Trends in prevalence of chronic disease and multimorbidity in Ontario,Canada

BACKGROUND:New case-mix tools from the Canadian Institute for Health Information offer a novel way of exploring the prevalence of chronic disease and multimorbidity using diagnostic data. We took a comprehensive approach to determine whether the prevalence of chronic disease and multimorbidity has been rising in Ontario, Canada.METHODS:In this observational study, we applied case-mix methodology to a population-based cohort. We used 10 years of patient-level data (fiscal years 2008/09 to 2017/18) from multiple care settings to compute the rolling 5-year prevalence of 85 chronic diseases and multimorbidity (i.e., the co-occurrence of 2 or more diagnoses). Diseases were further classified based on type and severity. We report both crude and age- and sex-standardized trends.RESULTS:The number of patients with chronic disease increased by 11.0% over the 10-year study period to 9.8 million in 2017/18, and the number with multimorbidity increased 12.2% to 6.5 million. Overall increases from 2008/09 to 2017/18 in the crude prevalence of chronic conditions and multimorbidity were driven by population aging. After adjustments for age and sex, the prevalence of patients with ≥ 1 chronic conditions decreased from 70.2% to 69.1%, and the prevalence of multimorbidity decreased from 47.1% to 45.6%. This downward trend was concentrated in minor and moderate diseases, whereas the prevalence of many major chronic diseases rose, along with instances of extreme multimorbidity (≥ 8 conditions). Age- and sex-standardized resource intensity weights, which reflect relative expected costs associated with patient diagnostic profiles, increased 4.6%.INTERPRETATION:Evidence of an upward trend in the prevalence of chronic disease was mixed. However, the change in case mix toward more serious conditions, along with increasing patient resource intensity weights overall, may portend a future need for population health management and increased health system spending above that predicted by population aging.

Multimorbidity exists when a patient is diagnosed with 2 or more chronic diseases. Patients with multimorbidity present challenges for physicians managing their care and, as the proportion of these patients in the population increases, for health care system planning. The prevalence of multimorbidity and chronic disease has been strongly associated with primary care use, specialist consultations, number and intensity of inpatient hospital admissions and other types of care.^1–7 Among beneficiaries of fee-for-service Medicare in the United States, expenditures for those with 4 or more chronic diseases were reported to be 66 times higher than for those with none.⁸ One study found that most health spending growth (77.6%) in the US between 1987 and 2011 could be attributed to patients with 4 or more diseases.⁹Several recent studies have estimated the prevalence of chronic disease and multimorbidity in Canada.^3,10–13 Rates of multimorbidity ranged from 10% to 25%, owing to differences in classification systems used to identify chronic disease, including the choice of conditions, and variations in study population. Lack of standardization in measures of chronic disease prevalence and multimorbidity has hampered the evaluation of trends over time and across settings.Ontario provides an ideal setting to evaluate trends in the prevalence of chronic disease because patients have access to a comprehensive set of publicly funded services. The Canadian Institute for Health Information (CIHI) has created a system that maps patient diagnosis data from all health care settings to a set of 226 clinically meaningful health conditions, covering the full spectrum of acute and chronic morbidity (Jeffrey Hatcher, Canadian Institute for Health Information, Ottawa: personal communication, 2017). CIHI’s system has been independently compared with the Johns Hopkins ACG System; CIHI’s system was deemed to be more specific and less sensitive in classifying diagnoses, making it more conservative in identifying health conditions (S. Cheng, ICES, unpublished data, 2016). The purpose of this study was to evaluate trends in the prevalence of chronic disease and multimorbidity in Ontario using CIHI’s comprehensive disease classification system.     

Polyvanadate acts at the level of plasma membranes through α-adrenergic receptor and affects cellular calcium distribution and some oxidation activities

The activities of calcium-stimulated respiration, calcium uptake, α-glycero-phosphate dehydrogenase and rates of oxidation in state 3 and of H²O² generation, were found to increase and that of pyruvate dehydrogenase decrease in mitochondria isolated from livers of rats administered intraperitoneally or perfused with polyvanadate. Phenoxybenzamine, an antagonist of α-adrenergic receptor, effectively prevented these changes. It was also found that perfusion of the liver with polyvanadate reproduced one of the best characterized events of α-adrenergic activation-stimulation of protein kinase C in plasma membrane accompanied by its decrease in cytosol. These experiments indicate for the first time the α-adrenergic mimetic action of polyvanadate.     

Stimulation of NADH oxidation by xanthine oxidase and polyvanadate in presence of some dehydrogenases and flavin compounds

The rates of NADH oxidation in presence of xanthine oxidase increase to a small and variable extent on addition of high concentrations of lactate dehydrogenase and other dehydrogenases. This heat stable activity is similar to polyvanadate-stimulation with respect to pH profile and SOD sensitivity. Isocitric dehydrogenase (NADP-specific) showed heat labile, SOD-sensitive polyvanadate-stimulated NADH oxidation activity. Polyvanadate-stimulated SOD-sensitive NADH oxidation was also found to occur with riboflavin, FMN and FAD in presence of a non-specific protein, BSA, suggesting that some flavoproteins may possess this activity.     

Steroid metabolism by germ cells and spermatozoa in men after vasoepididymostomy.

The ability of germ cells (spermatocytes and spermatids) and spermatozoa present in human ejaculate to metabolize steroids was studied in men with obstructive infertility who had undergone vasoepididymostomy as corrective surgery. Steroid metabolism by spermatozoa in men who had undergone vasovasostomy was also investigated. Germ cells converted testosterone mainly to androstenedione. In addition to androstenedione, dihydrotestosterone and androstanediols were also formed in incubations using spermatids. Both types of germ cells converted estradiol to estrone. Spermatozoa from subjects who had undergone vasoepididymostomy or vasovasostomy converted testosterone to androstenedione as in normal men, while spermatozoa from infertile subjects converted testosterone mainly to dihydrotestosterone. Seminal fluid, free of germ cells, did not show steroid-metabolizing capability.     

Addition of a small hydrophobic segment from the head region to an amphipathic leucine zipper like motif of E. coli toxin hemolysin E enhances the peptide-induced permeability of zwitterionic lipid vesicles

To find out the sequence requirement of the H-205 peptide, containing an amphipathic leucine zipper motif corresponding to the amino acid (a.a.) region 205-234 of hemolysin E (HlyE) to induce efficient permeation in zwitterionic lipid vesicles, the peptide was extended at the N-terminal after the addition of seven amino acids from the predicted transmembrane region in the head domain of the protein-toxin. The new peptide, H-198 (a.a. 198-234) and a scrambled mutant peptide of the same size were synthesized, fluorescently labeled and characterized functionally and structurally. The results showed that H-198 induced significantly higher permeation in the zwitterionic PC/Chol lipid vesicles than its shorter version, H-205. H-198 formed large aggregates in the PC/Chol vesicles unlike H-205 and also adopted more helical structure in the membrane mimetic environments compared to that of H-205. Fluorescence energy transfer experiments by flow cytometry indicated that only H-198 but not its mutant or H-205 oligomerized in the zwitterionic lipid vesicles, while in the negatively charged lipid vesicles both H-198 and H-205 formed oligomeric assembly. The results suggest a probable role of the hydrophobic residues of the head domain of HlyE in inducing permeability in the zwitterionic lipid vesicles by the peptide derived from the a.a. 198-234 of the toxin.     

Use of the Population Grouping Methodology of the Canadian Institute for Health Information to predict high-cost health system users in Ontario

BACKGROUND:Prior research has consistently shown that the heaviest users account for a disproportionate share of health care costs. As such, predicting high-cost users may be a precondition for cost containment. We evaluated the ability of a new health risk predictive modelling tool, which was developed by the Canadian Institute for Health Information (CIHI), to identify future high-cost cases.METHODS:We ran the CIHI model using administrative health care data for Ontario (fiscal years 2014/15 and 2015/16) to predict the risk, for each individual in the study population, of being a high-cost user 1 year in the future. We also estimated actual costs for the prediction period. We evaluated model performance for selected percentiles of cost based on the discrimination and calibration of the model.RESULTS:A total of 11 684 427 individuals were included in the analysis. Overall, 10% of this population had annual costs exceeding $3050 per person in fiscal year 2016/17, accounting for 71.6% of total expenditures; 5% had costs above $6374 (58.2% of total expenditures); and 1% exceeded $22 995 (30.5% of total expenditures). Model performance increased with higher cost thresholds. The c-statistic was 0.78 (reasonable), 0.81 (strong) and 0.86 (very strong) at the 10%, 5% and 1% cost thresholds, respectively.INTERPRETATION:The CIHI Population Grouping Methodology was designed to predict the average user of health care services, yet performed adequately for predicting high-cost users. Although we recommend the development of a purpose-designed tool to improve model performance, the existing CIHI Population Grouping Methodology may be used — as is or in concert with additional information — for many applications requiring prediction of future high-cost users.

A substantial literature across health systems shows that the highest users of services account for disproportionate shares of the public costs of health care. It has recently been reported that more than three-quarters of individual health care costs in Ontario were incurred by just 10% of the population.¹ Similarly, an Ontario Ministry of Health and Long-Term Care (MOHLTC) analysis of inpatient and home care costs found that the top 5% of patients were responsible for 61% of spending in those domains.² Consistent findings have been reported for Manitoba, Alberta and British Columbia.^3–7Some of the highest-cost cases may be explained by rare, unpredictable events, but others arise in the presence of multiple chronic conditions. Research from the United States has suggested that spending on chronic conditions accounts for the majority of health care expenditures.⁸ Predicting high-cost users may help us to understand and better manage public spending on health care.Cognizant of this need, the Ontario MOHLTC developed a predictive model for high-cost users based on sociodemographic, utilization and clinical diagnostic characteristics.⁹ Although the model performed well, it relied on a coarse categorization of 20 diagnostic variables consisting of broadly defined chapters of the International Statistical Classification of Diseases and Related Health Problems, 10th Revision (ICD-10) and a small number of chronic conditions, which limited its utility for explaining predictions. Moreover, this model is not available for use outside the MOHLTC. As such, there is a need for a predictive model that can be applied more widely by researchers and other stakeholders with an interest in health policy and spending in Canada.The Canadian Institute for Health Information (CIHI) has recently released a new population-based case mix product, the Population Grouping Methodology, which uses diagnoses obtained from patient health care encounters in multiple settings to summarize the universe of diagnosis codes into a clinically meaningful set of 226 health conditions. The grouping and modelling methodologies are described in more detail in Appendix 1 (available at www.cmaj.ca/lookup/suppl/doi:10.1503/cmaj.191297/-/DC1) and in previous reports.^10,11 The CIHI grouping methodology was not designed to predict high-cost cases, and previous work has already shown that the model performs better for low- and moderate-cost users than for highest-cost users (i.e., those with annual costs exceeding $25 000).¹¹ We evaluated the suitability of CIHI’s model for predicting future high-cost users in Ontario by examining the predicted costs for individuals who exceeded the top 10%, 5%, and 1% thresholds of actual cost.