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Effect of the 2016 Kumamoto earthquakes on preventable hospital admissions: a retrospective cohort study in Japan.

ABSTRACT: The Kumamoto earthquakes struck Kumamoto prefecture, in the southwest part of Japan in April 2016. Physical and mental disorders presenting to hospital increased after the 2016 Kumamoto earthquakes. Impaired access to primary care due to the earthquakes may have contributed to this increase. However, it is not known whether the 2016 Kumamoto earthquakes affected access to primary care. The objective of the present study was to investigate the impact of the 2016 Kumamoto earthquakes on short-term health conditions by analysing ambulatory care sensitive conditions (ACSCs), using administrative data from Kumamoto prefecture.A retrospective cohort study.Residents enrolled in National Health Insurance or Late Elders' Health Insurance from Kumamoto prefecture, Japan.All hospital admissions due to ACSCs between 15 March and 16 May in each year from 2013 to 2016.ACSCs are defined as conditions for which appropriate primary care interventions could prevent admission to the hospital.We identified a total of 7921, 18 763 and 85?436 admissions for vaccine, acute and chronic preventable ACSCs, respectively, during the study period. Admissions within 7?days after the 2016 Kumamoto earthquakes increased to 32.6% (10.2, 59.5), 44.1% (27.0, 63.5) and 27.7% (20.2, 35.6) for vaccine-preventable, acute and chronic ACSCs, respectively. However, admissions for ACSCs did not change significantly 30 days after the earthquakes.The 2016 Kumamoto earthquakes were associated with increased hospital admissions for ACSCs. The impact of the earthquakes on admissions for ACSCs did not persist for more than 7?days.

SUBMITTER: Sasabuchi Y

PROVIDER: S-EPMC6082481 | biostudies-other | 2018 Jul

REPOSITORIES: biostudies-other

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Effect of the 2016 Kumamoto earthquakes on preventable hospital admissions: a retrospective cohort study in Japan.

Sasabuchi Yusuke Y Matsui Hiroki H Kotani Kazuhiko K Lefor Alan Kawarai AK Yasunaga Hideo H

BMJ open 20180712 7

<h4>Background and objectives</h4>The Kumamoto earthquakes struck Kumamoto prefecture, in the southwest part of Japan in April 2016. Physical and mental disorders presenting to hospital increased after the 2016 Kumamoto earthquakes. Impaired access to primary care due to the earthquakes may have contributed to this increase. However, it is not known whether the 2016 Kumamoto earthquakes affected access to primary care. The objective of the present study was to investigate the impact of the 2016 ...[more]

PMID: 30002010

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Project description:BackgroundPatients with multimorbidities have the greatest healthcare needs and generate the highest expenditure in the health system. There is an increasing focus on identifying specific disease combinations for addressing poor outcomes. Existing research has identified a small number of prevalent "clusters" in the general population, but the limited number examined might oversimplify the problem and these may not be the ones associated with important outcomes. Combinations with the highest (potentially preventable) secondary care costs may reveal priority targets for intervention or prevention. We aimed to examine the potential of defining multimorbidity clusters for impacting secondary care costs.Methods and findingsWe used national, Hospital Episode Statistics, data from all hospital admissions in England from 2017/2018 (cohort of over 8 million patients) and defined multimorbidity based on ICD-10 codes for 28 chronic conditions (we backfilled conditions from 2009/2010 to address potential undercoding). We identified the combinations of multimorbidity which contributed to the highest total current and previous 5-year costs of secondary care and costs of potentially preventable emergency hospital admissions in aggregate and per patient. We examined the distribution of costs across unique disease combinations to test the potential of the cluster approach for targeting interventions at high costs. We then estimated the overlap between the unique combinations to test potential of the cluster approach for targeting prevention of accumulated disease. We examined variability in the ranks and distributions across age (over/under 65) and deprivation (area level, deciles) subgroups and sensitivity to considering a smaller number of diseases. There were 8,440,133 unique patients in our sample, over 4 million (53.1%) were female, and over 3 million (37.7%) were aged over 65 years. No clear "high cost" combinations of multimorbidity emerged as possible targets for intervention. Over 2 million (31.6%) patients had 63,124 unique combinations of multimorbidity, each contributing a small fraction (maximum 3.2%) to current-year or 5-year secondary care costs. Highest total cost combinations tended to have fewer conditions (dyads/triads, most including hypertension) affecting a relatively large population. This contrasted with the combinations that generated the highest cost for individual patients, which were complex sets of many (6+) conditions affecting fewer persons. However, all combinations containing chronic kidney disease and hypertension, or diabetes and hypertension, made up a significant proportion of total secondary care costs, and all combinations containing chronic heart failure, chronic kidney disease, and hypertension had the highest proportion of preventable emergency admission costs, which might offer priority targets for prevention of disease accumulation. The results varied little between age and deprivation subgroups and sensitivity analyses. Key limitations include availability of data only from hospitals and reliance on hospital coding of health conditions.ConclusionsOur findings indicate that there are no clear multimorbidity combinations for a cluster-targeted intervention approach to reduce secondary care costs. The role of risk-stratification and focus on individual high-cost patients with interventions is particularly questionable for this aim. However, if aetiology is favourable for preventing further disease, the cluster approach might be useful for targeting disease prevention efforts with potential for cost-savings in secondary care.

Project description:ObjectivesTo determine whether high performing hospitals with low 30 day risk standardized readmission rates have a lower proportion of readmissions from specific diagnoses and time periods after admission or instead have a similar distribution of readmission diagnoses and timing to lower performing institutions.DesignRetrospective cohort study.SettingMedicare beneficiaries in the United States.ParticipantsPatients aged 65 and older who were readmitted within 30 days after hospital admission for heart failure, acute myocardial infarction, or pneumonia in 2007-09.Main outcome measuresReadmission diagnoses were classified with a modified version of the Centers for Medicare and Medicaid Services' condition categories, and readmission timing was classified by day (0-30) after hospital discharge. Hospital 30 day risk standardized readmission rates over the three years of study were calculated with public reporting methods of the US federal government, and hospitals were categorized with bootstrap analysis as having high, average, or low readmission performance for each index condition. High and low performing hospitals had ≥ 95% probability of having an interval estimate respectively less than or greater than the national 30 day readmission rate over the three year period of study. All remaining hospitals were considered average performers.ResultsFor readmissions in the 30 days after the index admission, there were 320,003 after 1,291,211 admissions for heart failure (4041 hospitals), 102,536 after 517,827 admissions for acute myocardial infarction (2378 hospitals), and 208,438 after 1,135,932 admissions for pneumonia (4283 hospitals). The distribution of readmissions by diagnosis was similar across categories of hospital performance for all three conditions. High performing hospitals had fewer readmissions for all common diagnoses. Median time to readmission was similar by hospital performance for heart failure and acute myocardial infarction, though was 1.4 days longer among high versus low performing hospitals for pneumonia (P<0.001). Findings were unchanged after adjustment for other hospital characteristics potentially associated with readmission patterns.ConclusionsHigh performing hospitals have proportionately fewer 30 day readmissions without differences in readmission diagnoses and timing, suggesting the possible benefit of strategies that lower risk of readmission globally rather than for specific diagnoses or time periods after hospital stay.

Project description:BackgroundEnhanced management of multimorbidity constitutes a major clinical challenge. Multimorbidity shows well-established causal relationships with the high use of health care resources and, specifically, with unplanned hospital admissions. Enhanced patient stratification is vital for achieving effectiveness through personalized postdischarge service selection.ObjectiveThe study has a 2-fold aim: (1) generation and assessment of predictive models of mortality and readmission at 90 days after discharge; and (2) characterization of patients' profiles for personalized service selection purposes.MethodsGradient boosting techniques were used to generate predictive models based on multisource data (registries, clinical/functional and social support) from 761 nonsurgical patients admitted in a tertiary hospital over 12 months (October 2017 to November 2018). K-means clustering was used to characterize patient profiles.ResultsPerformance (area under the receiver operating characteristic curve, sensitivity, and specificity) of the predictive models was 0.82, 0.78, and 0.70 and 0.72, 0.70, and 0.63 for mortality and readmissions, respectively. A total of 4 patients' profiles were identified. In brief, the reference patients (cluster 1; 281/761, 36.9%), 53.7% (151/281) men and mean age of 71 (SD 16) years, showed 3.6% (10/281) mortality and 15.7% (44/281) readmissions at 90 days following discharge. The unhealthy lifestyle habit profile (cluster 2; 179/761, 23.5%) predominantly comprised males (137/179, 76.5%) with similar age, mean 70 (SD 13) years, but showed slightly higher mortality (10/179, 5.6%) and markedly higher readmission rate (49/179, 27.4%). Patients in the frailty profile (cluster 3; 152/761, 19.9%) were older (mean 81 years, SD 13 years) and predominantly female (63/152, 41.4%, males). They showed medical complexity with a high level of social vulnerability and the highest mortality rate (23/152, 15.1%), but with a similar hospitalization rate (39/152, 25.7%) compared with cluster 2. Finally, the medical complexity profile (cluster 4; 149/761, 19.6%), mean age 83 (SD 9) years, 55.7% (83/149) males, showed the highest clinical complexity resulting in 12.8% (19/149) mortality and the highest readmission rate (56/149, 37.6%).ConclusionsThe results indicated the potential to predict mortality and morbidity-related adverse events leading to unplanned hospital readmissions. The resulting patient profiles fostered recommendations for personalized service selection with the capacity for value generation.

Project description:ObjectivesTo study the association between accidental opioid overdose and neurological, respiratory, cardiac and other serious adverse events and whether risk of these adverse events was elevated during hospital readmissions compared with initial admissions.DesignRetrospective cohort study.SettingPopulation-based study using linked administrative data in British Columbia, Canada.ParticipantsThe primary analysis included 2433 patients with 2554 admissions for accidental opioid overdose between 2006 and 2015, including 121 readmissions within 1 year of initial admission. The secondary analysis included 538 patients discharged following a total of 552 accidental opioid overdose hospitalizations and 11 040 matched controls from a cohort of patients with ≥180 days of prescription opioid use.Outcome measuresThe primary outcome was encephalopathy; secondary outcomes were adult respiratory distress syndrome, respiratory failure, pulmonary haemorrhage, aspiration pneumonia, cardiac arrest, ventricular arrhythmia, heart failure, rhabdomyolysis, paraplegia or tetraplegia, acute renal failure, death, a composite outcome of encephalopathy or any secondary outcome and total serious adverse events (all-cause hospitalisation or death). We analysed these outcomes using generalised linear models with a logistic link function.Results3% of accidental opioid overdose admissions included encephalopathy and 25% included one or more adverse events (composite outcome). We found no evidence of increased risk of encephalopathy (OR 0.57; 95% CI 0.13 to 2.49) or other outcomes during readmissions versus initial admissions. In the secondary analysis, <5 patients in each cohort experienced encephalopathy. Risk of the composite outcome (OR 2.15; 95% CI 1.48 to 3.12) and all-cause mortality (OR 2.13; 95% CI 1.18 to 3.86) were higher for patients in the year following overdose relative to controls.ConclusionsWe found no evidence that risk of encephalopathy or other adverse events was higher in readmissions compared with initial admissions for accidental opioid overdose. Risk of serious morbidity and mortality may be elevated in the year following an accidental opioid overdose.

Dataset Information

Effect of the 2016 Kumamoto earthquakes on preventable hospital admissions: a retrospective cohort study in Japan.

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Effect of the 2016 Kumamoto earthquakes on preventable hospital admissions: a retrospective cohort study in Japan.

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