简介：AbstractBackground:Despite progress towards End TB Strategy targets for reducing tuberculosis (TB) incidence and deaths by 2035, South Africa remains among the top ten high-burden tuberculosis countries globally. A large challenge lies in how policies to improve detection, diagnosis and treatment completion interact with social and structural drivers of TB. Detailed understanding and theoretical development of the contextual determinants of problems in TB care is required for developing effective interventions. This article reports findings from the pre-implementation phase of a study of TB care in South Africa, contributing to HeAlth System StrEngThening in Sub-Saharan Africa (ASSET)—a five-year research programme developing and evaluating health system strengthening interventions in sub-Saharan Africa. The study aimed to develop hypothetical propositions regarding contextual determinants of problems in TB care to inform intervention development to reduce TB deaths and incidence whilst ensuring the delivery of quality integrated, person-centred care.Methods:Theory-building case study design using the Context and Implementation of Complex Interventions (CICI) framework to identify contextual determinants of problems in TB care. Between February and November 2019, we used mixed methods in six public-sector primary healthcare facilities and one public-sector hospital serving impoverished urban and rural communities in the Amajuba District of KwaZulu-Natal Province, South Africa. Qualitative data included stakeholder interviews, observations and documentary analysis. Quantitative data included routine data on sputum testing and TB deaths. Data were inductively analysed and mapped onto the seven CICI contextual domains.Results:Delayed diagnosis was caused by interactions between fragmented healthcare provision; limited resources; verticalised care; poor TB screening, sputum collection and record-keeping. One nurse responsible for TB care, with limited integration of TB with other conditions, and policy focused on treatment adherence contributed to staff stress and limited consideration of patients’ psychosocial needs. Patients were lost to follow up due to discontinuity of information, poverty, employment restrictions and limited support for treatment side-effects. Infection control measures appeared to be compromised by efforts to integrate care.Conclusions:Delayed diagnosis, limited psychosocial support for patients and staff, patients lost to follow-up and inadequate infection control are caused by an interaction between multiple interacting contextual determinants. TB policy needs to resolve tensions between treating TB as epidemic and individually-experienced social problem, supporting interventions which strengthen case detection, infection control and treatment, and also promote person-centred support for healthcare professionals and patients.

简介：AbstractSouth Asian (SA) countries have been fighting with the pandemic novel coronavirus disease 2019 (COVID-19) since January 2020. Earlier, the country-specific descriptive study has been done. Nevertheless, as transboundary infection, the border sharing, shared cultural and behavioral practice, effects on the temporal and spatial distribution of COVID-19 in SA is still unveiled. Therefore, this study has been revealed the spatial hotspot along with descriptive output on different parameters of COVID-19 infection. We extracted data from the WHO and the worldometer database from the onset of the outbreak up to 15 May, 2020. Europe has the highest case fatality rate (CFR, 9.22%), whereas Oceania has the highest (91.15%) recovery rate from COVID-19. Among SA countries, India has the highest number of cases (85,790), followed by Pakistan (38,799) and Bangladesh (20,065). However, the number of tests conducted was minimum in this region in comparison with other areas. The highest CFR was recorded in India (3.21%) among SA countries, whereas Nepal and Bhutan had no death record due to COVID-19 so far. The recovery rate varies from 4.75% in the Maldives to 51.02% in Sri Lanka. In Bangladesh, community transmission has been recorded, and the highest number of cases were detected in Dhaka, followed by Narayanganj and Chattogram. We detected Dhaka and its surrounding six districts, namely Gazipur, Narsingdi, Narayanganj, Munshiganj, Manikganj, and Shariatpur, as the 99% confidence-based hotspot where Faridpur and Madaripur district as the 95% confidence-based spatial hotspots of COVID-19 in Bangladesh. However, we did not find any cold spots in Bangladesh. We identified three hotspots and three cold spots at different confidence levels in India. Findings from this study suggested the "Test, Trace, and Isolation" approach for earlier detection of infection to prevent further community transmission of COVID-19.