinformatics analysis and further validated their functions in CCA tumorigenesis and progression.Methods: The RNA-seq data of CCA were downloaded from public databases. Differentially expressed lncRNAs (DElncRNAs) were screened using R packages. Then, candidate OS- and DFS-related DElncRNAs were selected through Kaplan–Meier survival analysis. Furthermore, LASSO regression analyses were performed to establish two prognostic signatures, termed the OS and DFS signatures, respectively. Multivariate COX models and nomograms for overall survival (OS) and disease-free survival (DFS) were established based on OS/DFS signature and clinical data. Hub lncRNAs were identified and enrichment analyses performed to explore their potential functions. Finally, in vitro and in vivo models were used to validate the effects of the hub lncRNAs in CCA tumorigenesis and progression.Results: A total of 925 DElncRNAs were selected, from which six candidate OS-related lncRNAs and 15 candidate DFS-related lncRNAs were identified. The OS and DFS signatures were then established using four lncRNAs, respectively. We found that the OS signature and vascular invasion were significant independent risk factors for OS outcomes of CCA, while the DFS signature, vascular invasion and CA19-9 were significant independent risk factors for DFS outcomes of CCA. MiR4435-2HG and GAPLINC were selected as hub lncRNAs because they were included in both OS and DFS signatures. GO and KEGG enrichment analyses revealed that the two hub lncRNAs were involved in CCA tumorigenesis and progression. Finally, we constructed in vitro and in vivo models and revealed that the lncRNAs, MiR4435-2HG and GAPLINC can prompt CCA proliferation and migration in vitro and in vivo.Conclusions: The established OS and DFS signatures, which were based on DElncRNAs, are independent risk factors for OS and DFS of CCA patients, respectively. MIR4435-2HG and GAPLINC were identified as hub lncRNAs. In vitro and in vivo models revealed that MiR4435-2HG and GAPLINC can prompt CCA progression, which might be novel prognostic biomarkers and therapeutic targets for CCA.

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Nomogram for Predicting COVID-19 Disease Progression Based on Single-Center Data: Observational Study and Model Development

JMIR Medical Informatics ◽

10.2196/19588 ◽

2020 ◽

Vol 8 (9) ◽

pp. e19588

Author(s):

Tao Fan ◽

Bo Hao ◽

Shuo Yang ◽

Bo Shen ◽

Zhixin Huang ◽

...

Keyword(s):

Risk Factors ◽

Regression Analysis ◽

Clinical Characteristics ◽

Cox Regression ◽

Disease Onset ◽

Lasso Regression ◽

Prediction Ability ◽

Calibration Curves ◽

Independent Risk Factors ◽

Critical Patients

Background In late December 2019, a pneumonia caused by SARS-CoV-2 was first reported in Wuhan and spread worldwide rapidly. Currently, no specific medicine is available to treat infection with COVID-19. Objective The aims of this study were to summarize the epidemiological and clinical characteristics of 175 patients with SARS-CoV-2 infection who were hospitalized in Renmin Hospital of Wuhan University from January 1 to January 31, 2020, and to establish a tool to identify potential critical patients with COVID-19 and help clinical physicians prevent progression of this disease. Methods In this retrospective study, clinical characteristics of 175 confirmed COVID-19 cases were collected and analyzed. Univariate analysis and least absolute shrinkage and selection operator (LASSO) regression were used to select variables. Multivariate analysis was applied to identify independent risk factors in COVID-19 progression. We established a nomogram to evaluate the probability of progression of the condition of a patient with COVID-19 to severe within three weeks of disease onset. The nomogram was verified using calibration curves and receiver operating characteristic curves. Results A total of 18 variables were considered to be risk factors after the univariate regression analysis of the laboratory parameters (P<.05), and LASSO regression analysis screened out 10 risk factors for further study. The six independent risk factors revealed by multivariate Cox regression were age (OR 1.035, 95% CI 1.017-1.054; P<.001), CK level (OR 1.002, 95% CI 1.0003-1.0039; P=.02), CD4 count (OR 0.995, 95% CI 0.992-0.998; P=.002), CD8 % (OR 1.007, 95% CI 1.004-1.012, P<.001), CD8 count (OR 0.881, 95% CI 0.835-0.931; P<.001), and C3 count (OR 6.93, 95% CI 1.945-24.691; P=.003). The areas under the curve of the prediction model for 0.5-week, 1-week, 2-week and 3-week nonsevere probability were 0.721, 0.742, 0.87, and 0.832, respectively. The calibration curves showed that the model had good prediction ability within three weeks of disease onset. Conclusions This study presents a predictive nomogram of critical patients with COVID-19 based on LASSO and Cox regression analysis. Clinical use of the nomogram may enable timely detection of potential critical patients with COVID-19 and instruct clinicians to administer early intervention to these patients to prevent the disease from worsening.

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A Competing Risk Nomogram for Predicting Cancer-Specific Death of Patients With Maxillary Sinus Carcinoma

Frontiers in Oncology ◽

10.3389/fonc.2021.698955 ◽

2021 ◽

Vol 11 ◽

Author(s):

Mingbin Hu ◽

Xiancai Li ◽

Weiguo Gu ◽

Jinhong Mei ◽

Dewu Liu ◽

...

Keyword(s):

Maxillary Sinus ◽

Cumulative Incidence ◽

External Validation ◽

Competing Risk ◽

Validation Data ◽

Data Set ◽

Internal Validation ◽

Data Resource ◽

Seer Data ◽

Risk Of Cancer

ObjectivesHerein, we purposed to establish and verify a competing risk nomogram for estimating the risk of cancer-specific death (CSD) in Maxillary Sinus Carcinoma (MSC) patients.MethodsThe data of individuals with MSC used in this study was abstracted from the (SEER) Surveillance, Epidemiology, and End Results data resource as well as from the First Affiliated Hospital of Nanchang University (China). The risk predictors linked to CSD were identified using the CIF (cumulative incidence function) along with the Fine-Gray proportional hazards model on the basis of univariate analysis coupled with multivariate analysis implemented in the R-software. After that, a nomogram was created and verified to estimate the three- and five-year CSD probability.ResultsOverall, 478 individuals with MSC were enrolled from the SEER data resource, with a 3- and 5-year cumulative incidence of CSD after diagnosis of 42.1% and 44.3%, respectively. The Fine-Gray analysis illustrated that age, histological type, N stage, grade, surgery, and T stage were independent predictors linked to CSD in the SEER-training data set (n = 343). These variables were incorporated in the prediction nomogram. The nomogram was well calibrated and it demonstrated a remarkable estimation accuracy in the internal validation data set (n = 135) abstracted from the SEER data resource and the external validation data set (n = 200). The nomograms were well-calibrated and had a good discriminative ability with concordance indexes (c-indexes) of 0.810, 0.761, and 0.755 for the 3- and 5-year prognosis prediction of MSC-specific mortality in the training cohort, internal validation, and external validation cohort, respectively.ConclusionsThe competing risk nomogram constructed herein proved to be an optimal assistant tool for estimating CSD in individuals with MSC.

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Characterization of the Brazpd ii Cohort and Description of Trends in Peritoneal Dialysis Outcome across Time Periods

Peritoneal Dialysis International ◽

10.3747/pdi.2013.00282 ◽

2014 ◽

Vol 34 (7) ◽

pp. 714-723 ◽

Cited By ~ 25

Author(s):

Thyago Proença de Moraes ◽

Ana Elizabeth Figueiredo ◽

Ludimila Guedim de Campos ◽

Marcia Olandoski ◽

Pasqual Barretti ◽

...

Keyword(s):

Risk Factors ◽

Peritoneal Dialysis ◽

Clinical Practice ◽

Risk Model ◽

Patient Survival ◽

Patient Characteristics ◽

Initial Therapy ◽

Competing Risk ◽

Technique Survival ◽

Independent Risk Factors

Observational studies from different regions of the world provide valuable information in patient selection, clinical practice, and their relationship to patient and technique outcome. The present study is the first large cohort providing patient characteristics, clinical practice, patterns and their relationship to outcomes in Latin America. The objective of the present study was to characterize the cohort and to describe the main determinants of patient and technique survival, including trends over time of peritoneal dialysis (PD) initiation and treatment. This was a nationwide cohort study in which all incident adult patients on PD from 122 centers were studied. Patient demographics, socioeconomic and laboratory values were followed from December 2004 to January 2011 and, for comparison purposes, divided into 3 groups according to the year of starting PD: 2005/06, 2007/08 and 2009/10. Patient survival and technique failure (TF) were analyzed using the competing risk model of Fine and Gray. All patients active at the end of follow-up were treated as censored. In contrast, all patients who dropped the study for any reason different from the primary event of interest were treated as competing risk. Significance was set to a p level of 0.05. A total of 9,905 patients comprised the adult database, 7,007 were incident and 5,707 remained at least 90 days in PD. The main cause of dropout was death (54%) and of TF was peritonitis (63%). Technique survival at 1, 2, 3, 4, and 5 years was 91%, 84%, 77%, 68%, and 58%, respectively. There was no change in TF during the study period but 3 independent risk factors were identified: lower center experience, lower age, and automated PD (APD) as initial therapy. Cardiovascular disease (36%) was the main cause of death and the overall patient survival was 85%, 74%, 64%, 54%, and 48% at 1, 2, 3, 4, and 5 years, respectively. Patient survival improved along all study periods: compared to 2005/2006, patients starting at 2007/2008 had a relative risk reduction (SHR) of 0.83 (95% confidence interval [CI] 0.72 – 0.95); and starting in 2009/2010 of 0.69 (95% CI 0.57 – 0.83). The independent risk factors for mortality were diabetes, age > 65 years, previous hemodialysis, starting PD modality, white race, low body mass index (BMI), low educational level, center experience, length of pre-dialysis care, and the year of starting PD. We observed an improvement in patient survival along the years. This finding was sustained even after correction for several confounders and using a competing risk approach. On the other hand, no changes in technique survival were found.

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Using Nomograms to Predict the PPCs of Patients With Diffuse Peritonitis Undergoing Emergency Gastrointestinal Surgery

Frontiers in Medicine ◽

10.3389/fmed.2021.705713 ◽

2021 ◽

Vol 8 ◽

Author(s):

Qiong Xue ◽

Yu Zhu ◽

Ying Wang ◽

Jian-Jun Yang ◽

Cheng-Mao Zhou

Keyword(s):

Risk Factors ◽

Gastrointestinal Surgery ◽

Test Group ◽

Pulmonary Complications ◽

Diffuse Peritonitis ◽

Lasso Regression ◽

Physical Status Classification ◽

Independent Risk Factors ◽

Auc Value ◽

Preoperative Albumin

Objective: To develop and validate a nomogram model for predicting postoperative pulmonary complications (PPCs) in patients with diffuse peritonitis undergoing emergency gastrointestinal surgery.Methods: We used the least absolute shrinkage and selection operator (LASSO) regression model to analyze the independent risk factors for PPCs in patients with diffuse peritonitis who underwent emergency gastrointestinal surgery. Using R, we developed and validated a nomogram model for predicting PPCs in patients with diffuse peritonitis undergoing emergency gastrointestinal surgery.Results: The LASSO regression analysis showed that AGE, American Society of Anesthesiologists physical status classification (ASA), DIAGNOSIS, platelets (on the 3rd day after surgery), cholesterol (on the 3rd day after surgery), ALBUMIN (on the first day after surgery), and preoperative ALBUMIN were independent risk factors for PPCs in patients with diffuse peritonitis undergoing emergency gastrointestinal surgery. The area under the curve (AUC) value of the nomogram model in the training group was 0.8240; its accuracy was 0.7000, and its sensitivity was 0.8658. This demonstrates that the nomogram has a high prediction value. Also in the test group, the AUC value of the model established by the variables AGE, ASA, and platelets (on the 3rd day after surgery), cholesterol (on the 3rd day after surgery), ALBUMIN (on the first day after surgery), and preoperative ALBUMIN was 0.8240; its accuracy was 0.8000; and its specificity was 0.8986. In the validation group, the same results were obtained. The results of the clinical decision curve show that the benefit rate was also high.Conclusion: Based on the risk factors AGE, ASA, DIAGNOSIS, platelets (on the 3rd day after surgery), cholesterol (on the 3rd day after surgery), ALBUMIN (on the first day after surgery), and preoperative ALBUMIN, the nomogram model established in this study for predicting PPCs in patients with diffuse peritonitis undergoing emergency gastrointestinal surgery has high accuracy and discrimination.

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Prediction of Short-Term Mortality With Renal Replacement Therapy in Patients With Cardiac Surgery-Associated Acute Kidney Injury

Frontiers in Cardiovascular Medicine ◽

10.3389/fcvm.2021.738947 ◽

2021 ◽

Vol 8 ◽

Author(s):

Huiyong Han ◽

Ziang Wen ◽

Jianbo Wang ◽

Peng Zhang ◽

Qian Gong ◽

...

Keyword(s):

Risk Factors ◽

Cardiac Surgery ◽

Sofa Score ◽

Kidney Injury ◽

High Apache ◽

Factors Influencing ◽

Apache Iii ◽

Long Time ◽

Independent Risk Factors ◽

High Lactate

Objective: We aimed to: (1) explore the risk factors that affect the prognosis of cardiac surgery-associated acute kidney injury (CS-AKI) in patients undergoing renal replacement therapy (RRT) and (2) investigate the predictive value of the Acute Physiology and Chronic Health Evaluation (APACHE) III score, Sequential Organ Failure Assessment (SOFA) score, and Vasoactive-Inotropic Score (VIS) for mortality risk in patients undergoing RRT.Methods: Data from patients who underwent cardiac surgery from January 2015 through February 2021 were retrospectively reviewed to calculate the APACHE III score, SOFA score, and VIS on the first postoperative day and at the start of RRT. Various risk factors influencing the prognosis of the patients during treatment were evaluated; the area under the receiver operating characteristics curve (AUCROC) was used to measure the predictive ability of the three scores. Independent risk factors influencing mortality were analyzed using multivariable binary logistic regression.Results: A total of 90 patients were included in the study, using 90-day survival as the end point. Of those patients, 36 patients survived, and 54 patients died; the mortality rate reached 60%. At the start of RRT, the AUCROC of the APACHE III score was 0.866 (95% CI: 0.795–0.937), the VIS was 0.796 (95% CI: 0.700–0.892), and the SOFA score was 0.732 (95% CI: 0.623–0.842). The AUCROC-value of the APACHE III score on the first postoperative day was 0.790 (95% CI: 0.694–0.885). After analyzing multiple factors, we obtained the final logistic regression model with five independent risk factors at the start of RRT: a high APACHE III score (OR: 1.228, 95% CI: 1.079–1.397), high VIS (OR: 1.147, 95% CI: 1.021–1.290), low mean arterial pressure (MAP) (OR: 1.170, 95% CI: 1.050–1.303), high lactate value (OR: 1.552, 95% CI: 1.032–2.333), and long time from AKI to initiation of RRT (OR: 1.014, 95% CI: 1.002–1.027).Conclusion: In this study, we showed that at the start of RRT, the APACHE III score and the VIS can accurately predict the risk of death in patients undergoing continuous RRT for CS-AKI. The APACHE III score on the first postoperative day allows early prediction of patient mortality risk. Predictors influencing patient mortality at the initiation of RRT were high APACHE III score, high VIS, low MAP, high lactate value, and long time from AKI to the start of RRT.

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Correlates of preschoolers’ screen time in China: parental factors

10.21203/rs.3.rs-1162022/v1 ◽

2022 ◽

Author(s):

Xinyao Wang ◽

Sunyue Ye

Keyword(s):

Risk Factors ◽

Screen Time ◽

Dose Effect ◽

Physical And Mental Health ◽

Parental Factors ◽

Factors Influencing ◽

Independent Risk Factors ◽

The Relationship

Abstract Background With the advent of the electronic age, the long-term screen time (ST) of preschoolers in China is relatively high and is on the rise, which is likely to affect preschoolers’ physical and mental health. This study aimed to explore the factors influencing ST in preschoolers, especially the role of parental factors, and to provide a basis for the prevention, control, and intervention of ST in preschoolers in China. Methods A questionnaire was completed by the parents of 1,546 preschoolers from four kindergartens in Pinghu City, Zhejiang Province, China, and a logistic regression model was used to analyze the correlates of excessive ST in preschoolers. Results A total of 43.8% of preschoolers, of which 50.3% were boys and 49.7% were girls, had > 1 hour/day of ST. For older preschoolers, greater screen accessibility, greater frequency of eating in front of a screen, longer ST of parents, and unclear rules of screen behavior were the risk factors for ST being > 1 hour/day (P < 0.05). After adjusting for confounders, the relationship between the ST of fathers and ST of preschoolers was still significant (P < 0.01), and the dose-effect relationship was observed (P < 0.001). Conclusion Prolonged parental ST (especially of fathers) and lack of rules for screen behavior of were independent risk factors for prolonged preschoolers’ ST in this study.

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