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Patients with more comorbidities have better detection of chronic conditions, but poorer management and control: findings from six middle-income countries

Abstract

Background

The burden of non-communicable diseases (NCDs) is rising rapidly in middle-income countries (MICs), where NCDs are often undiagnosed, untreated and uncontrolled. How comorbidity impacts diagnosis, treatment, and control of NCDs is an emerging area of research inquiry and have important clinical implications as highlighted in the recent National Institute for Health and Care Excellence guidelines for treating patients suffering from multiple NCDs. This is the first study to examine the association between increasing numbers of comorbidities with being undiagnosed, untreated, and uncontrolled for NCDs, in 6 large MICs.

Methods

Cross-sectional analysis of the World Health Organisation Study of Global Ageing and Adult Health (WHO SAGE) Wave 1 (2007–10), which consisted of adults aged ≥18 years from 6 populous MICs, including China, Ghana, India, Mexico, Russia and South Africa (overall n = 41, 557).

Results

A higher number of comorbidities was associated with better odds of diagnosis for hypertension, angina, and arthritis, and higher odds of having treatment for hypertension and angina. However, more comorbidities were associated with increased odds of uncontrolled hypertension, angina, arthritis, and asthma. Comorbidity with concordant conditions was associated with improved diagnosis and treatment of hypertension and angina.

Conclusion

Patients with more comorbidities have better diagnosis of chronic conditions, but this does not translate into better management and control of these conditions. Patients with multiple NCDs are high users of health services and are at an increased risk of adverse health outcomes. Hence, improving their access to care is a priority for healthcare systems.

Peer Review reports

What we know now and knowledge gaps

  • The burden of non-communicable diseases (NCDs) is rising rapidly in middle-income countries (MICs), where NCDs are often undiagnosed, untreated and uncontrolled.

  • From the limited number of studies in high-income countries, there is preliminary evidence that more comorbidities negatively impact treatment and control of NCDs. There are also mixed results on how concordant and discordant comorbidities influence treatment and control.

  • There is a specific knowledge gap in MICs on how more comorbidities with a NCD is associated with the diagnosis, treatment, and control of the NCD.

  • There is also a knowledge gap in MICs on how concordant and discordant comorbidities with a NCD are associated with the diagnosis, treatment, and control of the NCD.

What this study adds

  • A higher number of comorbidities was associated with better diagnosis of some NCDs.

  • However, this did not translate into better management and control of NCDs. More comorbidities associated with even worse control of NCDs.

  • Comorbidity with concordant conditions was associated with improved diagnosis and treatment, compared to comorbidity with discordant conditions.

  • Patients with more comorbidities have better diagnosis of chronic conditions, but this does not translate into better management and control of these conditions. Patients with multiple NCDs are high users of health services and are at an increased risk of adverse health outcomes. Hence, improving their access to care is a priority for healthcare systems.

Background

Non-communicable diseases (NCDs) are the leading cause of global disease burden with 85% of premature mortality due to NCDs occurring in low- and-middle income countries [1]. In middle-income countries (MICs), there is a high prevalence of multiple chronic conditions in young adults, and not only in the elderly [2]. Poor chronic disease outcomes in MICs pose a major hurdle to attain the health target 3.4 of United Nation’s Sustainable Development Goals, which is to reduce premature mortality from NCDs by a third by 2030 [3].

How comorbidity, defined as the presence of co-occurring NCDs in an individual [4, 5], impacts diagnosis, treatment, and control of NCDs is an emerging area of research inquiry and has important clinical implications as highlighted in the recent National Institute for Health and Care Excellence guidelines for treating patients suffering from multiple NCDs [6]. A small number of studies in high-income countries show mixed results on the implications of comorbidities on the management and control of single NCDs, such as hypertension or diabetes [7,8,9,10]. A study in the United States revealed that the proportion of persons with uncontrolled hypertension increased as the number of unrelated comorbid NCDs increased [9].

In contrast to this finding, a recent ecological study in the United Kingdom found that unrelated and related co-occurring NCDs could either be associated with better or worse treatment outcomes for patients with diabetes [8]. To the best of our knowledge, there are very few studies on this topic in MICs, and there is a need to fill the gap on comorbidities in MICs [11]. Findings from the small number of studies in high-income countries may not be applicable to MICs, as their health systems vary substantially and patients in MICs tend to have less frequent visits with well-trained healthcare providers [2, 12].

This study aims to examine how more comorbidities is associated with being undiagnosed, untreated, and uncontrolled for NCDs in 6 high-population MICs. We also investigate the odds of NCDs being undiagnosed, untreated, and uncontrolled, when comorbidities are concordant versus discordant. This paper considers the implications of the research findings for clinicians and policymakers on the revision of health structures and policies to improve management and control of NCDs in the context of MICs.

Methods

Sample and data

We used cross-sectional data from the World Health Organisation Study of Global Ageing and Adult Health (WHO SAGE) Wave 1 (2007–10) which collected nationally representative samples of people aged 50+ years in China, Ghana, India, Mexico, Russia and South Africa, with a smaller sample of adults aged 18–49 years in each country for comparison [13]. SAGE contains information on sociodemographic characteristics, anthropometrics and biomarkers, NCDs, healthcare utilisation, quality of life and well-being, social cohesion, and impact on caregiving [13]. Face-to-face interviews were conducted in all countries, using a combination of computer-assisted personal interview, and paper and pencil [13].

The original total sample size of adults aged ≥18 years was 44,089 (China: 15,009, India: 12,198, Ghana: 5563, Russia: 4350, Mexico: 2744, South Africa: 4225). We excluded those who had missing values on outcome variables and covariates (5.7% of entire sample). Final sample size was 41,557 (China: 14,906, India: 11,159, Ghana: 5067, Russia: 4330, Mexico: 2618, South Africa: 3477).

Variables

Figure 4 in Appendix summarises predicting variables and outcomes.

Chronic conditions

SAGE collected information on 9 NCDs including hypertension, angina, arthritis, asthma, chronic lung disease (CLD), diabetes, cataract, stroke, and depression. All 9 NCDs had questions on self-reported diagnosis and treatment. Subjects self-reported the NCD if they answered affirmatively to: “Have you ever been diagnosed with …? ”. Subjects self-reported being treated for the NCD if they answered affirmatively to: “Have you been taking medication or other treatment for it during the ( … last 2 weeks / … last 12 months)?”

SAGE had symptom-based assessment or physical measurements for only 6 of 9 NCDs. Hence only these 6 of 9 NCDs could be assessed in this study for being undiagnosed, or for being uncontrolled. These were hypertension, angina, arthritis, asthma, CLD, and depression.

For hypertension, physical measurement of blood pressure was taken, and subjects with hypertension had systolic blood pressure ≥ 140 mmHg or diastolic blood pressure ≥ 90 mmHg [14]. For angina, depression, arthritis, asthma, and CLD, symptom-based assessments were according to validated symptom scales derived through a standard algorithm based on a set of symptomatic questions from SAGE survey (i.e. Rose questionnaire for angina [15, 16], Composite International Diagnostic Interview for depression [17, 18], receiver operating characteristic curve analysis that generated an algorithm for arthritis diagnosis by symptoms [19]). These methods were consistent with SAGE individual country reports published by WHO [20, 21]. Table 3 in Appendix shows details on the symptom-based assessment.

Predicting variables

The first predicting variable was number of comorbidities. Subjects were categorised by number of diagnosed comorbidities: 0, 1, 2, 3 and 4+ comorbidities. The second predicting variable was co-occurrence of each NCD with only concordant NCDs, only discordant NCDs, and only depression. Concordant NCDs were those that represented parts of the same overall pathophysiologic risk profile [7]. For example, studies considered diabetes and hypertension “concordant” due to similar pathophysiologic risk profiles or were more likely the focus of a similar disease complex and management plan [7, 22]. Conditions considered “discordant” were not directly related in pathogenesis or did not share an underlying predisposing factor [9]. For example, asthma and arthritis are considered “discordant” to diabetes and hypertension [9]. For our study, concordant sets of NCDs included (i) hypertension, angina, stroke, diabetes [9, 22]; (ii) diabetes, cataract [23]; (iii) asthma, CLD [24]. Two NCDs, arthritis and depression, did not have any concordant NCDs.

Outcomes

Firstly, we examined the associations between being undiagnosed, untreated, and uncontrolled for NCDs with increasing comorbidity, and secondly, the associations between being undiagnosed, untreated and uncontrolled for NCDs when the NCD co-occurs with only concordant NCDs, only discordant NCDs, and only depression.

Undiagnosed subjects did not have self-reported diagnosis of the NCD by a medical professional but had the NCD based on SAGE assessment. Untreated subjects self-reported previous diagnosis of the NCD by a medical professional but self-reported not having treatment (medications, lifestyle changes, therapy, and/or counselling). Subjects who did not have treatment in the last 2 weeks and in the last 12 months were referred to in this study as (‘unT-last 2 weeks’) and (‘unT-last 12 months’), respectively. Uncontrolled subjects self-reported being both diagnosed and treated for the NCD, but had symptoms of the NCD based on SAGE assessment. Uncontrolled subjects who had treatment in the last 2 weeks were referred to as ‘unC-T-last 2 weeks’ and those who had treatment in the last 12 months were referred to as ‘unC-T-last 12 months’. Figure 5 and Table 4 in Appendix describe the detailed definitions of being undiagnosed, untreated, and uncontrolled.

Covariates

Covariates were age (18-49 years, 50-64 years, 65 + years), sex, marital status (married, not married), education (primary or less, secondary, tertiary and above), wealth quintiles, residence (rural, urban), and health insurance (with/without insurance).

Statistical analysis

We summarised subject characteristics by country with pooled data. For each NCD, we examined the prevalence of subjects with 0, 1, 2, 3, and 4+ comorbid conditions.

For the 6 NCDs that had symptom-based assessment, we presented the prevalence of undiagnosed subjects as their number of comorbidities increased from 0 to 1, 2, 3, and 4+. For all 9 NCDs, we presented prevalence of untreated subjects as the number of comorbidities increased from 0 to 1, 2, 3, and 4+. For the 6 NCDs that had symptom-based assessment, we presented the prevalence of uncontrolled subjects as the number of comorbidities increased from 0 to 1, 2, 3, and 4+. In addition, we presented the prevalence of being undiagnosed, untreated, and uncontrolled when each NCD co-occurred with only concordant NCDs, only discordant NCDs, and only depression.

We conducted a series of multivariable logistic regression analyses. For each NCD, we obtained adjusted odds ratios (AORs) of being undiagnosed, untreated and uncontrolled: Firstly, in association with greater number of comorbidities, and secondly, in association with the co-occurrence of only concordant NCDs, versus only discordant NCDs, and versus only depression. We adjusted for country fixed effects and covariates in all the regression models. The data analyses were weighted to account for the complex, multi-stage design of the SAGE survey. We performed statistical analyses using Stata 15∙1(StataCorp).

Results

Sample characteristics

We presented subjects’ characteristics by country in Table 1. Median age was 58 (IQR = 51–68) years. The prevalence of subjects with 2 or more NCDs was overall 19%, and was 38.7% in Russia, 17.7% in South Africa, 17.2% in Ghana, 16.8% in Mexico, 14.8% in India, and 13.9% in China. Using pooled data, 43.0% were male, 22.5% were aged above 70 years, 61.3% had primary school education or less, 18.8% were from the lowest income quintile, 52.5% resided in rural areas, and 48.1% did not have insurance. Table 5 in Appendix displays the prevalence of subjects with 0, 1, 2, 3, and 4+ comorbidities for each NCD.

Table 1 Sample characteristics of the population of China, India, Ghana, Russia, Mexico, and South Africa

Undiagnosed NCDs

Table 2 shows the prevalence of subjects undiagnosed for each NCD, which ranged from 42.8% for undiagnosed arthritis to 62.2% for undiagnosed angina.

Table 2 Prevalence of undiagnosed, untreated, and uncontrolled subjects for each non-communicable disease

More comorbidities were associated with decreased odds of undiagnosed hypertension, angina and arthritis, but not for asthma, CLD, and depression (Fig. 1a). Comorbidity with concordant conditions was associated with decreased odds of undiagnosed hypertension and angina, but not for arthritis, asthma, CLD, and depression (Fig. 1b).

Fig. 1
figure1

a Odds of being undiagnosed for each non-communicable disease (NCD), with increasing comorbidity. +Multivariable logistic regression adjusted for age, sex, marital status, education level, income level, residence, insurance, and country fixed effects. b Odds of being undiagnosed for each non-communicable disease (NCD), with only concordant NCDs, only discordant NCDs, and only depression. +Multivariable logistic regression adjusted for age, sex, marital status, education level, income level, residence, insurance, and country fixed effects. ^Concordant NCDs: (a) hypertension, angina, diabetes, stroke; (b) asthma, chronic lung disease

Untreated NCDs

Table 2 shows the prevalence of subjects untreated for each NCD, with highest prevalence for CLD (91.2% ‘unT-last 2 weeks’, 84.8% ‘unT-last 12 months’), and lowest prevalence for diabetes (33.8% ‘unT-last 2 weeks’) and hypertension (17.4% ‘unT-last 12 months’).

More comorbidities were associated with decreased odds of untreated hypertension and angina, but not arthritis, asthma, CLD, diabetes, depression and stroke for ‘unT-last 12 months’ (Fig. 2a). Similar results were seen for untreated hypertension and angina for ‘unT-last 2 weeks’ (Figure 6a in Appendix). In contrast, comorbidity was associated with increased odds of untreated diabetes.

Fig. 2
figure2

a Adjusted odds ratios of being untreated in the last 12 months (unT-last 12 months) for each non-communicable disease (NCD), with increasing comorbidity. +Multivariable logistic regression adjusted for age, sex, marital status, education level, income level, residence, insurance, and country fixed effects. Note: Cataract was only assessed for being untreated in the last 5 years. b Odds of being untreated in the last 12 months (unT-last 12 months) for each non-communicable disease (NCD), with only concordant NCDs, only discordant NCDs, and only depression. +Multivariable logistic regression adjusted for age, sex, marital status, education level, income level, residence, insurance, and country fixed effects^Concordant NCDs: (a) hypertension, angina, diabetes, stroke; (b) diabetes, cataract; (c) asthma, chronic lung disease. Note: Cataract was only assessed for being untreated in the last 5 years.

Comorbidity with concordant conditions was associated with decreased odds of untreated hypertension and angina, but not arthritis, asthma, CLD, diabetes, depression and stroke (Fig. 2b, Figure 6b in Appendix).

Uncontrolled NCDs

Table 2 shows the prevalence of subjects uncontrolled for each NCD, with highest prevalence for angina (83.6% ‘unC-T-last 2 weeks’; 77.8% ‘unC-T-last 12 months’), and lowest prevalence for depression (50.3% ‘unC-T-last 2 weeks’; 62.2% ‘unC-T-last 12 months’).

More comorbidities were associated with increased odds of uncontrolled hypertension, angina, arthritis, and asthma, but not CLD and depression for ‘unC-T-last 12 months’ (Fig. 3a). Similar results were seen for ‘unC-T-last 2 weeks’ (Figure 7a in Appendix).

Fig. 3
figure3

a Odds of being uncontrolled (unC-T-last 12 months) for each non-communicable disease (NCD), with increasing comorbidity. +Multivariable logistic regression adjusted for age, sex, marital status, education level, income level, residence, insurance, and country fixed effects. b Odds of being uncontrolled (unC-T-last 12 months) for each non-communicable disease (NCD), with only concordant NCDs, only discordant NCDs, and only depression. +Multivariable logistic regression adjusted for age, sex, marital status, education level, income level, residence, insurance, and country fixed effects. ^Concordant NCDs: (a) hypertension, angina, diabetes, stroke; (b) diabetes, cataract; (c) asthma, chronic lung disease

Comorbidity with concordant conditions was not associated with decreased nor increased odds of being uncontrolled for all NCDs (Fig. 3b, Figure 7b in Appendix).

Tables 6-15 in Appendix show prevalence of being undiagnosed, untreated and uncontrolled for each NCD, in association with greater comorbidity, and with having only concordant NCDs, only discordant NCDs, and only depression.

Discussion

Principal findings

More comorbidities were associated with better diagnosis of hypertension, angina and arthritis, and better odds of having treatment for hypertension and angina. However, more comorbidities were associated with worse control of hypertension, angina, arthritis, and asthma.

Comorbidity with concordant conditions was associated with decreased odds of undiagnosed and untreated hypertension and angina. Comorbidity with concordant conditions was not associated with decreased nor increased odds of being uncontrolled for NCDs.

Previous literature

Our finding on the positive effect of comorbidities on diagnosis is consistent with the small number of existing articles. Subjects with more comorbidities likely resulted in more frequent visits to, and interactions with multiple health providers. Healthcare professionals were more likely to detect previously undiagnosed co-occurring conditions [25, 26], such as hypertension, angina, and arthritis in this particular study. More comorbidities and increased frequency of healthcare visits were likely associated with a greater tendency for routinely taken blood pressure measurements to indicate hypertension, and for patients to self-report “chest pains” and arthritis symptoms [27,28,29]. For other conditions such as CLD and depression that require non-routine checks and are less self-reported, they may still be undiagnosed despite more patients having more comorbidities and more frequent healthcare visits [30,31,32,33].

Our finding on more comorbidities being associated with decreased odds of untreated hypertension and angina was not consistent with the small amount of existing literature, which mostly showed that comorbidity was associated with increased odds of untreated conditions. Existing studies showed that patients had difficulty coping with complex treatment regiments from polypharmacy, and had poor adherence from adverse drug events and high out-of-pocket expenditures on medicines [6, 34,35,36]. The difference in our findings and current literature is likely explained by our study examining only whether subjects were taking treatment or not, and did not assess if treatment was adequate, in terms of adherence to medicines (i.e. dose, frequency, duration, administration (e.g. techniques for subcutaneous insulin injections or inhaler use), route (oral, parenteral)), and to lifestyle modifications [37]. Our study showed that with more comorbidities, subjects had higher odds of taking treatment, but we expect that in reality, with more comorbidities, odds of treatment adherence and having adequate treatment would decline.

Our finding on more comorbidities being associated with poorer control is also consistent with the little amount of existing literature. The difficulty controlling NCDs tend to be exacerbated with more co-occurring conditions, and patients with comorbidities were less likely to have certain NCDs addressed [7, 9, 10]. Our study also showed that concordant comorbidities were associated with decreased odds of both undiagnosed and untreated hypertension and angina. Previously undiagnosed concordant NCDs may have a higher tendency to be simultaneously diagnosed during consultations with physicians for the primary NCD of interest, and concordant NCDs are more likely to have better co-ordination of care [6, 9, 38].

Our study’s finding that discordant conditions were not associated with poorer control of NCDs is generally in contrast to the literature. The small number of existing studies have revealed that discordant comorbidities, which have different pathophysiology and management plans, compromise the quality of care of the patient [7, 9, 39]. For example, a study in the United States on hypertension found that patients with discordant conditions were less likely to have controlled hypertension [9]. The existing literature is from high-income countries with likely higher adherence to treatment [40,41,42], and in contrast, adherence to treatment may be lower in MICs due to financial constraints and lower health literacy [36, 43]. Hence this non-adherence in MICs may explain the lack of difference between concordant versus discordant comorbidities in the association with control of NCDs.

Strengths and limitations

This is the first study on high-population MICs that investigates the associations between comorbidity and the odds of being undiagnosed, untreated, and uncontrolled for NCDs. Self-reported diagnosis of NCDs may be under-reported and symptom-based assessment of NCDs may not correlate with true medical status [13, 44, 45]. Additionally, stigma could be a reason for under-reporting of depression in MICs [32, 46]. These limitations may have implications on under-estimations of associations in the study. However, previous work suggests this may not be a substantial problem as SAGE incorporated measures to minimise these issues [45]. There may be differential survey responses across countries. However, the survey methodology included strategies to detect and correct for systematic reporting biases in health interview surveys, such as vignette methods and objective performance tests [44]. Strategies were used to improve data comparability, such as utilising common definitions of concepts, common data collection methods and translations, rigorous sample design, and post-hoc harmonisation [44].

Additionally, this survey only asked if subjects were taking treatment (medicines, lifestyle changes), but did not measure self-reported treatment adherence (i.e. dosage, frequency, duration, etc) [37]. There are limitations of the Rose questionnaire for assessing angina, including it being short in length, and that subjects with mental health disorders may be more likely to have false positive results [47]. However, large cohort studies showed that the Rose questionnaire had good predictive ability, and false positives from individuals with mental health conditions may be minimal [47,48,49,50]. This study did not adjust for the number of visits to a general practitioner in clinics to avoid possible over-adjustment bias. The associations between more comorbidities with better detection of previously undiagnosed NCDs [51], could be mediated by more clinic visits and interactions with healthcare professionals from having more comorbidities [12, 25, 26, 52]. The study was based on 9 NCDs, so future work could examine more conditions. For example, a large-scale Scotland study included 40 NCDs [53]. The study’s cross-sectional design does not allow for causal interpretations, and studies that use prospective cohort designs could examine how comorbidities cause treatment and control of NCDs in subjects that are followed-up prospectively, such as over a few years [6].

Clinical and policy implications

Our study revealed that subjects with more comorbidity had better detection of NCDs, but control was worse with more comorbidity. There are three principle possible explanations. First, it may be related to poor access to care, whereby patients with multiple chronic conditions are getting treatment for their conditions (perhaps from a pharmacy) but there is a lack of access to care from the primary care system [54,55,56,57]. It could also relate to cost, such as a lack of comprehensive universal coverage fee at the point of care [36, 58,59,60]. Second, it could be from suboptimal adherence to medications [59, 60]. It is well documented that adherence drops as polypharmacy rises [34, 36, 61]. The lack of intentional ongoing monitoring by physicians and allied health professionals might compound this problem, as regular follow-up could include checks on adherence and reinforce the importance of taking the prescribed medications. Third, it could be due to a lack of effectiveness of medication prescribed for each condition in patients with multiple chronic conditions [62]. For most treatments of individual NCDs, the evidence is based on randomised controlled trials which exclude patients with multiple NCDs [62,63,64]. Thus what works in a patient with a single condition, may not work in a patient with the same NCD with comorbidities [65].

In reality, the problem may be a combination of these three possibilities. Primary healthcare clinicians need to improve the follow up on patients, in order to assess possible discontinuation of certain treatments due to adverse drug events, and the financial constraints that limit patients’ ability to go for follow up check-ups and refilling prescriptions [36, 56, 57]. Another clinical implication is on prioritising NCDs, whereby clinicians should assess NCD profiles of patients, prioritise treatment for patients who would have the greatest clinical benefit of better management and control, and consider personal preferences of patients for managing various NCDs [6, 66,67,68].

Policies could prioritise NCDs that have higher prevalence or burden, such as focusing on lowering out-of-pocket expenditures for follow-up visits to healthcare providers and medicines [36, 69]. Policies also need to address continual accessibility to healthcare after first diagnosis. In MICs, it is not uncommon for patients to travel from rural residences to the urban areas to seek medical treatment, limiting their ability to regularly visit healthcare services and continue treatment, which results in suboptimal control of NCDs [52, 70, 71]. In addition, there are complexities of insurance policies. Health insurance from employers may be limited to certain districts or urban areas in MICs, and it is possible that healthcare for different conditions are covered by health providers in different physical locations [72, 73]. This problem aggravates the already existing fragmentation of care from utilising multiple health providers and services [53, 74].

Fragmented healthcare is a challenge for patients, policymakers, and clinicians. Reorganising healthcare structures particularly primary care, would benefit individuals with multiple chronic conditions who often have difficulty managing treatment due to polypharmacy, have multiple health providers and medical appointments, high financial burden, and suboptimal control of conditions [6, 11, 53, 74].

Conclusion

Patients with multiple chronic conditions may have better detection of some chronic conditions, but this does not translate into better management of these conditions. As these patients are high users of health services and are at increased risk of adverse health outcomes, improving their access to care is a priority for health systems. Clinical guidelines should move away from the current focus on single diseases and be tailored to better suit the needs of patients with multiple chronic conditions.

Availability of data and materials

No additional data available.

Abbreviations

AOR:

Adjusted odds ratio

CLD:

Chronic lung disease

MICs:

Middle-income countries

NCD:

Non-communicable disease;

WHO SAGE:

World Health Organisation Study of Global Ageing and Adult Health

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Acknowledgements

The abstract of this manuscript was presented at the European Public Health Conference 2019 (G Sum, G C H Koh, S W Mercer, Y W Lim, A Majeed, B Oldenburg, J T Lee, Patients with more comorbidities have better detection but poorer management of chronic diseases, European Journal of Public Health, Volume 29, Issue Supplement_4, November 2019, ckz185.030, https://0-doi-org.brum.beds.ac.uk/10.1093/eurpub/ckz185.030).

Funding

Sum G was funded by the President’s Graduate Fellowship, National University of Singapore. Lee JT was supported by the Ministry of Education Singapore, Tier 1 grant (R-608-000-162-114). Koh G was supported by the Singapore Ministry of Health’s National Medical Research Council under the Centre Grant Programme- Singapore Population Health Improvement Centre (NMRC/CG/C026/2017_NUHS). Each funding body played a role in funding the analysis and interpretation of data, and writing of the manuscript.

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Affiliations

Authors

Contributions

SG and LJT contributed to the conception and design of this research. SG conducted the data analyses and interpreted the data. SG wrote the first, second, and subsequent drafts of the paper. All authors contributed to the interpretation of findings and substantially revised the manuscript for important intellectual content. All authors read and approved the final manuscript.

Corresponding author

Correspondence to Grace Sum.

Ethics declarations

Ethics approval and consent to participate

The National University of Singapore Institutional Review Board (NUS IRB) approved the secondary data analysis of WHO SAGE Wave 1. The WHO SAGE study received human subjects testing and ethics council approval from research review boards local to each participating site, and from the WHO Ethical Review Committee. Written informed consent was obtained by WHO SAGE from each respondent before interview and examination. The list of institutions involved in each country is listed below:

Ghana: Department of Community Health, University of Ghana Medical School; Department of Community Health, University of Ghana Medical School.

China: National Center for Chronic and Noncommunicable Disease Control and Prevention, China Center for Disease Control and Prevention, Beijing, China; Shanghai Municipal Center for Disease Control and Prevention, Shanghai, China.

India: International Institute for Population Sciences, Mumbai, India.

Mexico: National Institute of Public Health, Cuernavaca, Mexico.

Russian Federation: National Research Institute of Public Health (FSBI, RAMS) Moscow, Russian Federation.

South Africa: Human Sciences Research Council, Pretoria, South Africa; University of the Witwatersrand, Johannesburg, South Africa.

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Not applicable.

Competing interests

Lee JT is an editorial board member of BMC Public Health, but he had no role in the editorial process of this paper.

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Appendix

Appendix

Fig. 4
figure4

Predicting and outcome variables

Fig. 5
figure5

Classification of subjects who are undiagnosed, untreated, and uncontrolled

Fig. 6
figure6

a Adjusted odds ratios of being untreated in the last 2 weeks (unT-last 2weeks) for each non-communicable disease (NCD), with increasing comorbidity. +Multivariable logistic regression adjusted for age, sex, marital status, education level, income level, residence, insurance, and country fixed effects. Note: Cataract was only assessed for being untreated in the last 5 years (see Figure 6a). b Odds of being untreated in the last 2 weeks (unT-last 2weeks) for each non-communicable disease (NCD), with only concordant NCDs, only discordant NCDs, and only depression. +Multivariable logistic regression adjusted for age, sex, marital status, education level, income level, residence, insurance, and country fixed effects. ^Concordant NCDs: (a) hypertension, angina, diabetes, stroke; (b) diabetes, cataract; (c) asthma, chronic lung disease. Note: Cataract was only assessed for being untreated in the last 5 years (see Figure 6b)

Fig. 7
figure7

a Odds of being uncontrolled (unC-T-last 2weeks) for each non-communicable disease (NCD), with increasing comorbidity. +Multivariable logistic regression adjusted for age, sex, marital status, education level, income level, residence, insurance, and country fixed effects. b Odds of being uncontrolled (unC-T-last 2weeks) for each non-communicable disease (NCD), with only concordant NCDs, only discordant NCDs, and only depression. +Multivariable logistic regression adjusted for age, sex, marital status, education level, income level, residence, insurance, and country fixed effects. ^Concordant NCDs: (a) hypertension, angina, diabetes, stroke; (b) diabetes, cataract; (c) asthma, chronic lung disease

Table 3 Algorithms used to ascertain presence of non-communicable diseases
Table 4 Definitions of prevalence of each chronic condition being undiagnosed, untreated, and uncontrolled
Table 5 Prevalence of comorbidity for each non-communicable disease
Table 6 Prevalence of undiagnosed subjects for each non-communicable disease, with increasing number of comorbidities
Table 7 Prevalence of undiagnosed subjects for each non-communicable disease (NCD), with diagnosed concordant NCDs only, discordant NCDs only, and depression only
Table 8 Prevalence of untreated subjects for each non-communicable disease, with increasing comorbidities. (untreated in the last 2 weeks)
Table 9 Prevalence of untreated subjects for each non-communicable disease (NCD), for comorbidity with only concordant NCDs, only discordant NCDs, and only depression. (untreated in the last 2 weeks)
Table 10 Prevalence of untreated subjects for each non-communicable disease, with increasing comorbidity. (untreated in the last 12 months)
Table 11 Prevalence of untreated subjects for each non-communicable disease (NCD), for comorbidity with only concordant NCDs, only discordant NCDs, and only depression. (untreated in the last 12 months)
Table 12 Prevalence of uncontrolled subjects for each non-communicable disease, with increasing comorbidity. (treated in the last 2 weeks)
Table 13 Prevalence of uncontrolled subjects for each non-communicable disease (NCD), for comorbidity with only concordant NCDs, only discordant NCDs, and only depression. (treated in the last 2 weeks)
Table 14 Prevalence of uncontrolled subjects for each non-communicable disease, with increasing comorbidity (treated in last 12 months)
Table 15 Prevalence of uncontrolled subjects for each non-communicable disease (NCD), for comorbidity with only concordant NCDs, discordant NCDs, and depression. (treated in the last 12 months)

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Sum, G., Koh, G.CH., Mercer, S.W. et al. Patients with more comorbidities have better detection of chronic conditions, but poorer management and control: findings from six middle-income countries. BMC Public Health 20, 9 (2020). https://0-doi-org.brum.beds.ac.uk/10.1186/s12889-019-8112-3

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Keywords

  • Comorbidity
  • Non-communicable diseases
  • Chronic conditions
  • Ageing
  • Health monitoring
  • Access to care