Identifying obesity-related multimorbidity combinations in the United States

Comorbidity of patients with noncommunicable diseases in general practice. Eurasian guidelines

Создание руководства поддержано Советом по терапевтическим наукам отделения клинической медицины Российской академии наук.

Quality of life and associated factors among community-dwelling adults with multimorbidity in Shanghai, China: A cross-sectional study

AIM

To compare the quality of life of patients with and without multimorbidity and investigate potential factors related to the quality of life in patients with multimorbidity.

DESIGN

A descriptive cross-sectional study.

METHODS

This study included 1778 residents with chronic diseases, including single disease (1255 people, average age: 60.78 ± 9.42) and multimorbidity (523 people, average age: 64.03 ± 8.91) groups, who were recruited from urban residents of Shanghai through a multistage, stratified, probability proportional to size sampling method. The quality of life was measured using the World Health Organization Quality of Life Questionnaire. The socio-demographic data and psychological states were measured using a self-made structured questionnaire, Self-rating Anxiety Scale, and Self-rating Depression Scale. Differences in demographic characteristics were estimated using Pearson's chi-squared test, and independent t-test or one-way ANOVA followed by S-N-K test was used to compare the mean quality of life. Multiple linear regression analysis was conducted to identify risk factors for multimorbidity.

RESULTS

There were differences in age, education, income, and BMI between single-disease and multimorbidity groups, but no differences in gender, marriage, and occupation. Multimorbidity had lower quality of life, reflected in all four domains. Multiple linear regression analyses showed that low level of education, low income, number of diseases, depression, and anxiety were negatively related to quality of life in all domains.

Multimorbidity patterns across race/ethnicity as stratified by age and obesity

The objective of our study is to assess differences in prevalence of multimorbidity by race/ethnicity. We applied the FP-growth algorithm on middle-aged and elderly cohorts stratified by race/ethnicity, age, and obesity level. We used 2016–2017 data from the Cerner HealthFacts electronic health record data warehouse. We identified disease combinations that are shared by all races/ethnicities, those shared by some, and those that are unique to one group for each age/obesity level. Our findings demonstrate that even after stratifying by age and obesity, there are differences in multimorbidity prevalence across races/ethnicities. There are multimorbidity combinations distinct to some racial groups—many of which are understudied. Some multimorbidities are shared by some but not all races/ethnicities. African Americans presented with the most distinct multimorbidities at an earlier age. The identification of prevalent multimorbidity combinations amongst subpopulations provides information specific to their unique clinical needs.

Self-rated health and multimorbidity in patients with type 2 diabetes

The relationship between multimorbidity and self-rated health is well established. This study examined self-rated health in relation to multimorbidity, glycaemia and body weight specifically in adults with type 2 diabetes. Bootstrapped hierarchical logistic regression and structural equation modelling (SEM) were used to analyse survey data from 280 adults with type 2 diabetes. The odds of 'fair/bad/very bad' self-rated health increased 10-fold in patients with three (OR = 10.11 (3.36-30.40)) and four conditions (OR = 10.58 (2.9-38.25)), irrespective of glycaemic control (p < 0.001). The relationship between multimorbidity and perceived health was more pronounced in male patients. SEM generated a model with good fit, χ2 (CMIN) = 5.10, df = 3, p = 0.164, χ2 (CMIN)/df = 1.70, RMSEA = 0.05, CFI = 0.97, TLI = 0.95 and NFI = 0.94; self-rated health mediated relations between multimorbidity and BMI. Overall, this study highlights the potential of self-rated health to mediate relationships between multimorbidity and BMI, but not glycaemic control, in adults with type 2 diabetes.

Issues with Variability in EHR Data About Race and Ethnicity: A Descriptive Analysis of the National COVID Cohort Collaborative Data Enclave (Preprint)

Background

The adverse impact of COVID-19 on marginalized and under-resourced communities of color has highlighted the need for accurate, comprehensive race and ethnicity data. However, a significant technical challenge related to integrating race and ethnicity data in large, consolidated databases is the lack of consistency in how data about race and ethnicity are collected and structured by health care organizations.

Objective

This study aims to evaluate and describe variations in how health care systems collect and report information about the race and ethnicity of their patients and to assess how well these data are integrated when aggregated into a large clinical database.

Methods

At the time of our analysis, the National COVID Cohort Collaborative (N3C) Data Enclave contained records from 6.5 million patients contributed by 56 health care institutions. We quantified the variability in the harmonized race and ethnicity data in the N3C Data Enclave by analyzing the conformance to health care standards for such data. We conducted a descriptive analysis by comparing the harmonized data available for research purposes in the database to the original source data contributed by health care institutions. To make the comparison, we tabulated the original source codes, enumerating how many patients had been reported with each encoded value and how many distinct ways each category was reported. The nonconforming data were also cross tabulated by 3 factors: patient ethnicity, the number of data partners using each code, and which data models utilized those particular encodings. For the nonconforming data, we used an inductive approach to sort the source encodings into categories. For example, values such as “Declined” were grouped with “Refused,” and “Multiple Race” was grouped with “Two or more races” and “Multiracial.”

Results

“No matching concept” was the second largest harmonized concept used by the N3C to describe the race of patients in their database. In addition, 20.7% of the race data did not conform to the standard; the largest category was data that were missing. Hispanic or Latino patients were overrepresented in the nonconforming racial data, and data from American Indian or Alaska Native patients were obscured. Although only a small proportion of the source data had not been mapped to the correct concepts (0.6%), Black or African American and Hispanic/Latino patients were overrepresented in this category.

Conclusions

Differences in how race and ethnicity data are conceptualized and encoded by health care institutions can affect the quality of the data in aggregated clinical databases. The impact of data quality issues in the N3C Data Enclave was not equal across all races and ethnicities, which has the potential to introduce bias in analyses and conclusions drawn from these data. Transparency about how data have been transformed can help users make accurate analyses and inferences and eventually better guide clinical care and public policy.

Increases in multimorbidity with weight class in the United States

Little is known regarding how multimorbidity combinations associated with obesity change with increase in body weight. This study employed data from the national Cerner HealthFacts Data Warehouse to identify changes in multimorbidity patterns by weight class using network analysis. Networks were generated for 154 528 middle-aged patients in the following categories: normal weight, overweight, and classes 1, 2, and 3 obesity. The results show significant differences (P-value<0.05) in prevalence by weight class for all but three of 82 diseases considered. The percentage of patients with multimorbidity (excluding obesity) increases from in 55.1% in patients with normal weight, to 57.88% with overweight, 70.39% with Class 1 obesity, 73.99% with Class 2 obesity, and 71.68% in Class 3 obesity, increasing most substantially with the progression from overweight to class 1 obesity. Most prevalent disease clusters expand from only hypertension and dorsalgia in normal weight, to add joint disorders in overweight, lipidemias in class 1 obesity, diabetes in class 2 obesity, and sleep disorders and chronic kidney disease in class 3 obesity. Recognition of multimorbidity patterns associated with weight increase is essential for true precision care of obesity-associated chronic conditions and can help clinicians identify and address preclinical disease before additional complications arise.

Disability assessment in an Italian cohort of patients with obesity using an International Classification of Functioning, Disability and Health (ICF)-derived questionnaire

BACKGROUND

Obesity is a clinical condition that contributes to the development of related disability in different areas (physical, psychological and social). Multidisciplinary treatment calls for specific instruments able to evaluate all related functional problems. We have developed a tool (an ICF-based assessment instrument, the ICF-OB schedule) to evaluate obesity-related disability, composed of an inventory of 71-items from the WHO International Classification of Functioning, Disability and Health (ICF).

AIM

The aim of the present study was to validate this new tool for the definition of obesity-related disability. We also sought to examine the relationship between obesity disability, an index of multimorbidity (Cumulative Illness Rating Scale [CIRS]) and a well-validated score of perceived obesity-related disability (Italian Obesity Society Test for Obesity-Related Disability [TSD-OC]).

DESIGN

Process validation of the ICF-OB schedule.

SETTING

Baseline conditions of out- and in-patients.

POPULATION

A large cohort of obese patients recruited from 9 multidisciplinary centers belonging to the Italian Obesity Society (SIO) network, which provide specialized obesity care.

METHODS

A total of 353 patients (F: 70%, age: 50.2±12.7yrs, BMI: 41.4±8.3kg/m²) were enrolled between January 2017 and June 2018. The ICF-OB was used to define patients' functioning and disability profiles in order to set and appraise rehabilitation goals.

RESULTS

We described the distribution of body functions (BF), body structures (BS) and activities and participations (A&P) categories and the agreement rates were significant for the majority of these. The ICF-OB was more often significantly associated, and with stronger coefficients, with patients' comorbidities as described by the CIRS rather than with Body Mass Index (BMI). The TSD-OC also presented a strong association with A&P indexes.

CONCLUSIONS

The complexity of clinical condition, that generates disability in obesity might be well identified with the use of this new instrument that appear significant related to the perceived disability for each patients and also with their multimorbidity.

CLINICAL REHABILITATION IMPACT

The ICF-OB shows great promise as a tool for goal setting in the rehabilitation of obese patients.

Untangling the complexity of multimorbidity with machine learning

The prevalence of multimorbidity has been increasing in recent years, posing a major burden for health care delivery and service. Understanding its determinants and impact is proving to be a challenge yet it offers new opportunities for research to go beyond the study of diseases in isolation. In this paper, we review how the field of machine learning provides many tools for addressing research challenges in multimorbidity. We highlight recent advances in promising methods such as matrix factorisation, deep learning, and topological data analysis and how these can take multimorbidity research beyond cross-sectional, expert-driven or confirmatory approaches to gain a better understanding of evolving patterns of multimorbidity. We discuss the challenges and opportunities of machine learning to identify likely causal links between previously poorly understood disease associations while giving an estimate of the uncertainty on such associations. We finally summarise some of the challenges for wider clinical adoption of machine learning research tools and propose some solutions.

Multimorbidity and complex multimorbidity in Brazilian rural workers

OBJECTIVE

To estimate the prevalence of multimorbidity and complex multimorbidity in rural workers and their association with sociodemographic characteristics, occupational contact with pesticides, lifestyle and clinical condition.

METHODS

This is a cross-sectional epidemiological study with 806 farmers from the main agricultural municipality of the state of Espírito Santo/Brazil, conducted from December 2016 to April 2017. Multimorbidity was defined as the presence of two or more chronic diseases in the same individual, while complex multimorbidity was classified as the occurrence of three or more chronic conditions affecting three or more body systems. Socio-demographic data, occupational contact with pesticides, lifestyle data and clinical condition data were collected through a structured questionnaire. Binary logistic regression was conducted to identify risk factors for multimorbidity.

RESULTS

The prevalence of multimorbidity among farmers was 41.5% (n = 328), and complex multimorbidity was 16.7% (n = 132). More than 77% of farmers had at least one chronic illness. Hypertension, dyslipidemia and depression were the most prevalent morbidities. Being 40 years or older (OR 3.33, 95% CI 2.06-5.39), previous medical diagnosis of pesticide poisoning (OR 1.89, 95% CI 1.03-3.44), high waist circumference (OR 2.82, CI 95% 1.98-4.02) and worse health self-assessment (OR 2.10, 95% CI 1.52-2.91) significantly increased the chances of multimorbidity. The same associations were found for the diagnosis of complex multimorbidity.

CONCLUSION

We identified a high prevalence of multimorbidity and complex multimorbidity among the evaluated farmers. These results were associated with increased age, abdominal fat, pesticide poisoning, and poor or fair health self-assessment. Public policies are necessary to prevent, control and treat this condition in this population.