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It is common to use multiple field sampling methods when implementing wildlife surveys to compare method efficacy or cost efficiency, integrate distinct pieces of information provided by separate methods, or evaluate method-specific biases and misclassification error. Existing models that combine information from multiple field methods or sampling devices permit rigorous comparison of method-specific detection parameters, enable estimation of additional parameters such as false-positive detection probability, and improve occurrence or abundance estimates, but with the assumption that the separate sampling methods produce detections independently of one another. This assumption is tenuous if methods are paired or deployed in close proximity simultaneously, a common practice that reduces the additional effort required to implement multiple methods and reduces the risk that differences between method-specific detection parameters are confounded by other environmental factors. We develop occupancy and spatial capture–recapture models that permit covariance between the detections produced by different methods, use simulation to compare estimator performance of the new models to models assuming independence, and provide an empirical application based on American marten (Martes americana) surveys using paired remote cameras, hair catches, and snow tracking. Simulation results indicate existing models that assume that methods independently detect organisms produce biased parameter estimates and substantially understate estimate uncertainty when this assumption is violated, while our reformulated models are robust to either methodological independence or covariance. Empirical results suggested that remote cameras and snow tracking had comparable probability of detecting present martens, but that snow tracking also produced false-positive marten detections that could potentially substantially bias distribution estimates if not corrected for. Remote cameras detected marten individuals more readily than passive hair catches. Inability to photographically distinguish individual sex did not appear to induce negative bias in camera density estimates; instead, hair catches appeared to produce detection competition between individuals that may have been a source of negative bias. Our model reformulations broaden the range of circumstances in which analyses incorporating multiple sources of information can be robustly used, and our empirical results demonstrate that using multiple field-methods can enhance inferences regarding ecological parameters of interest and improve understanding of how reliably survey methods sample these parameters.

We propose and illustrate a hierarchical Bayesian approach for matching statistical records observed on different occasions. We show how this model can be profitably adopted both in record linkage problems and in capture–recapture setups, where the size of a finite population is the real object of interest. There are at least two important differences between the proposed model-based approach and the current practice in record linkage. First, the statistical model is built up on the actually observed categorical variables and no reduction (to 0–1 comparisons) of the available information takes place. Second, the hierarchical structure of the model allows a two-way propagation of the uncertainty between the parameter estimation step and the matching procedure so that no plug-in estimates are used and the correct uncertainty is accounted for both in estimating the population size and in performing the record linkage. We illustrate and motivate our proposal through a real data example and simulations.

To evaluate the success of a stocking program in Fox Lake, Minnesota, adult (≥76 cm total length) Muskellunge were captured with large nearshore trap nets and individually marked with passive integrated transponder tags during the 2011–2013 and 2015–2017 spawning seasons; then, capture–recapture data were analyzed at two different time scales. Despite substantial sampling effort, daily capture histories within a single season only supported closed‐population abundance estimates for both sexes in half the years; estimates were imprecise, and there was evidence of trap shyness or violation of the short‐term closure assumption in some years. Jolly–Seber models over all years supported relatively precise abundance estimates for both sexes every year, as well as estimates of annual survival, recruitment, and population growth rate. Link–Barker Jolly–Seber models provided estimates of population growth rate λ ≈ 1 indicating that per‐capita annual recruitment rates of only about 0.15–0.20 were adequate to maintain the adult population given the high annual apparent survival rates of 0.80 for adult females and 0.89 for adult males. POPAN Jolly–Seber models revealed that about 80 adult females and 90–126 adult males were vulnerable to capture each year in the 385 ha lake, and about 16–18 fish of each sex recruited to the adult population annually. This study illustrates the importance of open‐population models with multiple years of data to evaluate the abundance and population dynamics of a low‐density, long‐lived species.

While the number of detected COVID-19 infections are widely available, an understanding of the extent of undetected cases is urgently needed for an effective tackling of the pandemic. The aim of this work is to estimate the true number of COVID-19 (detected and undetected) infections in several European countries. The question being asked is: How many cases have actually occurred?We propose an upper bound estimator under cumulative data distributions, in an open population, based on a day-wise estimator that allows for heterogeneity. The estimator is data-driven and can be easily computed from the distributions of daily cases and deaths. Uncertainty surrounding the estimates is obtained using bootstrap methods.We focus on the ratio of the total estimated cases to the observed cases at April 17th. Differences arise at the country level, and we get estimates ranging from the 3.93 times of Norway to the 7.94 times of France. Accurate estimates are obtained, as bootstrap-based intervals are rather narrow.Many parametric or semi-parametric models have been developed to estimate the population size from aggregated counts leading to an approximation of the missed population and/or to the estimate of the threshold under which the number of missed people cannot fall (i.e. a lower bound). Here, we provide a methodological contribution introducing an upper bound estimator and provide reliable estimates on the dark number, i.e. how many undetected cases are going around for several European countries, where the epidemic spreads differently.

Background Most estimates for End Stage Kidney Disease (ESKD) prevalence and incidence are based on renal replacement therapy (RRT) registers. However, not all people with ESKD will commence RRT and estimates based only on RRT registry data will underestimate the true burden of ESKD in the community. This study estimates the total number of Northern Territory (NT) residents with ESKD including: those receiving RRT, those diagnosed but not receiving RRT and an estimate of “undiagnosed” cases. Methods Four data sources were used to identify NT residents with a diagnosis of ESKD: public hospital admissions, Australia and New Zealand Dialysis and Transplant Registry registrations, death registrations and, for the Aboriginal population only, electronic primary care records. Three data sources contained information recorded between 1 July 2008 and 31 December 2013, death registration data extended to 31 December 2014 to capture 2013 prevalent cases. A capture–recapture method was used to estimate both diagnosed and undiagnosed cases by making use of probability patterns of overlapping multiple data sources. Results In 2013, the estimated ESKD prevalence in the NT Aboriginal population was 11.01 (95% confidence interval (CI) 10.24–11.78) per 1000, and 0.90 (95% CI 0.76–1.05) per 1000 in the NT non-Aboriginal population. The age-adjusted rates were 17.97 (95% CI 17.82–18.11) and 1.07 (95% CI 1.05–1.09) per 1000 in the NT Aboriginal and non-Aboriginal populations respectively. The proportion of individuals receiving RRT was 71.4% of Aboriginal and 75.5% of non-Aboriginal prevalent ESKD cases. The age-adjusted ESKD incidence was also greater for the Aboriginal (5.26 (95% CI 4.44–6.08) per 1000 population) than non-Aboriginal population (0.36 (95% CI 0.25–0.47) per 1000). Conclusion This study provides comprehensive estimates of the burden of ESKD including those cases that are not identified in relevant health data sources. The results are important for informing strategies to reduce the total burden of ESKD and to manage the potential unmet demand, particularly from comparatively young Aboriginal patients who may be suitable for RRT but do not currently access the services for social, geographic or cultural reasons.

Growing evidence supports a relationship between neighborhood-level characteristics and important health outcomes. One source of neighborhood data includes commercial databases integrated with geographic information systems to measure availability of certain types of businesses or destinations that may have either favorable or adverse effects on health outcomes; however, the quality of these data sources is generally unknown. This study assessed the concordance of two commercial databases for ascertaining the presence, locations, and characteristics of businesses. Businesses in the St. Louis, Missouri area were selected based on their four-digit Standard Industrial Classification (SIC) codes and classified into 14 business categories. Business listings in the two commercial databases were matched by standardized business name within specified distances. Concordance and coverage measures were calculated using capture–recapture methods for all businesses and by business type, with further stratification by census-tract-level population density, percent below poverty, and racial composition. For matched listings, distance between listings and agreement in four-digit SIC code, sales volume, and employee size were calculated. Overall, the percent agreement was 32% between the databases. Concordance and coverage estimates were lowest for health-care facilities and leisure/entertainment businesses; highest for popular walking destinations, eating places, and alcohol/tobacco establishments; and varied somewhat by population density. The mean distance (SD) between matched listings was 108.2 (179.0) m with varying levels of agreement in four-digit SIC (percent agreement = 84.6%), employee size (weighted kappa = 0.63), and sales volume (weighted kappa = 0.04). Researchers should cautiously interpret findings when using these commercial databases to yield measures of the neighborhood environment.

Objectives. To demonstrate how we applied the capture–recapture method for population estimation directly in a rural Appalachian county (Cabell County, WV) to estimate the number of people who inject drugs (PWID). Methods. We conducted 2 separate 2-week periods of data collection in June (“capture”) and July (“recapture”) 2018. We recruited PWID from a syringe services program and in community locations where PWID were known to congregate. Participants completed a survey that included measures related to sociodemographics, substance use, and HIV and hepatitis C virus prevention. Results. In total, 797 surveys were completed; of these surveys, 49.6% (n = 395) reflected PWID who reported injection drug use in the past 6 months and Cabell County residence. We estimated that there were 1857 (95% confidence interval = 1147, 2567) PWID in Cabell County. Among these individuals, most reported being White (83.4%), younger than 40 years (70.9%), and male (59.5%). The majority reported injecting heroin (82.0%), methamphetamine (71.0%), and fentanyl (56.3%) in the past 6 months. Conclusions. Capture–recapture methods can be applied in rural settings to estimate the size of PWID populations.

Assessing the population dynamics and demography of long-lived species can be challenging due to longer lifespans and slow reproductive rates. The population status and demography of the Asian elephant was studied from Dec 2006 to May 2008 at Mudumalai Tiger Reserve. The results were compared with previous studies (1985 and 2000) conducted in the study area to understand temporal changes in the elephant population. Distance sampling and mark-recapture methods were used to estimate elephant density in wet and dry seasons. A total of 651.5 km was sampled using transect method. For the capture–recapture method, 17 routes were monitored fortnightly. The sampling effort was 128.5 km. The estimated elephant density based on capture–recapture and distance sampling was 3.4 and 3.6/km respectively. The current estimate is higher than previous estimate (1/km in 1985 and 2.4/km in 2000). The adult male to adult female ratio was 1:20. Tuskless male makhnas accounted for 20% of the male population. Demography data showed that there has been an increase in the number of males in the population. However, the lack of older bulls in the population and the mortality of males through retaliatory killing require further investigation. Natural causes such as disease, injuries, and predation by tigers (two calves) accounted for a major percent (61%) of mortality. Sex-biased changes in mortalities have occurred over time. While adult and sub-adult male mortalities were higher (83%) in the earlier study period, adult female mortalities were higher (54.5%) in the present study. Despite the skewed sex ratio, elephant population has increased in the study area.

Background Continuous surveillance of death can measure health status of the population, reflect social development of a region, thus promote health service development in the region and improve the health level of local residents. Liangshan Yi Autonomous Prefecture was a poverty-stricken region in Sichuan province, China. While at the end of 2020, as the announcement of its last seven former severely impoverished counties had shaken off poverty, Liangshan declared victory against poverty. Since it is well known that the mortality and cause of death structure will undergo some undesirable changes as the economy develops, this study aimed to reveal the distribution of deaths, as well as analyze the latest mortality and death causes distribution characteristics in Liangshan in 2020, so as to provide references for the decision-making on health policies and the distribution of health resources in global poverty-stricken areas. Methods Liangshan carried out the investigation on underreporting deaths among population in its 11 counties in 2018, and combined with the partially available data from underreporting deaths investigation data in 2020 and the field experience, we have estimated the underreporting rates of death in 2020 using capture-recapture (CRC) method. The crude mortality rate, age-standardized mortality rate, proportion and rank of the death causes, potential years of life lost (PYLL), average years of life lost (AYLL), potential years of life lost rate (PYLLR), standardized potential years of life lost (SPYLL), premature mortality from non-communicable diseases (premature NCD mortality), life expectancy and cause-eliminated life expectancy were estimated and corrected. Results In 2020, Liangshan reported a total of 16,850 deaths, with a crude mortality rate of 608.75/100,000 and an age-standardized mortality rate of 633.50/100,000. Male mortality was higher than female mortality, while 0-year-old mortality of men was lower than women’s. The former severely impoverished counties’ age-standardized mortality and 0-year-old mortality were higher than those of the non-impoverished counties. The main cause of death spectrum was noncommunicable diseases (NCDs), and the premature NCD mortality of four major NCDs were 14.26% for the overall population, 19.16% for men and 9.27% for women. In the overall population, the top five death causes were heart diseases (112.07/100,000), respiratory diseases (105.85/100,000), cerebrovascular diseases (87.03/100,000), malignant tumors (73.92/100,000) and injury (43.89/100,000). Injury (64,216.78 person years), malignant tumors (41,478.33 person years) and heart diseases (29,647.83 person years) had the greatest burden on residents in Liangshan, and at the same time, the burden of most death causes on men were greater than those on women. The life expectancy was 76.25 years for overall population, 72.92 years for men and 80.17 years for women, respectively, all higher than the global level (73.3, 70.8 and 75.9 years). Conclusions Taking Liangshan in China as an example, this study analyzed the latest death situation in poverty-stricken areas, and proposed suggestions on the formulation of health policies in other poverty-stricken areas both at home and abroad.

Background: Reliable prevalence estimates of family violence in adults and children are difficult to obtain. Most are based on surveys or registration counts, whose research designs and methods are often questionable, making the results difficult to compare. This article presents an alternative approach. Methods: The capture–recapture method (CRC), which makes it possible to estimate unknown numbers in a partly hidden population, was applied to data from eight collaborating organizations in Haarlem, The Netherlands. Results: Uniform data registration took place over a 7-month period. The 1-year prevalence rate for adult victims of family violence was estimated to be 2.0% of the adult population (95% CI: 1.3–3.1). For victims of child abuse, it was 1.5–2.5%, and for child witnesses of spouse-abuse, it was 1.2–2.1%, though small numbers made these results more uncertain. Only ∼20% of all victims in the study were known to one or more of the participating organizations. Our results accorded quite well with results obtained by general health surveys in the Netherlands. Conclusions: CRC appears to be a valid and feasible research method for estimating the prevalence of family violence and child abuse. It can be used to complement other methods, especially in young children, in whom valid results are otherwise difficult to obtain.