BMI and Cause-Specific Hospital Admissions and Costs: The UK Biobank Cohort Study

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.

Challenges in obtaining accurate anthropometric measures for adults with severe obesity: A community-based study

AIMS

The number of people with severe obesity (BMI ⩾40 kg/m²) is increasing rapidly, but is poorly documented, partly as a result of inappropriate standard anthropometric measurement methods for community-based people.

METHODS

As part of a broader study, people receiving care services and with severe obesity were visited at home. The people were assessed for measurements using different weighing scales and a standard portable stadiometer. If the stadiometer could not be used, their half arm span and knee height were measured to estimate their height using standard predictive equations.

RESULTS

Measurements were taken for 15 women and 10 men (n = 25) aged 40-87 years (mean 62 years). Weights ranged from 98.4 to 211.8 kg (mean 150 kg), with 16 participants requiring bariatric scales. For the six people who were unable to stand, we used wheelchair scales (n = 1), bed weighing scales (n = 2), routine weights from care home records (n = 2) or weight data from hospital records (n = 1). The standard portable stadiometer could only be used for one person; the others required alternative measures from which to estimate height. Large body habitus obscured bony landmarks, meaning alternative measures gave diverse heights. Fourteen participants had a ⩾8 cm difference in height between estimates from half arm span and knee height measurements.

CONCLUSIONS

Standard practice commonly does not provide reliable measurements for people with severe obesity, particularly those with mobility difficulties. An inability to measure weight and height accurately can exclude people from appropriate care, obscuring the true numbers affected and the effectiveness of future service planning. Safe community care requires the availability of specialist scales and standardised methods for height estimation appropriate for older and disabled people with severe obesity.

Simple approaches to characterising multiple long-term conditions (multimorbidity) and rates of emergency hospital admission: Findings from 495,465 UK Biobank participants

BACKGROUND

Numerous approaches are used to characterise multiple long-term conditions (MLTC), including counts and indices. Few studies have compared approaches within the same dataset. We aimed to characterise MLTC using simple approaches, and compare their prevalence estimates of MLTC and associations with emergency hospital admission in the UK Biobank.

METHODS

We used baseline data from 495,465 participants (age 38-73 years) to characterise MLTC using four approaches: Charlson index (CI), Byles index (BI), count of 43 conditions (CC) and count of body systems affected (BC). We defined MLTC as more than two conditions using CI, BI and CC, and more than two body systems using BC. We categorised scores (incorporating weightings for the indices) from each approach as 0, 1, 2 and 3+. We used linked hospital episode statistics and performed survival analyses to test associations with an endpoint of emergency hospital admission or death over 5 years.

RESULTS

The prevalence of MLTC was 44% (BC), 33% (CC), 6% (BI) and 2% (CI). Higher scores using all approaches were associated with greater outcome rates independent of sex and age group. For example, using CC, compared with score 0, score 2 had 1.95 (95% CI: 1.91, 1.99) and a score of 3+ had 3.12 (95% CI: 3.06, 3.18) times greater outcome rates. The discriminant value of all approaches was modest (C-statistics 0.60-0.63).

CONCLUSIONS

The counts classified a greater proportion as having MLTC than the indices, highlighting that prevalence estimates of MLTC vary depending on the approach. All approaches had strong statistical associations with emergency hospital admission but a modest ability to identify individuals at risk.

Associations of handgrip strength with morbidity and all-cause mortality of cardiometabolic multimorbidity

BACKGROUND

Cardiometabolic multimorbidity (CM) is an increasing public health and clinical concern. However, predictors for the development and prognosis of CM are poorly understood. The aims of this study were to investigate the relation between handgrip strength (HGS) and the risk of CM and to examine the association of HGS with all-cause mortality risk among patients with CM.

METHODS

This prospective cohort study involved 493,774 participants from the UK Biobank. CM was defined as the simultaneous occurrence of two or more of the following conditions: type 2 diabetes, stroke, and coronary heart disease (CHD). Cox proportional hazards models were performed to estimate hazard ratios (HRs) and 95% confidence intervals (95% CIs).

RESULTS

During a median follow-up of 12.1 years, 4701 incident CM cases were documented among participants with none cardiometabolic disease at baseline. Compared with the fourth quartile (Q4), the multivariable adjusted HR (95% CI) value of Q1 of HGS for developing CM was 1.46 (1.34-1.60). In participants with one cardiometabolic disease at baseline, participants in Q1 of HGS also possessed higher risk of CM than those in Q4, with HRs (95% CIs) being 1.35 (1.23-1.49) in patients with type 2 diabetes, 1.23 (1.04-1.46) in patients with stroke, and 1.23 (1.11-1.36) in patients with CHD. For participants with CM at recruitment, HGS was also associated with the risk of all-cause mortality (Q1 vs. Q4 HR: 1.57, 95% CI: 1.36-1.80).

CONCLUSIONS

Our study provided novel evidence that HGS could be an independent predictor of morbidity and all-cause mortality of CM.

Obesity, metabolic risk and adherence to healthy lifestyle behaviours: prospective cohort study in the UK Biobank

BACKGROUND

Contested evidence suggests that obesity confers no risk to health in people who have a healthy lifestyle, particularly if there are no metabolic complications of obesity. The aim was to examine the association between adherence to lifestyle recommendations and the absence of metabolic complications on the incident or fatal cardiovascular disease and all-cause mortality across different categories of body mass index (BMI).

METHODS

This contemporary prospective cohort study included 339,902 adults without cardiovascular disease at baseline, recruited between 2006 and 2010 from the UK Biobank and followed until 2018-2020. The main exposures were four healthy lifestyle behaviours: never smoker, alcohol intake ≤ 112g/ week, 150 min moderate physical activity or 75 min vigorous activity/week, ≥ 5 servings of fruit or vegetables/day, and we assessed these overall and across the BMI groups. Metabolic complications of excess adiposity were hypertension, diabetes and hyperlipidaemia, and we examined whether obesity was associated with increased risk in the absence of these complications. The outcomes were all-cause mortality, death from, and incident cardiovascular disease (CVD).

RESULTS

Individuals who met four lifestyle recommendations but had excess weight had higher all-cause mortality; for BMI 30-34.9 kg/m2, the hazard ratio (HR) was 1.42 (95% confidence interval 1.20 to 1.68), and for BMI ≥ 35 kg/m2, HR was 2.17 (95% CI 1.71 to 2.76). The risk was lower, but still increased for people with no metabolic complications; for all-cause mortality, BMI 30-34.9 kg/m2 had an HR of 1.09 (95% CI 0.99 to 1.21), and BMI ≥ 35 kg/m2 had an HR of 1.44 (95% CI 1.19 to 1.74) for all-cause mortality. Similar patterns were found for incident and fatal CVD.

CONCLUSIONS

Meeting healthy lifestyle recommendations, or the absence of metabolic complications of obesity offsets some, but not all, of the risk of subsequent CVD, and premature mortality in people with overweight or obesity. Offering support to achieve and maintain a healthy weight and to adopt healthy behaviours are likely to be important components in effective preventative healthcare.

Mendelian randomization analysis of the causal impact of body mass index and waist-hip ratio on rates of hospital admission

We analyze how measures of adiposity - body mass index (BMI) and waist hip ratio (WHR) - causally influence rates of hospital admission. Conventional analyses of this relationship are susceptible to omitted variable bias from variables that jointly influence both hospital admission and adipose status. We implement a novel quasi-Poisson instrumental variable model in a Mendelian randomization framework, identifying causal effects from random perturbations to germline genetic variation. We estimate the individual and joint effects of BMI, WHR, and WHR adjusted for BMI. We also implement multivariable instrumental variable methods in which the causal effect of one exposure is estimated conditionally on the causal effect of another exposure. Data on 310,471 participants and over 550,000 inpatient admissions in the UK Biobank were used to perform one-sample and two-sample Mendelian randomization analyses. The results supported a causal role of adiposity on hospital admissions, with consistency across all estimates and sensitivity analyses. Point estimates were generally larger than estimates from comparable observational specifications. We observed an attenuation of the BMI effect when adjusting for WHR in the multivariable Mendelian randomization analyses, suggesting that an adverse fat distribution, rather than a higher BMI itself, may drive the relationship between adiposity and risk of hospital admission.