Early effects of COVID-19 on maternal and child health service disruption in Mozambique.

This article is part of the Research Topic 'Health Systems Recovery in the Context of COVID-19 and Protracted Conflict'. Introduction: After the World Health Organization declared COVID-19 a pandemic, more than 184 million cases and 4 million deaths had been recorded worldwide by July 2021. These are likely to be underestimates and do not distinguish between direct and indirect deaths resulting from disruptions in health care services. The purpose of our research was to assess the early impact of COVID-19 in 2020 and early 2021 on maternal and child healthcare service delivery at the district level in Mozambique using routine health information system data, and estimate associated excess maternal and child deaths. Methods: Using data from Mozambique's routine health information system (SISMA, Sistema de Informação em Saúde para Monitoria e Avaliação), we conducted a time-series analysis to assess changes in nine selected indicators representing the continuum of maternal and child health care service provision in 159 districts in Mozambique. The dataset was extracted as counts of services provided from January 2017 to March 2021. Descriptive statistics were used for district comparisons, and district-specific time-series plots were produced. We used absolute differences or ratios for comparisons between observed data and modeled predictions as a measure of the magnitude of loss in service provision. Mortality estimates were performed using the Lives Saved Tool (LiST). Results: All maternal and child health care service indicators that we assessed demonstrated service delivery disruptions (below 10% of the expected counts), with the number of new users of family planing and malaria treatment with Coartem (number of children under five treated) experiencing the largest disruptions. Immediate losses were observed in April 2020 for all indicators, with the exception of treatment of malaria with Coartem. The number of excess deaths estimated in 2020 due to loss of health service delivery were 11,337 (12.8%) children under five, 5,705 (11.3%) neonates, and 387 (7.6%) mothers. Conclusion: Findings from our study support existing research showing the negative impact of COVID-19 on maternal and child health services utilization in sub-Saharan Africa. This study offers subnational and granular estimates of service loss that can be useful for health system recovery planning. To our knowledge, it is the first study on the early impacts of COVID-19 on maternal and child health care service utilization conducted in an African Portuguese-speaking country.

Global influenza surveillance systems to detect the spread of influenza-negative influenza-like illness during the COVID-19 pandemic: Time series outlier analyses from 2015-2020.

BACKGROUND: Surveillance systems are important in detecting changes in disease patterns and can act as early warning systems for emerging disease outbreaks. We hypothesized that analysis of data from existing global influenza surveillance networks early in the COVID-19 pandemic could identify outliers in influenza-negative influenza-like illness (ILI). We used data-driven methods to detect outliers in ILI that preceded the first reported peaks of COVID-19. METHODS AND FINDINGS: We used data from the World Health Organization's Global Influenza Surveillance and Response System to evaluate time series outliers in influenza-negative ILI. Using automated autoregressive integrated moving average (ARIMA) time series outlier detection models and baseline influenza-negative ILI training data from 2015-2019, we analyzed 8,792 country-weeks across 28 countries to identify the first week in 2020 with a positive outlier in influenza-negative ILI. We present the difference in weeks between identified outliers and the first reported COVID-19 peaks in these 28 countries with high levels of data completeness for influenza surveillance data and the highest number of reported COVID-19 cases globally in 2020. To account for missing data, we also performed a sensitivity analysis using linear interpolation for missing observations of influenza-negative ILI. In 16 of the 28 countries (57%) included in this study, we identified positive outliers in cases of influenza-negative ILI that predated the first reported COVID-19 peak in each country; the average lag between the first positive ILI outlier and the reported COVID-19 peak was 13.3 weeks (standard deviation 6.8). In our primary analysis, the earliest outliers occurred during the week of January 13, 2020, in Peru, the Philippines, Poland, and Spain. Using linear interpolation for missing data, the earliest outliers were detected during the weeks beginning December 30, 2019, and January 20, 2020, in Poland and Peru, respectively. This contrasts with the reported COVID-19 peaks, which occurred on April 6 in Poland and June 1 in Peru. In many low- and middle-income countries in particular, the lag between detected outliers and COVID-19 peaks exceeded 12 weeks. These outliers may represent undetected spread of SARS-CoV-2, although a limitation of this study is that we could not evaluate SARS-CoV-2 positivity. CONCLUSIONS: Using an automated system of influenza-negative ILI outlier monitoring may have informed countries of the spread of COVID-19 more than 13 weeks before the first reported COVID-19 peaks. This proof-of-concept paper suggests that a system of influenza-negative ILI outlier monitoring could have informed national and global responses to SARS-CoV-2 during the rapid spread of this novel pathogen in early 2020.

Emergency Medical Service Utilization and Response Following COVID-19 Emergency and Stay-at-Home Policies: An Interrupted Time-Series Analysis.

Objective Examine changing emergency medical services (EMS) utilization and response patterns associated with coronavirus disease 2019 (COVID-19) emergency declaration and stay-at-home orders during the first year of the COVID-19 pandemic. Methods We conducted an uncontrolled interrupted time series analysis of EMS calls (January 1, 2019 - March 1, 2021) in Marin County, California analyzing call volume (All calls, n=46,055); patient refusal of EMS care or transport and patient care resolved on scene (Calls with opportunity for transport; n=37,401); and call severity (Transported calls; n=27,887). Results Pre-COVID-19 (1/1/2019-3/2/2020), EMS transported patients were predominately female (50.6%), 80+ years old (31.6%), and Marin County residents (68.0%). During COVID-19 (3/3/2020-3/1/2021), EMS transported patients were predominately male (52.7%), 35-64 years old (29.8%), and Marin County residents (70.4%). After the first stay-at-home order on 3/17/2020, call volume immediately decreased by 48% (adjusted incidence rate ratio [aIRR]=0.52; 95% CI=0.35,0.79) for children (0-15 years) and 34% for adults 80+ years (aIRR=0.66;95% CI=0.46,0.95). The odds of a transported call being prioritized as severe doubled (adjusted odds ratio [aOR]=2.26; 95% CI=1.11,4.59). Though transport refusals increased by 69% for children after the first order (aOR, 1.69 [95% CI, 1.13-2.52]), immediately following the second order on 12/8/2020, transport refusals decreased by 30% for children but increased 38-40% for adults 35-79 years (aOR=1.40 [95% CI=1.04-1.89] for 35-64 years; 1.38 [95% CI=1.02-1.87] for 65-79 years). Calls resolved on scene by EMS increased after the first order among all ages and after the second order for adults 16-79 years.  Conclusions Call volume reduced for children and older adults after the first COVID-19 stay-at-home order. Changes in call severity, patient care refusals, and on-scene care provided by EMS indicated a changing role for EMS during the outbreak.

The impact of the COVID-19 pandemic on health services utilization in China: Time-series analyses for 2016-2020.

BACKGROUND: The aim of this study is to quantify the effects of the SARS-CoV-2 pandemic on health services utilization in China using over four years of routine health information system data. METHODS: We conducted a retrospective observational cohort study of health services utilization from health facilities at all levels in all provinces of mainland China. We analyzed monthly all-cause health facility visits and inpatient volume in health facilities before and during the SARS-CoV-2 outbreak using nationwide routine health information system data from January 2016 to June 2020. We used interrupted time series analyses and segmented negative binomial regression to examine changes in healthcare utilization attributable to the pandemic. Stratified analyses by facility type and by provincial Human Development Index (HDI) - an area-level measure of socioeconomic status - were conducted to assess potential heterogeneity in effects. FINDINGS: In the months before the SARS-CoV-2 outbreak, a positive secular trend in patterns of healthcare utilization was observed. After the SARS-CoV-2 outbreak, we noted statistically significant decreases in all indicators, with all indicators achieving their nadir in February 2020. The magnitude of decline in February ranged from 63% (95% CI 61-65%; p<0•0001) in all-cause visits at hospitals in regions with high HDI and 71% (95% CI 70-72%; p<0•0001) in all-cause visits at primary care clinics to 33% (95% CI 24-42%; p<0•0001) in inpatient volume and 10% (95% CI 3-17%; p = 0•0076) in all-cause visits at township health centers (THC) in regions with low HDI. The reduction in health facility visits was greater than that in the number of outpatients discharged (51% versus 48%; p<0•0079). The reductions in both health facility visits and inpatient volume were greater in hospitals than in primary health care facilities (p<0•0001) and greater in developed regions than in underdeveloped regions (p<0•0001). Following the nadir of health services utilization in February 2020, all indicators showed statistically significant increases. However, even by June 2020, nearly all indicators except outpatient and inpatient volume in regions with low HDI and inpatient volume in private hospitals had not achieved their pre-SARS-COV-2 forecasted levels. In total, we estimated cumulative losses of 1020.5 (95% CI 951.2- 1089.4; P<0.0001) million or 23.9% (95% CI 22.5-25.2%; P<0.0001) health facility visits, and 28.9 (95% CI 26.1-31.6; P<0.0001) million or 21.6% (95% CI 19.7-23.4%; P<0.0001) inpatients as of June 2020. INTERPRETATION: Inpatient and outpatient health services utilization in China declined significantly after the SARS-CoV-2 outbreak, likely due to changes in patient and provider behaviors, suspension of health facilities or their non-emergency services, massive mobility restrictions, and the potential reduction in the risk of non-SARS-COV-2 diseases. All indicators rebounded beginning in March but most had not recovered to their pre-SARS-COV-2 levels as of June 2020. FUNDING: The National Natural Science Foundation of China (No. 72042014).