Measuring disability-adjusted life years (DALYs) due to low back pain in Malta - Sarah Cuschieri; Grant M. A. Wyper; Neville Calleja; Vanessa Gorasso; Brecht Devleesschauwer (BMC, 2020)
Abstract Background Low back pain (LBP) is a public health concern and a leading cause of ill health. A high prevalence of musculoskeletal complaints has been reported for Malta, a small European state. The aim was to estimate for the first time the burden of LBP at population level in Malta in terms of disability-adjusted life years (DALYs) and compare to estimates obtained by the Global Burden of Disease (GBD) study. Method The Maltese European Health Interview Survey dataset for 2015 provided the LBP prevalence data through representative self-reported history of chronic LBP within the past 12 months in combination with limitations to daily activities. Proportions of LBP severity (with and without leg pain - mild, moderate, severe and most severe) and their corresponding disability weights followed values reported in the GBD study. Years lived with disability (YLD) for LBP were estimated for the whole population by age and sex. Since LBP does not carry any mortality, YLD reflected DALYs. The estimated local DALYs per 100,000 were compared to the GBD 2017 study results for Malta for the same year. Results LBP with activity limitation gave a point prevalence of 6.4% (95% Uncertainty Interval [UI] 5.7-7.2%) (5.6% males [95% UI 4.6-6.6%]; 7.3% females [95% UI 6.2-8.4%]), contributing to a total of 23,649 (95% UI 20,974-26,463) Maltese suffering from LBP. The LBP DALYs were of 716 (95% UI 558-896) per 100,000. Females experienced higher LBP burden (739 [95% UI 575-927] DALYs per 100,000) than males (693 [95% UI 541-867] DALYs per 100,000). Our DALY estimates were lower than those reported by the GBD 2017 study (i.e., 1829 [95% UI 1300-2466] per 100,000). Conclusions LBP imposes a substantial burden on the Maltese population. Differences observed between national estimates and those of the GBD study suggest the integration of updated locally sourced data into the model and encouraging local contributors in order to improve the DALY estimates of each country.