Journal of multimorbidity and comorbidity最新文献

Introduction: Several low-and middle-income countries are undergoing rapid epidemiological transition with a rising burden of non-communicable diseases (NCDs). South Africa (SA) is a country with one of the largest HIV epidemics worldwide and a growing burden of NCDs where the collision of these epidemics poses a major public health challenge.

Methods: Using data from a large nationally representative survey, the South Africa Demographic and Health Survey (SADHS 2016), we conducted a geospatial analysis of several diseases including HIV, tuberculosis (TB), cardiovascular, respiratory, and metabolic diseases to identify areas with a high burden of co-morbidity within the country. We explored the spatial structure of each disease and associations between diseases using different spatial and visual data methodologies. We also assessed the individual level co-occurrence of HIV and the other diseases included in the analysis.

Results: The spatial distribution for HIV prevalence showed that this epidemic is most intense in the eastern region of the country, mostly within the Gauteng, Mpumalanga, and Kwazulu-Natal provinces. In contrast, chronic diseases had their highest prevalence rates the southern region of the country, particularly in the Eastern and Western Cape provinces. Individual-level analyses were consistent with the spatial correlations and found no statistically significant associations between HIV infection and the presence of any NCDs.

Conclusions: We found no evidence of geospatial overlap between the HIV epidemic and NCDs in SA. These results evidence the complex epidemiological landscape of the country, characterized by geographically distinct areas exhibiting different health burdens. The detailed description of the heterogenous prevalence of HIV and NCDs in SA reported in this study could be a useful tool to inform and direct policies to enhance targeted health service delivery according to the local health needs of each community.

Background: Most people living with multiple long-term condition multimorbidity (MLTC-M) are under 65 (defined as 'early onset'). Earlier and greater accrual of long-term conditions (LTCs) may be influenced by the timing and nature of exposure to key risk factors, wider determinants or other LTCs at different life stages. We have established a research collaboration titled 'MELD-B' to understand how wider determinants, sentinel conditions (the first LTC in the lifecourse) and LTC accrual sequence affect risk of early-onset, burdensome MLTC-M, and to inform prevention interventions.

Aim: Our aim is to identify critical periods in the lifecourse for prevention of early-onset, burdensome MLTC-M, identified through the analysis of birth cohorts and electronic health records, including artificial intelligence (AI)-enhanced analyses.

Design: We will develop deeper understanding of 'burdensomeness' and 'complexity' through a qualitative evidence synthesis and a consensus study. Using safe data environments for analyses across large, representative routine healthcare datasets and birth cohorts, we will apply AI methods to identify early-onset, burdensome MLTC-M clusters and sentinel conditions, develop semi-supervised learning to match individuals across datasets, identify determinants of burdensome clusters, and model trajectories of LTC and burden accrual. We will characterise early-life (under 18 years) risk factors for early-onset, burdensome MLTC-M and sentinel conditions. Finally, using AI and causal inference modelling, we will model potential 'preventable moments', defined as time periods in the life course where there is an opportunity for intervention on risk factors and early determinants to prevent the development of MLTC-M. Patient and public involvement is integrated throughout.

Background: Multimorbidity is a major challenge to health and social care systems around the world. There is limited research exploring the wider contextual determinants that are important to improving care for this cohort. In this study, we aimed to elicit and prioritise determinants of improved care in people with multiple conditions.

Methods: A three-round online Delphi study was conducted in England with health and social care professionals, data scientists, researchers, people living with multimorbidity and their carers.

Results: Our findings suggest a care system which is still predominantly single condition focused. 'Person-centred and holistic care' and 'coordinated and joined up care', were highly rated determinants in relation to improved care for multimorbidity. We further identified a range of non-medical determinants that are important to providing holistic care for this cohort.

Conclusions: Further progress towards a holistic and patient-centred model is needed to ensure that care more effectively addresses the complex range of medical and non-medical needs of people living with multimorbidity. This requires a move from a single condition focused biomedical model to a person-based biopsychosocial approach, which has yet to be achieved.

Objective: Social, biological and environmental factors in early-life, defined as the period from preconception until age 18, play a role in shaping the risk of multiple long-term condition multimorbidity. However, there is a need to conceptualise these early-life factors, how they relate to each other, and provide conceptual framing for future research on aetiology and modelling prevention scenarios of multimorbidity. We develop a conceptual framework to characterise the population-level domains of early-life determinants of future multimorbidity.

Method: This work was conducted as part of the Multidisciplinary Ecosystem to study Lifecourse Determinants and Prevention of Early-onset Burdensome Multimorbidity (MELD-B) study. The conceptualisation of multimorbidity lifecourse determinant domains was shaped by a review of existing research evidence and policy, and co-produced with public involvement via two workshops.

Results: Early-life risk factors incorporate personal, social, economic, behavioural and environmental factors, and the key domains discussed in research evidence, policy, and with public contributors included adverse childhood experiences, socioeconomics, the social and physical environment, and education. Policy recommendations more often focused on individual-level factors as opposed to the wider determinants of health discussed within the research evidence. Some domains highlighted through our co-production process with public contributors, such as religion and spirituality, health screening and check-ups, and diet, were not adequately considered within the research evidence or policy.

Conclusions: This co-produced conceptualisation can inform research directions using primary and secondary data to investigate the early-life characteristics of population groups at risk of future multimorbidity, as well as policy directions to target public health prevention scenarios of early-onset multimorbidity.

The primary objective was to quantify the influences of care delivery teams on the outcomes of patients with multimorbidity. Electronic medical record data on 68,883 patient care encounters (i.e., 54,664 patients) were extracted from the Arkansas Clinical Data Repository. Social network analysis assessed the minimum care team size associated with improved care outcomes (i.e., hospitalizations, days between hospitalizations, and cost) of patients with multimorbidity. Binomial logistic regression further assessed the influence of the presence of seven specific clinical roles. When compared to patients without multimorbidity, patients with multimorbidity had a higher mean age (i.e., 47.49 v. 40.61), a higher mean dollar amount of cost per encounter (i.e., $3,068 v. $2,449), a higher number of hospitalizations (i.e., 25 v. 4), and a higher number of clinicians engaged in their care (i.e., 139,391 v. 7,514). Greater network density in care teams (i.e., any combination of two or more Physicians, Residents, Nurse Practitioners, Registered Nurses, or Care Managers) was associated with a 46-98% decreased odds of having a high number of hospitalizations. Greater network density (i.e., any combination of two or more Residents or Registered Nurses) was associated with 11-13% increased odds of having a high cost encounter. Greater network density was not significantly associated with having a high number of days between hospitalizations. Analyzing the social networks of care teams may fuel computational tools that better monitor and visualize real-time hospitalization risk and care cost that are germane to care delivery.

Background: Comorbidities are important risk factors of severe COVID-19 complications. Their impact during the Omicron wave among vaccinated and unvaccinated COVID-19 cases is not well documented.

Purpose: The objective of this study was to estimate the association between the number of comorbidities and the risk of hospitalization, intensive care unit (ICU) admission, and death among vaccinated and unvaccinated confirmed adult COVID-19 cases during the Omicron wave.

Research design and study sample: We performed a cohort study of COVID-19 adult cases of primo-infection occurring during the Omicron wave, from December 5, 2021 to January 9, 2022 using surveillance database of the province of Québec, Canada. The database included all laboratory-confirmed cases in the province and the related information on 21 pre-existing comorbidities, hospitalization, ICU admission, death related to COVID-19 and vaccination status.

Analysis: We performed a robust Poisson regression model to estimate the impact of the number of comorbidities on each complication by vaccination status adjusted for age, sex, socioeconomic status, and living environment.

Results: We observed that the risk of complication increased for each additional comorbidity in both vaccinated and unvaccinated individuals and that this risk was systematically higher among unvaccinated individuals. Compared with vaccinated individuals without comorbidities (reference group), the risks of hospitalization, ICU admission, and death were respectively: 9X (95% CI [7.77-12.01]), 13X (95% CI [8.74-18.87]), and 12X (95% CI [7.57-18.91]) higher in vaccinated individuals with ≥3 comorbidities; 22X (95% CI [19.07-25.95]), 45X (95% CI [29.06-69.67]) and 38X (95% CI [23.62-61.14]) higher in unvaccinated individuals with ≥3 comorbidities.

Conclusion: Our results support the importance of promoting vaccination in all individuals, and especially those with pre-existing medical conditions, to reduce severe complications, even during the Omicron wave.

Background: Low- and middle-income countries (LMICs), including South Africa, are currently experiencing multiple epidemics: HIV and the rising burden of non-communicable diseases (NCDs), leading to different patterns of multimorbidity (the occurrence of two or more chronic conditions) than experienced in high income settings. These adversely affect health outcomes, increase patients' perceived burden of treatment, and impact the workload of self-management. This paper outlines the methods used in a qualitative study exploring burden of treatment among people living with HIV/NCD multimorbidity in South Africa.

Methods: We undertook a comparative qualitative study to examine the interaction between individuals' treatment burden (self-management workload) and their capacity to take on this workload, using the dual lenses of Burden of Treatment Theory (BoTT) and Cumulative Complexity Model (CuCoM) to aid conceptualisation of the data. We interviewed 30 people with multimorbidity and 16 carers in rural Eastern Cape and urban Cape Town between February-April 2021. Data was analysed through framework analysis.

Findings: This paper discusses the methodological procedures considered when conducting qualitative research among people with multimorbidity in low-income settings in South Africa. We highlight the decisions made when developing the research design, recruiting participants, and selecting field-sites. We also explore data analysis processes and reflect on the positionality of the research project and researchers.

Conclusion: This paper illustrates the decision-making processes conducting this qualitative research and may be helpful in informing future research aiming to qualitatively investigate treatment burden among patients in LMICs.

Multimorbidity has been framed as a pressing global health challenge that exposes the limits of systems organised around single diseases. This article seeks to expand and strengthen current thinking around multimorbidity by analysing its construction within the field of global health. We suggest that the significance of multimorbidity lies not only in challenging divisions between disease categories but also in what it reveals about the culture and history of transnational biomedicine. Drawing on social research from sub-Saharan Africa to ground our arguments, we begin by describing the historical processes through which morbidity was made divisible in biomedicine and how the single disease became integral not only to disease control but to the extension of biopolitical power. Multimorbidity, we observe, is hoped to challenge single disease approaches but is assembled from the same problematic, historically-loaded categories that it exposes as breaking down. Next, we highlight the consequences of such classificatory legacies in everyday lives and suggest why frameworks and interventions to integrate care have tended to have limited traction in practice. Finally, we argue that efforts to align priorities and disciplines around a standardised biomedical definition of multimorbidity risks retracing the same steps. We call for transdisciplinary work across the field of global health around a more holistic, reflexive understanding of multimorbidity that foregrounds the culture and history of translocated biomedicine, the intractability of single disease thinking, and its often-adverse consequences in local worlds. We outline key domains within the architecture of global health where transformation is needed, including care delivery, medical training, the organisation of knowledge and expertise, global governance, and financing.

Background: Multidisciplinary Teams (MDTs) has been suggested as an intervention to overcome some of the complexities experienced by people with diabetes and comorbidities in terms of diagnosis and treatment. However, evidence concerning MDTs within the diabetes field remains sparse.

Objective: This review aims to identify and map available evidence on key characteristics of MDTs in the context of diagnosis and treatment in people with diabetes and comorbidities.

Methods: This review followed the PRISMA-ScR guidelines. Databases PubMed, EMBASE, and CINAHL were systematically searched for studies assessing any type of MDT within the context of diagnosis and treatment in adult people (≥ 18 years) with diabetes and comorbidities/complications. Data extraction included details on study characteristics, MDT interventions, digital health solutions, and key findings.

Results: Overall, 19 studies were included. Generally, the MDTs were characterized by high heterogeneity. Four overall components characterized the MDTs: Both medical specialists and healthcare professionals (HCPs) of different team sizes were represented; interventions spanned elements of medication, assessment, nutrition, education, self-monitoring, and treatment adjustment; digital health solutions were integrated in 58% of the studies; MDTs were carried out in both primary and secondary healthcare settings with varying frequencies. Generally, the effectiveness of the MDTs was positive across different outcomes.

Conclusions: MDTs are characterized by high diversity in their outline yet seem to be effective and cost-effective in the context of diagnosis and treatment of people with diabetes and comorbidities. Future research should investigate the cross-sectorial collaboration to reduce care fragmentation and enhance care coordination.