Measuring multimorbidity in hospitalised patients using linked hospital episode data: comparison of two measures

Novel linkage approach to join community-acquired and national data

BACKGROUND

Community optometrists in Scotland have performed regular free-at-point-of-care eye examinations for all, for over 15 years. Eye examinations include retinal imaging but image storage is fragmented and they are not used for research. The Scottish Collaborative Optometry-Ophthalmology Network e-research project aimed to collect these images and create a repository linked to routinely collected healthcare data, supporting the development of pre-symptomatic diagnostic tools.

METHODS

As the image record was usually separate from the patient record and contained minimal patient information, we developed an efficient matching algorithm using a combination of deterministic and probabilistic steps which minimised the risk of false positives, to facilitate national health record linkage. We visited two practices and assessed the data contained in their image device and Practice Management Systems. Practice activities were explored to understand the context of data collection processes. Iteratively, we tested a series of matching rules which captured a high proportion of true positive records compared to manual matches. The approach was validated by testing manual matching against automated steps in three further practices.

RESULTS

A sequence of deterministic rules successfully matched 95% of records in the three test practices compared to manual matching. Adding two probabilistic rules to the algorithm successfully matched 99% of records.

CONCLUSIONS

The potential value of community-acquired retinal images can be harnessed only if they are linked to centrally-held healthcare care data. Despite the lack of interoperability between systems within optometry practices and inconsistent use of unique identifiers, data linkage is possible using robust, almost entirely automated processes.

Relationship between the health poverty vulnerability and multimorbidity patterns identified with latent class analysis aged 45 years or more adults in Northwestern China: A cross-section study

This study aims to identify multimorbidity patterns and examine whether health poverty vulnerability (HPV) varies among adults aged 45 years or more. Data from 4338 participants were extracted from a Chinese cross-sectional study. Latent class analysis was used to identify multimorbidity patterns based on 11 self-reported chronic diseases. A 3-stage feasible generalized least-squares method was used to measure the HPV. The associations and influencing factors were analyzed using the Tobit model. The mean HPV values were 0.105 ± 0.225 and 0.329 ± 0.357, based on extreme poverty and those of low- and middle-income countries' poverty line, respectively. Four latent multimorbidity patterns were identified, comprising hypertension (57.33%), cardiovascular diseases (19.94%), the musculoskeletal system (13.09%), and spine (9.64%). The HPV value from hypertension (coefficient [Coef] =0.03, 95% confidence interval (CI) = 0.00-0.05) was significantly higher than that of the musculoskeletal system based on extreme poverty. In addition, the HPV values for hypertension (Coef =0.08, 95% CI = 0.05-0.11), spine (Coef =0.06, 95% CI = 0.02-0.11), and cardiovascular diseases (Coef =0.07, 95% CI = 0.03-0.11) were significantly high based on low- and middle-income countries' poverty line. Age ≥75 years, registered poor households, catastrophic medical expenditure, and toilet style were major risk factors. Although the multimorbidity pattern-induced HPV has been significant improved on extreme poverty, it still poses a very serious challenge with regard to low- to middle-income countries' poverty line. The sensitivity analysis proved the robustness of the results. Policymakers should focus on adults with 3 multimorbidity patterns, namely, registered poor households, age ≥75 years, and catastrophic health expenditure, to adopt targeted interventions to prevent and eliminate HPV.

Acute physiology and chronic health evaluation score and mortality of patients admitted to intermediate care units of a hospital in a low- and middle-income country: A cross-sectional study from Pakistan

Background

Intermediate care units (IMCUs) serve as a bridge between general wards and intensive care units by providing close monitoring and rapid response to medical emergencies. We aim to identify the common acute medical conditions in patients admitted to IMCU and compare the predicted mortality of these conditions by acute physiology and chronic health evaluation-II (APACHE-II) score with actual mortality.

Methods

A cross-sectional study was conducted at a tertiary care hospital from 2017 to 2019. All adult internal medicine patients admitted to IMCUs were included. Acute conditions were defined as those of short duration (<3 weeks) that require hospitalization. The APACHE-II score was used to determine the severity of these patients' illnesses.

Results

Mean (standard deviation [SD]) age was 62 (16.5) years, and 493 (49.2%) patients were male. The top three acute medical conditions were acute and chronic kidney disease in 399 (39.8%), pneumonia in 303 (30.2%), and urinary tract infections (UTIs) in 211 (21.1%). The mean (SD) APACHE-II score of these patients was 12.5 (5.4). The highest mean APACHE-II (SD) score was for acute kidney injury (14.7 ± 4.8), followed by sepsis/septic shock (13.6 ± 5.1) and UTI (13.4 ± 5.1). Sepsis/septic shock was associated with the greatest mortality (odds ratio [OR]: 6.9 [95% CI (confidence interval): 4.5-10.6]), followed by stroke (OR: 3.9 [95% CI: 1.9-8.3]) and pneumonia (OR: 3.0 [95% CI: 2.0-4.5]).

Conclusions

Sepsis/septic shock, stroke, and pneumonia are the leading causes of death in our IMCUs. The APACHE-II score predicted mortality for most acute medical conditions but underestimated the risk for sepsis and stroke.

Age, sex, and socioeconomic differences in multimorbidity measured in four ways: UK primary care cross-sectional analysis

BACKGROUND

Multimorbidity poses major challenges to healthcare systems worldwide. Definitions with cut-offs in excess of ≥2 long-term conditions (LTCs) might better capture populations with complexity but are not standardised.

AIM

To examine variation in prevalence using different definitions of multimorbidity.

DESIGN AND SETTING

Cross-sectional study of 1 168 620 people in England.

METHOD

Comparison of multimorbidity (MM) prevalence using four definitions: MM2+ (≥2 LTCs), MM3+ (≥3 LTCs), MM3+ from 3+ (≥3 LTCs from ≥3 International Classification of Diseases, 10th revision chapters), and mental-physical MM (≥2 LTCs where ≥1 mental health LTC and ≥1 physical health LTC are recorded). Logistic regression was used to examine patient characteristics associated with multimorbidity under all four definitions.

RESULTS

MM2+ was most common (40.4%) followed by MM3+ (27.5%), MM3+ from 3+ (22.6%), and mental-physical MM (18.9%). MM2+, MM3+, and MM3+ from 3+ were strongly associated with oldest age (adjusted odds ratio [aOR] 58.09, 95% confidence interval [CI] = 56.13 to 60.14; aOR 77.69, 95% CI = 75.33 to 80.12; and aOR 102.06, 95% CI = 98.61 to 105.65; respectively), but mental-physical MM was much less strongly associated (aOR 4.32, 95% CI = 4.21 to 4.43). People in the most deprived decile had equivalent rates of multimorbidity at a younger age than those in the least deprived decile. This was most marked in mental-physical MM at 40-45 years younger, followed by MM2+ at 15-20 years younger, and MM3+ and MM3+ from 3+ at 10-15 years younger. Females had higher prevalence of multimorbidity under all definitions, which was most marked for mental-physical MM.

CONCLUSION

Estimated prevalence of multimorbidity depends on the definition used, and associations with age, sex, and socioeconomic position vary between definitions. Applicable multimorbidity research requires consistency of definitions across studies.

Identifying multimorbidity clusters in an unselected population of hospitalised patients

Multimorbidity (multiple coexisting chronic health conditions) is common and increasing worldwide, and makes care challenging for both patients and healthcare systems. To ensure care is patient-centred rather than specialty-centred, it is important to know which conditions commonly occur together and identify the corresponding patient profile. To date, no studies have described multimorbidity clusters within an unselected hospital population. Our aim was to identify and characterise multimorbidity clusters, in a large, unselected hospitalised patient population. Linked inpatient hospital episode data were used to identify adults admitted to hospital in Grampian, Scotland in 2014 who had ≥ 2 of 30 chronic conditions diagnosed in the 5 years prior. Cluster analysis (Gower distance and Partitioning around Medoids) was used to identify groups of patients with similar conditions. Clusters of conditions were defined based on clinical review and assessment of prevalence within patient groups and labelled according to the most prevalent condition. Patient profiles for each group were described by age, sex, admission type, deprivation and urban–rural area of residence. 11,389 of 41,545 hospitalised patients (27%) had ≥ 2 conditions. Ten clusters of conditions were identified: hypertension; asthma; alcohol misuse; chronic kidney disease and diabetes; chronic kidney disease; chronic pain; cancer; chronic heart failure; diabetes; hypothyroidism. Age ranged from 51 (alcohol misuse) to 79 (chronic heart failure). Women were a higher proportion in the chronic pain and hypothyroidism clusters. The proportion of patients from the most deprived quintile of the population ranged from 6% (hypertension) to 14% (alcohol misuse). Identifying clusters of conditions in hospital patients is a first step towards identifying opportunities to target patient-centred care towards people with unmet needs, leading to improved outcomes and increased efficiency. Here we have demonstrated the face validity of cluster analysis as an exploratory method for identifying clusters of conditions in hospitalised patients with multimorbidity.

Positive scores on the 4AT delirium assessment tool at hospital admission are linked to mortality, length of stay and home time: two-centre study of 82,770 emergency admissions

BACKGROUND

Studies investigating outcomes of delirium using large-scale routine data are rare. We performed a two-centre study using the 4 'A's Test (4AT) delirium detection tool to analyse relationships between delirium and 30-day mortality, length of stay and home time (days at home in the year following admission).

METHODS

The 4AT was performed as part of usual care. Data from emergency admissions in patients ≥65 years in Lothian, UK (n = 43,946) and Salford, UK (n = 38,824) over a period of $\sim$3 years were analysed using logistic regression models adjusted for age and sex.

RESULTS

4AT completion rates were 77% in Lothian and 49% in Salford. 4AT scores indicating delirium (≥4/12) were present in 18% of patients in Lothian, and 25% of patients in Salford. Thirty-day mortality with 4AT ≥4 was 5.5-fold greater than the 4AT 0/12 group in Lothian (adjusted odds ratio (aOR) 5.53, 95% confidence interval [CI] 4.99-6.13) and 3.4-fold greater in Salford (aOR 3.39, 95% CI 2.98-3.87). Length of stay was more than double in patients with 4AT scores of 1-3/12 (indicating cognitive impairment) or ≥ 4/12 compared with 4AT 0/12. Median home time at 1 year was reduced by 112 days (Lothian) and 61 days (Salford) in the 4AT ≥4 group (P < 0.001).

CONCLUSIONS

Scores on the 4AT used at scale in practice are strongly linked with 30-day mortality, length of hospital stay and home time. The findings highlight the need for better understanding of why delirium is linked with poor outcomes and also the need to improve delirium detection and treatment.

Prevalence of secondary care multimorbidity in mid-life and its association with premature mortality in a large longitudinal cohort study

OBJECTIVES

Multimorbidity is the coexistence of two or more health conditions in an individual. Multimorbidity in younger adults is increasingly recognised as an important challenge. We assessed the prevalence of secondary care multimorbidity in mid-life and its association with premature mortality over 15 years of follow-up, in the Aberdeen Children of the 1950s (ACONF) cohort.

METHOD

A prospective cohort study using linked electronic health and mortality records. Scottish ACONF participants were linked to their Scottish Morbidity Record hospital episode data and mortality records. Multimorbidity was defined as two or more conditions and was assessed using healthcare records in 2001 when the participants were aged between 45 and 51 years. The association between multimorbidity and mortality over 15 years of follow-up (to ages 60-66 years) was assessed using Cox proportional hazards regression. There was also adjustment for key covariates: age, gender, social class at birth, intelligence at age 7, secondary school type, educational attainment, alcohol, smoking, body mass index and adult social class.

RESULTS

Of 9625 participants (51% males), 3% had multimorbidity. The death rate per 1000 person-years was 28.4 (95% CI 23.2 to 34.8) in those with multimorbidity and 5.7 (95% CI 5.3 to 6.1) in those without. In relation to the reference group of those with no multimorbidity, those with multimorbidity had a mortality HR of 4.5 (95% CI 3.4 to 6.0) over 15 years and this association remained when fully adjusted for the covariates (HR 2.5 (95% CI 1.5 to 4.0)).

CONCLUSION

Multimorbidity prevalence was 3% in mid-life when measured using secondary care administrative data. Multimorbidity in mid-life was associated with premature mortality.