User fee reduction and removal policies have been the object of extensive research, but little rigorous evidence exists on their sustained effects in relation to use of delivery care services, and no evidence exists on the effects of partial reduction compared with full removal of user fees. We aimed to fill these knowledge gaps by assessing sustained effects of both partial reduction and complete removal of user fees on utilization of facility-based delivery. Our study took place in four districts in the Sahel region of Burkina Faso, where the national user fee reduction policy (SONU) launched in 2007 (lowering fees at point of use by 80%) co-existed with a user fee removal pilot launched in 2008. We used Health Management Information System data to construct a controlled interrupted time-series analysis and examine both immediate and sustained effects of SONU and the pilot from January 2004 to December 2014. We found that both SONU and the pilot led to a sustained increase in the use of facility-based delivery. SONU produced an accumulative increase of 31.4% (P < 0.01) over 8 years in the four study districts. The pilot further enhanced utilization and produced an additional increase of 23.2% (P < 0.001) over 6 years. These increasing trends did not continue to reach full coverage, i.e. ensuring that all women had a facility-based delivery. Instead, they stabilized 3 years and 4 years after the onset of SONU and the pilot, respectively. Our study provides further evidence that user fee reduction and removal policies are effective in increasing service use in the long term. However, they alone are not sufficient to achieve full coverage. This calls for the need to implement additional measures, targeting for instance geographical barriers and knowledge gaps, to achieve the target of all women delivering in the presence of a skilled attendant.

Objectives In this cluster-randomised controlled study (CoV-Surv Study), four different “active” SARS-CoV-2 testing strategies for general population surveillance are evaluated for their effectiveness in determining and predicting the prevalence of SARS-CoV-2 infections in a given population. In addition, the costs and cost-effectiveness of the four surveillance strategies will be assessed. Further, this trial is supplemented by a qualitative component to determine the acceptability of each strategy. Findings will inform the choice of the most effective, acceptable and affordable strategy for SARS-CoV-2 surveillance, with the most effective and cost-effective strategy becoming part of the local public health department’s current routine health surveillance activities. Investigating its everyday performance will allow us to examine the strategy’s applicability to real time prevalence prediction and the usefulness of the resulting information for local policy makers to implement countermeasures that effectively prevent future nationwide lockdowns. The authors would like to emphasize the importance and relevance of this study and its expected findings in the context of population-based disease surveillance, especially in respect to the current SARS-CoV-2 pandemic. In Germany, but also in many other countries, COVID-19 surveillance has so far largely relied on passive surveillance strategies that identify individuals with clinical symptoms, monitor those cases who then tested positive for the virus, followed by tracing of individuals in close contact to those positive cases. To achieve higher effectiveness in population surveillance and to reliably predict the course of an outbreak, screening and monitoring of infected individuals without major symptoms (about 40% of the population) will be necessary. While current testing capacities are also used to identify such asymptomatic cases, this rather passive approach is not suitable in generating reliable population-based estimates of the prevalence of asymptomatic carriers to allow any dependable predictions on the course of the pandemic. To better control and manage the SARS-CoV-2 pandemic, current strategies therefore need to be complemented by an active surveillance of the wider population, i.e. routinely conducted testing and monitoring activities to identify and isolate infected individuals regardless of their clinical symptoms. Such active surveillance strategies will enable more effective prevention of the spread of the virus as they can generate more precise population-based parameters during a pandemic. This essential information will be required in order to determine the best strategic and targeted short-term countermeasures to limit infection spread locally. Trial design This trial implements a cluster-randomised, two-factorial controlled, prospective, interventional, single-blinded design with four study arms, each representing a different SARS-CoV-2 testing and surveillance strategy. Participants Eligible are individuals age 7 years or older living in Germany’s Rhein-Neckar Region who consent to provide a saliva sample (all four arms) after completion of a brief questionnaire (two arms only). For the qualitative component, different samples of study participants and non-participants (i.e. eligible for study, but refuse to participate) will be identified for additional interviews. For these interviews, only individuals age 18 years or older are eligible. Intervention and comparator Of the four surveillance strategies to be assessed and compared, Strategy A1 is considered the gold standard for prevalence estimation and used to determine bias in other arms. To determine the cost-effectiveness, each strategy is compared to status quo, defined as the currently practiced passive surveillance approach. Strategy A1: Individuals (one per household) receive information and study material by mail with instructions on how to produce a saliva sample and how to return the sample by mail. Once received by the laboratory, the sample is tested for SARS-CoV-2 using Reverse Transcription Loop-mediated Isothermal Amplification (RT-LAMP). Strategy A2: Individuals (one per household) receive information and study material by mail with instructions on how to produce their own as well as saliva samples from each household member and how to return these samples by mail. Once received by the laboratory, the samples are tested for SARS-CoV-2 using RT-LAMP. Strategy B1: Individuals (one per household) receive information by mail on how to complete a brief pre-screening questionnaire which asks about COVID-19 related clinical symptoms and risk exposures. Only individuals whose pre-screening score crosses a defined threshold, will then receive additional study material by mail with instructions on how to produce a saliva sample and how to return the sample by mail. Once received by the laboratory, the saliva sample is tested for SARS-CoV-2 using RT-LAMP. Strategy B2: Individuals (one per household) receive information by mail on how to complete a brief pre-screening questionnaire which asks about COVID-19 related clinical symptoms. Only individuals whose pre-screening score crosses a defined threshold, will then receive additional study material by mail with instructions how to produce their own as well as saliva samples from each household member and how to return these samples by mail. Once received by the laboratory, the samples are tested for SARS-CoV-2 using RT-LAMP. In each strategy, RT-LAMP positive samples are additionally analyzed with qPCR in order to minimize the number of false positives. Main outcomes The identification of the one best strategy will be determined by a set of parameters. Primary outcomes include costs per correctly screened person, costs per positive case, positive detection rate, and precision of positive detection rate. Secondary outcomes include participation rate, costs per asymptomatic case, prevalence estimates, number of asymptomatic cases per study arm, ratio of symptomatic to asymptomatic cases per study arm, participant satisfaction. Additional study components (not part of the trial) include cost effectiveness of each of the four surveillance strategies compared to passive monitoring (i.e. status quo), development of a prognostic model to predict hospital utilization caused by SARS-CoV-2, time from test shipment to test application and time from test shipment to test result, and perception and preferences of the persons to be tested with regard to test strategies. Randomisation Samples are drawn in three batches of three continuous weeks. Randomisation follows a two-stage process. First, a total of 220 sampling points have been allocated to the three different batches. To obtain an integer solution, the Cox-algorithm for controlled rounding has been used. Afterwards, sample points have been drawn separately per batch, following a probability proportional to size (PPS) random sample. Second, for each cluster the same number of residential addresses is randomly sampled from the municipal registries (self-weighted sample of individuals). The 28,125 addresses drawn per municipality are then randomly allocated to the four study arms A1, A2, B1, and B2 in the ratio 5 to 2.5 to 14 to 7 based on the expected response rates in each arm and the sensitivity and specificity of the pre-screening tool as applied in strategy B1 and B2. Based on the assumptions, this allocation should yield 2500 saliva samples in each strategy. Although a municipality can be sampled by multiple batches and the overall number of addresses per municipality might vary, the number of addresses contacted in each arm is kept constant. Blinding (masking) The design is single-blinded, meaning the staff conducting the SARS-CoV-2 tests are unaware of the study arm assignment of each single participant and test sample. Sample sizes Total sample size for the trial is 10,000 saliva samples equally allocated to the four study arms (i.e. 2,500 participants per arm). For the qualitative component, up to 60 in-depth interviews will be conducted with about 30 study participants (up to 15 in each arm A and B) and 30 participation refusers (up to 15 in each arm A and B) purposefully selected from the quantitative study sample to represent a variety of gender and ages to explore experiences with admission or rejection of study participation. Up to 25 asymptomatic SARS-CoV-2 positive study participants will be purposefully selected to explore the way in which asymptomatic men and women diagnosed with SARS-CoV-2 give meaning to their diagnosis and to the dialectic between feeling concurrently healthy and yet also being at risk for transmitting COVID-19. In addition, 100 randomly selected study participants will be included to explore participants’ perspective on testing processes and implementation. Trial Status Final protocol version is “Surveillance_Studienprotokoll_03Nov2020_v1_2” from November 3, 2020. Recruitment started November 18, 2020 and is expected to end by or before December 31, 2020. Trial registration The trial is currently being registered with the German Clinical Trials Register (Deutsches Register Klinischer Studien), DRKS00023271 (https://www.drks.de/drks_web/navigate.do?navigationId=trial. HTML&TRIAL_ID=DRKS00023271). Retrospectively registered 30 November 2020. Full protocol The full protocol is attached as an additional file, accessible from the Trials website (Additional file 1). In the interest in expediting dissemination of this material, the familiar formatting has been eliminated; this Letter serves as a summary of the key elements of the full protocol.

BackgroundAlzheimer’s disease (AD) is the most common cause of dementia among the elderly and is characterized by loss of memory and other cognitive functions. An increase in AChE (a key enzyme in the cholinergic nervous system) levels around β-amyloid plaques and neurofibrillary tangles is a common feature of AD neuropathology. Amnesic effects of scopolamine (acetylcholine receptor antagonist) can be investigated in various behavioral tests such as Morris water maze, object recognition, Y-maze, and passive avoidance. In the scope of this paper, we report the anti-AChE, anti-BChE properties of the isolated compound and the in vivo effects of the methanolic extract of Xylia xylocarpa (MEXX) on scopolamine-induced memory deficit.ResultsIn further phytochemistry study, a new hopan-type triterpenoid, (3β)-hopan-3-ol-28,22-olide (1), together with twenty known compounds were isolated (2–21). Compound 1, 2, 4, 5, 7–9, and 11–13 exhibited potent acetylcholinesterase (AChE) inhibitory activity in a concentration-dependent manner with IC50 values ranging from 54.4 to 94.6 μM. Compound 13 was also shown anti-butyrylcholinesterase (BChE) activity with an IC50 value of 42.7 μM. The Morris water Y-maze, Y-maze, and object recognition test were also carried out.ConclusionsIt is noteworthy that MEXX is effective when administered orally to mice, experimental results are consistent with the traditional use of this medicinal plant species.Electronic supplementary materialThe online version of this article (doi:10.1186/s13065-016-0197-5) contains supplementary material, which is available to authorized users.

Research aims: This study focuses on the correlation between tax revenue, investment, and economic growth, taking into account the non-linear effects of tax revenue.Design/Methodology/Approach: Macro data of nine countries in ASEAN (including Brunei, Cambodia, Indonesia, Laos, Malaysia, Philippines, Singapore, Thailand, and Vietnam) in 2000 - 2020 were extracted from the World Bank database. This research employed panel data estimations.Research findings: This study found statistical evidence of a negative effect of tax revenue on economic growth. However, when considering the non-linear effects of tax revenue, the empirical findings showed that higher tax revenue could reduce the disadvantages of tax impacts to boost economic growth. The negative effect of taxes is as obvious as the economic growth theories, but it depends on the taxation revenue. Lower tax revenue may encourage saving and investment, but it also leads to an increased government deficit, reducing economic growth through government debt, spending and investment. Moreover, this study provides consistent evidence of investment’s positive effect on economic growth in ASEAN countries during the research period.Theoretical contribution/Originality: The theoretical contribution provides evidence on the direct effect of tax revenue and investment on economic growth with a broader understanding of the tax’s non-linear effects and investment contributions in the ASEAN. The study confirms the vital role of government activity in regulating the development of the economy through taxation and investment. Practitioner/Policy implication: The severe impact of the COVID-19 pandemic has increased macroeconomic uncertainties, including uncertainty over savings, investment, and spending, potentially leading to tax revenue and investment losses. It, in turn, affects economic activities, so it requires careful consideration. Learned lessons from this study can prepare for future economic shocks and financial crises to reduce negative impacts on economic growth, including their adverse tax revenue effects.Research limitation: This study is limited by looking at the tax revenue ratio overview, which ignores the tax structure due to the lack of data collection. The following studies need to clarify the tax structure of ASEAN countries to determine which tax gives a negative impact/and which tax has a positive effect on economic growth.

Background Antigen-based rapid diagnostic tests (Ag-RDT) have been implemented in hospitals and nursing homes to screen for infectious individuals without symptoms suggestive of SARS-CoV-2 infections and to prevent entry into these high-risk settings. Despite their benefits for screening, the cost of large-scale implementation is largely understudied. Our study presents evidence on their implementation costs in high-risk settings. This study aimed to estimate the economic costs of implementing Ag-RDT-based screening for SARS-CoV-2 in two tertiary care hospitals (University Hospital Heidelberg - UKHD, and Charité – Universitätsmedizin Berlin) and one nursing home in Germany. Methods We adopted a health system perspective and followed the three sequential steps to costing: identification of resources, measurement of resource consumption, and valuation of costs. Data on resource consumption were collected between October 2020 and April 2021 through various techniques and data sources. The cost estimation considered all costs along the screening algorithm including PCR confirmation tests for positive cases. We estimated the costs for the two implementation modalities observed: staff dedicated exclusively to screening and staff not dedicated exclusively to screening. Furthermore, cost estimations were performed under both observed capacity use and hypothetical capacity use assumptions (60, 80 and 100%). Results Our study indicates that the average cost per Ag-RDT is highly dependent on the capacity use and implementation mode. Staff time and test kits are the two main cost drivers of implementing the large-scale screening programs for SARS-CoV-2 using Ag-RDTs. For hospitals, the average cost per test in UKHD was €30.12 (capacity observed); €14.56 (non-dedicated mode); €19.47, €16.37, €14.53 at 60, 80, 100% capacity respectively (dedicated mode); and at Charité €13.10 (non-dedicated mode). For the nursing home the estimated average cost per test was €15.03 (non-dedicated mode). Conclusions The information on the estimated costs by mode of implementation and capacity use may support the planning of Ag-RDT-based covid-19 screening programs suitable for each institution. Further research is needed to cost this screening strategy for COVID-19 in other high-risk, high-income settings to reach generalizability.

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In spite of the wide attention performance-based financing (PBF) has received over the past decade, no evidence is available on its impacts on quantity and mix of service provision nor on its interaction with parallel health financing interventions. Our study aimed to examine the PBF impact on quantity and mix of service provision in Burkina Faso, while accounting for the parallel introduction of a free healthcare policy. We used Health Management Information System data from 838 primary-level health facilities across 24 districts and relied on an interrupted time-series analysis with independent controls. We placed two interruptions, one to account for PBF and one to account for the free healthcare policy. In the period before the free healthcare policy, PBF produced significant but modest increases across a wide range of maternal and child services, but a significant decrease in child immunization coverage. In the period after the introduction of the free healthcare policy, PBF did not affect service provision in intervention compared with control facilities, possibly indicating a saturation effect. Our findings indicate that PBF can produce modest increases in service provision, without altering the overall service mix. Our findings, however, also indicate that the introduction of other health financing reforms can quickly crowd out the effects produced by PBF. Further qualitative research is required to understand what factors allow healthcare providers to increase the provision of some, but not all services and how they react to the joint implementation of PBF and free health care.

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