Potentially Preventable Hospitalisations Research Articles

Remoteness and socioeconomic status reduce access to specialist mental health care across Australia.

Estimate impact of socioeconomic factors and remoteness from tertiary hospital on incidence/duration of Australian mental health admissions. Retrospective analysis of incidence/duration of public mental health unit admissions (2018-19). Covariates included Indigenous population, potentially preventable hospitalisations (PPH) and socioeconomic disadvantage. Regional distance from hospital was correlated with socioeconomic disadvantage (ρ: p < 0.01). Population identifying as Aboriginal or Torres Strait Islander was associated with distance from hospital, socioeconomic disadvantage and PPH (ρ: p < 0.01). Bed days per capita was explained (R2adj: 0.48) by distance and socioeconomic disadvantage (p < 0.0001). A 1% increase in distance from hospital was associated with a 0.37% decrease in per capita bed days. Admission rate per capita across Queensland and WA was explained (R2adj: 0.36) by distance, education/occupation and state (p < 0.05). Across Queensland and WA a 1% increase in distance from hospital was associated with a 0.05% decreased incidence of admission. Rural Australians face high mental illness burden, socioeconomic disadvantage and limited service provision. Overcoming the additional disadvantages of reduced likelihood of admission to and reduced time in hospital with increasing distance from hospital will require increased outreach proportional to remoteness.

Potentially preventable hospitalisations in rural community-dwelling patients.

Potentially preventable hospitalisations (PPH) are a common occurrence. Knowing the factors associated with PPH may allow high-risk patients to be identified and healthcare resources to be better allocated, and these factors may differ between urban and rural locations. To determine factors associated with PPH in an Australian rural population. A retrospective review of admitted patients' demographic and clinical data was used to describe and model the factors associated with PPH, using an age- and sex-matched control group of non-admitted patients. This study is based in a multi-site rural general practice, Tasmania. The study included patients aged ≥18 years residing in the Huon-Bruny Island region of Tasmania, who were active patients at a rural general practice and were admitted to a public hospital for a PPH between 1 July 2016 and 30 June 2019. Main outcome measure is overnight admission to hospital for a PPH. Predictors with a significant odds ratio (OR) in the final model were being single/unmarried (OR 2.43; 95% confidence interval (CI) 1.38-4.28), higher Charlson Comorbidity Index score (OR 1.40; 95% CI 1.13-1.74) and the number of general practice visits in the preceding 12 months (OR 1.09; 95% CI 1.05-1.14). This study found that being single and having a higher comorbidity burden were the strongest independent risk factors for PPH in a rural population. Demographic and socioeconomic factors appeared to be as, if not more, important than medical factors and warrant attention when considering the design of programmes to reduce PPH risk in rural communities.

Identifying patterns of potentially preventable hospitalisations in people living with dementia

BackgroundOlder Australians make up 46% of all potentially preventable hospitalisations (PPHs) and people living with dementia are at significantly greater risk. While policy reforms aim to reduce PPHs, there is currently little evidence available on what drives this, especially for people living with dementia. This study examines patterns of PPHs in people living with dementia to inform service delivery and the development of evidence-based interventions.MethodsWe used the Victorian Admitted Episodes Dataset from Victoria, Australia, to extract data for people aged 50 and over with a diagnosis of dementia between 2015 and 2016. Potentially avoidable admissions, known as ambulatory care sensitive conditions (ACSCs), were identified. The chi-square test was used to detect differences between admissions for ACSCs and non-ACSCs by demographic, geographical, and administrative factors. Predictors of ACSCs admissions were analysed using univariate and multiple logistic regression.ResultsOf the 8156 hospital records, there were 3884 (48%) ACSCs admissions, of which admissions for urinary tract infections accounted for 31%, followed by diabetes complications (21%). Mean bed-days were 8.26 for non-ACSCs compared with 9.74 for ACSCs (p ≤ 0.001). There were no differences between admissions for ACSCs and non-ACSCs by sex, marital status, region (rural vs metro), and admission source (private accommodation vs residential facility). Culture and language predicted ASCS admission rates in the univariate regression analyses, with ACSC admission rates increasing by 20 and 29% if English was not the preferred language or if an interpreter was required, respectively. Results from the multiple regression analysis confirmed that language was a significant predictor of ACSC admission rates.ConclusionsImproved primary health care may help to reduce the most common causes of PPHs for people living with dementia, particularly for those from culturally and linguistically diverse backgrounds.

Rural Patients' Perceptions of Their Potentially Preventable Hospitalisation: A Qualitative Study.

Potentially preventable hospitalisations (PPHs) occur when patients receive hospital care for a condition that could have been more appropriately managed in the primary healthcare setting. It is anticipated that the causes of PPHs in rural populations may differ from those in urban populations; however, this is understudied. Semi-structured interviews with 10 rural Australian patients enabled them to describe their recent PPH experience. Reflexive thematic analysis was used to identify the common factors that may have led to their PPH. The analysis revealed that most participants had challenges associated with their health and its optimal self-management. Self-referral to hospital with the belief that this was the only treatment option available was also common. Most participants had limited social networks to call on in times of need or ill health. Finally, difficulty in accessing primary healthcare, especially urgently or after-hours, was described as a frequent cause of PPH. These qualitative accounts revealed that patients describe nonclinical risk factors as contributing to their recent PPH and reinforces that the views of patients should be included when designing interventions to reduce PPHs.

Healthcare Providers' Perceptions of Potentially Preventable Rural Hospitalisations: A Qualitative Study.

Potentially preventable hospitalisations (PPHs) are common in rural communities in Australia and around the world. Healthcare providers have a perspective on PPHs that may not be accessible by analysing routine patient data. This study explores the factors that healthcare providers believe cause PPHs and seeks to identify strategies for preventing them. Physicians, nurses, paramedics, and health administrators with experience in managing rural patients with PPHs were recruited from southern Tasmania, Australia. Semi-structured telephone interviews were conducted, and reflexive thematic analysis was used to analyse the data. Participants linked health literacy, limited access to primary care, and perceptions of primary care services with PPH risk. The belief that patients did not have a good understanding of where, when, and how to manage their health was perceived to be linked to patient-specific health literacy challenges. Access to primary healthcare was impacted by appointment availability, transport, and financial constraints. In contrast, it was felt that the prompt, comprehensive, and free healthcare delivered in hospitals appealed to patients and influenced their decision to bypass rural primary healthcare services. Strategies to reduce PPHs in rural Australian communities may include promoting health literacy, optimising the delivery of existing services, and improving social support structures.

Predicting Future Geographic Hotspots of Potentially Preventable Hospitalisations Using All Subset Model Selection and Repeated K-Fold Cross-Validation.

Long-term future prediction of geographic areas with high rates of potentially preventable hospitalisations (PPHs) among residents, or “hotspots”, is critical to ensure the effective location of place-based health service interventions. This is because such interventions are typically expensive and take time to develop, implement, and take effect, and hotspots often regress to the mean. Using spatially aggregated, longitudinal administrative health data, we introduce a method to make such predictions. The proposed method combines all subset model selection with a novel formulation of repeated k-fold cross-validation in developing optimal models. We illustrate its application predicting three-year future hotspots for four PPHs in an Australian context: type II diabetes mellitus, heart failure, chronic obstructive pulmonary disease, and “high risk foot”. In these examples, optimal models are selected through maximising positive predictive value while maintaining sensitivity above a user-specified minimum threshold. We compare the model’s performance to that of two alternative methods commonly used in practice, i.e., prediction of future hotspots based on either: (i) current hotspots, or (ii) past persistent hotspots. In doing so, we demonstrate favourable performance of our method, including with respect to its ability to flexibly optimise various different metrics. Accordingly, we suggest that our method might effectively be used to assist health planners predict excess future demand of health services and prioritise placement of interventions. Furthermore, it could be used to predict future hotspots of non-health events, e.g., in criminology.

What could prevent chronic condition admissions assessed as preventable in rural and metropolitan contexts? An analysis of clinicians’ perspectives from the DaPPHne study

IntroductionReducing potentially preventable hospitalisations (PPH) is a priority for health services. This paper describes the factors that clinicians perceived contributed to preventable admissions for angina, diabetes, congestive heart failure (CHF) and chronic obstructive pulmonary disease (COPD), and what they considered might have been done in the three months leading up to an admission to prevent it.MethodsThe study was conducted in a rural and a metropolitan health district in NSW, Australia. Expert Panels reviewed detailed case reports to assess preventability. For those admissions identified as preventable, comments from clinicians indicating what they perceived could have made a difference and/or been done differently to prevent each of the preventable admissions were analysed qualitatively.Results148 (46%) of 323 admissions were assessed as preventable. Across the two districts, the most commonly identified groups of contributing factors to preventable admissions were: ‘Systems issues: Community based services missing or inadequate or not referred to’; ‘Patient issues: Problems with adherence/self-management’; and ‘Clinician issues: GP care inadequate’. In some instances, important differences drove these groups of factors. For example, in the rural district ‘Systems issues: Community based services missing or inadequate or not referred to’ was largely driven by social and welfare support services missing/inadequate/not referred to, whereas in the metropolitan district it was largely driven by community nursing, allied health, care coordination or integrated care services missing/inadequate/not referred to. Analyses revealed the complexity of system, clinician and patient factors contributing to each admission. Admissions for COPD (rural) and CHF (metropolitan) admissions showed greatest complexity.Discussion and conclusionThese findings suggest preventability of individual admissions is complex and context specific. There is no single, simple solution likely to reduce PPH. Rather, an approach addressing multiple factors is required. This need for comprehensiveness may explain why many programs seeking to reduce PPH have been unsuccessful.

Potentially preventable hospitalisations for physical health conditions in community mental health service users: a population-wide linkage study.

Mental health (MH) service users have increased prevalence of chronic physical conditions such as cardio-respiratory diseases and diabetes. Potentially Preventable Hospitalisations (PPH) for physical health conditions are an indicator of health service access, integration and effectiveness, and are elevated in long term studies of people with MH conditions. We aimed to examine whether PPH rates were elevated in MH service users over a 12-month follow-up period more suitable for routine health indicator reporting. We also examined whether MH service users had increased PPH rates at a younger age, potentially reflecting the younger onset of chronic physical conditions. A population-wide data linkage in New South Wales (NSW), Australia, population 7.8 million. PPH rates in 178 009 people using community MH services in 2016-2017 were compared to population rates. Primary outcomes were crude and age- and disadvantage-standardised annual PPH episode rate (episodes per 100 000 population), PPH day rate (hospital days per 100 000) and adjusted incidence rate ratios (AIRR). MH service users had higher rates of PPH admission (AIRR 3.6, 95% CI 3.5-3.6) and a larger number of hospital days (AIRR 5.2, 95% CI 5.2-5.3) than other NSW residents due to increased likelihood of admission, more admissions per person and longer length of stay. Increases were greatest for vaccine-preventable conditions (AIRR 4.7, 95% CI 4.5-5.0), and chronic conditions (AIRR 3.7, 95% CI 3.6-3.7). The highest number of admissions and relative risks were for respiratory and metabolic conditions, including chronic obstructive airways disease (AIRR 5.8, 95% CI 5.5-6.0) and diabetic complications (AIRR 5.4, 95% CI 5.1-5.8). One-quarter of excess potentially preventable bed days in MH service users were due to vaccine-related conditions, including vaccine-preventable respiratory illness. Age-related increases in risk occurred earlier in MH service users, particularly for chronic and vaccine-preventable conditions. PPH rates in MH service users aged 20-29 were similar to population rates of people aged 60 and over. These substantial differences were not explained by socio-economic disadvantage. PPHs for physical health conditions are substantially increased in people with MH conditions. Short term (12-month) PPH rates may be a useful lead indicator of increased physical morbidity and less accessible, integrated or effective health care. High hospitalisation rates for vaccine-preventable respiratory infections and hepatitis underline the importance of vaccination in MH service users and suggests potential benefits of prioritising this group for COVID-19 vaccination.

Study of potentially preventable hospitalisations (PPH) for chronic conditions: what proportion are preventable and what factors are associated with preventable PPH?

IntroductionThe proportion of potentially preventable hospitalisations (PPH) which are actually preventable is unknown, and little is understood about the factors associated with individual preventable PPH. The Diagnosing Potentially Preventable Hospitalisations...

Evaluating continuity of care incorporating a time protective effect of general practitioner care on diabetes related potentially preventable hospitalisations: An application of threshold effects model

Background Continuity of care with a general practitioner (GP) is vital for management of chronic conditions including diabetes as it provides proactive care facilitating opportunities to prevent or delay progression of disease.&#x0D; Aim To capture the proportion of time people with diabetes are under the protective effect of contact with a GP using the Cover Index and its effect on potentially preventable hospitalisation (PPH) and length of stay (LOS).&#x0D; Methods The linked self-report and administrative health service data of 267,153 participants in the 45 and Up study in New South Wales (NSW) in 2006-2009, obtained from the NSW Centre for Health Record Linkage and the Australian Government Department of Human Services, were used. A cohort of 21,965 people aged 45+ years identified with diabetes before July 2009 and followed up to 2015 were included in the analysis. Time duration protective effect of GP contact was estimated according to severity of diabetes using threshold effect models and then used to calculate the Cover Index. The effect of levels of GP cover on PPH and unplanned PPH and related LOS were estimated using negative binomial models weighted for the inverse probability of treatment to control for observed pre-treatment covariate imbalance.&#x0D; Results Perfect GP cover was observed among 56% of people in the study cohort. Compared with low GP cover, having perfect GP cover was significantly associated with lower PPH (IRR 0.21, 95%CI 0.13-0.35), LOS for PPH (IRR 0.12, 95%CI 0.06-0.24), unplanned PPH (IRR 0.36, 95%CI 0.19-0.68) and LOS for unplanned PPH (IRR 0.39, 95%CI 0.17-0.85) after controlling for the observed pre-treatment covariate imbalance.&#x0D; Conclusion This study suggests that longitudinal continuity of care measured by incorporating a time protective effect of a GP (cover) may be an important factor associated with reduction in PPHs and length of stay independent of other measures of longitudinal continuity.

Do general practice management and/or team care arrangements reduce avoidable hospitalisations in Central and Eastern Sydney, Australia?

BackgroundThe number of people living with chronic health conditions is increasing in Australia. The Chronic Disease Management program was introduced to Medicare Benefits Schedule (MBS) to provide a more structured approach to managing patients with chronic conditions and complex care needs. The program supports General Practitioners (GP)s claiming for up to one general practice management plan (GPMP) and one team care arrangement (TCA) every year and the patient claiming for up to five private allied health visits. We describe the profile of participants who claimed for GPMPs and/or TCAs in Central and Eastern Sydney (CES) and explore if GPMPs and/or TCAs are associated with fewer emergency hospitalisations (EH)s or potentially preventable hospitalisations (PPH)s over the following 5 years.MethodsThis research used the CES Primary and Community Health Cohort/Linkage Resource (CES-P&CH) based on the 45 and Up Study to identify a community-dwelling population in the CES region. There were 30,645 participants recruited within the CES area at baseline. The CES-P&CH includes 45 and Up Study questionnaire data linked to MBS data for the period 2006–2014. It also includes data from the Admitted Patient Data Collection, Emergency Department Data Collection and Deaths Registry linked by the NSW Centre for Health Record Linkage.ResultsWithin a two-year health service utilisation baseline period 22% (5771) of CES participants had at least one claim for a GPMP and/or TCA. Having at least one claim for a GPMP and/or TCA was closely related to the socio-demographic and health needs of participants with higher EHs and PPHs in the 5 years that followed. However, after controlling for confounding factors such as socio-demographic need, health risk, health status and health care utilization no significant difference was found between having claimed for a GPMP and/or TCA during the two-year health service utilisation baseline period and EHs or PPHs in the subsequent 5 years.ConclusionsThe use of GPMPs and/or TCAs in the CES area appears well-targeted towards those with chronic and complex care needs. There was no evidence to suggest that the use of GPMPs and /or TCAs has prevented hospitalisations in the CES region.

Potentially preventable hospitalisations in children: a comparison of definitions

ObjectiveTo compare admission rate, cumulative incidence and social distribution of potentially preventable hospitalisations (PPHs) among children according to the current Australian adult definition, and the child definition developed in New...

Relationship between nursing home quality indicators and potentially preventable hospitalisation

BackgroundHospitalisations are very common among nursing home residents and many of these are deemed inappropriate or preventable. Little is known about whether clinical care quality is related to hospitalisation, especially...

Corrigendum to: Preventable hospitalisations in regional Queensland: potential for primary health?

Objective The aims of this study were to: (1) use local health data to examine potentially preventable hospitalisations (PPHs) as a proportion of total hospital separations and estimated costs to a large regional hospital in northern Queensland, including differences associated with Indigenous status; and (2) identify priority conditions and discuss issues related to strategic local primary health intervention.Methods A cross-sectional analysis was conducted using Queensland Hospital Admitted Patient Data Collection data (July 2012-June 2014) restricted to 51087 separations generated by 29485 local residents. PPHs were identified from the International Statistical Classification of Diseases and Related Health Problems 10th Revision Australian Modification (ICD-10-AM) and procedure codes using National Healthcare Agreement definitions. Age-standardised separation rates were calculated using Australian 2001 reference population and associated economic costs were estimated using Australian-refined diagnosis related groups.Results Eleven per cent (n=5488) of all hospital separations were classified as PPH, and most were for common chronic (n=2486; 45.3%) and acute (n=2845; 51.8%) conditions. Because many acute presentations reflect chronic underlying disease, chronic conditions account for up to 76.5% of all PPHs. Age-standardised PPH rates were 3.4-fold higher for Indigenous than non-Indigenous people. Associated 2-year costs were AU$32.7million, which was 10.7% of estimated total health care expenditure for hospital separations, and were higher for Indigenous (14.9%) than non-Indigenous (9.7%) people.Conclusions High hospitalisation rates and costs for common preventable chronic conditions represent opportunities for primary healthcare interventions. In particular, community-level health services need to be more responsive to the needs of local Indigenous families.What is known about the topic? PPH rates are used as a measure of timely access to quality primary health care, and are incrementally higher in regional and remote areas than in major cities. Investment in primary healthcare services has been shown to significantly reduce costs associated with avoidable hospitalisations.What does this paper add? This study used local health data to identify the most common PPH conditions presenting to a large regional hospital in northern Queensland, including estimation of costs and differences associated with Indigenous status. Recommendations are made to strengthen primary healthcare and reduce hospital-related costs.What are the implications for practitioners? Interventions to address high PPH rates should be tailored to meet the needs of the local population. Primary health strategies targeting common chronic conditions provide the greatest opportunity to reduce avoidable hospitalisations and costs in this regional area. Investment in collaborative, evidence-based interventions is recommended and justified, especially for Indigenous Australians.

EXPLORING GEOSPATIAL VARIATION IN DIABETES-RELATED PRIMARY HEALTH CARE SERVICE UTILISATION AND POTENTIALLY PREVENTABLE HOSPITALISATIONS IN WESTERN AUSTRALIA

Abstract. Greater investments and improvements in primary health care (PHC) can provide benefits in reducing the high costs of hospital admissions. Potentially preventable hospitalisations (PPH) are a health system performance indicator used to evaluate access to and effectiveness of community-based health services. The Western Australia Department of Health obtained detailed primary health care data, for the first time at the postcode level scale, and analysed its associations with PPH information for selected conditions. PHC data obtained from the Commonwealth Department of Health for the financial year 2013/14 was Medicare Benefits Schedule (MBS) and Pharmaceutical Benefits Scheme (PBS) records at postcode level. In this paper we explore the sensitivity of various benchmarks of spatial zonings for comparison of diabetes-related primary health care utilisation and potentially preventable hospitalisations and then examine the relationship between them among the various spatial zonings. From the geospatial visualisation and analysis undertaken, conclusions are drawn about the patterns and relationships between diabetes-related primary health care utilisation and potentially preventable hospitalisations. The scale of spatial zonings used for comparison is important as too large or too small areas may mask out the relative geospatial variation of diabetes-related PHC utilisation and PPH evident among postcode areas.

Identifying patterns of general practitioner service utilisation and their relationship with potentially preventable hospitalisations in people with diabetes: The utility of a cluster analysis approach

AimsWe aimed to characterise use of general practitioners (GP) simultaneously across multiple attributes in people with diabetes and examine its impact on diabetes related potentially preventable hospitalisations (PPHs). MethodsFive-years of panel data from 40,625 adults with diabetes were sourced from Western Australian administrative health records. Cluster analysis (CA) was used to group individuals with similar patterns of GP utilisation characterised by frequency and recency of services. The relationship between GP utilisation cluster and the risk of PPHs was examined using multivariable random-effects negative binomial regression. ResultsCA categorised GP utilisation into three clusters: moderate; high and very high usage, having distinct patient characteristics. After adjusting for potential confounders, the rate of PPHs was significantly lower across all GP usage clusters compared with those with no GP usage; IRR = 0.67 (95%CI: 0.62–0.71) among the moderate, IRR = 0.70 (95%CI 0.66–0.73) high and IRR = 0.76 (95%CI 0.72–0.80) very high GP usage clusters. ConclusionsCombination of temporal factors with measures of frequency of use of GP services revealed patterns of primary health care utilisation associated with different underlying patient characteristics. Incorporation of multiple attributes, that go beyond frequency-based approaches may better characterise the complex relationship between use of GP services and diabetes-related hospitalisation.

Sociodemographic variations in the amount, duration and cost of potentially preventable hospitalisation for chronic conditions among Aboriginal and non-Aboriginal Australians: a period prevalence study of linked public hospital data

ObjectivesTo determine disparities in rates, length of stay (LOS) and hospital costs of potentially preventable hospitalisations (PPH) for selected chronic conditions among Aboriginal and non-Aboriginal South Australians (SA), then examine...

The utility of historical electoral roll records and their effect on the association of regularity of GP contact and potentially preventable diabetic hospitalisations

ABSTRACTObjectivesCorrectly ascertaining person-time at risk is paramount to longitudinal studies of health services research and relies on the ability to track the status of individuals throughout the study. Administrative health data contain limited information on where study subjects live in the time between episodes of health service use. Accurate ascertainment of person-time at risk is important particularly when it varies differentially across exposure groups or covariates. Historical electoral roll data allows better specification of person-time and also provides longitudinal information on geographic location facilitating the inclusion of accessibility and socio-economic status longitudinally. This study evaluated the utility of Australian historical electoral roll data to capture place of residence throughout the time line of a whole of population longitudinal cohort study over 20 years to better ascertain person-time at risk and changes in socio-economic status (SES).ApproachThe association between regularity of GP contact and potentially preventable hospitalisations (PPHs) in WA was modelled using person-level linked data where the time at risk and socio-economic status for both the exposure (regularity of GP contact) and outcome (PPHs) was assumed to be constant throughout the follow up until death. The analysis was then repeated incorporating historical (longitudinal) electoral roll data. These data partitioned follow up time and socio-economic status according to location of residence within the State to better characterise access and SES and included removal (out-of-State/country migration) and re-enrolment records.ResultsSubstantial differences were found in the number of people at risk (46,625 (13%) of people were never at risk) and person-time at risk (reduction of 473,708 (22%) person-years at risk) when cross-sectional electoral roll and health administrative data were augmented using historical electoral roll data. Substantial changes in residential postcode (up to 25 changes) were observed and these impacted on the accessibility and SES classification across the duration of the study. These changes significantly impacted the magnitude of the relationship between GP contacts on PPHs determined by models.ConclusionsCurrently cross-sectional electoral roll data are available to researchers in WA solely for the purposes of identifying and characterising a cohort at baseline. However these data are longitudinal and contain important information that improve analyses. Information on their utility is important so as to leverage their availability from the Australian Electoral Commission.

Child Social Exclusion Risk and Child Health Outcomes in Australia.

IntroductionThis paper studies the relationship between the risk of child social exclusion, as measured by the Child Social Exclusion (CSE) index and its individual domains, and child health outcomes at the small area level in Australia. The CSE index is Australia’s only national small-area index of the risk of child social exclusion. It includes five domains that capture different components of social exclusion: socio-economic background, education, connectedness, housing and health services.MethodsThe paper used data from the National Centre for Social and Economic Modelling (NATSEM), University of Canberra for the CSE Index and its domains and two key Australian Institute of Health and Welfare (AIHW) data sources for the health outcome measures: the National Hospital Morbidity Database and the National Mortality Database.ResultsThe results show positive associations between rates of both of the negative health outcomes: potentially preventable hospitalisations (PPH) and avoidable deaths, and the overall risk of child social exclusion as well as with the index domains. This analysis at the small-area level can be used to identify and study areas with unexpectedly good or bad health outcomes relative to their estimated risk of child social exclusion. We show that children’s health outcomes are worse in remote parts of Australia than what would be expected solely based on the CSE index.ConclusionsThe results of this study suggest that developing composite indices of the risk of child social exclusion can provide valuable guidance for local interventions and programs aimed at improving children’s health outcomes. They also indicate the importance of taking a small-area approach when conducting geographic modelling of disadvantage.

Diagnosing Potentially Preventable Hospitalisations (DaPPHne): protocol for a mixed-methods data-linkage study

IntroductionRates of potentially preventable hospitalisations (PPH) are used as a proxy measure of effectiveness of, or access to community-based health services. The validity of PPH as an indicator in Australia...