scholarly journals Depression and risk of hospitalisations and rehospitalisations for ambulatory care-sensitive conditions in Denmark: a population-based cohort study

BMJ Open ◽  
2015 ◽  
Vol 5 (12) ◽  
pp. e009878 ◽  
Author(s):  
Dimitry S Davydow ◽  
Morten Fenger-Grøn ◽  
Anette Riisgaard Ribe ◽  
Henrik Søndergaard Pedersen ◽  
Anders Prior ◽  
...  
Keyword(s):  
Cohort Study ◽  
Ambulatory Care ◽  
Population Based ◽  
Ambulatory Care Sensitive Conditions ◽  
Ambulatory Care Sensitive ◽  
Sensitive Conditions

scholarly journals Osteoarthritis and risk of hospitalization for ambulatory care sensitive conditions: A general population-based cohort study

Rheumatology ◽  
2021 ◽  
Author(s):  
Ali Kiadaliri ◽  
Martin Englund
Keyword(s):  
Ambulatory Care ◽  
Hazard Model ◽  
Population Based ◽  
Incidence Rates ◽  
Ambulatory Care Sensitive Conditions ◽  
Chronic Obstructive ◽  
Main Diagnosis ◽  
Increased Risk ◽  
Ambulatory Care Sensitive ◽  
Sensitive Conditions

Abstract Objective To determine the association between osteoarthritis (OA) and risk of hospitalization for ambulatory care sensitive conditions (HACSCs). Methods We included all individuals aged 40–85 years who resided in Skåne, Sweden on 31st December 2005 with at least one healthcare consultation during 1998–2005 (n = 515 256). We identified those with a main diagnosis of OA between January 1, 1998 and December 31, 2016. People were followed from January 1st 2006 until an HACSC, death, relocation outside Skåne, or December 31st 2016 (whichever occurred first). OA status was treated as a time-varying covariate (those diagnosed before January 1, 2006 considered as exposed for whole study period). We assessed relative (hazard ratios (HRs) using Cox proportional hazard model) and absolute (hazard difference using additive hazard model) effects of OA on HACSCs adjusted for potential confounders. Results Crude incidence rates of HACSCs were 239 (95% CI 235, 242) and 151 (150, 152) per 10 000 person-years among OA and non-OA persons, respectively. The OA persons had an increased risk of HACSCs (HR [95% CI] 1.11 [1.09, 1.13]) and its subcategories of medical conditions except chronic obstructive pulmonary disease (HR [95% CI] 0.86 [0.81, 0.90]). There were 20 (95% CI 16, 24) more HACSCs per 10 000 person-years in OA compared with non-OA persons. While HRs for knee and hip OA were generally comparable, only knee OA was associated with increased risk of hospitalization for diabetes. Conclusion OA is associated with an increased risk of HACSCs, highlighting the urgent need to improve outpatient care for OA patients.

scholarly journals Predicting hospitalizations related to ambulatory care sensitive conditions with machine learning for population health planning: derivation and validation cohort study

2021 ◽  
Author(s):  
Seung Eun Yi ◽  
Vinyas Harish ◽  
Jahir M. Gutierrez ◽  
Mathieu Ravaut ◽  
Kathy Kornas ◽  
...  
Keyword(s):  
Machine Learning ◽  
Cohort Study ◽  
Ambulatory Care ◽  
Population Health ◽  
Health Planning ◽  
Health Data ◽  
Ambulatory Care Sensitive Conditions ◽  
Administrative Health Data ◽  
Ambulatory Care Sensitive ◽  
Sensitive Conditions

AbstractObjectiveTo predict older adults’ risk of avoidable hospitalization related to ambulatory care sensitive conditions (ACSC) using machine learning applied to administrative health data of Ontario, Canada.Design, Setting, and ParticipantsA retrospective cohort study was conducted on a large cohort of all residents covered under a single-payer system in Ontario, Canada over the period of 10 years, between 2008 and 2017. The study included 1.85 million Ontario residents between 65 and 74 years old at any time throughout the study period.Data sourcesAdministrative health data from Ontario, Canada obtained from the ICES Data Repository.Main outcome measuresRisk of hospitalizations due to ACSCs one year after the observation period.ResultsThe study used a total of 1,854,116 patients, split into train, validation, and test sets. The ACSC incidence rates among the data points were 1.1% for all sets. The final XGBoost model achieved an AUC of 80.5% on the held-out test set, and the predictions were well-calibrated. When ranking the predictions made by the model, those at the top 5% of risk as predicted by the model captured 37.4% of those presented with an ACSC-related hospitalization. A variety of features such as the previous number of ambulatory care visits, presence of ACSC-related hospitalizations during the observation window, age, rural residence, and prescription of certain medications were contributors to the prediction. Our model was also able to capture the geospatial heterogeneity of ACSC risk in the province of Ontario, and especially the elevated risk in rural and marginalized regions.ConclusionsThis study aimed to predict the 1-year risk of hospitalization from a series of ambulatory-care sensitive conditions in seniors aged 65 to 74 years old with a single, large-scale machine learning model. The model shows the potential to inform population health planning and interventions to reduce the burden of ACSC-related hospitalizations.

scholarly journals Do hospitalisations for ambulatory care sensitive conditions reflect low access to primary care? An observational cohort study of primary care usage prior to hospitalisation

BMJ Open ◽  
2017 ◽  
Vol 7 (8) ◽  
pp. e015704 ◽  
Author(s):  
Sabine I Vuik ◽  
Gianluca Fontana ◽  
Erik Mayer ◽  
Ara Darzi
Keyword(s):  
Primary Care ◽  
Cohort Study ◽  
Ambulatory Care ◽  
Observational Cohort Study ◽  
Ambulatory Care Sensitive Conditions ◽  
Observational Cohort ◽  
Gp Services ◽  
Access To Primary Care ◽  
Ambulatory Care Sensitive ◽  
Sensitive Conditions

ObjectivesTo explore whether hospitalisations for ambulatory care sensitive conditions (ACSCs) are associated with low access to primary care.DesignObservational cohort study over 2008 to 2012 using the Clinical Practice Research Datalink (CPRD) and Hospital Episode Statistics (HES) databases.SettingEnglish primary and secondary care.ParticipantsA random sample of 300 000 patients.Main outcome measuresEmergency hospitalisation for an ACSC.ResultsOver the long term, patients with ACSC hospitalisations had on average 2.33 (2.17 to 2.49) more general practice contacts per 6 months than patients with similar conditions who did not require hospitalisation. When accounting for the number of diagnosed ACSCs, age, gender and GP practice through a nested case–control method, the difference was smaller (0.64 contacts), but still significant (p<0.001).In the short-term analysis, measured over the 6 months prior to hospitalisation, patients used more GP services than on average over the 5 years. Cases had significantly (p<0.001) more primary care contacts in the 6 months before ACSC hospitalisations (7.12, 95% CI 6.95 to 7.30) than their controls during the same 6 months (5.57, 95% CI 5.43 to 5.72). The use of GP services increased closer to the time of hospitalisation, with a peak of 1.79 (1.74 to 1.83) contacts in the last 30 days before hospitalisation.ConclusionsThis study found no evidence to support the hypothesis that low access to primary care is the main driver of ACSC hospitalisations. Other causes should also be explored to understand how to use ACSC admission rates as quality metrics, and to develop the appropriate interventions.

scholarly journals Identifying priorities for primary care investment in Ireland through a population-based analysis of avoidable hospital admissions for ambulatory care sensitive conditions (ACSC)

BMJ Open ◽  
2019 ◽  
Vol 9 (11) ◽  
pp. e028744 ◽  
Author(s):  
Geraldine McDarby ◽  
Breda Smyth
Keyword(s):  
Primary Care ◽  
Ambulatory Care ◽  
Integrated Care ◽  
Hospital Admissions ◽  
Acute Hospital ◽  
Ambulatory Care Sensitive Conditions ◽  
Hospital System ◽  
Bed Days ◽  
Ambulatory Care Sensitive ◽  
Sensitive Conditions

BackgroundIn 2016, the Irish acute hospital system operated well above internationally recommended occupancy targets. Investment in primary care can prevent hospital admissions of ambulatory care sensitive conditions (ACSCs).ObjectiveTo measure the impact of ACSCs on acute hospital capacity in the Irish public system and identify specific care areas for enhanced primary care provision.DesignNational Hospital In-patient Enquiry System data were used to calculate 2011–2016 standardised bed day rates for selected ACSC conditions. A prioritisation exercise was undertaken to identify the most significant contributors to bed days within our hospital system. Poisson regression was used to determine change over time using incidence rate ratios (IRR).ResultsIn 2016 ACSCs accounted for almost 20% of acute public hospital beds (n=871 328 bed days) with adults over 65 representing 69.1% (n=602 392) of these. Vaccine preventable conditions represented 39.1% of ACSCs. Influenza and pneumonia were responsible for 99.8% of these, increasing by 8.2% (IRR: 1.02; 95% CI 1.02 to 1.03) from 2011 to 2016. Pyelonephritis represented 47.6% of acute ACSC bed days, increasing by 46.5% (IRR: 1.07; 95% CI 1.06 to 1.08) over the 5 years examined.ConclusionsPrioritisation for targeted investment in integrated care programmes is enabled through analysis of ACSC’s in terms of acute hospital bed days. This analysis demonstrates that primary care investment in integrated care programmes for respiratory ACSC’s from prevention to rehabilitation at scale could assist with bed capacity in acute hospitals in Ireland. In adults 65 years and over, including chronic obstructive pulmonary disease patients, the current analysis supports targeting community based pulmonary rehabilitation including pneumococcal and influenza vaccination programmes in order to reduce the burden of infection and hospitalisations. Further exploration of pyelonephritis is necessary in order to ascertain patient profile and appropriateness of admissions.

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