METHODS: We conducted a secondary analysis of data from a prior systematic review of costs of influenza and other respiratory illnesses in LMICs and contacted authors to obtain data on cost of illness (COI) for laboratory-confirmed influenza-like illness and acute respiratory infection. We calculated the COI by household income strata and calculated the out-of-pocket (OOP) cost as a proportion of household income.
RESULTS: We included 11 studies representing 11 LMICs. OOP expenses, as a proportion of annual household income, were highest among the lowest income quintile in 10 of 11 studies: in 4/4 studies among the general population, in 6/7 studies among children, 2/2 studies among older adults, and in the sole study for adults with chronic medical conditions. COI was generally higher for hospitalizations compared with outpatient illnesses; median OOP costs for hospitalizations exceeded 10% of annual household income among the general population and children in Kenya, as well as for older adults and adults with chronic medical conditions in China.
CONCLUSIONS: The findings indicate that influenza and acute respiratory infections pose a considerable economic burden, particularly from hospitalizations, on the lowest income households in LMICs. Future evaluations could investigate specific drivers of COI in low-income household and identify interventions that may address these, including exploring household coping mechanisms. Cost-effectiveness analyses could incorporate health inequity analyses, in pursuit of health equity.
RESULTS: Quality Implementation Framework (QIF) was adopted to develop the breast cancer module as part of the in-house EMR system used at UMMC, called i-Pesakit©. The completion of the i-Pesakit© Breast Cancer Module requires management of clinical data electronically, integration of clinical data from multiple internal clinical departments towards setting up of a research focused patient data governance model. The 14 QIF steps were performed in four main phases involved in this study which are (i) initial considerations regarding host setting, (ii) creating structure for implementation, (iii) ongoing structure once implementation begins, and (iv) improving future applications. The architectural framework of the module incorporates both clinical and research needs that comply to the Personal Data Protection Act.
CONCLUSION: The completion of the UMMC i-Pesakit© Breast Cancer Module required populating EMR including management of clinical data access, establishing information technology and research focused governance model and integrating clinical data from multiple internal clinical departments. This multidisciplinary collaboration has enhanced the quality of data capture in clinical service, benefited hospital data monitoring, quality assurance, audit reporting and research data management, as well as a framework for implementing a responsive EMR for a clinical and research organization in a typical middle-income country setting. Future applications include establishing integration with external organization such as the National Registration Department for mortality data, reporting of institutional data for national cancer registry as well as data mining for clinical research. We believe that integration of multiple clinical visit data sources provides a more comprehensive, accurate and real-time update of clinical data to be used for epidemiological studies and audits.