Dataset Information

Prevalence, determinants and patterns of multimorbidity in primary care: a systematic review of observational studies.

ABSTRACT:

Introduction

Multimorbidity is a major concern in primary care. Nevertheless, evidence of prevalence and patterns of multimorbidity, and their determinants, are scarce. The aim of this study is to systematically review studies of the prevalence, patterns and determinants of multimorbidity in primary care.

Methods

Systematic review of literature published between 1961 and 2013 and indexed in Ovid (CINAHL, PsychINFO, Medline and Embase) and Web of Knowledge. Studies were selected according to eligibility criteria of addressing prevalence, determinants, and patterns of multimorbidity and using a pretested proforma in primary care. The quality and risk of bias were assessed using STROBE criteria. Two researchers assessed the eligibility of studies for inclusion (Kappa=?0.86).

Results

We identified 39 eligible publications describing studies that included a total of 70,057,611 patients in 12 countries. The number of health conditions analysed per study ranged from 5 to 335, with multimorbidity prevalence ranging from 12.9% to 95.1%. All studies observed a significant positive association between multimorbidity and age (odds ratio [OR], 1.26 to 227.46), and lower socioeconomic status (OR, 1.20 to 1.91). Positive associations with female gender and mental disorders were also observed. The most frequent patterns of multimorbidity included osteoarthritis together with cardiovascular and/or metabolic conditions.

Conclusions

Well-established determinants of multimorbidity include age, lower socioeconomic status and gender. The most prevalent conditions shape the patterns of multimorbidity. However, the limitations of the current evidence base means that further and better designed studies are needed to inform policy, research and clinical practice, with the goal of improving health-related quality of life for patients with multimorbidity. Standardization of the definition and assessment of multimorbidity is essential in order to better understand this phenomenon, and is a necessary immediate step.

SUBMITTER: Violan C

PROVIDER: S-EPMC4105594 | biostudies-literature | 2014

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Prevalence, determinants and patterns of multimorbidity in primary care: a systematic review of observational studies.

Violan Concepció C Foguet-Boreu Quintí Q Flores-Mateo Gemma G Salisbury Chris C Blom Jeanet J Freitag Michael M Glynn Liam L Muth Christiane C Valderas Jose M JM

PloS one 20140721 7

<h4>Introduction</h4>Multimorbidity is a major concern in primary care. Nevertheless, evidence of prevalence and patterns of multimorbidity, and their determinants, are scarce. The aim of this study is to systematically review studies of the prevalence, patterns and determinants of multimorbidity in primary care.<h4>Methods</h4>Systematic review of literature published between 1961 and 2013 and indexed in Ovid (CINAHL, PsychINFO, Medline and Embase) and Web of Knowledge. Studies were selected ac ...[more]

PMID: 25048354

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Project description:BackgroundPeople with osteoarthritis are at a high risk of cardiovascular disease (CVD). Detecting CVD risk factors in this high-risk population will help to improve CVD outcomes. Primary care electronic health records (EHRs) provide opportunities for the surveillance of CVD risk factors in the osteoarthritis population. This paper aimed to systematically review evidence of prevalence estimates of CVD risk factors in people with osteoarthritis derived from primary care EHRs.MethodsEight databases including MEDLINE were systematically searched till January 2019. Observational studies using primary care EHRs data to estimate the prevalence of six CVD risk factors in people with osteoarthritis were included. A narrative review was conducted to summarize study results.ResultsSix studies were identified. High heterogeneity between studies prevented the calculation of pooled estimates. One study reported the prevalence of smoking (12.5%); five reported hypertensions (range: 19.7%-55.5%); four reported obesities (range: 34.4%-51.6%); two reported dyslipidemias (6.0%, 13.3%); five reported diabetes (range: 5.2%-18.6%); and one reported chronic kidney disease (1.8%) in people with osteoarthritis. One study reported a higher prevalence of hypertension (Odds Ratio (OR) 1.25, 95% confidence interval (CI) 1.19-1.32), obesity (OR 2.44, 95%CI 2.33-2.55), dyslipidemia (OR 1.24, 95%CI 1.14-1.35) and diabetes (1.11, 95%CI 1.02-1.22) in the osteoarthritis population compared with the matched non-osteoarthritis population.ConclusionsFrom studies identified in this review that had used primary care EHRs, prevalence estimates of CVD risk factors were higher in people with osteoarthritis compared with those without. These estimates may provide baseline frequency of CVD risk factors in osteoarthritis patients in primary care, although this is limited by the small number of studies and high heterogeneity. Further studies of frequency, using primary care EHRs, will help to answer whether this data source can be used for evaluating approaches to manage CVD risk factors in osteoarthritis patients.

Project description:BACKGROUND:Unintended pregnancy has significant consequences for the health and welfare of women and children. Despite this, a number of studies with inconsistent findings were conducted to reduce unintended pregnancy in Ethiopia; unavailability of a nationwide study that determines the prevalence of unintended pregnancy and its determinants is an important research gap. Thus, this study was conducted to determine the overall prevalence of unintended pregnancy and its determinants in Ethiopia. METHODS:We searched from Google Scholar, PubMed, Science Direct, Web of Science, CINAHL, and Cochrane Library databases for studies. Each of the original studies was assessed using a tool for the risk of bias of observational studies. The heterogeneity of studies was also assessed using I2 test statistics. Data were pooled and a random effect meta-analysis model was fitted to provide the overall prevalence of unintended pregnancy and its determinants in Ethiopia. In addition, the subgroup analyses were performed to investigate how the prevalence of unintended pregnancy varies across different groups of studies. RESULTS:Twenty-eight studies that satisfy the eligibility criteria were included. We found that the overall prevalence of unintended pregnancy in Ethiopia was 28% (95% CI: 26-31). The subgroup analyses showed that the highest prevalence of unintended pregnancy was observed from the Oromiya region (33.8%) followed by Southern Nations Nationalities and Peoples' region (30.6%) and the lowest was in Harar. In addition, the pooled prevalence of unintended pregnancy was 26.4% (20.8-32.4) and 30.0% (26.6-33.6) for community-based cross-sectional and institution-based cross-sectional studies respectively. The pooled analysis showed that not communicating with one's husband about family planning was more likely to lead to unintended pregnancy (OR: 3.56, 95%CI: 1.68-7.53). The pooled odds ratio also showed that unintended pregnancy is more likely among women who never use family planning methods (OR: 2.08, 95%CI: 1.18-3.69). Furthermore, the narrative review of this study showed that maternal education, age, and household wealth index are strongly associated with an unintended pregnancy. CONCLUSIONS:In this study, the prevalence of unintended pregnancy was high. Lack of spousal communication, never using family planning, maternal education, and household wealth level were significantly associated with an unintended pregnancy. This study implies the need to develop plans and policies to improve the awareness of contraceptive utilization and strengthen spousal communication related to pregnancy.

Project description:BackgroundInternationally, there is an 'evidence-practice gap' in the rate healthcare professionals assess tobacco use and offer cessation support in clinical practice, including primary care. Evidence is needed for implementation strategies enacted in the 'real-world'.AimTo identify implementation strategies aiming to increase smoking cessation treatment provision in primary care, their effectiveness, cost-effectiveness and any perceived facilitators and barriers for effectiveness.Methods'Embase', 'Medline', 'PsycINFO', 'CINAHL', 'Global Health', 'Social Policy & Practice', 'ASSIA Applied Social Sciences Index and Abstracts' databases, and grey literature sources were searched from inception to April 2021. Studies were included if they evaluated an implementation strategy implemented on a nation-/state-wide scale, targeting any type of healthcare professional within the primary care setting, aiming to increase smoking cessation treatment provision.Primary outcome measuresimplementation strategy identification, and effectiveness (practitioner-/patient-level).Secondary outcome measuresperceived facilitators and barriers to effectiveness, and cost-effectiveness. Studies were assessed using the Risk Of Bias In Non-randomized Studies of Interventions (ROBINS-I) tool. A narrative synthesis was conducted using the Expert Recommendations for Implementing Change (ERIC) compilation and the Consolidated Framework for Implementation Research (CFIR).ResultsOf 49 included papers, half were of moderate/low risk of bias. The implementation strategy domains identified involved utilizing financial strategies, changing infrastructure, training and educating stakeholders, and engaging consumers. The first three increased practitioner-level smoking status recording and cessation advice provision. Interventions in the utilizing financial strategies domain also appeared to increase smoking cessation (patient-level). Key facilitator: external policies/incentives (tobacco control measures and funding for public health and cessation clinics). Key barriers: time and financial constraints, lack of free cessation medications and follow-up, deprioritisation and unclear targets in primary care, lack of knowledge of healthcare professionals, and unclear messaging to patients about available cessation support options. No studies assessed cost-effectiveness.ConclusionsSome implementation strategy categories increased the rate of smoking status recording and cessation advice provision in primary care. We found some evidence for interventions utilizing financial strategies having a beneficial impact on cessation. Identified barriers to effectiveness should be reduced. More pragmatic approaches are recommended, such as hybrid effectiveness-implementation designs and utilising Multiphase Optimization Strategy methodology.Protocol registrationPROSPERO:CRD42021246683.

Project description:BackgroundMultimorbidity is increasingly prevalent and represents a major challenge in primary care. Patients with multimorbidity are potentially more likely to experience safety incidents due to the complexity of their needs and frequency of their interactions with health services. However, rigorous syntheses of the link between patient safety incidents and multimorbidity are not available. This review examined the relationship between multimorbidity and patient safety incidents in primary care.MethodsWe followed our published protocol (PROSPERO registration number: CRD42014007434). Medline, Embase and CINAHL were searched up to May 2015. Study design and quality were assessed. Odds ratios (OR) and 95% confidence intervals (95% CIs) were calculated for the associations between multimorbidity and two categories of patient safety outcomes: 'active patient safety incidents' (such as adverse drug events and medical complications) and 'precursors of safety incidents' (such as prescription errors, medication non-adherence, poor quality of care and diagnostic errors). Meta-analyses using random effects models were undertaken.ResultsEighty six relevant comparisons from 75 studies were included in the analysis. Meta-analysis demonstrated that physical-mental multimorbidity was associated with an increased risk for 'active patient safety incidents' (OR = 2.39, 95% CI = 1.40 to 3.38) and 'precursors of safety incidents' (OR = 1.69, 95% CI = 1.36 to 2.03). Physical multimorbidity was associated with an increased risk for active safety incidents (OR = 1.63, 95% CI = 1.45 to 1.80) but was not associated with precursors of safety incidents (OR = 1.02, 95% CI = 0.90 to 1.13). Statistical heterogeneity was high and the methodological quality of the studies was generally low.ConclusionsThe association between multimorbidity and patient safety is complex, and varies by type of multimorbidity and type of safety incident. Our analyses suggest that multimorbidity involving mental health may be a key driver of safety incidents, which has important implication for the design and targeting of interventions to improve safety. High quality studies examining the mechanisms of patient safety incidents in patients with multimorbidity are needed, with the goal of promoting effective service delivery and ameliorating threats to safety in this group of patients.

Project description:ObjectiveTo determine the effectiveness of interventions designed to improve outcomes in patients with multimorbidity in primary care and community settings.DesignSystematic review.Data sourcesMedline, Embase, CINAHL, CAB Health, Cochrane central register of controlled trials, the database of abstracts of reviews of effectiveness, and the Cochrane EPOC (effective practice and organisation of care) register (searches updated in April 2011).Eligibility criteriaRandomised controlled trials, controlled clinical trials, controlled before and after studies, and interrupted time series analyses reporting on interventions to improve outcomes for people with multimorbidity in primary care and community settings. Multimorbidity was defined as two or more chronic conditions in the same individual. Outcomes included any validated measure of physical or mental health and psychosocial status, including quality of life outcomes, wellbeing, and measures of disability or functional status. Also included were measures of patient and provider behaviour, including drug adherence, utilisation of health services, acceptability of services, and costs.Data selectionTwo reviewers independently assessed studies for eligibility, extracted data, and assessed study quality. As meta-analysis of results was not possible owing to heterogeneity in participants and interventions, a narrative synthesis of the results from the included studies was carried out.Results10 studies examining a range of complex interventions totalling 3407 patients with multimorbidity were identified. All were randomised controlled trials with a low risk of bias. Two studies described interventions for patients with specific comorbidities. The remaining eight studies focused on multimorbidity, generally in older patients. Consideration of the impact of socioeconomic deprivation was minimal. All studies involved complex interventions with multiple components. In six of the 10 studies the predominant component was a change to the organisation of care delivery, usually through case management or enhanced multidisciplinary team work. In the remaining four studies, intervention components were predominantly patient oriented. Overall the results were mixed, with a trend towards improved prescribing and drug adherence. The results indicated that it is difficult to improve outcomes in this population but that interventions focusing on particular risk factors in comorbid conditions or functional difficulties in multimorbidity may be more effective. No economic analyses were included, although the improvements in prescribing and risk factor management in some studies could provide potentially important cost savings.ConclusionsEvidence on the care of patients with multimorbidity is limited, despite the prevalence of multimorbidity and its impact on patients and healthcare systems. Interventions to date have had mixed effects, although are likely to be more effective if targeted at risk factors or specific functional difficulties. A need exists to clearly identify patients with multimorbidity and to develop cost effective and specifically targeted interventions that can improve health outcomes.

Project description:ObjectiveTo identify observational studies which used data from more than one primary care electronic health record (EHR) database, and summarise key characteristics including: objective and rationale for using multiple data sources; methods used to manage, analyse and (where applicable) combine data; and approaches used to assess and report heterogeneity between data sources.DesignA systematic review of published studies.Data sourcesPubmed and Embase databases were searched using list of named primary care EHR databases; supplementary hand searches of reference list of studies were retained after initial screening.Study selectionObservational studies published between January 2000 and May 2018 were selected, which included at least two different primary care EHR databases.Results6054 studies were identified from database and hand searches, and 109 were included in the final review, the majority published between 2014 and 2018. Included studies used 38 different primary care EHR data sources. Forty-seven studies (44%) were descriptive or methodological. Of 62 analytical studies, 22 (36%) presented separate results from each database, with no attempt to combine them; 29 (48%) combined individual patient data in a one-stage meta-analysis and 21 (34%) combined estimates from each database using two-stage meta-analysis. Discussion and exploration of heterogeneity was inconsistent across studies.ConclusionsComparing patterns and trends in different populations, or in different primary care EHR databases from the same populations, is important and a common objective for multi-database studies. When combining results from several databases using meta-analysis, provision of separate results from each database is helpful for interpretation. We found that these were often missing, particularly for studies using one-stage approaches, which also often lacked details of any statistical adjustment for heterogeneity and/or clustering. For two-stage meta-analysis, a clear rationale should be provided for choice of fixed effect and/or random effects or other models.

Project description:BackgroundThe co-design of health care enables patient-centredness by partnering patients, clinicians and other stakeholders together to create services.MethodsWe conducted a systematic review of co-designed health interventions for people living with multimorbidity and assessed (a) their effectiveness in improving health outcomes, (b) the co-design approaches used and (c) barriers and facilitators to the co-design process with people living with multimorbidity. We searched MEDLINE, EMBASE, CINAHL, Scopus and PsycINFO between 2000 and March 2022. Included experimental studies were quality assessed using the Cochrane risk of bias tool (ROB-2 and ROBINS-I).ResultsWe screened 14,376 reports, with 13 reports meeting the eligibility criteria. Two reported health and well-being outcomes: one randomised clinical trial (n = 134) and one controlled cohort (n = 1933). Outcome measures included quality of life, self-efficacy, well-being, anxiety, depression, functional status, healthcare utilisation and mortality. Outcomes favouring the co-design interventions compared to control were minimal, with only 4 of 17 outcomes considered beneficial. Co-design approaches included needs assessment/ideation (12 of 13), prototype (11 of 13), pilot testing (5 of 13) (i.e. focus on usability) and health and well-being evaluations (2 of 13). Common challenges to the co-design process include poor stakeholder interest, passive participation, power imbalances and a lack of representativeness in the design group. Enablers include flexibility in approach, smaller group work, advocating for stakeholders' views and commitment to the process or decisions made.ConclusionsIn this systematic review of co-design health interventions, we found that few projects assessed health and well-being outcomes, and the observed health and well-being benefits were minimal. The intensity and variability in the co-design approaches were substantial, and challenges were evident. Co-design aided the design of novel services and interventions for those with multimorbidity, improving their relevance, usability and acceptability. However, the clinical benefits of co-designed interventions for those with multimorbidity are unclear.

Dataset Information

Prevalence, determinants and patterns of multimorbidity in primary care: a systematic review of observational studies.

Introduction

Methods

Results

Conclusions

Publications

Prevalence, determinants and patterns of multimorbidity in primary care: a systematic review of observational studies.

Similar Datasets

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