Population Estimates Research Articles

Drug use patterns and health problems among people who use drugs in Guinea-Bissau (2022): A cross-sectional survey using respondent-driven sampling

BackgroundLittle data exists on the use of cocaine, methamphetamine, tramadol and heroin or related health conditions in Guinea Bissau. We aimed to estimate drug use practices and the prevalence of selected blood-borne infections, depression and population size estimates of people who use injectable drugs in Guinea-Bissau. MethodsWe used respondent-driven sampling to recruit adults who use injectable drugs in this cross sectional survey in three cities (Bissau, Bafatá and Gabú) between July and September 2022. Participants completed an interviewer administered survey enquiring about sociodemographic characteristics, drug use practices and mental health. Rapid diagnostic testing was done for HIV, hepatitis B surface antigen (HBsAg) and hepatitis C antibody (anti-HCV). Data was weighted in RDS-Analyst using self-reported network size and Gile's Sequential Sampling Estimator. Population size estimates were generated using the two point capture-recapture method. ResultsOverall, 750 participants were recruited. People who use drugs were estimated to be mostly unemployed males aged between 25 and 49 years. Methamphetamine and crack cocaine were most commonly used. Prevalence of ever injecting ranged from 6 % to 44 %. Between 44 % and 52 % of people experience symptoms of depression. Prevalence ranges from 1.9 % to 5.2 % for HIV, and 5.7–8.3 % for HBsAg and 0.42–0.66 % for anti-HCV. The population estimates of people who use injectable drugs were 1637 in Bissau, 1314 in Bafatá and 424 in Gabú. ConclusionMethamphetamine and crack cocaine are the most commonly used injectable drugs in Guinea-Bissau. Symptoms of depression are common among people who use drugs in the country. Access to evidence-based drug use treatment and harm reduction interventions that integrate mental health care services are needed to improve the health and wellbeing of people who use drugs in Guinea-Bissau.

A drone-based population survey of Delacour's langur (Trachypithecus delacouri) in the karst forests of northern Vietnam

The Critically Endangered Delacour's langur (Trachypithecus delacouri) is presently found only in a few isolated karst areas in northern Vietnam, where conducting field surveys has proven challenging due to the difficult terrain. Accurate population estimates from a scientifically sound method are needed to inform management of the species. From October to December 2022, we used a drone equipped with optical and thermal cameras to survey the species in Kim Bang Forest, a critical site for the species. We estimated langur abundance from the resulting point count data using N-mixture models including abiotic and biotic variables. We also compared the effectiveness and efficiency of the drone method with two commonly used ground-based methods. The drone survey recorded 16 groups with 104 individuals. We estimated a density of 0.87 groups per km2 and a population of 25 groups and 175 individuals. This estimate is 80–113 % higher than previous ground-based estimates, attributed primarily to the higher area coverage by the drone survey. The estimate reaffirms the conservation importance of Kim Bang Forest for the species. The modelling also indicated that Delacour's langur abundance was correlated negatively with Assamese macaque (Macaca assamensis) presence and positively with vegetation productivity. Other variables (elevation, terrain ruggedness and distance to forest edge) were much less important in explaining langur abundance. Compared to the ground-based methods, the drone approach proved effective and resource-efficient for surveying Delacour's langurs. We recommend the drone method for future Delacour's langur surveys, with potential applicability to other arboreal mammals in difficult-to-access karst forests.

ESTUDO CLÍNICO-EPIDEMIOLÓGICO DE CASOS NOTIFICADOS POR HANSENÍASE ENTRE OS ANOS DE 2019 A 2023 EM UM MUNICÍPIO DO TOCANTINS

INTRODUCTION: Leprosy is an infectious disease with a chronic evolution caused by the bacillus Mycobacterium leprae, which, although curable, is still endemic in several parts of the world, and among these countries we have Brazil, it is associated with social inequalities and a lack of basic structure. This study aimed to analyze the incidence and epidemiological profile of Leprosy cases in Porto Nacional, Tocantins, between 2019 and 2023. METHODOLOGY: A quantitative approach was used, based on retrospective data collected from the Notifiable Diseases Information System (SINAN). Sex, Operational Classification, Initial Therapeutic Scheme and Bacilloscopy. Notification forms in ignored and blank fields above 70% unfilled or that do not provide conclusive data will be excluded. The incidence calculation was carried out based on population estimates provided by IBGE. RESULTS: The data reveal that there was an increase in the total number of reported cases of Leprosy in the Municipality of Porto Nacional during the period analyzed. The majority of cases affected men (52.15%). DISCUSSION: The discussion of the results highlights the need for more effective public policies to control the spread of the disease, especially in endemic regions such as Porto Nacional – TO. FINAL CONSIDERATIONS: The clinical-epidemiological analysis of Leprosy cases in Porto Nacional highlights the urgent need for more effective interventions to control the disease and prevent new cases, breaking the transmission chain.

Estimating the rates of dementia using administrative data linked to cohort studies.

Current estimates of dementia and Alzheimer's disease incidence and prevalence are required to understand the health needs of the elderly. We used two Australia cohort studies, administrative datasets, and data linkage techniques to estimate dementia rates in Australia. The study used Australian Longitudinal Study on Women's Health and the Health in Men Cohort Study. Records of dementia were obtained from linked sources and incidence and prevalence estimates were produced. Capture-recapture methods were used to estimate numbers of dementia cases not identified through data linkage. There were 3399 (28.5%) men with dementia identified from any source and 3767 (34.8%) women. Rates of dementia incidence and prevalence were similar between sexes but were raised in men once estimates of unidentified cases were included. Cohort studies and linked administrative data can be used together to produce current estimates of dementia prevalence and incidence comparable to other population estimates.

Population size and structure of Grant’s gazelle and lesser kudu in Geralle National Park, Southeastern Ethiopia

Background Grant’s gazelle and lesser kudu remain widespread within and outside protected areas. Current pressures on their populations, human encroachment and disturbance associated habitat modifications, and excessive grazing pose further threats to the species. The estimation of density and abundance of species has significant value for sustainable wildlife management in Geralle National Park (GNP) and also contributes towards a more accurate global population estimate. Result Using distance sampling methods, the lowest Akaike Information Criterion (AIC) (close to zero) and Chi-square tests (P value > 0.05) showed that the hazard rate key function with an un-equal interval group model was selected for estimation of density and abundance. The density of species was 1.7 ± 0.5, 2.07 ± 0.7, gazelle/km2 and 1.39 ± 0.3, 1.92 ± 0.42, lesser kudu/km2 during the dry and wet seasons, respectively. Population density, abundance, and structure showed habitat and seasonal differences in observation. More individuals of both species were recorded during the wet season as compared to the dry season. Grassland was favored by Grant’s gazelle, while lesser kudu preferred woodlands. Both species exhibited a female-biased sex ratio, indicating potential for future population growth prospects. Conclusion It can be concluded that GNP is home to viable populations of Grant’s gazelle and lesser kudu, and season has influenced population abundance and distribution due to resource availability variations among seasons. The female-biased sex ratio indicates the future population growth prospects for the two species.

A NEW CONJUGATE GRADIENT METHOD BASED ON LOGISTIC MAPPING FOR UNCONSTRAINED OPTIMIZATION AND ITS APPLICATION IN REGRESSION ANALYSIS

The study tackles the critical need for efficient optimization techniques in unconstrained optimization problems, where conventional techniques often suffer from slow and inefficient convergence. There is still a need for algorithms that strike a balance between computational efficiency and robustness, despite advancements in gradient-based techniques. This work introduces a novel conjugate gradient algorithm based on the logistic mapping formula. As part of the methodology, descent conditions are established, and the suggested algorithm's global convergence properties are thoroughly examined. Comprehensive numerical experiments are used for empirical validation, and the new algorithm is compared to the Polak-Ribière-Polyak (PRP) algorithm. The suggested approach performs better than the PR algorithm, according to the results, and is more efficient since it needs fewer function evaluations and iterations to reach convergence. Furthermore, the usefulness of the suggested approach is demonstrated by its actual use in regression analysis, notably in the modelling of population estimates for the Kurdistan Region of Iraq. In contrast to conventional least squares techniques, the method maintains low relative error rates while producing accurate predictions. All things considered, this study presents the novel conjugate gradient algorithm as an effective tool for handling challenging optimisation problems in both theoretical and real-world contexts.

Risk of mycobacterial infections in a cohort of silicosis patients with autoimmune rheumatic diseases

Objective: To evaluate the incidence rates of mycobacterial infections in silicosis patients with systemic autoimmune rheumatic disease (ARD). Methods: This was a retrospective cohort of silicosis patients between January of 1999 and December of 2023. We compared the incidence of tuberculosis and nontuberculous mycobacterial disease (NTM) in patients with silicosis with and without ARD. We also compared the tuberculosis incidence in the overall cohort with general Brazilian population estimates. Results: The study comprised 369 silicosis patients, of whom 35 (9.5%) had ARD. Having ARD did not affect the cumulative incidence of mycobacterial diseases. The risk of tuberculosis was higher in the cohort when compared with that in the adult Brazilian male population (age-adjusted incidence rate ratio = 20.46; 95% CI 14.89-28.13). Conclusions: In this cohort of patients with silicosis, ARD was not associated with the incidence of mycobacterial diseases.

Abstract 4122008: Bayesian modeling to forecast mortality due to Ischemic Heart Disease, Stroke, and Peripheral Artery Disease in Brazil until 2040

Introduction: Atherosclerotic cardiovascular disease, specifically ischemic heart disease (IHD), stroke, and peripheral artery disease (PAD) together are among the leading causes of cardiovascular mortality in Brazil. As the obesity, hypertension, and diabetes rates in the Brazil continue to increase, projecting mortality rates related to these conditions are important for guiding health policy. Hypothesis: We hypothesized that there are differences in the future trends of IHD, stroke and PAD in Brazil. Aims: We sought to forecast the trends in IHD, stroke, and PAD in Brazil until 2040 to fill in this important knowledge gap and inform public health policy development. Methods: We collected the annual death counts for IHD, stroke, and PAD and the mid-year population estimates in 5-year age brackets (40 – 79 years) between 1990 and 2019 from the Global Burden of Disease. We calculated the annual crude and age-standardized mortality rates (per 100 000 residents) for IHD, stroke and PAD between 1990 and 2019. Using this observed data, we estimated the projected mortality rates for these conditions till 2040 using Bayesian age-period-cohort (BAPC) models. Results: Brazil`s population (aged 40-79 years) will increase by 237.5% between 1990 and 2040. Nationally, compared to 2019, the age standardized IHD mortality is projected to reduce by 31.66 % [2019: 131.13, 2040:89.6 (IQR: 65.83, 113.37)] and stroke rates are estimated to reduce by 16.75% [2019: 91.09, 2040: 75.83 (IQR: 51.68, 99.98)]. However, PAD rates are projected to increase by 1.2% in the same period [2019: 1.58, 2040: 1.66 (IQR: 1.24, 2.07)]. The age standardized IHD and stroke mortality rates will reduce in males and females. However, the age standardized PAD rates will increase in females [2019: 1.33, 2040: 1.48 (IQR: 1.06, 1.89), 11.28% difference] and remain reasonably constant among males. Conclusion: In Brazil, the age standardized rates for IHD and stroke mortality are projected to decline till 2040. However, PAD related mortality is likely to be an important issue among females. Our findings highlight the need for early and targeted interventions to address these potential cardiovascular health disparities.

COVID-19 latent age-specific mortality in US states: a county-level spatio-temporal analysis with counterfactuals

During the COVID-19 pandemic, which spanned much of 2020–2023 and beyond, daily case and death counts were recorded globally. In this study, we examined available mortality counts and associated case counts, with a focus on the estimation missing information related to age distributions. In this paper, we explored a model-based paradigm for generating age distributions of mortality counts in a spatio-temporal context. We pursued this aim by employing Bayesian spatio-temporal lagged dependence models for weekly mortality at the county level. We compared three US states at the county level: South Carolina (SC), Ohio, and New Jersey (NJ). Models were developed for mortality counts using Bayesian spatio-temporal constructs, incorporating both dependence on current and cumulative case counts and lagged dependence on previous deaths. Age dependence was predicted based on total deaths in proportion to population estimates. This latent age field was generated as counterfactuals and then compared to observed deaths within age groups. The optimal retrospective space–time models for weekly mortality counts were those with lagged dependence and a function of caseload. Added random effects were found to vary across states: Ohio favored a spatially correlated model, while SC and NJ favored a simpler formulation. The generation of age-specific latent fields was performed for SC only and compared to a 15-month, 13-county data set of observed >65 age population. It is possible to model spatio-temporal variations in mortality at the county level with lagged dependencies, spatial effects, and case dependencies. In addition, it is also possible to generate latent age-specific fields based on estimates of death risk (using population proportions or more sophisticated modeling approaches). More detailed data will be needed to make more calibrated comparisons for future epidemic monitoring. The proposed discrepancy tool could serve as a useful resource for public health planners in tailoring interventions during epidemic situations.

An OpenStreetMap derived building classification dataset for the United States

Building classification is crucial for population estimation, traffic planning, urban planning, and emergency response applications. Although essential, such data is often not readily available. To alleviate this problem, this work presents a comprehensive dataset by providing residential/non-residential building classification covering the entire United States. We developed a dataset of building types based on building footprints and the available OpenStreetMap information. The dataset is validated using authoritative ground truth data for select counties in the U.S., which shows a high precision for non-residential building classification and a high recall for residential buildings. In addition to the building classifications, this dataset includes detailed information on the OpenStreetMap data used in the classification process. A major result of this work is the resulting dataset of classifying 67,705,475 buildings. We hope that this data is of value to the scientific community, including urban and transportation planners.

A rural ambulance service in Northern Togo improves access to emergency care for women with obstetric complications.

Maternal mortality remains high in sub-Saharan Africa, with little progress made in the last 20 years. The provision of emergency obstetric care has been shown to have the greatest effect in reducing maternal mortality in countries with high maternal mortality ratios, especially when paired with an emergency transport service. Integrate Health has partnered with the Togolese Ministry of Health to improve maternal and child health via the integration of a free ambulance service into a pre-existing primary care model. In this study, we aim to describe the implementation of this service and assess its effectiveness on access to emergency obstetric care by estimating its coverage of women with obstetric complications. This is a retrospective cross-sectional study using routinely collected data from ambulance logbooks. The study was restricted to pregnant or postpartum woman in four districts of Northern Togo. For each patient transported, the variables collected included date of transport, destination, patient information, kilometres travelled, and reason for transport. Complicated obstetric cases were defined by reason for transport and included maternal haemorrhage, complicated birth, and signs of danger. Estimated coverage of major obstetric complications was calculated using population estimates per fiscal year, the birth rate (3.7%) in Togo, and the assumption that 15% of pregnant women will have a complication. Between July 2020 and June 2023, there were 2926 maternal patients transported by the ambulance service. Of these, 1030 were reported as complicated obstetric cases. Estimated coverage of obstetric complications increased over time and as the programme expanded, from 18% in 2020 to 35.7% in 2021, and 66.5% in 2022. Our findings demonstrate that implementing a rural ambulance service in a region with historically high maternal mortality rates may improve maternal access to emergency obstetric care. The success of our ambulance service was likely due to the fact that it is free, available 24/7, easily accessible, operated by trained staff and community members, and integrated into a pre-existing primary care programme with well-resourced health care centres.

Foxtrot migration and dynamic over-wintering range of an Arctic raptor

Advances in tracking technologies have revealed the diverse migration patterns of birds, which are critical for range mapping and population estimation. Population trends are usually estimated in breeding ranges where birds remain stationary, but for species that breed in remote areas like the Arctic, these trends are often assessed in over-wintering ranges. Assessing population trends during the wintering season is challenging due to the extensive movements of birds in these ranges, which requires a deep understanding of the movement dynamics. However, these movements remain understudied, particularly in the mid-latitudes, where many Arctic breeders overwinter, increasing uncertainty in their ranges and numbers. Here, we show that the Arctic breeding raptor Rough-legged buzzard, which overwinters in the mid-latitudes, has a specific wintering strategy. After migrating ca. 1500 km from the Arctic to mid-latitudes, the birds continue to move throughout the entire over-wintering period, traveling another 1000 km southwest and then back northeast as the snowline advances. This continuous movement makes their wintering range dynamic throughout the season. In essence, this movement represents an extension of the quick migration process, albeit at a slower pace, and we have termed this migration pattern ‘foxtrot migration’, drawing an analogy to the alternating fast and slow movements of the foxtrot dance. These results highlight the potential errors in range mapping from single mid-winter surveys and emphasize the importance of this migration pattern in assessing the conservation status of bird species. Understanding this migration pattern could help to correctly estimate bird populations in over-wintering ranges, which is especially important for species that nest in hard-to-reach regions such as the Arctic.

Foxtrot migration and dynamic over-wintering range of an Arctic raptor.

Advances in tracking technologies have revealed the diverse migration patterns of birds, which are critical for range mapping and population estimation. Population trends are usually estimated in breeding ranges where birds remain stationary, but for species that breed in remote areas like the Arctic, these trends are often assessed in over-wintering ranges. Assessing population trends during the wintering season is challenging due to the extensive movements of birds in these ranges, which requires a deep understanding of the movement dynamics. However, these movements remain understudied, particularly in the mid-latitudes, where many Arctic breeders overwinter, increasing uncertainty in their ranges and numbers. Here, we show that the Arctic breeding raptor Rough-legged buzzard, which overwinters in the mid-latitudes, has a specific wintering strategy. After migrating ca. 1500 km from the Arctic to mid-latitudes, the birds continue to move throughout the entire over-wintering period, traveling another 1000 km southwest and then back northeast as the snowline advances. This continuous movement makes their wintering range dynamic throughout the season. In essence, this movement represents an extension of the quick migration process, albeit at a slower pace, and we have termed this migration pattern 'foxtrot migration', drawing an analogy to the alternating fast and slow movements of the foxtrot dance. These results highlight the potential errors in range mapping from single mid-winter surveys and emphasize the importance of this migration pattern in assessing the conservation status of bird species. Understanding this migration pattern could help to correctly estimate bird populations in over-wintering ranges, which is especially important for species that nest in hard-to-reach regions such as the Arctic.

Designing and Validating a Survey for National-Level Data During the COVID-19 Pandemic in Sri Lanka: Cross-Sectional Mobile Phone Surveys.

The COVID-19 pandemic has generated a demand for timely data, resulting in a surge of mobile phone surveys for tracking the impacts of and responses to the pandemic. Mobile phone surveys have become a preferred mode of data collection across low- and middle-income countries. This study piloted 2 population-based, cross-sectional mobile phone surveys among Sri Lankan residents in 2020 and 2021 during the COVID-19 pandemic. The surveys aimed to gather data on knowledge, attitudes, and practices, vaccine acceptability, availability, and barriers to COVID-19 testing, and use of a medicine distribution service. The study used Surveda, an open-source survey tool developed by the NCD (noncommunicable disease) Mobile Phone Survey Data 4 Health Initiative, for data collection and management. The surveys were conducted through interactive voice response using automated, prerecorded messages in Sinhala, Tamil, and English. The sample design involved random sampling of mobile phone numbers, stratified by sex, proportional to the general population. Eligibility criteria varied between surveys, targeting adults aged 35 years and older with any noncommunicable disease for the first survey and all adults for the second survey. The data were adjusted to population estimates, and statistical analysis was conducted using SAS (SAS Institute) and R software (R Core Team). Descriptive statistics, Rao-Scott chi-square tests, and z tests were used to analyze the data. Response rates, cooperation rates, and productivity of the sampling approach were calculated. In the first survey, n=5001, the overall response rate was 7.5%, with a completion rate of 85.6%. In the second survey, n=1250, the overall response rate was 10.9%, with a completion rate of 61.9%. Approximately 3 out of 4 adults reported that they avoided public places (888/1175, 75.6%), more than two-thirds avoided public transportation (808/1173, 68.9%), and 9 out of 10 practiced physical distancing (1046/1167, 89.7%). Approximately 1 out of 10 Sri Lankan persons reported being tested for COVID-19, and the majority of those received a polymerase chain reaction test (112/161, 70%). Significantly more males than females reported being tested for COVID-19 (98/554, 17.8% vs 61/578, 10.6%, respectively; P<.001). Finally, the majority of adult Sri Lankan people reported that they definitely or probably would get the COVID-19 vaccination (781/1190, 65.7%). The surveys revealed that, overall, the adult Sri Lankan population adhered to COVID-19 mitigation strategies. These findings underscore the use of mobile phone surveys in swiftly and easily providing essential data to inform a country's response during the COVID-19 pandemic, obviating the need for face-to-face data collection.

Delivery-Related Maternal Morbidity and Mortality Among Patients With Cardiac Disease.

To assess the risk of severe maternal morbidity (SMM) and mortality among pregnant patients with cardiovascular disease (CVD). This was a retrospective cohort study of U.S. delivery hospitalizations from 2010 to 2020 using weighted population estimates from the National Inpatient Sample database. The primary objective was to evaluate the risk of SMM and maternal mortality among patients with CVD at delivery hospitalization. International Classification of Diseases, Ninth and Tenth Revision, Clinical Modification codes were used to identify delivery hospitalizations, CVD, and SMM events. Multivariable logistic regression analyses were performed to compare SMM and mortality risk among patients with CVD and those without CVD. Given the substantial racial and ethnic disparities in SMM, mortality, and CVD burden, secondary objectives included evaluating SMM and mortality across racial and ethnic groups and assessing the population attributable fraction within each group. Lastly, subgroup analyses of SMM by underlying CVD diagnoses (eg, congenital heart disease, chronic heart failure) were performed. Variables used in the regression models included socioeconomic and demographic maternal characteristics, maternal comorbidities, and pregnancy-specific complications. Among 38,374,326 individuals with delivery hospitalizations, 203,448 (0.5%) had CVD. Patients with CVD had an increased risk of SMM (11.6 vs 0.7%, adjusted odds ratio [aOR] 12.5, 95% CI, 12.0-13.1) and maternal death (538 vs 5 per 100,000 delivery hospitalizations, aOR 44.1, 95% CI, 35.4-55.0) compared with those without CVD. Patients with chronic heart failure had the highest SMM risk (aOR 354.4, 95% CI, 301.0-417.3) among CVD categories. Black patients with CVD had a higher risk of SMM (aOR 15.9, 95% CI, 14.7-17.1) than those without CVD with an adjusted population attributable fraction of 10.5% (95% CI, 10.0-11.0%). CVD in pregnancy is associated with increased risk of SMM and mortality, with the highest risk of SMM among patients with chronic heart failure. Although CVD affects less than 1% of the pregnant population, it contributes to nearly 1 in 10 SMM events in the United States.

Bayesian Model Projecting Cardiovascular Disease Related Mortality Trends in the United States.

As future cardiovascular disease mortality trends have public health implications, we aimed to project ischemic heart disease (IHD), cerebrovascular disease (CeVD), and heart failure (HF) mortality rates for adults (40-79 years). In this population-level study, we linked the yearly mortality rates (per 100 000 US residents) (2000-2019) with IHD, CeVD, or HF as the primary cause of death from the Centers for Disease Control and Prevention Wide-Ranging Online Data for Epidemiologic Research with the midyear US population estimates (2000-2035) for adults (40-79 years). We calculated the observed age-standardized mortality rates (2000-2019) (per 100 000 residents) (aSMR) and fitted Bayesian age-period-cohort models to project aSMR for IHD, CeVD, and HF up to 2035 in the United States. Between 2019 (last year of observed data) and 2035 (last year of projected results), the US population (40-79 years) will increase by 16% and age. The IHD aSMR will reduce from 111.9 (in 2019) to 81.8 (66.7-96.9) in 2035, an effect observed for all age groups. The CeVD aSMR will remain comparable between 2019 (37.4) and 2035 (38.6 [30.7-46.5]). The HF aSMR will increase from 16.5 in 2019 to reach 30.9 (13-48.8) in 2035; such increases were observed in all age groups. In the United States, between 2020 and 2035, the aSMR for IHD is expected to decrease, for CeVD will remain stable, and for HF will increase substantially. These data can inform resource allocation for future public health initiatives.

Population estimate and habitat association of Grant’s gazelle (Nanger granti Brooke, 1872) in the Ene Forest of Dale Sadi district, western Ethiopia

Grant’s gazelles (Nanger granti) are classified as of least concern by the IUCN, although their number is declining due to several factors. A few research studies have been conducted on Grant’s gazelle in Ethiopia. Thus, the present study was carried out to determine the population size and habitat association of Grant gazelle in the Ene Forest of western Ethiopia, comprising the dry and wet seasons. The study area was stratified into four habitats: woodland, mixed woodland, riverine forest, and grassland habitats. The data were collected using the direct observation technique. The data were analyzed using descriptive statistics and Pearson’s chi-square (χ2) test. The average estimated Grant gazelle population was 136 ± 23 individuals, with a density of 9/km2. The adult male-to-adult female sex ratio was 1:1.40 and 1:1.26 during the wet and dry seasons, respectively. The largest herd size (N = 6) was observed during the wet season, and the smallest (N = 4) was observed during the dry season. The highest numbers of Grant gazelles were observed in the grassland habitat during the wet season and in the woodland during the dry season. More Gazelles (N = 65) were observed in the woodland habitat compared to the other habitat types. The continued existence of the Grant’s gazelle population in the area and the suitability of the environment depend significantly on ongoing assessments of habitat change and management intervention.

Rapid Zooplankton Assessment: Evaluating a Tool for Ecosystem‐Based Fisheries Management in the Large Marine Ecosystems of Alaska

ABSTRACTEcosystem‐based fisheries management (EBFM) remains an aspirational goal for management throughout the world. One of the primary limitations of EBFM is the incorporation of basic lower trophic level information, particularly for zooplankton, despite the importance of zooplankton to fish. The generation of zooplankton abundance estimates requires significant time and expertise to generate. The rapid zooplankton assessment (RZA) is introduced as a tool whereby nontaxonomic experts may produce rapid zooplankton counts shipboard that can be applied to management in near real time. Zooplankton are rapidly counted shipboard and placed into three broad groups of zooplankton relevant to higher trophic levels: large copepods (&gt; 2 mm), small copepods (&lt; 2 mm), and euphausiids. A Bayesian, hierarchical linear regression modeling approach was used to validate the relationship between RZA abundances and laboratory‐processed abundances to ensure the rapid method is a reliable indicator. Additional factors likely to impact the accuracy of the RZA abundance predictions were added to the initial regression model: RZA sorter, survey, season, and large marine ecosystem (Bering Sea, Chukchi/Beaufort Sea, and Gulf of Alaska). We tested models that included the random effect of sorter nested within survey, which improved fits for both large copepods (Bayes R2 = 0.80) and euphausiids (Bayes R2 = 0.84). These factors also improved the fit for small copepods when the fixed effect of season was also included (Bayes R2 = 0.65). Additional RZA data were used to predict laboratory‐processed abundances for each zooplankton category and the results were consistent with model training data: large copepods (Bayes R2 = 0.80), small copepods (Bayes R2 = 0.64), and euphausiids (Bayes R2 = 0.88). The Bayesian models were therefore able to predict laboratory‐processed abundances with an associated error when accounting for these fixed and random effects. To demonstrate the utility of zooplankton data in management, zooplankton time series from the Bering Sea shelf were shown to vary in relation to warm and cold conditions. This variability impacted commercially important fish, notably Walleye Pollock (Gadus chalcogrammus), and these time series were used by managers using a risk table approach. The RZA method provides a rapid zooplankton population estimation in near real time that can be applied to the management process quickly, thus helping to fill a gap in EBFM.

Pengaruh Kualitas Produk, Brand Image, dan Promosi Terhadap Keputusan Pembelian pada E-Commerce Shopee: Studi Empiris pada Konsumen E-Commerce Shopee Kabupaten Demak

Determining the impact of advertising, brand image, and product quality on Shopee e-commerce purchase decisions is the goal of this study. The Demak Regency's e-commerce clientele is the subject of this study; nevertheless, the precise population estimate is unknown. One hundred respondents were chosen for the sample using the target sampling technique. The hypothesis was tested using multiple linear regression analysis, and its validity was assessed using the F and t tests. The findings of the study indicate that: (1) brand image has no influence on purchasing decisions; (2) product quality has a positive and substantial effect on purchasing decisions; and (3) promotion has a positive and significant effect on purchasing decisions.

Protocol for an ambidirectional cohort study on long COVID and the healthcare needs, use and barriers to access health services in a large city in Southeast Brazil

IntroductionPost-COVID-19 condition, or syndrome, also known as long COVID, is an infection-associated chronic condition that can develop after a SARS-CoV-2 infection and last at least 3 months to years. Despite...