Gabrielle Davie

Minimum Purchasing Age for Alcohol and Traffic Crash Injuries Among 15- to 19-Year-Olds in New Zealand

OBJECTIVES In 1999, New Zealand lowered the minimum purchasing age for alcohol from 20 to 18 years. We tested the hypothesis that this increased traffic crash injuries among 15- to 19-year-olds. METHODS Poisson regression was used to compute incidence rate ratios for the after to before incidence of alcohol-involved crashes and hospitalized injuries among 18- to 19-year-olds and 15- to 17-year-olds (20- to 24-year-olds were the reference). RESULTS Among young men, the ratio of the alcohol-involved crash rate after the law change to the period before was 12% larger (95% confidence interval [CI]=1.00, 1.25) for 18- to 19-year-olds and 14% larger (95% CI=1.01, 1.30) for 15- to 17-year-olds, relative to 20- to 24-year-olds. Among young women, the equivalent ratios were 51% larger (95% CI=1.17, 1.94) for 18- to 19-year-olds and 24% larger (95% CI=0.96, 1.59) for 15- to 17-year-olds. A similar pattern was observed for hospitalized injuries. CONCLUSIONS Significantly more alcohol-involved crashes occurred among 15-to 19-year-olds than would have occurred had the purchase age not been reduced to 18 years. The effect size for 18- to 19-year-olds is remarkable given the legal exceptions to the pre-1999 law and its poor enforcement.

Accuracy of injury coding under ICD-9 for New Zealand public hospital discharges

Objective: To determine the level of accuracy in coding for injury principal diagnosis and the first external cause code for public hospital discharges in New Zealand and determine how these levels vary by hospital size. Method: A simple random sample of 1800 discharges was selected from the period 1996–98 inclusive. Records were obtained from hospitals and an accredited coder coded the discharge independently of the codes already recorded in the national database. Results: Five percent of the principal diagnoses, 18% of the first four digits of the E-codes, and 8% of the location codes (5th digit of the E-code), were incorrect. There were no substantive differences in the level of incorrect coding between large and small hospitals. Conclusions: Users of New Zealand public hospital discharge data can have a high degree of confidence in the injury diagnoses coded under ICD-9-CM-A. A similar degree of confidence is warranted for E-coding at the group level (for example, fall), but not, in general, at higher levels of specificity (for example, type of fall). For those countries continuing to use ICD-9 the study provides insight into potential problems of coding and thus guidance on where the focus of coder training should be placed. For those countries that have historical data coded according to ICD-9 it suggests that some specific injury and external cause incidence estimates may need to be treated with more caution.

Predicting BMI in young adults from childhood data using two approaches to modelling adiposity rebound

OBJECTIVE:To identify the age of adiposity rebound and the value of its associated BMI and examine their association with BMI at ages 18 and 21 y for males and females.DESIGN:A longitudinal study of a large cohort of people born in Dunedin, New Zealand between 1972–1973.SUBJECTS:Four hundred and seventy-four males and 448 females aged between birth and 21 y.MEASUREMENTS:BMI was derived from measurements of weight and height made when the participants were born and at intervals from age 3–21 y.RESULTS:When a random coefficients model was fitted to the data for those who had five or more measures of BMI between age 3 and age 18 y, adiposity rebound occurred at 6.0 y of age for boys and 5.6 y for girls. The values of BMI associated with these were 15.7 kg/m2 for boys and 15.5 kg/m2 for girls. The correlations between age at adiposity rebound and BMI at ages 18 and 21 y were between −0.72 and −0.65 for boys and −0.59 and −0.47 for girls. These were higher than those derived from fitting individual curves or from deriving the adiposity rebound from data collected up to age 11 y. The correlation between BMI at age 7 y and BMI at ages 18 and 21 y were 0.70 and 0.61 for boys and 0.56 and 0.52 for girls. The correlations between measures of skeletal maturity at age 7 y and adiposity rebound were statistically significant for boys but not for girls.CONCLUSIONS:BMI in early adulthood was associated with both age of adiposity rebound and BMI at that age. As the correlations between BMI at age 7 y and BMI at ages 18 and 21 y were similar in magnitude, BMI at age 7 y may be a more practical way of predicting BMI in early adulthood.

High prevalence of asymptomatic vitamin D and iron deficiency in East African immigrant children and adolescents living in a temperate climate

Objectives: Vitamin D deficiency (VDD) is common in immigrant children with increased skin pigmentation living in higher latitudes. We assessed the pattern of and risk factors for VDD in immigrant East African children living in Melbourne (latitude 37°49′ South). Study design: A prospective survey of 232 East African children attending a clinic in Melbourne. Data were collected by questionnaire, medical assessment and laboratory tests. Results: Low 25-hydroxyvitamin D (25-OHD) levels (<50 nmol/l) occurred in 87% of children, and VDD (25-OHD <25 nmol/l) in 44%. Risk factors included age <5 years, female gender, increased time in Australia, decreased daylight exposure and winter/spring season. Anaemia (20%), vitamin A deficiency (20%) and iron deficiency (19%) were also identified. Conclusions: Asymptomatic VDD is common in East African immigrant children residing at a temperate latitude. Risk factors for VDD limit endogenous vitamin D production. Screening of immigrant children with increased skin pigmentation for VDD, anaemia, iron and vitamin A deficiency is appropriate. VDD in adolescent females identifies an increased risk of future infants with VDD.

Trends and determinants of excess winter mortality in New Zealand: 1980 to 2000

BackgroundAlthough many countries experience an increase in mortality during winter, the magnitude of this increase varies considerably, suggesting that some winter excess may be avoidable. Conflicting evidence has been presented on the role of gender, region and deprivation. Little has been published on the magnitude of excess winter mortality (EWM) in New Zealand (NZ) and other Southern Hemisphere countries.MethodsMonthly mortality rates per 100,000 population were calculated from routinely collected national mortality data for 1980 to 2000. Generalised negative binomial regression models were used to compare mortality rates between winter (June–September) and the warmer months (October–May).ResultsFrom 1980–2000 around 1600 excess winter deaths occurred each year with winter mortality rates 18% higher than expected from non-winter rates. Patterns of EWM by age group showed the young and the elderly to be particularly vulnerable. After adjusting for all major covariates, the winter:non-winter mortality rate ratio from 1996–2000 in females was 9% higher than in males. Mortality caused by diseases of the circulatory system accounted for 47% of all excess winter deaths from 1996–2000 with mortality from diseases of the respiratory system accounting for 31%. There was no evidence to suggest that patterns of EWM differed by ethnicity, region or local-area based deprivation level. No decline in seasonal mortality was evident over the two decades.ConclusionEWM in NZ is substantial and at the upper end of the range observed internationally. Interventions to reduce EWM are important, but the surprising lack of variation in EWM by ethnicity, region and deprivation, provides little guidance for how such mortality can be reduced.

Prospective outcomes of injury study

Background: In New Zealand (NZ), 20% of adults report a disability, of which one-third is caused by injury. No prospective epidemiological studies of predictors of disability following all-cause injury among New Zealanders have been undertaken. Internationally, studies have focused on a limited range of predictors or specific injuries. Although these studies provide useful insights, applicability to NZ is limited given the importance of NZ’s unique macro-social factors, such as NZ’s no-fault accident compensation and rehabilitation scheme, the Accident Compensation Corporation (ACC). Objectives: (1) To quantitatively determine the injury, rehabilitation, personal, social and economic factors leading to disability outcomes following injury in NZ. (2) To qualitatively explore experiences and perceptions of injury-related outcomes in face-to-face interviews with 15 Māori and 15 other New Zealanders, 6 and 12 months after injury. Setting: Four geographical regions within NZ. Design: Prospective cohort study with telephone interviews 1, 4 and 12 months after injury. Participants: 2500 people (including 460 Māori), aged 18–64 years, randomly selected from ACC’s entitlement claims register (people likely to be off work for at least 1 week or equivalent). Data: Telephone interviews, electronic hospital and ACC injury data. Exposures include demographic, social, economic, work-related, health status, participation and/or environmental factors. Outcome measures: Primary: disability (including WHODAS II) and health-related quality of life (including EQ-5D). Secondary: participation (paid and unpaid activities), life satisfaction and costs. Analysis: Separate regression models will be developed for each of the outcomes. Repeated measures outcomes will be modelled using general estimating equation models and generalised linear mixed models.

Prospective Outcomes of Injury Study: recruitment, and participant characteristics, health and disability status

The Prospective Outcomes of Injury Study aims to identify predictors of disability following injury. Participants were selected from the entitlement claims register of New Zealands no-fault compensation insurer, the Accident Compensation Corporation, and followed up by interview for 2 years. This report describes changes to intended Prospective Outcomes of Injury Study methods and key characteristics of the cohort, with an emphasis on general health and disability before injury and soon afterwards. There were 2856 injured participants in the first interview, which occurred 3.2 months (median) after injury. The recruitment period was extended to enable inclusion of sufficient Māori participants. At the first interview, most participants were experiencing worse health status and increased disability compared to before injury, despite less than one-third reporting admission to hospital because of their injury. Analysis of outcome predictors related to post-injury function, disability and return-to-work soon after injury and 1 year later is now under way.

Prevalence and predictors of sub-acute phase disability after injury among hospitalised and non-hospitalised groups: a longitudinal cohort study

Introduction To reduce the burden on injury survivors and their supporters, factors associated with poor outcomes need to be identified so that timely post-injury interventions can be implemented. To date, few studies have investigated outcomes for both those who were hospitalised and those who were not. Aim To describe the prevalence and to identify pre-injury and injury-related predictors of disability among hospitalised and non-hospitalised people, three months after injury. Methods Participants in the Prospective Outcomes of Injury Study were aged 18–64 years and on an injury entitlement claims register with New Zealands no-fault injury compensation insurer, following referral by healthcare professionals. A wide range of pre-injury demographic, health and injury-related characteristics were collected at interview. Participants were categorised as ‘hospitalised’ if they were placed on New Zealands National Minimum Data Set within seven days of the injury event. Injury severity scores (NISS) and 12 injury categories were derived from ICD-10 codes. WHODAS assessed disability. Multivariable analyses examined relationships between explanatory variables and disability. Results Of 2856 participants, 2752 (96%) had WHODAS scores available for multivariable analysis; 673 were hospitalised; 2079 were not. Disability was highly prevalent among hospitalised (53.6%) and non-hospitalised (39.4%) participants, three-months after injury. In both groups, pre-injury disability, obesity and higher injury severity were associated with increased odds of post-injury disability. A range of other factors were associated with disability in only one group: e.g. female, ≥2 chronic conditions and leg fracture among hospitalised; aged 35–54 years, trouble accessing healthcare, spine or lower extremity sprains/dislocations and assault among non-hospitalised. Significance Disability was highly prevalent among both groups yet, with a few exceptions, factors associated with disability were not common to both groups. Where possible, including a range of injured people in studies, hospitalised and not, will increase understanding of the burden of disability in the sub-acute phase.

Improving the predictive ability of the ICD-based Injury Severity Score

Objective: To assess whether the use of integrated hospitalization and mortality data sources and/or the inclusion of comorbidity improve the predictive ability of the International Classification of Disease (ICD)-based Injury Severity Score (ICISS). Design: Models using either the ICISS based solely on hospital discharge data or one of nine modified ICISSs as the predictor variable were assessed on their ability to predict survival using logistic regression modeling. Setting: New Zealand. Patients or subjects: Inpatients, with an S00–T89 ICD-10-AM principal diagnosis, and fatalities, with any S00–T89 ICD-10-AM diagnosis, occurring in 2000–2003. Interventions: None. Main outcome measures: Models were compared in terms of their discrimination (concordance), calibration, and goodness-of-fit. Results: 186 835 cases including 9968 deaths met the inclusion criterion. The modified ICISS that included both mortality data and Charlson comorbid conditions at the ICD-10-AM level had the best concordance and high calibration. Calibration curves indicated that scores using hospital discharge data only to calculate survival risk ratios underestimated mortality, whereas scores using hospital discharge and mortality data overestimated mortality. Conclusions: Valid measurement of injury severity is important for both meaningful research and surveillance and to assist in classifying information to meet specific injury policy, prevention, and control needs. This study suggests that the predictive ability of ICISS would be improved if both mortality and comorbidity data were included in its calculation.

Factors predicting work status 3 months after injury: results from the Prospective Outcomes of Injury Study.

Objective Few studies examine predictors of work status following injury beyond injuries presenting to a hospital or emergency department. This paper examines the combined influences of socio-demographic, occupational, injury and pre-existing health and lifestyle factors as predictors of work status 3 months after hospitalised and non-hospitalised injury in a cohort of injured New Zealand workers. Design Prospective cohort study. Setting The Prospective Outcomes of Injury Study, New Zealand. Participants 2626 workforce active participants were identified from the Prospective Outcomes of Injury Study; 11 participants with missing outcome responses were excluded. Primary and secondary outcome measures The primary outcome of interest was ‘not working’ at the time of interview. Results 720 (27%) reported ‘not working’ 3 months after injury. The most important pre-injury predictors of not working following injury found by multidimensional modelling were as follows: low or unknown income, financial insecurity, physical work tasks, temporary employment, long week schedules, obesity, perceived threat to life and hospital admission. Contrary to expectations, workers reporting less frequent exercise pre-injury had lower odds of work absence. Pre-injury psychosocial and health factors were not associated with not working. Conclusion Certain pre-injury socio-demographic, physical work, work organisation, lifestyle and injury-related factors were associated with not working 3 months after injury. If these findings are confirmed, intervention strategies aimed at improving return to work should address multiple dimensions of both the worker and the workplace.