Browsing by Author "Leerapan, Borwornsom"

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    Associations between the stringency of COVID-19 containment policies and health service disruptions in 10 countries
    (2023) Reddy, Tarylee; Kapoor, Neena R.; Kubota, Shogo; Doubova, Svetlana V.; Asai, Daisuke; Mariam, Damen H.; Ayele, Wondimu; Mebratie, Anagaw D.; Thermidor, Roody; Sapag Muñoz de la Peña, Jaime; Bedregal, Paula; Passi-Solar, Álvaro; Gordon-Strachan, Georgiana; Dulal, Mahesh; Gadeka, Dominic D.; Mehata, Suresh; Margozzini Maira, Paula; Leerapan, Borwornsom; Rittiphairoj, Thanitsara; Kaewkamjornchai, Phanuwich; Nega, Adiam; Awoonor-Williams, John K.; Kruk, Margaret E.; Arsenault, Catherine
    Background Disruptions in essential health services during the COVID-19 pandemic have been reported in several countries. Yet, patterns in health service disruption according to country responses remain unclear. In this paper, we investigate associations between the stringency of COVID-19 containment policies and disruptions in 31 health services in 10 low- middle- and high-income countries in 2020. Methods Using routine health information systems and administrative data from 10 countries (Chile, Ethiopia, Ghana, Haiti, Lao People’s Democratic Republic, Mexico, Nepal, South Africa, South Korea, and Thailand) we estimated health service disruptions for the period of April to December 2020 by dividing monthly service provision at national levels by the average service provision in the 15 months pre-COVID (January 2019-March 2020). We used the Oxford COVID-19 Government Response Tracker (OxCGRT) index and multi-level linear regression analyses to assess associations between the stringency of restrictions and health service disruptions over nine months. We extended the analysis by examining associations between 11 individual containment or closure policies and health service disruptions. Models were adjusted for COVID caseload, health service category and country GDP and included robust standard errors. Findings Chronic disease care was among the most affected services. Regression analyses revealed that a 10% increase in the mean stringency index was associated with a 3.3 percentage-point (95% CI -3.9, -2.7) reduction in relative service volumes. Among individual policies, curfews, and the presence of a state of emergency, had the largest coefficients and were associated with 14.1 (95% CI -19.6, 8.7) and 10.7 (95% CI -12.7, -8.7) percentage-point lower relative service volumes, respectively. In contrast, number of COVID-19 cases in 2020 was not associated with health service disruptions in any model. Conclusions Although containment policies were crucial in reducing COVID-19 mortality in many contexts, it is important to consider the indirect effects of these restrictions. Strategies to improve the resilience of health systems should be designed to ensure that populations can continue accessing essential health care despite the presence of containment policies during future infectious disease outbreaks.
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    Tracking health system performance in times of crisis using routine health data: lessons learned from a multicountry consortium
    (2023) Turcotte-Tremblay, Anne-Marie; Leerapan, Borwornsom; Akweongo, Patricia; Amponsah, Freddie; Aryal, Amit; Asai, Daisuke; Awoonor-Williams, John K.; Ayele, Wondimu; Bauhoff, Sebastian; Doubova, Svetlana V.; Gadeka, Dominic D.; Dulal, Mahesh; Gage, Anna; Gordon-Strachan, Georgiana; Haile-Mariam, Damen; Joseph, Jean P.; Kaewkamjornchai, Phanuwich; Kapoor, Neena R.; Gelaw, Solomon K.; Kim, Min K.; Kruk, Margaret E.; Kubota, Shogo; Margozzini Maira, Paula; Mehata, Suresh; Mthethwa, Londiwe; Nega, Adiam; Oh, Juhwan; Park, Soo K.; Passi-Solar, Alvaro; Perez Cuevas, Ricardo E.; Reddy, Tarylee; Rittiphairoj, Thanitsara; Sapag Muñoz de la Peña, Jaime; Thermidor, Roody; Tlou, Boikhutso; Arsenault, Catherine
    COVID-19 has prompted the use of readily available administrative data to track health system performance in times of crisis and to monitor disruptions in essential healthcare services. In this commentary we describe our experience working with these data and lessons learned across countries. Since April 2020, the Quality Evidence for Health System Transformation (QuEST) network has used administrative data and routine health information systems (RHIS) to assess health system performance during COVID-19 in Chile, Ethiopia, Ghana, Haiti, Lao People’s Democratic Republic, Mexico, Nepal, South Africa, Republic of Korea and Thailand. We compiled a large set of indicators related to common health conditions for the purpose of multicountry comparisons. The study compiled 73 indicators. A total of 43% of the indicators compiled pertained to reproductive, maternal, newborn and child health (RMNCH). Only 12% of the indicators were related to hypertension, diabetes or cancer care. We also found few indicators related to mental health services and outcomes within these data systems. Moreover, 72% of the indicators compiled were related to volume of services delivered, 18% to health outcomes and only 10% to the quality of processes of care. While several datasets were complete or near-complete censuses of all health facilities in the country, others excluded some facility types or population groups. In some countries, RHIS did not capture services delivered through non-visit or nonconventional care during COVID-19, such as telemedicine. We propose the following recommendations to improve the analysis of administrative and RHIS data to track health system performance in times of crisis: ensure the scope of health conditions covered is aligned with the burden of disease, increase the number of indicators related to quality of care and health outcomes; incorporate data on nonconventional care such as telehealth; continue improving data quality and expand reporting from private sector facilities; move towards collecting patient-level data through electronic health records to facilitate quality-of-care assessment and equity analyses; implement more resilient and standardized health information technologies; reduce delays and loosen restrictions for researchers to access the data; complement routine data with patient-reported data; and employ mixed methods to better understand the underlying causes of service disruptions.