Sociodemographic and lifestyle predictors of incident hospital admissions with multimorbidity in a general population, 1999-2019: the EPIC-Norfolk cohort

Predictive model for vitamin C levels in hyperinsulinemic individuals based on age, sex, waist circumference, low-density lipoprotein, and immune-associated serum proteins

Vitamin C (ascorbic acid) is an important water-soluble antioxidant associated with decreased oxidative stress in type 2 diabetes (T2D) patients. A previous targeted plasma proteomic study has indicated that ascorbic acid is associated with markers of the immune system in healthy subjects. However, the association between the levels of ascorbic acid and blood biomarkers in subjects at risk of developing T2D is still unknown. Serum ascorbic acid was measured by ultra-performance liquid chromatography and serum proteins were quantified by untargeted liquid-chromatography mass spectrometry in 25 hyperinsulinemia subjects that were randomly assigned a high dairy intake diet or an adequate dairy intake diet for 6 weeks, then crossed-over after a 6-week washout period. Spearman correlation followed by gene ontology analyses were performed to identify biological pathways associated with ascorbic acid. Finally, machine learning analysis was performed to obtain a specific serum protein signature that could predict ascorbic acid levels. After adjustments for waist circumference, LDL, HDL, fasting insulin, fasting blood glucose, age, gender, and dairy intake; serum ascorbic acid correlated positively with different aspects of the immune system. Machine learning analysis indicated that a signature composed of 21 features that included 17 proteins (mainly from the immune system), age, sex, waist circumference, and LDL could predict serum ascorbic acid levels in hyperinsulinemia subjects. In conclusion, the result reveals a correlation as well as modulation between serum ascorbic acid levels and proteins that play vital roles in regulating different aspects of the immune response in individuals at risk of T2D. The development of a predictive signature for ascorbic acid will further help the assessment of ascorbic acid status in clinical settings.

Hospital admissions and mortality over 20 years in community-dwelling older people: findings from the Hertfordshire Cohort Study

BACKGROUND

Demographic changes worldwide are leading to pressures on health services, with hospital admissions representing an important contributor. Here, we report admission types experienced by older people and examine baseline risk factors for subsequent admission/death, from the community-based Hertfordshire Cohort Study.

METHODS

2997 participants (1418 women) completed a baseline questionnaire and clinic visit to characterize their health. Participants were followed up from baseline (1998-2004, aged 59-73 years) until December 2018 using UK Hospital Episode Statistics and mortality data, which report clinical outcomes using ICD-10 coding. Baseline characteristics in relation to the risk of admission/death during follow-up were examined using sex-stratified univariate logistic regression.

RESULTS

During follow-up, 36% of men and 26% of women died and 93% of men and 92% of women had at least one hospital admission; 6% of men and 7% of women had no admissions and were alive at end of follow-up. The most common types of admission during follow-up were cardiovascular (ever experienced: men 71%, women 68%) and respiratory (men 40%, women 34%). In both sexes, baseline risk factors that were associated (p < 0.05) with admission/death during follow-up were older age, poorer SF-36 physical function, and poorer self-rated health. In men, manual social class and a history of smoking, and in women, higher BMI, not owning one's home, and a minor trauma fracture since age 45, were also risk factors for admission/death.

CONCLUSIONS

Sociodemographic factors were related to increased risk of admission/death but a small proportion experienced no admissions during this period, suggesting that healthy ageing is achievable.

Impact of individual, household, and area characteristics on health and social care outcomes for people with multimorbidity: Protocol for a multilevel analysis

BACKGROUND

Multimorbidity is one of the greatest challenges facing health and social care systems globally. It is associated with high rates of health service use, adverse healthcare events, and premature death. Despite its importance, little is known about the effects of contextual determinants such as household and area characteristics on health and care outcomes for people with multimorbidity. This study protocol presents a plan for the examination of associations between individual, household, and area characteristics with important health and social care outcomes.

METHODS

The study will use a cross-section of data from the SAIL Databank on 01 January 2019 and include all people alive and registered with a Welsh GP. The cohort will be stratified according to the presence or absence of multimorbidity, defined as two or more long-term conditions. Multilevel models will be used to examine covariates measured for individuals, households, and areas to account for social processes operating at different levels. The intra-class correlation coefficient will be calculated to determine the strength of association at each level of the hierarchy. Model outcomes will be any emergency department attendance, emergency hospital or care home admission, or mortality, within the study follow-up period.

DISCUSSION

Household and area characteristics might act as protective or risk factors for health and care outcomes for people with multimorbidity, in which case results of the analyses can be used to guide clinical and policy responses for effective targeting of limited resources.

Household and area determinants of emergency department attendance and hospitalisation in people with multimorbidity: a systematic review

OBJECTIVES

Multimorbidity is one of the greatest challenges facing healthcare internationally. Emergency department (ED) attendance and hospitalisation rates are higher in people with multimorbidity, but most research focuses on associations with individual characteristics, ignoring household or area mediators of service use.

DESIGN

Systematic review reported using the synthesis without meta-analysis framework.

DATA SOURCES

Twelve electronic databases (1 January 2000-21 September 2021): MEDLINE/OVID, Embase, Global Health, PsycINFO, ASSIA, CAB Abstracts, Science Citation Index Expanded/ISI Web of Science, Scopus, Cumulative Index to Nursing and Allied Health Literature, Sociological Abstracts, the Cochrane Library, and OpenGrey.

ELIGIBILITY CRITERIA

Adults aged ≥16 years, with multimorbidity. Exposure(s) were household and/or area determinants of health. Outcomes were ED attendance and/or hospitalisation. The literature search was limited to publications in English.

DATA EXTRACTION AND SYNTHESIS

Independent double screening of titles and abstracts to select relevant full-text studies. Methodological quality was assessed using an adaptation of the Newcastle-Ottawa Quality Assessment Scale tool. Given high study heterogeneity, narrative synthesis was performed.

RESULTS

After deduplication, 10 721 titles and abstracts were screened, and 142 full-text articles were reviewed, of which 10 were eligible for inclusion. In people with multimorbidity, household food insecurity was associated with hospitalisation (OR 1.58 (95% CI 1.06 to 2.36) in concordant multimorbidity). People with multimorbidity living in the most versus least deprived areas attended ED more frequently (8.9% (95% CI 8.6 to 9.1) in most versus 6.3% (95% CI 6.1 to 6.6) in least), had higher rates of hospitalisation (26% in most versus 22% in least), and higher probability of hospitalisation (6.4% (95% CI 5.8 to 7.2) in most versus 4.2% (95% CI 3.8 to 4.7) in least). There was non-conclusive evidence that household income is associated with ED attendance and hospitalisation. No statistically significant relationships were found between marital status, living with others with multimorbidity, or rurality with ED attendance or hospitalisation.

CONCLUSIONS

There is some evidence that household and area contexts mediate associations of multimorbidity with ED attendance and hospitalisation, but firm conclusions are constrained by the small number of studies published and study design heterogeneity. Further research is required on large population samples using robust analytical methods.

PROSPERO REGISTRATION NUMBER

CRD42021283515.

The Perceived Risk of Hospitalization in Primary Health Care – The Importance of Multidimensional Assessment

Ageing has increased the use of health services, with a corresponding rise in avoidable hospitalizations. We aimed to assess and characterize the perceived risk of hospitalization in primary health care (PHC). 118 individuals aged ≥65 years, PHC patients, were assessed using the Community Risk Assessment Instrument by their General Practitioner, who identified their perceived risk of hospitalization, at one year. The instrument is composed of three domains (mental state, daily living activities (ADLs) state and medical state). Multivariate logistic regression was used to identify the best model to predict the risk of hospitalization. Four models were estimated, one for each domain and one with all the variables of the instrument. 58.5% were identified as being at risk of hospitalization. The best predictive models are those that include functionality assessment variables (ADL model and Community Assessment of Risk Instrument model). The model that includes all the variables of three domains presents the best predictive value. Mobility problems (Odds Ratio (OR) 16.18 [CI: 1.63–160.53]), meal preparation (OR 10.93 [CI: 1.59–75.13]), communication (OR 6.91 [CI: 1.37–34.80]) and palliative care (OR 4.84 [CI: 1.14–20.58]) are the best predictors of hospitalization risk. The use of multidimensional assessment tools can allow the timely identification of people at risk, contributing to a reduction in hospitalizations.

Charlson Comorbidity Index: A Critical Review of Clinimetric Properties

The present critical review was conducted to evaluate the clinimetric properties of the Charlson Comorbidity Index (CCI), an assessment tool designed specifically to predict long-term mortality, with regard to its reliability, concurrent validity, sensitivity, incremental and predictive validity. The original version of the CCI has been adapted for use with different sources of data, ICD-9 and ICD-10 codes. The inter-rater reliability of the CCI was found to be excellent, with extremely high agreement between self-report and medical charts. The CCI has also been shown either to have concurrent validity with a number of other prognostic scales or to result in concordant predictions. Importantly, the clinimetric sensitivity of the CCI has been demonstrated in a variety of medical conditions, with stepwise increases in the CCI associated with stepwise increases in mortality. The CCI is also characterized by the clinimetric property of incremental validity, whereby adding the CCI to other measures increases the overall predictive accuracy. It has been shown to predict long-term mortality in different clinical populations, including medical, surgical, intensive care unit (ICU), trauma, and cancer patients. It may also predict in-hospital mortality, although in some instances, such as ICU or trauma patients, the CCI did not perform as well as other instruments designed specifically for that purpose. The CCI thus appears to be clinically useful not only to provide a valid assessment of the patient's unique clinical situation, but also to demarcate major diagnostic and prognostic differences among subgroups of patients sharing the same medical diagnosis.

Assessment of common infections and incident dementia using UK primary and secondary care data: a historical cohort study

BACKGROUND

Common infections have been associated with dementia risk; however, evidence is scarce. We aimed to investigate the association between common infections and dementia in adults (≥65 years) in a UK population-based cohort study.

METHODS

We did a historical cohort study of individuals who were 65 years and older with no history of dementia or cognitive impairment using the Clinical Practice Research Datalink linked to Hospital Episode Statistics between Jan 1, 2004, and Dec 31, 2018. Multivariable Cox proportional hazard regression models were used to estimate the association between time-updated previous common infections (sepsis, pneumonia, other lower respiratory tract infections, urinary tract infections, and skin and soft tissue infections) and incident dementia diagnosis. We also tested for effect modification by diabetes since it is an independent risk factor for dementia and co-occurs with infection.

FINDINGS

Between Jan 1, 2004, and Dec 31, 2018, our study included 989 800 individuals (median age 68·6 years [IQR 65·0-77·0]; 537 602 [54·3%] women) of whom 402 204 (40·6%) were diagnosed with at least one infection and 56 802 (5·7%) had incident dementia during a median follow-up of 5·2 years (IQR 2·3-9·0). Dementia risk increased in those with any infection (adjusted hazard ratio [HR] 1·53 [95% CI 1·50-1·55]) compared with those without infection. HRs were highest for sepsis (HR 2·08 [1·89-2·29]) and pneumonia (HR 1·88 [1·77-1·99]) and for infections leading to hospital admission (1·99 [1·94-2·04]). HRs were also higher in individuals with diabetes compared with those without diabetes.

INTERPRETATION

Common infections, particularly those resulting in hospitalisation, were associated with an increased risk of dementia persisting over the long term. Whether reducing infections lowers the risk of subsequent dementia warrants evaluation.

FUNDING

Alzheimer's Society, Wellcome Trust, and the Royal Society.