The burden of hospital admissions for skeletal dysplasias in Sri Lanka: a population-based study

Abstract

Abstract Background Skeletal dysplasias (SD) are a diverse group of rare disorders in the chondro-osseous tissue that can have a significant impact on patient's functionality. The worldwide prevalence of SD at birth is approximately 1:5000 births. To date, disease burden and trends of SD in the Sri Lankan population have not been described in any epidemiological study. Our aim was to evaluate the burden and the current trends in hospital admissions for SD in the Sri Lankan population. A retrospective evaluation of hospital admissions for SD during 2017-2020 was performed using population-based data from the eIMMR database which covers government hospitals in the entire country. The trends in hospital admissions for SD by calendar year, age, and types of SD were described using appropriate summary statistics. Results Respective crude admission rates of SD in the years 2017, 2018, 2019 and 2020 were 5.2, 8.1, 8.0, and 6.5 per million population. A female predominance was noted during the studied period (1.4: 1). Of all reported cases majority (n=268; 44.2%) were children less than 4 years. Each year, 0-4 years age group represented 40% to 47% of the total hospital admissions. More than half of the cases were reported from Colombo (28.1%) and Kandy (25.4%) districts combined. Sixty percent of cases were diagnosed as osteogenesis imperfecta (OI). Rising trends were observed in the hospital admissions for osteogenesis imperfecta, achondroplasia and osteopetrosis, while other SD types collectively showed a relatively stable trend. Conclusion This preliminary study revealed a female predominance of SD and a relatively high admission rate of osteogenesis imperfecta in the Sri Lankan population. A distinct trend was not visible in the studied years probably due to the impact on hospital services due to COVID- Pandemic. Future research on the healthcare burden on families affected by SD is required to better understand the overall cost of care and identify therapies that reduce admission rates. This study highlights the value of analysing population-based data on rare diseases to improve healthcare in low-resource countries.