Epidemiology and impact of multimorbidity in primary care: a retrospective cohort study

Salisbury, Chris; Johnson, Leigh; Purdy, Sarah; Valderas, José M; Montgomery, Alan

doi:10.3399/bjgp11x548929

Cited by 745 publications

(745 citation statements)

References 29 publications

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“…Similar to other studies, older age (77-78% of this cohort 65+ years old) showed the highest odds of medical comorbidity. 8,14,27 However, among those with at least 50% service-connected disability, the highest odds of comorbidity type were driven by mental health.…”

Section: Discussionmentioning

confidence: 99%

“…The synergistic effect of the greater number of medical and mental health comorbidities present in more disadvantaged populations is a contributing factor to the gap in health equity evidenced in the literature. [7][8][9][13][14][15][16][17][18] The prevalence of coexisting medical and mental health conditions is clear among disadvantaged populations, with a convincing base of evidence. One study in a Scottish population (1.75 million) found that individuals in the most socioeconomically deprived areas had 10-15 years' earlier onset of multimorbidity and 11-fold higher odds of both physical and mental health disorders than their wealthier counterparts.…”

Section: Discussionmentioning

confidence: 99%

“…2,7,8 Multimorbidity increases with age, and is associated with a variety of untoward health outcomes, including poor functional status, lower quality of life, higher healthcare utilization, and higher mortality. 2,[9][10][11][12] It is conceptualized not simply as a list of diseases conferring varying degrees of health risk, but as non-randomly occurring clusters of diseases within populations.…”

Section: Introductionmentioning

confidence: 99%

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Geographic and Racial/Ethnic Variations in Patterns of Multimorbidity Burden in Patients with Type 2 Diabetes

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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“…Multimorbidity is quite common in elderly people as it affects more than 60 % of those aged 65 and above. This proportion rises with age, together with the number of simultaneous underlying conditions [3].…”

Section: Introductionmentioning

confidence: 98%

Preventing and managing herpes zoster: key actions to foster healthy aging

et al. 2015

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Population aging is the demographic phenomenon characterizing all countries in the world, and it is challenging the national infrastructures, in particular health systems. However, aging itself is not associated with increased medical spending, but disability and comorbidity that affect older individuals are the actual drivers for health expenditures. Therefore, if people age in better health, medical spending may be significantly reduced. Preventative interventions proved to be effective in reducing/preventing disease and disability and often found to be cost effective, include diet and exercise interventions, medications, routine disease screenings, and immunizations. Vaccination can protect older citizens against life-threatening diseases, such as influenza, pneumococcal infections, tetanus, and against diseases which adversely impact their quality of life, such as herpes zoster (HZ). Including HZ vaccination in its citizens' lifetime immunization calendar can reinforce Europe's commitment toward active, healthy aging. This paper outlines the consensus statement of a group of Italian experts on HZ.

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“…Moreover, cases of multimorbidity are strongly associated with age (e. g. Salisbury et al, 2011). For instance, a study carried out in a sample of Spanish adults shows that the group of persons aged 65 and over presented the highest prevalence of multimorbidity; concretely, 67.…”

Section: Introductionmentioning

confidence: 99%

Development of a Predictive Model of Elderly Patients at Risk of Future Hospital Admission at Primary Care Centres in Valencia (Spain)

Doñate-Martínez¹,

Ródenas-Rigla²,

Garcés‐Ferrer³

2017

Eur. j. interdiscip. stud.

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This paper presents the development of a new predictive model of elders at risk of suffering hospital admissions in the subsequent year in Valencia (Spain), based on primary care experts' discussions and consensus. The study involves three main stages. Firstly, Focus Group methodology with six primary care experts to design the first set of variables of the model. Subsequently, two retrospective studies to analyse the performance of the selected variables in a pilot sample (n=107) and to design the predictive model in a development sample (n=343). Data was collected from electronic medical records and consulting the professional of reference. Logistic regression analysis identified five variables as predictors of hospital admissions during the subsequent year of the development cohort: diagnosis of chronic heart diseases, chronic respiratory diseases, diabetes, presence of palliative care and number of previous visits to the hospital emergency department. A risk scoring system was developed for each patient from 0 to 1, with a cut-off point of 0. 5. The model had a sensitivity of 42%, specificity of 96% and AUC of 0. 764. Our predictive model identifies with moderate efficiency elderly patients at risk of suffering future hospital admissions. Additionally, this first screening could be extended through a second phase aimed to assess social variables which are very relevant in the current economic context in Spain. Further research is needed to validate these results with larger samples, and to explore their applicability in other health and social care settings.

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Epidemiology and impact of multimorbidity in primary care: a retrospective cohort study

Cited by 745 publications

References 29 publications

Geographic and Racial/Ethnic Variations in Patterns of Multimorbidity Burden in Patients with Type 2 Diabetes

Geographic and Racial/Ethnic Variations in Patterns of Multimorbidity Burden in Patients with Type 2 Diabetes

Preventing and managing herpes zoster: key actions to foster healthy aging

Development of a Predictive Model of Elderly Patients at Risk of Future Hospital Admission at Primary Care Centres in Valencia (Spain)

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