Declines in health service use during the Coronavirus Disease 2019 (COVID-19) pandemic could have important effects on population health. In this study, we used an interrupted time series design to assess the immediate effect of the pandemic on 31 health services in two low-income (Ethiopia and Haiti), six middle-income (Ghana, Lao People’s Democratic Republic, Mexico, Nepal, South Africa and Thailand) and high-income (Chile and South Korea) countries. Despite efforts to maintain health services, disruptions of varying magnitude and duration were found in every country, with no clear patterns by country income group or pandemic intensity. Disruptions in health services often preceded COVID-19 waves. Cancer screenings, TB screening and detection and HIV testing were most affected (26–96% declines). Total outpatient visits declined by 9–40% at national levels and remained lower than predicted by the end of 2020. Maternal health services were disrupted in approximately half of the countries, with declines ranging from 5% to 33%. Child vaccinations were disrupted for shorter periods, but we estimate that catch-up campaigns might not have reached all children missed. By contrast, provision of antiretrovirals for HIV was not affected. By the end of 2020, substantial disruptions remained in half of the countries. Preliminary data for 2021 indicate that disruptions likely persisted. Although a portion of the declines observed might result from decreased needs during lockdowns (from fewer infectious illnesses or injuries), a larger share likely reflects a shortfall of health system resilience. Countries must plan to compensate for missed healthcare during the current pandemic and invest in strategies for better health system resilience for future emergencies.
Background To improve care for patients with chronic diseases, a recent policy initiative in Thailand focused on strengthening primary care based on the concept of Chronic Care Model (CCM). This study aimed to assess the perception of patients about the health care services after the implementation. Methods We conducted a cross-sectional survey of 4071 patients with hypertension and/or diabetes registered with 27 primary care units and 11 hospital non-communicable diseases (NCDs) clinics in 11 provinces. The patients were interviewed using a validated questionnaire of the Patient Assessment of Chronic Illness Care. Upgraded primary care units (PCUs) were ordinary PCUs with the multi-professional team including a physician. Trained upgraded PCUs were upgraded PCUs with the training input. Structural equation modeling was used to create subscale scores for CCM and 5 A model characteristics. Mixed effect logistic models were employed to examine the association of subscales (high vs low score) of patient perception of the care quality with type of PCUs. Results Compared to hospital NCD clinics, ordinary PCUs were the best in the odds of receiving high score for every CCM subscale (ORs: 1.46–1.85; p < 0.05), whereas the trained upgraded PCUs were better in terms of follow-up (ORs:1.37; p < 0.05), and the upgraded PCU did not differ in all domains. According to the 5 A model subscales, patient assessment also revealed better performance of ordinary PCUs in all domains compared to hospital NCD clinics whereas upgraded PCUs and trained upgraded PCUs did so in some domains. Seeing the same doctor on repeated visits (ORs: 1.82–2.17; p < 0.05) or having phone contacts with the providers (ORs:1.53–1.99; p < 0.05) were found beneficial using CCM subscales and the 5A model subscales. However, patient assessment by both subscales did not demonstrate a statistically significant association across health insurance status. Conclusions The policy implementation might not satisfy the patients’ perception on quality of chronic care according to the CCM and the 5A model subscale. However, the arrangement of chronic care with patients seeing the same doctors or patients having telephone contact with healthcare providers may satisfy the patients’ perceived needs.
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.
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