TY - JOUR
T1 - Trajectories in long-term condition accumulation and mortality in older adults
T2 - A group-based trajectory modelling approach using the English Longitudinal Study of Ageing
AU - Chalitsios, Christos V.
AU - Santoso, Cornelia
AU - Nartey, Yvonne
AU - Khan, Nusrat
AU - Simpson, Glenn
AU - Islam, Nazrul
AU - Stuart, Beth
AU - Farmer, Andrew
AU - Dambha-Miller, Hajira
N1 - Publisher Copyright:
© Author(s) (or their employer(s)) 2024. Re-use permitted under CC BY. Published by BMJ.
PY - 2024/7/11
Y1 - 2024/7/11
N2 - Objectives To classify older adults into clusters based on accumulating long-term conditions (LTC) as trajectories, characterise clusters and quantify their associations with all-cause mortality. Design We conducted a longitudinal study using the English Longitudinal Study of Ageing over 9 years (n=15 091 aged 50 years and older). Group-based trajectory modelling was used to classify people into clusters based on accumulating LTC over time. Derived clusters were used to quantify the associations between trajectory memberships, sociodemographic characteristics and all-cause mortality by conducting regression models. Results Five distinct clusters of accumulating LTC trajectories were identified and characterised as: 'no LTC' (18.57%), 'single LTC' (31.21%), 'evolving multimorbidity' (25.82%), 'moderate multimorbidity' (17.12%) and 'high multimorbidity' (7.27%). Increasing age was consistently associated with a larger number of LTCs. Ethnic minorities (adjusted OR=2.04; 95% CI 1.40 to 3.00) were associated with the 'high multimorbidity' cluster. Higher education and paid employment were associated with a lower likelihood of progression over time towards an increased number of LTCs. All the clusters had higher all-cause mortality than the 'no LTC' cluster. Conclusions The development of multimorbidity in the number of conditions over time follows distinct trajectories. These are determined by non-modifiable (age, ethnicity) and modifiable factors (education and employment). Stratifying risk through clustering will enable practitioners to identify older adults with a higher likelihood of worsening LTC over time to tailor effective interventions to prevent mortality.
AB - Objectives To classify older adults into clusters based on accumulating long-term conditions (LTC) as trajectories, characterise clusters and quantify their associations with all-cause mortality. Design We conducted a longitudinal study using the English Longitudinal Study of Ageing over 9 years (n=15 091 aged 50 years and older). Group-based trajectory modelling was used to classify people into clusters based on accumulating LTC over time. Derived clusters were used to quantify the associations between trajectory memberships, sociodemographic characteristics and all-cause mortality by conducting regression models. Results Five distinct clusters of accumulating LTC trajectories were identified and characterised as: 'no LTC' (18.57%), 'single LTC' (31.21%), 'evolving multimorbidity' (25.82%), 'moderate multimorbidity' (17.12%) and 'high multimorbidity' (7.27%). Increasing age was consistently associated with a larger number of LTCs. Ethnic minorities (adjusted OR=2.04; 95% CI 1.40 to 3.00) were associated with the 'high multimorbidity' cluster. Higher education and paid employment were associated with a lower likelihood of progression over time towards an increased number of LTCs. All the clusters had higher all-cause mortality than the 'no LTC' cluster. Conclusions The development of multimorbidity in the number of conditions over time follows distinct trajectories. These are determined by non-modifiable (age, ethnicity) and modifiable factors (education and employment). Stratifying risk through clustering will enable practitioners to identify older adults with a higher likelihood of worsening LTC over time to tailor effective interventions to prevent mortality.
KW - epidemiology
KW - geriatric medicine
KW - public health
UR - http://www.scopus.com/inward/record.url?scp=85198598099&partnerID=8YFLogxK
U2 - 10.1136/bmjopen-2023-074902
DO - 10.1136/bmjopen-2023-074902
M3 - Article
C2 - 38991683
AN - SCOPUS:85198598099
SN - 2044-6055
VL - 14
JO - BMJ Open
JF - BMJ Open
IS - 7
M1 - e074902
ER -