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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]

The primary duty of law enforcement agencies is to ensure that a victim has the necessary information and access to the relevant tools required to seek justice. In India, complex cases such as bodily offences and property crimes capture the work and efforts of many agencies involved; however, cases related to missing persons are not often accorded similar priority or seriousness. The COVID-19 pandemic and subsequent lockdowns have added further challenges to this scenario. The government-mandated lockdowns in Tamil Nadu generally exacerbated difficult socio-economic and living conditions, thereby directly or indirectly contributing to an increased load of missing person cases. This study aims to assess and identify the impact of mobility on reporting and registration of missing persons. By adopting an auto-regressive neural networks method, this study uses a counterfactual analysis of registered missing person cases during the government-mandated lockdowns in response to the global pandemic in 2020 and 2021. The registered cases are calculated based on the daily count of cases for eleven years in Tamil Nadu, India. The lockdowns identify eight different time windows to determine the impact of mobility on the registration of cases. While there has been no significant or drastic change over the pre-pandemic period, during the pandemic, especially during the restrictive phases of the pandemic, there was a sharp fall in cases compared to the counterfactual predicted (effect sizes: −0.981 and −0.74 in 2020 and 2021), signalling towards a choked mechanism of reporting. In contrast, when most mobility restrictions were removed, an increase in cases (effect sizes of +0.931 and 0.834 in 2020 and 2021) pointed to restored and enabled reporting channels. The research findings emphasise the significance of mobility as a factor in influencing the reporting and registration of missing persons and the need to ensure this continues to help families find redress. [ABSTRACT FROM AUTHOR]