In the absence of a viable pharmaceutical intervention for SARS-CoV-2, governments have implemented a range of non-pharmaceutical interventions (NPIs) to curb the spread of infection of the virus and the disease caused by the virus, now known as COVID-19. Given the associated social and economic costs, it is critical to enumerate the individual impacts of NPIs to aid in decision-making moving forward. We used globally reported SARS-CoV-2 cases to fit a Bayesian model framework to estimate transmission associated with NPIs in 26 countries and 34 US states. Using a mixed effects model with country level random effects, we compared the relative impact of other NPIs to national-level household confinement measures and evaluated the impact of NPIs on the global trajectory of the COVID-19 pandemic over time. We observed heterogeneous impacts of the easing of restrictions and estimated an overall reduction in infection of 23% (95% CI: 18-27%) associated with household confinement, 10% (95% CI: 1-18%) with limits on gatherings, 12% (95% CI: 5-19%) with school closures and 17% (95% CI: 6-28%) with mask policies. We estimated a 12% (95% CI: 9-15%) reduction in transmission associated with NPIs overall. The implementation of NPIs have substantially reduced acceleration of COVID-19. At this early time point, we cannot determine the impact of the easing of restrictions and there is a need for continual assessment of context specific effectiveness of NPIs as more data become available.

Background The proposed National Health Insurance (NHI) system aims to re-engineer primary healthcare (PHC) in South Africa, envisioning both private sector providers and public sector clinics as independent contracting units to the NHI Fund. In 2017, 16% of the South African population had private medical insurance and predominately utilised private providers. However, it is estimated that up to 28% of the population access private PHC services, with a meaningful segment of the low-income, uninsured population paying for these services out-of-pocket. The study objective was to characterise the health seeking behaviour of low-income, patients accessing PHC services in both the public and private sectors, patient movement between sectors, and factors influencing their facility choice. Methods We conducted once-off patient interviews on a random sample of 153 patients at 7 private PHC providers (primarily providing services to the low-income mostly uninsured patient population) and their matched public PHC clinic (7 facilities). Results The majority of participants were economically active (96/153, 63%), 139/153 (91%) did not have health insurance, and 104/153 (68%) earned up to $621/month. A multiple response question found affordability (67%) and convenience (60%) were ranked as the most important reasons for choosing to usually access care at public clinics (48%); whilst convenience (71%) and quality of care (59%) were key reasons for choosing the private sector (32%). There is movement between sectors: 23/76 (30%) of those interviewed at a private facility and 8/77 (10%) of those interviewed at a public facility indicated usually accessing PHC services at a mix of private and public facilities. Results indicate cycling between the private and public sectors with different factors influencing facility choice. Conclusions It is imperative to understand the potential impact on where PHC services are accessed once affordability is mitigated through the NHI as this has implications on planning and contracting of services under the NHI.

Background: The proposed National Health Insurance (NHI) system aims to re-engineer primary healthcare (PHC) in South Africa, envisioning both private sector providers and public sector clinics as independent contracting units to the NHI Fund. In 2017, 16% of the South African population had private medical insurance and predominately utilised private providers. However, it is estimated that up to 28% of the population access private PHC services, with a meaningful segment of the low-income, uninsured population paying for these services out-of-pocket. The study objective was to characterise the health seeking behaviour of low-income, uninsured patients accessing PHC services in both the public and private sectors, patient movement between sectors, and factors influencing their facility choice. Methods: We conducted once-off patient interviews on a random sample of 153 patients at 7 private PHC providers (primarily providing services to the low-income uninsured patient population) and their matched public PHC clinic (7 facilities). Results: The majority of participants were economically active (96/153, 63%), 139/153 (91%) did not have health insurance, and 104/153 (68%) earned up to $621/month. A multiple response question found affordability (67%) and convenience (60%) were ranked as the most important reasons for choosing to usually access care at public clinics (48%); whilst convenience (71%) and quality of care (59%) were key reasons for choosing the private sector (32%). There is movement between sectors: 23/76 (30%) of those interviewed at a private facility and 8/77 (10%) of those interviewed at a public facility indicated usually accessing PHC services at a mix of private and public facilities. Results indicate cycling between the private and public sectors with different factors influencing facility choice. Conclusions: It is imperative to understand the potential impact on where PHC services are accessed once affordability is mitigated through the NHI as this has implications on planning and contracting of services under the NHI.

Background Assessment of data quality is essential to successful monitoring & evaluation of tuberculosis (TB) services. South Africa uses the Three Interlinked Electronic Register (TIER.Net) to monitor TB diagnoses and treatment outcomes. We assessed the quality of routine programmatic data as captured in TIER.Net. Methods We reviewed 277 records from routine data collected for adults who had started TB treatment for drug-sensitive (DS-) TB between 10/2018-12/2019 from 15 facilities across three South African districts using three sources and three approaches to link these (i.e., two approaches compared TIER.NET with the TB Treatment Record while the third approach compared all three sources of TB data: the TB treatment record or patient medical file; the TB Identification Register; and the TB module in TIER.Net). We report agreement and completeness of demographic information and key TB-related variables across all three data sources. Results In our first approach we selected 150 patient records from TIER.Net and found all but one corresponding TB Treatment Record (99%). In our second approach we were also able to find a corresponding TIER.Net record from a starting point of the paper-based, TB Treatment Record for 73/75 (97%) records. We found fewer records 55/75 (73%) in TIER.Net when we used as a starting point records from the TB Identification Register. Demographic information (name, surname, date of birth, and gender) was accurately reported across all three data sources (matching 90% or more). The reporting of key TB-related variables was similar across both the TB Treatment Record and the TB module in TIER.Net (p>0.05). We observed differences in completeness and moderate agreement (Kappa 0.41–0.60) for site of disease, TB treatment outcome and smear microscopy or X-ray as a diagnostic test (p<0.05). We observed more missing items for the TB Treatment record compared to TIER.Net; TB treatment outcome date and site of disease specifically. In comparison, TB treatment start dates as well as HIV-status recording had higher concordance. HIV status and lab results appeared to be more complete in the TB module in TIER.Net than in the TB Treatment Records, and there was “good/substantial” agreement (Kappa 0.61–0.80) for HIV status. Discussion and conclusion Our key finding was that the TB Module in TIER.Net was more complete in some key variables including TB treatment outcome. Most TB patient records we reviewed were found on TIER.Net but there was a noticeable gap of TB Identification patient records from the paper register as compared to TIER.Net, including those who tested TB-negative or HIV-negative. There is evidence of complete and “good/substantial” data quality for key TB-related variables, such as “First GeneXpert test result” and “HIV status.” Improvements in data completeness of TIER.Net compared to the TB Treatment Record are the most urgent area for improvement, especially recording of TB treatment outcomes.