scholarly journals A 10-year review of child injury hospitalisations, health outcomes and treatment costs in Australia

2017 ◽  
Vol 24 (5) ◽  
pp. 344-350 ◽  
Author(s):  
Rebecca J Mitchell ◽  
Kate Curtis ◽  
Kim Foster
Keyword(s):  
Injury Severity ◽  
Negative Binomial ◽  
Developmental Stages ◽  
Negative Binomial Regression ◽  
Mortality Data ◽  
Child Injury ◽  
Injured Child ◽  
Childhood Injury ◽  
Usual Residence ◽  
Cox Proportional Hazard Regression

BackgroundChildhood injury is a leading cause of hospitalisation, yet there has been no comprehensive examination of child injury and survival over time in Australia. To examine the characteristics, temporal trend and survival for children who were hospitalised as a result of injury in Australia.MethodA retrospective examination of linked hospitalisation and mortality data for injured children aged 16 years or less during 1 July 2001 to 30 June 2012. Negative binomial regression examined change in injury hospitalisation trends. Cox proportional hazard regression examined the association of risk factors on 30-day survival.ResultsThere were 6 86 409 injury hospitalisations, with an age-standardised rate of 1489 per 1 00 000 population (95% CI 1485.3 to 1492.4) in Australia. Child injury hospitalisation rates did not change over the 10-year period. For every severely injured child, there are at least 13 children hospitalised with minor or moderate injuries. The total cost of child injury hospitalisations was $A2.1 billion (annually $A212 million). Falls (38.4%) were the most common injury mechanism. Factors associated with a higher risk of 30-day mortality were: child was aged ≤10 years, higher injury severity, head injury, injured in a transport incident or following drowning and submersion or other threats to breathing, during self-harm and usual residence was regional/remote Australia.ConclusionsChildhood injury hospitalisation rates have not reduced in 10 years. Children’s patterns of injury change with age, and priorities for injury prevention alter according to developmental stages. The development of a national multisectorial childhood injury monitoring and prevention strategy in Australia is long overdue.

scholarly journals Education-related inequalities in cause-specific mortality: First estimates for Australia using individual-level linked census and mortality data

2020 ◽  
Author(s):  
Jennifer Welsh ◽  
Grace Joshy ◽  
Lauren Morgan ◽  
Kay Soga ◽  
Hsei-Di Law ◽  
...  
Keyword(s):  
High Education ◽  
Causes Of Death ◽  
Negative Binomial ◽  
International Comparisons ◽  
Negative Binomial Regression ◽  
Medical Intervention ◽  
Mortality Data ◽  
Individual Level ◽  
Specific Mortality ◽  
Inequalities In Mortality

Background: Socioeconomic inequalities in mortality are evident in all high-income countries and ongoing monitoring is recommended using linked census-mortality data. Using such data, we provide first estimates of education-related inequalities in cause-specific mortality in Australia, suitable for international comparisons. Methods: Using Australian Census (2016) linked to 13-months of Death Registrations data (2016-17), we estimated relative rates (RR) and rate differences (RD, per100 000 person-years), comparing rates in low (no qualifications) and intermediate (secondary school) with high education (tertiary), for individual causes of death (among those 25-84y) and grouped according to preventability (25-74y), separately by sex and age group, adjusting for age, using negative binomial regression. Results: Among 13.9M people contributing 14 452 732 person-years, 84 743 deaths occurred. We observed inequalities in most causes of death for each age-sex group. Among men aged 25-44y, absolute and relative inequalities (low versus high education) were largest for injuries, e.g. transport accidents (RR=10.1 [95%CI: 5.4-18.7], RD=21.1 [15.9-26.3]). Among those aged 45-64y, inequalities were greatest for chronic diseases, e.g. lung cancer (men RR=6.6 [4.9-8.9], RD=55.6 [51.1-60.1]) and ischaemic heart disease (women RR=5.8 [3.7-9.1], RD=19.2 [17.0-21.5]), with similar patterns for people aged 65-84y. When grouped according to preventability, inequalities were large for causes amenable to behaviour change and medical intervention for all ages and causes amenable to injury prevention among young men. Conclusions: Australian education-related inequalities in mortality are substantial, generally higher than international estimates, and related to preventability. Findings highlight opportunities to reduce them and the potential to improve the health of the population.

scholarly journals Using Linked Whole-Of-Population Data to Estimate Education-Related Inequalities in Cause-Specific Mortality in Australia

2020 ◽  
Vol 5 (5) ◽  
Author(s):  
Jennifer Welsh ◽  
Grace Joshy ◽  
Lauren Moran ◽  
Kay Soga ◽  
Hsei Di Law ◽  
...  
Keyword(s):  
Cause Of Death ◽  
Brain Cancer ◽  
Negative Binomial ◽  
Health Behaviours ◽  
Negative Binomial Regression ◽  
Population Data ◽  
Medical Intervention ◽  
Mortality Data ◽  
Registration Data ◽  
Specific Mortality

IntroductionOfficial Australian estimates of socioeconomic inequalities in cause-specific mortality have been based on area-level socioeconomic measures. Using area-level measures is known to underestimate inequalities. Objectives and ApproachUsing recently released census linked to mortality data, we estimate education-related inequalities in cause-specific mortality for Australia. We used 2016 Australian Census and Death Registration data (2016-17) linked via a Person Linkage Spine (linkage rates: 92% and 97%, respectively) from the Multi-Agency Data Integration Project (MADIP). Education, from the Census, was categorised as low (no secondary school graduation or other qualification), intermediate (secondary graduation with/without other non-tertiary qualifications) and high (tertiary qualification). Cause of death was coded according to the underlying cause of death using the ICD-10. We used negative binomial regression to estimate relative rates (RR) for cause-specific mortality at ages 25-84 years, in the 12-months following Census, comparing low vs high education, separately by sex and 20-year age group, adjusting for age. Results80,317 deaths occurred among 13,856,202 people. For those aged 25-44 years, relative inequalities were large for causes related to injury and smaller for lesspreventable deaths (e.g. for men, suicide RR=5.6, 95%CI: 4.1-7.5 and brain cancer RR=1.3, 0.6-3.1). For those aged 45-64, inequalities were large for causes related to health behaviours and amenable to medical intervention, e.g. lung cancer (men RR= 6.4, 4.7-8.8) and ischaemic heart disease (women RR=5.0, 3.2-7.7), and were small for less preventable causes e.g. brain cancer (women RR=0.9, 0.6-1.3). Patterns among those aged 65-84years were similar to those aged 45-64 years. Conclusion / ImplicationsIn Australia, inequalities in mortality are substantial. Our findings highlight the health burden from inequalities, opportunities for prevention and provide insights on targets to effectively reduce them.

scholarly journals Education inequalities in adult all-cause mortality: first national data for Australia using linked census and mortality data

2019 ◽  
Vol 49 (2) ◽  
pp. 511-518
Author(s):  
Rosemary J Korda ◽  
Nicholas Biddle ◽  
John Lynch ◽  
James Eynstone-Hinkins ◽  
Kay Soga ◽  
...  
Keyword(s):  
Negative Binomial ◽  
Census Data ◽  
International Comparisons ◽  
Negative Binomial Regression ◽  
Mortality Rates ◽  
Mortality Data ◽  
Age Group ◽  
National Data ◽  
Registration Data ◽  
All Cause Mortality

Abstract Background National linked mortality and census data have not previously been available for Australia. We estimated education-based mortality inequalities from linked census and mortality data that are suitable for international comparisons. Methods We used the Australian Bureau of Statistics Death Registrations to Census file, with data on deaths (2011–2012) linked probabilistically to census data (linkage rate 81%). To assess validity, we compared mortality rates by age group (25–44, 45–64, 65–84 years), sex and area-inequality measures to those based on complete death registration data. We used negative binomial regression to quantify inequalities in all-cause mortality in relation to five levels of education [‘Bachelor degree or higher’ (highest) to ‘no Year 12 and no post-secondary qualification’ (lowest)], separately by sex and age group, adjusting for single year of age and correcting for linkage bias and missing education data. Results Mortality rates and area-based inequality estimates were comparable to published national estimates. Men aged 25–84 years with the lowest education had age-adjusted mortality rates 2.20 [95% confidence interval (CI): 2.08‒2.33] times those of men with the highest education. Among women, the rate ratio was 1.64 (1.55‒1.74). Rate ratios were 3.87 (3.38‒4.44) in men and 2.57 (2.15‒3.07) in women aged 25–44 years, decreasing to 1.68 (1.60‒1.76) in men and 1.44 (1.36‒1.53) in women aged 65–84 years. Absolute education inequalities increased with age. One in three to four deaths (31%) was associated with less than Bachelor level education. Conclusions These linked national data enabled valid estimates of education inequality in mortality suitable for international comparisons. The magnitude of relative inequality is substantial and similar to that reported for other high-income countries.

scholarly journals Association between homicide rates and suicide rates: a countrywide longitudinal analysis of 5507 Brazilian municipalities

BMJ Open ◽  
2020 ◽  
Vol 10 (11) ◽  
pp. e040069
Author(s):  
Daiane Borges Machado ◽  
Keltie McDonald ◽  
Luis F S Castro-de-Araujo ◽  
Delan Devakumar ◽  
Flávia Jôse Oliveira Alves ◽  
...  
Keyword(s):  
Negative Binomial ◽  
Negative Binomial Regression ◽  
Population Based ◽  
Sensitivity Analyses ◽  
Secondary Outcome ◽  
Mortality Data ◽  
Homicide Rates ◽  
Suicide Rates ◽  
Socioeconomic And Demographic Factors ◽  
The Relationship

ObjectiveTo estimate the association between homicide and suicide rates in Brazilian municipalities over a period of 7 years.DesignWe conducted a longitudinal ecological study using annual mortality data from 5507 Brazilian municipalities between 2008 and 2014. Multivariable negative binomial regression models were used to examine the relationship between homicide and suicide rates. Robustness of results was explored using sensitivity analyses to examine the influence of data quality, population size, age and sex on the relationship between homicide and suicide rates.SettingA nationwide study of municipality-level data.ParticipantsMortality data and corresponding population estimates for municipal populations aged 10 years and older.Primary and secondary outcome measuresAge-standardised suicide rates per 100 000.ResultsMunicipal suicide rates were positively associated with municipal homicide rates; after adjusting for socioeconomic and demographic factors, a doubling of the homicide rate was associated with 22% increase in suicide rate (rate ratio=1.22, 95% CI: 1.13 to 1.33). A dose–response effect was observed with 4% increase in suicide rates at the third quintile, 9% at the fourth quintile and 12% at the highest quintile of homicide rates compared with the lowest quintile. The observed effect estimates were robust to sensitivity analyses.ConclusionsMunicipalities with higher homicide rates have higher suicide rates and the relationship between homicide and suicide rates in Brazil exists independently of many sociodemographic and socioeconomic factors. Our results are in line with the hypothesis that changes in homicide rates lead to changes in suicide rates, although a causal association cannot be established from this study. Suicide and homicide rates have increased in Brazil despite increased community mental health support and incarceration, respectively; therefore, new avenues for intervention are needed. The identification of a positive relationship between homicide and suicide rates suggests that population-based interventions to reduce homicide rates may also reduce suicide rates in Brazil.

scholarly journals PEMODELAN DATA KEMATIAN BAYI DENGAN GEOGRAPHICALLY WEIGHTED NEGATIVE BINOMIAL REGRESSION

2017 ◽  
Vol 9 (2) ◽  
pp. 95
Author(s):  
Riza F. Ramadhan ◽  
Robert Kurniawan
Keyword(s):  
Infant Mortality ◽  
Negative Binomial ◽  
Gaussian Function ◽  
Negative Binomial Regression ◽  
Kernel Functions ◽  
Mortality Data ◽  
Weighting Matrix ◽  
Binomial Regression ◽  
Fixed Kernel ◽  
Best Fit

Overdispersion phenomenon and the influence of location or spatial aspect on data are handled using Binomial Geographically Weighted Regression (GWNBR). GWNBR is the best solution to form a regression analysis that is specific to each observation’s location. The analysis resulted in parameter value which different from one observation to another between location. The Weighting Matrix Selection is done before doing The GWNBR modeling. Different weighting  will resulted in different model. Thus this study aims to  investigate the best fit model using infant mortality data that is produced by some kind of weighting such as fixed kernel Gaussian, fixed kernel Bisquare, adaptive kernel Gaussian and adaptive kernal Bisquare in GWNBR modeling. This region study covers all the districts/municipalities in Java because the number of observations are more numerous and have more diverse characteristics. The study shows that out of four kernel functions, infant mortality data in Java2012, the best fit model is produced by fixed kernel Gaussian function. Besides that GWNBR with fixed kernel Gaussian also shows better result than the poisson regression and negative binomial regression for data modeling on  infant mortality based on the value of AIC and Deviance.                                                                                    Keywords:   GWNBR, infant mortality, fixed gaussian, fixed bisquare, adaptive gaussian, adaptive bisquare.

Blood Utilization at a Level I Trauma Center: Is this as Good as it Gets?

2009 ◽  
Vol 75 (8) ◽  
pp. 693-698
Author(s):  
Sebron W. Harrison ◽  
Russell L. Griffin ◽  
Jeffrey D. Kerby ◽  
Marisa B. Marques ◽  
Loring W. Rue ◽  
...  
Keyword(s):  
Injury Severity Score ◽  
Severity Score ◽  
Trauma Center ◽  
Injury Severity ◽  
Negative Binomial ◽  
Negative Binomial Regression ◽  
Trauma Patients ◽  
Severely Injured ◽  
Level I ◽  
Rbc Transfusion

Recognition of the adverse effects of allogeneic blood resulted in the decreased use of red blood cell (RBC) transfusion in surgical practice in the 1990s. Our objective was to evaluate patterns of RBC transfusion utilization among trauma patients during the current decade. Blunt trauma patients admitted to a regional trauma center between 2000 and 2007 were identified (n = 16,011). Annual trends in RBC utilization were estimated (negative binomial regression for continuous dependent variables and logistic regression for dichotomous variables). Models were stratified by Injury Severity Score to adjust for injury severity. Although the proportion of patients receiving a blood transfusion within 48 hours of hospitalization significantly increased ( P < 0.0001), there was no significant change in the rate of units transfused ( P = 0.5152) among transfused patients. After stratification by Injury Severity Score, a significantly decreasing trend in the proportion of severely injured patients transfused was observed ( P = 0.0243). Annual variation in the relatively less injured groups was not significant. In the current decade, transfusion utilization at a Level I trauma center has demonstrated minimal variation on a year-to-year basis. Among the severely injured, the temporal decrease in relatively early utilization of RBC transfusion may reflect increasing inclination to accept a greater degree of anemia in higher acuity patients.

scholarly journals Socioeconomic and comorbid factors affecting mortality and length of stay in COVID-19

2021 ◽  
Author(s):  
Adam Delora ◽  
Ashlynn Mills ◽  
David Jacobson ◽  
Brendon Cornett ◽  
W. Frank Peacock ◽  
...  
Keyword(s):  
Length Of Stay ◽  
Hospital Mortality ◽  
Negative Binomial ◽  
Private Insurance ◽  
Negative Binomial Regression ◽  
Mortality Data ◽  
Factors Affecting ◽  
Black Patients ◽  
Testing Patient ◽  
Insurance Rates

Background The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic exposed and exacerbated health disparities between socioeconomic groups. Our purpose was to determine which disparities are most prevalent and their impact on length of stay (LoS) and in hospital mortality in patients diagnosed with Covid-19. Methods De-Identified data for patients who tested positive for COVID-19 was abstracted from the HCA enterprise database. Data was binned into summary tables. A negative binomial regression with LoS as the dependent variable and a logistic regression of in-hospital mortality data, using age, insurance status, sex, comorbidities as the dependent variables, were performed. Results From March 1, 2020 to August 23, 2020, of 111,849 covid testing patient records, excluding those with missing data (n=7), without confirmed COVID-19 (n=27,225), and those from a carceral environment (n=1,861), left 84,624 eligible patients. Compared to the US population, the covid cohort had more black patients (23.17% vs 13.4%). Compared to the white cohort, the black cohort had higher private insurance rates (28.52% vs. 23.68%), shorter LoS (IRR=0.97 CI=0.95-0.99, P<0.01) and lower adjusted mortality (OR 0.81, 95% CI 0.75-0.97). Increasing age was associated with increased mortality and LoS. Patients with Medicare or Medicaid had longer LoS (IRR=1.07, 95% CI=1.04-1.09) and higher adjusted mortality rates (OR=1.11, 95% CI=1-1.23) than those with private insurance Conclusion Conclusions We found that when blacks have higher rates of private insurance, they have shorter hospitalizations and lower mortality than whites, when diagnosed with Covid-19. Some other psychiatric and medical conditions also significantly impacted outcomes in patients with Covid-19.

A control chart based on Pearson residuals for a negative binomial regression: application to infant mortality data

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Sandra García-Bustos ◽  
Nadia Cárdenas-Escobar ◽  
Ana Debón ◽  
César Pincay
Keyword(s):  
Infant Mortality ◽  
Control Chart ◽  
Negative Binomial ◽  
Negative Binomial Regression ◽  
Mortality Data ◽  
Negative Binomial Regression Model ◽  
Content Type ◽  
Pearson Residuals ◽  
Binomial Regression ◽  
The Mean

PurposeThe study aims to design a control chart based on an exponentially weighted moving average (EWMA) chart of Pearson's residuals of a model of negative binomial regression in order to detect possible anomalies in mortality data.Design/methodology/approachIn order to evaluate the performance of the proposed chart, the authors have considered official historical records of death of children of Ecuador. A negative binomial regression model was fitted to the data, and a chart of the Pearson residuals was designed. The parameters of the chart were obtained by simulation, as well as the performances of the charts related to changes in the mean of death.FindingsWhen the chart was plotted, outliers were detected in the deaths of children in the years 1990–1995, 2001–2006, 2013–2015, which could show that there are underreporting or an excessive growth in mortality. In the analysis of performances, the value of λ = 0.05 presented the fastest detection of changes in the mean death.Originality/valueThe proposed charts present better performances in relation to EWMA charts for deviance residuals, with a remarkable advantage of the Pearson residuals, which are much easier to interpret and calculate. Finally, the authors would like to point out that although this paper only applies control charts to Ecuadorian infant mortality, the methodology can be used to calculate mortality in any geographical area or to detect outbreaks of infectious diseases.

scholarly journals Community Predictors of COVID-19 Cases and Deaths in Massachusetts: Evaluating Changes Over Time Using Geospatially Refined Data

2021 ◽  
Author(s):  
Keith Spangler ◽  
Prasad Patil ◽  
Xiaojing Peng ◽  
Jonathan Levy ◽  
Kevin Lane ◽  
...  
Keyword(s):  
Negative Binomial ◽  
Phase 1 ◽  
Negative Binomial Regression ◽  
Mortality Data ◽  
Phase 2 ◽  
Patient Privacy ◽  
Term Care ◽  
Address Data ◽  
Rate Ratios ◽  
Over Time

Background: The COVID-19 pandemic has highlighted the need for targeted local interventions given substantial heterogeneity within cities and counties. Publicly available case data are typically aggregated to the city or county level to protect patient privacy, but more granular data are necessary to identify and act upon community-level risk factors that can change over time. Methods: Individual COVID-19 case and mortality data from Massachusetts were geocoded to residential addresses and aggregated into two time periods: “Phase 1” (March–June 2020) and “Phase 2” (September 2020–February 2021). Institutional cases associated with long-term care facilities, prisons, or homeless shelters were identified using address data and modeled separately. Census tract sociodemographic and occupational predictors were drawn from the 2015-2019 American Community Survey. We used mixed-effects negative binomial regression to estimate incidence rate ratios (IRRs), accounting for town-level spatial autocorrelation. Results: Case incidence was elevated in census tracts with higher proportions of Black and Latinx residents, with larger associations in Phase 1 than Phase 2. Case incidence associated with proportion of essential workers was similarly elevated in both Phases. Mortality IRRs had differing patterns from case IRRs, decreasing less substantially between Phases for Black and Latinx populations and increasing between Phases for proportion of essential workers. Mortality models excluding institutional cases yielded stronger associations for age, race/ethnicity, and essential worker status. Conclusions: Geocoded home address data can allow for nuanced analyses of community disease patterns, identification of high-risk subgroups, and exclusion of institutional cases to comprehensively reflect community risk.

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