Mapping the global research landscape and knowledge gaps on multimorbidity: a bibliometric study

Abstract

BackgroundTo summarize global research trends and activities on multimorbidity; then to assess the knowledge gaps and to identify implications for knowledge exchange between high income countries (HICs) and low– and middle– income countries (LMICs).MethodsA comprehensive search was conducted to identify research publications on multimorbidity in the Web of ScienceTM, as well as diabetes, depression, hypertension, and Chronic Obstructive Pulmonary Disease (COPD). The time frame for the search was from 1900 to June, 2016. Information (such as publication date, subject category, author, country of origin, title, abstract, and keywords) were extracted and the full texts were obtained for the co–citation analysis. Data were linked with the life expectancy at birth (years) and Gross National Income (GNI). Co–citation and hierarchal clustering analysis was used to map the trends and research networks with CiteSpace II (JAVA freeware, copyright Chaomei Chen, http://cluster.cis.drexel.edu/~cchen/citespace/).FindingsWe identified 2864 relevant publications as at June 2016, with the first paper on this topic indexed in 1974 from Germany, but 80% were published after 2010. Further analysis yielded two knowledge gaps: (1) compared with single conditions (diabetes, hypertension, depression, and COPD), there is a mismatch between the high prevalence of multimorbidity and its research outputs (ratio of articles on multimorbidity vs other four single conditions is 1:13–150); (2) although a total of 76 countries have contributed to this research area, only 5% of research originated from LMICs where 73% of non–communicable disease (NCD) related deaths had occurred. Additional analysis showed the median year of first publication occurred 15 years later in the LMICs compared with HICs (2010 vs 1995); and longer life expectancy was associated with exponentially higher publication outputs (Pearson correlation coefficient r = 0.95) at the global level. The life expectancy at the median year (1994) of first publication was 66.1, with the gap between LMICs and HICs 7.9 (68.4 vs 76.3).ConclusionsThis study confirms substantial knowledge gaps in the research agenda on multimorbidity, with input urgently needed to move us forward worldwide, especially for and in LMICs. There is the possibility that LMICs can learn from and collaborate with HICs in this area.

Multimorbidity among middle‐aged and older persons in urban China: Prevalence, characteristics and health service utilization

“…Multimorbidity has been recognized as a critical public health issue . Although many researchers have studied multimorbidity and its impact on healthcare systems in high‐income countries, the issues in low‐ and middle‐income countries (LMIC) are less often discussed . With population aging, LMIC have been undergoing a health transition, emphasizing the rising impact of non‐negligible infectious diseases and of emerging chronic diseases .…”

“…[2][3][4] Although many researchers have studied multimorbidity and its impact on healthcare systems in high-income countries, the issues in low-and middle-income countries (LMIC) are less often discussed. 1,5 With population aging, LMIC have been undergoing a health transition, emphasizing the rising impact of non-negligible infectious diseases and of emerging chronic diseases. 6 Multimorbidity is expected to play an important role in medical education and health service delivery systems in China and other countries alike.…”

Mining comorbidity patterns using retrospective analysis of big collection of outpatient records

“…Studies on multimorbidity are a great challenge given the mismatch between the high prevalence of this condition and relatively smaller number of research papers [9], which is partly due to lack of data. Machine learning (ML) is the basic technology used in such studies.…”

Coping with multiple chronic conditions: An integrative review

“…Terms such as multiple chronic conditions, comorbidity, and multimorbidity were used interchangeably and varied measurements for MCCs were used (Xu, Mishra, & Jones, 2017).…”