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COVID-19 has impacted the healthcare system across the globe. The study will span three pandemic waves in 2020, 2021, and 2022. The goal is to learn how the pandemic affects antenatal care (ANC) and emergency delivery care for pregnant women in Tamil Nadu, India, and how medical services respond. The study employs counterfactual analysis to evaluate the causal impact of the pandemic. A feedforward in combination with a simple auto-regressive neural network (AR-Net) is used to predict the daily number of calls for ambulance services (CAS). Three categories of the daily CAS count between January 2016 and December 2022 are utilised. The total CAS includes all types of medical emergencies; the second group pertains to planned ANC for high-risk pregnant women and the third group comprises CAS from pregnant women for medical emergencies. The second wave's infection and mortality rates were up to six times higher than the first. The phases in wave-II, post-wave-II, wave-III, and post-wave-III experienced a significant increase in both total IFT (inter-facility transfer) and total non-IFT calls covering all emergencies relative to the counterfactual, as evidenced by reported effect sizes of 1 and a range of 0.65 to 0.85, respectively. This highlights overwhelmed health services. In Tamil Nadu, neither emergency prenatal care nor planned prenatal care was affected by the pandemic. In contrast, the increase in actual emergency-related IFT calls during wave-II, post-wave-II, wave-III, and post-wave-III was 62%, 160%, 141%, and 165%, respectively, relative to the counterfactual. During the same time periods, the mean daily CAS related to prenatal care increased by 47%, 51%, 38%, and 38%, respectively, compared to pre-pandemic levels. The expansion of ambulance services and increased awareness of these services during wave II and the ensuing phases of Covid-19 pandemic have enhanced emergency care delivery for all, including obstetric and neonatal cohorts. [ABSTRACT FROM AUTHOR]

This study investigates the important role of attendant factors, such as road traffic victims' access to trauma centres, the robustness of health infrastructure, and the responsiveness of police and emergency services in the incidence of Road Traffic Injuries (RTI) during the pandemic-induced COVID-19 lockdowns. The differential effects of the first and second waves of the pandemic concerning perceived health risk and legal restrictions provide us with a natural experiment that helps us differentiate between the impact of attendant factors and the standard relationship between mobility and Road Traffic Injuries. The authors use the auto-regressive recurrent neural network method on two population levels–Tamil Nadu (TN), a predominantly rural state, and Chennai, the most significant metropolitan city of the state, to draw causal inference through counterfactual predictions on daily counts of road traffic deaths and Road Traffic Injuries. During the first wave of the pandemic, which was less severe than the second wave, the traffic flow was correlated to Road Traffic Death/Road Traffic Injury. In the second wave's partial and post lockdown phases, an unprecedented fall of over 70% in Road Traffic Injury—Grievous as against Road Traffic Injury—Minor was recorded. Attendant factors, such as the ability of the victim to approach relief centres, the capability of health and other allied infrastructures, transportation and medical treatment of road traffic crash victims, and minimal access to other emergency services, including police, assumed greater significance than overall traffic flow in the incidence of Road Traffic Injury in the more severe second wave. These findings highlight the significant role these attendant factors play in producing the discrepancy between the actual road traffic incident rate and the officially registered rate. Thus, our study enables practitioners to observe the mobility-adjusted actual incidence rate devoid of factors related to reporting and registration of accidents. [ABSTRACT FROM AUTHOR]