Discussion
This study has shown that in rural Burkina Faso, multimorbidity, defined as two or more conditions, is common, and present in nearly one quarter of the population over 40 years old. Multimorbidity is more common in women, among those who are older, those who have formal education, unmarried individuals and those of relatively higher economic status. Although limited by the number of conditions that we could collect, we found that discordant multimorbidity was as common as concordant multimorbidity, highlighting that patterns of multimorbidity are complex in this population. Multimorbidity was independently associated with a greater prevalence of frailty, worse disability, lower quality of life and lower walk speed. There was no significant difference between those with discordant versus concordant multimorbidity, after adjustment for number of conditions. However, multimorbidity with MH alone, or multimorbidity with MH and NCDs had worse measurements for all outcomes, except from time to chair rise, compared with multimorbidity with only NCDs.
Multimorbidity is a little-explored topic in low-resource settings18 and this is one of the first papers reporting the prevalence of multimorbidity such settings. There has been one previous study from Burkina Faso, conducted in a major city in 2010, which found an even higher prevalence of multimorbidity (65%) than in this study.32 However, the earlier study population was older (>60 years of age) and data were collected on a wider range of conditions, many of which were highly prevalent (eg, osteoarthritis), and which encompassed a range of health states somewhat broader than our definition of a disease (eg, visual impairment, malnutrition). In our sample, the prevalence of multimorbidity was higher (35.2%) among those of 60 years of age compared with younger adults, but not nearly as high as in the previous study from Burkina Faso. Thus, it is possible either that our results underestimate the prevalence of broad multimorbidity in rural Burkina Faso, or that previous work overestimated the prevalence, depending on the definition of what constitutes a disease that is adopted.
We are not aware of any previous study that has looked at the patterns (or morbidity domains in which disease is occurring) of multimorbidity or ascertained its associations with frailty, disability, quality of life and physical performance in West Africa or other LICs defined as least developed. Our findings are of particular importance, because, although we cannot demonstrate causality in this cross-sectional study, it highlights that multimorbidity is at least associated with important health and well-being consequences, including limitations in physical function and decreased self-reported quality of life.
Knowledge of who suffers from multimorbidity and how morbidities are grouped can help with the design of healthcare services to treat the multimorbid patient, and support in the development of guidelines for healthcare practitioners of which other conditions to look for when a person presents with a single disease.
In contrast to findings from high-income countries,22 51 52 our study has found that even when controlling for age, wealthier people are more likely to have multimorbidity than those who are poorer, this is especially the case for multimorbidity with NCDs. This may reflect the shifting patterns of diseases and risk factors with individual wealth seen as countries’ gross domestic product improves.53 Obesity in particular affects those who are relatively wealthy in LICs and LMICs and associated cardiometabolic diseases affect the highest wealth quintile.53 The findings may also reflect that wealthy people have better access to healthcare and are more likely to have their conditions diagnosed,52 54 although work has shown that healthcare access is universally poor in LICs.55 56 While we have found that the socioeconomic associations with multimorbidity in this low-income country are different to those seen in high-income countries, we have shown that the associations in terms of functional outcomes are similar across socioeconomic strata. Thus, ultimately, the meaning of multimorbidity in terms of consequences to the patients, the healthcare system which treats them and the families that care for them, is likely to be similar no matter what income level patients come from.
We have shown that in this population, having two or more conditions in the discordant multimorbidity category is just as likely as having two or more conditions in the same domain; thus, in healthcare structures that have hitherto provided siloed care, adopting a more holistic care provision strategy could provide an avenue towards better health. The high prevalence of discordant multimorbidity highlights that such approaches cannot be limited to the management of concordant clusters of morbidities, such as diabetes, hypertension and dyslipidaemia; such an approach has failed to provide effective, joined-up care in high-income countries,57 and replicating this model in developing countries would therefore be a missed opportunity. However, because the number of conditions we investigated in this study were limited, we were unable to conduct a more nuanced cluster analysis.
Frailty, disability and reduced quality of life are strongly and independently associated with increasing number of conditions. However, these findings also indicate that discordant multimorbidity is no different to concordant multimorbidity in its association with poor individual outcomes, when controlling for number of conditions. This is in contrast with what has been found previously with patients with discordant multimorbidities typically having poorer health-related quality of life and worse clinical outcomes.6 13 58 Interestingly, we found that people who have multimorbidity with only MH conditions or multimorbidity with MH conditions and NCDs have worse outcomes than those with only NCDs. The finding that people with NCDs and MH have worse outcomes than those with NCDs alone has been found in other countries, including high income countries (HICs).6 However, our findings suggest that those with multimorbidity in the domain of MH alone were the worse off in terms of outcomes that we assessed. A reason for this could be that people with NCDs have greater access to healthcare and have their needs addressed more thoroughly than people with MH conditions.59 A previous paper from South Africa found that patterns of multimorbidity affect healthcare utilisation, potentially supporting our hypothesis;13 however, this finding needs to be explored in further studies.
It is not possible from this cross-sectional analysis to know if the relationship between increasing numbers of morbidities and frailty, disability, low quality of life and poor physical performance is causal. However, given life expectancy in Burkina Faso increased from 49.5 in 1990 to 60.8 in 2017,31 knowledge of high prevalence of multimorbidity and these associations is cause for concern in a growing population with few healthcare resources.
Hence, preventative and management interventions needs to be done in order to ensure that the health system can deal with this increasing burden of disease and its downstream consequences.
The mismatch between prevalence of multimorbidity and research output in LMICs (only 5% of multimorbidity research originates from LMICs) is well known.6 18 This study contributes to filling that knowledge gap, yet further research on the clusters of multimorbidity, their associations with adverse outcomes and health system solutions to prevent and manage multimorbidity is urgently required. Data collection that extends beyond a priori defined morbidities commonly seen in high-incomes settings, to include locally determined and prioritised conditions must increasingly drive this area of research.
This study has several limitations. Even though multimorbidity prevalence was high, the number of conditions evaluated was limited, and it is likely that our multimorbidity prevalence estimates are conservative. Moreover, many of the NCDs (cancer, heart and respiratory disease) and HIV in our study relied on self-report of a diagnosis from a medical professional. However, many people in this population lack the financial resources or awareness to seek medical care for their conditions, further underestimating true prevalence and perhaps overestimating multimorbidity prevalence in people in the high income category. There is especially likely to be under-reporting of HIV in our study, due to the stigma associated with the condition. While we relied mostly on self-reported diagnoses in this study, restricting our analysis to objective measurements did not substantially change our results (online supplementary appendix table 3). We were not able to capture information on all chronic conditions, for example, osteoarthritis or visual impairment, in our survey, meaning that our estimates should be considered a lower bound on the true prevalence of multimorbidity in this population. However, we intentionally captured as broad spectrum of conditions as possible, and reported on 11 common conditions, similar to that seen in other studies in similar settings.12
This study is also cross-sectional, limiting our ability to assess causation. Nonetheless, longitudinal studies in high-income countries have pointed to a causal relationship between multimorbidity and outcomes similar to those assessed here.60 61 Finally, participants were selected only from one area of Burkina Faso and hence our findings may not generalise nationally or internationally.