Coverage Estimates Research Articles

The 2020 immunization programme landscape: Piloting an assessment metric to evaluate the maturity of national immunization programmes across the life course

BackgroundThe World Health Organization (WHO) encourages countries to provide appropriate vaccinations for children, adolescents, and relevant adult populations. Childhood programme have been the focus of global investments, but recent pandemics have increasingly demonstrated the value of life course vaccination. Our objective is to compare national life course immunization programmatic maturity prior to mass COVID-19 vaccine introduction, the largest adult vaccination programme, globally. As coverage estimates (typically used to assess childhood programmes) are not available for adult vaccinations, this analysis pilots a standardized quantitative metric of programmatic maturity. MethodsThrough consultation with vaccination experts, we developed a standardized approach to assess national immunization programme maturity across the life course. In accordance with expert input, five vaccines were selected to represent delivery across the life course: diphtheria tetanus toxoid and pertussis (DTP); measles (MCV) second dose; human papillomavirus (HPV) final dose; pneumococcal conjugate (PCV) final dose; and seasonal influenza annual dose. Experts recommended inclusion of the following indicators for each vaccine: a legal mandate (national policy), experience delivering the vaccine (programme duration), and vaccine use (uptake for relevant populations). We developed a metric accordingly that provides up to 5 points per vaccine (“vaccine specific maturity score”) which when summed forms the “life course maturity score”, with a maximum score of 25. We analysed the prevalence of national policies, experience, and use by region and World Bank income group. ResultsMore than 55% of the 194 WHO Member States had childhood vaccine policies for all three of the vaccines considered (DTP, MCV, and PCV) compared to 60% for HPV (proxy for adolescent vaccination programme) and 52% for seasonal influenza (proxy for adult vaccination programme). Childhood vaccination programmes (e.g., MCV and DTP) had the highest vaccine specific maturity scores, while seasonal influenza and HPV vaccination programmes had much lower scores. The national life course maturity scores ranged from 1 to 23, with a global median of 12 (IQR: 8; 16). DiscussionThe piloted metric provides an overview of the maturity of life course immunization programmes. The metric is structured to be a flexible, rapid resource that can be used to assess other combinations of vaccines across the life course. The findings from this paper provide a baseline of immunization programme maturity for childhood, adolescent, and adult vaccination programmes immediately prior to the COVID-19 vaccine introduction. This maturity score, or adaptations of this approach, could be used to monitor the trajectory of national immunization programme maturity across the life course in the years ahead.

Coverage estimation of benthic habitat features by semantic segmentation of underwater imagery from South-eastern Baltic reefs using deep learning models

Underwater imagery (UI) is an important and sometimes the only tool for mapping hard-bottom habitats. With the development of new camera systems, from hand-held or simple “drop-down” cameras to ROV/AUV-mounted video systems, video data collection has increased considerably. However, the processing and analysing of vast amounts of imagery can become very labour-intensive, thus making it ineffective both time-wise and financially. This task could be simplified if the processes or their intermediate steps could be done automatically. Luckily, the rise of AI applications for automatic image analysis tasks in the last decade has empowered researchers with robust and effective tools. In this study, two ways to make UI analysis more efficient were tested with eight dominant visual features of the Southeastern Baltic reefs: 1) the simplification of video processing and expert annotation efforts by skipping the video mosaicking step and reducing the number of frames analysed; 2) the application of semantic segmentation of UI using deep learning models. The results showed that the annotation of individual frames provides similar results compared to 2D mosaics; moreover, the reduction of frames by 2–3 times resulted in only minor differences from the baseline. Semantic segmentation using the PSPNet model as the deep learning architecture was extensively evaluated, applying three variants of validation. The accuracy of segmentation, as measured by the intersection-over-union, was mediocre; however, estimates of visual coverage percentages were fair: the difference between the expert annotations and model-predicted segmentation was less than 6–8%, which could be considered an encouraging result.

Substantial but spatially heterogeneous progress in male circumcision for HIV prevention in South Africa

BackgroundVoluntary medical male circumcision (VMMC) reduces the risk of male HIV acquisition by 60%. Programmes to provide VMMCs for HIV prevention have been introduced in sub-Saharan African countries with high HIV burden. Traditional circumcision is also a long-standing male coming-of-age ritual, but practices vary considerably across populations. Accurate estimates of circumcision coverage by age, type, and time at subnational levels are required for planning and delivering VMMCs to meet targets and evaluating their impacts on HIV incidence.MethodsWe developed a Bayesian competing risks time-to-event model to produce region-age-time-type specific probabilities and coverage of male circumcision with probabilistic uncertainty. The model jointly synthesises data from household surveys and health system data on the number of VMMCs conducted. We demonstrated the model using data from five household surveys and VMMC programme data to produce estimates of circumcision coverage for 52 districts in South Africa between 2008 and 2019.ResultsNationally, in 2008, 24.1% (95% CI: 23.4–24.8%) of men aged 15–49 were traditionally circumcised and 19.4% (18.9–20.0%) were medically circumcised. Between 2010 and 2019, 4.25 million VMMCs were conducted. Circumcision coverage among men aged 15–49 increased to 64.0% (63.2–64.9%) and medical circumcision coverage to 42% (41.3–43.0%). Circumcision coverage varied widely across districts, ranging from 13.4 to 86.3%. The average age of traditional circumcision ranged between 13 and 19 years, depending on local cultural practices.ConclusionSouth Africa has made substantial, but heterogeneous, progress towards increasing medical circumcision coverage. Detailed subnational information on coverage and practices can guide programmes to identify unmet need to achieve national and international targets.

Elimination of Mother-to-Child Transmission of Hepatitis B Virus in Gulf Cooperation Council Countries: Current Status and Future Prospects.

The WHO member states endorsed the goal to eliminate mother-to-child transmission (EMTCT) of hepatitis B virus (HBV) by 2030, which requires achievement of ≥90% coverage with timely hepatitis B birth dose (HepB-BD), three doses of the hepatitis B vaccine (HepB3), and a hepatitis B surface antigen (HBsAg) seroprevalence ≤0.1% in children. We assessed the progress made to achieve EMTCT of HBV in Gulf Cooperation Council (GCC) countries. Data was extracted from National Viral Hepatitis Strategic Frameworks and WHO hepatitis B vaccination coverage estimates during 2018-2022 for all GCC countries. We also reviewed the literature to summarize the prevalence of HBsAg in children. During 2018-2022, coverage with timely HepB-BD and HepB3 was >90% in all countries. All newborns irrespective of whether parents are nationals or immigrants/expatriates receive HepB-BD and other routine immunization vaccines. Prevalence of HBsAg among children was available in three of six GCC countries; it ranged from 0% in Qatar and Saudi Arabia to 0.4% in Oman. Five countries reported screening pregnant women for HBsAg, and three provided antiviral treatment of those eligible, and hepatitis B immunoglobulin to exposed newborns. In conclusion, all GCC countries achieved hepatitis B vaccination targets and countries with available data have either achieved or are close to achieving EMTCT of HBV. Remaining countries need to implement hepatitis B serosurveys to track progress to EMTCT of HBV.

Estimating immunization coverage at the district level: A case study of measles and diphtheria-pertussis-tetanus-Hib-HepB vaccines in Ethiopia.

Ethiopia has made significant progress in the last two decades in improving the availability and coverage of essential maternal and child health services including childhood immunizations. As Ethiopia keeps momentum towards achieving national immunization goals, methods must be developed to analyze routinely collected health facility data and generate localized coverage estimates. This study leverages the District Health Information Software (DHIS2) platform to estimate immunization coverage for the first dose of measles vaccine (MCV1) and the third dose of diphtheria-pertussis-tetanus-Hib-HepB vaccine (Penta3) across Ethiopian districts ("woredas"). Monthly reported numbers of administered MCV1 and Penta3 immunizations were extracted from public facilities from DHIS2 for 2017/2018-2021/2022 and corrected for quality based on completeness and consistency across time and districts. We then utilized three sources for the target population (infants) to compute administrative coverage estimates: Central Statistical Agency, DHIS2, and WorldPop. The Ethiopian Demographic and Health Surveys were used as benchmarks to which administrative estimates were adjusted at the regional level. Administrative vaccine coverage was estimated for all woredas, and, after adjustments, was bounded within 0-100%. In regions with the highest immunization coverage, MCV1 coverage would range from 83 to 100% and Penta3 coverage from 88 to 100% (Addis Ababa, 2021/2022); MCV1 from 8 to 100% and Penta3 from 4 to 100% (Tigray, 2019/2020). Nationally, the Gini index for MCV1 was 0.37, from 0.13 (Harari) to 0.37 (Somali); for Penta3, it was 0.36, from 0.16 (Harari) to 0.36 (Somali). The use of routine health information systems, such as DHIS2, combined with household surveys permits the generation of local health services coverage estimates. This enables the design of tailored health policies with the capacity to measure progress towards achieving national targets, especially in terms of inequality reductions.

Covid-19 pandemic and equity of global human papillomavirus vaccination: descriptive study of World Health Organization-Unicef vaccination coverage estimates

ObjectiveTo analyse progress in global vaccination against human papillomavirus (HPV) during the covid-19 pandemic, with a particular focus on equity.DesignDescriptive study of World Health Organization-Unicef vaccination coverage estimates.SettingWHO-Unicef estimates of...

Hepatitis A vaccination coverage survey in 24-month-old children living in Brazilian capitals, 2020.

To estimate hepatitis A vaccination coverage in 24-month-old children and identify factors associated with non-vaccination. This was a survey involving a sample stratified by socioeconomic strata in capital cities (2020-2022), with coverage estimates and 95% confidence intervals (95%CI), the factor analysis was performed using the prevalence ratio (PR) by means of Poisson regression. Among 31,001 children, hepatitis A coverage was 88.1% (95%CI 86.8;89.2). Regarding socioeconomic strata (A/B), the variable immigrant parents/guardians was associated with non-vaccination (PR = 1.91; 95%CI 1.09;3.37); in strata C/D, children of Asian race/skin color (PR = 4.69; 95%CI 2.30;9.57), fourth-born child or later (PR = 1.68; 95%CI 1.06;2 .66), not attending daycare/nursery (PR = 1.67; 95%CI 1.24;2.24) and mother with paid work (PR = 1.42; 95%CI 1.16;1.74) were associated with non-vaccination. Hepatitis A coverage was below the target (95%), suggesting that specificities of social strata should be taken into consideration. Hepatitis A vaccination coverage was 88%. Non-vaccination was greater in children with immigrant guardians (strata A/B); of Asian race/skin color, fourth-born child or later, those not attending daycare/nursery and mother with paid work (C/D strata). The results of this study contributed to the Ministry of Health and Health Departments in monitoring vaccination coverage and identifying factors that may negatively impact hepatitis A vaccination coverage. Further research is needed on the impact of migration on hepatitis A vaccination and vaccination in general. Health managers should be attentive to the different factors affecting vaccination among social strata.

Reliability of information recorded on the National Immunization Program Information System.

To analyze the reliability of records held on the National Immunization Program Information System (SI-PNI) in a subsample of children included in the national vaccination coverage survey in Brazilian state capitals and Federal District in 2020. This was a study of agreement between data recorded on vaccination cards (doses and dates) and on the SI-PNI for 4050 children with full coverage at 24 months. Data on 3587 children were held on the SI-PNI, with losses of 11% (95%CI: 10;12). Total agreement between doses and dates in the two sources was 86% (95%CI: 86;87), however taking each dose and vaccine individually, variation was greater, with 32% of data in only one source. Part of the information was not recorded, but the discrepancy can be considered small. Nonetheless, underrecording of doses and children can compromise vaccination coverage estimates, altering the numerator and denominator data. Subsample of 4,050 children, among those completing the full schedule at 24 months studied in the national survey, 11% had not been recorded on the SI-PNI, 32% had unrecorded doses (doses or dates) and there was 8% disagreement between vaccination cards and SI-PNI records. Recognizing the difficulties faced by the SI-PNI and the discrepancies between sources is essential for adopting initiatives to improve data quality, so as to avoid inaccurate estimates of childhood vaccination coverage. This study is expected to contribute to improving the quality of records and the usability of data for monitoring vaccination coverage of the immunization program from the local to the national level.

Triangulation of Routine Antenatal HIV Prevalence Data and Adjusted HIV Estimates in Mozambique.

Routine health system data are central to monitoring HIV trends. In Mozambique, the reported number of women receiving antenatal care (ANC) and antiretroviral therapy for prevention of mother-to-child transmission (PMTCT) has exceeded the Spectrum-estimated number of pregnant women since 2017. In some provinces, reported HIV prevalence in pregnant women has declined faster than epidemiologically plausible. We hypothesized that these issues are linked and caused by programmatic overenumeration of HIV-negative pregnant women at ANC. We triangulated program-reported ANC client numbers with survey-based fertility estimates and facility birth data adjusted for the proportion of facility births. We used survey-reported ANC attendance to produce adjusted time series of HIV prevalence in pregnant women, adjusted for hypothesized program double counting. We calibrated the Spectrum HIV estimation models to adjusted HIV prevalence data to produce adjusted adult and pediatric HIV estimates. ANC client numbers were not consistent with facility birth data or modeled population estimates indicating ANC data quality issues in all provinces. Adjusted provincial ANC HIV prevalence in 2021 was median 45% [interquartile range 35%-52% or 2.3 percentage points (interquartile range 2.5-3.5)] higher than reported HIV prevalence. In 2021, calibrating to adjusted antenatal HIV prevalence lowered PMTCT coverage to less than 100% in most provinces and increased the modeled number of new child infections by 35%. The adjusted results better reconciled adult and pediatric antiretroviral treatment coverage and antenatal HIV prevalence with regional fertility estimates. Adjusting HIV prevalence in pregnant women using nationally representative household survey data on ANC attendance produced estimates more consistent with surveillance data. The number of children living with HIV in Mozambique has been substantially underestimated because of biased routine ANC prevalence. Renewed focus on HIV surveillance among pregnant women would improve PMTCT coverage and pediatric HIV estimates.

Oral microbiota distinguishes patients with osteosarcoma from healthy controls.

The human microbiota plays a key role in cancer diagnosis, pathogenesis, and treatment. However, osteosarcoma-associated oral microbiota alterations have not yet been unraveled. The aim of this study was to explore the characteristics of oral microbiota in osteosarcoma patients compared to healthy controls, and to identify potential microbiota as a diagnostic tool for osteosarcoma. The oral microbiota was analyzed in osteosarcoma patients (n = 45) and matched healthy controls (n = 90) using 16S rRNA MiSeq sequencing technology. The microbial richness and diversity of the tongue coat were increased in osteosarcoma patients as estimated by the abundance-based coverage estimator indices, the Chao, and observed operational taxonomy units (OTUs). Principal component analysis delineated that the oral microbial community was significant differences between osteosarcoma patients and healthy controls. 14 genera including Rothia, Halomonas, Rhodococcus, and Granulicatella were remarkably reduced, whereas Alloprevotella, Prevotella, Selenomonas, and Campylobacter were enriched in osteosarcoma. Eventually, the optimal four OTUs were identified to construct a microbial classifier by the random forest model via a fivefold cross-validation, which achieved an area under the curve of 99.44% in the training group (30 osteosarcoma patients versus 60 healthy controls) and 87.33% in the test group (15 osteosarcoma patients versus 30 healthy controls), respectively. Notably, oral microbial markers validated strong diagnostic potential distinguishing osteosarcoma patients from healthy controls. This study comprehensively characterizes the oral microbiota in osteosarcoma and reveals the potential efficacy of oral microbiota-targeted biomarkers as a noninvasive biological diagnostic tool for osteosarcoma.

The Changes, Aggregation Processes, and Driving Factors for Soil Fungal Communities during Tropical Forest Restoration.

Soil fungal communities play crucial roles in mediating the functional associations between above- and belowground components during forest restoration. Forest restoration shapes the alterations in plant and soil environments, which exerts a crucial effect on soil fungal assemblages. However, the changes, assembly processes, and driving factors of soil fungi communities during tropical forest restoration are still uncertain. We used Illumina high-throughput sequencing to identify the changes of soil fungal communities across a tropical secondary forest succession chronosequence (i.e., 12-, 42-, and 53-yr stages) in Xishuangbanna. During forest restoration, the dominant taxa of soil fungi communities shifted from r- to K-strategists. The relative abundance of Ascomycota (r-strategists) decreased by 10.0% and that of Basidiomycota (K-strategists) increased by 4.9% at the 53-yr restoration stage compared with the 12-yr stage. From the 12-yr to 53-yr stage, the operational taxonomic unit (OTU), abundance-based coverage estimator (ACE), Chao1, and Shannon index of fungal communities declined by 14.5-57.4%. Although the stochastic processes were relatively important in determining fungal assemblages at the late stage, the fungal community assembly was dominated by deterministic processes rather than stochastic processes. The shifts in soil properties resulting from tropical forest restoration exerted significant effects on fungal composition and diversity. The positive effects of microbial biomass carbon, readily oxidizable carbon, and soil water content explained 11.5%, 9.6%, and 9.1% of the variations in fungal community composition, respectively. In contrast, microbial biomass carbon (40.0%), readily oxidizable carbon (14.0%), and total nitrogen (13.6%) negatively contributed to the variations in fungal community diversity. Our data suggested that the changes in fungal composition and diversity during tropical forest restoration were primarily mediated by the positive or negative impacts of soil carbon and nitrogen pools.

Bias-Adjusted Predictions of County-Level Vaccination Coverage from the COVID-19 Trends and Impact Survey.

The potential for selection bias in nonrepresentative, large-scale, low-cost survey data can limit their utility for population health measurement and public health decision making. We developed an approach to bias adjust county-level COVID-19 vaccination coverage predictions from the large-scale US COVID-19 Trends and Impact Survey. We developed a multistep regression framework to adjust for selection bias in predicted county-level vaccination coverage plateaus. Our approach included poststratification to the American Community Survey, adjusting for differences in observed covariates, and secondary normalization to an unbiased reference indicator. As a case study, we prospectively applied this framework to predict county-level long-run vaccination coverage among children ages 5 to 11 y. We evaluated our approach against an interim observed measure of 3-mo coverage for children ages 5 to 11 y and used long-term coverage estimates to monitor equity in the pace of vaccination scale up. Our predictions suggested a low ceiling on long-term national vaccination coverage (46%), detected substantial geographic heterogeneity (ranging from 11% to 91% across counties in the United States), and highlighted widespread disparities in the pace of scale up in the 3 mo following Emergency Use Authorization of COVID-19 vaccination for 5- to 11-y-olds. We relied on historical relationships between vaccination hesitancy and observed coverage, which may not capture rapid changes in the COVID-19 policy and epidemiologic landscape. Our analysis demonstrates an approach to leverage differing strengths of multiple sources of information to produce estimates on the time scale and geographic scale necessary for proactive decision making. Designing integrated health measurement systems that combine sources with different advantages across the spectrum of timeliness, spatial resolution, and representativeness can maximize the benefits of data collection relative to costs. The COVID-19 pandemic catalyzed massive survey data collection efforts that prioritized timeliness and sample size over population representativeness.The potential for selection bias in these large-scale, low-cost, nonrepresentative data has led to questions about their utility for population health measurement.We developed a multistep regression framework to bias adjust county-level vaccination coverage predictions from the largest public health survey conducted in the United States to date: the US COVID-19 Trends and Impact Survey.Our study demonstrates the value of leveraging differing strengths of multiple data sources to generate estimates on the time scale and geographic scale necessary for proactive public health decision making.

A Deep Learning Approach to Estimate Halimeda incrassata Invasive Stage in the Mediterranean Sea

Invasive algae, such as Halimeda incrassata, alter marine biodiversity in the Mediterranean Sea. Monitoring these changes over time is crucial for assessing the health of coastal environments and preserving local species. However, this monitoring process is resource-intensive, requiring taxonomic experts and significant amounts of time. Recently, deep learning approaches have attempted to automate the detection of certain seagrass species like Posidonia oceanica and Halophila ovalis from two different strategies: seagrass coverage estimation and detection. This work presents a novel approach to detect Halimeda incrassata and estimate its coverage, independently of the invasion stage of the algae. Two merging methods based on the combination of the outputs of an object detection network (YOLOv5) and a semantic segmentation network (U-net) are developed. The system achieves an F1-scoreof 84.2% and a Coverage Error of 5.9%, demonstrating its capability to accurately detect Halimeda incrassata and estimate its coverage independently of the invasion stage.

Estimating county-level vaccination coverage using small area estimation with the National Immunization Survey-Child

The National Immunization Survey-Child (NIS-Child) provides annual vaccination coverage estimates in the United States for children aged 19 through 35 months, nationally, for each state, and for select local areas and territories. There is a need for vaccination coverage estimates for smaller geographic areas to support local authority planning and identify counties with potentially low vaccination coverage for possible further intervention. We describe small area estimation methods using 2008-2018 NIS-Child data to generate county-level estimates for children up to two years of age born 2007-2011 and 2012-2016. We applied an empirical best linear unbiased prediction method to combine direct estimates of vaccination coverage with model-based prediction using county-level predictors regarding health and demographic characteristics. We review the predictors commonly selected for the small area models and note multiple predictors related to barriers to vaccination.

A novel strategy for estimating biomass of submerged aquatic vegetation in lake integrating UAV and Sentinel data

Submerged aquatic vegetation (SAV) plays a fundamental ecological role in mediating carbon cycling within lakes, and its biomass is essential to assess the carbon sequestration potential of lake ecosystems. Remote sensing (RS) offers a powerful tool for large-scale SAV biomass retrieval. Given the underwater location of SAV, the spectral signal in RS data often exhibits weakness, capturing primarily horizontal structure rather than volumetric information crucial for biomass assessment. Fortunately, easily-measured SAV coverage can serve as an intermediary variable for difficultly-quantified SAV biomass inversion. Nevertheless, obtaining enough SAV coverage samples matching satellite image pixels for robust model development remains problematic. To overcome this challenge, we employed a UAV to acquire high-precision data, thereby replacing manual SAV coverage sample collection. In this study, we proposed an innovative strategy integrating unmanned aerial vehicle (UAV) and satellite data to invert large-scale SAV coverage, and subsequently estimate the biomass of the dominant SAV population (Potamogeton pectinatus) in Ulansuhai Lake. Firstly, a coverage-biomass model (R2 = 0.93, RMSE = 0.8 kg/m2) depicting the relationship between SAV coverage and biomass was developed. Secondly, in a designed experimental area, a high-precision multispectral image was captured by a UAV. Based on the Normalized Difference Water Index (NDWI), the UAV-based image was classified into non-vegetated and vegetated areas, thereby generating an SAV distribution map. Leveraging spatial correspondence between satellite pixels and the UAV-based SAV distribution map, the proportion of SAV within each satellite pixel, referred to as SAV coverage, was computed, and a coverage sample set matched with satellite pixels was obtained. Subsequently, based on the sample set, a satellite-scale SAV coverage estimation model (R2 = 0.78, RMSE = 14.05 %) was constructed with features from Sentinel-1 and Sentinel-2 data by XGBoost algorithm. Finally, integrating the coverage-biomass model with the obtained coverage inversion results, fresh biomass of SAV in Ulansuhai Lake was successfully estimated to be approximately 574,600 tons.

Updates on syringe coverage and service uptake among needle and syringe programs in the United States, 2019–2020

BackgroundAs injection drug use has increased in the US, so too has the prevalence of receptive syringe sharing. Since the 1980s, Needle and Syringe Programs (NSPs) have been an important source of clean injection equipment and disposal of used syringes. This study reports national syringe coverage and examines the impact of program attributes on organizational-level service uptake, defined as number of syringes distributed per participant contact per year. MethodsIn 2019 and 2020, we administered an annual cross-sectional survey to NSPs operating in the US (n = 260). A national estimate of coverage was calculated by dividing the total number of syringes distributed by the 2019 and 2020 population estimate of people who inject drugs (PWID). Frequency distributions and percentages were calculated for categorical variables (e.g., funding, census region, distribution policy/modality), and median and interquartile ranges (IQR) were calculated for continuous variables (e.g., participant contacts, syringes distributed). Bivariate and multivariable mixed effects logistic regression models were used to estimate the odds ratio associated with organizational characteristics on increasing service uptake at the NSP level. ResultsFrom 2019 to 2020, the total number of participant contacts by NSPs increased from 871,976 to 898,891, and the number of syringes distributed increased from 92,648,529 to 113,071,748. The national coverage estimate increased from 29.5 (95 % CI = 15.0, 58.2) to 35.8 (95 % CI = 18.2, 70.6) syringes per PWID. Fifty-eight percent of NSPs increased service uptake in 2020 as compared to the previous year. NSPs that received government funding and NSPs that changed to a less restrictive syringe distribution policy were more likely to increase service uptake (aOR 1.80, 95 % CI = 1.01, 3.22 and aOR 3.33, 95 % CI = 1.11, 9.94, respectively). Syringe distribution modalities also diversified, with more NSPs reaching participants via backpacking/outreach, fixed site pop-ups, mobile delivery, mail-based delivery, leaving supplies out, and secondary distribution. ConclusionBoth governmental investment in harm reduction programming and needs-based distribution of syringes increased service uptake and thus should be expanded and sustained to reduce harms associated with injection drug use.

Analysis of subgingival bacterial and fungal diversity in patients with peri-implantitis based on 16sRNA and internal transcribed spacer sequencing.

Aim: To analyze subgingival fungal diversity in peri-implant inflammation patients and their relationship with bacteria. Methods: We collected saliva samples from four groups. 16sRNA and internal transcribed spacer sequencing was performed preceded by quantitative PCR and enzyme-linked immunosorbent assay tests. Analyses were done usingR and Cytoscape software. Results: Significant differences were observed in the Abundance-based Coverage Estimator(ACE) index between control and peri-implantitis samples. Basidiomycota was the dominant fungal species, while Firmicutes dominated thebacteria. The most abundant fungal and bacterial species were 's_unclassified g Apiotrichum' and 's_unclassified g Streptococcus', respectively. Dothiorella was strongly associated with immunoglobulin G levels, with positive correlations between specific microorganisms and peri-implantitis in Q-PCR. Conclusion: Our findings have significant clinical implications, suggesting specific fungal and bacterial taxaroles in peri-implant inflammation.

Cost-effectiveness of human papillomavirus (HPV) vaccination in Burkina Faso: a modelling study

BackgroundAfrica has some of the highest cervical cancer incidence and mortality rates globally. Burkina Faso launched a human papillomavirus (HPV) vaccination programme for 9-year-old girls in 2022 with support from Gavi, the Vaccine Alliance (Gavi). An economic evaluation of HPV vaccination is required to help sustain investment and inform decisions about optimal HPV vaccine choices.MethodsWe used a proportionate outcomes static cohort model to evaluate the potential impact and cost-effectiveness of HPV vaccination for 9-year-old girls over a ten-year period (2022–2031) in Burkina Faso. The primary outcome measure was the cost (2022 US$) per disability-adjusted life year (DALY) averted from a limited societal perspective (including all vaccine costs borne by the government and Gavi, radiation therapy costs borne by the government, and all other direct medical costs borne by patients and their families). We evaluated four vaccines (CERVARIX®, CECOLIN®, GARDASIL-4®, GARDASIL-9®), comparing each to no vaccination (and no change in existing cervical cancer screening and treatment strategies) and to each other. We combined local estimates of HPV type distribution, healthcare costs, vaccine coverage and costs with GLOBOCAN 2020 disease burden data and clinical trial efficacy data. We ran deterministic and probabilistic uncertainty analyses.ResultsHPV vaccination could prevent 37–72% of cervical cancer cases and deaths. CECOLIN® had the most favourable cost-effectiveness (cost per DALY averted < 0.27 times the national gross domestic product [GDP] per capita). When cross-protection was included, CECOLIN® remained the most cost-effective (cost per DALY averted < 0.20 times the national GDP per capita), but CERVARIX® provided greater health benefits (66% vs. 48% reduction in cervical cancer cases and deaths) with similar cost-effectiveness (cost per DALY averted < 0.28 times the national GDP per capita, with CECOLIN® as the comparator). We estimated the annual cost of the vaccination programme at US$ 2.9, 4.1, 4.4 and 19.8 million for CECOLIN®, GARDASIL-4®, CERVARIX® and GARDASIL-9®, respectively. A single dose strategy reduced costs and improved cost-effectiveness by more than half.ConclusionHPV vaccination is cost-effective in Burkina Faso from a limited societal perspective. A single dose strategy and/or alternative Gavi-supported HPV vaccines could further improve cost-effectiveness.

A Practical Guide to Pilot Testing Community-Based Vaccination Coverage Surveys.

Pilot testing is crucial when preparing any community-based vaccination coverage survey. In this paper, we use the term pilot test to mean informative work conducted before a survey protocol has been finalized for the purpose of guiding decisions about how the work will be conducted. We summarize findings from seven pilot tests and provide practical guidance for piloting similar studies. We selected these particular pilots because they are excellent models of preliminary efforts that informed the refinement of data collection protocols and instruments. We recommend survey coordinators devote time and budget to identify aspects of the protocol where testing could mitigate project risk and ensure timely assessment yields, credible estimates of vaccination coverage and related indicators. We list specific items that may benefit from pilot work and provide guidance on how to prioritize what to pilot test when resources are limited.

Formula omitted]-Coverage Reliability for Wireless Multihop Network incorporating Boundary Effect

Coverage is one of the crucial performance measures for Wireless Multihop Networks (WMN) and is widely explored by researchers. The coverage performance of the network indicates how effectively the deployed network is able to provide the required coverage for a given period of time. The coverage performance of a finite network is influenced by different factors such as the presence of obstacles in the signal propagation path, the shape and size of the network deployment region, sensor node characteristics, and many others. Therefore, it is necessary to compute the performance of the network before its actual deployment in the desired region. The existing paper proposes an analytical solution for the network coverage considering sensor node failure and boundary effect using three different sensing models i.e., Boolean, Shadow Fading, and Elfes, separately. The Boolean Sensing Model (BSM) is deterministic and ignores the channel randomness, whereas the other two are probabilistic sensing models and are more realistic as they also consider the channel randomness caused by environmental factors. In the existing research, coverage estimation is determined separately for each individual model. However, coverage estimation for all three models and coverage reliability under boundary conditions have not been explored in the literature yet. Coverage reliability is another crucial metric determining how long a network can provide the required coverage. This paper aims to compute the Network Coverage Reliability (NCR) of a WMN deployed in given environments and with sensor node failure conditions. We have proposed an analytical solution to estimate the NCR of a WMN deployed in a finite circular region considering boundary effect using all three models viz., BSM, Shadowing & Multipath Fading (SMF), and Elfes Sensing Model (ESM) assuming sensor node failure condition. The Mean Time to Failure (MTTF) parameter has been determined for all three models and based on that the value of NCR is estimated. The proposed work considers κ-coverage reliability with 1≤κ≤3 where κ is the number of distinct sensor node required to cover the target and render desired NCR for different path fading and non-fading sensing models. The analytical results are validated through simulation runs and found to be consistent. Further, we have analyzed the impact of different parameters, such as the number of sensor node, sensing range, SMF parameters, sensor failure rate, and required reliability on the κ-coverage and κ-coverage reliability, referred to as NCR.