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Abraha Hailu,1 Kibreab Gidey,1 Mohamedawel Mohamedniguss Ebrahim,2 Yonas Berhane,1 Teklay Gebrehaweria Baraki,1 Tesfay Hailemariam,1 Ataklti Negash,1 Habtamu Mesele,1 Tekleab Desta,1 Haylsh Tsegay,1 Mulubirhan Assefa,3 Alemayehu Bayray3 1Department of Internal Medicine, School of Medicine, Mekelle University-College of Health Sciences, Mekelle, Tigray, Ethiopia; 2Department of Biostatistics, School of Public Health, Mekelle University-College of Health Sciences, Mekelle, Tigray, Ethiopia; 3Department of Epidemiology, School of Public Health, Mekelle University-College of Health Sciences, Mekelle, Tigray, EthiopiaCorrespondence: Abraha Hailu, Department of Internal Medicine, Cardiology Unit, School of Medicine, Mekelle University-College of Health Sciences, Mekelle, Tigray, Ethiopia, Tel +251911413363, Email abrahahailu7@gmail.comBackground: Documentation of cardiovascular diseases (CVDs) including major contributors to hospitalization and their outcomes in sub-Saharan Africa and Ethiopia is important to prioritize further research, prevention and treatment strategies.Objective: This study aimed to describe the clinical profiles of CVD admissions, risk factors, and patient outcomes at Ayder Comprehensive Specialized Hospital (ACSH) in Tigray, Ethiopia.Methods: Data of patients with the diagnosis of CVD was collected prospectively from Nov. 1, 2017 – Oct. 31, 2018 for patients admitted to medical wards and ICU of ACSH. Socio-demographic profiles, final admission diagnosis, duration of hospital stay and outcomes of hospitalization were variables recorded using a data abstraction checklist. Diseases were categorized using the World Health Organization’s International Classification of Diseases (ICD)-10 coding system. Data was analyzed using SPSS version 26.Results: Of the 2084 admissions over 1 year period, 767 (36.5%) had CVDs. The mean age of the study population was 53.4 ± 19.4 years with 55% females. Leading CVD admissions were stroke (35%), Venous thromboembolism (17%), rheumatic heart disease (RHD) (16.2%), ischemic heart disease (IHD) (11.3%) and cardiomyopathy (CMP) (7.4%). Heart Failure (HF) was found in 300 (39.1%) patients with structural heart disease. The structural cardiac lesions in those with HF were RHD (36%), CMP (19%), IHD (18%) and Cor pulmonale (9%). The in-hospital mortality of all CVD admissions was 13.2%. Of the total 101 CVD deaths most were because of stroke (n=37, 36.6%) followed by IHD (18.8%). Hypertension was the most frequent risk factor (28.8%).Conclusion: This study shows stroke to be the most common form of CVD and RHD to be the main reason for HF among hospital admissions in Northern Ethiopia, ACSH. Hypertension is the leading reported risk factor and hence the need to give high priority to hypertension and RHD control programs.Keywords: cardiovascular disease, admissions, outcomes, sub-Saharan Africa, Ethiopia

Abraha Hailu,1 Kibreab Gidey,1 Mohamedawel Mohamedniguss Ebrahim,2 Yonas Berhane,1 Teklay Gebrehawaria,1 Tesfay Hailemariam,1 Atakelti Negash,1 Habtamu Mesele,1 Tekleab Desta,1 Haylsh Tsegay,1 Mulubirhan Assefa Alemayohu,3 Alemayehu Bayray3 1Department of Internal Medicine, School of Medicine, Mekelle University - College of Health Sciences, Mekelle, Tigray, Ethiopia; 2Department of Biostatistics, School of Public Health, Mekelle University - College of Health Sciences, Mekelle, Tigray, Ethiopia; 3Department of Epidemiology, School of Public Health, Mekelle University - College of Health Sciences, Mekelle, Tigray, EthiopiaCorrespondence: Abraha Hailu Weldegerima, Department of Internal Medicine, Cardiology Unit, Mekelle University College of Health Sciences, Mekelle, Tigray, Ethiopia, Tel +251-91-141-3363, Email abrahahailu7@gmail.comBackground: Previous works show that noncommunicable diseases (NCDs) are on the rise in developing nations, including Ethiopia, in the background of preexisting communicable diseases (CDs). Data on hospital admission in sub-Saharan Africa are scarce, particularly prospectively collected, and hence this study was done to close the gap in acquiring accurate diagnosis usually faced with retrospective data. We aimed to describe medical admission patterns, their outcomes and determinants at Ayder Comprehensive Specialized Hospital (ACSH) in Tigray, Ethiopia.Methods: Data were collected prospectively from November 1, 2017 to October 31, 2018 for all medical admissions. Sociodemographic profiles, final diagnosis, outcome of hospitalization, and complications were recorded using a data-abstraction checklist. Data were analyzed using SPSS 26.Results: There were 2084 medical admissions over 1 year. Median age was 45 years (IQR 31). A majority (1107, 53.1%) were male. The age-group 25– 34 years had the most admissions (19.0%). NCDs constituted 68.2% of admissions. According to the International Classification of Diseases, patients were admitted most frequently due to disease of the circulatory system (36.7%) followed by infectious or parasitic diseases (24.1%). Of NCDs, cardiovascular diseases contributed to 53.9%, followed by cancers (8.6%). Of CDs, HIV/AIDS contributed to 31.3%, followed by tuberculosis (22.8%). A total of 1375 (66%) were discharged improved, while 311 (14.9%) died. On multivariate analysis, the in-hospital mortality was significantly higher (18.7%) for CDs than NCDs (13.2%, AOR 1.8, 95% CI 1.4– 2.3; P< 0.001). Bed occupancy of ACSH medical wards and ICU was 54%.Conclusion: NCDs were leading causes of medical admissions in ACSH. The presence of CDs and the development of complications were associated with higher risk of death. Attention should be given by policy-makers to curb the higher morbidity from NCDs and mortality from CDs.Keywords: medical admissions, mortality, Ayder, Ethiopia

Dietary diversity is a measure of food consumption that reflects household access to a variety of foods and is also a proxy for nutrient adequacy of the diet of individuals. Dietary diversity is useful because it is correlated with nutrient intake as well as various anthropometric measures in children. This study was carried out to assess the status of individual dietary diversity score (IDDS) of children under five years old (6-59 months) and its determinants in the Southern Zone of Tigray, Ethiopia. A survey of 320 households having children 6-59 months old was conducted using a cross-sectional descriptive study with both qualitative and quantitative study designs following the FAO guidelines for the dietary diversity and a semi-structured questionnaire for demographic and socio-economic factors. Data for the individual dietary diversity (IDDS) indicator were collected by asking the respondent (mother or caregiver) to recall the foods provided during the previous 24 hour period. Data analysis was conducted by using STATA 12 software. The mean IDDS in the study areas was found to be below four, which is the acceptable minimum level. The traditional diet of children was cereal dominated and low in micronutrient rich food groups in all the assessed Kebeles (1). Being a model (2) farmer, household's farmland endowment, female headship of the household, and experience in special meals preparation and participation in nutrition related training of household members were positively associated with dietary diversity of children in the study areas. In order to overcome the low IDDS of children in the areas and to improve their nutritional status through consumption of diverse foods, it is important to make agricultural production more nutrition sensitive especially on diversification of nutrient dense agricultural products. Key words: Agriculture-Nutrition linkage, dietary diversity, food group, micronutrients, 24-hour recall, INTRODUCTION Good nutrition and health are essential for child growth and development, body maintenance and protection from both infectious and non-communicable diseases in adult life. Adequate nutrition and a healthy [...]

Tesfay Gebreamlak Hailemariam,1 Girum Sebsibie Teshome2 1Ahadu Primary Hospital, Shire Town, Tigray, Ethiopia; 2School of Nursing and Midwifery, College of Medicine and Health Science, University of Rwanda, Kigali, RwandaCorrespondence: Tesfay Gebreamlak Hailemariam, Department of Pediatrics and Child health, Ahadu Primary Hospital, Post Box 4412, Shire Town, Tigray, Ethiopia, Tel +251914788713, Email tesfay.gebreamlak@yahoo.comBackground: Young child feeding practice is the important modifiable factor influencing the health of children. Optimal young child feeding practice is safe, promotes sensory and cognitive development and help to protect from common childhood illnesses.Objective: The purpose of this study was to assess young children feeding practices and its associated factors among mothers in Tigray Region, Ethiopia.Methods: A community-based cross-sectional study design was used. The study was conducted from March 8 to April 8, 2024, in selected zone of Tigray Regional state, Ethiopia. Data was collected from 600 mothers using structured interview questionnaire with Multi-stage sampling technique and kept on the Kobo Collect website. Data was analyzed by SPSS version 27. Frequency distributions, Bi-variate and Multivariate analysis was done and at P value < 0.05 considered as statistically significant.Results: The overall poor feeding practice was 59.2%. Separated mothers (AOR = 7.222, 95% CI (2.080, 25.079)), farmer mothers (AOR = 2.933, 95% CI (1.030, 8.354)), and unemployed fathers (AOR = 1.947, 95% CI (1.005, 3.770)) showed higher odds of poor feeding practices, respectively. Home deliveries (AOR = 8.454, 95% CI (4.100, 17.433)), cesarean sections (AOR = 7.454, 95% CI (3.100, 16.33)) and vacuum delivery, (AOR = 3.902, 95% CI (1.277, 11.925)) were also linked to higher odds of poor feeding practices, respectively. Regular postnatal care (AOR = 0.547, 95% CI (0.390, 0.768)), higher educational level of parents (AOR 0.356 95% CI (0.170, 0.746)) and refrigerator ownership (AOR = 0.589, 95% CI (0.418, 0.831)) have lower odds of poor feeding practices, respectively.Conclusion: The overall poor feeding practice was 59.2%. Separated mothers, farmer mothers, unemployed fathers, home deliveries, cesarean sections and vacuum delivery were associated with increased poor feeding practice. Higher educational level of parents, regular postnatal care and refrigerator ownership have prevention effect of poor feeding practice.Keywords: young children, feeding practice, Tigray, Ethiopia

Abstract Recent literature highlights the potential of animal pollinator-dependent (PD) crops in enhancing food and nutrition security, although there is a lack of detailed household-level estimates. In this study, we investigate the nutrient composition, productivity, and contribution of PD and pollinator-independent (PI) crops to household nutrition in four sub-Saharan African (SSA) countries. We also evaluate the impact of reallocating resources from PI crops to PD crops on nutrient deficiencies, utilizing nationally representative panel data from three waves and over 30,000 household-year observations. Our findings reveal that PD crops exhibit higher micronutrient content per unit, albeit with lower macronutrient content compared to PI crops. PI crops have higher yield of calories per hectare while PD crops have higher vitamin A yield per hectare. However, protein and iron yield for PD and PI crops varies across countries. PI crops predominantly contribute to macronutrients and iron, while PD crops significantly contribute to vitamin A production. Our econometric results demonstrate that increasing the cultivation of PD crops relative to PI crops reduces the prevalence of nutrient deficiencies and increases crop income without compromising macronutrients production. This suggests that greater investment in PD crop production can be an integral approach to achieving nutrition security in SSA.

Arguably the most popular remote-sensing products are classified images. However, there are no definitive procedures to assess classification accuracy that simultaneously consider resources available and field efforts. The explosive usage of unmanned aerial systems (UAS) in land surveys adds new challenges to classification assessment, as orthorectified images usually contain significant artifacts. This study aims to identify the optimal ratio between training and validation sample size within a supervised classification approach applied to UAS orthophotos. As a case study, we used a wetland area west of Portland, OR, USA, treated with various glyphosate formulations to control Phalaris arundinacea, commonly known as reed canary grass. A completely randomized design with five replications and six glyphosate formulations was used to assess P. arundinacea vigor following repeated herbicide applications. The change in P. arundinacea vitality was monitored with high-resolution four-band imagery acquired with a SlantRange 3PX camera installed on a DJI Matrice 210. The orthophotos created from images were produced with Pix4D, which was subsequently preprocessed with ERDAS Imagine 2020 to reduce the noise, shadows, and artifacts. All images were classified with the maximum likelihood classification algorithm. Simple random and stratified random sampling methods were applied to collect training and validation samples, evaluating eight ratios of training to validation samples to assess their classification accuracy. We found that increasing the training-to-validation sample size ratio enhances accuracy, with the 3:1 ratio being the most reliable in classifying P. arundinacea vigor. Our study provides evidence that image preprocessing and enhancement are essential for UAS-based imagery.

Gebrerufael Solomon Tsegay,1 Gebremedhin Berhe Gebregergs,2 Gidey Gomera Weleslassie,1 Teklehaimanot Tekle Hailemariam1 1Department of Physiotherapy, School of Medicine, College of Health Sciences and Ayder Comprehensive Specialized Hospital, Mekelle University, Mekelle, Tigray, Ethiopia; 2Department of Epidemiology, School of Public Health, College of Health Sciences, Mekelle University, Mekelle, Tigray, EthiopiaCorrespondence: Gebrerufael Solomon Tsegay, College of health Sciences, Mekelle University, P.O. Box 1871, Mekelle, Tigray, Ethiopia, Tel +251914041691, Email gebrerufael.solomon@mu.edu.etAbstract: There are contradicting findings regarding the effect of thoracic spine manipulation in decreasing pain and disability in patients with chronic mechanical neck pain. Hence, the purpose of this review was to evaluate the current evidence on the effectiveness of thoracic spine thrust manipulation in decreasing pain intensity, and neck disability among subjects with chronic mechanical neck pain. We made a comprehensive search of literature published between 2010 and 2020 from the electronic databases of PubMed, Cumulative Index to Nursing and Allied Health Literature (CINAHL), Cochrane Library, and Physiotherapy Evidence Database (PEDro). We adhered to Preferred Reporting Items for Systematic reviews and Meta-analysis (PRISMA). The methodological quality was assessed by the PEDro scale, and the level of evidence rated by Grading of Recommendations Assessment, Development, and Evaluation (GRADE) software. Finally, a meta-analysis was done using RevMan 5.3 with a random-effects model to calculate the mean difference (MD) and 95% confidence intervals for pain and disability. Eight eligible randomized controlled trials were identified with a total of 457 participants. The quality assessment of the included studies was found to be fair in quality (mean PEDro score of 6.63/10). The overall GRADE of the review showed low to moderate level of evidence. Effect size estimates of the studies showed modest differences in pain reduction, as it revealed a significant effect in Visual Analog Scale ((VAS) 0– 100mm) (MD − 12.46; 95% CI: − 17.29, − 7.64), and Pain Numeric Rating Scale ((PNRS) 0– 10 pts) (MD − 0.8; 95% CI: − 1.60, − 0.10). The thoracic manipulation also resulted in a significant effect in decreasing neck disability, in which the mean difference in Neck disability index (NDI) showed (MD − 6.46; 95% CI: − 10.43 − 2.50). This review suggested that thoracic spine manipulation was effective in reducing pain and neck disability in all adults with chronic mechanical neck pain compared to other interventions.Keywords: chronic neck pain, manual therapy, mechanical neck pain, neck pain, spinal manipulation and thoracic manipulation

Tree height is a crucial variable in forestry science. In the current study, an accurate height prediction model for Juniperus procera Hochst. ex Endl. trees were developed, using a nonlinear mixed-effects modeling approach on 1215 observations from 101 randomly established plots in the Chilimo Dry Afromontane Forest in Ethiopia. After comparing 14 nonlinear models, the most appropriate base model was selected and expanded as a mixed-effects model, using the sample plot as a grouping factor, and adding stand-level variables to increase the model’s prediction ability. Using a completely independent dataset of observations, the best sampling alternative for calibration was determined using goodness-of-fit criteria. Our findings revealed that the Michaelis–Menten model outperformed the other models, while the expansion to the mixed-effects model significantly improved the height prediction. On the other hand, incorporating the quadratic mean diameter and the stem density slightly improved the model’s prediction ability. The fixed-effects of the selected model can also be used to predict the mean height of Juniperus procera trees as a marginal solution. The calibration response revealed that a systematic selection of the three largest-diameter trees at the plot level is the most effective for random effect estimation across new plots or stands.

Understanding the human-forest relationship is key to sustainable development and forest management. The increase of agricultural land expansion mainly focuses on satisfying the competing interest amongst stakeholders and rural households, which neglects the impact of forest losses. The study looked at rural households' farmland expansion dynamics in Adaba (Oromia Region) and Adiyo, located in the Southern Nations, Nationalities, and People's Region (SNNR) of Ethiopia. Thus, the study identified the role of push and pull factors in rural households’ agricultural land expansion using primary and secondary data. We found that the livelihood of the rural households significantly impacts farming land expansion at the expense of forest resources (deforestation). In addition, we noticed that the increase in household size and living expenses, followed by the need for income diversification, are amongst the major drivers directly causing farmland expansion. In contrast, economic pressure was generally found to be the key push factor for most rural households to expand their farming land. Hence, some farmers were “pulled” by the current economic return and market attraction of timber due to the booming market demand, which was seen as one of the reasons for forest loss. These findings also reported the negative impacts of agricultural expansion on biodiversity and the environment, which need alternative agricultural development pathways.

Aim of study: To develop and cross-validate simultaneous modeling systems for estimating components and total tree aboveground biomass and carbon of Litsea glutinosa in an agroforestry model with cassava. Area of study: In the Central Highlands of Vietnam, the agroforestry model widely planted on fallow land of ethnic minorities is a mixture of 65% L. glutinosa in combination with 35% cassava (Manihot esculenta). Material and methods: Twenty-two 300-m2 circular sample plots were located, representing the range of tree age, plantation density, and a 6-7 year rotation cycle. In each sample plot, one selected tree with a diameter at breast height equal to the plot quadratic mean diameter was destructively sampled. The relationships among tree aboveground biomass and carbon (AGB/AGC) and their components with dendrometric variables diameter, height, age, and crown area were examined using factor analysis. To fit systems of equations for AGB/AGC and their components, we compared two methods: weighted nonlinear least-squares (WNLS) and weighted nonlinear seemingly unrelated regression (WNSUR). Main results: The results of the leave-one-out cross-validation showed that the simultaneous WNSUR approach to modeling systems of four tree components, total biomass, and carbon provided better results than independent WNLS models. Research highlights: The simultaneous WNSUR modeling system provided improved and reliable estimates of tree components, total biomass, and carbon for L. glutinosa in an agroforestry model with cassava compared to independently fitted WNLS models.

Abstract We developed spatial Bayesian hierarchical models to assess potential climate change impacts on suitable habitat for five important tree species in the Pacific northwestern United States (California, Oregon, and Washington). Individual‐species models were fit with presence–absence data from forest inventory field plots and spatial relationships were specified through a conditional autoregressive model. This modeling approach allowed us to visualize uncertainty in response curves, map current and future prediction uncertainty, and provide interval estimates for change. Upward elevational or northward latitudinal shifts in climatically suitable habitat were projected for all species. Climate change impacts were the most damaging for noble fir (Abies procera), for which 79%–100% of the current range was projected to become climatically unsuitable by the 2080s. Although coastal Douglas‐fir (Pseudotsuga menziesii var. menziesii) has been projected by others to gain habitat in Canada, within our study area we projected a net loss of climatically suitable habitat (ca. 8000–31,400 km2) under three of four future climate scenarios. A net loss in habitat was also projected for Oregon white oak (Quercus garryana) under three of four scenarios, with 40%–60% of the current range becoming unsuitable. Although there was no net loss of habitat for forest land blue oak under any scenario, other factors like competition may inhibit blue oak (Quercus douglasii) and white oak from occupying areas projected to increase in climatic suitability. Additionally, between 13% and 32% of blue oak's current range was projected to become unsuitable; some of these areas aligned with dieback following the 2012–2015 California drought, which our data set predates. Unlike the other four species, we projected a 17%–25% increase in climatically suitable habitat for California black oak (Quercus kelloggii), although 1%–20% of the current range was still projected to become unsuitable. Our findings indicate that, although some species will face more pressure in tracking climatically suitable habitat than others, climate change will impact the location of suitable habitat for many species.

Forest management decisions affect carbon stock and rates of sequestration. One subject of debate is the rotation age that will optimize sequestration over extended periods. Some argue that shorter rotations facilitate greater sequestration rates due to the accelerated growth rates of younger trees compared to mature or old-growth trees. Others maintain that frequent harvesting will not allow forest carbon to rebound after each subsequent rotation, and thus more extended periods between clearcutting is the superior choice. These contrasting viewpoints are mirrored regarding the impact of thinning treatments, in that either thinning will enhance forest carbon uptake by facilitating improved and sustained r growth of residual trees or removing any above-ground biomass will outweigh the yields. This study aims to compare the different suites of management decisions and identify practical combinations of rotation ages and thinning applications that will optimize carbon sequestration while meeting other objectives over a 240-year projection timeframe. Stand development under different harvest rotations and thinning specifications was modeled using a Forest Vegetation Simulator (FVS). We found that site productivity was the primary determinant in stand-above-ground carbon dynamics under various management scenarios. Thus, the optimal rotation age/thinning treatment combinations differed between site classes. High productivity stands were estimated to sequester the most above-ground live carbon with 60-year rotations with a low-intensity thin at age 40. Moderately productive stands performed the best with 80-year rotations when two low-intensity thinning treatments were applied between harvests. For high and moderate productivity stands, estimates of gross carbon increased when two low or moderate-intensity thinning treatments were applied within 80- or 120-year rotations. High-intensity thinning treatments reduced total carbon sequestered over the 240-year projection timeframe for all productivity levels and rotation ages, except for low productivity stands under 120-year rotations.

We developed a system for modeling the growth and yield of planted teak (Tectona grandis L.f.) for small diameter products under varying management regimes in the tropical Central Highlands of Viet Nam. We compared an independent and simultaneous system of models to predict dominant height (Ho), quadratic mean diameter (Dg), averaged tree height (Hg) with Dg, and mean tree volume (V) versus stand age (A). In addition, the model system performance with and without site index (SI) and stand density (N) as covariates were compared using K-fold cross-validation. The best modeling system was obtained with the simultaneously fit models that included SI and N and were in the form of: Dg=Dm/(1 + a × exp(-b × A)) × exp[e1 × (SI– 15) + e2/1000 × (N – 722)]; Hg=Hm × exp(-a × exp(-b × A)) × exp[e1 × (SI– 15) + e2/1000 × (N – 722)]; and V=π4×104Dg2×Hg×0.45; where Dm, Hm, a, b, e1and e2 were the parameters to be estimated. These models will help predict the growth and yield of teak planted for different planting schemes, includings monoculture, agroforestry, and forest enrichment planting in this region.

Rapid climate change over the coming century will impact suitable habitat for many tree species. In response to these changes in climate, areas that become unsuitable will see higher mortality and lower growth and recruitment. Therefore, early detection of demographic trends is critical for effective forest management. Recent 10-year remeasurement data from the United States (US) Department of Agriculture (USDA) Forest Service’s Forest Inventory and Analysis (FIA) Program’s national annual inventory of forest land provides an ideal data set for analyzing such trends over large areas. However, failure to distinguish between areas of future habitat contraction and expansion or persistence when estimating demographic trends may mask species’ shifts. We used remeasurement data to compare observed tree demographic rates with projected impacts of climate change for five important tree species in the Pacific Northwest. Projected impacts were based on spatial-Bayesian hierarchical models of species distributions, which were used to project areas where habitat would persist (remain climatically suitable), expand (become suitable), or contract (become unsuitable) under four future climate scenarios for the 2080s. We compared estimates of mortality and net-growth between these areas of shifting suitability and a naïve division of habitat based on elevation and latitude. Within these regions, we assessed the sustainability of mortality and determined that observational data suggest that climate change impacts were already being felt in some areas by some species. While there is an extensive literature on bioclimatic species distribution models, this work demonstrates they can be adapted to the practical problem of detecting early climate-related trends using national forest inventory data. Of the species examined, California black oak (Quercus kelloggii) had the most notable instances of observed data suggesting population declines in the core of its current range.

Gebremedhin Haile, Teklehaimanot Tekle Hailemariam, Tsiwaye Gebreyesus Haile Department of Physiotherapy, School of Medicine, College of Health Sciences and Ayder Comprehensive Specialized Hospital, Mekelle University, Mekelle, EthiopiaCorrespondence: Gebremedhin Haile Email gebremedhinhaile88@gmail.comAbstract: Non-specific chronic low back pain is the most common self-reported kind of musculoskeletal ache associated with substantial health and socioeconomic problem and responsible for most years lived with a disability as compared with any other medical condition. So treating chronic non-specific low back pain is one of the main problems faced among physical therapists in the rehabilitation area. The effects of ultrasound for patients with non-specific chronic low back pain remain unknown, however it is commonly used to treat clients with low back pain in rehabilitation setting. Therefore, the main aim of this review was to evaluate the up-to-date confirmation in the efficacy of ultrasound therapy on the treatment of non-specific chronic low back pain. A comprehensive search of four computerized electronic databases was performed to identify the effectiveness of ultrasound therapy on the management of chronic non-specific low back pain. Searching was done through the Google Scholar, PubMed, ScienceDirect, and Physiotherapy Evidence Databases (PEDro) and reported using preferred reporting items for systematic reviews and meta-analyses guidelines. The qualities of articles were appraised by the PEDro scale. The primary outcome measure visual analog scale was used. Six randomized clinical trials with a total sample size of 699 patients from the electronic database published in English were identified. In this review, the effect of UST in five articles was statically significant in reducing the visual analog scale (p< 0.05) score. So this systematic review found ultrasound therapy could be an alternative treatment to reduce the intensity of pain in subjects with non-specific chronic LBP.Keywords: back pain, biophysical agents, effectiveness, low back pain, rehabilitation and ultrasound therapy

Gender-based intra-household resource distributions constitute an important role in technology adoption-related decisions. Using a non-unitary household model and fitting an empirical adoption model for men and women individually and jointly, we examine how gender-based ownership, management and control of resources are correlated with adoptions of a portfolio of climate change adaptation strategies. Our analysis is based on recent household survey data with detailed household plot-level information about adaptation strategies, plot ownership, farm management decisions, and the right to control outputs from the plots among spouses within the household. The observed patterns of women’s ownership, management and economic rights of land suggest that all are important aspects of women’s bargaining power. We found compelling evidence regarding the overlapping effects of the different gender-linked-land-based rights on the adoption of a portfolio of adaptation strategies. The empirical findings emphasize the importance of incorporating gender-based intra-household interactions and joint resource ownership and decision making in climate change adaptation.

This paper examines how smallholder farmers are differentially affected by agricultural expansion in their communities and landscapes. Contributing to the debate on intensification versus expansion, and implications for sustainability, we employ mixed methods research with smallholders in four communities in Ghana and Ethiopia to explore the impact of agricultural expansion among different social groups (men, women, the young, older, the poor and rich community members) across different timescales. Surveys were conducted with 200 households per community on livelihoods, land management practices and involvement in agricultural expansion. Focus group discussions were conducted with different categories of farming households to support the initial surveys.Results indicate that agricultural expansion may have both negative and positive effects on livelihood outcomes depending on timescale, participation in expansion, choices of other households, and individual roles in the household. Short-term wins are likely to result in losses in the long-term due to changing conditions. Households that have not expanded may lose benefits such as food and income from nature, due to the externalities resulting from the activities of expanders.

Stands are the primary unit for tactical and operational forest planning. Forest managers can use remote-sensing-based forest inventories to precisely estimate attributes of interest at the stand scale. However, remote-sensing-based inventories typically rely on models relating remote-sensing information to forest attributes for fixed area plots with accurate coordinates. The collection of that kind of ground data is expensive and time-consuming. Furthermore, remote-sensing-based inventories provide precise descriptions of the forest when the remote-sensing data were collected, but they inevitably become outdated as the forest evolves. Fay–Herriot (FH), models can be used with ground information from variable radius plots even if the plot coordinates are unknown. Thus, they provide an efficient way to update old remote-sensing-based inventories or develop new ones when fixed radius plots are unavailable. In addition, FH models are well described in the small-area estimation literature and allow reporting estimation uncertainties, which is key to incorporating quality controls to remote-sensing inventories. We compared two scenarios developed in the Willamette National Forest, OR, United States, to produce stand-level estimates of above-ground biomass (AGB), and Volume (V) for natural and managed stands. The first, Case 1, was developed using auxiliary data from a recent lidar acquisition. The second, Case 2, was developed to update an old remote-sensing-based inventory. Results showed that FH models allowed for improvements in efficiency with respect to direct stand-level estimates obtained using only field data for both case scenarios and both typologies of stands. Average improvements in efficiency in natural stands were 37.36% for AGB and 33.10% for Volume for FH models from Case 1 and 20.19% for AGB and 19.25 for V for Case 2. For managed stands, average improvements for Case 1 were 2.29 and 19.92% for AGB and V, respectively, and for Case 2, improvements were 15.55% for AGB and 16.05% for V.

Quantifying above-ground biomass changes, ΔAGB, is key for understanding carbon dynamics. National Forest Inventories, NFIs, aims at providing precise estimates of ΔAGB relying on model-assisted estimators that incorporate auxiliary information to reduce uncertainty. Poststratification estimators, PS, are commonly used for this task. Recently proposed endogenous poststratification, EPS, methods have the potential to improve the precision of PS estimates of ΔAGB. Using the state of Oregon, USA, as a testing area, we developed a formal comparison between three EPS methods, traditional PS estimators used in the region, and the Horvitz-Thompson, HT, estimator. Results showed that gains in performance with respect to the HT estimator were 9.71% to 19.22% larger for EPS than for PS. Furthermore, EPS methods easily accommodated a large number of auxiliary variables, and the inclusion of independent predictions of ΔAGB as an additional auxiliary variable resulted in further gains in performance.

Currently, quantifying phenology at landscape to regional scales is not feasible with field data or near-surface sensors. Consequently, the spatial and temporal complexity of phenology has been assessed using satellite-based estimates (land surface phenology, LSP). While estimates from Moderate Resolution Imaging Spectroradiometer (MODIS) capture intraannual patterns of phenology, they have relatively low spatial resolution. Estimates from sensors like Landsat capture finer spatial detail but are often limited by Landsat’s temporal resolution. We implemented a spatio-temporal image fusion method on the Google Earth Engine (GEE) platform and used the resulting dense time series of images to estimate intraannual LSP at 30-meter resolution. We utilized Landsat 8 surface reflectance and MODIS NBAR (Nadir BRDF-Adjusted Reflectance; MCD43A4) images from 2016 and 2017 in the interior Pacific Northwest of the United States. Images predicted from the GEE image fusion algorithm were evaluated with true Landsat observations and compared with the accuracy achieved by executing the original ESTARFM algorithm. Excluding snow and cloud obscured observations, the algorithm produced approximately 215 observations per 30-meter pixel in 2017. Root mean squared prediction error (RMSPE) of Normalized Difference Vegetation Index (NDVI) for the GEE predicted images ranged from 0.032 to 0.066, and the RMSPE for the original ESTARFM predicted images from the ranged from 0.027 to 0.064. Phenometric estimates were evaluated with near-surface sensors (PhenoCams) in shrubland, conifer, and agricultural sites and field observations of phenology in grassland, open-pine, and mixed-conifer sites. Although phenometric estimates were dissimilar at all PhenoCam sites, the general temporal pattern of the GEE image fusion and PhenoCam time series was often similar. The start of season derived from the GEE image fusion time series had closer correspondence to the PhenoCam-derived start of season at the shrubland site (13 days) than the agriculture and conifer sites. The end of season was closest at one of the conifer sites and the agriculture site (22 and 31 days, respectively). Trends of some of the field-based phenology observations aligned with phenometrics estimated from the image fusion time series. At the grassland and open-pine field sites, the phenometrics from GEE image fusion were associated with phenophase trends of dominant plant functional types. Though characterizing LSP within the interior Pacific Northwest remains a challenge, this study demonstrates that image fusion implemented in GEE can produce a densified time series capable of capturing seasonal trends in NDVI related to vegetation phenology, which can be used to estimate intraannual phenometrics.

Commercial thinning (CT) is an important tool that meets a diverse set of forest management objectives, including the generation of intermediate revenue, promotion of regeneration tree growth, and the modification of vertical and horizontal fuel structure for wildfire mitigation. Using a set of 653 fixed radius plots and a coincident LiDAR acquisition, we compared three different classification methods to predict CT eligibility for Douglas-fir (Pseudotsuga menziesii) stands in southwestern Oregon. We assessed logistic regression (LOG), random forests (RF), XGBoost (XGB) to classify areas eligible for CT operations based on three structural attributes, volume (VOL), basal area (BA) and Curtis’ Relative Density index (CRD). We also assessed their predictive performance and reliability via cross-validation at different sample sizes. We used the area under the receiver operating characteristic curve (AUC) as our primary performance measure. Estimated AUCs were 0.86, 0.77 and 0.68 for XGB, RF and LOG, respectively. We observed that classifier performance stabilized between sample sizes of 200 and 300 plots, which suggests that the development of a CT eligibility classifier is appropriate for operational applications of the method with similar sample sizes and large area attributes.

Purpose. Chronic kidney disease is a challenging disease and global public health problem. The burden of chronic kidney disease and hemodialysis is increasing in Ethiopia, but few studies explored the lived experiences of chronic kidney disease patients receiving hemodialysis. This study explored the lived experiences of chronic kidney disease patients receiving hemodialysis, in the Felege Hiwot Comprehensive Specialized Hospital, Bahir Dar City, Northwest Ethiopia, 2019. Methods. A phenomenological study design was conducted with 12 chronic kidney disease patients receiving hemodialysis between September 1 and October 30, 2019. A purposive sampling technique was used to select participants, and a semistructured in-depth interview guide was used to collect the data. The investigators audio-taped the interviews and then transcribed them verbatim. Finally, the transcribed data were imported to Atlas.ti™-7 software for coding, and then, thematic analysis was done. Transferability, dependability, credibility, and conformability were embedded to ensure data quality. Results. In this study, six major themes were emerged: (1) the seriousness of the disease, (2) challenges to get hemodialysis, (3) financial constraint, (4) restricted life, (5) feeling of dependency, and (6) psychological impacts. Conclusion. The restrictive nature of the disease affects a participant’s financial status which makes it challenging to obtain the service and increases feelings of dependency. These circumstances impact the psychology of the participants. We would recommend that every patient with hemodialysis needs social and psychological support. We would also recommend the need to extend the study to other areas of the country to confirm or disconfirm the findings.

Abstract Background Volume and taper equations are essential for obtaining estimates of total and merchantable stem volume. Taper functions provide advantages to merchantable volume equations because they estimate diameter inside or outside bark at specific heights on the stem, enabling the estimation of total and merchantable stem volume, volume of individual logs, and a height at a given diameter. Methods Using data collected from 1218 trees (1093 Douglas-fir (Pseudotsuga menziesii (Mirbel) Franco) and 125 western hemlock (Tsuga heterophylla)), we evaluated the performance of one simple polynomial function and four variable-exponent taper functions in predicting upper stem diameter. Sample trees were collected from different parts of the states of Oregon, Washington, and California. We compared inside-bark volume estimates obtained from the selected taper equation with estimates obtained from a simple logarithmic volume equation for the data obtained in this study and the equations used by the Forest Inventory and Analysis program in the Pacific Northwest (FIA-PNW) in the state of California and western half of the states of Oregon and Washington. Results Variable exponent taper equations were generally better than the simple polynomial taper equations. The FIA-PNW volume equations performed fairly well but volume equations with fewer parameters fitted in this study provided comparable results. The RMSE obtained from taper-based volume estimates were also comparable with the RMSE of the FIA-PNW volume equations for Douglas-fir and western hemlock trees respectively. Conclusions The taper equations fitted in this study provide added benefit to the users over the FIA-PNW volume equations by enabling the users to predict diameter at any height, height to a given diameter, and merchantable volume in addition to cubic volume including top and stump (CVTS) of Douglas-fir and western hemlock trees in the Pacific Northwest. The findings of this study also give more confidence to the users of FIA-PNW volume equations.

Airborne laser scanning (ALS) acquisitions provide piecemeal coverage across the western US, as collections are organized by local managers of individual project areas. In this study, we analyze different factors that can contribute to developing a regional strategy to use information from completed ALS data acquisitions and develop maps of multiple forest attributes in new ALS project areas in a rapid manner. This study is located in Oregon, USA, and analyzes six forest structural attributes for differences between: (1) synthetic (i.e., not-calibrated), and calibrated predictions, (2) parametric linear and semiparametric models, and (3) models developed with predictors computed for point clouds enclosed in the areas where field measurements were taken, i.e., “point-cloud predictors”, and models developed using predictors extracted from pre-rasterized layers, i.e., “rasterized predictors”. Forest structural attributes under consideration are aboveground biomass, downed woody biomass, canopy bulk density, canopy height, canopy base height, and canopy fuel load. Results from our study indicate that semiparametric models perform better than parametric models if no calibration is performed. However, the effect of the calibration is substantial in reducing the bias of parametric models but minimal for the semiparametric models and, once calibrations are performed, differences between parametric and semiparametric models become negligible for all responses. In addition, minimal differences between models using point-cloud predictors and models using rasterized predictors were found. We conclude that the approach that applies semiparametric models and rasterized predictors, which represents the easiest workflow and leads to the most rapid results, is justified with little loss in accuracy or precision even if no calibration is performed.

The use of individual tree detection methods to support forest management inventories has been a research topic for over two decades, but a formal assessment of these methods to produce stand-level and region-level predictions of forest attributes and measures of error is lacking. We employed model-based estimation methods in conjunction with the semi-individual tree crown approach (s-ITC) to produce predictions and measures of error for tree volume (VOL), basal area (BA), stem density (DEN), and quadratic mean diameter (QMD) at the scale of forest stands and the entire study region. We compared the s-ITC approach against the area-based approach (ABA) for predictions of region-level and stand-level attributes via model-based root mean squared errors (RMSEs). The study was conducted at the Panther Creek watershed in Oregon, USA using a set of 78 field plots and aerial lidar information. For region-level attributes, s-ITC RMSEs demonstrated changes between −31% and 17% relative to ABA models. At the stand level, median s-ITC RMSEs generally increased, with changes between −29% and 414% relative to ABA models, but demonstrated important reductions in stands where segmentation provided large increases in sample size and was less prone to extrapolation than ABA models. The ABA demonstrated smaller RMSEs in stands without sampled population units for all variables. Our findings motivate further research into niche applications where s-ITC models may consistently outperform ABA models.

Forest inventories require estimates and measures of uncertainty for subpopulations such as management units. These units often times hold a small sample size, so they should be regarded as small areas. When auxiliary information is available, different small area estimation methods have been proposed to obtain reliable estimates for small areas. Unit level empirical best linear unbiased predictors (EBLUP) based on plot or grid unit level models have been studied more thoroughly than area level EBLUPs, where the modelling occurs at the management unit scale. Area level EBLUPs do not require a precise plot positioning and allow the use of variable radius plots, thus reducing fieldwork costs. However, their performance has not been examined thoroughly. We compared unit level and area level EBLUPs, using LiDAR auxiliary information collected for inventorying 98,104 ha coastal coniferous forest. Unit level models were consistently more accurate than area level EBLUPs, and area level EBLUPs were consistently more accurate than field estimates except for large management units that held a large sample. For stand density, volume, basal area, quadratic mean diameter, mean height and Lorey's height, root mean squared errors (rmses) of estimates obtained using area level EBLUPs were, on average, 1.43, 2.83, 2.09, 1.40, 1.32 and 1.64 times larger than those based on unit level estimates, respectively. Similarly, direct field estimates had rmses that were, on average, 1.37, 1.45, 1.17, 1.17, 1.26, and 1.38 times larger than rmses of area level EBLUPs. Therefore, area level models can lead to substantial gains in accuracy compared to direct estimates, and unit level models lead to very important gains in accuracy compared to area level models, potentially justifying the additional costs of obtaining accurate field plot coordinates.

Accurate estimates of growth and structural changes are key for forest management tasks such as determination of optimal rotation times, optimal rotation times, site indices and for identifying areas experiencing difficulties to regenerate. Estimation of structural changes, especially for biomass, is also key to quantify greenhouse gas (GHG) emissions/sequestration. We compared two different modeling strategies to estimate changes in V, BA and B, at three different spatial aggregation levels using auxiliary information from two light detection and ranging (LiDAR) flights. The study area is Blacks Mountains Experimental Forest, a ponderosa pine dominated forest in Northern California for which two LiDAR acquisitions separated by six years were available. Analyzed strategies consisted of (1) directly modeling the observed changes as a function of the LiDAR auxiliary information ( δ -modeling method) and (2) modeling V, BA and B at two different points in time, including a term to account for the temporal correlation, and then computing the changes as the difference between the predicted values of V, BA and B for time two and time one. We analyzed predictions and measures of uncertainty at three different level of aggregation (i.e., pixels, stands or compartments and the entire study area). Results showed that changes were very weakly correlated with the LiDAR auxiliary information. Both modeling alternatives provided similar results with a better performance of the δ -modeling for the entire study area; however, this method also showed some inconsistencies and seemed to be very prone to extrapolation problems. The y -modeling method, which seems to be less prone to extrapolation problems, allows obtaining more outputs that are flexible and can outperform the δ -modeling method at the stand level. The weak correlation between changes in structural attributes and LiDAR auxiliary information indicates that pixel-level maps have very large uncertainties and estimation of change clearly requires some degree of spatial aggregation; additionally, in similar environments, it might be necessary to increase the time lapse between LiDAR acquisitions to obtain reliable estimates of change.

Bamboo forests play an important role in achieving the objectives of the United Nations program on Reducing Emission from Deforestation and Forest Degradation. We developed and validated a modeling system that simultaneously estimate aboveground biomass and its components for a common bamboo species (Bambusa procera A. Chev. and A. Camus) in tropical forests. Eighty-three bamboo culms were destructively sampled from seventeen 100 m2 sample plots located in different parts of the Central Highlands in Viet Nam to obtain total plant aboveground biomass (AGB) and its components. We examined the performance of weighted nonlinear models fit by maximum likelihood and weighted nonlinear seemingly unrelated regression fit by generalized least squares for predicting bamboo biomass. The simultaneous estimation of AGB and its components produced higher reliability than the models of components and total developed separately. With a large number of bamboo species, it may not be feasible to develop species- specific biomass models, hence genus-specific allometric models may be considered.

Using lidar for large-scale forest management can improve operational and management decisions. Using multi-temporal lidar sampling and remeasured field inventory data collected from 78 plots in the Panther Creek Watershed, Oregon, USA, we evaluated the performance of different fixed and mixed models in estimating change in aboveground biomass ( ∆ AGB ) and cubic volume including top and stump ( ∆ CVTS ) over a five-year period. Actual values of CVTS and AGB were obtained using newly fitted volume and biomass equations or the equations used by the Pacific Northwest unit of the Forest Inventory and Analysis program. Estimates of change based on fixed and mixed-effect linear models were more accurate than change estimates based on differences in LIDAR-based estimates. This may have been due to the compounding of errors in LIDAR-based estimates over the two time periods. Models used to predict volume and biomass at a given time were, however, more precise than the models used to predict change. Models used to estimate ∆ CVTS were not as accurate as the models employed to estimate ∆ AGB . Final models had cross-validation root mean squared errors as low as 40.90% for ∆ AGB and 54.36% for ∆ CVTS .

Background Depending on tree and site characteristics crown biomass accounts for a significant portion of the total aboveground biomass in the tree. Crown biomass estimation is useful for different purposes including evaluating the economic feasibility of crown utilization for energy production or forest products, fuel load assessments and fire management strategies, and wildfire modeling. However, crown biomass is difficult to predict because of the variability within and among species and sites. Thus the allometric equations used for predicting crown biomass should be based on data collected with precise and unbiased sampling strategies. In this study, we evaluate the performance different sampling strategies to estimate crown biomass and to evaluate the effect of sample size in estimating crown biomass. Methods Using data collected from 20 destructively sampled trees, we evaluated 11 different sampling strategies using six evaluation statistics: bias, relative bias, root mean square error (RMSE), relative RMSE, amount of biomass sampled, and relative biomass sampled. We also evaluated the performance of the selected sampling strategies when different numbers of branches (3, 6, 9, and 12) are selected from each tree. Tree specific log linear model with branch diameter and branch length as covariates was used to obtain individual branch biomass. Results Compared to all other methods stratified sampling with probability proportional to size estimation technique produced better results when three or six branches per tree were sampled. However, the systematic sampling with ratio estimation technique was the best when at least nine branches per tree were sampled. Under the stratified sampling strategy, selecting unequal number of branches per stratum produced approximately similar results to simple random sampling, but it further decreased RMSE when information on branch diameter is used in the design and estimation phases. Conclusions Use of auxiliary information in design or estimation phase reduces the RMSE produced by a sampling strategy. However, this is attained by having to sample larger amount of biomass. Based on our finding we would recommend sampling nine branches per tree to be reasonably efficient and limit the amount of fieldwork.

While lidar-based forest inventory methods have been widely demonstrated, performances of methods to predict tree diameters with airborne lidar (lidar) are not well understood. One cause for this is that the performance metrics typically used in studies for prediction of diameters can be difficult to interpret, and may not support comparative inferences between sampling designs and study areas. To help with this problem we propose two indices and use them to evaluate a variety of lidar and k nearest neighbor (k-NN) strategies for prediction of tree diameter distributions. The indices are based on the coefficient of determination (R2), and root mean square deviation (RMSD). Both of the indices are highly interpretable, and the RMSD-based index facilitates comparisons with alternative (non-lidar) inventory strategies, and with projects in other regions. K-NN diameter distribution prediction strategies were examined using auxiliary lidar for 190 training plots distribute across the 800 km2 Savannah River Site in South Carolina, USA. We evaluate the performance of k-NN with respect to distance metrics, number of neighbors, predictor sets, and response sets. K-NN and lidar explained 80% of variability in diameters, and Mahalanobis distance with k = 3 neighbors performed best according to a number of criteria.

This contribution complements Forest Ecosystems’ Thematic Series on “Forest Observational Studies”. We provide essential clarification regarding the definition and purpose of long-term field studies, review some of the extensive literature and discuss different approaches to collecting field data. We also describe two newly established forest observational networks that serve to illustrate the scope and diversity of forest field studies. The first is a large-scale network of forest observational studies in prominent natural forest ecosystems in China. The second example demonstrates observational studies in mixed and uneven-aged pine-oak forests which are selectively managed by local communities in Mexico. We summarize the potential for analysing and modeling forest ecosystems within interdisciplinary projects and provide argumentation in favour of long-term institutional commitment to maintaining forest observational field studies.

Forest surveys provide critical information for many diverse interests. Data are often collected from samples, and from these samples, maps of resources and estimates of aerial totals or averages are required. In this paper, two approaches for mapping and estimating totals; the spatial linear model (SLM) and k-NN (k-Nearest Neighbor) are compared, theoretically, through simulations, and as applied to real forestry data. While both methods have desirable properties, a review shows that the SLM has prediction optimality properties, and can be quite robust. Simulations of artificial populations and resamplings of real forestry data show that the SLM has smaller empirical root-mean-squared prediction errors (RMSPE) for a wide variety of data types, with generally less bias and better interval coverage than k-NN. These patterns held for both point predictions and for population totals or averages, with the SLM reducing RMSPE from 9% to 67% over some popular k-NN methods, with SLM also more robust to spatially imbalanced sampling. Estimating prediction standard errors remains a problem for k-NN predictors, despite recent attempts using model-based methods. Our conclusions are that the SLM should generally be used rather than k-NN if the goal is accurate mapping or estimation of population totals or averages.

There are few allometric equations available for dipterocarp forests, despite the fact that this forest type covers extensive areas in tropical Southeast Asia. This study aims to develop a set of equations to estimate tree aboveground biomass (AGB) in dipterocarp forests in Vietnam and to validate and compare their predictive performance with allometric equations used for dipterocarps in Indonesia and pantropical areas. Diameter at breast height (DBH), total tree height (H), and wood density (WD) were used as input variables of the nonlinear weighted least square models. Akaike information criterion (AIC) and residual plots were used to select the best models; while percent bias, root mean square percentage error, and mean absolute percent error were used to compare their performance to published models. For mixed-species, the best equation was AGB = 0.06203 × DBH 2.26430 × H 0.51415 × WD 0.79456 . When applied to a random independent validation dataset, the predicted values from the generic equations and the dipterocarp equations in Indonesia overestimated the AGB for different sites, indicating the need for region-specific equations. At the genus level, the selected equations were AGB = 0.03713 × DBH 2.73813 and AGB = 0.07483 × DBH 2.54496 for two genera, Dipterocarpus and Shorea, respectively, in Vietnam. Compared to the mixed-species equations, the genus-specific equations improved the accuracy of the AGB estimates. Additionally, the genus-specific equations showed no significant differences in predictive performance in different regions (e.g., Indonesia, Vietnam) of Southeast Asia.

Biomass estimates are required for reporting carbon, assessing feedstock availability, and assessing forest fire threat. We developed diameter- and height-based biomass equations for Western hemlock (Tsuga heterophylla (Raf.) Sarg.) and red alder (Alnus rubra Bong.) trees in Western Oregon. A system of component biomass equations was fitted simultaneously with a constrained seemingly unrelated regression. Additionally, a linear model that predicts total aboveground biomass as a function of DBH and height was also fitted. The predicted total biomass was then apportioned to different components according to the predicted proportions from beta, Dirichlet, and multinomial log-linear regressions. Accuracy of these methods differed between species with higher root mean squared error (RMSE) being produced in red alder trees. Within species, the accuracy of the equation for bole biomass was better than the equations for other components. None of these methods stood out as a clear winner, but the multinomial log-linear regression produced marginally better results compared to other methods in terms of RMSE, except for Western hemlock bark biomass and red alder bole and branch biomass. The equations based on a seemingly unrelated regression provided lower RMSEs for those species-component combinations.

Alternative treatments for visceral leishmaniasis (VL) are required in East Africa. Paromomycin sulphate (PM) has been shown to be efficacious for VL treatment in India.A multi-centre randomized-controlled trial (RCT) to compare efficacy and safety of PM (20 mg/kg/day for 21 days) and PM plus sodium stibogluconate (SSG) combination (PM, 15 mg/kg/day and SSG, 20 mg/kg/day for 17 days) with SSG (20 mg/kg/day for 30 days) for treatment of VL in East Africa. Patients aged 4-60 years with parasitologically confirmed VL were enrolled, excluding patients with contraindications. Primary and secondary efficacy outcomes were parasite clearance at 6-months follow-up and end of treatment, respectively. Safety was assessed mainly using adverse event (AE) data.The PM versus SSG comparison enrolled 205 patients per arm with primary efficacy data available for 198 and 200 patients respectively. The SSG & PM versus SSG comparison enrolled 381 and 386 patients per arm respectively, with primary efficacy data available for 359 patients per arm. In Intention-to-Treat complete-case analyses, the efficacy of PM was significantly lower than SSG (84.3% versus 94.1%, difference = 9.7%, 95% confidence interval, CI: 3.6 to 15.7%, p = 0.002). The efficacy of SSG & PM was comparable to SSG (91.4% versus 93.9%, difference = 2.5%, 95% CI: -1.3 to 6.3%, p = 0.198). End of treatment efficacy results were very similar. There were no apparent differences in the safety profile of the three treatment regimens.The 17 day SSG & PM combination treatment had a good safety profile and was similar in efficacy to the standard 30 day SSG treatment, suggesting suitability for VL treatment in East Africa.www.clinicaltrials.govNCT00255567.

Abstract Background Even though remarkable progress has been achieved, HIV/AIDS continues to be a major global health priority. HIV discordant relationship is one of the emerging issues in HIV prevention endeavour. In Ethiopia, very little is known about HIV-serodiscordant couples particularly how they manage their sexual relationship and fertility desire. Therefore, we conduct this study with the aim of exploring the experiences of HIV discordant couples about their sexual life, and fertility desire in the context of long-term relationships in Addis Ababa, Ethiopia. Methods A grounded theory approach was employed using in-depth interviews among 36 informants in ART/PMTCT centers of three public hospitals, a health center and one PLHIV association in Addis Ababa. Theoretical sampling was used to recruit 28 clients who lived in a discordant relationship and eight health care providers as key informants. Data collection and analysis were undertaken simultaneously using a constant comparison. The analysis was facilitated using OpenCode software. Results A grounded theory pertaining to sexual life and desire to have a child among HIV discordant couples emerged as “maintaining the relationship” as a core category. Couples pass through a social process of struggle to maintain their relationship. The causal conditions for couples to enter into the process of struggle to maintain their relationship were collectively categorized as “Entering in-to a transition” (knowing HIV serostatus) and this includes mismatch of desire to have a child, controversy on safe sex versus desire to have a child, and undeniable change in sexual desire and practice through time were the features in entering into-transition. Then after the transition, couples engaged in certain actions/strategies that are categorized as “dealing with discordancy” such as entertaining partner’s interest by scarifying once self interest to maintain their relationship. Conclusions HIV discordant couples’ relationship is filled with controversies of maintaining relationship versus fear of getting infected. The findings of this study have suggested the need to view discordant couple’s actions as a process of maintaining their relationship in the context of eminent risks. Further study should be done among HIV discordant couples to assess the fitness of the current model in different setups and population. In addition, a study could begin to test the hypotheses proposed in this study.

Postnatal care immediately after birth is a crucial component to save lives of mothers and the newborns. A paucity of evidence indicated that women's unwillingness to receive care after birth is a remained challenge in resource-limited settings in general and in Ethiopia in particular. This study aimed to assess the utilization of postnatal care within 48 h after birth and its determinants in northwest Ethiopia. A community-based cross-sectional study was conducted from October to November 2020. A total of 811 women who had birth within the last one year were involved in the study. Both random and fixed effects were reported using an adjusted odds ratio with a 95% confidence interval and p value of 0.05. The study revealed that 13.3% (95% CI 10.9-15.7%) of women attended postnatal care visits within 48 h after birth. Attending secondary and above education (AOR = 2.39; 95% CI 1.18, 4.84); health extension workers home visiting during pregnancy (AOR = 1.74; 95% CI 1.08, 2.82); birth order (AOR = 2.07; 95% CI 1.03, 4.14); place of delivery (AOR = 7.49; 95% CI 3.73, 15.06); and short distance to health facility (AOR = 4.19; 95% CI 1.64, 10.70) were significantly associated with postnatal care utilization within 48 h after birth. Postnatal care utilization within 48 h is low in rural northwest Ethiopia compared to findings from most resource-limited settings. The existing health system should consider multilevel intervention strategies focusing on maternal health education, engaging community health workers in health promotion, and ensuring physical accessibility of healthcare facilities to be more effective in improving postnatal care utilization within 48 h., (© 2024. The Author(s).)

Background: Drug-resistant tuberculosis (DR-TB) remains a threat to public health. Shorter regimens have been proposed as potentially valuable treatments for multidrug or rifampicin resistant tuberculosis (MDR/RR-TB). We undertook a systematic review and network meta-analysis to evaluate the efficacy and safety of shorter MDR/RR-TB regimens., Methods: We searched PubMed/MEDLINE, Cochrane Center for Clinical Trials (CENTRAL), Scopus, ClinicalTrials.gov, WHO International Clinical Trials Registry Platform, US Food and Drug Administration, and Chinese Clinical Trial Registry for primary articles published from 2013 to July 2023. Favorable (cured and treatment completed) and unfavorable (treatment failure, death, loss to follow-up, and culture conversion) outcomes were assessed as the main efficacy outcomes, while adverse events were assessed as the safety outcomes. The network meta-analysis was performed using R Studio version 4.3.1 and the Netmeta package. The study protocol adhered to the PRISMA-NMA guidelines and was registered in PROSPERO (CRD42023434050)., Result: We included 11 eligible studies (4 randomized control trials and 7 cohorts) that enrolled 3,548 patients with MDR/RR-TB. Treatment with a 6-month combination of BdqLzdLfxZTrd/Eto/H had two times more favorable outcomes [RR 2.2 (95% CI 1.22, 4.13), P = 0.0094], followed by a 9-11 month combination of km/CmMfx/LfxPtoCfzZEHh [RR1.67 (95% CI 1.45, 1.92), P < 0.001] and a 6-month BdqPaLzdMfx [RR 1.64 (95% CI 1.24, 2.16), P < 0.0005] compared to the standard longer regimens. Treatment with 6 months of BdqPaLzdMfx [RR 0.33 (95% CI 0.2, 0.55), P < 0.0001] had a low risk of severe adverse events, followed by 6 months of BdqPaLzd [RR 0.36 (95% CI 0.22, 0.59), P ≤ 0.001] and BdqPaLzdCfz [RR 0.54 (95% CI 0.37, 0.80), P < 0.0001] than standard of care., Conclusion: Treatment of patients with RR/MDR-TB using shorter regimens of 6 months BdqLzdLfxZTrd/Eto/H, 9-11 months km/CmMfx/LfxPtoCfzZEHh, and 6 months BdqPaLzdMfx provides significantly higher cure and treatment completion rates compared to the standard longer MDR/RR-TB. However, 6BdqPaLzdMfx, 6BdqPaLzd, and 6BdqPaLzdCfz short regimens are significantly associated with decreased severity of adverse events. The findings are in support of the current WHO-recommended 6-month shorter regimens., (© 2024. The Author(s).)

Background: The midwife-led model of care is woman-centered and based on the premise that pregnancy and childbirth are normal life events, and the midwife plays a fundamental role in coordinating care for women and linking with other health care professionals as required. Worldwide, this model of care has made a great contribution to the reduction of maternal and child mortality. For example, the global under-5 mortality rate fell from 42 deaths per 1,000 live births in 2015 to 39 in 2018. The neonatal mortality rate fell from 31 deaths per 1,000 live births in 2000 to 18 deaths per 1,000 in 2018. Even if this model of care has a pivotal role in the reduction of maternal and newborn mortality, in recent years it has faced many challenges., Objective: To explore facilitators and barriers to a midwife-led model of care at a public health institution in Dire Dawa, Eastern Ethiopia, in 2021., Methodology: A qualitative approach was conducted at Dire Dawa public health institution from March 1-April 30, 2022. Data was collected using a semi-structured, in-depth interview tool guide, focused group discussions, and key informant interviews. A convenience sampling method was implemented to select study participants, and the data were analyzed thematically using computer-assisted qualitative data analysis software Atlas.ti7. The thematic analysis with an inductive approach goes through six steps: familiarization, coding, generating themes, reviewing themes, defining and naming themes, and writing up., Result: Two major themes were driven from facilitators of the midwife-led model of care (professional pride and good team spirit), and seven major themes were driven from barriers to the midwife-led model of care (lack of professional development, shortage of resources, unfair risk or hazard payment, limited organizational power of midwives, feeling of demoralization absence of recognition from superiors, lack of work-related security)., Conclusion: The midwifery-led model of care is facing considerable challenges, both pertaining to the management of the healthcare service locally and nationally. A multidisciplinary and collaborative effort is needed to solve those challenges., (© 2024. The Author(s).)

Background: IT has brought remarkable change in bridging the digital gap in resource-constrained regions and advancing the health care system worldwide. Community-based information systems and mobile apps have been extensively developed and deployed to quantify and support health services delivered by community health workers. The success and failure of a digital health information system depends on whether and how it is used. Ethiopia is scaling up its electronic community health information system (eCHIS) to support the work of health extension workers (HEWs). For successful implementation, more evidence was required about the factors that may affect the willingness of HEWs to use the eCHIS., Objective: This study aimed to assess HEWs' intentions to use the eCHIS for health data management and service provision., Methods: A cross-sectional study design was conducted among 456 HEWs in 6 pilot districts of the Central Gondar zone, Northwest Ethiopia. A Unified Theory of Acceptance and Use of Technology model was used to investigate HEWs' intention to use the eCHIS. Data were cleaned, entered into Epi-data (version 4.02; EpiData Association), and exported to SPSS (version 26; IBM Corp) for analysis using the AMOS 23 Structural Equation Model. The statistical significance of dependent and independent variables in the model was reported using a 95% CI with a corresponding P value of <.05., Results: A total of 456 HEWs participated in the study, with a response rate of 99%. The mean age of the study participants was 28 (SD 4.8) years. Our study revealed that about 179 (39.3%; 95% CI 34.7%-43.9%) participants intended to use the eCHIS for community health data generation, use, and service provision. Effort expectancy (β=0.256; P=.007), self-expectancy (β=0.096; P=.04), social influence (β=0.203; P=.02), and hedonic motivation (β=0.217; P=.03) were significantly associated with HEWs' intention to use the eCHIS., Conclusions: HEWs need to be computer literate and understand their role with the eCHIS. Ensuring that the system is easy and enjoyable for them to use is important for implementation and effective health data management., (©Tesfahun Hailemariam, Asmamaw Atnafu, Lemma Gezie, Jens Kaasbøll, Jorn Klein, Binyam Tilahun. Originally published in JMIR Human Factors (https://humanfactors.jmir.org), 04.03.2024.)

Background and Aims: Hirschsprung's disease (HSD) remains a common cause of pediatric intestinal obstruction. Barium contrast enema (BE) is the primary imaging modality for the evaluation of clinically suspected cases. Here, we aimed to assess the diagnostic accuracy of BE in children with clinically suspected HSD when compared to a gold standard full-thickness rectal biopsy (FTRB)., Methods: We recruited and consecutively enrolled children with clinically suspected HSD at two tertiary teaching hospitals. Participants underwent BE imaging and two radiologists interpreted the findings independently. Participants further underwent FTRB by pediatric surgeons as the confirmatory test. Sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and receiver operating characteristics (ROC) with the area under the curve (AUC) were calculated on Stata version 14.2, taking FTRB as the standard., Results: We enrolled 55 cases, of which 49 completed the evaluation and were included in the final analysis. The median age was 9.4 months (interquartile range: 2-24], with a male-to-female ratio of 4.4:1. The sensitivity, specificity, PPV, and NPV of BE were 0.95 (95% confidence interval [CI] [0.81-0.99]), 0.73 (95% CI [0.39-0.94]), 0.92 (95% CI [0.82-0.97]), and 0.80 (95% CI [0.50-0.94]), respectively. On AUC, the diagnostic accuracy of BE compared to the confirmatory FTRB was 0.84 (95% CI [0.69-0.98]). The diagnostic accuracy was higher in neonates (ROC: 1.00) when compared to infants (ROC: 0.83) or those above 1 year of age (ROC: 0.798). HSD-suggestive BE findings were associated with absence of ganglion cells on FTRB ( χ 2  = 23.301, p  < 0.001). Inverted rectosigmoid ratio and transition zone were more sensitive in detecting HSD of 0.92 (95% CI [0.74-0.98]) and 0.81 (95% CI [0.63-0.92]), respectively., Conclusion: BE is sufficiently accurate in the diagnosis of children with HSD, suggesting BE would likely be used to inform surgical management in settings where confirmatory biopsy is lacking. However, clinical judgment is warranted in interpreting negative BE findings., Competing Interests: The authors declare no conflict of interest., (© 2024 The Authors. Health Science Reports published by Wiley Periodicals LLC.)