Regional variation of avoidable hospitalisations in a universal health care system: a register-based cohort study from Finland 1996−2013

Manderbacka, Kristiina; Arffman, Martti; Satokangas, Markku; Keskimäki, Ilmo

doi:10.1136/bmjopen-2019-029592

Cited by 12 publications

(14 citation statements)

References 19 publications

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“…Few studies have assessed over time the development of geographic distribution 23 , 24 or variation in ACSC rates, 25 , 26 but describe these only with area-level factors. 23 , 24 It is still unclear how a comprehensive array of area-level factors over time contributes to geographic variation in ACSCs, when individual SEP and health status are adjusted for.…”

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confidence: 99%

Individual and Area-level Factors Contributing to the Geographic Variation in Ambulatory Care Sensitive Conditions in Finland

et al. 2020

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Individual and Area-level Factors Contributing to the Geographic Variation in Ambulatory Care Sensitive Conditions in Finland

et al. 2020

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“…This is to be expected because the state of health in the general population has improved over time. The number of ACSC hospitalisations has also decreased over time in Finland [20]. However, patients whose condition deteriorates to the point that they need inpatient care in a hospital are likely to be at a similar risk of dying regardless of year.…”

Section: Discussionmentioning

confidence: 99%

Mortality related to ambulatory care sensitive hospitalisations in Finland

Partanen

Arffman

Manderbacka

et al. 2020

Scand J Public Health

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Aims: Hospitalisations for ambulatory care sensitive conditions are used as an outcome indicator of access to and quality of primary care. Evidence on mortality related to these hospitalisations is scarce. This study analysed the effect of ambulatory care sensitive condition hospitalisations to subsequent mortality and time or geographical trends in the mortality indicating variations in ambulatory care sensitive conditions outcomes. Methods: This retrospective cohort study used individual-level data from national registers concerning ambulatory care sensitive condition hospitalisations. Crude and age-adjusted 365-day mortality rates for the first ambulatory care sensitive condition-related admission were calculated for vaccine-preventable, acute, and chronic ambulatory care sensitive conditions separately, and for three time periods stratified by gender. The mortality rates were also compared to mortality in the general Finnish population to assess the excess mortality related to ambulatory care sensitive condition hospitalisations. Results: The data comprised a total of 712,904 ambulatory care sensitive condition hospital admissions with the crude 365-day mortality rate of 14.2 per 100 person-years. Mortality for those hospitalised for vaccine-preventable conditions was approximately 10-fold compared to the general population and four-fold in chronic and acute conditions. Of the 10 most common ambulatory care sensitive conditions, bacterial pneumonia and influenza and congestive heart failure were associated with highest age-standardised mortality rates. Conclusions: Hospitalisations for ambulatory care sensitive conditions were shown to be associated with excess mortality in patients compared to the general population. Major differences in mortality were found between different types of ambulatory care sensitive condition admissions. There were also minor differences in mortality between hospital districts. These differences are important to consider when using preventable hospital admissions as an indicator of primary care performance.

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“…To address this deficiency, and in order to effectively guide health policy planners, it is essential to be able to accurately predict PPH hotspots several years into the future. However, while some longitudinal studies have been undertaken (e.g., see [ 4 , 37 , 38 , 39 , 40 ]), to the best of our knowledge, only one previous study has explicitly predicted, and evaluated the prediction of, future PPH hotspots: Duckett and Griffiths (2016) used past periods of persistently high PPH rates to predict areas expected to exhibit correspondingly high rates in the future [ 7 ]. Several subsequent studies have employed this approach in Australia [ 39 , 41 ].…”

Section: Introductionmentioning

confidence: 99%

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

Tuson

Turlach

Murray

et al. 2021

IJERPH

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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.

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Regional variation of avoidable hospitalisations in a universal health care system: a register-based cohort study from Finland 1996−2013

Cited by 12 publications

References 19 publications

Individual and Area-level Factors Contributing to the Geographic Variation in Ambulatory Care Sensitive Conditions in Finland

Individual and Area-level Factors Contributing to the Geographic Variation in Ambulatory Care Sensitive Conditions in Finland

Mortality related to ambulatory care sensitive hospitalisations in Finland

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

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