A multilevel analysis on the relationship between neighbourhood poverty and public hospital utilization: is the high Indigenous morbidity avoidable?

Potentially preventable hospitalisations in rural community-dwelling patients

BACKGROUND

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.

AIM

To determine factors associated with PPH in an Australian rural population.

METHODS

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.

RESULTS

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).

CONCLUSIONS

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.

Disparities in Human Papillomavirus vaccination coverage among adolescents in Australia: A geospatial analysis

AIM

This ecological study aimed to examine the geographic patterns in Human Papillomavirus (HPV) vaccination rates among boys and girls aged 15 years across locations in Australia, in addition to assessing contextual area-level factors that may explain the variations in HPV vaccination coverage.

METHODS

Aggregate HPV vaccination data for Australian girls and boys aged 15 years from 2015 to 16 was obtained from the Australian Institute of Health and Welfare for each Statistical Area level 4 (SA4). A Gradient Boosting Machine learning model was applied to assess the predictors' importance for the study outcomes. Geographically weighted regression (GWR) models were run to assess whether substantially different relationships between predictors and outcomes occur at different locations in space.

RESULTS

Completed HPV vaccination across the 88 SA4 regions ranged from 57.6% to 90.6% among girls, and from 53.6% to 85.5% among boys. The 2016 SEIFA Index of Economic Resources was the variable with the highest contribution to the predictions of both girls' and boys' HPV vaccination rates. Selected predictors explained 45% and 72% of the geographic variance in vaccination rates among boys and girls, respectively. Normalised coefficients for both GWR models showed a high variation in the associations between predictors and HPV vaccination rates across regions.

CONCLUSION

Socioeconomic and education factors were important predictors for HPV vaccination rates among Australian boys and girls aged 15 years, although no variable presented a uniform effect on HPV vaccination across SA4 regions. Important spatial heterogeneity in the effect of predictors was identified across the study area.

Melioidosis in the remote Katherine region of northern Australia

Melioidosis is endemic in the remote Katherine region of northern Australia. In a population with high rates of chronic disease, social inequities, and extreme remoteness, the impact of melioidosis is exacerbated by severe weather events and disproportionately affects First Nations Australians. All culture-confirmed melioidosis cases in the Katherine region of the Australian Top End between 1989–2021 were included in the study, and the clinical features and epidemiology were described. The diversity of Burkholderia pseudomallei strains in the region was investigated using genomic sequencing. From 1989–2021 there were 128 patients with melioidosis in the Katherine region. 96/128 (75%) patients were First Nations Australians, 72/128 (56%) were from a very remote region, 68/128 (53%) had diabetes, 57/128 (44%) had a history of hazardous alcohol consumption, and 11/128 (9%) died from melioidosis. There were 9 melioidosis cases attributable to the flooding of the Katherine River in January 1998; 7/9 flood-associated cases had cutaneous melioidosis, five of whom recalled an inoculating event injury sustained wading through flood waters or cleaning up after the flood. The 126 first-episode clinical B. pseudomallei isolates that underwent genomic sequencing belonged to 107 different sequence types and were highly diverse, reflecting the vast geographic area of the study region. In conclusion, melioidosis in the Katherine region disproportionately affects First Nations Australians with risk factors and is exacerbated by severe weather events. Diabetes management, public health intervention for hazardous alcohol consumption, provision of housing to address homelessness, and patient education on melioidosis prevention in First Nations languages should be prioritised.

Melioidosis, caused by the environmental bacterium Burkholderia pseudomallei, disproportionately affects Australian First Nations peoples in the Northern Territory of Australia. The Katherine region has some of the highest rates of homelessness in Australia, and social inequity impacts health outcomes for First Nations people whose access to care is further complicated by remoteness. In this study, we describe the clinical features and epidemiology of melioidosis in the Katherine region over a 32-year period. Almost three quarters of melioidosis cases were First Nations Australians, over half lived in a very remote region, and diabetes and hazardous alcohol consumption were common risk factors. Following a severe flooding event in the region in 1998, a spike in cases of melioidosis was seen, the majority presenting as skin and soft tissue infections. The B. pseudomallei isolates in the study were extremely genetically diverse, reflecting the large geographic area of the Katherine region. With predicted climate change-driven increases in severe weather events and subsequent increases in melioidosis cases, public health priorities in the region should include addressing high rates of homelessness and hazardous alcohol consumption, optimisation of diabetes management, and ongoing education in First Nations languages regarding prevention of B. pseudomallei exposure.

Admission and readmission rate incidences from deprived areas-impact of a classical or multi-dimensional model

INTRODUCTION

Classical deprivation instruments use a factor analytical approach relying on a smaller number of dimensions, factors or components. Multi-dimensional deprivation models attempt classification in fine detail-even down to street level.

METHODS

Single-centre retrospective cohort study using routinely collected aggregated and anonymised data on emergency medical admissions (96,526 episodes in 50,731 patients; 2002-2016). We calculated admission/readmission rate incidences for the 74 small areas within the hospital catchment area. We compared a classical Small Area Health Research Unit (SAHRU) to the multi-dimensional POBAL Haase and Pratschke Deprivation Index for Small Areas (POBAL) deprivation instrument and their deprivation ranks for two Irish censuses (2006/ 2011).

RESULTS

There was poor agreement between the instruments of the Deprivation Ranks by Quintile-with agreement in 46 and 42% of small areas for the respective 2006 and 2011 censuses. The classical model (SAHRU) suggested more areas with severe deprivation (Q5 66 and 55%) compared with POBAL (Q5 32 and 24%) from the respective censuses. SAHRU classical instrument had a higher prediction level incidence rate ratio (IRR) 1.48 (95% CI 1.47, 1.49)) compared with POBAL IRR 1.28 (95% CI 1.27, 1.28) and systematically lower estimates of hospital admission and readmission rate incidences. Earlier Census data modelled more powerfully, suggesting a long latency between social circumstances and the ultimate expression of the emergency medical admission.

CONCLUSION

Deprivation influences hospital incidence rates for emergency medical admissions and readmissions; instruments focusing at the very small area (individual or street level) have a utility but appear inferior in terms of representing the population risk of environmental/socio-economic factors which seem best approximated at a larger scale.

Persons with disability, social deprivation and an emergency medical admission

BACKGROUND

The community level of disability and social deprivation may result in an emergency hospitalisation; we have examined the annual admission incidence rate for emergency medical conditions in relation to the community prevalence of such factors.

METHODS

All emergency medical admissions (96,305 episodes in 50,612 patients) within the institution's catchment area were examined between 2002 and 2016. The frequency of disability, level of full-time carers and unemployment for the 74 electoral divisions of the catchment area was regressed against admission rates; incidence rate ratios (IRR) were calculated using truncated Poisson regression.

RESULTS

Disability was present in 12.1% of the catchment area population (95% CI = 9.7-15.0). The annual admission incidence rates/1000 population across disability quintiles for the more affluent areas increased from Q1 7.6 (95% CI = 7.4-7.8) to Q5 27.3 (95% CI = 27.0-27.5) and for the more deprived area from Q1 16.6 (95% CI = 16.4, 16.8) to and Q5 40.4 (95% CI = 40.1-40.7). Disability status influenced the overall admission IRR (compared with Q1/Q3) for Q4/Q5 1.11 (95% CI = 1.09-1.13) showing an increased rate of hospitalisation for the more deprived areas. Community disability levels interacted with local area unemployment and frequency of full-time carers; as they increased, a linear relationship between disability and the admission rate incidence was demonstrated.

CONCLUSION

Local catchment area disability prevalence rates in addition to social deprivation factors are an important determinant of the annual incidence rate of emergency medical admissions.

Influence of social deprivation, overcrowding and family structure on emergency medical admission rates

BACKGROUND

Patients from deprived backgrounds have a higher in-patient mortality following emergency medical admission.

AIM

To evaluate the influence of Deprivation Index, overcrowding and family structure on hospital admission rates.

DESIGN

Retrospective cohort study.

METHODS

All emergency medical admissions from 2002 to 2013 were evaluated. Based on address, each patient was allocated to an electoral division, whose small area population statistics were available from census data. Patients were categorized by quintile of Deprivation Index, overcrowding and family structure, and these were evaluated against hospital admission rate, calculated as rate/1000 population. Univariate and multivariable risk estimates (Odds Ratios or Incidence Rate Ratios) were calculated, using logistic or zero truncated Poisson regression as appropriate.

RESULTS

There were 66 861 admissions in 36 214 patients over the 12-year study period. Deprivation Index quintile independently predicted the admission rate, with rates of Q1 12.0 (95% CI 11.8-12.2), Q2 19.5 (95% CI 19.3-19.6), Q3 33.7 (95% CI 33.3-34.0), Q4 31.4 (95% CI 31.2-31.6) and Q5 38.1 (95% CI 37.7-38.5). Similarly the proportions of families with children <15 years old, was an independent predictor of the admission rate with rates of Q1 20.8 (95% CI 20.4-21.1), Q2 23.0 (95% CI 22.7-23.3), Q3 32.2 (95% CI 31.9-32.5), Q4 32.4 (95% CI 32.2-32.7) and Q5 37.2 (95% CI 36.6-37.8). The proportion of families with children ≥15-years old was also predictive but quintile of overcrowding was only predictive in the univarate model.

CONCLUSION

Deprivation Index and family structure strongly predict emergency medical hospital admission rates.

Social deprivation and the rate of emergency medical admission for older persons

BACKGROUND

Deprivation Status increases the annual admission incidence of emergency medical admissions; the extent to which deprivation influences the admission of older persons is less well known.

AIM

To examine whether deprivation within a hospital catchment area influences emergency medical admissions for the elderly population.

DESIGN

The relationship between Deprivation Status, Dependency Ratio (population proportion of non-working age (<15 or ≥65 years) and age for all emergency admissions (82 368 episodes of 44 628 patients), over a 13-year period, were examined and ranked by quintile.

METHODS

Univariate and multi-variable risk estimates (incidence rate ratios) were calculated, using truncated Poisson regression.

RESULTS

The Dependency Ratio and the Deprivation index independently predicted the annual incidence rate of medical emergencies; however, when calculated for older persons, the corresponding incidence rate ratios showed a falling trend with increasing Deprivation Status-Q2 0.51 (95% confidence interval [CI]: 0.50, 0.52), Q3 0.59 (95% CI: 0.58, 0.60), Q4 0.51 (95% CI: 0.50, 0.52) and Q5 0.37 (95% CI: 0.36, 0.38). Thus, with increasing Deprivation Status, the proportion of total admission from the ≥65-year cohort fell substantially.

CONCLUSION

The admission incidence rate for emergency medical patients is strongly influenced by the catchment area Deprivation Status. However, because of its greater impact on the younger population, increasing deprivation alters the ratio of younger to older persons as a proportion of total emergency admissions.

Deprivation influences the emergency admission rate of ambulatory care sensitive conditions

Ambulatory care sensitive conditions (ACSCs) are a group of conditions judged to be suitable for healthcare efficiency initiatives to reduce the rate of hospital admissions. All emergency medical admissions to an Irish hospital between 2002 and 2013 were assessed for ACSCs. They were categorised by quintile of deprivation index and evaluated against hospital admission rate. Univariable and multivariable risk estimates were calculated, using logistic regression or zero-truncated Poisson regression. There were 66,861 admissions in 36,214 patients. ACSCs represented 66.4% of admissions. The rate of ACSC admissions increased with deprivation index, Q1 10.4 (95% confidence interval (CI) 10.2-10.5), Q2 17.3 (95% CI 17.2-17.5), Q3 34.0 (95% CI 33.7-34.2), Q4 30.2 (95% CI 30.0-30.4) and Q5 44.5 (95% CI 43.8- 45.1) (p<0.001), corresponding incidence rate ratios compared with Q1 were: Q2 1.67 (95% CI 1.64-1.70), Q3 3.28 (95% CI 3.22-3.33), Q4 2.92 (95% CI 2.87-2.97) and Q5 4.29 (95% CI 4.20-4.39) (p<0.001). ACSCs are common in acute medical admissions and are strongly influenced by the underlying social demographics of the population.

Deprivation index and dependency ratio are key determinants of emergency medical admission rates

BACKGROUND

Patients from deprived backgrounds have a higher in-patient mortality following an emergency medical admission; there has been debate as to the extent to which deprivation and population structure influences hospital admission rate.

METHODS

All emergency medical admissions to an Irish hospital over a 12-year period (2002-2013) categorized by quintile of Deprivation Index and Dependency Ratio (proportion of population <15 or ≥ 65 years) from small area population statistics (SAPS), were evaluated against hospital admission rates. Univariate and multivariable risk estimates (Odds Ratios (OR) or Incidence Rate Ratios (IRR)) were calculated, using logistic or zero truncated Poisson regression as appropriate.

RESULTS

66,861 admissions in 36,214 patients occured during the study period. The Deprivation Index quintile independently predicted the admission rate/1000 population, Q1 9.4 (95%CI 9.2 to 9.7), Q2 16.8 (95%CI 16.6 to 17.0), Q3 33.8 (95%CI 33.5 to 34.1), Q4 29.6 (95%CI 29.3 to 29.8) and Q5 45.4 (95%CI 44.5 to 46.2). Similarly the population Dependency Ratio was an independent predictor of the admission rate with adjusted predicted rates of Q1 20.8 (95%CI 20.5 to 21.1), Q2 19.2 (95%CI 19.0 to 19.4), Q3 27.6 (95%CI 27.3 to 27.9), Q4 43.9 (95%CI 43.5 to 44.4) and Q5 34.4 (95%CI 34.1 to 34.7). A high concurrent Deprivation Index and Dependency Ratio were associated with very high admission rates.

CONCLUSION

Deprivation Index and population Dependency Ratio are key determinants of the rate of emergency medical admissions.

Social deprivation, population dependency ratio and an extended hospital episode - Insights from acute medicine

BACKGROUND

Patients from deprived backgrounds have a higher in-patient mortality following an emergency medical admission; this study aimed to investigate the extent to which Deprivation status and the population Dependency Ratio influenced extended hospital episodes.

METHODS

All Emergency Medical admissions (75,018 episodes of 41,728 patients) over 12 years (2002-2013) categorized by quintile of Deprivation Index and Population Dependency Rates (proportion of non-working/working) were evaluated against length of stay (LOS). Patients with an Extended LOS (ELOS), >30 days, were investigated, by Deprivation status, Illness Severity and Co-morbidity status. Univariate and multi-variable risk estimates (Odds Rates or Incidence Rate Ratios) were calculated, using truncated Poisson regression.

RESULTS

Hospital episodes with ELOS had a frequency of 11.5%; their median LOS (IQR) was 55.0 (38.8, 97.6) days utilizing 57.6% of all bed days by all 75,018 emergency medical admissions. The Deprivation Index independently predicted the rate of such ELOS admissions; these increased approximately five-fold (rate/1000 population) over the Deprivation Quintiles with model adjusted predicted admission rates of for Q1 0.93 (95% CI: 0.86, 0.99), Q22.63 (95% CI: 2.55, 2.71), Q3 3.84 (95% CI: 3.77, 3.91), Q4 3.42 (95% CI: 3.37, 3.48) and Q5 4.38 (95% CI: 4.22, 4.54). Similarly the Population Dependency Ratio Quintiles (dependent to working structure of the population by small area units) independently predicted extended LOS admissions.

CONCLUSION

The admission of patients with an ELOS is strongly influenced by the Deprivation status and the population Dependency Ratio of the catchment area. These factors interact, with both high deprivation and Dependency cohorts having a major influence on the numbers of emergency medical admission patients with an extended hospital episode.

Multilevel analysis of the impact of school-level tobacco policies on adolescent smoking: the case of Michigan

BACKGROUND

In efforts to curb and prevent youth smoking, school tobacco policies have become an important and effective strategy. This study explores the degrees and types of tobacco-free school policy (TFSP) enforcement that are associated with adolescent smoking.

METHODS

A multilevel analysis was performed using 983 students who are nested in 14 schools. The individual-level data are drawn from the 2009 Michigan Youth Risk Behavior Survey. The school-level data are drawn from the 2008 School Health Profiles survey.

RESULTS

Two factors are associated with lower adolescent smoking: greater punishment for TFSP violation and more tobacco control communication efforts. By contrast, the factors associated with higher adolescent smoking are designation of a tobacco-free school zone and school-level smoking.

CONCLUSIONS

This study theoretically and methodologically guides researchers to test TFSP effectiveness in other states. Three strategic implications emerge: (1) schools should provide a consistent antismoking message in smoke-free environments; (2) schools should integrate TFSP into a comprehensive tobacco control initiative, including community-wide tobacco control programs and messages; and (3) the way a specific TFSP is promoted and communicated could determine how effective it is.

Health inequity in the Northern Territory, Australia

INTRODUCTION

Understanding health inequity is necessary for addressing the disparities in health outcomes in many populations, including the health gap between Indigenous and non-Indigenous Australians. This report investigates the links between Indigenous health outcomes and socioeconomic disadvantage in the Northern Territory of Australia (NT).

METHODS

Data sources include deaths, public hospital admissions between 2005 and 2007, and Socio-Economic Indexes for Areas from the 2006 Census. Age-sex standardisation, standardised rate ratio, concentration index and Poisson regression model are used for statistical analysis.

RESULTS

There was a strong inverse association between socioeconomic status (SES) and both mortality and morbidity rates. Mortality and morbidity rates in the low SES group were approximately twice those in the medium SES group, which were, in turn, 50% higher than those in the high SES group. The gradient was present for most disease categories for both deaths and hospital admissions. Residents in remote and very remote areas experienced higher mortality and hospital morbidity than non-remote areas. Approximately 25-30% of the NT Indigenous health disparity may be explained by socioeconomic disadvantage.

CONCLUSIONS

Socioeconomic disadvantage is a shared common denominator for the main causes of deaths and principal diagnoses of hospitalisations for the NT population. Closing the gap in health outcomes between Indigenous and non-Indigenous populations will require improving the socioeconomic conditions of Indigenous Australians.

Deprivation as an outcome determinant in emergency medical admissions

BACKGROUND

Deprivation in the general population predicts mortality. We have investigated its relevance to an acute medical admission, using a database of all emergency admissions to St James' Hospital, Dublin, over a 10-year period (2002-11).

METHODS

All emergency admissions, based on geocoding of residence, were allocated to a Small Area Health Research Unit division, with a corresponding deprivation index. We then examined this index as a univariate (unadjusted) and independent (adjusted) predictor of 30-day in-hospital mortality.

RESULTS

The 30-day in-hospital mortality, over the 10-year period, was higher for those in the upper half of the deprivation distribution (9.6 vs. 8.6%; P = 0.002). Indeed, there was a stepwise increase in 30-day mortality over the quintiles of deprivation from 7.3% (Quintile 1) to 8.8, 10.0, 10.0 and 9.3%, respectively. Univariate logistic regression of the deprivation indices (quintiles) against outcome showed an increased risk (P = 0.002) of a 30-day death with odds ratios (ORs), respectively (compared with lowest deprivation quintile) of 1.39 [95% confidence intervals (CI) 1.21, 1.58], 1.47 (95% CI 1.29, 1.68), 1.44 (95% CI 1.26, 1.64) and 1.39 (95% CI 1.22, 1.59). The deprivation index was an independent predictor of outcome in a model when adjusted for illness severity and co-morbidity. The fully adjusted OR for a 30-day death was increased by 31% (P = 0.001) for patients in the upper half of the deprivation index distribution (OR 1.35; 95% CI 1.23, 1.48; P < 0.001).

CONCLUSION

Deprivation, independent of co-morbidity or acute illness severity, is an independent predictor of 30-day mortality in acute medical admissions.