Abstract
Background
Multimorbidity, the co-occurrence of multiple chronic conditions, is commonly managed in primary care and increasingly prevalent. Researchers are increasingly studying the unique combinations of chronic conditions and their associations with health service utilisation. However, published studies largely focus on common dyads and triads of chronic conditions. This study aims to determine the prevalence and combinations of multimorbidity among older adults managed in Singapore’s primary care.
Methods
A cross-sectional analysis of all Singapore citizens and permanent residents aged ≥50 years who visited any polyclinic between July 2017 to June 2018 was conducted. Patient diagnoses up to year 2012 were reviewed using 23 chronic condition categories to define multimorbidity. All multimorbidity combinations and their prevalence were analysed.
Results
Among 502,036 polyclinic patients aged ≥50 years, 59.9% had multimorbidity. Of 4,194,050 possible multimorbidity combinations, 13,959 were observed. Combinations comprised between three to 13 chronic conditions. The most prevalent and costly combinations consisted three to four chronic conditions: (1) hyperlidaemia/hypertension/diabetes; (2) hyperlidaemia/hypertension/diabetes/functional limitation; (3) hyperlidaemia/hypertension/functional limitation; (4) hyperlipidaemia/hypertension/diabetes/cardiovascular disease; (5) hyperlipidaemia/hypertension/diabetes/arthritis; (6) hyperlipidaemia/hypertension/arthritis; (7) hyperlipidaemia/hypertension/cardiovascular disease; (8) hyperlipidaemia/hypertension/diabetes/obesity; (9) hyperlipidaemia/hypertension/diabetes/stroke; (10) hyperlipidaemia/hypertension/arthritis/functional limitation; (11) hyperlipidaemia/hypertension/diabetes/kidney.
Conclusion
Although a wide variety of combinations were observed, hyperlipidaemia and hypertension were common in the most prevalent and costly combinations. As current management of chronic conditions focusses on disease-specific clinical outcomes, findings from this study suggest a need to optimise clinical outcomes for common multimorbidity combinations rather than simply aggregating outcomes for all conditions within the combination.
Introduction
Multimorbidity may be broadly defined as the coexistence of multiple chronic conditions in an individual. 1 As the population ages worldwide, the prevalence of multimorbidity is expected to increase. Across many health systems, patients with multimorbidity are commonly managed in primary care. They also incur higher costs and have poorer outcomes than those without multimorbidity.2,3
A systematic review of 126 studies involving nearly 15.4 million people across 54 countries found that the global prevalence of multimorbidity is approximately 37.2%, with higher rates observed in females (39.4%) and among those over 60 years of age (51%). 4 The prevalence varies by region, being highest in South America (45.7%) and North America (43.1%), highlighting that multimorbidity is a widespread and growing health challenge worldwide. In our study population of 254,377 patients with at least one chronic condition, the prevalence of multimorbidity was 62.4%, underscoring its significant impact on the local healthcare context. 5
Management of multimorbidity can be challenging, as clinical practice guidelines are conventionally developed based on evidence focused on single diseases. The application of multiple single-disease clinical guidelines in the development of a care plan for patients with multimorbidity can lead to treatment burden and potentially introduce drug-drug interactions.6,7 Instead, the overall optimal treatment plan for patients with multimorbidity will likely need to be contextualised to the combination of chronic conditions. There is a need to develop guidelines to specifically meet the needs of patients with multimorbidity. 8 Thus, it will be crucial to understand which conditions commonly co-occur, and their impact on healthcare costs, to inform the development of appropriate protocols for multimorbidity management.
To date, much of the literature estimating the prevalence of multimorbidity across various health systems focusses on the counts of conditions.9,10 Other studies also examined the prevalence of specific combinations of chronic conditions (i.e., multimorbidity combinations), but have mostly been limited to dyad and triad combinations.11–13 Limiting the management of patients with multimorbidity to the management of each of the specific conditions individually overlook the heterogeneity of multimorbidity, which can have undesirable implications for these patients. The dearth of literature on commonly occurring multimorbidity combinations also has implications for costs and resource projections.
In the city state of Singapore, patients with multimorbidity may be managed across a variety of care venues – primary care, specialist outpatient centres (SOC) and tertiary hospitals – either in the public or private settings, or a mix of both. While private primary care clinics meet about 80% of the total primary care demand, public primary care accounts for approximately 52% of the chronic disease burden, with a sizeable proportion of patients with chronic conditions seeking care across the 26 polyclinics islandwide.14,15 These polyclinics provide subsidised outpatient medical care to Singapore residents, and are located across various regions in the country. They offer a range of services including management of acute and chronic conditions, preventive health screenings and selected diagnostic services. 16 As polyclinics are strategically situated in residential towns and cater to a large proportion of Singapore’s population–particularly older adults and those with chronic conditions–they provide an important setting for understanding multimorbidity patterns in a national context.
In contrast, public hospitals provide approximately 83% of all tertiary care demand, where patients routinely seek subsidised chronic disease care from specialists. 17 The prevalence of multimorbidity in Singapore is estimated to range between 26% to 89%,12,18–20 with an estimated annual per capita cost to the public primary care sector ranging from SGD303 to SGD695.12,21 The variation in the clinical and economic impact of multimorbidity reported by studies is due, in part, to the inconsistencies in the definition of multimorbidity, 22 making it challenging to plan for appropriate resource distribution for effective disease management.
The burden of multimorbidity on the local health system is further compounded by the rapidly ageing population in Singapore. It is estimated that about one in four Singaporeans will be older than 65 years by 2030. 23 Given the proportion of older residents in Singapore reporting three or more chronic diseases have doubled between 2009 to 2017, 24 the resultant socioeconomic and disease burden due to multimorbidity are expected to rise significantly.
Therefore, this study aims to determine the burden of multimorbidity, based on prevalence as well as costs, among older adults in the public primary care setting in Singapore to better inform clinical and policy decisions in the management of multimorbidity.
Methods
This study utilised administrative healthcare data obtained from the Ministry of Health (MOH), Singapore. The datasets comprised national-level healthcare visits and claims records from all public healthcare institutions, including polyclinics, SOCs, emergency departments (ED) and hospitals. The datasets were compiled from multiple sources and linked at the individual level using anonymised identifiers by MOH, Singapore. They include demographic information, clinical diagnoses and billing records. While this dataset provides a fairly comprehensive view of healthcare use in the public sector, data from private healthcare institutions were not available at the time of the study and therefore not included in the analysis.
A cross-sectional analysis of all patient attendances at any of the 21 polyclinics between 1 July 2017 to 30 June 2018 was conducted (five new polyclinics were opened from 2019 to 2025). For the purpose of this analysis, ‘older adults’ are defined as Singapore citizens or permanent residents aged 50 years and older. Our study included only older adults with at least one chronic condition and made at least one doctor visit to a polyclinic in the duration of interest. Patient attendances to polyclinics up to 1 January 2012 were also reviewed to identify diagnoses of chronic conditions to generate prevalence estimates. All analyses were conducted using data de-identified by MOH. Waiver of informed consent requirements and approval of the study were granted by the National University of Singapore Institutional Review Board (NUS-IRB-2023-1056).
In this study, multimorbidity was defined as the presence of three or more chronic conditions based on the consensus-derived definition of multimorbidity to identify a smaller number of patients with higher needs.5,25 We employed a list of 23 condition categories, contextualised to the primary care setting in Singapore. 25 A “chronic condition” was identified based on four parameters: (1) lasting six months or longer; (2) being recurrent or having a persistent course; (3) impacting the patient in one or more domains, such as limitations in activities of daily living (ADLs) or instrumental activities of daily living (IADLs), physical disability, mortality, or psychological impairment; and (4) requiring long-term follow-up, as established through consensus using a Delphi study conducted by our team. 25 All possible combinations of multimorbidity observed within the study population were analysed.
All the 21 polyclinics utilised electronic medical records to document diagnoses for each clinical encounter, using a pre-approved and harmonised list of ICD-10 codes established by MOH. The research team mapped the ICD-10 diagnosis codes from all polyclinics to the 23 condition categories derived from the Delphi study, based on matching ICD-10 codes. 25
To estimate the economic impact of multimorbidity to the public healthcare system, healthcare costs and health service utilisation (HSU) incurred by the study population at public healthcare institutions were also analysed. HSU was defined as any visit to the polyclinic, SOC, ED or any hospital admissions in the following year of analysis. That is, HSU of patients between 1 July 2018 to 30 June 2019 were analysed, and presented as average HSU. For the purpose of this analysis, dental visits were excluded. Average length of stay was estimated using only inpatient admissions; while hospital admissions included both inpatient admission and day surgery. Admissions into community hospitals or other long-term care facilities were excluded.
Patients with missing information, such as demographic information, and those at the end of life were excluded. For the purpose of this analysis, end of life was defined using a proxy outcome of deaths between 1 July 2018 to 30 June 2019.
Healthcare financing in Singapore is underpinned by a multipayer framework, in which a proportion of public healthcare costs are subsidised by the government, and remaining co-payments in the public and private sectors are covered by various sources including statutory financing schemes, private voluntary health insurance, employer medical benefits and out-of-pocket payments. 26 To estimate healthcare costs, we used the pre-subsidy bill to patients in the period that the HSU were incurred, and presented as annual per capita healthcare costs. Hospital costs referred to costs from day surgeries and inpatient stays. SOC normative costs were used to estimate SOC costs due to data unavailability. Of note, HSU and costs incurred in the community hospitals or other long-term care facilities were not included due to data unavailability. All costs are presented in Singapore dollars, rounded to the nearest dollar. Linear regressions were used to examine the associations between multimorbidity and healthcare costs, while adjusting for age. All analyses were performed using Stata/MP 16.1 (Stata Corporation, College Station, Texas, US).
Results
Demographic characteristics of study population.
Annual per capita costs incurred by multimorbidity status.
aAdjusted for age.
Crude prevalence rates of multimorbidity conditions in study population.
(COPD: chronic obstructive pulmonary disease, TIA: transient ischaemic attack).
aOther mental health conditions include schizophrenia, bipolar disorder, childhood mental health disorders like attention deficit hyperactivity disorder, autism, and obsessive-compulsive disorder, etc.
bChronic pain not coded across healthcare setting studied.
Commonly occurring combinations of multimorbidity in the polyclinics.
aCombinations A–J were the top 10 prevalent combinations. Combination K was the 10th costliest combination.
bFunctional limitation.
cCardiovascular disease (angina, myocardial infarction, atrial fibrillation, poor circulation of lower limbs).
dArthritis &/or rheumatoid arthritis.
eStroke and transient ischaemic attack.
fKidney disease or failure.
Mean healthcare costs of the most costly combinations of multimorbidity.
aCombinations A–J were the top 10 combinations with highest total healthcare costs. Combination K was the 10th most prevalent combination.
bNon-primary care costs refer to the aggregate of costs incurred within the polyclinic, SOC, ED and hospital.
cCardiovascular disease (angina, myocardial infarction, atrial fibrillation, poor circulation of lower limbs).
dFunctional limitation.
eArthritis &/or rheumatoid arthritis.
fStroke and transient ischaemic attack.
gKidney disease or failure.
Median healthcare costs of the most costly combinations of multimorbidity.
aCombinations A–J were the top 10 combinations with highest total healthcare costs. Combination K was the 10th most prevalent combination.
bNon-primary care costs refer to the aggregate of costs incurred within the polyclinic, SOC, ED and hospital.
cCardiovascular disease (angina, myocardial infarction, atrial fibrillation, poor circulation of lower limbs).
dFunctional limitation.
eArthritis &/or rheumatoid arthritis.
fStroke and transient ischaemic attack.
gKidney disease or failure.
Of note, the costliest combination corresponds with the most prevalent combination: hyperlipidaemia/hypertension/diabetes had the highest prevalence of 8.0% and the highest total healthcare cost of SGD163,765,386. When looking at total per capita costs across all care settings, hyperlipidaemia/hypertension/diabetes/kidney disease/failure had the highest per capita costs at SGD7,293 (SD: SGD14,510). It was also the combination that had the highest per capita costs in the SOC at SGD1,372 (SD: SGD1,799), ED at SGD188 (SD: SGD437) and hospital at SGD4,783 (SD: SGD13,441). Hyperlipidaemia/hypertension/diabetes/CVD had the highest per capita cost in the polyclinic at SGD974 (SD: SGD667). Similar trends were also observed when looking at the median costs of these combinations of multimorbidity.
The ratio of non-primary care costs to primary care costs for each of these 11 combinations of multimorbidity were also estimated. Combinations with higher prevalence tended to have a lower non-primary care to primary cost ratios.
Discussion
To the best of our knowledge, this is the first study that analysed all possible multimorbidity combinations within the public primary care setting in Singapore. It describes the prevalence of multimorbidity among those aged 50 years and older and compares the common multimorbidity combinations against total healthcare costs incurred across all public healthcare settings.
Hypertension, hyperlipidaemia and diabetes were observed to be the most prevalent chronic conditions in this population, consistent with the National Primary Care Survey 2010, 15 which revealed that these three conditions were among the top five common conditions seen in the local primary care setting. Unsurprisingly, the triad of hypertension/hyperlipidemia/diabetes was found to be the most prevalent combination of multimorbidity and incurred the highest healthcare costs. This finding is also consistent with other local studies in the primary care setting, 5 as well as community-dwelling adults. 27 Other studies also support the finding that the combination hypertension/hyperlipidaemia/diabetes incurred the highest healthcare costs in Singapore.5,12
Of note, hypertension and hyperlipidaemia co-occurred in all prevalent and costly combinations. This is aligned with international studies from a plethora of high-income countries which found that patients with multimorbidity tend to exhibit both conditions.28–32 Considering the current disease-specific focussed management of chronic conditions, this finding underscores the importance of harmonising the management of commonly occurring multimorbidity combinations, pointing to a potential for the development of clinical practice guidelines that provide clinical guidance for the management of combinations of diseases including hypertension and hyperlipidaemia, given their common co-occurrence. 22
A significant proportion of international studies focus on specific combinations of chronic conditions, typically dyads or triads, to analyse multimorbidity, which may undermine its true complexity. Our approach of considering all possible combinations of chronic conditions provides a more comprehensive picture of the burden of multimorbidity in the primary care population in Singapore. Despite differences in methodology, our findings are consistent with the literature, which has similarly demonstrated that conditions such as hypertension, diabetes, and hyperlipidaemia were among the most prevalent conditions within populations with multimorbidity from other high-income settings such as Belgium, 11 France 33 and the United States. 34 Additionally, our results are aligned with international literature which found that prevalent combinations of multimorbidity tend to have greater contributions to healthcare costs.35,36
While majority of the prevalent combinations overlapped with the costly combinations, there were two exceptions. Hyperlipidaemia/hypertension/arthritis/functional limitation was observed to be prevalent but not costly, while hyperlipidaemia/hypertension/diabetes/kidney disease/failure was considered costly but not prevalent. Further, the combination of hyperlipidaemia/hypertension/diabetes/kidney disease/failure had the highest total per capita costs, which were in part attributed to the high per capita costs incurred at the tertiary settings. This finding sheds light on HSU and costs potentially being driven by other factors such as disease complexity, in addition to prevalence. 37 Our study also found that multimorbidity combinations with higher prevalence in the primary care setting tend to have a lower non-primary care to primary care cost ratio. This may potentially be attributed to an attenuation in tertiary care utilisation with an increased primary care utilisation. 38 Further studies will be required to confirm such associations among patients with multimorbidity. If confirmed, this will have implications for right-siting patients to the care settings most appropriate to manage their conditions, as well as on resource planning for the health system at large.
Beyond the health system implications, multimorbidity also significantly affects individuals’ quality of life. 11 Patients often experience fragmented care, treatment burden and challenges in navigating complex care pathways, especially in the face of social and functional limitations. 6 At the macro level, the increasing prevalence of multimorbidity with population aging places strain on healthcare financing and workforce capacity, with long-term implications for population health and economic productivity.39,40 By providing insights into the distribution and impact of multimorbidity in a real-world primary care context, our findings underscore the need for more integrated, person-centred models of care.
Our study has a few key strengths. Firstly, we applied an extensive list of chronic conditions contextualised to the local primary care setting to identify multimorbidity. We also used national-level data from all public polyclinics in Singapore, which make up the majority of chronic care attendances locally. Furthermore, cost data used in this study included subsidies and co-payments, providing a better estimate of healthcare costs.
This study is also not without limitations. A major limitation of this study is the lack of data from the private healthcare setting, which may lead to an underestimation of the prevalence and costs of multimorbidity. However, given the high proportion of patients with multimorbidity seeking care in the public sector compared with the private sector, the omission of data from the private setting will unlikely alter the nature of the study results. The data used in our analysis, while comprehensive, may be subject to limitations due to its dated nature as well. While our analysis adjusted for key sociodemographic factors such as age and sex, we were unable to include a robust measure of socioeconomic status due to data limitations. Although healthcare subsidy level was considered as a potential proxy, the data was incomplete across the study population and thus excluded from the final analysis. Furthermore, the HSU patterns and common multimorbidity combinations may have changed due to the potential impact of interventions such as the roll-out of Healthier SG in July 2023 which include incentives for private primary care to play a greater role in chronic care. 41 Finally, we collected and analysed the data from all polyclinics at an aggregate level and did not account for potential variations among individual polyclinics.
Conclusion
Although the combinations of multimorbidity can be heterogeneous, the most prevalent chronic conditions – hyperlipidaemia and hypertension – were found to commonly co-occur in both costly and prevalent combinations in a population of older adults seeking chronic care at the public primary care setting. Given that current management of chronic conditions tends to focus on disease-specific clinical outcomes, findings from this study suggest a need to optimise clinical outcomes of common multimorbidity combinations rather than the simple aggregation of clinical outcomes for all conditions within the multimorbidity combination.
Footnotes
Acknowledgements
The team would like to thank Ms Deanette Pang, Mr Ng Shaowei, Dr Manojkumar Kharbanda, Dr Chua Ying Xian, Mr Samuel Ng, Dr Low Lian Leng, and Dr Andrew Fang for their contributions to the study.
Ethical considerations
Waiver of informed consent requirements and approval of the study were granted by the National University of Singapore Institutional Review Board (NUS-IRB reference code: NUS-IRB-2023-1056).
Author contributions
VK – Conceptualisation, Data curation, Formal analysis, Methodology, Writing (original draft). ST – Conceptualisation, Formal analysis, Methodology, Writing (review & editing). GCHK, ESL – Conceptualisation, Formal analysis, Methodology, Writing (review & editing). SZS, MS, LL, NCT – Formal analysis, Methodology, Writing (review & editing).
Funding
The authors received no financial support for the research, authorship, and/or publication of this article.
Declaration of conflicting interests
The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.
Data Availability Statement
The datasets analysed in this study contain confidential information and are not publicly available. Due to these confidentiality restrictions, the data cannot be shared.