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Community prevalence and dyad disease pattern of multimorbidity in China and India: a systematic review
  1. Xinyi Zhang1,2,
  2. Asutosh Padhi3,
  3. Ting Wei2,
  4. Shangzhi Xiong4,
  5. Jie Yu4,
  6. Pengpeng Ye4,5,
  7. Wenijng Tian1,
  8. Hongru Sun1,
  9. David Peiris4,
  10. Devarsetty Praveen3,4,6,
  11. Maoyi Tian1,4
  1. 1School of Public Health, Harbin Medical University, Harbin, Heilongjiang, China
  2. 2The George Institute for Global Health, China, Beijing, China
  3. 3The George Institute for Global Health, India, Hyderabad, Telangana, India
  4. 4The George Institute for Global Health, Faculty of Medicine and Health, University of New South Wales, Sydney, New South Wales, Australia
  5. 5National Centre for Non-Communicable Disease Control and Prevention, Chinese Centre for Disease Control and Prevention, Beijing, China
  6. 6Prasanna School of Public Health, Manipal Academy of Higher Education, Manipal, Karnataka, India
  1. Correspondence to Dr Maoyi Tian; maoyi.tian{at}hrbmu.edu.cn

Abstract

Background Driven by the increasing life expectancy, China and India, the two most populous countries in the world are experiencing a rising burden of multimorbidity. This study aims to explore community prevalence and dyad patterns of multimorbidity in China and India.

Methods We conducted a systematic review of five English and Chinese electronic databases. Studies involving adults 18 years or older at a community level, which reported multimorbidity prevalence and/or patterns were included. A modified Newcastle-Ottawa Scale was used for quality assessment. Despite large heterogeneity among reported studies, a systematic synthesis of the results was conducted to report the findings.

Results From 13 996 studies retrieved, 59 studies met the inclusion criteria (46 in China, 9 in India and 4 in both). The median prevalence of multimorbidity was 30.7% (IQR 17.1, 49.4), ranging from 1.5% to 90.5%. There was a large difference in multimorbidity prevalence between China and India, with median prevalence being 36.1% (IQR 19.6, 48.8) and 28.3% (IQR 8.9, 56.8), respectively. Among 27 studies that reported age-specific prevalence, 19 studies found multimorbidity prevalence increased with age, while 8 studies observed a paradoxical reduction in the oldest age group. Of the 34 studies that reported sex-specific prevalence, 86% (n=32) observed a higher prevalence in females. The most common multimorbidity patterns from 14 studies included hypertensive diseases combined with diabetes mellitus, arthropathies, heart diseases and metabolic disorders. All included studies were rated as fair or poor quality.

Conclusion Multimorbidity is highly prevalent in China and India with hypertensive diseases and other comorbidities being the most observed patterns. The overall quality of the studies was low and there was a lack of representative samples in most studies. Large epidemiology studies, using a common definition of multimorbidity and national representative samples, with sex disaggregation are needed in both countries.

PROSPERO registration number CRD42020176774.

  • public health
  • hypertension
  • systematic review
  • arthritis
  • epidemiology

Data availability statement

Data are available in a public, open access repository. Data and analyses will be available on request from the corresponding author (MT).

http://creativecommons.org/licenses/by-nc/4.0/

This is an open access article distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited, appropriate credit is given, any changes made indicated, and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/4.0/.

http://dx.doi.org/10.1136/bmjgh-2022-008880

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    WHAT IS ALREADY KNOWN ON THIS TOPIC

    • People living with multimorbidity have poorer quality of life, physical and mental health functions and experience worse health outcomes.

    • The burden of multimorbidity in China and India is growing but limited evidence is available from the current literature.

    WHAT THIS STUDY ADDS

    • The prevalence and pattern of multimorbidity were affected by multiple factors, which leads to heterogenous definitions of multimorbidity.

    • The most important factors include age, number of diseases and method of disease measurement.

    • The reported prevalence of multimorbidity in China and India was lower than that in high-income countries but is rising.

    • Most studies reported a higher prevalence in females than males.

    • The most common patterns of multimorbidity involved hypertensive diseases.

    HOW THIS STUDY MIGHT AFFECT RESEARCH, PRACTICE OR POLICY

    • Consensus on a standardised definition for multimorbidity is needed to guide future research.

    • Multimorbidity intervention or preventive practice should focus more on the more vulnerable population.

    • A priority area for addressing multimorbidity includes hypertensive diseases combined with any of the following disease: diabetes mellitus, arthropathies, heart diseases and metabolic disorders.

    • High-quality large-scale studies with representative samples are needed to better understand the growing burden of multimorbidity in China and India.

    Introduction

    Globally, over the past few decades, better healthcare and improved living conditions have led to an improvement in life expectancy. However, these improvements have been accompanied by an increased co-occurrence of multiple chronic conditions in an individual, referred to as ‘multimorbidity’ and ‘comorbidity’. Multimorbidity is commonly defined as the presence of more than one health condition with long duration, each condition is either a physical non-communicable disease, a mental health condition or an infectious disease, within an individual.1 Compared with comorbidity, multimorbidity co-exists several medical conditions within one person, without focus on one condition above the others (ie, no index condition).2 Multimorbidity is subject to the interactions or causal relationships among different diseases, and some disease patterns share similar diagnostic factors, which lead to more complex healthcare needs and different aetiological analysis compared with the single disease.3 4 Irrespectively, multimorbidity is associated with poorer quality of life, worse clinical outcomes, higher levels of disability and premature mortality.5 6 However, the disease combinations associated with the greatest burden on quality of life remains unclear.7 Evidence on the prevalence of multimoribidity and the interactions across different disease conditions in the same individual is essential for improving the medical practice for people with multimorbidity.3 8

    China and India, the two most populated countries, account for about 36% of the world population together.9 Both countries are subject to substantial global burden of diseases, and multimorbidity is becoming a major concern, especially in the older populations.10 11 A study from 9 provinces in China with >17 000 participants over 45 years old found about 42% of the participants having two or more chronic conditions.12 While in a study in India, among 9852 participants with age 60 years and above, the number was 30.7%.13 A number of systematic reviews have examined the prevalence of multimorbidity globally, but only a few studies included data from low-income and middle-income counties (LMICs) and even those were conducted before 2015.14–18 Moreover, the only one that reported the patterns of multimorbidity was limited to studies from high-income countries (HICs).17 As such, there is no reliable up-to-date estimates of prevalence and patterns of multimorbidity in China and India. This study aims to bridge the knowledge gap by determining the current prevalence and identifying patterns of multimorbidity at the community levels in both countries.

    Methods

    We conducted a systematic review of published studies reporting prevalence and patterns of multimorbidity among adults residing in China and India, in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses checklist. Details are available in the online supplemental material 1. The study was registered in PROSPERO (CRD42020176774).

    Supplemental material

    Eligibility criteria

    Articles were selected based on the following inclusion criteria: (1) original studies reporting prevalence and/or disease patterns of multimorbidity (ie, several medical conditions co-existing within one person with no selection of an index disease condition); (2) studies with participants aged 18 years and above; (3) studies conducted in China (including Mainland China, Hong Kong, Taiwan and Macau) or India; (4) studies conducted at the community level and (5) studies that published in either English or Chinese language, with no limitation on the publication time (from inception to the date of search, 21 April 2020).

    Any studies that focused on participants from a specific population group (such as prisoners, miners or earthquake victims), or that began with a preliminary selection of index disease (eg, multimorbidity prevalence among patients with diabetes), or studies conducted in the hospital settings were excluded.

    Search strategy

    We conducted an online literature search in ‘Medline’, ‘EMBASE’, ‘Cochrane’ and ‘Web of Science’ for English articles, and ‘China National Knowledge Infrastructure’ for Chinese articles. We used both Medical Subject Heading (MeSH) terms and keywords to conduct the search in English databases. The MeSH terms included “multimorbidity”, “comorbidity” and their variations. The searched keywords included “multimorbidity”, “comorbidity”, “multiple chronic conditions”, “prevalence”, “epidemiology”, “China” and “India”. The detailed search strategy is listed in online supplemental material 2.

    Study selection and data extraction

    Three independent reviewers (XZ, AP and TW) screened the search results according to the eligibility criteria. Disagreements were resolved through discussion with the senior researchers (DPr and MT), where necessary. A standardised data extraction form was developed (online supplemental material 3) in Excel (Microsoft Excel for Office 365) for entering key study characteristics, including the publication year, study type, study population and sample size, sex and age of participants, data source, study location (urban or rural), number of diseases, prevalence, patterns, all single disease conditions and the top five prevalent single disease conditions (if available). Prior to the formal data collection, two reviewers (XZ and AP) started with a calibration exercise to ensure consistent and reproducible results. During the calibration exercise, we randomly selected 10% of the included articles with each reviewer independently extracting the data. After the calibration exercise, three reviewers (XZ, AP and TW) started to extract the data separately for the rest of the included studies. MT and DPr were consulted if there were any disagreements. All disagreements were resolved by group consensus.

    Quality assessment

    Study quality was independently assessed by three reviewers (XZ, AP and TW) using the modified Newcastle-Ottawa Scale for cross-sectional studies.19 The scale consisted of three domains, selection (representativeness of the sample, sample size, non-respondents, ascertainment of the exposure), comparability (comparability of different outcome groups) and outcome (confounding factors are controlled, assessment of outcome and statistical test). A study is awarded a maximum of one star for each numbered item within the selection and outcome domains. A maximum of two stars is given for comparability domain. Studies awarded seven or eight stars were considered as good quality, five to six stars as fair quality, while four stars or below as poor quality. Score disagreements were resolved by group discussions and consensus. Detailed quality assessment is included in online supplemental material 4.

    Data synthesis

    We used the International Classification of Diseases-10th revision (ICD-10) coding system to standardise the classification of all listed diseases identified from the review. In this study, the block categories of the ICD-10 coding system was used to synthesise the data, primarily due to two reasons. First, many studies reported a general term of the disease condition, like ‘heart disease’. The reported ‘heart disease’ may refer to ‘ischaemic heart disease’ or ‘other forms of heart diseases’. It is thus unlikely to classify these general terms into a single disease condition. Second, the reporting of the disease conditions of the multimorbidity is heterogenous between the studies. There is a lack of uniformed standard to report multimorbidity disease conditions from the included studies. All disease conditions were coded by an experienced clinician (JY) using Excel (Microsoft Excel for Office 365). Due to inconsistent categorisation for participants’ age across the included studies, the following rules were applied for data synthesis: (1) if the prevalence was reported in an age group with lower and upper limits, the mean age was used; (2) if the prevalence was reported for an age group with a lower limit only (eg, participants >85 years), 5 years was added to the lower limit and used; (3) if the prevalence was reported for an age group with an upper limit only (eg, participants younger than 60 years), 5 years was subtracted from the upper limit and used. Multimorbidity patterns were extracted from the studies, but only dyad disease patterns were synthesised in this study because very few studies reported combinations of more than two disease conditions. The prevalence was presented with the median value and IQR. Weighting according to samples size was not considered. In studies reporting sex-specific data, the difference in prevalence between female and male was reported, while the 95% CI for the difference between gender was generated by STATA V.15 (StataCorp, College Station, Texas, USA). Heterogeneity across studies was evaluated using I2 statistic. Meta-analysis or quantitative pooling was not performed due to the very high heterogeneity (I2 ≥99%) of the included studies.

    Patient and public involvement

    Patients and/or the public were not involved in the design, or conduct, or reporting, or dissemination plans of this research.

    Results

    We identified 13 996 articles from the initial search. After removing the duplicates, 10 860 studies were selected for title and abstract screening. There were 10 685 studies excluded from this screening phase, mainly due to these studies describing the illness co-existed with a particular disease of interest, that is, comorbidity, but not multimorbidity. A total of 175 studies remained for full-text review, with a total of 59 articles included for final analysis and synthesis (figure 1).

    Figure 1
    Figure 1

    Study flow chart. CNKI, China National Knowledge Infrastructure.

    Study characteristics

    The key characteristics of the studies are listed in online supplemental material 3. There are 46 studies conducted in China (43 studies in Mainland China, 1 study in Hong Kong and 2 studies in Taiwan),12 20–64 9 studies in India13 65–72 and 4 studies in both countries.73–76 The studies reported both countries used the data from WHO study on global ageing and adult health. There were eight studies using data from China Health and Retirement Longitudinal Study but analysis with different age group or data collection time point.12 35 40 41 43 47 60 62 Most studies (n=46) included participants from both urban and rural areas, while there were four studies specifically in rural areas and nine in urban areas. All included studies were designed as cross-sectional studies, with three studies involving data collection at multiple time points. The age of the participants ranged between 18 years and 113 years. There was one study that enrolled participants from 15 years of age and above, but with no available data to specifically report the prevalence and patterns for the age group between 15 and 18 years.72 There were 26 studies with study participants >60 years. The number of co-existing disease conditions included in multimorbidity assessment varied from 2 to >40.

    Prevalence of multimorbidity

    The median prevalence of multimorbidity was found to be 30.7% (IQR 17.1, 49.4), ranging from 1.5% to 90.5% (table 1). Forty-four studies (75%) reported a prevalence <50%, while 15 studies reported a prevalence >50%. Thirty-four studies reported sex-specific data. The median prevalence in females was 34.2% (IQR 14.7, 48.2), and 30.8% (IQR 15.3, 43.1) in males. Of the 46 studies conducted in both urban and rural areas, 12 studies reported urban-specific and rural-specific data in which the median prevalence was 25.0% (IQR 11.1, 33.2) in urban, while 25.5% (IQR 8.3, 28.4) in rural areas. The prevalence of multimorbidity based on primary data collection was 41.6% (IQR 20.8, 56.8), and that of those based on secondary data analysis was 30.1% (IQR 13.5, 44.6). Among the Chinese participants, the median prevalence was 36.1% (IQR 19.6, 48.8), while 28.3% (IQR 8.9, 56.8) for Indian participants. There was no substantial difference in prevalence between studies with representative samples and those without (31.2% vs 30.4%). The prevalence increased with age and the number of co-existing disease conditions, whereas decreased with the sample size (table 1).

    View this table:
    Table 1

    Prevalence of multimorbidity by age

    Prevalence of multimorbidity by sex and age

    Out of the 59 studies, 34 studies reported 37 datasets for sex-specific analysis (one study contained three datasets, and one study contained one dataset from China and one from India). Figure 2 shows the differences in prevalence between female and male. Among the 37 datasets, 86% (n=32) reported females had a higher prevalence than males, with only 5 studies showing otherwise.

    Figure 2
    Figure 2

    Multimorbidity prevalence between female and male. *The 2010 dataset of Fu, 2014. †The 2005 dataset of Fu, 2014. ‡The 2000 dataset of Fu, 2014. Panel A: forest plot showing multimorbidity prevalence difference between female and male in studies with youngest age of enrolled participants between 18 and 34 years. Panel B: forest plot showing multimorbidity prevalence difference between female and male in studies with youngest age of enrolled participants between 35 and 59 years. Panel C: forest plot showing multimorbidity prevalence difference between female and male in studies with youngest age of enrolled participants >60 years.

    Out of the 59 studies, only 27 studies reported the prevalence of multimorbidity by age group (21 from China and 6 from India) (figure 3). In general, the prevalence of multimorbidity increased with age, prior to the age of 60 years, but this pattern changed for age groups >60 years. Of note, there were eight studies that saw a reduction in the middle-old or oldest-old age groups (>75 years old). More than half of these studies used country-level representative datasets, and all eight studies enrolled participants aged 45 years and above.12 34 47 59 61–63 67 Among the 27 studies, the highest prevalence was 90.8% for an age group >70 years old,58 while the lowest was 1.6% for an age group between 18 and 24 years old.23

    Figure 3
    Figure 3

    Age-specific prevalence of multimorbidity. ♦: Sample size <1000; ▲: Sample size between 1000 and 10 000; ●: Sample size >10 000. Panel A: prevalence of multimorbidity by age group in China. Panel B: prevalence of multimorbidity by age group in India.

    Patterns of multimorbidity

    Seventeen datasets from 14 studies (1 study reported patterns in 4 datasets) reported the pattern of multimorbidity. Figure 4 shows the prevalence of dyad disease patterns identified in each study. The top four frequently occurring dyad disease patterns were hypertensive diseases and diabetes mellitus (n=9; median prevalence: 10.1%, IQR: 6.6, 15.8), hypertensive diseases and arthropathies (n=8; median prevalence: 11.6%, IQR: 3.6, 17.9), hypertensive diseases and heart diseases (n=7; median prevalence: 7.5%, IQR: 4.9, 23.3) and hypertensive diseases and metabolic disorders (n=6; median prevalence: 10.7%, IQR: 9.5, 19.4). The dyad disease pattern with highest prevalence was hypertensive diseases and diseases of oesophagus, stomach and duodenum (56.6%), while the lowest prevalence was other diseases of the respiratory system and arthropathies (1.1%). Hypertensive diseases were the most frequent disease condition in all dyad disease patterns, followed by arthropathies and diabetes mellitus.

    Figure 4
    Figure 4

    The prevalence of different dyads patterns of multimorbidity in included studies. The numbers in brackets represent the frequency of pattern in the studies. Colours in green: studies with youngest age of enrolled participants between 18 and 34 years; colours in blue: studies with youngest age of enrolled participants between 35 and 59 years; colours in red: studies with youngest age of enrolled participants >60 years. Digestive Dis, Oother diseases of the digestive system; DM, Diabetes mellitus; Eye Dis, Diseases of the eye and adnexa; General symptoms, General symptoms and signs; HTN, hypertensive Diseases; Liver Dis, Diseases of liver; Respiratory Dis, Other diseases of the respiratory system; Sexual infections, Iinfections with a predominantly sexual mode of transmission; Stomach Dis, Diseases of esophagus, stomach and duodenum.

    Single disease conditions

    The number of single disease conditions identified from the included 59 studies varied from 2 to >40. The most frequent single disease conditions were hypertensive diseases (n=58), diabetes mellitus (n=57), cerebrovascular diseases (n=48), heart diseases (n=47), arthropathies (n=43) and chronic lower respiratory diseases (n=41).

    The prevalence of each single disease condition was reported in most of the studies (n=44). The most prevalent single disease conditions were hypertensive diseases (median: 30.2%, IQR 20.9, 46.5), arthropathies (median: 21.2%, IQR 13.5, 32.7), diseases of oesophagus, stomach and duodenum (median: 20.3%, IQR 11.4, 24.5), metabolic disorders (median: 13.9%, IQR 5.6, 34.2) and heart diseases (median: 13.0%, IQR 6.5, 15.4).

    Sample size of the included studies

    The sample size varied widely from <100 to >2 million. There were only three studies with >1 million participants, one from India and two from Taiwan, China.23 38 72 All these studies were secondary analysis from the routinely collected data, such as national health survey or health insurance database, whereas studies with fewer than 1000 participants were primary data collected from the community surveys.51 53 59 65 69 71

    Quality assessment

    All included studies were cross-sectional studies and none of them were rated as good quality (7–8 stars). Thirty-nine studies were rated as fair quality (5–6 stars), while 20 were rated as poor quality (4 stars or below) (online supplemental material 4).

    Discussion

    This review found that the prevalence of multimorbidity varies widely across the studies and is largely affected by the study participants’ age and sex. In addition, the patterns of multimorbidity are associated with the most prevalent single disease conditions, such as hypertensive diseases, diabetes mellitus, arthropathies, heart diseases and metabolic disorders.

    In most studies, the prevalence of multimorbidity increased with age. In contrast, a previous review found an S-shaped relationship between age and prevalence, with <20% before 40 years old and >70% between the age of 40 and 70 years, remaining plateaued after the age of 70 years.77 This might be explained by the wide age range of the study participants, spanning from 10 years and below to 80 years and above in one single study. Whereas in this review, the widest age range was from 18 years to 70 years and above. Nevertheless, the middle-age group (from 35 years to 64 years) seems to be the most vulnerable population to multimorbidity, which is similar to the pattern identified as the previous studies.15 17 77 The plateaued or decreased pattern in the oldest age group may be due to the bias in multimorbidity assessment, such as the potential under-reporting of mental health diseases or self-reported disease outcomes.78 79 This may also be attributable to potential survival bias, where people who suffered from multimorbidity died prematurely in the very old age group. This explanation is supported by a previous study that found people with more co-existing disease condition having higher mortality risk.80

    Moreover, our study also found that the prevalence of multimorbidity increased with the number of diseases that are included in the analysis. This finding is consistent with previous studies that found the number of diseases considered in the analysis largely affected the estimated prevalence of multimorbidity.81 82 For example, a previous study that included 335 disease conditions in the multimorbidity analysis found the prevalence of multimorbidity increased with the number of observed diseases.1 Although there is no uniform standard for the number of disease types to be included in the multimorbidity analysis, the number and type of disease conditions should be determined according to the prevalent single disease condition in the study population.77 82

    In our study, we found the prevalence of multimorbidity was higher in females than males, which is consistent with the previous review.15 A large population-based study in West Asia addressed the importance to increase awareness about multimorbidity risk factors among females.83 Despite the fact that average life expectancy of females are higher than males,11 84 females experience greater challenges in health and quality of life.85Although such findings may portrait females as more vulnerable to multimorbidity, two factors should be considered. First, sex-wise comparison can be highly affected by the sex-specific disease conditions that were included in the multimorbidity analysis. For example, one study investigated disorders of breast and pelvic inflammatory disease in the multimorbidity assessment, which only applied to females with zero prevalence in males.27 Likewise, this also applied to males due to male-specific diseases such as prostate issues. Second, higher detection rates of multimorbidity among females may be due to higher utilisation of health services, such as health screening during pregnancy.86 This highlights the need to conduct high-quality studies to control confounders and gender-specific disease conditions to provide robust evidence on sex-specific multimorbidity prevalence.

    The overall prevalence of multimorbidity is found to be higher in China than in India. One likely reason might be that Indian studies included in this review had wider age range than the Chinese studies, which led to a reduced overall prevalence because the prevalence of multimorbidity is lower in younger age groups. Moreover, the number of disease conditions included in the Indian datasets were smaller than the Chinese datasets, which may have reduced the overall prevalence. In addition, the prevalence of the most prevalent single disease conditions was higher in Chinese studies compared with Indian studies.11 87 In addition, we found far more Chinese studies than Indian studies in this review. The reasons to explain this are threefolds. First, Chinese language database is available whereas India does not have such database in local language apart from English. Second, there are multiple studies (n=8) from China that used one single database from China Health and Retirement Longitudinal Study. Third, all regions in Greater China are included in this review. Besides Mainland China, Hong Kong, Macau and Taiwan were included as well. We also found the overall prevalence reported in our review to be much lower than those reported in studies conducted in HICs. For example, a systematic review and meta-analysis that included 31 HICs reported the pooled prevalence of multimorbidity at the community settings in HICs to be 37.9% (95% CI: 32.5 to 43.4),15 while another review covering 52 articles from 30 HICs, with >60 million older adults reported the overall prevalence of multimorbidity to be 66.1% (IQR: 54.4, 76.6).18 One potential reason to explain for this difference, in addition to the likely lower reporting rates in LMICs than that in HICs, could be the premature deaths occurring in LMICs from non-communicable diseases and multimorbidity.88 With improvement in health prevention and management, people in China and India can be expected to live longer with multimorbidity.

    In our study, the most frequent and prevalent disease condition is hypertensive disease. This finding aligns with the epidemiological studies in China and India. The prevalence of hypertension is 23.2% in China (adults over 18 years between 2012 and 2015),89 while 25.3% in India (adults over 18 years between 2012 and 2014),87 making it as the leading health problem in both countries. This finding is also consistent with the studies from HICs that found hypertension to be the most prevalent disease condition in multimorbidity analyses.17 18 Nonetheless, in this review, diseases such as dementia, depression and anxiety were not reported as prevalent or frequent diseases compared with other studies,82 90 which may be due to the under-reporting or underdiagnosis of the non-symptoms-based conditions.78 Last but not least, HIV infection, which is widely considered as a chronic condition because of its life-long disease duration and antiretroviral therapy,91 92 was not mentioned in any of the currently included studies. Patients with HIV, however, are more vulnerable to chronic diseases such as diabetes mellitus and kidney diseases, in addition to HIV-associated complications.93 94 Therefore, if the included studies considered HIV infection in their multimorbidity analyses, the prevalence and patterns of multimorbidity may have been presented differently.

    The common patterns of multimorbidity identified from this review are hypertensive diseases combined with diabetes mellitus, arthropathies, heart disease and metabolic disorders, which is different compared with the previous studies.17 75 95 One possible explanation for the various patterns across the studies is the non-unified classification of the diseases compared with previous studies. In this review, the block category of ICD-10 coding system was used to code all disease conditions. For example, one of the most commonly reported multimorbidity pattern in another study involved hyperlipidaemia,92 but that was coded into its block category as metabolic disease in this study. This highlights the need to standardise disease classification for multimorbidity assessment.

    In our review, only three studies used routinely collected data, with large sample size, to conduct multimorbidity assessment, spanning ages 18 to >65 years. Despite routinely collected data, such as health insurance claims or electronic health records, been applied in many aspects,96 97 its use in multimorbidity assessment remains limited. This may be due to the concerns about the accessibility, accuracy and reliability of the routinely collected data.98 Routinely collected data can serve as a promising tool to better understand the epidemiology of multimorbidity due to its volume and representativeness, but still depending on the quality and completeness of the data. For example, mental health assessment or dietary patterns may not be collected routinely. It might be necessary to establish an integrated health information system of integrating the existing routinely collected data systems,99 maximising the use of such data, to comprehensively and timely understand the burden of multimorbidity.

    Strengths and limitations

    To our knowledge, our study is the first systematic review examining the prevalence and patterns of multimorbidity in both Chinese and Indian population. There are a few limitations that we acknowledge. First, we did not include grey literatures or unpublished papers in the data synthesis, despite a rigorous search from the major English and Chinese databases. Second, we used ICD-10 coding system to code the disease conditions, but the outcomes may differ if we used other coding systems. Third, we only analysed the dyad patterns of multimorbidity in this study due to limited studies reporting disease patterns with more than two disease conditions. Moreover, our study is limited by the large heterogeneity among the included studies in their sample sizes, data collection methods and numbers of diseases included in the multimorbidity analyses. Meta-analysis was therefore not possible to be performed to estimate the overall pooled prevalence of multimorbidity. Lastly, this review was restricted to studies that involved participants only from China and India, which may limit the generalisability of the findings to a broader population in LMICs.

    Nonetheless, this review represents an updated and comprehensive synthesis of evidence about multimorbidity prevalence and patterns in Chinese and Indian community settings. The findings should support researchers and clinical practices to conduct future studies to further explore the prevention and management of multimorbidity.

    Conclusion

    With rapid ageing and improvements in living standards and health services, multimorbidity is likely to become a growing health problem among Chinese and Indian people. To date, China and India have begun to realise the impact of multimorbidity. More studies have tended to report the prevalence and patterns of multimorbidity in the past 5 years, but the results varied across different studies. This may in part be due to a lack of standardised definition of multimorbidity and methodological issues in conducting these studies. It is therefore important to apply common definition of multimorbidity, use routinely collected data as disruptive technology, to conduct robust large-scale research to determine the prevalence and patterns of multimorbidity in China and India, and other LMICs, among people of different age groups, to provide further evidence for the development of effective interventions for multimorbidity management.

    Data availability statement

    Data are available in a public, open access repository. Data and analyses will be available on request from the corresponding author (MT).

    Ethics statements

    Patient consent for publication

    References

    1. ↵
      1. van den Akker M,
      2. Buntinx F,
      3. Metsemakers JF, et al
      . Multimorbidity in general practice: prevalence, incidence, and determinants of co-occurring chronic and recurrent diseases. J Clin Epidemiol 1998;51:367–75.doi:10.1016/S0895-4356(97)00306-5pmid:http://www.ncbi.nlm.nih.gov/pubmed/9619963
      OpenUrlCrossRefPubMedWeb of Science
    2. ↵
      1. van den Akker M,
      2. Buntinx F,
      3. Knottnerus J
      . Comorbidity or multimorbidity what’s in a name? A review of literature. Europ J Gen Pract 1996;2:65–70.
      OpenUrl
    3. ↵
      1. Moffat K,
      2. Mercer SW
      . Challenges of managing people with multimorbidity in today’s healthcare systems. BMC Fam Pract 2015;16:129.doi:10.1186/s12875-015-0344-4pmid:http://www.ncbi.nlm.nih.gov/pubmed/26462820
      OpenUrlCrossRefPubMed
    4. ↵
      1. Schäfer I,
      2. von Leitner E-C,
      3. Schön G, et al
      . Multimorbidity patterns in the elderly: a new approach of disease clustering identifies complex interrelations between chronic conditions. PLoS One 2010;5:e15941.doi:10.1371/journal.pone.0015941pmid:http://www.ncbi.nlm.nih.gov/pubmed/21209965
      OpenUrlCrossRefPubMed
    5. ↵
      1. Mujica-Mota RE,
      2. Roberts M,
      3. Abel G, et al
      . Common patterns of morbidity and multi-morbidity and their impact on health-related quality of life: evidence from a national survey. Qual Life Res 2015;24:909–18.doi:10.1007/s11136-014-0820-7pmid:http://www.ncbi.nlm.nih.gov/pubmed/25344816
      OpenUrlCrossRefPubMed
    6. ↵
      1. Gallo JJ,
      2. Hwang S,
      3. Joo JH, et al
      . Multimorbidity, depression, and mortality in primary care: randomized clinical trial of an evidence-based depression care management program on mortality risk. J Gen Intern Med 2016;31:380–6.doi:10.1007/s11606-015-3524-ypmid:http://www.ncbi.nlm.nih.gov/pubmed/26432693
      OpenUrlCrossRefPubMed
    7. ↵
      1. Sheridan PE,
      2. Mair CA,
      3. Quiñones AR
      . Associations between prevalent multimorbidity combinations and prospective disability and self-rated health among older adults in Europe. BMC Geriatr 2019;19:198.doi:10.1186/s12877-019-1214-zpmid:http://www.ncbi.nlm.nih.gov/pubmed/31351469
      OpenUrlPubMed
    8. ↵
      1. Wallace E,
      2. Salisbury C,
      3. Guthrie B, et al
      . Managing patients with multimorbidity in primary care. BMJ 2015;350:h176.doi:10.1136/bmj.h176pmid:http://www.ncbi.nlm.nih.gov/pubmed/25646760
      OpenUrlFREE Full Text
    9. ↵
      1. World Bank
      . Population 2020. World development indicators database, 2021. Available: https://databank.worldbank.org/data/download/POP.pdf [Accessed 18 Dec 2021].
    10. ↵
      1. Pati S,
      2. Swain S,
      3. Metsemakers J, et al
      . Pattern and severity of multimorbidity among patients attending primary care settings in Odisha, India. PLoS One 2017;12:e0183966.doi:10.1371/journal.pone.0183966pmid:http://www.ncbi.nlm.nih.gov/pubmed/28910309
      OpenUrlPubMed
    11. ↵
      1. Wang LM,
      2. Chen ZH,
      3. Zhang M, et al
      . [Study of the prevalence and disease burden of chronic disease in the elderly in China]. Zhonghua Liu Xing Bing Xue Za Zhi 2019;40:277–83.doi:10.3760/cma.j.issn.0254-6450.2019.03.005pmid:http://www.ncbi.nlm.nih.gov/pubmed/30884604
      OpenUrlPubMed
    12. ↵
      1. Cheng YY,
      2. Cao Z,
      3. Hou J
      . Investigation and association analysis of multimorbidity in middle-aged and elderly population in China. Chinese J Dis Cont Prevent 2019;23:625–9.
      OpenUrl
    13. ↵
      1. Mini GK,
      2. Thankappan KR
      . Pattern, correlates and implications of non-communicable disease multimorbidity among older adults in selected Indian states: a cross-sectional study. BMJ Open 2017;7:e013529.doi:10.1136/bmjopen-2016-013529pmid:http://www.ncbi.nlm.nih.gov/pubmed/28274966
      OpenUrlAbstract/FREE Full Text
    14. ↵
      1. Pati S,
      2. Swain S,
      3. Hussain MA, et al
      . Prevalence and outcomes of multimorbidity in South Asia: a systematic review. BMJ Open 2015;5:e007235.doi:10.1136/bmjopen-2014-007235pmid:http://www.ncbi.nlm.nih.gov/pubmed/26446164
      OpenUrlPubMed
    15. ↵
      1. Nguyen H,
      2. Manolova G,
      3. Daskalopoulou C, et al
      . Prevalence of multimorbidity in community settings: a systematic review and meta-analysis of observational studies. J Comorb 2019;9:2235042X19870934.doi:10.1177/2235042X19870934pmid:http://www.ncbi.nlm.nih.gov/pubmed/31489279
      OpenUrlPubMed
    16. ↵
      1. Hu X,
      2. Huang J,
      3. Lv Y, et al
      . Status of prevalence study on multimorbidity of chronic disease in China: systematic review. Geriatr Gerontol Int 2015;15:1–10.doi:10.1111/ggi.12340pmid:http://www.ncbi.nlm.nih.gov/pubmed/25163532
      OpenUrlPubMed
    17. ↵
      1. Violan C,
      2. Foguet-Boreu Q,
      3. Flores-Mateo G, et al
      . Prevalence, determinants and patterns of multimorbidity in primary care: a systematic review of observational studies. PLoS One 2014;9:e102149.doi:10.1371/journal.pone.0102149pmid:http://www.ncbi.nlm.nih.gov/pubmed/25048354
      OpenUrlCrossRefPubMed
    18. ↵
      1. Ofori-Asenso R,
      2. Chin KL,
      3. Curtis AJ, et al
      . Recent patterns of multimorbidity among older adults in high-income countries. Popul Health Manag 2019;22:127–37.doi:10.1089/pop.2018.0069pmid:http://www.ncbi.nlm.nih.gov/pubmed/30096023
      OpenUrlPubMed
    19. ↵
      1. Wells GSB,
      2. O’Connell D
      . The Newcastle-Ottawa Scale (NOS) for assessing the quality of Nonrandomised studies in meta-analyses, 2004. Available: http://www.ohrica/programs/clinical_epidemiology_oxford_htm [Accessed 18 Jan 2020].
    20. ↵
      1. Wong SYS,
      2. Mercer SW,
      3. Woo J, et al
      . The influence of multi-morbidity and self-reported socio-economic standing on the prevalence of depression in an elderly Hong Kong population. BMC Public Health 2008;8:119.doi:10.1186/1471-2458-8-119pmid:http://www.ncbi.nlm.nih.gov/pubmed/18410677
      OpenUrlCrossRefPubMed
    21. ↵
      1. Chi M-ju,
      2. Lee C-yi,
      3. Wu S-chong
      . Multiple morbidity combinations impact on medical expenditures among older adults. Arch Gerontol Geriatr 2011;52:e210–4.doi:10.1016/j.archger.2010.11.013pmid:http://www.ncbi.nlm.nih.gov/pubmed/21131068
      OpenUrlPubMed
    22. ↵
      1. Wu F,
      2. Guo Y,
      3. Kowal P, et al
      . Prevalence of major chronic conditions among older Chinese adults: the study on global AGEing and adult health (SAGE) wave 1. PLoS One 2013;8:e74176.doi:10.1371/journal.pone.0074176pmid:http://www.ncbi.nlm.nih.gov/pubmed/24069278
      OpenUrlCrossRefPubMed
    23. ↵
      1. Fu S,
      2. Huang N,
      3. Chou Y-J
      . Trends in the prevalence of multiple chronic conditions in Taiwan from 2000 to 2010: a population-based study. Prev Chronic Dis 2014;11:e187.doi:10.5888/pcd11.140205pmid:http://www.ncbi.nlm.nih.gov/pubmed/25340359
      OpenUrlPubMed
    24. ↵
      1. Wang HHX,
      2. Wang JJ,
      3. Wong SYS, et al
      . Epidemiology of multimorbidity in China and implications for the healthcare system: cross-sectional survey among 162,464 community household residents in southern China. BMC Med 2014;12:188.doi:10.1186/s12916-014-0188-0pmid:http://www.ncbi.nlm.nih.gov/pubmed/25338506
      OpenUrlCrossRefPubMed
    25. ↵
      1. Wong MCS,
      2. Liu J,
      3. Zhou S, et al
      . The association between multimorbidity and poor adherence with cardiovascular medications. Int J Cardiol 2014;177:477–82.doi:10.1016/j.ijcard.2014.09.103pmid:http://www.ncbi.nlm.nih.gov/pubmed/25443249
      OpenUrlPubMed
    26. ↵
      1. Wang R,
      2. Yan Z,
      3. Liang Y, et al
      . Prevalence and patterns of chronic disease pairs and multimorbidity among older Chinese adults living in a rural area. PLoS One 2015;10:e0138521.doi:10.1371/journal.pone.0138521pmid:http://www.ncbi.nlm.nih.gov/pubmed/26394368
      OpenUrlPubMed
    27. ↵
      1. Wang SB,
      2. D'Arcy C,
      3. Yu YQ, et al
      . Prevalence and patterns of multimorbidity in northeastern China: a cross-sectional study. Public Health 2015;129:1539–46.doi:10.1016/j.puhe.2015.06.013pmid:http://www.ncbi.nlm.nih.gov/pubmed/26210070
      OpenUrlPubMed
    28. ↵
      1. Chin WY,
      2. Choi EPH,
      3. Wan EYF, et al
      . Health-related quality of life mediates associations between multi-morbidity and depressive symptoms in Chinese primary care patients. Fam Pract 2016;33:61–8.doi:10.1093/fampra/cmv090pmid:http://www.ncbi.nlm.nih.gov/pubmed/26567547
      OpenUrlCrossRefPubMed
    29. ↵
      1. Wang S,
      2. Li B,
      3. Ungvari GS, et al
      . Poor mental health status and its associations with demographic characteristics and chronic diseases in Chinese elderly. Soc Psychiatry Psychiatr Epidemiol 2016;51:1449–55.doi:10.1007/s00127-016-1271-ypmid:http://www.ncbi.nlm.nih.gov/pubmed/27539269
      OpenUrlCrossRefPubMed
    30. ↵
      1. Gu J,
      2. Chao J,
      3. Chen W, et al
      . Multimorbidity in the community-dwelling elderly in urban China. Arch Gerontol Geriatr 2017;68:62–7.doi:10.1016/j.archger.2016.09.001pmid:http://www.ncbi.nlm.nih.gov/pubmed/27654809
      OpenUrlPubMed
    31. ↵
      1. Kunna R,
      2. San Sebastian M,
      3. Stewart Williams J
      . Measurement and decomposition of socioeconomic inequality in single and multimorbidity in older adults in China and Ghana: results from the WHO study on global AGEing and adult health (SAGE). Int J Equity Health 2017;16:79.doi:10.1186/s12939-017-0578-ypmid:http://www.ncbi.nlm.nih.gov/pubmed/28506233
      OpenUrlPubMed
    32. ↵
      1. Lin H,
      2. Li Q,
      3. Hu Y, et al
      . The prevalence of multiple non-communicable diseases among middle-aged and elderly people: the Shanghai Changfeng study. Eur J Epidemiol 2017;32:159–63.doi:10.1007/s10654-016-0219-6pmid:http://www.ncbi.nlm.nih.gov/pubmed/27999971
      OpenUrlPubMed
    33. ↵
      1. Wang X-X,
      2. Lin W-Q,
      3. Chen X-J, et al
      . Multimorbidity associated with functional independence among community-dwelling older people: a cross-sectional study in Southern China. Health Qual Life Outcomes 2017;15:73.doi:10.1186/s12955-017-0635-7pmid:http://www.ncbi.nlm.nih.gov/pubmed/28412945
      OpenUrlPubMed
    34. ↵
      1. Chen H,
      2. Chen Y,
      3. Cui B
      . The association of multimorbidity with healthcare expenditure among the elderly patients in Beijing, China. Arch Gerontol Geriatr 2018;79:32–8.doi:10.1016/j.archger.2018.07.008pmid:http://www.ncbi.nlm.nih.gov/pubmed/30086414
      OpenUrlPubMed
    35. ↵
      1. Chen H,
      2. Cheng M,
      3. Zhuang Y, et al
      . Multimorbidity among middle-aged and older persons in urban China: prevalence, characteristics and health service utilization. Geriatr Gerontol Int 2018;18:1447–52.doi:10.1111/ggi.13510pmid:http://www.ncbi.nlm.nih.gov/pubmed/30178629
      OpenUrlPubMed
    36. ↵
      1. Cheung JTK,
      2. Yu R,
      3. Wu Z, et al
      . Geriatric syndromes, multimorbidity, and disability overlap and increase healthcare use among older Chinese. BMC Geriatr 2018;18:147.doi:10.1186/s12877-018-0840-1pmid:http://www.ncbi.nlm.nih.gov/pubmed/29940868
      OpenUrlPubMed
    37. ↵
      1. Bao X-Y,
      2. Xie Y-X,
      3. Zhang X-X, et al
      . The association between multimorbidity and health-related quality of life: a cross-sectional survey among community middle-aged and elderly residents in southern China. Health Qual Life Outcomes 2019;17:107.doi:10.1186/s12955-019-1175-0pmid:http://www.ncbi.nlm.nih.gov/pubmed/31234889
      OpenUrlPubMed
    38. ↵
      1. Hu R-H,
      2. Hsiao F-Y,
      3. Chen L-J, et al
      . Increasing age- and gender-specific burden and complexity of multimorbidity in Taiwan, 2003-2013: a cross-sectional study based on nationwide claims data. BMJ Open 2019;9:e028333.doi:10.1136/bmjopen-2018-028333pmid:http://www.ncbi.nlm.nih.gov/pubmed/31182451
      OpenUrlAbstract/FREE Full Text
    39. ↵
      1. Lai FTT,
      2. Guthrie B,
      3. Wong SYS, et al
      . Sex-specific intergenerational trends in morbidity burden and multimorbidity status in Hong Kong community: an age-period-cohort analysis of repeated population surveys. BMJ Open 2019;9:e023927.doi:10.1136/bmjopen-2018-023927pmid:http://www.ncbi.nlm.nih.gov/pubmed/30782718
      OpenUrlAbstract/FREE Full Text
    40. ↵
      1. Yao SS,
      2. Cao GY,
      3. Han L
      . Prevalence and patterns of multimorbidity in a nationally representative sample of older Chinese: results from the China health and retirement longitudinal study. J Gerontol A Biol Sci Med Sci 2019;75:1974–80.
      OpenUrl
    41. ↵
      1. Yi JY,
      2. Kim H,
      3. Chi I
      . Urban-rural differences in multimorbidity and associated factors in China and Korea: a population-based survey study. Geriatr Gerontol Int 2019;19:1157–64.doi:10.1111/ggi.13771pmid:http://www.ncbi.nlm.nih.gov/pubmed/31529597
      OpenUrlPubMed
    42. ↵
      1. You L,
      2. Yu Z,
      3. Zhang X, et al
      . Association between multimorbidity and depressive symptom among community-dwelling elders in eastern China. Clin Interv Aging 2019;14:2273–80.doi:10.2147/CIA.S221917pmid:http://www.ncbi.nlm.nih.gov/pubmed/31908437
      OpenUrlPubMed
    43. ↵
      1. Zhang R,
      2. Lu Y,
      3. Shi L, et al
      . Prevalence and patterns of multimorbidity among the elderly in China: a cross-sectional study using national survey data. BMJ Open 2019;9:e024268.doi:10.1136/bmjopen-2018-024268pmid:http://www.ncbi.nlm.nih.gov/pubmed/31427309
      OpenUrlAbstract/FREE Full Text
    44. ↵
      1. Qin HY,
      2. ZW Q
      . The investigation of depression in the elderly comorbided with chronic somatic disorders in the community of Pudong new area. J Clin Psychiat 2012;22:169–71.
      OpenUrl
    45. ↵
      1. Jia Y,
      2. Mei YY,
      3. Sheng CQ
      . Investigation and correlation analysis of multimorbidity of chronic diseases among residents aged 55 years and over. Chinese Gen Pract 2016;19:683–7.
      OpenUrl
    46. ↵
      1. Zhang KK,
      2. Zhu ML,
      3. Liu XH
      . Investigation of multimorbidity and geriatric syndromes in the elder people in Beijing communities. Chinese J Pract Int Med 2016;36:419–21.
      OpenUrl
    47. ↵
      1. Qian HS,
      2. AX M
      . Multimorbidity status and its riskfactors to multimorbidity in middle-agedpeople in China. Chinese J Drug Evaluat 2017;34:476–80.
      OpenUrl
    48. ↵
      1. Wang JF,
      2. Zhang ZH,
      3. Cui Y
      . Analysis on combinations of multimorbidity in the elderly in Shanghai communities and the influence factors. Geriatr Health Care 2017;23:97–101.
      OpenUrl
    49. ↵
      1. You Y,
      2. Li N,
      3. Wu M
      . Analysis of the prevalence and influencing factors of non-communicable chronic diseases among urban residents in Liaoning Province. Chinese J Prevent Cont Chron Dis 2017;25:810–7.
      OpenUrl
    50. ↵
      1. Chen J,
      2. Yang XH,
      3. Zeng LP
      . Analyzing the status of chronic illnesses and its comorbid mode of community elderly residents in Xindu district. Chinese Health Serv Manag 2018;8:573–615.
      OpenUrl
    51. ↵
      1. Chen JX,
      2. Zheng XL
      . Investigation on the current situation of chronic diseases and rconomic burden of elderly residents in Yanqiao community. Economic Res Manag 2018;9:107–12.
      OpenUrl
    52. ↵
      1. Qiu Y,
      2. Liu Y,
      3. Ren W
      . Features of elderly outpatients seeking primary care in the General Hospital. Chinese Gen Pract 2018;21:2686–9.
      OpenUrl
    53. ↵
      1. Zhang GF,
      2. Wang H,
      3. GM Y
      . Survey on chronic diseases in longevous old people living in villages or towns of Zhongxiang City, Hubei Province. China J Multple Organ Diseases in the Elderly 2018;17:92–6.
      OpenUrl
    54. ↵
      1. Zhang JY,
      2. Cong SJ,
      3. Mai YQ
      . Investigation and epidemiology analysis of chronic disease common diseases in elderly people in Zhongshan City. J Modern Med Health 2018;34:1320–3.
      OpenUrl
    55. ↵
      1. Zhong CY,
      2. Zhou DQ,
      3. Mao J
      . Multicenter epidemiological characteristics study of special chronic diseases in 9476 elderly with the outpatient service in Zunyi. Mod Prevent Med 2018;45:3676–80.
      OpenUrl
    56. ↵
      1. Chang R,
      2. Dong XL,
      3. Wang YZ
      . The prevalence and risk factors of diabetes and hypertension in Deqing rural community. Chinese J Dis Cont Prevent 2019;23:640–55.
      OpenUrl
    57. ↵
      1. Feng X,
      2. Hua ZL,
      3. Zhuo Q
      . Analysis of status and comorbid patterns of major chronic diseases among middle-aged and elderly residents in a City. Modern Prevent Med 2019;46:3728–53.
      OpenUrl
    58. ↵
      1. Han SH,
      2. Zhang PJ,
      3. JL L
      . Analysis of prevalence and comorbidity factors of hypertension,diabetes mellitus and hyperlipidemia in 35-75 years old residents in Hubei province. J Appl Prevent Med 2019;25:356–60.
      OpenUrl
    59. ↵
      1. Hou YT,
      2. Jiang DD,
      3. Liu XJ
      . Prevalence and related factors of comorbidity of chronic diseases among community elderly in Wuhan City. Chinese J Public Health 2020;36:1604–7.
      OpenUrl
    60. ↵
      1. Jin XZ,
      2. Lu Y
      . Prevalence of comorbidity among the elderly and its impact on healthcare expenditure in China. Chinese Gen Pract 2019;22:4166–72.
      OpenUrl
    61. ↵
      1. Li X,
      2. Cai L,
      3. Wang XM
      . The prevalence of five common chronic diseases and multi-morbidity and its relationship with socioeconomic position among rural older adults in Yunnan Province. Chinese J Dis Cont Prevent 2019;23:630–4.
      OpenUrl
    62. ↵
      1. Yan W,
      2. Lu Y,
      3. Zhang R
      . Multimorbidity status of the elderly in China-research based on CHARLS data. Chinese J Dis Cont Prevent 2019;23:426–30.
      OpenUrl
    63. ↵
      1. Zhang H,
      2. SG Q,
      3. ZX L
      . Multimorbidity of common chronic disease among community-dwelling older adults in six provinces. Capital J Pub Health 2019;13:122–5.
      OpenUrl
    64. ↵
      1. Liu W,
      2. DH Y,
      3. Jin H
      . Analysis and evaluation of multiple drug use among elderly patients with multiple chronic conditions in community. Chinese Gen Pract 2020;23:1592–8.
      OpenUrl
    65. ↵
      1. Banjare P,
      2. Pradhan J
      . Socio-economic inequalities in the prevalence of multi-morbidity among the rural elderly in Bargarh district of Odisha (India). PLoS One 2014;9:e97832.doi:10.1371/journal.pone.0097832pmid:http://www.ncbi.nlm.nih.gov/pubmed/24902041
      OpenUrlCrossRefPubMed
    66. ↵
      1. Pati S,
      2. Agrawal S,
      3. Swain S, et al
      . Non communicable disease multimorbidity and associated health care utilization and expenditures in India: cross-sectional study. BMC Health Serv Res 2014;14:451.doi:10.1186/1472-6963-14-451pmid:http://www.ncbi.nlm.nih.gov/pubmed/25274447
      OpenUrlCrossRefPubMed
    67. ↵
      1. Arokiasamy P,
      2. Uttamacharya U,
      3. Jain K
      . Multi-morbidity, functional limitations, and self-rated health among older adults in India: cross-sectional analysis of LASI pilot survey, 2010. SAGE Open 2015;5.
    68. ↵
      1. Pati S,
      2. Swain S,
      3. Hussain MA, et al
      . Prevalence, correlates, and outcomes of multimorbidity among patients attending primary care in Odisha, India. Ann Fam Med 2015;13:446–50.doi:10.1370/afm.1843pmid:http://www.ncbi.nlm.nih.gov/pubmed/26371265
      OpenUrlAbstract/FREE Full Text
    69. ↵
      1. Gupta A,
      2. Girdhar S,
      3. Chaudhary A, et al
      . Patterns of multimorbidity among elderly in an urban area of North India. J Evol Med Dent Sci 2016;5:936–41.doi:10.14260/jemds/2016/218
      OpenUrl
    70. ↵
      1. Tripathy JP,
      2. Thakur JS,
      3. Jeet G, et al
      . Prevalence and determinants of comorbid diabetes and hypertension: evidence from non communicable disease risk factor STEPS survey, India. Diabetes Metab Syndr 2017;11:459–65.doi:10.1016/j.dsx.2017.03.036pmid:http://www.ncbi.nlm.nih.gov/pubmed/28395951
      OpenUrlPubMed
    71. ↵
      1. Jain K,
      2. Arokiasamy P
      . Urbanization, multi-morbidities and preference for health care facility: an insight from Rajasthan, India. J Urban Reg Analys 2018;10:143–76.
      OpenUrl
    72. ↵
      1. Bischops AC,
      2. Manne-Goehler J,
      3. Jaacks LM, et al
      . The prevalence of concurrently raised blood glucose and blood pressure in India: a cross-sectional study of 2035 662 adults. J Hypertens 2019;37:1822–31.doi:10.1097/HJH.0000000000002114pmid:http://www.ncbi.nlm.nih.gov/pubmed/31368919
      OpenUrlPubMed
    73. ↵
      1. Arokiasamy P,
      2. Uttamacharya U,
      3. Jain K, et al
      . The impact of multimorbidity on adult physical and mental health in low- and middle-income countries: what does the study on global ageing and adult health (SAGE) reveal? BMC Med 2015;13:178.doi:10.1186/s12916-015-0402-8pmid:http://www.ncbi.nlm.nih.gov/pubmed/26239481
      OpenUrlCrossRefPubMed
    74. ↵
      1. Lee JT,
      2. Hamid F,
      3. Pati S, et al
      . Impact of noncommunicable disease multimorbidity on healthcare utilisation and out-of-pocket expenditures in middle-income countries: cross sectional analysis. PLoS One 2015;10:e0127199.doi:10.1371/journal.pone.0127199pmid:http://www.ncbi.nlm.nih.gov/pubmed/26154083
      OpenUrlCrossRefPubMed
    75. ↵
      1. Garin N,
      2. Koyanagi A,
      3. Chatterji S, et al
      . Global multimorbidity patterns: a cross-sectional, population-based, multi-country study. J Gerontol A Biol Sci Med Sci 2016;71:205–14.doi:10.1093/gerona/glv128pmid:http://www.ncbi.nlm.nih.gov/pubmed/26419978
      OpenUrlCrossRefPubMed
    76. ↵
      1. Stubbs B,
      2. Vancampfort D,
      3. Veronese N, et al
      . Multimorbidity and perceived stress: a population-based cross-sectional study among older adults across six low- and middle-income countries. Maturitas 2018;107:84–91.doi:10.1016/j.maturitas.2017.10.007pmid:http://www.ncbi.nlm.nih.gov/pubmed/29169587
      OpenUrlPubMed
    77. ↵
      1. Fortin M,
      2. Stewart M,
      3. Poitras M-E, et al
      . A systematic review of prevalence studies on multimorbidity: toward a more uniform methodology. Ann Fam Med 2012;10:142–51.doi:10.1370/afm.1337pmid:http://www.ncbi.nlm.nih.gov/pubmed/22412006
      OpenUrlAbstract/FREE Full Text
    78. ↵
      1. Violán C,
      2. Foguet-Boreu Q,
      3. Hermosilla-Pérez E, et al
      . Comparison of the information provided by electronic health records data and a population health survey to estimate prevalence of selected health conditions and multimorbidity. BMC Public Health 2013;13:251.doi:10.1186/1471-2458-13-251pmid:http://www.ncbi.nlm.nih.gov/pubmed/23517342
      OpenUrlCrossRefPubMed
    79. ↵
      1. Schram MT,
      2. Frijters D,
      3. van de Lisdonk EH, et al
      . Setting and registry characteristics affect the prevalence and nature of multimorbidity in the elderly. J Clin Epidemiol 2008;61:1104–12.doi:10.1016/j.jclinepi.2007.11.021pmid:http://www.ncbi.nlm.nih.gov/pubmed/18538993
      OpenUrlCrossRefPubMedWeb of Science
    80. ↵
      1. Emerging Risk Factors Collaboration,
      2. Di Angelantonio E,
      3. Kaptoge S, et al
      . Association of cardiometabolic multimorbidity with mortality. JAMA 2015;314:52–60.doi:10.1001/jama.2015.7008pmid:http://www.ncbi.nlm.nih.gov/pubmed/26151266
      OpenUrlCrossRefPubMed
    81. ↵
      1. Fortin M,
      2. Hudon C,
      3. Haggerty J, et al
      . Prevalence estimates of multimorbidity: a comparative study of two sources. BMC Health Serv Res 2010;10:111.doi:10.1186/1472-6963-10-111pmid:http://www.ncbi.nlm.nih.gov/pubmed/20459621
      OpenUrlCrossRefPubMed
    82. ↵
      1. Diederichs C,
      2. Berger K,
      3. Bartels DB
      . The measurement of multiple chronic diseases--a systematic review on existing multimorbidity indices. J Gerontol A Biol Sci Med Sci 2011;66:301–11.doi:10.1093/gerona/glq208pmid:http://www.ncbi.nlm.nih.gov/pubmed/21112963
      OpenUrlCrossRefPubMedWeb of Science
    83. ↵
      1. Alimohammadian M,
      2. Majidi A,
      3. Yaseri M, et al
      . Multimorbidity as an important issue among women: results of a gender difference investigation in a large population-based cross-sectional study in West Asia. BMJ Open 2017;7:e013548.doi:10.1136/bmjopen-2016-013548pmid:http://www.ncbi.nlm.nih.gov/pubmed/28490550
      OpenUrlAbstract/FREE Full Text
    84. ↵
      1. Kumari M,
      2. Mohanty SK
      . Caste, religion and regional differentials in life expectancy at birth in India: cross-sectional estimates from recent national family health survey. BMJ Open 2020;10:e035392.doi:10.1136/bmjopen-2019-035392pmid:http://www.ncbi.nlm.nih.gov/pubmed/32819936
      OpenUrlAbstract/FREE Full Text
    85. ↵
      1. Kanesarajah J,
      2. Waller M,
      3. Whitty JA, et al
      . Multimorbidity and quality of life at mid-life: a systematic review of general population studies. Maturitas 2018;109:53–62.doi:10.1016/j.maturitas.2017.12.004pmid:http://www.ncbi.nlm.nih.gov/pubmed/29452782
      OpenUrlCrossRefPubMed
    86. ↵
      1. Sayer GP,
      2. Britt H
      . Sex differences in morbidity: a case of discrimination in general practice. Soc Sci Med 1996;42:257–64.doi:10.1016/0277-9536(95)00097-6pmid:http://www.ncbi.nlm.nih.gov/pubmed/8928034
      OpenUrlCrossRefPubMed
    87. ↵
      1. Geldsetzer P,
      2. Manne-Goehler J,
      3. Theilmann M, et al
      . Diabetes and hypertension in India: a nationally representative study of 1.3 million adults. JAMA Intern Med 2018;178:363–72.doi:10.1001/jamainternmed.2017.8094pmid:http://www.ncbi.nlm.nih.gov/pubmed/29379964
      OpenUrlPubMed
    88. ↵
      1. World Health Organization
      . Noncommunicable diseases factsheet, 2019. Available: http://www.who.int/news-room/fact-sheets/detail/noncommunicable-diseases [Accessed 18 Jan 2020].
    89. ↵
      1. Wang Z,
      2. Chen Z,
      3. Zhang L, et al
      . Status of hypertension in China: results from the China hypertension survey, 2012-2015. Circulation 2018;137:2344–56.doi:10.1161/CIRCULATIONAHA.117.032380pmid:http://www.ncbi.nlm.nih.gov/pubmed/29449338
      OpenUrlAbstract/FREE Full Text
    90. ↵
      1. Harrison C,
      2. Britt H,
      3. Miller G, et al
      . Examining different measures of multimorbidity, using a large prospective cross-sectional study in Australian general practice. BMJ Open 2014;4:e004694.doi:10.1136/bmjopen-2013-004694pmid:http://www.ncbi.nlm.nih.gov/pubmed/25015470
      OpenUrlAbstract/FREE Full Text
    91. ↵
      1. Deeks SG,
      2. Lewin SR,
      3. Havlir DV
      . The end of AIDS: HIV infection as a chronic disease. Lancet 2013;382:1525–33.doi:10.1016/S0140-6736(13)61809-7pmid:http://www.ncbi.nlm.nih.gov/pubmed/24152939
      OpenUrlCrossRefPubMedWeb of Science
    92. ↵
      1. Oni T,
      2. McGrath N,
      3. BeLue R, et al
      . Chronic diseases and multi-morbidity-a conceptual modification to the WHO ICCC model for countries in health transition. BMC Public Health 2014;14:575.doi:10.1186/1471-2458-14-575pmid:http://www.ncbi.nlm.nih.gov/pubmed/24912531
      OpenUrlCrossRefPubMed
    93. ↵
      1. Swanepoel CR,
      2. Atta MG,
      3. D'Agati VD, et al
      . Kidney disease in the setting of HIV infection: conclusions from a Kidney Disease: Improving Global Outcomes (KDIGO) Controversies Conference. Kidney Int 2018;93:545–59.doi:10.1016/j.kint.2017.11.007pmid:http://www.ncbi.nlm.nih.gov/pubmed/29398134
      OpenUrlCrossRefPubMed
    94. ↵
      1. Noubissi EC,
      2. Katte J-C,
      3. Sobngwi E
      . Diabetes and HIV. Curr Diab Rep 2018;18:125.doi:10.1007/s11892-018-1076-3pmid:http://www.ncbi.nlm.nih.gov/pubmed/30294763
      OpenUrlPubMed
    95. ↵
      1. Zhang Y,
      2. Misra R,
      3. Sambamoorthi U
      . Prevalence of multimorbidity among Asian Indian, Chinese, and non-hispanic white adults in the United States. Int J Environ Res Public Health 2020;17:17093336.doi:10.3390/ijerph17093336pmid:32403412
      OpenUrlPubMed
    96. ↵
      1. Pickrell WO,
      2. Kerr MP
      . SUDEP and mortality in epilepsy: the role of routinely collected healthcare data, registries, and health inequalities. Epilepsy Behav 2020;103:106453.doi:10.1016/j.yebeh.2019.106453pmid:http://www.ncbi.nlm.nih.gov/pubmed/31427266
      OpenUrlPubMed
    97. ↵
      1. Biggin F,
      2. Emsley HCA,
      3. Knight J
      . Routinely collected patient data in neurology research: a systematic mapping review. BMC Neurol 2020;20:431.doi:10.1186/s12883-020-01993-wpmid:http://www.ncbi.nlm.nih.gov/pubmed/33243167
      OpenUrlPubMed
    98. ↵
      1. Behrendt C-A
      . Routinely collected data from health insurance claims and electronic health records in vascular research - a success story and way to go. Vasa 2020;49:85–6.doi:10.1024/0301-1526/a000847pmid:http://www.ncbi.nlm.nih.gov/pubmed/32091976
      OpenUrlPubMed
    99. ↵
      1. Xiong S,
      2. Cai C,
      3. Jiang W, et al
      . Primary health care system responses to non-communicable disease prevention and control: a scoping review of national policies in Mainland China since the 2009 health reform. Lancet Regional Health - Western Pacific 2022;396:100390.doi:10.1016/j.lanwpc.2022.100390

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    Footnotes

    • DPr and MT are joint senior authors.

    • Handling editor Seye Abimbola

    • Twitter @davidpeiris, @MaoyiT

    • Contributors MT, DPr, XZ and AP contributed to the conception and design of this research. XZ, AP and TW participated in screening and data extraction. XZ, TW, JY and YP carried out the analysis and interpretation of data. XZ and MT wrote the first draft of the paper. SX, DPe, YP, DPr, WT and HS provided critical comments for the manuscript and revised the first and subsequent drafts. All authors approved the submission. MT acts as the guarantor, responsible for the overall content.

    • Funding The authors have not declared a specific grant for this research from any funding agency in the public, commercial or not-for-profit sectors.

    • Competing interests None declared.

    • Patient and public involvement Patients and/or the public were not involved in the design, or conduct, or reporting, or dissemination plans of this research.

    • Provenance and peer review Not commissioned; externally peer reviewed.

    • Supplemental material This content has been supplied by the author(s). It has not been vetted by BMJ Publishing Group Limited (BMJ) and may not have been peer-reviewed. Any opinions or recommendations discussed are solely those of the author(s) and are not endorsed by BMJ. BMJ disclaims all liability and responsibility arising from any reliance placed on the content. Where the content includes any translated material, BMJ does not warrant the accuracy and reliability of the translations (including but not limited to local regulations, clinical guidelines, terminology, drug names and drug dosages), and is not responsible for any error and/or omissions arising from translation and adaptation or otherwise.