Hospital charges associated with motorcycle crash factors: a quantile regression analysis

The economic burden of malaria inpatients and its determinants during China's elimination stage

Background

Malaria burden is still worrisome, while empirical evidence from malaria-eliminated countries including China may provide inspiration for the world.

Objective

This study aimed to investigate China's malaria hospitalization costs and explore its determinants.

Methods

Stratified multistage sampling across provincial, municipal, and county hospitals was conducted in 2017. All the malaria medical records were retrieved from 2014 to 2016 in 70 hospitals. Parametric and non-parametric methods were employed to estimate hospitalization costs, and the non-parametric bootstrap was used to compare hospitalization costs among sample areas and assessed the uncertainty of its differences. Quantile regressions were conducted to identify the determinants of hospitalization costs.

Results

The median hospitalization costs of 1633 malaria inpatients were 628 USD. Medication and laboratory tests accounted for over 70% of total expenditure. The median reimbursement rate was 41.87%, and this number was even lower in higher-level hospitals (<35%) and among the New Rural Cooperative Medical Scheme (<40%). Finally, health insurance type, hospital tier, clinical units, unknown fever, and comorbidity were the main determinants of hospitalization costs.

Conclusion

The disparity of health protection for malaria hospitalization between rural and urban areas was noteworthy. Equivocal diagnosis and comorbidity are contributors of high cost as well. A reasonable payment system and enhanced capacities to treat malaria in a cost-effective way are suggested to reassure malaria economic burden.

Socioeconomic and Demographic Factors Effect in Association with Driver's Medical Services after Crashes

Motor vehicle crashes are the third leading cause of preventable-injury deaths in the United States. Previous research has found links between the socioeconomic characteristics of driver residence zip codes and crash frequencies. The objective of the study is to extend earlier work by investigating whether the socioeconomic characteristics of a driver’s residence zip code influence their likelihood of resulting in post-crash medical services. Data were drawn from General Use Model (GUM) data for police crash reports linked to hospital records in Kentucky, Utah, and Ohio. Zip-code-level socioeconomic data from the American Community Survey were also incorporated into analyses. Logistic regression models were developed for each state and showed that the socioeconomic variables such as educational attainment, median housing value, gender, and age have p-values < 0.001 when tested against the odds of seeking post-crash medical services. Models for Kentucky and Utah also include the employment-to-population ratio. The results show that in addition to age and gender, educational attainment, median housing value and rurality percentage at the zip code level are associated with the likelihood of a driver seeking follow-up medical services after a crash. It is concluded that drivers from areas with lower household income and lower educational attainment are more likely to seek post-crash medical services, primarily in emergency departments. Female drivers are also more likely to seek post-crash medical services.

Data linkage for crash outcome assessment: Linking police-reported crashes, emergency response data, and trauma registry records

INTRODUCTION

Traffic crash reports lack detailed information about emergency medical service (EMS) responses, the injuries, and the associated treatments, limiting the ability of safety analysts to account for that information. Integrating data from other sources can enable a better understanding of characteristics of serious crashes and further explain variance in injury outcomes. In this research, an approach is proposed and implemented to link crash data to EMS run data, patient care reports, and trauma registry data.

METHOD

A heuristic framework is developed to match EMS run reports to crashes through time, location, and other indicators present in both datasets. Types of matches between EMS and crashes were classified. To investigate the fidelity of the match approach, a manual review of a sample of data was conducted. A comparative bias analysis was implemented on several key variables.

RESULTS

72.2% of EMS run reports matched to a crash record and 69.3% of trauma registry records matched with a crash record. Females, individuals between 11 and 20 years old, and individuals involved in single vehicle or head on crashes were more likely to be present in linked data sets. Using the linked data sets, relationships between EMS response time and reported injury in the crash report, and between police-reported injury and injury severity score were examined.

CONCLUSION

Linking data from other sources can greatly enhance the information available to address road safety issues, data quality issues, and more. Linking data has the potential to result in biases that must be investigated as they relate to the use-case for the data.

PRACTICAL IMPLICATIONS

This research resulted in a transferable heuristic approach that can be used to link data sets that are commonly collected by agencies across the world. It also provides guidance on how to check the linked data for biases and errors.

Emergency department visits by pediatric patients sustained as a passenger on a motorcycle

OBJECTIVE

Currently only 5 out of the 50 states in the United States have laws restricting the age of passengers permitted to ride on a motorcycle. This study sought to characterize the visits by patients under the age of 16 to U.S. emergency departments (EDs) for injuries sustained as a passenger on a motorcycle.

METHODS

In this retrospective cohort study, data were obtained from the Nationwide Emergency Department Sample (NEDS) for the years 2006 to 2011. Pediatric patients who were passengers on a motorcycle that was involved in a crash were identified using International Classification of Diseases, Ninth Revision (ICD-9) External Cause of Injury codes. We also examined gender, age, disposition, regional differences, common injuries, and charges.

RESULTS

Between 2006 and 2011 there were an estimated 9,689 visits to U.S. EDs by patients under the age of 16 who were passengers on a motorcycle involved in a crash. The overall average patient age was 9.4 years, and they were predominately male (54.5%). The majority (85%) of these patients were treated and released. The average charges for discharged patients were $2,116.50 and amounted to roughly $17,500,000 during the 6 years. The average cost for admission was $51,446 per patient and totaled over $54 million. The most common primary injuries included superficial contusions; sprains and strains; upper limb fractures; open wounds of head, neck, and trunk; and intracranial injuries.

CONCLUSION

Although there were only about 9,700 visits to U.S. EDs for motorcycle crashes involving passengers less than 16 years old for 2006 to 2011, the total cost of visits that resulted in either ED discharge or hospital admission amounted to over $71 million.

Linkage of traffic crash and hospitalization records with limited identifiers for enhanced public health surveillance

BACKGROUND

Motor vehicle traffic (MVT) crashes kill or seriously injure approximately 4250 people in New York City (NYC) each year. Traditionally, NYC surveillance practices use hospitalization and crash data separately to monitor trends in MVT-related injuries, but key information linking crash circumstances to health outcomes is lost when analyzing these data sources in isolation. Our objective was to match crash reports to hospitalization records to create a traffic injury surveillance dataset that can be used to describe crash circumstances and related injury outcomes. The linkage of the two systems presents a unique challenge since the system tracking crashes and the system tracking hospitalizations and emergency department (ED) visits lack key identifying data such as names and dates of birth.

METHODS

NYC Department of Transportation provided electronic records based on reports of motor vehicle crashes submitted to the New York State Department of Motor Vehicles for all crashes occurring in NYC from 2009 to 2013. New York Statewide Planning and Research Cooperative System (SPARCS) ED and hospitalization administrative data from NYC hospitals were used to identify unintentional MVT-related injuries using external cause of injury codes. Since the two systems do not share unique individual identifiers, probabilistic record linkage was conducted using LinkSolv9.0. Sensitivity/specificity calculations and chi-square analyses of linkage rates were conducted to assess linkage results.

RESULTS

From 2009-2013, there were 1,054,344 individuals involved in MVT crashes in NYC and 280,340 ED visits and hospitalizations from MVT-related injuries. There were 145,003 linked pairs, giving a linkage rate of 52% of the total MVT-related hospital records. This linkage had a sensitivity of 74% and a specificity of 93%. Linkage rates were comparable by age, sex, crash role, collision type, hospital county, injury location, hospital type, and hospital status, indicating no apparent biases in the match by these variables.

CONCLUSIONS

Performing a probabilistic linkage between MVT crash reports and hospitalization records is possible with a limited set of identifying variables. These linked data will inform traffic safety policies by providing new information on how crash circumstances translate to health outcomes.

Classifying, measuring and improving the quality of data in trauma registries: A review of the literature

INTRODUCTION

Globally, injury is a major cause of death and disability. Improvements in trauma care have been driven by trauma registries. The capacity of a trauma registry to inform improvements in the quality of trauma care is dependent upon the quality of data. The literature on data quality in disease registries is inconsistent and ambiguous; methods used for classifying, measuring, and improving data quality are not standardised. The aim of this study was to review the literature to determine the methods used to classify, measure and improve data quality in trauma registries.

METHODS

A scoping review of the literature was performed. Databases were searched using the term "trauma registry" and its synonyms, combined with multiple terms denoting data quality. There was no restriction on year. Full-length manuscripts were included if the classification, measurement or improvement of data quality in one or more trauma registries was a study objective. Data were abstracted regarding registry demographics, study design, data quality classification, and the reported methods used to measure and improve the pre-defined data quality dimensions of accuracy, completeness and capture.

RESULTS

Sixty-nine publications met the inclusion criteria. Four publications classified data quality. The most frequently described methods for measuring data accuracy (n=47) were checks against other datasets (n=18) and checks of injury coding (n=17). The most frequently described methods for measuring data completeness (n=47) were the percentage of included cases, for a given variable or list of variables, for which there was an observation in the registry (n=29). The most frequently described methods for measuring data capture (n=37) were the percentage of cases in a linked reference dataset that were also captured in the primary dataset being evaluated (n=24). Most publications dealing with the measurement of a dimension of data quality did not specify the methods used; most publications dealing with the improvement of data quality did not specify the dimension being targeted.

CONCLUSION

The classification, measurement and improvement of data quality in trauma registries is inconsistent. To maintain confidence in the usefulness of trauma registries, the metrics and reporting of data quality need to be standardised.