The increased prevalence of diabetes and its significant impact on use of health care services, particularly hospitals, is a concern for health planners. This paper explores the risk factors for all-cause hospitalisation and the excess risk due to diabetes in a large sample of older Australians.
The study population was 263,482 participants in the 45 and Up Study. The data assessed were linked records of hospital admissions in the 12 months following completion of a baseline questionnaire. All cause and ambulatory care sensitive admission rates and length of stay were examined. The associations between demographic characteristics, socioeconomic status, lifestyle factors, and health and wellbeing and risk of hospitalisation were explored using zero inflated Poisson (ZIP) regression models adjusting for age and gender. The ratios of adjusted relative rates and 95% confidence intervals were calculated to determine the excess risk due to diabetes.
Prevalence of diabetes was 9.0% (n = 23,779). Age adjusted admission rates for all-cause hospitalisation were 631.3 and 454.8 per 1,000 participant years and the mean length of stay was 8.2 and 7.1 days respectively for participants with and without diabetes. In people with and without diabetes, the risk of hospitalisation was associated with age, gender, household income, smoking, BMI, physical activity, and health and wellbeing. However, the increased risk of hospitalisation was attenuated for participants with diabetes who were older, obese, or had hypertension or hyperlipidaemia and enhanced for those participants with diabetes who were male, on low income, current smokers or who had anxiety or depression.
This study is one of the few studies published to explore the impact of diabetes on hospitalisation in a large non-clinical population, the 45 and Up Study. The attenuation of risk associated with some factors is likely to be due to correlation between diabetes and factors such as age and obesity. The increased risk in association with other factors such as gender and low income in participants with diabetes is likely to be due to their synergistic influence on health status and the way services are accessed.
Healthy aging; Diabetes; Hospital admission; Record linkage; Cohort study; Socioeconomic status; Health and wellbeing
To investigate variation in rates of cataract surgery in New South Wales, Australia by area of residence for Aboriginal and non-Aboriginal adults.
Observational data linkage study of hospital admissions.
Two hundred eighty-nine thousand six hundred forty-six New South Wales residents aged 30 years and over admitted to New South Wales hospitals for 444 551 cataract surgery procedures between 2001 and 2008.
Analysis of linked routinely collected hospital data using direct standardization and multilevel negative binomial regression models accounting for clustering of individuals within Statistical Local Areas.
Main Outcome Measures
Age-standardized cataract surgery rates and adjusted rate ratios.
Aboriginal people had lower rates of cataract procedures than non-Aboriginal people of the same age and sex, living in the same Statistical Local Area (adjusted rate ratio 0.71, 95% confidence interval 0.68–0.75). There was significant variation in cataract surgery rates across Statistical Local Areas for both Aboriginal and non-Aboriginal people, with the disparity greater in major cities and less disadvantaged areas. Rates of surgery were lower for Aboriginal than non-Aboriginal people in most Statistical Local Areas, but in a few, the rates were similar or higher for Aboriginal people.
Aboriginal people in New South Wales received less cataract surgery than non-Aboriginal people, despite evidence of higher cataract rates. This disparity was greatest in urban and wealthier areas. Higher rates of surgery for Aboriginal people observed in some specific locations are likely to reflect the availability of public ophthalmology services, targeted services for Aboriginal people and higher demand for surgery in these populations.
Aboriginal health; cataract surgery; data linkage; disadvantage
To investigate the nature and potential implications of under-reporting of morbidity information in administrative hospital data.
Setting and participants
Retrospective analysis of linked self-report and administrative hospital data for 32 832 participants in the large-scale cohort study (45 and Up Study), who joined the study from 2006 to 2009 and who were admitted to 313 hospitals in New South Wales, Australia, for at least an overnight stay, up to a year prior to study entry.
Agreement between self-report and recording of six morbidities in administrative hospital data, and between-hospital variation and predictors of positive agreement between the two data sources.
Agreement between data sources was good for diabetes (κ=0.79); moderate for smoking (κ=0.59); fair for heart disease, stroke and hypertension (κ=0.40, κ=0.30 and κ =0.24, respectively); and poor for obesity (κ=0.09), indicating that a large number of individuals with self-reported morbidities did not have a corresponding diagnosis coded in their hospital records. Significant between-hospital variation was found (ranging from 8% of unexplained variation for diabetes to 22% for heart disease), with higher agreement in public and large hospitals, and hospitals with greater depth of coding.
The recording of six common health conditions in administrative hospital data is highly variable, and for some conditions, very poor. To support more valid performance comparisons, it is important to stratify or control for factors that predict the completeness of recording, including hospital depth of coding and hospital type (public/private), and to increase efforts to standardise recording across hospitals. Studies using these conditions for risk adjustment should also be cautious of their use in smaller hospitals.
To quantify the independent roles of geography and Indigenous status in explaining disparities in Potentially Preventable Hospital (PPH) admissions between Indigenous and non-Indigenous Australians.
Design, setting and participants
Analysis of linked hospital admission data for New South Wales (NSW), Australia, for the period July 1 2003 to June 30 2008.
Main outcome measures
Age-standardised admission rates, and rate ratios adjusted for age, sex and Statistical Local Area (SLA) of residence using multilevel models.
PPH diagnoses accounted for 987,604 admissions in NSW over the study period, of which 3.7% were for Indigenous people. The age-standardised PPH admission rate was 76.5 and 27.3 per 1,000 for Indigenous and non-Indigenous people respectively. PPH admission rates in Indigenous people were 2.16 times higher than in non-Indigenous people of the same age group and sex who lived in the same SLA. The largest disparities in PPH admission rates were seen for diabetes complications, chronic obstructive pulmonary disease and rheumatic heart disease. Both rates of PPH admission in Indigenous people, and the disparity in rates between Indigenous than non-Indigenous people, varied significantly by SLA, with greater disparities seen in regional and remote areas than in major cities.
Higher rates of PPH admission among Indigenous people are not simply a function of their greater likelihood of living in rural and remote areas. The very considerable geographic variation in the disparity in rates of PPH admission between Indigenous and non-Indigenous people indicates that there is potential to reduce unwarranted variation by characterising outlying areas which contribute the most to this disparity.
The presence and severity of pelvic inflammatory disease (PID) symptoms are thought to vary by microbiological etiology but there is limited empirical evidence. We sought to estimate and compare the rates of hospitalisation for PID temporally related to diagnoses of gonorrhoea and chlamydia.
All women, aged 15–45 years in the Australian state of New South Wales (NSW), with a diagnosis of chlamydia or gonorrhoea between 01/07/2000 and 31/12/2008 were followed by record linkage for up to one year after their chlamydia or gonorrhoea diagnosis for hospitalisations for PID. Standardised incidence ratios compared the incidence of PID hospitalisations to the age-equivalent NSW population.
A total of 38,193 women had a chlamydia diagnosis, of which 483 were hospitalised for PID; incidence rate (IR) 13.9 per 1000 person-years of follow-up (PYFU) (95%CI 12.6–15.1). In contrast, 1015 had a gonorrhoea diagnosis, of which 45 were hospitalised for PID (IR 50.8 per 1000 PYFU, 95%CI 36.0–65.6). The annual incidence of PID hospitalisation temporally related to a chlamydia or gonorrhoea diagnosis was 27.0 (95%CI 24.4–29.8) and 96.6 (95%CI 64.7–138.8) times greater, respectively, than the age-equivalent NSW female population. Younger age, socio-economic disadvantage, having a diagnosis prior to 2005 and having a prior birth were also associated with being hospitalised for PID.
Chlamydia and gonorrhoea are both associated with large increases in the risk of PID hospitalisation. Our data suggest the risk of PID hospitalisation is much higher for gonorrhoea than chlamydia; however, further research is needed to confirm this finding.
Adjustment for the differing risk profiles of patients is essential to the use of administrative hospital data for epidemiological research. Smoking is an important factor to include in such adjustments, but the accuracy of the diagnostic codes denoting smoking in hospital records is unknown. The aims of this study were to measure the validity of current smoking and ever smoked status identified from diagnoses in hospital records using a range of algorithms, relative to self-reported smoking status; and to examine whether the misclassification of smoking identified through hospital data is differential or non-differential with respect to common exposures and outcomes. Data from the baseline questionnaire of the 45 and Up Study, completed by 267,153 residents of New South Wales (NSW), Australia, aged 45 years and older, were linked to the NSW Admitted Patient Data Collection. Patients who had been admitted to hospital for an overnight stay between 1 July 2005 and the date of completion of the questionnaire (1 January 2006 to 2 March 2009) were included. Smokers were identified by applying a range of algorithms to hospital admission histories, and compared against self-reported smoking in the questionnaire (‘gold standard’). Sensitivities for current smoking ranged from 59% to 84%, while specificities were 94% to 98%. Sensitivities for ever smoked ranged from 45% to 74% and specificities were 93% to 97%. For the majority of algorithms, sensitivities and/or specificities differed significantly according to principal diagnosis, number of comorbidities, socioeconomic status, residential remoteness, Indigenous status, 28 day readmission and 365 day mortality. The identification of smoking through diagnoses in hospital data results in differential misclassification. Risk adjustment based on smoking identified from these data will yield potentially misleading results. Systematic capture of information about smoking in hospital records using a mandatory item would increase the utility of administrative data for epidemiological research.
Several studies have demonstrated the effects of health behaviours on risk of chronic diseases and mortality, but none have investigated their contribution to potentially preventable hospitalisation (PPH). We aimed to quantify the effects on risk of PPH of six health behaviours: smoking; alcohol consumption; physical activity; fruit and vegetables consumption; sitting time; and sleeping time.
Prospective observational study in New South Wales, Australia.
267,006 men and women aged 45 years and over.
PPH admissions and mortality during follow-up according to individual positive health behaviours (non-smoking, <14 alcoholic drinks per week, ≥2.5 hours of physical activity per week, ≥2 servings of fruit and 5 servings of vegetables per day, <8 hours sitting and ≥7 hours sleeping per day) and the total number of these behaviours.
During an average of 3 years follow-up, 20971 (8%) participants had at least one PPH admission. After adjusting for potential confounders, participants who reported all six positive health behaviours at baseline had 46% lower risk of PPH admission (95% CI 0.48–0.61), compared to those who reported having only one of these behaviours. Based on these risk estimates, approximately 29% of PPH admissions in Australians aged 45 years and over were attributable to not adhering to the six health behaviours. Estimates were similar for acute, chronic and vaccine-preventable categories of PPH admissions.
Individual and combined positive health behaviours were associated with lower risk of PPH admission. These findings suggest that there is a significant opportunity to reduce PPH by promoting healthy behaviours.
There is strong evidence based on previous studies that ethnicity and socioeconomic status are important determinants of diversity in the occurrence of diabetes. However, the independent roles of socioeconomic status, country of birth and lifestyle factors in the occurrence of type 2 diabetes have not been clearly identified. This study investigated the relationships between socioeconomic status, country of birth and type 2 diabetes in a large diverse sample of residents of New South Wales, Australia, and aged 45 years and over.
The analysis used self-reported baseline questionnaire data from 266,848 participants in the 45 and Up Study. Educational attainment, work status and income were used as indicators of socioeconomic status. Logistic regression models were built to investigate associations between socioeconomic status, country of birth and type 2 diabetes.
The adjusted odds of type 2 diabetes were significantly higher for people born in many overseas countries, compared to Australian-born participants. Compared with participants who had a university degree or higher qualification, the adjusted odds ratio (OR) for diabetes was higher in all other educational categories. Diabetes was more prevalent in people who were retired, unemployed or engaged in other types of work, compared with people who were in paid work. The prevalence of diabetes was higher in people with lower incomes. Compared with people who earned more than $50,000, the adjusted OR for diabetes was 2.05 (95% CI 1.95-2.14) for people who had an income less than $20,000 per annum. The relationships between socioeconomic factors and country of birth and diabetes were attenuated slightly when all were included in the model. Addition of smoking, obesity and physical activity to the model had marked impacts on adjusted ORs for some countries of birth, but relationships between diabetes and all measures of socioeconomic status and country of birth remained strong and significant.
Country of birth and socioeconomic status are independent predictors of type 2 diabetes. However, in this population, country of birth had a stronger association with type 2 diabetes.
Type 2 diabetes; Country of birth; Ethnicity; Socioeconomic status
Australian Aboriginal children experience earlier, more frequent and more severe otitis media, particularly in remote communities, than non-Aboriginal children. Insertion of ventilation tubes is the main surgical procedure for otitis media. Our aim was to quantify inequalities in ventilation tube insertion (VTI) procedures between Australian Aboriginal and non-Aboriginal children, and to explore the influence of birth characteristics, socioeconomic background and geographical remoteness on this inequality.
Retrospective cohort study using linked hospital and mortality data from July 2000 to December 2008.
Setting and participants
A whole-of-population cohort of 653 550 children (16 831 Aboriginal and 636 719 non-Aboriginal) born in a New South Wales hospital between 1 July 2000 and 31 December 2007 was included in the analysis.
First VTI procedure.
VTI rates were lower in Aboriginal compared with non-Aboriginal children (incidence rate (IR), 4.3/1000 person-years; 95% CI 3.8 to 4.8 vs IR 5.8/1000 person-years; 95% CI 5.7 to 5.8). Overall, Aboriginal children were 28% less likely than non-Aboriginal children to have ventilation tubes inserted (age-adjusted and sex-adjusted rate ratios (RRs) 0.72; 95% CI 0.64 to 0.80). After adjusting additionally for geographical remoteness, Aboriginal children were 19% less likely to have ventilation tubes inserted (age-adjusted and sex-adjusted RR 0.81; 95% CI 0.73 to 0.91). After adjusting separately for private patient/health insurance status and area socioeconomic status, there was no significant difference (age-adjusted and sex-adjusted RR 0.96; 95% CI 0.86 to 1.08 and RR 0.93; 95% CI 0.83 to 1.04, respectively). In the fully adjusted model, there were no significant differences in VTI rates between Aboriginal and non-Aboriginal children (RR 1.06; 95% CI 0.94 to 1.19).
Despite a much higher prevalence of otitis media, Aboriginal children were less likely to receive VTI procedures than their non-Aboriginal counterparts; this inequality was largely explained by differences in socioeconomic status and geographical remoteness.
Epidemiology; Public Health
Prevalence studies usually depend on self-report of disease status in survey data or administrative data collections and may over- or under-estimate disease prevalence. The establishment of a linked data collection provided an opportunity to explore the accuracy and completeness of capture of information about diabetes in survey and administrative data collections.
Baseline questionnaire data at recruitment to the 45 and Up Study was obtained for 266,848 adults aged 45 years and over sampled from New South Wales, Australia in 2006–2009, and linked to administrative data about hospitalisation from the Admitted Patient Data Collection (APDC) for 2000–2009, claims for medical services (MBS) and pharmaceuticals (PBS) from Medicare Australia data for 2004–2009. Diabetes status was determined from response to a question ‘Has a doctor EVER told you that you have diabetes’ (n = 23,981) and augmented by examination of free text fields about diagnosis (n = 119) or use of insulin (n = 58). These data were used to identify the sub-group with type 1 diabetes. We explored the agreement between self-report of diabetes, identification of diabetes diagnostic codes in APDC data, claims for glycosylated haemoglobin (HbA1c) in MBS data, and claims for dispensed medication (oral hyperglycaemic agents and insulin) in PBS data.
Most participants with diabetes were identified in APDC data if admitted to hospital (79.3%), in MBS data with at least one claim for HbA1c testing (84.7%; 73.4% if 2 tests claimed) or in PBS data through claim for diabetes medication (71.4%). Using these alternate data collections as an imperfect ‘gold standard’ we calculated sensitivities of 83.7% for APDC, 63.9% (80.5% for two tests) for MBS, and 96.6% for PBS data and specificities of 97.7%, 98.4% and 97.1% respectively. The lower sensitivity for HbA1c may reflect the use of this test to screen for diabetes suggesting that it is less useful in identifying people with diabetes without additional information. Kappa values were 0.80, 0.70 and 0.80 for APDC, MBS and PBS respectively reflecting the large population sample under consideration. Compared to APDC, there was poor agreement about identifying type 1 diabetes status.
Self-report of diagnosis augmented with free text data indicating diabetes as a chronic condition and/or use of insulin among medications used was able to identify participants with diabetes with high sensitivity and specificity compared to available administrative data collections.
Primary health care; Cohort studies; Diabetes mellitus; Record linkage; Health service data; Quality of health care; Validation study; Sensitivity and specificity; Older age; English language
Approximately 14% of Australian women smoke during pregnancy. Although the risk of adverse outcomes is reduced by smoking cessation, less than 35% of Australian women quit smoking spontaneously during pregnancy. Evidence for the efficacy of bupropion, varenicline or nicotine replacement therapy as smoking cessation aids in the non-pregnant population suggest that pharmacotherapy for smoking cessation is worth exploring in women of childbearing age. Currently, little is known about the utilisation, effectiveness and safety of pharmacotherapies for smoking cessation during pregnancy; neither the extent to which they are used prior to pregnancy nor whether their use has changed in response to related policy reforms. The Smoking MUMS (Maternal Use of Medications and Safety) Study will explore these issues using linked person-level data for a population-based cohort of Australian mothers.
Methods and analysis
The cohort will be assembled by linking administrative health records for all women who gave birth in New South Wales or Western Australia since 2003 and their children, including records relating to childbirth, use of pharmaceuticals, hospital admissions, emergency department presentations and deaths. These longitudinal linked data will be used to identify utilisation of smoking cessation pharmacotherapies during and between pregnancies and to explore the associated smoking cessation rates and maternal and child health outcomes. Subgroup and temporal analyses will identify potential differences between population groups including indigenous mothers and social security recipients and track changes associated with policy reforms that have made alternative smoking cessation pharmacotherapies available.
Ethics and dissemination
Ethical approval has been obtained for this study. To enhance the translation of the project's findings into policy and practice, policy and clinical stakeholders will be engaged through a reference group and a policy forum will be held. Outputs from the project will include scientific papers and summary reports designed for policy audiences.
Epidemiology; Perinatology; Preventive Medicine; Primary Care
The rate of total knee arthroplasty surgery (TKA) is rising in Australia despite varying impacts of TKA on physical function (PF) in population-based studies. There are potentially modifiable risk factors that could enhance PF after TKA, so we evaluated (1) the levels of PF in persons with TKA and the rest of the population, (2) potentially modifiable characteristics of those reporting poor PF after TKA.
Nested case–control study.
Population-based cohort study in New South Wales, Australia.
Members of a large (n=267 151) cohort study recruited by a self-completed, mailed questionnaire from 2006 to 2008. After exclusions (for hip arthroplasty, partial TKA, missing important variables and mismatching TKA status between self-reported and hospital record data), this study included 205 148 participants.
Primary and secondary outcomes
Primary outcome, Medical Outcomes Study Physical Function scale (MOS-PF). Secondary outcome, dispensings of analgesics or anti-inflammatory drugs.
We found 2916 TKA participants and 202 232 participants with no TKA (confirmed across datasets). Persons with TKA had a lower MOS-PF (59.9, 95% CI 58.5 to 60.6) than those without TKA (83.8, 95% CI 83.7 to 83.9). In the matched analysis, the TKA group had a lower MOS-PF (59.9, 95% CI 59.9 to 62.4) than those without TKA (68.4, 95% CI 67.8 to 69.0). In persons with TKA, lower levels of MOS-PF were associated with low self-rated health, high psychological distress, comorbidity, greater age, recent treatment for osteoarthritis and use of paracetamol. Women had an MOS-PF that was 11.6 points (95% CI 9.5 to 13.8) lower than men.
Several modifiable risk factors have been identified to influence PF in persons receiving TKA, with notable differences between sexes. The importance of these risk factors should be examined in incident TKA to test if early identification and intervention for individuals can improve outcomes.
Potentially preventable hospitalisation (PPH) has been adopted widely by international health systems as an indicator of the accessibility and overall effectiveness of primary care. The Assessing Preventable Hospitalisation InDicators (APHID) study will validate PPH as a measure of health system performance in Australia and Scotland. APHID will be the first large-scale study internationally to explore longitudinal relationships between primary care and PPH using detailed person-level information about health risk factors, health status and health service use.
Methods and analysis
APHID will create a new longitudinal data resource by linking together data from a large-scale cohort study (the 45 and Up Study) and prospective administrative data relating to use of general practitioner (GP) services, dispensing of pharmaceuticals, emergency department presentations, hospital admissions and deaths. We will use these linked person-level data to explore relationships between frequency, volume, nature and costs of primary care services, hospital admissions for PPH diagnoses, and health outcomes, and factors that confound and mediate these relationships. Using multilevel modelling techniques, we will quantify the contributions of person-level, geographic-level and service-level factors to variation in PPH rates, including socioeconomic status, country of birth, geographic remoteness, physical and mental health status, availability of GP and other services, and hospital characteristics.
Ethics and dissemination
Participants have consented to use of their questionnaire data and to data linkage. Ethical approval has been obtained for the study. Dissemination mechanisms include engagement of policy stakeholders through a reference group and policy forum, and production of summary reports for policy audiences in parallel with the scientific papers from the study.
Epidemiology; Health Services Administration & Management; Primary Care; Public Health; Statistics & Research Methods
Large-scale population biobanks are critical for future research integrating epidemiology, genetic, biomarker and other factors. Little is known about the factors influencing participation in biobanks. This study compares the characteristics of biobank participants with those of non-participants, among members of an existing cohort study.
Individuals aged 45 and over participating in The 45 and Up Study and living ≤20km from central Wagga Wagga, New South Wales (NSW), Australia (rural/regional area) or ≤10km from central Parramatta, NSW (urban area) (n=2340) were invited to join a biobank, giving a blood sample and having additional measures taken, including height, weight, waist circumference, heart rate and blood pressure.
The overall uptake of the invitation to participate was 33% (762/2340). The response rate was 41% (410/1002) among participants resident in the regional area, and 26% (352/1338) among those resident in the urban area. Characteristics associated with significantly decreased participation were being aged 80 and over versus being aged 45–64 (participation rate ratio: RR = 0.45, 95%CI 0.34-0.60), not being born in Australia versus being born in Australia (0.69, 0.59-0.81), having versus not having a major disability (0.54, 0.38-0.76), having full-time caregiving responsibilities versus not being a full-time carer (0.62, 0.42-0.93) and being a current smoker versus never having smoked (0.66, 0.50-0.89). Factors associated with increased participation were being in part-time work versus not being in paid work (1.24, 1.07-1.44) and having an annual household income of ≥$50,000 versus <$20,000 (1.50, 1.26-1.80).
A range of socio-economic, health and lifestyle factors are associated with biobank participation among members of an existing cohort study, with factors relating to health-seeking behaviours and access difficulties or time limitations being particularly important. If more widespread participation in biobanking is desired, particularly to ensure sufficient numbers among those most affected by these issues, specific efforts may be required to increase participation in certain groups such as migrants, the elderly, and those in poor health. Whilst caution should be exercised when generalising estimates of absolute prevalence from biobanks, estimates for many internal comparisons are likely to remain valid.
Various options exist for collecting biospecimens and biomarkers from cohort study participants, and these have important logistic, resource and scientific implications. Evidence on how different collection methods affect participation and data quality is lacking. This parallel-design randomised trial, the Link-Up Study, involved blood sample donation and other data collection among participants in an existing cohort study, The 45 and Up Study. It aimed to investigate the relation of fasting status, reminder letters and data collection site to response rates, data quality and biospecimen yield.
Individuals aged 45 and over participating in The 45 and Up Study and living ≤20 km from central Wagga Wagga, NSW (regional area) or ≤10 km from central Parramatta, NSW (urban area) (n = 2340) were randomised, stratified by area of residence, to be invited to give a blood sample and additional data by attending either a clinic established specifically for the trial, with an appointment time (“dedicated clinic”, n = 1336) or an existing local commercial pathology centre (n = 1004). Within dedicated clinic groups, participants were randomised into fasting (n = 668) or non-fasting (n = 668) and, at the Parramatta pathology centre site, reminder letter after two weeks (n = 336) or no reminder (n = 334).
Overall, 33% (762/2340) of invitees took part in the Link-Up Study; 41% (410/1002) among regional and 26% (352/1338) among urban-area residents (p < 0.0001). At the dedicated clinics, response rates were 38% (257/668) not fasting and 38% fasting (257/668) (participation rate ratio (RR) = 1.00, 95%CI 0.91-1.08, p = 0.98). The response rate was 22% among individuals randomised to attend the Parramatta pathology centre without a reminder and 23% among those sent a reminder letter (RR = 1.01, 0.93-1.09, p = 0.74). In total, the response rate was 38% (514/1336) at the dedicated clinics and 25% (248/1004) at the pathology centres (RR = 0.67, 0.56-0.78, p < 0.01); measures of height, weight and systolic and diastolic blood pressure did not vary materially between these groups, nor did the median number of aliquots of plasma, buffy coat and red cells collected.
Among cohort study participants, response rates for an additional study involving biospecimen collection, but not data quality or average biospecimen yield, were considerably higher at dedicated clinics than at existing commercial pathology sites.
Biobank; Response rate; Fasting status; Reminder; Biospecimens
Available evidence suggests that smokers have a lower propensity than others to use primary care services. But previous studies have incorporated only limited adjustment for confounding and mediating factors such as income, access to services and health status. We used data from a large prospective cohort study (the 45 and Up Study), linked to administrative claims data, to quantify the relationship between smoking status and use of primary care services, including specific preventive services, in a contemporary Australian population.
Baseline questionnaire data from the 45 and Up Study were linked to administrative claims (Medicare) data for the 12-month period following study entry. The main outcome measures were Medicare benefit claimed for unreferred services, out-of-pocket costs (OOPC) paid, and claims for specific preventive services (immunisations, health assessments, chronic disease management services, PSA tests and Pap smears). Rate ratios with 95% confidence intervals were estimated using a hierarchical series of models, adjusted for predisposing, access- and health-related factors. Separate hurdle (two part) regression models were constructed for Medicare benefit and OOPC. Poisson models with robust error variance were used to model use of each specific preventive service.
Participants included 254,382 people aged 45 years and over of whom 7.3% were current smokers. After adjustment for predisposing, access- and health-related factors, current smokers were very slightly less likely to have claimed Medicare benefit than never smokers. Among those who claimed benefit, current smokers claimed similar total benefit, but recent quitters claimed significantly greater benefit, compared to never-smokers. Current smokers were around 10% less likely than never smokers to have paid any OOPC. Current smokers were 15-20% less likely than never smokers to use immunisations, Pap smears and prostate specific antigen tests.
Current smokers were less likely than others to use primary care services that incurred out of pocket costs, and specific preventive services. This was independent of a wide range of predisposing, access- and health-related factors, suggesting that smokers have a lower propensity to seek health care. Smokers may be missing out on preventive services from which they would differentially benefit.
Heart disease is a leading cause of the gap in burden of disease between Aboriginal and non-Aboriginal Australians. Our study investigated short- and long-term mortality after admission for Aboriginal and non-Aboriginal people admitted with acute myocardial infarction (AMI) to public hospitals in New South Wales, Australia, and examined the impact of the hospital of admission on outcomes.
Admission records were linked to mortality records for 60047 patients aged 25–84 years admitted with a diagnosis of AMI between July 2001 and December 2008. Multilevel logistic regression was used to estimate adjusted odds ratios (AOR) for 30- and 365-day all-cause mortality.
Aboriginal patients admitted with an AMI were younger than non-Aboriginal patients, and more likely to be admitted to lower volume, remote hospitals without on-site angiography. Adjusting for age, sex, year and hospital, Aboriginal patients had a similar 30-day mortality risk to non-Aboriginal patients (AOR: 1.07; 95% CI 0.83-1.37) but a higher risk of dying within 365 days (AOR: 1.34; 95% CI 1.10-1.63). The latter difference did not persist after adjustment for comorbid conditions (AOR: 1.12; 95% CI 0.91-1.38). Patients admitted to more remote hospitals, those with lower patient volume and those without on-site angiography had increased risk of short and long-term mortality regardless of Aboriginal status.
Improving access to larger hospitals and those with specialist cardiac facilities could improve outcomes following AMI for all patients. However, major efforts to boost primary and secondary prevention of AMI are required to reduce the mortality gap between Aboriginal and non-Aboriginal people.
Hospital performance; Acute myocardial infarction; Ischaemic heart disease; Aboriginal health; Health outcomes; Multilevel modelling; Data linkage
To examine the effect of childbearing and maternal breastfeeding on a woman's subsequent risk of developing type 2 diabetes.
RESEARCH DESIGN AND METHODS
Using information on parity, breastfeeding, and diabetes collected from 52,731 women recruited into a cohort study, we estimated the risk of type 2 diabetes using multivariate logistic regression.
A total of 3,160 (6.0%) women were classified as having type 2 diabetes. Overall, nulliparous and parous women had a similar risk of diabetes. Among parous women, there was a 14% (95% CI 10–18%, P < 0.001) reduced likelihood of diabetes per year of breastfeeding. Compared to nulliparous women, parous women who did not breastfeed had a greater risk of diabetes (odds ratio 1.48, 95% CI 1.26–1.73, P < 0.001), whereas for women breastfeeding, the risk was not significantly increased.
Compared with nulliparous women, childbearing women who do not breastfeed have about a 50% increased risk of type 2 diabetes in later life. Breastfeeding substantially reduces this excess risk.
In Australia, the Home and Community Care (HACC) program provides services in the community to frail elderly living at home and their carers. Surprisingly little is known about the health of people who use these services. In this study we sought to describe health-related factors associated with use of HACC services, and to identify potential opportunities for targeting preventive services to those at high risk.
We obtained questionnaire data from the 45 and Up Study for 103,041 men and women aged 45 years and over, sampled from the general population of New South Wales, Australia in 2006-2007, and linked this with administrative data about HACC service use. We compared the characteristics of HACC clients and non-clients according to a range of variables from the 45 and Up Study questionnaire, and estimated crude and adjusted relative risks for HACC use with generalized linear models.
4,978 (4.8%) participants used HACC services in the year prior to completing the questionnaire. Increasing age, female sex, lower pre-tax household income, not having a partner, not being in paid work, Indigenous background and living in a regional or remote location were strongly associated with HACC use. Overseas-born people and those speaking languages other than English at home were significantly less likely to use HACC services. People who were underweight, obese, sedentary, who reported falling in the past year, who were current smokers, or who ate little fruit or vegetables were significantly more likely to use HACC services. HACC service use increased with decreasing levels of physical functioning, higher levels of psychological distress, and poorer self-ratings of health, eyesight and memory. HACC clients were more likely to report chronic health conditions, in particular diabetes, stroke, Parkinson's disease, anxiety and depression, cancer, heart attack or angina, blood clotting problems, asthma and osteoarthritis.
HACC clients have high rates of modifiable lifestyle risk factors and health conditions that are amenable to primary and secondary prevention, presenting the potential for implementing preventive health care programs in the HACC service setting.
There is little empirical evidence regarding the generalisability of relative risk estimates from studies which have relatively low response rates or are of limited representativeness. The aim of this study was to investigate variation in exposure-outcome relationships in studies of the same population with different response rates and designs by comparing estimates from the 45 and Up Study, a population-based cohort study (self-administered postal questionnaire, response rate 18%), and the New South Wales Population Health Survey (PHS) (computer-assisted telephone interview, response rate ~60%).
Logistic regression analysis of questionnaire data from 45 and Up Study participants (n = 101,812) and 2006/2007 PHS participants (n = 14,796) was used to calculate prevalence estimates and odds ratios (ORs) for comparable variables, adjusting for age, sex and remoteness. ORs were compared using Wald tests modelling each study separately, with and without sampling weights.
Prevalence of some outcomes (smoking, private health insurance, diabetes, hypertension, asthma) varied between the two studies. For highly comparable questionnaire items, exposure-outcome relationship patterns were almost identical between the studies and ORs for eight of the ten relationships examined did not differ significantly. For questionnaire items that were only moderately comparable, the nature of the observed relationships did not differ materially between the two studies, although many ORs differed significantly.
These findings show that for a broad range of risk factors, two studies of the same population with varying response rate, sampling frame and mode of questionnaire administration yielded consistent estimates of exposure-outcome relationships. However, ORs varied between the studies where they did not use identical questionnaire items.
Better communication is often suggested as fundamental to increasing the use of research evidence in policy, but little is known about how researchers and policy makers work together or about barriers to exchange. This study explored the views and practice of policy makers and researchers regarding the use of evidence in policy, including: (i) current use of research to inform policy; (ii) dissemination of and access to research findings for policy; (iii) communication and exchange between researchers and policy makers; and (iv) incentives for increasing the use of research in policy.
Separate but similar interview schedules were developed for policy makers and researchers. Senior policy makers from NSW Health and senior researchers from public health and health service research groups in NSW were invited to participate. Consenting participants were interviewed by an independent research company.
Thirty eight policy makers (79% response rate) and 41 researchers (82% response rate) completed interviews. Policy makers reported rarely using research to inform policy agendas or to evaluate the impact of policy; research was used more commonly to inform policy content. Most researchers reported that their research had informed local policy, mainly by increasing awareness of an issue. Policy makers reported difficulty in accessing useful research syntheses, and only a third of researchers reported developing targeted strategies to inform policy makers of their findings. Both policy makers and researchers wanted more exchange and saw this as important for increasing the use of research evidence in policy; however, both groups reported a high level of involvement by policy makers in research.
Policy makers and researchers recognise the potential of research to contribute to policy and are making significant attempts to integrate research into the policy process. These findings suggest four strategies to assist in increasing the use of research in policy: making research findings more accessible to policy makers; increasing opportunities for interaction between policy makers and researchers; addressing structural barriers such as research receptivity in policy agencies and a lack of incentives for academics to link with policy; and increasing the relevance of research to policy.
Correction to Banks E, Jorm L, Lujic S, Rogers K. Health, ageing and private health insurance: baseline results from the 45 and Up Study cohort. ANZ Health Policy 2009; 6: 16.
This study investigates the relationships between health and lifestyle factors, age and private health insurance (PHI) in a large Australian population-based cohort study of people aged 45 years and over; the 45 and Up Study. Unlike previous Australian analyses of relationships between health, lifestyle and PHI, it incorporates adjustment for multiple confounding socioeconomic and demographic factors. Recruitment into the 45 and Up Study began in February 2006 and these analyses relate to the first 103,042 participants who joined the study prior to July 2008.
The proportion with PHI decreased with increasing age. The factors independently and most strongly associated with having PHI were: higher income; higher educational attainment; not holding a health care concession card; not being of Aboriginal/Torres Strait Islander origin; being a non-smoker; high levels of self-rated health and functional capacity; and low levels of psychological distress. These factors increased the probability of having PHI by 16% to 125%, compared to individuals without these characteristics. PHI coverage was significantly but only marginally higher in people reporting non-melanoma skin cancer (adjusted RR 1.04, 95%CI 1.03–1.05), prostate cancer (1.09, 1.06–1.11) or an enlarged prostate (1.07, 1.06–1.09), those reporting a family history of a range of conditions (e.g. 1.02, 1.01–1.03 for a family history of heart disease; 1.03, 1.02–1.04 for a family history of prostate cancer) and lower in people reporting diabetes (0.92, 0.91–0.94) or stroke (0.91, 0.88–0.94), compared to people who did not have these medical or family histories. PHI was higher in those reporting certain surgical procedures with RRs (95%CI) of 1.12 (1.09–1.15) for hip replacement, 1.10 (1.08–1.13) for knee replacement and 1.12 (1.09–1.15) for prostatectomy, compared to those not reporting these interventions.
Compared to the rest of the study population, those with PHI are richer, better educated, more health conscious, in better health and more likely to use certain discretionary health services. Hence, PHI use is generally highest among those with the least need for health care. Whether or not people have PHI is more strongly associated with demographic and lifestyle factors than with health status.
At present, we have very limited ability to compare public health activity across jurisdictions and countries, or even to ascertain differences in what is considered to be a public health activity. Existing standardised health classifications do not capture important dimensions of public health, which include its functions, the methods and interventions used to achieve these, the health issues and determinants of health that public health activities address, the resources and infrastructure they use, and the settings in which they occur. A classification that describes these dimensions will promote consistency in collecting and reporting information about public health programs, expenditure, workforce and performance. This paper describes the development of an initial version of such a classification.
We used open-source Protégé software and published procedures to construct an ontology of public health, which forms the basis of the classification. We reviewed existing definitions of public health, descriptions of public health functions and classifications to develop the scope, domain, and multidimensional class structure of the ontology. These were then refined through a series of consultations with public health experts from across Australia, culminating in an initial classification framework.
The public health classification consists of six top-level classes: public health 'Functions'; 'Health Issues'; 'Determinants of Health'; 'Settings'; 'Methods' of intervention; and 'Resources and Infrastructure'. Existing classifications (such as the international classifications of diseases, disability and functioning and external causes of injuries) can be used to further classify large parts of the classes 'Health Issues', 'Settings' and 'Resources and Infrastructure', while new subclass structures are proposed for the classes of public health 'Functions', 'Determinants of Health' and 'Interventions'.
The public health classification captures the important dimensions of public health activity. It will facilitate the organisation of information so that it can be used to address questions relating to any of these dimensions, either singly or in combination. The authors encourage readers to use the classification, and to suggest improvements.
To help improve incident preparedness this study assessed socio-demographic and socio-economic predictors of perceived risk of terrorism within Australia and willingness to comply with public safety directives during such incidents.
The terrorism perception question module was incorporated into the New South Wales Population Health Survey and was completed by a representative sample of 2,081 respondents in early 2007. Responses were weighted against the New South Wales population.
Multivariate analyses indicated that those with no formal educational qualifications were significantly more likely (OR = 2.10, 95%CI:1.32–3.35, p < 0.001) to think that a terrorist attack is very or extremely likely to occur in Australia and also more likely (OR = 3.62, 95%CI:2.25–5.83, p < 0.001) to be very or extremely concerned that they or a family member would be directly affected, compared to those with a university-level qualification. Speaking a language other than English at home predicted high concern (very/extremely) that self or family would be directly affected (OR = 3.02, 95%CI:2.02–4.53, p < 0.001) and was the strongest predictor of having made associated changes in living (OR = 3.27, 95%CI:2.17–4.93, p < 0.001). Being female predicted willingness to evacuate from public facilities. Speaking a language other than English at home predicted low willingness to evacuate.
Low education level is a risk factor for high terrorism risk perception and concerns regarding potential impacts. The pattern of concern and response among those of migrant background may reflect secondary social impacts associated with heightened community threat, rather than the direct threat of terrorism itself. These findings highlight the need for terrorism risk communication and related strategies to address the specific concerns of these sub-groups as a critical underpinning of population-level preparedness.