Publications by authors named "Louisa R Jorm"

49 Publications

Does a Prescription-based Comorbidity Index Correlate with the American Society of Anesthesiologists Physical Status Score and Mortality After Joint Arthroplasty? A Registry Study.

Clin Orthop Relat Res 2021 Jul 7. Epub 2021 Jul 7.

Quality Use of Medicines and Pharmacy Research Centre, Clinical and Health Sciences, University of South Australia, Adelaide, Australia.

Background: When analyzing the outcomes of joint arthroplasty, an important factor to consider is patient comorbidities. The presence of multiple comorbidities has been associated with longer hospital stays, more postoperative complications, and increased mortality. The American Society of Anesthesiologists (ASA) physical status classification system score is a measure of a patient's overall health and has been shown to be associated with complications and mortality after joint arthroplasty. The Rx-Risk score is another measure for determining the number of different health conditions for which an individual is treated, with a possible score ranging from 0 to 47.

Questions/purposes: For patients undergoing THA or TKA, we asked: (1) Which metric, the Rx-Risk score or the ASA score, correlates more closely with 30- and 90-day mortality after TKA or THA? (2) Is the Rx-Risk score correlated with the ASA score?

Methods: This was a retrospective analysis of the Australian Orthopaedic Association National Joint Replacement Registry (AOANJRR) database linked to two other national databases, the National Death Index (NDI) database and the Pharmaceutical Benefits Scheme (PBS), a dispensing database. Linkage to the NDI provided outcome information on patient death, including the fact of and date of death. Linkage to the PBS was performed to obtain records of all medicines dispensed to patients undergoing a joint replacement procedure. Patients were included if they had undergone either a THA (119,076 patients, 131,336 procedures) or TKA (182,445 patients, 215,712 procedures) with a primary diagnosis of osteoarthritis, performed between 2013 and 2017. We excluded patients with missing ASA information (THA: 3% [3055 of 119,076]; TKA: 2% [4095 of 182,445]). This left 127,761 primary THA procedures performed in 116,021 patients (53% [68,037 of 127,761] were women, mean age 68 ± 11 years) and 210,501 TKA procedures performed in 178,350 patients (56% [117,337 of 210,501] were women, mean age 68 ± 9 years) included in this study. Logistic regression models were used to determine the concordance of the ASA and Rx-Risk scores and 30-day and 90-day postoperative mortality. The Spearman correlation coefficient (r) was used to estimate the correlation between the ASA score and Rx-Risk score. All analyses were performed separately for THAs and TKAs.

Results: We found both the ASA and Rx-Risk scores had high concordance with 30-day mortality after THA (ASA: c-statistic 0.83 [95% CI 0.79 to 0.86]; Rx-Risk: c-statistic 0.82 [95% CI 0.79 to 0.86]) and TKA (ASA: c-statistic 0.73 [95% CI 0.69 to 0.78]; Rx-Risk: c-statistic 0.74 [95% CI 0.70 to 0.79]). Although both scores were strongly associated with death, their correlation was moderate for patients undergoing THA (r = 0.45) and weak for TKA (r = 0.38). However, the median Rx-Risk score did increase with increasing ASA score. For example, for THAs, the median Rx-Risk score was 1, 3, 5, and 7 for ASA scores 1, 2, 3, and 4, respectively. For TKAs, the median Rx-Risk score was 2, 4, 5, and 7 for ASA scores 1, 2, 3, and 4, respectively.

Conclusion: The ASA physical status and RxRisk were associated with 30-day and 90-day mortality; however, the scores were only weakly to moderately correlated with each other. This suggests that although both scores capture a similar level of patient illness, each score may be capturing different aspects of health. The Rx-Risk may be used as a complementary measure to the ASA score.

Level Of Evidence: Level III, therapeutic study.
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http://dx.doi.org/10.1097/CORR.0000000000001895DOI Listing
July 2021

Psychotropic medicine prescribing and polypharmacy for people with dementia entering residential aged care: the influence of changing general practitioners.

Med J Aust 2021 Jul 1. Epub 2021 Jul 1.

Dementia Centre for Research Collaboration, University of New South Wales, Sydney, NSW.

Objective: To examine relationships between changing general practitioner after entering residential aged care and overall medicines prescribing (including polypharmacy) and that of psychotropic medicines in particular.

Design: Retrospective data linkage study.

Setting, Participants: 45 and Up Study participants in New South Wales with dementia who were PBS concession card holders and entered permanent residential aged care during January 2010 - June 2014 and were alive six months after entry.

Main Outcome Measures: Inverse probability of treatment-weighted numbers of medicines dispensed to residents and proportions of residents dispensed antipsychotics, benzodiazepines, and antidepressants in the six months after residential care entry, by most frequent residential care GP category: usual (same as during two years preceding entry), known (another GP, but known to the resident), or new GP.

Results: Of 2250 new residents with dementia (mean age, 84.1 years; SD, 7.0 years; 1236 women [55%]), 625 most frequently saw their usual GPs (28%), 645 saw known GPs (29%), and 980 saw new GPs (44%). The increase in mean number of dispensed medicines after residential care entry was larger for residents with new GPs (+1.6 medicines; 95% CI, 1.4-1.9 medicines) than for those attended by their usual GPs (+0.7 medicines; 95% CI, 0.4-1.1 medicines; adjusted rate ratio, 2.42; 95% CI, 1.59-3.70). The odds of being dispensed antipsychotics (adjusted odds ratio [aOR], 1.59; 95% CI, 1.18-2.12) or benzodiazepines (aOR, 1.69; 95% CI, 1.25-2.30), but not antidepressants (aOR, 1.32; 95% CI, 0.98-1.77), were also higher for the new GP group. Differences between the known and usual GP groups were not statistically significant.

Conclusions: Increases in medicine use and rates of psychotropic dispensing were higher for people with dementia who changed GP when they entered residential care. Facilitating continuity of GP care for new residents and more structured transfer of GP care may prevent potentially inappropriate initiation of psychotropic medicines.
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http://dx.doi.org/10.5694/mja2.51153DOI Listing
July 2021

Patient and hospital factors associated with 30-day readmissions after coronary artery bypass graft (CABG) surgery: a systematic review and meta-analysis.

J Cardiothorac Surg 2021 Jun 10;16(1):172. Epub 2021 Jun 10.

Centre for Big Data Research in Health, University of New South Wales (UNSW) Sydney, Kensington, Australia.

Background: Readmission after coronary artery bypass graft (CABG) surgery is associated with adverse outcomes and significant healthcare costs, and 30-day readmission rate is considered as a key indicator of the quality of care. This study aims to: quantify rates of readmission within 30 days of CABG surgery; explore the causes of readmissions; and investigate how patient- and hospital-level factors influence readmission.

Methods: We conducted systematic searches (until June 2020) of PubMed and Embase databases to retrieve observational studies that investigated readmission after CABG. Random effect meta-analysis was used to estimate rates and predictors of 30-day post-CABG readmission.

Results: In total, 53 studies meeting inclusion criteria were identified, including 8,937,457 CABG patients. The pooled 30-day readmission rate was 12.9% (95% CI: 11.3-14.4%). The most frequently reported underlying causes of 30-day readmissions were infection and sepsis (range: 6.9-28.6%), cardiac arrythmia (4.5-26.7%), congestive heart failure (5.8-15.7%), respiratory complications (1-20%) and pleural effusion (0.4-22.5%). Individual factors including age (OR per 10-year increase 1.12 [95% CI: 1.04-1.20]), female sex (OR 1.29 [1.25-1.34]), non-White race (OR 1.15 [1.10-1.21]), not having private insurance (OR 1.39 [1.27-1.51]) and various comorbidities were strongly associated with 30-day readmission rates, whereas associations with hospital factors including hospital CABG volume, surgeon CABG volume, hospital size, hospital quality and teaching status were inconsistent.

Conclusions: Nearly 1 in 8 CABG patients are readmitted within 30 days and the majority of these are readmitted for noncardiac causes. Readmission rates are strongly influenced by patients' demographic and clinical characteristics, but not by broadly defined hospital characteristics.
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http://dx.doi.org/10.1186/s13019-021-01556-1DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8194115PMC
June 2021

Impact of pre-surgery hospital transfer on time to surgery and 30-day mortality for people with hip fractures.

Med J Aust 2021 07 11;215(2):87-88. Epub 2021 May 11.

Falls, Balance and Injury Research Centre, Neuroscience Research Australia, Sydney, NSW.

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http://dx.doi.org/10.5694/mja2.51083DOI Listing
July 2021

Commentary: Towards machine learning-enabled epidemiology.

Authors:
Louisa R Jorm

Int J Epidemiol 2021 01;49(6):1770-1773

Centre for Big Data Research in Health, Faculty of Medicine, University of New South Wales, Sydney, Australia.

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http://dx.doi.org/10.1093/ije/dyaa242DOI Listing
January 2021

The impact of dementia on aged care service transitions in the last five years of life.

Age Ageing 2021 Jun;50(4):1159-1165

Centre for Healthy Brain Ageing (CHeBA), School of Psychiatry, University of New South Wales, Sydney, New South Wales, Australia.

Objective: To investigate the impact of dementia on aged care service use at end-of-life.

Methods: Our retrospective data linkage study in New South Wales, Australia, used survey data from participants in the 45 and Up Study who died between July 2011-June 2014 linked to routinely collected administrative data for 2006-2014. We investigated movement between aged care "states" (No Services, Home Care including Home Support and Low-and High-Level Home Care and Residential Care) in the last five years of life. The dementia cohort comprised decedents with a dementia diagnosis recorded in hospital records, death certificates or who had claims for dementia-specific medicines prior to death (n = 2,230). The comparison cohort were decedents with no dementia diagnosis, matched 1:1 on age-at-death, sex, income and location.

Results: Compared to those without dementia, people with dementia were more likely to: use home care (67 versus 60%, P < 0.001), enter residential care (72 versus 30%, P < 0.001) and stay longer in residential care (median 17.9 versus 12.7 months, P < 0.001). Five years before death, more people with dementia were within residential care (6 versus 4%; RR = 1.61, 95%CI = 1.23-2.10) and these rates diverged at the end-of-life (69 versus 28%, RR = 2.48, 95%CI = 2.30-2.66). Use of home-based care was higher among people with dementia five years from death (20 versus 17%; RR = 1.15, 95%CI = 1.02-1.30) but lower at end-of-life (13 versus 24%, RR = 0.55, 95%CI = 0.49-0.63).

Conclusion: Dementia-specific aged care trajectories were dominated by residential care. Home care use declined towards end-of-life for people with dementia and may not be meeting their needs.
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http://dx.doi.org/10.1093/ageing/afaa254DOI Listing
June 2021

Medications used disproportionately during pregnancy: Priorities for research on the risks and benefits of medications when used during pregnancy.

Pharmacoepidemiol Drug Saf 2021 01 16;30(1):53-64. Epub 2020 Sep 16.

Centre for Big Data Research in Health (CBDRH), University of New South Wales, Sydney, New South Wales, Australia.

Purpose: To identify medications used disproportionately more or less among pregnant women relative to women of childbearing age.

Methods: Medication use among pregnant women in New South Wales, Australia was identified using linked perinatal and pharmaceutical dispensing data from 2006 to 2012. Medication use in women of childbearing age (including pregnant women) was identified using pharmaceutical dispensing data for a 10% random sample of the Australian population. Pregnant social security beneficiaries (n = 111 612) were age-matched (1:3) to female social security beneficiaries in the 10% sample. For each medication, the risk it was dispensed during pregnancy relative to being dispensed during an equivalent time period among matched controls was computed. Medications were mapped to Australian pregnancy risk categories.

Results: Of the 181 included medications, 35 were statistically significantly more commonly dispensed to pregnant women than control women. Of these, 23 are categorised as posing no increased risk to the foetus. Among medications suspected of causing harm or having insufficient safety data, the strongest associations were observed for hydralazine, ondansetron, dalteparin sodium and ranitidine. Use was less likely during pregnancy than control periods for 127 medications, with the strongest associations observed for hormonal contraceptives and progestogens.

Conclusions: Most medications found to be used disproportionately more by pregnant women are indicated for pregnancy-related problems. A large number of medications were used disproportionately less among pregnant women, where avoidance of some of these medications may pose a greater risk of harm. For many other medications avoided during pregnancy, current data are insufficient to inform this risk-benefit assessment.
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http://dx.doi.org/10.1002/pds.5131DOI Listing
January 2021

Evidence-practice gaps in P2Y inhibitor use after hospitalisation for acute myocardial infarction: findings from a new population-level data linkage in Australia.

Intern Med J 2020 Aug 25. Epub 2020 Aug 25.

Cardiac Clinical Network, Agency for Clinical Innovation, Sydney, Australia.

Background: P2Y inhibitor therapy is recommended for 12 months in patients hospitalised for acute myocardial infarction (AMI) unless bleeding risk is high.

Aims: To describe real-world use of P2Y inhibitor therapy following AMI hospitalisation.

Methods: We used population-level linked hospital data to identify all patients discharged from a public hospital with a primary diagnosis of AMI between July 2011-June 2013 in New South Wales and Victoria, Australia. We used dispensing claims to examine dispensing of a P2Y inhibitor (clopidogrel, prasugrel or ticagrelor) within 30 days of discharge, and multilevel models to identify predictors of post-discharge dispensing and persistence of therapy to one year.

Results: We identified 31 848 patients hospitalised for AMI, of whom 56.8% were dispensed a P2Y inhibitor within 30 days of discharge. The proportion of patients with a post-discharge dispensing varied between hospitals (interquartile range: 25.0%-56.5%), and significant between-hospital variation remained after adjusting for patient characteristics. Patients factors associated with the lowest likelihood of post-discharge dispensing were having undergone coronary artery bypass grafting (OR:0.17, 95% CIs:0.15-0.20), having oral anticoagulants dispensed 180 days before or 30 days after discharge (OR:0.39, 95% CIs:0.35-0.44), major bleeding (OR:0.68, 95% CIs:0.61-0.76) or being aged ≥85 (OR:0.68, 95% CIs:0.62-0.75). 26.8% of patients who were dispensed a P2Y inhibitor post-discharge discontinued therapy within one year.

Conclusion: Post-hospitalisation use of P2Y inhibitor therapy in AMI patients is low, and varies substantially by hospital of discharge. Our findings suggest strategies addressing both health system (hospital and physician) and patient factors are needed to close this evidence-practice gap. This article is protected by copyright. All rights reserved.
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http://dx.doi.org/10.1111/imj.15036DOI Listing
August 2020

Measuring dementia incidence within a cohort of 267,153 older Australians using routinely collected linked administrative data.

Sci Rep 2020 05 29;10(1):8781. Epub 2020 May 29.

Centre for Big Data Research in Health, University of New South Wales, Sydney, New South Wales, Australia.

To estimate dementia incidence rates using Australian administrative datasets and compare the characteristics of people identified with dementia across different datasets. This data linkage study used a cohort of 267,153 from the Australian 45 and Up Study. Participants completed a survey in 2006-2009 and subsequent dementia was identified through pharmaceutical claims, hospitalisations, aged care eligibility assessments, care needs at residential aged care entry and death certificates. Age-specific, and age-standardised incidence rates, incidence rate ratios and survival from first dementia diagnosis were estimated. Estimated age-standardised dementia incidence rates using all linked datasets was 16.8 cases per 1000 person years for people aged 65+. Comparing incidence rates to the global published rates suggested 77% of cases were identified but this varied by age with highest coverage among those aged 80-84 years (92%). Incidence rate ratios were inconsistent across datasets for: sex, socio-economic disadvantage, size of support network, marital status, functional limitations and diabetes. Median survival from first dementia diagnosis ranged from 1.80 years in the care needs dataset to 3.74 years in the pharmaceutical claims dataset. Characteristics of people identified with dementia in different administrative datasets reflect the factors that drive interaction with specific services; this may introduce bias in observational studies using a single data-source to identify dementia.
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http://dx.doi.org/10.1038/s41598-020-65273-wDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7260191PMC
May 2020

Use of smoking cessation pharmacotherapies during pregnancy is not associated with increased risk of adverse pregnancy outcomes: a population-based cohort study.

BMC Med 2020 02 5;18(1):15. Epub 2020 Feb 5.

Centre for Big Data Research in Health, Faculty of Medicine, University of New South Wales (UNSW), Sydney, NSW, 2052, Australia.

Background: Varenicline, bupropion and nicotine replacement therapy (NRT) are three effective pharmacotherapies for smoking cessation, but data about their safety in pregnancy are limited. We assessed the risk of adverse perinatal outcomes and major congenital anomalies associated with the use of these therapies in pregnancy in Australia.

Methods: Perinatal data for 1,017,731 deliveries (2004 to 2012) in New South Wales and Western Australia were linked to pharmaceutical dispensing, hospital admission and death records. We identified 97,875 women who smoked during pregnancy; of those, 233, 330 and 1057 were exposed to bupropion, NRT and varenicline in pregnancy, respectively. Propensity scores were used to match exposed women to those who were unexposed to any smoking therapy (1:10 ratio). Propensity scores and gestational age at exposure were used to match varenicline-exposed to NRT-exposed women (1:1 ratio). Time-dependent Cox proportional hazards models estimated hazard ratios (HR) with 95% confidence intervals (95% CI) for any adverse perinatal event (a composite of 10 unfavourable maternal and neonatal outcomes) and any major congenital anomaly.

Results: The risk of any adverse perinatal event was not significantly different between bupropion-exposed and unexposed women (39.2% versus 39.3%, HR 0.93, 95% CI 0.73-1.19) and between NRT-exposed and unexposed women (44.8% vs 46.3%, HR 1.02, 95% CI 0.84-1.23), but it was significantly lower in women exposed to varenicline (36.9% vs 40.1%, HR 0.86, 95% CI 0.77-0.97). Varenicline-exposed infants were less likely than unexposed infants to be born premature (6.5% vs 8.9%, HR 0.72, 95% CI 0.56-0.92), be small for gestational age (11.4% vs 15.4%, HR 0.68, 95% CI 0.56-0.83) and have severe neonatal complications (6.6% vs 8.2%, HR 0.74, 95% CI 0.57-0.96). Among infants exposed to varenicline in the first trimester, 2.9% had a major congenital anomaly (3.5% in unexposed infants, HR 0.91, 95% CI 0.72-1.15). Varenicline-exposed women were less likely than NRT-exposed women to have an adverse perinatal event (38.7% vs 51.4%, HR 0.58, 95% CI 0.33-1.05).

Conclusions: Pregnancy exposure to smoking cessation pharmacotherapies does not appear to be associated with an increased risk of adverse birth outcomes. Lower risk of adverse birth outcomes in varenicline-exposed pregnancies is inconsistent with recommendations that NRT be used in preference to varenicline.
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http://dx.doi.org/10.1186/s12916-019-1472-9DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7001233PMC
February 2020

Impact of Prior Home Care on Length of Stay in Residential Care for Australians With Dementia.

J Am Med Dir Assoc 2020 06 1;21(6):843-850.e5. Epub 2020 Feb 1.

Centre for Big Data Research in Health, University of New South Wales, Sydney, New South Wales, Australia.

Objectives: To assess the impact of home care on length-of-stay within residential care.

Design: A retrospective observational data-linkage study.

Setting And Participants: In total there were 3151 participants from the 45 and Up Study in New South Wales, Australia with dementia who entered residential care between 2010 and 2014.

Methods: Survey data collected from 2006‒2009 were linked to administrative data for 2006‒2016. The highest level of home care a person accessed prior to residential care was defined as no home care, home support, low-level home care, and high-level home care. Multinomial logistic regression and Cox proportional hazards were used to investigate differences in activities of daily living, behavioral, and complex healthcare scales at entering residential care; and length-of-stay in residential care.

Results: People with prior high-level home care entered residential care needing higher assistance compared with the no home care group: activities of daily living [odds ratio (OR) 3.41, 95% confidence interval (CI) 2.14‒5.44], behavior (OR 2.61, 95% CI 1.69‒4.03), and complex healthcare (OR 2.02, 95% CI 1.06‒3.84). They had a higher death rate, meaning shorter length-of-stay in residential care (<2 years after entry: hazard ratio 1.12; 95% CI 0.89‒1.42; 2-4 years: hazard ratio 1.49; 95% CI 1.01‒2.21). Those using low-level home care were less likely to enter residential care needing high assistance compared to the no home care group (activities of daily living: OR 0.61, 95% CI 0.45‒0.81; behavioral: OR 0.72, 95% CI 0.54‒0.95; complex healthcare: OR 0.51, 95% CI 0.33‒0.77). There was no difference between the home support and no home care groups.

Conclusions: High-level home care prior to residential care may help those with dementia stay at home for longer, but the low-level care group entered residential care at low assistance levels, possibly signaling lack of informal care and barriers in accessing higher-level home care.

Implications: Better transition options from low-level home care, including more timely availability of high-level care packages, may help people with dementia remain at home longer.
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http://dx.doi.org/10.1016/j.jamda.2019.11.023DOI Listing
June 2020

Evidence-Practice Gaps in Postdischarge Initiation With Oral Anticoagulants in Patients With Atrial Fibrillation.

J Am Heart Assoc 2019 12 4;8(24):e014287. Epub 2019 Dec 4.

Safer Care Victoria Melbourne Australia.

Background Oral anticoagulant (OAC) therapy reduces the risk of stroke in people with atrial fibrillation (AF), and is considered best practice; however, there is little Australian evidence around the uptake of OACs in this population. Methods and Results We used linked hospital admissions, pharmaceutical dispensing claims, medical services, and mortality data for people in Australia's 2 most populous states (July 2010 to June 2015). Among OAC-naïve people hospitalized with AF, we estimated initiation of OAC therapy within 30 days of discharge, and persistence with therapy in the first year. We analyzed both outcomes using multivariable Cox regression. In 71 184 people with AF (median age 78 years, 49% female), 22.7% initiated OAC therapy. Initiation was lowest in July to December 2011 (17.0%) and highest in July to December 2014 (30.1%) after subsidy of the direct OACs. In adjusted analyses, initiation was most likely in people with a CHADS-VA score ≥7 (versus 0) (hazard ratio=6.25, 95% CI 5.08-7.69), and a history of venous thromboembolism (hazard ratio=2.65, 95% CI 2.49-2.83). Of the people who initiated OAC therapy, 39.9% discontinued within 1 year; a lower risk of discontinuation was associated with a CHADS-VA score ≥7 (versus 0) (hazard ratio=0.22, 95% CI 0.14-0.35), or initiation on a direct OAC (versus warfarin) (hazard ratio=0.55, 95% CI 0.50-0.60). Conclusions We found that OAC therapy was severely underutilized in people hospitalized with AF, even among high-risk individuals. Reasons for this underuse, whether patient, prescriber, or hospital related, should be identified and addressed to reduce stroke-related morbidity and mortality in people with AF.
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http://dx.doi.org/10.1161/JAHA.119.014287DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6951075PMC
December 2019

Do hospitals influence geographic variation in admission for preventable hospitalisation? A data linkage study in New South Wales, Australia.

BMJ Open 2019 02 22;9(2):e027639. Epub 2019 Feb 22.

Centre for Big Data Research in Health, University of New South Wales, Sydney, New South Wales, Australia.

Objective: Preventable hospitalisations are used internationally as a performance indicator for primary care, but the influence of other health system factors remains poorly understood. This study investigated between-hospital variation in rates of preventable hospitalisation.

Setting: Linked health survey and hospital admissions data for a cohort study of 266 826 people aged over 45 years in the state of New South Wales, Australia.

Method: Between-hospital variation in preventable hospitalisation was quantified using cross-classified multiple-membership multilevel Poisson models, adjusted for personal sociodemographic, health and area-level contextual characteristics. Variation was also explored for two conditions unlikely to be influenced by discretionary admission practice: emergency admissions for acute myocardial infarction (AMI) and hip fracture.

Results: We found significant between-hospital variation in adjusted rates of preventable hospitalisation, with hospitals varying on average 26% from the state mean. Patients served more by community and multipurpose facilities (smaller facilities primarily in rural areas) had higher rates of preventable hospitalisation. Community hospitals had the greatest between-hospital variation, and included the facilities with the highest rates of preventable hospitalisation. There was comparatively little between-hospital variation in rates of admission for AMI and hip fracture.

Conclusions: Geographic variation in preventable hospitalisation is determined in part by hospitals, reflecting different roles played by community and multipurpose facilities, compared with major and principal referral hospitals, within the community. Care should be taken when interpreting the indicator simply as a performance measure for primary care.
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http://dx.doi.org/10.1136/bmjopen-2018-027639DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6398792PMC
February 2019

Identifying patients using antidepressants for the treatment of depression: A predictive algorithm for use in pharmaceutical and medical claims data.

Pharmacoepidemiol Drug Saf 2019 03 25;28(3):354-361. Epub 2019 Jan 25.

Centre for Big Data Research in Health (CBDRH), UNSW Sydney, Sydney, NSW, Australia.

Purpose: Records of antidepressant dispensings are often used as a surrogate measure of depression. However, as antidepressants are frequently prescribed for indications other than depression, this is likely to result in misclassification. This study aimed to develop a predictive algorithm that identifies patients using antidepressants for the treatment of depression.

Methods: Pharmaceutical Benefits Scheme (PBS) and Medicare Benefits Schedule (MBS) claims data were linked to follow-up questionnaires (completed in 2012-2013) for participants of the 45 and Up Study-a cohort study of residents of New South Wales, Australia, aged 45 years and older. The sample composed participants who were dispensed an antidepressant in the 30 days prior to questionnaire completion (n = 3162). An algorithm based on patient characteristics, pharmaceutical dispensings, and claims for mental health services was built using group-lasso interaction network (glinternet), with self-reported receipt of treatment for depression as the outcome. The predictive performance of the algorithm was assessed via bootstrap resampling.

Results: The algorithm composes 15 main effects and 11 interactions, with type of antidepressant dispensed and claims for mental health services the strongest predictors. The ability of the algorithm to discriminate between antidepressant users with and without depression was 0.73. At a predicted probability cut-off of 0.6, specificity was 93.8% and sensitivity was 23.6%.

Conclusions: Using this algorithm with a high probability cut-off yields high specificity and facilitates the exclusion of individuals using antidepressants for indications other than depression, thereby mitigating the risk of confounding by indication when evaluating the outcomes of antidepressant use.
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http://dx.doi.org/10.1002/pds.4739DOI Listing
March 2019

Maternal and perinatal outcomes associated with the use of renin-angiotensin system (RAS) blockers for chronic hypertension in early pregnancy.

Pregnancy Hypertens 2018 Oct 3;14:156-161. Epub 2018 Oct 3.

Centre for Big Data Research in Health, University of New South Wales, Sydney, Australia.

Objective: Previous research reported greater risk of adverse perinatal outcomes associated with first trimester exposure to angiotensin converting enzyme inhibitors (ACEIs) in comparison to unexposed pregnancies among non-hypertensive women. We examined the relationship between first trimester exposure to ACEIs and angiotensin receptor blockers (ARBs), and maternal and perinatal outcomes, whilst controlling for the underlying hypertension.

Study Design: We performed a population-based cohort study among 130,061 pregnancies resulting in birth in NSW, Australia between 2005 and 2012. Birth data were linked to hospital discharge and pharmaceutical dispensing records. After restricting to women with chronic hypertension, 67 and 73 pregnancies exposed to ACEIs and ARBs respectively during the first trimester were compared with 316 pregnancies exposed to methyldopa.

Study Outcomes: Preterm delivery, caesarean section, low birth weight, small for gestational age and Apgar score <7.

Results: Compared to pregnancies exposed to methyldopa, the adjusted odds ratio (aOR) for ACEI exposure was 0.5 (95% CI: 0.2-1.1) for preterm delivery, 1.6 (0.8-3.1) for caesarean section, 0.6 (0.2-1.3) for LBW and 0.8 (0.4-1.9) for SGA. The corresponding aORs and confidence intervals for ARB exposure were 0.7 (0.3-1.5), 1.2 (0.6-2.6), 1.3 (0.7-2.6), and 1.2 (0.6-2.4).

Conclusion: No association between early pregnancy exposure to ACEIs and ARBs and perinatal outcomes was observed, however, the possibility of an association cannot be ruled out due to limited power. Nonetheless, this study suggests that the magnitude of risk is smaller than that reported previously.
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http://dx.doi.org/10.1016/j.preghy.2018.09.010DOI Listing
October 2018

Overcoming the data drought: exploring general practice in Australia by network analysis of big data.

Med J Aust 2018 07 9;209(2):68-73. Epub 2018 Jul 9.

Centre for Big Data Research in Health, UNSW Sydney, Sydney, NSW.

Objectives: To investigate the organisation and characteristics of general practice in Australia by applying novel network analysis methods to national Medicare claims data.

Design: We analysed Medicare claims for general practitioner consultations during 1994-2014 for a random 10% sample of Australian residents, and applied hierarchical block modelling to identify provider practice communities (PPCs).

Participants: About 1.7 million patients per year.

Main Outcome Measures: Numbers and characteristics of PPCs (including numbers of providers, patients and claims), proportion of bulk-billed claims, continuity of care, patient loyalty, patient sharing.

Results: The number of PPCs fluctuated during the 21-year period; there were 7747 PPCs in 2014. The proportion of larger PPCs (six or more providers) increased from 32% in 1994 to 43% in 2014, while that of sole provider PPCs declined from 50% to 39%. The median annual number of claims per PPC increased from 5000 (IQR, 40-19 940) in 1994 to 9980 (190-23 800) in 2014; the proportion of PPCs that bulk-billed all patients was lowest in 2004 (21%) and highest in 2014 (29%). Continuity of care and patient loyalty were stable; in 2014, 50% of patients saw the same provider and 78% saw a provider in the same PPC for at least 75% of consultations. Density of patient sharing in a PPC was correlated with patient loyalty to that PPC.

Conclusions: During 1994-2014, Australian GP practice communities have generally increased in size, but continuity of care and patient loyalty have remained stable. Our novel approach to the analysis of routinely collected data allows continuous monitoring of the characteristics of Australian general practices and their influence on patient care.
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http://dx.doi.org/10.5694/mja17.01236DOI Listing
July 2018

Using Weighted Hospital Service Area Networks to Explore Variation in Preventable Hospitalization.

Health Serv Res 2018 08 22;53 Suppl 1:3148-3169. Epub 2017 Sep 22.

MRC/CSO Social and Public Health Sciences Unit, University of Glasgow, Glasgow, UK.

Objective: To demonstrate the use of multiple-membership multilevel models, which analytically structure patients in a weighted network of hospitals, for exploring between-hospital variation in preventable hospitalizations.

Data Sources: Cohort of 267,014 people aged over 45 in NSW, Australia.

Study Design: Patterns of patient flow were used to create weighted hospital service area networks (weighted-HSANs) to 79 large public hospitals of admission. Multiple-membership multilevel models on rates of preventable hospitalization, modeling participants structured within weighted-HSANs, were contrasted with models clustering on 72 hospital service areas (HSAs) that assigned participants to a discrete geographic region.

Data Collection/extraction Methods: Linked survey and hospital admission data.

Principal Findings: Between-hospital variation in rates of preventable hospitalization was more than two times greater when modeled using weighted-HSANs rather than HSAs. Use of weighted-HSANs permitted identification of small hospitals with particularly high rates of admission and influenced performance ranking of hospitals, particularly those with a broadly distributed patient base. There was no significant association with hospital bed occupancy.

Conclusion: Multiple-membership multilevel models can analytically capture information lost on patient attribution when creating discrete health care catchments. Weighted-HSANs have broad potential application in health services research and can be used across methods for creating patient catchments.
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http://dx.doi.org/10.1111/1475-6773.12777DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6056604PMC
August 2018

Tobacco policy reform and population-wide antismoking activities in Australia: the impact on smoking during pregnancy.

Tob Control 2018 09 4;27(5):552-559. Epub 2017 Aug 4.

Centre for Big Data Research in Health, School of Population and Global Health, UNSW Sydney, Sydney, Australia.

Introduction: This study examined the impact of antismoking activities targeting the general population and an advertising campaign targeting smoking during pregnancy on the prevalence of smoking during pregnancy in New South Wales (NSW), Australia.

Methods: Monthly prevalence of smoking during pregnancy was calculated using linked health records for all pregnancies resulting in a birth (800 619) in NSW from 2003 to 2011. Segmented regression of interrupted time series data assessed the effects of the extension of the ban on smoking in enclosed public places to include licensed premises (evaluated in combination with the mandating of graphic warnings on cigarette packs), television advertisements targeting smoking in the general population, print and online magazine advertisements targeting smoking during pregnancy and increased tobacco tax. Analyses were conducted for all pregnancies, and for the population stratified by maternal age, parity and socioeconomic status. Further analyses adjusted for the effect of the Baby Bonus maternity payment.

Results: Prevalence of smoking during pregnancy decreased from 2003 to 2011 overall (0.39% per month), and for all strata examined. For pregnancies overall, none of the evaluated initiatives was associated with a change in the trend of smoking during pregnancy. Significant changes associated with increased tobacco tax and the extension of the smoking ban (in combination with graphic warnings) were found in some strata.

Conclusions: The declining prevalence of smoking during pregnancy between 2003 and 2011, while encouraging, does not appear to be directly related to general population antismoking activities or a pregnancy-specific campaign undertaken in this period.
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http://dx.doi.org/10.1136/tobaccocontrol-2017-053715DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6109232PMC
September 2018

Compliance with telephone triage advice among adults aged 45 years and older: an Australian data linkage study.

BMC Health Serv Res 2017 08 1;17(1):512. Epub 2017 Aug 1.

Centre for Big Data Research in Health-Faculty of Medicine, UNSW Sydney (The University of New South Wales), Sydney, NSW, 2052, Australia.

Background: Middle-aged and older patients are prominent users of telephone triage services for timely access to health information and appropriate referrals. Non-compliance with advice to seek appropriate care could potentially lead to poorer health outcomes among those patients. It is imperative to assess the extent to which middle-aged and older patients follow triage advice and how this varies according to their socio-demographic, lifestyle and health characteristics as well as features of the call.

Methods: Records of calls to the Australian healthdirect helpline (July 2008-December 2011) were linked to baseline questionnaire data from the 45 and Up Study (participants age ≥ 45 years), records of emergency department (ED) presentations, hospital admissions, and medical consultation claims. Outcomes of the call included compliance with the advice "Attend ED immediately"; "See a doctor (immediately, within 4 hours, or within 24 hours)"; "Self-care"; and self-referral to ED or hospital within 24 h when given a self-care or low-urgency care advice. Multivariable logistic regression was used to investigate associations between call outcomes and patient and call characteristics.

Results: This study included 8406 adults (age ≥ 45 years) who were subjects of 11,088 calls to the healthdirect helpline. Rates of compliance with the advices "Attend ED immediately", "See a doctor" and "Self-care" were 68.6%, 64.6% and 77.5% respectively, while self-referral to ED within 24 h followed 7.0% of calls. Compliance with the advice "Attend ED immediately" was higher among patients who had three or more positive lifestyle behaviours, called after-hours, or stated that their original intention was to attend ED, while it was lower among those who lived in rural and remote areas or reported high or very high levels of psychological distress. Compliance with the advice "See a doctor" was higher in patients who were aged ≥65 years, worked full-time, or lived in socio-economically advantaged areas, when another person made the call on the patient's behalf, and when the original intention was to seek care from an ED or a doctor. It was lower among patients in rural and remote areas and those taking five medications or more. Patients aged ≥65 years were less likely to comply with the advice "Self-care". The rates of self-referral to ED within 24 h were greater in patients from disadvantaged areas, among calls made after-hours or by another person, and when the original intention was to attend ED. Patients who were given a self-care or low-urgency care advice, whose calls concerned bleeding, cardiac, gastrointestinal, head and facial injury symptoms, were more likely to self-refer to ED.

Conclusions: Compliance with telephone triage advice among middle-age and older patients varied substantially according to both patient- and call-related factors. Knowledge about the patients who are less likely to comply with telephone triage advice, and about characteristics of calls that may influence compliance, will assist in refining patient triage protocols and referral pathways, training staff and tailoring service design and delivery to achieve optimal patient compliance.
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http://dx.doi.org/10.1186/s12913-017-2458-yDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5539620PMC
August 2017

Data cleaning and management protocols for linked perinatal research data: a good practice example from the Smoking MUMS (Maternal Use of Medications and Safety) Study.

BMC Med Res Methodol 2017 Jul 11;17(1):97. Epub 2017 Jul 11.

Centre for Big Data Research in Health, Faculty of Medicine, UNSW Sydney (The University of New South Wales), Sydney, NSW, 2052, Australia.

Background: Data cleaning is an important quality assurance in data linkage research studies. This paper presents the data cleaning and preparation process for a large-scale cross-jurisdictional Australian study (the Smoking MUMS Study) to evaluate the utilisation and safety of smoking cessation pharmacotherapies during pregnancy.

Methods: Perinatal records for all deliveries (2003-2012) in the States of New South Wales (NSW) and Western Australia were linked to State-based data collections including hospital separation, emergency department and death data (mothers and babies) and congenital defect notifications (babies in NSW) by State-based data linkage units. A national data linkage unit linked pharmaceutical dispensing data for the mothers. All linkages were probabilistic. Twenty two steps assessed the uniqueness of records and consistency of items within and across data sources, resolved discrepancies in the linkages between units, and identified women having records in both States.

Results: State-based linkages yielded a cohort of 783,471 mothers and 1,232,440 babies. Likely false positive links relating to 3703 mothers were identified. Corrections of baby's date of birth and age, and parity were made for 43,578 records while 1996 records were flagged as duplicates. Checks for the uniqueness of the matches between State and national linkages detected 3404 ID clusters, suggestive of missed links in the State linkages, and identified 1986 women who had records in both States.

Conclusions: Analysis of content data can identify inaccurate links that cannot be detected by data linkage units that have access to personal identifiers only. Perinatal researchers are encouraged to adopt the methods presented to ensure quality and consistency among studies using linked administrative data.
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http://dx.doi.org/10.1186/s12874-017-0385-6DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5504784PMC
July 2017

Emergency Department Attendance after Telephone Triage: A Population-Based Data Linkage Study.

Health Serv Res 2018 04 29;53(2):1137-1162. Epub 2017 Mar 29.

Centre for Big Data Research in Health, UNSW, Sydney, NSW, Australia.

Objective: To investigate compliance with telephone helpline advice to attend an emergency department (ED) and the acuity of patients who presented to ED following a call.

Data Sources/collection Methods: In New South Wales (NSW), Australia, 2009-2012, all (1.04 million) calls to a telephone triage service, ED presentations, hospital admissions and death registrations, linked using probabilistic data linkage.

Study Design: Population-based, observational cohort study measuring ED presentations within 24 hours of a call in patients (1) with dispositions to attend ED (compliance) and (2) low-urgency dispositions (self-referral), triage categories on ED presentation.

Principal Findings: A total of 66.5 percent of patients were compliant with dispositions to attend an ED. A total of 6.2 percent of patients with low-urgency dispositions self-referred to the ED within 24 hours. After age adjustment, healthdirect compliant patients were significantly less likely (7.8 percent) to receive the least urgent ED triage category compared to the general NSW ED population (16.9 percent).

Conclusions: This large population-based data linkage study provides precise estimates of ED attendance following calls to a telephone triage service and details the predictors of ED attendance. Patients who attend an ED compliant with a healthdirect helpline disposition are significantly less likely than the general ED population to receive the lowest urgency triage category on arrival.
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http://dx.doi.org/10.1111/1475-6773.12692DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5867179PMC
April 2018

Access to Subsidized Smoking Cessation Medications by Australian Smokers Aged 45 Years and Older: A Population-Based Cohort Study.

Nicotine Tob Res 2017 Mar;19(3):342-350

Centre for Health Research, School of Medicine, Western Sydney University, Penrith, New South Wales, Australia.

Introduction: The principal aim of this study was to assess the accessibility of subsidized cessation medications to socioeconomically disadvantaged smokers, including smokers living in regional and remote communities.

Methods: Analyses used baseline questionnaire and linked Pharmaceutical Benefits Scheme data for 18 686 regular smokers participating in the 45 and Up Study, a large-scale Australian cohort study of people aged 45 years and older. Participants who were dispensed nicotine replacement therapy, varenicline, or bupropion were identified from the Pharmaceutical Benefits Scheme data, which provide an essentially complete record of participants' access to subsidized pharmaceuticals. Associations between the supply of each pharmacotherapy and a range of sociodemographic and health-related variables were evaluated using multiple logistic regression.

Results: The odds that participants were supplied with a cessation medication declined markedly with increasing age for participants older than 60 years and were substantially higher for participants who smoked 20 or more cigarettes/day than for participants who smoked fewer than 10 cigarettes/day. Participants with no formal qualification and those residing in socioeconomically disadvantaged areas had higher odds of receiving nicotine replacement therapy or varenicline than university-educated participants and participants living in the least disadvantaged areas. There was no evidence that participants residing in regional and remote communities had lower odds of receiving a cessation medication than participants residing in major cities.

Conclusions: Older Australian smokers' access to cessation pharmacotherapies is determined predominantly by age and daily cigarette consumption and does not appear to be limited by educational achievement, socioeconomic disadvantage, or remoteness.

Implications: Promoting the use of cessation medications is a principal measure proposed to achieve Australia's National Tobacco Strategy 2012-2018 goal of reducing cigarette consumption among socioeconomically disadvantaged smokers. The results of this large-scale cohort study indicate that access to cessation pharmacotherapies is determined primarily by age and daily cigarette consumption, and is not limited by socioeconomic circumstances, providing some reassurance that existing government subsidies are sufficient to ensure that pharmaceutical aids are accessible to all Australian smokers.
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http://dx.doi.org/10.1093/ntr/ntw202DOI Listing
March 2017

Visualising linked health data to explore health events around preventable hospitalisations in NSW Australia.

BMJ Open 2016 09 7;6(9):e012031. Epub 2016 Sep 7.

MRC/CSO Social and Public Health Sciences Unit, University of Glasgow, Glasgow, UK.

Objective: To explore patterns of health service use in the lead-up to, and following, admission for a 'preventable' hospitalisation.

Setting: 266 950 participants in the 45 and Up Study, New South Wales (NSW) Australia

Methods: Linked data on hospital admissions, general practitioner (GP) visits and other health events were used to create visual representations of health service use. For each participant, health events were plotted against time, with different events juxtaposed using different markers and panels of data. Various visualisations were explored by patient characteristics, and compared with a cohort of non-admitted participants matched on sociodemographic and health characteristics. Health events were displayed over calendar year and in the 90 days surrounding first preventable hospitalisation.

Results: The visualisations revealed patterns of clustering of GP consultations in the lead-up to, and following, preventable hospitalisation, with 14% of patients having a consultation on the day of admission and 27% in the prior week. There was a clustering of deaths and other hospitalisations following discharge, particularly for patients with a long length of stay, suggesting patients may have been in a state of health deterioration. Specialist consultations were primarily clustered during the period of hospitalisation. Rates of all health events were higher in patients admitted for a preventable hospitalisation than the matched non-admitted cohort.

Conclusions: We did not find evidence of limited use of primary care services in the lead-up to a preventable hospitalisation, rather people with preventable hospitalisations tended to have high levels of engagement with multiple elements of the healthcare system. As such, preventable hospitalisations might be better used as a tool for identifying sicker patients for managed care programmes. Visualising longitudinal health data was found to be a powerful strategy for uncovering patterns of health service use, and such visualisations have potential to be more widely adopted in health services research.
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http://dx.doi.org/10.1136/bmjopen-2016-012031DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5020859PMC
September 2016

Variation in the use of primary care services for diabetes management according to country of birth and geography among older Australians.

Prim Care Diabetes 2016 Feb 1;10(1):66-74. Epub 2015 Aug 1.

Centre for Primary Health Care and Equity, UNSW Australia, UNSW Sydney, NSW 2052, Australia. Electronic address:

Aims: To investigate variation according to country of birth and geography in the use of primary care services funded through Medicare Australia-Australian universal health insurance-for diabetes annual cycle of care among older overseas-born Australians with type-2 diabetes.

Methods: Records of Medicare claims for medical services were linked to self-administered questionnaire data for people with type-2 diabetes enrolled in the 45 and Up Study, including 840 participants born in Italy, Greece, Vietnam, Lebanon, China, India, or the Philippines and 12,444 participants born in Australia, living in 195 statistical local areas (SLAs) in New South Wales, Australia. Study outcomes included ≥6 claims for general practitioner (GP) visits, at least one claim for specialist, optometrist, Practice Incentive Payment for completion of diabetes annual cycle of care (PIP), GP Management Plan or Team Care Arrangement (GPMP/TCA), allied health, blood tests for glycosylated haemoglobin (HbA1c) and cholesterol, and urine test for micro-albumin. Multivariable multilevel logistic regression was performed, controlling for personal socio-demographic and health characteristics and geographical area remoteness and socio-economic status.

Results: Compared with Australia-born participants, people born in Vietnam and China had significantly lower rates of claims for allied health services (odds ratio [OR] 0.14, 95% confidence interval [CI] 0.05-0.43, and OR 0.40, 95%CI 0.18-0.87, respectively), those born in Italy had lower rates of PIP claims (OR 0.60, 95%CI 0.39-0.92) and micro-albuminuria testings (OR 0.65, 95%CI 0.47-0.89), and those born in the Philippines had lower claims for specialist services (OR 0.59, 95%CI 0.38-0.91). Participants born in Greece and China (GP visits), Vietnam (optometrist services), and India (micro-albuminuria tests) were more likely to claims for these services than Australia-born people. Significant geographic variation was observed for all study outcomes, with the greatest variations in claims for allied health services (variation 9.3%, median odds ratio [MOR] 1.74, 95% credible interval [CrI] 1.60-2.01), PIP (7.8%, MOR 1.65, 95%CrI 1.55-1.83), and GPMP/TCA items (6.6%, MOR 1.58, 95%CrI 1.49-1.73).

Conclusions: Different approach among geographical areas and intervention programs for identified cultural groups and their providers are warranted to improve disparities in diabetes care.
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http://dx.doi.org/10.1016/j.pcd.2015.07.001DOI Listing
February 2016

Sociodemographic and health characteristics, rather than primary care supply, are major drivers of geographic variation in preventable hospitalizations in Australia.

Med Care 2015 May;53(5):436-45

*Centre for Health Research, University of Western Sydney, Sydney †The Sax Institute, Sydney, New South Wales ‡Australian National University Medical School, Australian National University, Canberra §Concord Clinical School, University of Sydney, Sydney, New South Wales, Australia ∥Health Economics Research Unit, University of Aberdeen, Aberdeen ¶MRC/CSO Social and Public Health Sciences Unit, University of Glasgow, Glasgow, UK.

Background: Geographic rates of preventable hospitalization are used internationally as an indicator of accessibility and quality of primary care. Much research has correlated the indicator with the supply of primary care services, yet multiple other factors may influence these admissions.

Objective: To quantify the relative contributions of the supply of general practitioners (GPs) and personal sociodemographic and health characteristics, to geographic variation in preventable hospitalization.

Methods: Self-reported questionnaire data for 267,091 participants in the 45 and Up Study, Australia, were linked with administrative hospital data to identify preventable hospitalizations. Multilevel Poisson models, with participants clustered in their geographic area of residence, were used to explore factors that explain geographic variation in hospitalization.

Results: GP supply, measured as full-time workload equivalents, was not a significant predictor of preventable hospitalization, and explained only a small amount (2.9%) of the geographic variation in hospitalization rates. Conversely, more than one-third (36.9%) of variation was driven by the sociodemographic composition, health, and behaviors of the population. These personal characteristics explained a greater amount of the variation for chronic conditions (37.5%) than acute (15.5%) or vaccine-preventable conditions (2.4%).

Conclusions: Personal sociodemographic and health characteristics, rather than GP supply, are major drivers of preventable hospitalization. Their contribution varies according to condition, and if used for performance comparison purposes, geographic rates of preventable hospitalization should be reported according to individual condition or potential pathways for intervention.
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http://dx.doi.org/10.1097/MLR.0000000000000342DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4396734PMC
May 2015

Smoking and potentially preventable hospitalisation: the benefit of smoking cessation in older ages.

Drug Alcohol Depend 2015 May 4;150:85-91. Epub 2015 Mar 4.

Centre for Big Data Research in Health, Faculty of Medicine, UNSW Australia, NSW 2052, Australia.

Aims: Reducing preventable hospitalisation is a priority for health systems worldwide. This study sought to quantify the contribution of smoking to preventable hospitalisation in older adults and the potential benefits of smoking cessation.

Methods: Self-reported smoking data for 267,010 Australian men and women aged 45+ years linked with administrative hospital data were analysed using Cox's models to estimate the effects on risk of hospitalisation for congestive heart failure (CHF), diabetes complications, chronic obstructive pulmonary disease (COPD) and angina. The impacts of smoking and quitting smoking were also quantified using risk advancement periods (RAP).

Results: The cohort included 7% current smokers, 36% former smokers and 57% never smokers. During an average follow-up of 2.7 years, 4% of participants had at least one hospitalisation for any of the study conditions (0.8% for CHF, 1.7% for diabetes complications, 0.8% for COPD and 1.4% for angina). Compared to never smokers, the adjusted hazard ratio for hospitalisation for any of the conditions for current smokers was 1.89 (95% CI 1.75-2.03), and the RAP was 3.8 years. There were strong dose-response relationships between smoking duration, smoking intensity and cumulative smoking dose on hospitalisation risk. The excess risk of hospitalisation and RAP for COPD was reduced within 5 years of smoking cessation across all age groups, but risk reduction for other conditions was only observed after 15 years.

Conclusion: Smoking is associated with increased risk of preventable hospitalisation for chronic conditions in older adults and smoking cessation, even at older ages, reduces this risk.
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http://dx.doi.org/10.1016/j.drugalcdep.2015.02.028DOI Listing
May 2015

The relationship between body mass index and hospitalisation rates, days in hospital and costs: findings from a large prospective linked data study.

PLoS One 2015 4;10(3):e0118599. Epub 2015 Mar 4.

National Centre for Epidemiology and Population Health, The Australian National University, Canberra, Australian Capital Territory, Australia; The Sax Institute, Sydney, New South Wales, Australia.

Background: Internationally there is limited empirical evidence on the impact of overweight and obesity on health service use and costs. We estimate the burden of hospitalisation-admissions, days and costs-associated with above-normal BMI.

Methods: Population-based prospective cohort study involving 224,254 adults aged ≥45y in Australia (45 and Up Study). Baseline questionnaire data (2006-2009) were linked to hospitalisation and death records (median follow-up 3.42y) and hospital cost data. The relationships between BMI and hospital admissions and days were modelled using zero-inflated negative binomial regression; generalised gamma models were used to model costs. Analyses were stratified by sex and age (45-64, 65-79, ≥80y), and adjusted for age, area of residence, education, income, smoking, alcohol-intake and private health insurance status. Population attributable fractions were also calculated.

Results: There were 459,346 admissions (0.55/person-year) and 1,483,523 hospital days (1.76/person-year) during follow-up. For ages 45-64y and 65-79y, rates of admissions, days and costs increased progressively with increments of above-normal BMI. Compared to BMI 22.5-<25kg/m2, rates of admissions and days were 1.64-2.54 times higher for BMI 40-50kg/m2; costs were 1.14-1.24 times higher for BMI 27.5-<30kg/m2, rising to 1.77-2.15 times for BMI 40-50kg/m2. The BMI-hospitalisation relationship was less clear for ≥80y. We estimated that among Australians 45-79y, around 1 in every 8 admissions are attributable to overweight and obesity (2% to overweight, 11% to obesity), as are 1 in every 6 days in hospital (2%, 16%) and 1 in every 6 dollars spent on hospitalisation (3%, 14%).

Conclusions: The dose-response relationship between BMI and hospital use and costs in mid-age and older Australians in the above-normal BMI range suggests even small downward shifts in BMI among these people could result in considerable reductions in their annual health care costs; whether this would result in long-term savings to the health care system is not known from this study.
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http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0118599PLOS
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4349828PMC
January 2016

Variation in the recording of common health conditions in routine hospital data: study using linked survey and administrative data in New South Wales, Australia.

BMJ Open 2014 Sep 3;4(9):e005768. Epub 2014 Sep 3.

Centre for Health Research, University of Western Sydney, Sydney, Australia.

Objectives: 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.

Outcome Measures: 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.

Results: 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.

Conclusions: 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.
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http://dx.doi.org/10.1136/bmjopen-2014-005768DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4158198PMC
September 2014

Linking birth records to hospital admission records enhances the identification of women who smoke during pregnancy.

Aust N Z J Public Health 2014 Jun;38(3):258-64

Centre for Health Research, University of Western Sydney, New South Wales.

Objective: Birth records and hospital admission records are valuable for research on maternal smoking, but individually are known to under-estimate smokers. This study investigated the extent to which combining data from these records enhances the identification of pregnant smokers, and whether this affects research findings such as estimates of maternal smoking prevalence and risk of adverse pregnancy outcomes associated with smoking.

Methods: A total of 846,039 birth records in New South Wales, Australia, (2001-2010) were linked to hospital admission records (delivery and antenatal). Algorithm 1 combined data from birth and delivery admission records, whereas algorithm 2 combined data from birth record, delivery and antenatal admission records. Associations between smoking and placental abruption, preterm birth, stillbirth, and low birthweight were assessed using multivariable logistic regression.

Results: Algorithm 1 identified 127,612 smokers (smoking prevalence 15.1%), which was a 9.6% and 54.6% increase over the unenhanced identification from birth records alone (prevalence 13.8%), and delivery admission records alone (prevalence 9.8%), respectively. Algorithm 2 identified a further 2,408 smokers from antenatal admission records. The enhancement varied by maternal socio-demographic characteristics (age, marital status, country of birth, socioeconomic status); obstetric factors (multi-fetal pregnancy, diabetes, hypertension); and maternity hospital. Enhanced and unenhanced identification methods yielded similar odds ratios for placental abruption, preterm birth, stillbirth and low birthweight.

Conclusions: Use of linked data improved the identification of pregnant smokers. Studies relying on a single data source should adjust for the under-ascertainment of smokers among certain obstetric populations.
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http://dx.doi.org/10.1111/1753-6405.12213DOI Listing
June 2014

The contribution of geography to disparities in preventable hospitalisations between indigenous and non-indigenous Australians.

PLoS One 2014 23;9(5):e97892. Epub 2014 May 23.

Centre for Health Research, University of Western Sydney, Sydney, New South Wales, Australia.

Objectives: 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.

Results: 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.

Conclusions: 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.
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http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0097892PLOS
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4032338PMC
February 2015
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