Prevalence, causes and impact of musculoskeletal impairment in Malawi: A national cluster randomized survey

Abstract

Background: There is a lack of accurate information on the prevalence and causes of musculoskeletal impairment (MSI) in low income countries. The WHO prevalence estimate does not help plan services for specific national income levels or countries. The aim of this study was to find the prevalence, impact, causes and factors associated with musculoskeletal impairment in Malawi. We wished to undertake a national cluster randomized survey of musculoskeletal impairment in Malawi, one of the UN Least Developed Countries (LDC), that involved a reliable sampling methodology with a case definition and diagnostic criteria that could clearly be related to the classification system used in the WHO International Classification of Functioning, Disability and Health (ICF).

Methods: A sample size of 1,481 households was calculated using data from the latest national census and an expected prevalence based on similar surveys conducted in Rwanda and Cameroon. We randomly selected clusters across the whole country through probability proportional to size sampling with an urban/rural and demographic split that matched the distribution of the population. In the field, randomization of households in a cluster was based on a ground bottle spin. All household members present were screened, and all MSI cases identified were examined in more detail by medical students under supervision, using a standardized interview and examination protocol. Data collection was carried out from 1st July to 30th August 2016. Extrapolation was done based on study size compared to the population of Malawi. MSI severity was classified using the parameters for the percentage of function outlined in the WHO International Classification of Functioning (ICF). A loss of function of 5-24% was mild, 25-49% was moderate and 50-90% was severe. The Malawian version of the EQ-5D-3L questionnaire was used, and EQ-5D index scores were calculated using population values from Zimbabwe, as a population value set for Malawi is not currently available. Chi-square test was used to test categorical variables. Odds ratio (OR) was calculated with a linear regression model adjusted for age, gender, location and education.

Results: A total of 8,801 individuals were enumerated in 1,481 households. Of the 8,548 participants that were screened and examined (response rate of 97.1%), 810 cases of MSI were diagnosed of which 18% (108) had mild, 54% (329) had moderate and 28% (167) had severe MSI as classified by ICF. There was an overall prevalence of MSI of 9.5% (CI 8.9-10.1). The prevalence of MSI increased with age, and was similar in men (9.3%) and women (9.6%). People without formal education were more likely to have MSI [13.3% (CI 11.8-14.8)] compared to those with formal education levels [8.9% (CI 8.1-9.7), p<0.001] for primary school and [5.9% (4.6-7.2), p<0.001] for secondary school. Overall, 33.2% of MSIs were due to congenital causes, 25.6% were neurological in origin, 19.2% due to acquired non-traumatic non-infective causes, 16.8% due to trauma and 5.2% due to infection. Extrapolation of these findings indicated that there are approximately one million cases of MSI in Malawi that need further treatment. MSI had a profound impact on quality of life. Analysis of disaggregated quality of life measures using EQ-5D showed clear correlation with the ICF class. A large proportion of patients with moderate and severe MSI were confined to bed, unable to wash or undress or unable to perform usual daily activities.

Conclusion: This study has uncovered a high prevalence of MSI in Malawi and its profound impact on a large proportion of the population. These findings suggest that MSI places a considerable strain on social and financial structures in this low-income country. The Quality of Life of those with severe MSI is considerably affected. The huge burden of musculoskeletal impairment in Malawi is mostly unattended, revealing an urgent need to scale up surgical and rehabilitation services in the country.

Fig 1. Number and diagnostic categories of MSI, by age group.

Y-axis: Number of cases.