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.

Ethnic disparities in preventable hospitalisation in England: an analysis of 916 375 emergency admissions

ObjectivesTo study ethnic inequalities in ambulatory care sensitive conditions (ACSC) in England.DesignObservational study of inpatient hospital admission database enhanced with ethnicity coding of patient surnames. The primary diagnosis of the first episode in spells with emergency admission were coded with definitions for acute ACSC, chronic ACSC and vaccine-preventable diseases.SettingNational Health Service England.Participants916 375 ACSC emergency admissions in 7 39 618 patients were identified between April 2011 and March 2012.Main outcome measuresORs of ACSC for each ethnic group relative to the White British majority group adjusted for age, sex and area deprivation.ResultsAcute ACSC admission risk adjusted for age and sex was particularly high among Other (OR 1.73; 95% CI 1.69 to 1.77) and Pakistani (1.51; 95% CI 1.48 to 1.54) compared with White British patients. For chronic ACSC, high risk was found among Other (2.02; 95% CI 1.97 to 2.08), Pakistani (2.07; 95% CI 2.02 to 2.12) and Bangladeshi (1.36; 95% CI 1.30 to 1.42). For vaccine-preventable diseases, other (2.42; 95% CI 2.31 to 2.54), Pakistani (1.94; 95% CI 1.85 to 2.04), Bangladeshi (1.48; 95% CI 1.36 to 1.62), Black African (1.45; 95% CI 1.36 to 1.54) and white other (1.38; 95% CI 1.33 to 1.43) groups. Elevated risk was only partly explained in analyses also adjusting for area deprivation.ConclusionsACSC admission was especially high among individuals of Bangladeshi, Pakistani, Black African, white other or other background with up to twofold differences compared with the white British group. This suggests that these ethnic groups are not receiving optimal primary care.

Identifying diabetes management opportunity areas in the USA

Using adherence to diabetes management guidelines as a case study, this paper applied a novel geospatial hot-spot and cold-spot methodology to identify priority counties to target interventions. Data for this study were obtained from the Dartmouth Atlas of Healthcare, the United States Census Bureau’s American Community Survey and the University of Wisconsin County Health Rankings. A geospatial approach was used to identify four tiers of priority counties for diabetes preventive and management services: diabetes management cold-spots, clusters of counties with low rates of adherence to diabetes preventive and management services (Tier D); Medicare spending hot-spots, clusters of counties with high rates of spending and were diabetes management cold-spots (Tier C); preventable hospitalisation hot-spots, clusters of counties with high rates of spending and are diabetes management cold-spots (Tier B); and counties that were located in a diabetes management cold-spot cluster, preventable hospitalisation hot-spot cluster and Medicare spending hot-spot cluster (Tier A). The four tiers of priority counties were geographically concentrated in Texas and Oklahoma, the Southeast and central Appalachia. Of these tiers, there were 62 Tier A counties. Rates of preventable hospitalisations and Medicare spending were higher in Tier A counties compared with national averages. These same counties had much lower rates of adherence to diabetes preventive and management services. The novel geospatial mapping approach used in this study may allow practitioners and policy makers to target interventions in areas that have the highest need. Further refinement of this approach is necessary before making policy recommendations.

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

ObjectivePreventable 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.SettingLinked 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.MethodBetween-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.ResultsWe 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.ConclusionsGeographic 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.

Drug Use in Prisoners

Drug use and crime seem inextricably linked. Law enforcement responses to drug use tend to funnel people who use drugs into the criminal justice system rather than treatment, and those drug users who are imprisoned often have multiple, co-occurring mental health problems and/or suffer from infectious diseases including HIV, hepatitis C, and tuberculosis. Prisons provide a rare but regrettable opportunity to identify and respond to these needs, but correctional policies with respect to drug use and related harms often diverge from the evidence. Where such responses are evidence-based, they are rarely delivered at scale. Drug use in prison remains common and, in the absence of evidence-based harm reduction measures, is high risk. Relapse to drug use after release from prison is normative, such that incarceration can at best be conceived of as an interruption in drug use. People released from prison are at markedly increased risk of drug-related harms including fatal drug overdose and preventable hospitalisation, and are at increased risk of reincarceration. Greater investment in independent, rigorous research on the epidemiology of substance use and related harms in people who cycle through prisons, and a renewed commitment to aligning correctional policy and practice with the evidence, will have measurable benefits for public health, public safety, and the public purse.

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 associations with area-level socioeconomic disadvantage and remoteness.SettingPeriod prevalence study using linked, administrative public hospital records.ParticipantsParticipants included all SA residents in 2005–2006 to 2010–2011. Analysis focused on those individuals experiencing chronic PPH as defined by the Australian Institute of Health and Welfare.Primary outcome measuresNumber and rates (unadjusted, then adjusted for sex and age) of chronic PPH, total LOS and direct hospital costs by Aboriginality.ResultsAboriginal SAs experienced higher risk of index chronic PPH compared with non-Aboriginals (11.5 and 6.2 per 1000 persons per year, respectively) and at younger ages (median age 48 vs 70 years). Once hospitalised, Aboriginal people experienced more chronic PPH events, longer total LOS with higher costs than non-Aboriginal people (2.6 vs 1.9 PPH per person; 11.7 vs 9.0 days LOS; at $A17 928 vs $A11 515, respectively). Compared with population average LOS, the standardised rate ratio of LOS among Aboriginal people increased by 0.03 (95% CI 0.00 to 0.07) as disadvantage rank increased and 1.04 (95% CI 0.63 to 1.44) as remoteness increased. Non-Aboriginal LOS also increased as disadvantage increased but at a lower rate (0.01 (95% CI 0.01 to 0.01)). Costs of Aboriginal chronic PPH increased by 0.02 (95% CI 0.00 to 0.06) for each increase in disadvantage and 1.18 (95% CI 0.80 to 1.55) for increased remoteness. Non-Aboriginal costs also increased as disadvantage increased but at lower rates (0.01 (95% CI 0.01 to 0.01)).ConclusionAboriginal people’s heightened risk of chronic PPH resulted in more time in hospital and greater cost. Systematic disparities in chronic PPH by Aboriginality, area disadvantage and remoteness highlight the need for improved uptake of effective primary care. Routine, regional reporting will help monitor progress in meeting these population needs.

Using weighted hospital service area networks to explore variation in preventable hospitalisation.

ABSTRACT ObjectivesMarkets of health care are created in health services research to attribute variation in performance to characteristics of the health system. Defining patient catchments to capture hospital-level variation poses particular difficulties, because many factors other than geography drive choice of hospital. Several methods using linked data have been developed to create patient catchments or ‘hospital service areas’ (HSAs), including patterns of patient flow and networks of patient and physician referrals, yet these discrete catchments often have poor patient loyalty and are unable to attribute variation to specific hospitals. This study sought to demonstrate the use of multiple membership multilevel models, which cluster people in one or more higher-level units (such as multiple hospitals), in exploring between-hospital variation of preventable hospitalisations. ApproachLinked hospital data from 267,014 participants in the 45 and Up Study, NSW Australia, with linkage by the NSW CHeReL, were used to create weighed hospital service area networks (HSANs) in which patterns of patient flow to large public hospitals within 593 postal areas were used to create a weighted probability of admission of participants to each hospital. Multiple membership multilevel Poisson models were used to explore variation in rates of preventable hospitalisation, clustering participants in hospitals using a weighted HSAN, and compared with models clustering participants in HSAs based on the most common hospital of admission. ResultsThe most common hospital of admission accounted for an average of 67% of all admissions in each postal area. There was significant variation in rates of preventable hospitalisation between all 79 large public hospitals when clustering participants in a weighted HSAN, which was more than twice as large as the variation between the 72 hospitals forming the basis of HSAs. The ranking of hospitals differed between modelling approaches, and the hospital with the highest rate of preventable hospitalisation wasn’t identified when using HSAs. There was no association between hospital bed occupancy rate and preventable hospitalisations when using either modelling approach. ConclusionQuantifying variation in health service use and outcomes is the cornerstone of creating accountable health care systems, yet much information is lost in creating discrete health catchments. Multiple membership multilevel models can help capture this uncertainty, and given they can be applied using extensions of current methodology, have potential to be used across a variety of methods for defining and analysing health care catchments.