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Forest structure depends both on extrinsic factors such as climate and on intrinsic properties such as community composition and diversity. Here, the authors use a dataset of stand structural complexity based on LiDAR measurements to build a global map of structural complexity for primary forests, and find that precipitation variables best explain global patterns of forest structural complexity.

Land surface temperature (LST) and air temperature (Tair) have been commonly used to analyze urban heat island (UHI) effects throughout the world, with noted variations based on vegetation distribution. This research has compared time series LST data acquired from the Moderate Resolution Imaging Spectroradiometer (MODIS) platforms, Landsat 7 Enhanced Thematic Mapper (ETM+) and Tair from weather stations in the Southern Ontario area. The influence of the spatial resolution, land cover, vegetated surfaces, and seasonality on the relationship between LST and in situ Tair were examined. The objective is to identify spatial and seasonal differences amongst these different spatial resolution LST products and Tair, along with the causes for variations at a localized scale. Results show that MODIS LST from Terra had stronger relationships with Landsat 7 LST than those from Aqua. Tair demonstrated weaker correlations with Landsat LST than with MODIS LST in sparsely vegetated and urban areas during the summer. Due to the winter’s ability to smooth heterogenous surfaces, both LST and Tair showed stronger relationships in winter than summer over every land cover, except with coarse spatial resolutions on forested surfaces.

Abstract Background The general practitioner contracting initiative (GPCI) is a health systems strengthening initiative piloted in the first phase of national health insurance (NHI) implementation in South Africa as it progresses towards universal health coverage (UHC). GPCI aimed to address the shortage of doctors in the public sector by contracting-in private sector general practitioners (GPs) to render services in public primary health care clinics. This paper explores the early inception and emergence of the GPCI. It describes three models of contracting-in that emerged and interrogates key factors influencing their evolution. Methods This qualitative multi-case study draws on three cases. Data collection comprised document review, key informant interviews and focus group discussions with national, provincial and district managers as well as GPs (n = 68). Walt and Gilson’s health policy analysis triangle and Liu’s conceptual framework on contracting-out were used to explore the policy content, process, actors and contractual arrangements involved. Results Three models of contracting-in emerged, based on the type of purchaser: a centralized-purchaser model, a decentralized-purchaser model and a contracted-purchaser model. These models are funded from a single central source but have varying levels of involvement of national, provincial and district managers. Funds are channelled from purchaser to provider in slightly different ways. Contract formality differed slightly by model and was found to be influenced by context and type of purchaser. Conceptualization of the GPCI was primarily a nationally-driven process in a context of high-level political will to address inequity through NHI implementation. Emergence of the models was influenced by three main factors, flexibility in the piloting process, managerial capacity and financial management capacity. Conclusion The GPCI models were iterations of the centralized-purchaser model. Emergence of the other models was strongly influenced by purchaser capacity to manage contracts, payments and recruitment processes. Findings from the decentralized-purchaser model show importance of local context, provincial capacity and experience on influencing evolution of the models. Whilst contract characteristics need to be well defined, allowing for adaptability to the local context and capacity is critical. Purchaser capacity, existing systems and institutional knowledge and experience in contracting and financial management should be considered before adopting a decentralized implementation approach.

Introduction: In South Africa over the past 20 years, immunisation has saved countless lives as well as prevented illnesses and disabilities. Despite this, vaccine-preventable illnesses remain a danger. The demand for and uptake of immunisation services are shaped by a variety of factors that can either act as barriers or facilitators to immunisation uptake. The aim of this project was to identify the supply and demand barriers and develop local strategies to improve childhood immunisation in four zero-dose districts in South Africa. Materials and Methods: This study used a mixed-method approach. In each of these four districts, 15 in-depth key informant interviews with health workers and local health managers and four focus group discussions (10 participants per focus group discussion) with community members and caregivers were held over a three-month period. Transcribed interviews were thematically analysed using qualitative analysis software (Nvivo®) into 10 factors as identified as important in influencing immunisation demand and uptake in previous studies. A further four were identified during the data analysis process. Results: Despite the varying role of factors affecting demand and uptake of immunisation services, three consistent findings stand out as major barriers across all districts. The first is interaction with healthcare staff. This clearly highlights the crucial role that the interactions between patients and staff play in shaping perceptions and behaviours related to immunisation services. The second is the overall experience of care at healthcare facilities. This emphasises the role that patient experience of services plays in perceptions and behaviours related to immunisation services. The third is family dynamics. This highlights the important role family dynamics play in shaping individuals’ decisions regarding immunisation uptake as well as the impact it has on the ability of people to access health services. Discussion: The role played by the different factors in the demand and uptake of immunisation services varied across the four districts examined in this study. Each of the districts presents a unique landscape where different factors have varying degrees of importance in affecting the utilisation of immunisation services. In some districts, certain factors are major barriers, clearly hindering the demand and uptake of immunisation services, while in others, these same factors might be a relatively minor barrier. This discrepancy highlights the unique nature of healthcare challenges across the districts and the need for tailored strategy recommendations to address them effectively.

BackgroundTuberculosis (TB) prevalence remains persistently high in many settings, with new or expanded interventions required to achieve substantial reductions. The HIV Prevention Trials Network (HPTN) 071 (PopART) community-randomised trial randomised 14 communities to receive the "PopART" intervention during 2014 to 2017 (7 arm A and 7 arm B communities) and 7 communities to receive standard-of-care (arm C). The intervention was delivered door-to-door by community HIV care providers (CHiPs) and included universal HIV testing, facilitated linkage to HIV care at government health clinics, and systematic TB symptom screening. The Tuberculosis Reduction through Expanded Anti-retroviral Treatment and Screening (TREATS) study aimed to measure the impact of delivering the PopART intervention on TB outcomes, in communities with high HIV and TB prevalence.Methods and findingsThe study population of the HPTN 071 (PopART) trial included individuals aged ≥15 years living in 21 urban and peri-urban communities in Zambia and South Africa, with a total population of approximately 1 million and an adult HIV prevalence of around 15% at the time of the trial. Two sputum samples for TB testing were provided to CHiPs by individuals who reported ≥1 TB suggestive symptom (a cough for ≥2 weeks, unintentional weight loss ≥1.5 kg in the last month, or current night sweats) or that a household member was currently on TB treatment. Antiretroviral therapy (ART) was offered universally at clinics in arm A and according to local guidelines in arms B and C. The TREATS study was conducted in the same 21 communities as the HPTN 071 (PopART) trial between 2017 and 2022, and TB prevalence was a co-primary endpoint of the TREATS study. The primary comparison was between the PopART intervention (arms A and B combined) and the standard-of-care (arm C). During 2019 to 2021, a TB prevalence survey was conducted among randomly selected individuals aged ≥15 years (approximately 1,750 per community in arms A and B, approximately 3,500 in arm C). Participants were screened on TB symptoms and chest X-ray, with diagnostic testing using Xpert-Ultra followed by culture for individuals who screened positive. Sputum eligibility was determined by the presence of a cough for ≥2 weeks, or ≥2 of 5 "TB suggestive" symptoms (cough, weight loss for ≥4 weeks, night sweats, chest pain, and fever for ≥2 weeks), or chest X-ray CAD4TBv5 score ≥50, or no available X-ray results. TB prevalence was compared between trial arms using standard methods for cluster-randomised trials, with adjustment for age, sex, and HIV status, and multiple imputation was used for missing data on prevalent TB. Among 83,092 individuals who were eligible for the survey, 49,556 (59.6%) participated, 8,083 (16.3%) screened positive, 90.8% (7,336/8,083) provided 2 sputum samples for Xpert-Ultra testing, and 308 (4.2%) required culture confirmation. Overall, estimated TB prevalence was 0.92% (457/49,556). The geometric means of 7 community-level prevalence estimates were 0.91%, 0.70%, and 0.69% in arms A, B, and C, respectively, with no evidence of a difference comparing arms A and B combined with arm C (adjusted prevalence ratio 1.14, 95% confidence interval, CI [0.67, 1.95], p = 0.60). TB prevalence was higher among people living with HIV than HIV-negative individuals, with an age-sex-community adjusted odds ratio of 2.29 [95% CI 1.54, 3.41] in Zambian communities and 1.61 [95% CI 1.13, 2.30] in South African communities. The primary limitations are that the study was powered to detect only large reductions in TB prevalence in the intervention arm compared with standard-of-care, and the between-community variation in TB prevalence was larger than anticipated.ConclusionsThere was no evidence that the PopART intervention reduced TB prevalence. Systematic screening for TB that is based on symptom screening alone may not be sufficient to achieve a large reduction in TB prevalence over a period of several years. Including chest X-ray screening alongside TB symptom screening could substantially increase the sensitivity of systematic screening for TB.Trial registrationThe TREATS study was registered with ClinicalTrials.gov Identifier: NCT03739736 on November 14, 2018. The HPTN 071 (PopART) trial was registered at ClinicalTrials.gov under number NCT01900977 on July 17, 2013.