scholarly journals Risk factors for the accuracy of the initial diagnosis of malaria cases in China: a decision-tree modelling approach

2022 ◽  
Vol 21 (1) ◽  
Author(s):  
Gang Li ◽  
Donglan Zhang ◽  
Zhuo Chen ◽  
Da Feng ◽  
Xinyan Cai ◽  
...  
Keyword(s):  
Decision Tree ◽  
Median Time ◽  
Health Centre ◽  
Diagnostic Errors ◽  
Initial Diagnosis ◽  
Categorical Variables ◽  
Time Interval ◽  
Insurance Status ◽  
Healthcare Facilities ◽  
Health Insurance Status

Abstract Background Early accurate diagnosis and risk assessment for malaria are crucial for improving patients’ terminal prognosis and preventing them from progressing to a severe or critical stage. This study aims to describe the accuracy of the initial diagnosis of malaria cases with different characteristics and the factors that affect the accuracy in the context of the agenda for a world free of malaria. Methods A retrospective study was conducted on 494 patients admitted to hospitals with a diagnosis of malaria from January 2014 through December 2016. Descriptive statistics were calculated, and decision tree analysis was performed to predict the probability of patients who may be misdiagnosed. Results Of the 494 patients included in this study, the proportions of patients seeking care in county-level, prefecture-level and provincial-level hospitals were 27.5% (n = 136), 26.3% (n = 130) and 8.3% (n = 41), respectively; the proportions of patients seeking care in clinic, township health centre and Centres for Disease Control and Prevention were 25.9% (n = 128), 4.1% (n = 20), and 7.9% (n = 39), respectively. Nearly 60% of malaria patients were misdiagnosed on their first visit, and 18.8% had complications. The median time from onset to the first visit was 2 days (IQR: 0-3 days), and the median time from the first visit to diagnosis was 3 days (IQR: 0–4 days). The decision tree classification of malaria patients being misdiagnosed consisted of six categorical variables: healthcare facilities for the initial diagnosis, time interval between onset and initial diagnosis, region, residence type, insurance status, and age. Conclusions Insufficient diagnostic capacity of healthcare facilities with lower administrative levels for the first visit was the most important risk factor in misdiagnosing patients. To reduce diagnostic errors, clinicians, government decision-makers and communities should consider strengthening the primary care facilities, the time interval between onset and initial diagnosis, residence type, and health insurance status.

scholarly journals Efficient Management Strategy of Malaria Patients in China: A Decision-Tree Modelling Approach

2021 ◽  
Author(s):  
Gang Li ◽  
Donglan Zhang ◽  
Zhuo Chen ◽  
Da Feng ◽  
Xinyan Cai ◽  
...  
Keyword(s):  
Decision Tree ◽  
Median Time ◽  
Diagnostic Errors ◽  
Initial Diagnosis ◽  
Categorical Variables ◽  
Time Interval ◽  
Insurance Status ◽  
Healthcare Facilities ◽  
Health Insurance Status ◽  
Control And Prevention

Abstract Background: Early accurate diagnosis and risk assessment for malaria are crucial for improving patients’ terminal prognosis, and preventing them from progressing to severe or critical state. This study aims to describe the accuracy of the initial diagnosis of malaria cases with different characteristics, as well as the factors that affect the accuracy in the context of the agenda for a world free of malaria.Methods: A retrospective study was conducted on 494 patients admitted to hospitals with a diagnosis of malaria from January 2014 through December 2016. Descriptive statistics were calculated and decision tree analysis was performed to predict the probability of patients who may be misdiagnosed.Results: Of the 494 patients included in this study, the proportions of patients seeking care in county-level, prefecture-level, and provincial-level hospitals were 27.5% (n=136), 26.3% (n=130), and 8.3% (n=41), respectively; the proportions of patients seeking care in clinic, township health center, and Centers for Disease Control and Prevention were 25.9% (n=128), 4.1% (n=20), and 7.9% (n=39), respectively. Nearly 60% of malaria patients were misdiagnosed on their first visit, and 18.8% of patients had complications. The median time from onset to the first visit was 2 days (IQR: 0 - 3 days), and the median time from the first visit to diagnosis was 3 days (IQR: 0 - 4 days). The decision tree classification of malaria patients being misdiagnosed consisted of six categorical variables: healthcare facilities for the initial diagnosis, time interval between onset and initial diagnosis, region, residence type, insurance status, and age.Conclusion: Insufficient diagnostic capacity of healthcare facilities with lower administrative levels for the first visit was the most important risk factor in misdiagnosing patients. Researchers and clinicians should consider strengthening the primary care facilities, time interval between onset and initial diagnosis, administrated residence, and health insurance status to reduce diagnostic errors.

Abstract 106: Association between Insurance Status and Presentation Delay in Young Patients with Acute Myocardial Infarction: Insights from the VIRGO Study

2014 ◽  
Vol 7 (suppl_1) ◽  
Author(s):  
Serene I Chen ◽  
Yongfei Wang ◽  
Rachel Dreyer ◽  
Kelly M Strait ◽  
Erica S Spatz ◽  
...  
Keyword(s):  
Health Insurance ◽  
Young Patients ◽  
Independent Effect ◽  
Time Interval ◽  
Insurance Status ◽  
Chi Square ◽  
Prehospital Delay ◽  
Health Insurance Status ◽  
Care Access ◽  
Chi Square Test

Background: Lacking health insurance and having financial concerns are known barriers to emergency care access in patients with AMI. However we do not know whether being uninsured or underinsured is associated with increased prehospital delay among young adults specifically, and whether this relationship varies by gender. Methods: We used data from the VIRGO study, a multicenter prospective study of women and men aged 18-55y with AMI (3,572). We excluded non-US patients (587) and those with unknown prehospital delay time (32) or insurance status (2). Participants were divided into 3 groups by health insurance status: 1. uninsured; 2. underinsured (insured but avoided care or medications due to cost concerns); 3. adequately insured (insured and without the above concerns). Prehospital delay was defined as the time interval between symptom onset and hospital presentation. The association between insurance groups and prehospital delay of >12 hours was examined using chi-square test. A hierarchical logistic model was used to evaluate the independent effect of insurance groups on prehospital delay of >12 hours adjusting for site clustering, demographics, medical history and clinical characteristics. Results: Of the 2951 young individuals with AMI in our study, 1987 were women (67.3%); the median age was 48 (IQR= 44, 52); 38% were underinsured; and 23%, uninsured. While women were less likely than men to be uninsured (21.8 vs 25.4%, P=0.03), they were more likely to be underinsured (60.5 vs 53.6%, P12 hours of prehospital delay: 38% of women and 29% of men (P=0.0001; figure). Insurance status was not associated with delays of >12 hrs in women (P=0.10) or men (P=0.57). After adjustments, the underinsured (OR 0.99; 95%CI 0.81, 1.19) and uninsured (OR 1.11; 95% CI 0.88, 1.39) did not have higher odds of having >12 hrs of prehospital delay compared to the adequately insured. Conclusion: More than 1 in 3 young individuals with AMI presented to hospitals beyond 12 hours after onset of symptoms. Insurance status was not associated with prehospital delays in this population. Although women are more likely to have >12 hours of prehospital delay, this difference is not attributable to differences in insurance status. Factors beyond the provision of insurance will need to be addressed to reduce delays.

scholarly journals Management of imported malaria cases and healthcare institutions in central China, 2012–2017: application of decision tree analysis

2019 ◽  
Vol 18 (1) ◽  
Author(s):  
Xi-Liang Wang ◽  
Jie-Bin Cao ◽  
Dan-Dan Li ◽  
Dong-Xiao Guo ◽  
Cheng-Da Zhang ◽  
...  
Keyword(s):  
Decision Tree ◽  
Descriptive Analysis ◽  
Henan Province ◽  
Initial Diagnosis ◽  
Imported Malaria ◽  
Central China ◽  
Time Interval ◽  
Valuable Insight ◽  
Malaria Surveillance ◽  
Tree Model

Abstract Background Imported malaria has been an important challenge for China. Fatality rates from malaria increased in China, particularly in Henan Province, primarily due to malpractice and misdiagnoses in healthcare institutions, and the level of imported malaria. This study aims to investigate the relationship between the state of diagnosis and subsequent complications among imported malaria cases at healthcare institutions, based on malaria surveillance data in Henan Province from 2012 to 2017. Methods A retrospective descriptive analysis was performed using data from the Centre for Disease Control and Prevention, Zhengzhou City, the capital of Henan Province. A decision tree method was exploited to provide valuable insight into the correlation between imported malaria cases and healthcare institutions. Results From 2012 to 2017, there were 371 imported malaria cases, mostly in males aged between 20 and 50 years, including 319 Plasmodium falciparum cases. First visits of 32.3%, 19.9% and 15.9% malaria cases for treatment were to provincial, municipal and county healthcare institutions, respectively. The time interval between onset and initial diagnosis of 284 cases (76.5%) and the time interval between initial diagnosis and final diagnosis of 197 cases (53.1%) was no more than 72 h. An apparent trend was found that there were notably fewer patients misdiagnosed at first visit to healthcare institutions of a higher administrative level; 12.5% of cases were misdiagnosed in provincial healthcare institutions compared to 98.2% in private clinics, leading to fewer complications at healthcare institutions of higher administrative level due to correct initial diagnosis. In the tree model, the rank of healthcare facilities for initial diagnosis, and number of days between onset and initial diagnosis, made a major contribution to the classification of initial diagnosis, which subsequently became the most significant factor influencing complications developed in the second tree model. The classification accuracy were 82.2 and 74.1%, respectively for the tree models of initial diagnosis and complications developed. Conclusion Inadequate seeking medical care by imported malaria patients, and insufficient capacity to diagnose malaria by healthcare institutions of lower administrative level were identified as major factors influencing complications of imported malaria cases in Henan Province. The lack of connection between uncommon imported malaria cases and superior medical resources was found to be the crucial challenge. A web-based system combined with WeChat to target imported malaria cases was proposed to cope with the challenge.

FLT3/Internal Tandem Duplication in Paired Presentation and Relapse Pediatric AML Samples.

Blood ◽  
2004 ◽  
Vol 104 (11) ◽  
pp. 94-94
Author(s):  
Jacqueline Cloos ◽  
Christian M. Zwaan ◽  
Sophie L. Corthals ◽  
Bianca F. Goemans ◽  
Quinten Waisfisz ◽  
...  
Keyword(s):  
Median Time ◽  
De Novo ◽  
Pcr Amplification ◽  
Patient Characteristics ◽  
Initial Diagnosis ◽  
Time Interval ◽  
Wild Type ◽  
De Novo Mutations ◽  
First Relapse ◽  
Pediatric Aml

Abstract We have previously reported that FLT3 internal tandem duplications (FLT3/ITD) were detectable in 11.5% of 234 pediatric AML samples at initial diagnoses, and conferred a poor prognosis (Zwaan et al, Blood 2003). However, no data were available on relapsed pediatric AML and on paired initial-relapse samples. Currently we have studied genomic DNA of 99 relapsed and 42 paired initial-relapse pediatric AML samples for FLT3/ITD. Samples were obtained from the AML-BFM-SG, the Dutch Childhood Oncology Group, and by centers participating in the International BFM Relapsed AML 2001/01 study. Of the 99 relapsed patients the median age was 10.2 years and 71% were boys. Median time from diagnosis to first relapse was 14 months. Patient characteristics of the 42 pairs were at diagnosis: median age 7.7 years, sex 80% boys; at relapse: median age 14.3 years. Median time from diagnosis to first relapse was 14 months. ITDs were detected by PCR amplification including a fluorescent labeled primer. Fragments were separated by capillary electrophoresis on an ABI 3100 and analyzed using Genescan software. Of the 99 relapsed samples, 20 were ITD positive (20.2%), with ITDs varying in length from 18–129 bp. ITDs occurred in 15% of children <10 years of age, and in 21% ≥ 10 years (p=0.5). ITD pos. patients were boys in 84% vs. 68% in wild type (wt) patients (p=0.16). WBC, blast percentage and FAB-types were not different between ITD and wt patients. The time-interval between diagnosis and relapse was significantly shorter in ITD pos. patients (10 months) than in wt ones (15 months, p=0.003). Flt3/ITD in paired initial vs relapse samples of pediatric AML Relapse ITD pos Relapse wild type Total Initial ITD pos 6 (14%) 4 (10%) 10 (24%) Initial wild type 4 (10%) 28 (66%) 32 (76%) Total 10 (24%) 32 (76%) 42 (100%) Two of the 42 paired samples had 2 different length mutations at initial diagnosis and only 1 at relapse. Four acquired an ITD at relapse (one at 2nd relapse), which varied from 21–129 bp. FAB-types were: 1xM1, 2xM2, 1x M6. Median time to first relapse was only 6 months. Four patients (FAB-types were 2xM1 and 2xM2) lost the ITD, which at diagnosis varied from 30–72 bp and interestingly the median time to relapse was relatively long (median of 30 months). All ITDs were confirmed by sequencing and they were all located in exon 14. We conclude that: a) detectable FLT3/ITD is more frequent in the relapsed patients (±20%) compared to our previous study with patients at initial diagnosis (11.5%); b) patients with initially detectable FLT3/ITD relapse earlier than wt patients; c) there were no significant differences in clinical and cell-biological characteristics between wt and ITD patients at relapse d) gain and loss of ITDs at relapse does occur e) patients who gain an ITD seem to relapse early, patients who lose an ITD relapse relatively late. The changes in ITD status may reflect true de novo mutations or a selection of specific clones. In the latter case a small ITD positive subclone was not detected. We will further elucidate this with specific PCRs we are currently developing. These results have relevance for Flt3-targeted therapy and minimal disease monitoring.

Identifying Key Fraud Indicators in the Automobile Insurance Industry Using SQL Server Analysis Services

2019 ◽  
Vol 64 (2) ◽  
pp. 53-71
Author(s):  
Botond Benedek ◽  
Ede László
Keyword(s):  
Neural Network ◽  
Decision Tree ◽  
Naive Bayes ◽  
Insurance Industry ◽  
Naïve Bayes ◽  
Sql Server ◽  
Categorical Variables ◽  
Automobile Insurance ◽  
Price Determination ◽  
Mining Tool

Abstract Customer segmentation represents a true challenge in the automobile insurance industry, as datasets are large, multidimensional, unbalanced and it also requires a unique price determination based on the risk profile of the customer. Furthermore, the price determination of an insurance policy or the validity of the compensation claim, in most cases must be an instant decision. Therefore, the purpose of this research is to identify an easily usable data mining tool that is capable to identify key automobile insurance fraud indicators, facilitating the segmentation. In addition, the methods used by the tool, should be based primarily on numerical and categorical variables, as there is no well-functioning text mining tool for Central Eastern European languages. Hence, we decided on the SQL Server Analysis Services (SSAS) tool and to compare the performance of the decision tree, neural network and Naïve Bayes methods. The results suggest that decision tree and neural network are more suitable than Naïve Bayes, however the best conclusion can be drawn if we use the decision tree and neural network together.

Health Insurance Status as a Predictor of Mode of Colon Cancer Detection but Not Stage at Diagnosis: Implications for Early Detection

2021 ◽  
pp. 003335492199917
Author(s):  
Lindsey A. Jones ◽  
Katherine C. Brewer ◽  
Leslie R. Carnahan ◽  
Jennifer A. Parsons ◽  
Blase N. Polite ◽  
...  
Keyword(s):  
Colon Cancer ◽  
Health Insurance ◽  
Early Detection ◽  
Late Stage ◽  
Early Stage ◽  
Insurance Status ◽  
Stage At Diagnosis ◽  
Health Insurance Status ◽  
Percentage Point Increase ◽  
Point Increase

Objective For colon cancer patients, one goal of health insurance is to improve access to screening that leads to early detection, early-stage diagnosis, and polyp removal, all of which results in easier treatment and better outcomes. We examined associations among health insurance status, mode of detection (screen detection vs symptomatic presentation), and stage at diagnosis (early vs late) in a diverse sample of patients recently diagnosed with colon cancer from the Chicago metropolitan area. Methods Data came from the Colon Cancer Patterns of Care in Chicago study of racial and socioeconomic disparities in colon cancer screening, diagnosis, and care. We collected data from the medical records of non-Hispanic Black and non-Hispanic White patients aged ≥50 and diagnosed with colon cancer from October 2010 through January 2014 (N = 348). We used logistic regression with marginal standardization to model associations between health insurance status and study outcomes. Results After adjusting for age, race, sex, and socioeconomic status, being continuously insured 5 years before diagnosis and through diagnosis was associated with a 20 (95% CI, 8-33) percentage-point increase in prevalence of screen detection. Screen detection in turn was associated with a 15 (95% CI, 3-27) percentage-point increase in early-stage diagnosis; however, nearly half (47%; n = 54) of the 114 screen-detected patients were still diagnosed at late stage (stage 3 or 4). Health insurance status was not associated with earlier stage at diagnosis. Conclusions For health insurance to effectively shift stage at diagnosis, stronger associations are needed between health insurance and screening-related detection; between screening-related detection and early stage at diagnosis; or both. Findings also highlight the need to better understand factors contributing to late-stage colon cancer diagnosis despite screen detection.

scholarly journals Health Insurance Coverage before and after the Affordable Care Act in the USA

Sci ◽  
2021 ◽  
Vol 3 (2) ◽  
pp. 25
Author(s):  
Jesse Patrick ◽  
Philip Q. Yang
Keyword(s):  
Health Insurance ◽  
National Health ◽  
Affordable Care Act ◽  
The United States ◽  
Insurance Status ◽  
Health Insurance Status ◽  
Full Implementation ◽  
Before And After ◽  
The Affordable Care Act ◽  
Partial Implementation

The Affordable Care Act (ACA) is at the crossroads. It is important to evaluate the effectiveness of the ACA in order to make rational decisions about the ongoing healthcare reform, but existing research into its effect on health insurance status in the United States is insufficient and descriptive. Using data from the National Health Interview Surveys from 2009 to 2015, this study examines changes in health insurance status and its determinants before the ACA in 2009, during its partial implementation in 2010–2013, and after its full implementation in 2014 and 2015. The results of trend analysis indicate a significant increase in national health insurance rate from 82.2% in 2009 to 89.4% in 2015. Logistic regression analyses confirm the similar impact of age, gender, race, marital status, nativity, citizenship, education, and poverty on health insurance status before and after the ACA. Despite similar effects across years, controlling for other variables, youth aged 26 or below, the foreign-born, Asians, and other races had a greater probability of gaining health insurance after the ACA than before the ACA; however, the odds of obtaining health insurance for Hispanics and the impoverished rose slightly during the partial implementation of the ACA, but somewhat declined after the full implementation of the ACA starting in 2014. These findings should be taken into account by the U.S. Government in deciding the fate of the ACA.

Intrapartum Basal Ganglia–Thalamic Pattern Injury and Radiologically Termed “Acute Profound Hypoxic–Ischemic Brain Injury” Are Not Synonymous

2020 ◽  
Author(s):  
Johan Smith ◽  
Regan Solomons ◽  
Lindi Vollmer ◽  
Eduard J. Langenegger ◽  
Jan W. Lotz ◽  
...  
Keyword(s):  
Brain Injury ◽  
Basal Ganglia ◽  
Caesarean Section ◽  
Median Time ◽  
Mode Of Delivery ◽  
Time Interval ◽  
Key Points ◽  
Admission Status ◽  
Sentinel Event ◽  
Magnetic Resonance Imaging Mri

Objective Human cases of acute profound hypoxic-ischemic (HI) injury (HII), in which the insult duration timed with precision had been identified, remains rare, and there is often uncertainty of the prior state of fetal health. Study Design A retrospective analysis of 10 medicolegal cases of neonatal encephalopathy-cerebral palsy survivors who sustained intrapartum HI basal ganglia-thalamic (BGT) pattern injury in the absence of an obstetric sentinel event. Results Cardiotocography (CTG) admission status was reassuring in six and suspicious in four of the cases. The median time from assessment by admission CTG or auscultation to birth was 687.5 minutes (interquartile range [IQR]: 373.5–817.5 minutes), while the median time interval between first pathological CTG and delivery of the infant was 179 minutes (IQR: 137–199.25 minutes). The mode of delivery in the majority of infants (60%) was by unassisted vaginal birth; four were delivered by delayed caesarean section. The median (IQR) interval between the decision to perform a caesarean section and delivery was 169 minutes (range: 124–192.5 minutes). Conclusion The study shows that if a nonreassuring fetal status develops during labor and is prolonged, a BGT pattern HI injury may result, in the absence of a perinatal sentinel event. Intrapartum BGT pattern injury and radiologically termed “acute profound HI brain injury” are not necessarily synonymous. A visualized magnetic resonance imaging (MRI) pattern should preferably solely reflect the patterns description and severity, rather than a causative mechanism of injury. Key Points

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