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Background: The burden of chronic non-communicable diseases (NCDs) is a growing public health concern. The availability of cost-of-illness data, particularly public healthcare costs for NCDs, is limited in Sub-Saharan Africa (SSA), yet such data evidence is needed for policy action. Objective: The objective of this study was to estimate the economic burden of type 2 diabetes (T2D) on Kenya's public healthcare system in 2021 and project costs for 2045. Methods: This was a cost-of-illness study using the prevalence-based bottom-up costing approach to estimate the economic burden of T2D in the year 2021. We further conducted projections on the estimated costs for the year 2045. The costs were estimated corresponding to the care, treatment, and management of diabetes and some diabetes complications based on the primary data collected from six healthcare facilities in Nairobi and secondary costing data from previous costing studies in low and middle-income countries (LMICs). The data capture and costing analysis were done in Microsoft Excel 16, and sensitivity analysis was conducted on all the parameters to estimate the cost changes. Results: The total cost of managing T2D for the healthcare system in Kenya was estimated to be US$ 635 million (KES 74,521 million) in 2021. This was an increase of US$ 2 million (KES 197 million) considering the screening costs of undiagnosed T2D in the country. The major cost driver representing 59% of the overall costs was attributed to T2D complications, with nephropathy having the highest estimated costs of care and management (US$ 332 million (KES 36, 457 million). The total cost for T2D was projected to rise to US$ 1.6 billion (KES 177 billion) in 2045. Conclusion: This study shows that T2D imposes a huge burden on Kenya's healthcare system. There is a need for government and societal action to develop and implement policies that prevent T2D, and appropriately plan care for those diagnosed with T2D. [ABSTRACT FROM AUTHOR]

Abstract Background The burden of chronic non-communicable diseases (NCDs) is a growing public health concern. The availability of cost-of-illness data, particularly public healthcare costs for NCDs, is limited in Sub-Saharan Africa (SSA), yet such data evidence is needed for policy action. Objective The objective of this study was to estimate the economic burden of type 2 diabetes (T2D) on Kenya’s public healthcare system in 2021 and project costs for 2045. Methods This was a cost-of-illness study using the prevalence-based bottom-up costing approach to estimate the economic burden of T2D in the year 2021. We further conducted projections on the estimated costs for the year 2045. The costs were estimated corresponding to the care, treatment, and management of diabetes and some diabetes complications based on the primary data collected from six healthcare facilities in Nairobi and secondary costing data from previous costing studies in low and middle-income countries (LMICs). The data capture and costing analysis were done in Microsoft Excel 16, and sensitivity analysis was conducted on all the parameters to estimate the cost changes. Results The total cost of managing T2D for the healthcare system in Kenya was estimated to be US$ 635 million (KES 74,521 million) in 2021. This was an increase of US$ 2 million (KES 197 million) considering the screening costs of undiagnosed T2D in the country. The major cost driver representing 59% of the overall costs was attributed to T2D complications, with nephropathy having the highest estimated costs of care and management (US$ 332 million (KES 36, 457 million). The total cost for T2D was projected to rise to US$ 1.6 billion (KES 177 billion) in 2045. Conclusion This study shows that T2D imposes a huge burden on Kenya’s healthcare system. There is a need for government and societal action to develop and implement policies that prevent T2D, and appropriately plan care for those diagnosed with T2D.

Abstract Background This study aims to propose a semi-automatic method for monitoring the waiting times of follow-up examinations within the National Health System (NHS) in Italy, which is currently not possible to due the absence of the necessary structured information in the official databases. Methods A Natural Language Processing (NLP) based pipeline has been developed to extract the waiting time information from the text of referrals for follow-up examinations in the Lombardy Region. A manually annotated dataset of 10 000 referrals has been used to develop the pipeline and another manually annotated dataset of 10 000 referrals has been used to test its performance. Subsequently, the pipeline has been used to analyze all 12 million referrals prescribed in 2021 and performed by May 2022 in the Lombardy Region. Results The NLP-based pipeline exhibited high precision (0.999) and recall (0.973) in identifying waiting time information from referrals’ texts, with high accuracy in normalization (0.948-0.998). The overall reporting of timing indications in referrals’ texts for follow-up examinations was low (2%), showing notable variations across medical disciplines and types of prescribing physicians. Among the referrals reporting waiting times, 16% experienced delays (average delay = 19 days, standard deviation = 34 days), with significant differences observed across medical disciplines and geographical areas. Conclusions The use of NLP proved to be a valuable tool for assessing waiting times in follow-up examinations, which are particularly critical for the NHS due to the significant impact of chronic diseases, where follow-up exams are pivotal. Health authorities can exploit this tool to monitor the quality of NHS services and optimize resource allocation.

Background: This study aims to propose a semi-automatic method for monitoring the waiting times of follow-up examinations within the National Health System (NHS) in Italy, which is currently not possible to due the absence of the necessary structured information in the official databases. Methods: A Natural Language Processing (NLP) based pipeline has been developed to extract the waiting time information from the text of referrals for follow-up examinations in the Lombardy Region. A manually annotated dataset of 10 000 referrals has been used to develop the pipeline and another manually annotated dataset of 10 000 referrals has been used to test its performance. Subsequently, the pipeline has been used to analyze all 12 million referrals prescribed in 2021 and performed by May 2022 in the Lombardy Region. Results: The NLP-based pipeline exhibited high precision (0.999) and recall (0.973) in identifying waiting time information from referrals' texts, with high accuracy in normalization (0.948-0.998). The overall reporting of timing indications in referrals' texts for follow-up examinations was low (2%), showing notable variations across medical disciplines and types of prescribing physicians. Among the referrals reporting waiting times, 16% experienced delays (average delay = 19 days, standard deviation = 34 days), with significant differences observed across medical disciplines and geographical areas. Conclusions: The use of NLP proved to be a valuable tool for assessing waiting times in follow-up examinations, which are particularly critical for the NHS due to the significant impact of chronic diseases, where follow-up exams are pivotal. Health authorities can exploit this tool to monitor the quality of NHS services and optimize resource allocation. [ABSTRACT FROM AUTHOR]

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