BackgroundMultimorbidity, the co-occurrence of two or more chronic conditions, is common among older adults and is known to be associated with high costs and gaps in quality of care. Population-based estimates of multimorbidity are not readily available, which makes future planning a challenge. We aimed to estimate the population-based prevalence and trends of multimorbidity in Ontario, Canada and to examine patterns in the co-occurrence of chronic conditions.MethodsThis retrospective cohort study includes all Ontarians (aged 0 to 105 years) with at least one of 16 common chronic conditions. Descriptive statistics were used to examine and compare the prevalence of multimorbidity by age and number of conditions in 2003 and 2009. The co-occurrence of chronic conditions among individuals with multimorbidity was also explored.ResultsThe prevalence of multimorbidity among Ontarians rose from 17.4% in 2003 to 24.3% in 2009, a 40% increase. This increase over time was evident across all age groups. Within individual chronic conditions, multimorbidity rates ranged from 44% to 99%. Remarkably, there were no dominant patterns of co-occurring conditions.ConclusionThe high prevalence of multimorbidity and numerous combinations of conditions suggests that single, disease-oriented management programs may be less effective or efficient tools for high quality care compared to person-centered approaches.Electronic supplementary materialThe online version of this article (doi:10.1186/s12889-015-1733-2) contains supplementary material, which is available to authorized users.

BackgroundMultimorbidity poses a significant clinical challenge and has been linked to greater health services use, including hospitalization; however, we have little knowledge about the influence of contextual factors on outcomes in this population. Objectives: To describe the extent to which the association between multimorbidity and hospitalization is modified by age, gender, primary care practice model, or continuity of care (COC) among adults with at least one chronic condition.MethodsA retrospective cohort study with linked population-based administrative data.Setting: Ontario, Canada. Cohort: All individuals 18 and older with at least one of 16 priority chronic conditions as of April 1, 2009 (baseline). Main Outcome Measures: Any hospitalization, 3 or more hospitalizations, non-medical discharge delay, and 30-day readmission within the 1 year following baseline.ResultsOf 5,958,514 individuals, 484,872 (8.1 %) experienced 646,347 hospitalizations. There was a monotonic increase in the likelihood of hospitalization and related outcomes with increasing multimorbidity which was modified by age, gender, and COC but not primary care practice model. The effect of increasing multimorbidity was greater in younger adults than older adults and in those with lower COC than with higher COC. The effect of increasing multimorbidity on hospitalization was greater in men than women but reversed for the other outcomes.ConclusionsThe effect of multimorbidity on hospitalization is influenced by age and gender, important considerations in the development of person-centred care models. Greater continuity of physician care lessened the effect of multimorbidity on hospitalization, further demonstrating the need for care continuity across providers for people with chronic conditions.Electronic supplementary materialThe online version of this article (doi:10.1186/s12913-016-1415-5) contains supplementary material, which is available to authorized users.

ObjectivesTo estimate the attributable costs of multimorbidity and assess whether the association between the level of multimorbidity and health system costs varies by socio-demographic factors in young (<65 years) and older (≥65 years) adults living in Ontario, Canada.DesignA population-based, retrospective cohort studySettingThe province of Ontario, CanadaParticipants6 639 089 Ontarians who were diagnosed with at least one of 16 selected medical conditions on 1 April 2009.Main outcome measuresFrom the perspective of the publicly funded healthcare system, total annual healthcare costs were derived from linked provincial health administrative databases using a person-level costing method. We used generalised linear models to examine the association between the level of multimorbidity and healthcare costs and the extent to which socio-demographic variables modified this association.ResultsAttributable total costs of multimorbidity ranged from C$377 to C$2073 for young individuals and C$1026 to C$3831 for older adults. The association between the degree of multimorbidity and healthcare costs was significantly modified by age (p<0.001), sex (p<0.001) and neighbourhood income (p<0.001) in both age groups, and the positive association between healthcare costs and levels of multimorbidity was statistically stronger for older than younger adults. For individuals aged 65 years or younger, the increase in healthcare costs was more gradual in women than in their male counterparts, however, for those aged 65 years or older, the increase in healthcare costs was significantly greater among women than men. Lastly, we also observed that the positive association between the level of multimorbidity and healthcare costs was significantly greater at higher levels of marginalisation.ConclusionSocio-demographic factors are important effect modifiers of the relationship between multimorbidity and healthcare costs and should therefore be considered in any discussion of the implementation of healthcare policies and the organisation of healthcare services aimed at controlling healthcare costs associated with multimorbidity.

BackgroundDespite research demonstrating the potential effectiveness of Telehomecare for people with Chronic Obstructive Pulmonary Disease and Heart Failure, broad-scale comprehensive evaluations are lacking. This article discusses the qualitative component of a mixed-method program evaluation of Telehomecare in Ontario, Canada. The objective of the qualitative component was to explore the multi-level factors and processes which facilitate or impede the implementation and adoption of the program across three regions where it was first implemented.MethodsThe study employs a multi-level framework as a conceptual guide to explore the facilitators and barriers to Telehomecare implementation and adoption across five levels: technology, patients, providers, organizations, and structures. In-depth semi-structured interviews and ethnographic observations with program stakeholders, as well as a Telehomecare document review were used to elicit key themes. Study participants (n = 89) included patients and/or informal caregivers (n = 39), health care providers (n = 23), technicians (n = 2), administrators (n = 12), and decision makers (n = 13) across three different Local Health Integration Networks in Ontario.ResultsKey facilitators to Telehomecare implementation and adoption at each level of the multi-level framework included: user-friendliness of Telehomecare technology, patient motivation to participate in the program, support for Telehomecare providers, the integration of Telehomecare into broader health service provision, and comprehensive program evaluation. Key barriers included: access-related issues to using the technology, patient language (if not English or French), Telehomecare provider time limitations, gaps in health care provision for patients, and structural barriers to patient participation related to geography and social location.ConclusionsThough Telehomecare has the potential to positively impact patient lives and strengthen models of health care provision, a number of key challenges remain. As such, further implementation and expansion of Telehomecare must involve continuous assessments of what is working and not working with all stakeholders. Increased dialogue, evaluation, and knowledge translation within and across regions to understand the contextual factors influencing Telehomecare implementation and adoption is required. This can inform decision-making that better reflects and addresses the needs of all program stakeholders.Electronic supplementary materialThe online version of this article (doi:10.1186/s12913-015-1196-2) contains supplementary material, which is available to authorized users.

Meeting diabetes testing goals has the potential to reduce hospitalizations for diabetes-related complications; however, this depends on types of coexisting chronic conditions and diabetes-related complications in patients with diabetes.

BackgroundDespite the high prevalence of osteoarthritis and the prominence of primary care in managing this condition, there is no systematic summary of quality indicators applicable for osteoarthritis care in primary care settings.ObjectivesThis systematic review aimed to identify evidence-based quality indicators for monitoring, evaluating and improving the quality of care for adults with osteoarthritis in primary care settings.MethodsOvid MEDLINE and Ovid EMBASE databases and grey literature, including relevant organizational websites, were searched from 2000 to 2015. Two reviewers independently selected studies if (i) the study methodology combined a systematic literature search with assessment of quality indicators by an expert panel and (ii) quality indicators were applicable to assessment of care for adults with osteoarthritis in primary care settings. Included studies were appraised using the Appraisal of Indicators through Research and Evaluation (AIRE) instrument. A narrative synthesis was used to combine the indicators within themes. Applicable quality indicators were categorized according to Donabedian’s ‘structure-process-outcome’ framework.ResultsThe search revealed 4526 studies, of which 32 studies were reviewed in detail and 4 studies met the inclusion criteria. According to the AIRE domains, all studies were clear on purpose and stakeholder involvement, while formal endorsement and use of indicators in practice were scarcely described. A total of 20 quality indicators were identified from the included studies, many of which overlapped conceptually or in content.ConclusionsThe process of developing quality indicators was methodologically suboptimal in most cases. There is a need to develop specific process, structure and outcome measures for adults with osteoarthritis using appropriate methodology.

BackgroundDespite the growing interest in assessing the quality of care for depression, there is little evidence to support measurement of the quality of primary care for depression. This study identified evidence-based quality indicators for monitoring, evaluating and improving the quality of care for depression in primary care settings.MethodsOvid MEDLINE and Ovid PsycINFO databases, and grey literature, including relevant organizational websites, were searched from 2000 to 2015. Two reviewers independently selected studies if (1) the study methodology combined a systematic literature search with assessment of quality indicators by an expert panel and (2) quality indicators were applicable to assessment of care for adults with depression in primary care settings. Included studies were appraised using the Appraisal of Indicators through Research and Evaluation (AIRE) instrument, which contains four domains and 20 items. A narrative synthesis was used to combine the indicators within themes. Quality indicators applicable to care for adults with depression in primary care settings were extracted using a structured form. The extracted quality indicators were categorized according to Donabedian’s ‘structure-process-outcome’ framework.ResultsThe search revealed 3838 studies. Four additional publications were identified through grey literature searching. Thirty-nine articles were reviewed in detail and seven met the inclusion criteria. According to the AIRE domains, all studies were clear on purpose and stakeholder involvement, while formal endorsement and usage of indicators in practice were scarcely described. A total of 53 quality indicators were identified from the included studies, many of which overlap conceptually or in content: 15 structure, 33 process and four outcome indicators. This study identified quality indicators for evaluating primary care for depression among adult patients.ConclusionsThe identified set of indicators address multiple dimensions of depression care and provide an excellent starting point for further development and use in primary care settings.Electronic supplementary materialThe online version of this article (doi:10.1186/s13643-017-0530-7) contains supplementary material, which is available to authorized users.

Background Since primary data collection can be time-consuming and expensive, surgical site infections (SSIs) could ideally be monitored using routinely collected administrative data. We derived and internally validated efficient algorithms to identify SSIs within 30 days after surgery with health administrative data, using Machine Learning algorithms. Methods All patients enrolled in the National Surgical Quality Improvement Program from the Ottawa Hospital were linked to administrative datasets in Ontario, Canada. Machine Learning approaches, including a Random Forests algorithm and the high-performance logistic regression, were used to derive parsimonious models to predict SSI status. Finally, a risk score methodology was used to transform the final models into the risk score system. The SSI risk models were validated in the validation datasets. Results Of 14,351 patients, 795 (5.5%) had an SSI. First, separate predictive models were built for three distinct administrative datasets. The final model, including hospitalization diagnostic, physician diagnostic and procedure codes, demonstrated excellent discrimination (C statistics, 0.91, 95% CI, 0.90–0.92) and calibration (Hosmer-Lemeshow χ2 statistics, 4.531, p = 0.402). Conclusion We demonstrated that health administrative data can be effectively used to identify SSIs. Machine learning algorithms have shown a high degree of accuracy in predicting postoperative SSIs and can integrate and utilize a large amount of administrative data. External validation of this model is required before it can be routinely used to identify SSIs.