Capture-recapture Estimates Research Articles

Novel hair snare and genetic methods for non-invasive bobcat detection.

Over the past 20 years, the use of non‐invasive hair snare surveys in wildlife research and management has become more prevalent. While these tools have been used to answer important research questions, these techniques often fail to gather information on elusive carnivores, such as bobcats (Lynx rufus). Due to the limited success of previous bobcat studies using hair snares which required active rubbing, this technique has largely fallen out of use, in favor of camera trapping. The goal of our study was to construct a novel, passive bobcat hair snare that could be deployed regardless of terrain or vegetation features, which would be effective for use in capture–recapture population estimation at a large spatial scale. This new hair snare was deployed in 1500 10‐km2 cells across West Virginia (USA) between two sampling seasons (2015–2016). Collected hair samples were analyzed with newly developed mitochondrial DNA primers specifically for felids and qPCR to determine species of origin, with enough sensitivity to identify samples as small as two bobcat hairs. Over the two years of the study, a total of 378 bobcat detections were recorded from 42,000 trap nights of sampling, for an overall rate of 0.9 detections/100 trap nights—nearly 2–6 times greater than any previous bobcat hair snare study. While the overall number of recaptured animals was low (n = 9), continued development of this platform should increase its usefulness in capture–recapture studies.

The Prevalence of Multiple Sclerosis in Liguria region, Italy, using the capture–recapture method

Abstract Background In recent years, there has been a growing interest in developing methods based on health administrative databases to estimate prevalence of chronic diseases, hoping that this would lead to more precise and reliable estimates. The capture-recapture method can be used to improve the prevalence of a disease reducing the bias of case underestimation. Objective To assess the prevalence of multiple sclerosis (MS) in the Liguria region in Italy using routinely collected healthcare data. Methods Data from three sources collected in Liguria Health Authority (A.Li.Sa.) databases - hospital discharge records, drug-dispensing records, disease-specific payment exemptions from copayment to health care - and a capture-recapture method with log-linear models were used to estimate the prevalence of MS. The accuracy of the case-finding algorithm was tested using a sample of people residents in Liguria Region with a definite diagnosis of MS extracted from clinical records of Liguria AISM Rehabilitation Service as reference standard. Results The case-finding algorithm to capture people with MS from routinely collected healthcare data used in our study found an observed crude prevalence of MS in Liguria region on 31 December 2017, of 202.4 cases per 100,000 inhabitants (95% CI: 195.3-209.5). After linkage to clinical data, the algorithm showed a sensitivity of 82.7%, with 22.1% of MS cases undetected on capture-recapture models. Consequently, the capture-recapture method prevalence estimate of 259.7 per 100,000 inhabitants (95% CI: 251.7-267.8) suggest that this region constitutes a high-risk area for MS. Conclusions Our initial study of crude MS prevalence showed an overall regional prevalence of MS of 202.4 cases per 100,000, capture-recapture analysis would add another 20%, to those figures. This method could be considered a good alternative to the population-based study design for estimating the prevalence of MS. Key messages Studies based on routinely collected healthcare data could help estimate the prevalence of chronic disease. To reduce the bias of case underestimation, the capture–recapture method can be useful.

Capture–Recapture Estimation of Characteristics of U.S. Local Food Farms Using a Web-Scraped List Frame

Abstract The emerging sectors of agriculture, such as organics, urban, and local food, tend to be dominated by farms that are smaller, more transient, more diverse, and more dispersed than the traditional farms in the rural areas of the United States. As a consequence, a list frame of all farms within one of these sectors is difficult to construct and, even with the best of efforts, is incomplete. The United States Department of Agriculture’s (USDA’s) National Agricultural Statistics Service (NASS) maintains a list frame of all known and potential U.S. farms and uses this list frame as the sampling frame for most of its surveys. Traditionally, NASS has used its area frame to assess undercoverage. However, getting a good measure of the incompleteness of the NASS list frame using an area frame is cost prohibitive for farms in these emerging sectors that tend to be located within and near urban areas. In 2016, NASS conducted the Local Food Marketing Practices (LFMP) survey. Independent samples were drawn from (1) the NASS list frame and (2) a web-scraped list of local food farms. Using these two samples and capture–recapture methods, the total number and sales of local food operations at the United States, regional, and state levels were estimated. To our knowledge, the LFMP survey is the first survey in which a web-scraped list frame has been used to assess undercoverage in a capture–recapture setting to produce official statistics. In this article, the methods are presented, and the challenges encountered are reviewed. Best practices and open research questions for conducting surveys using web-scraped list frames and capture–recapture methods are discussed.

Abundance estimation for line transect sampling: A comparison of distance sampling and spatial capture-recapture models.

Accurate and precise abundance estimation is vital for informed wildlife conservation and management decision-making. Line transect surveys are a common sampling approach for abundance estimation. Distance sampling is often used to estimate abundance from line transect survey data; however, search encounter spatial capture-recapture can also be used when individuals in the population of interest are identifiable. The search encounter spatial capture-recapture model has rarely been applied, and its performance has not been compared to that of distance sampling. We analyzed simulated datasets to compare the performance of distance sampling and spatial capture-recapture abundance estimators. Additionally, we estimated the abundance of North Atlantic right whales in the southeastern United States with two formulations of each model and compared the estimates. Spatial capture-recapture abundance estimates had lower root mean squared error than distance sampling estimates. Spatial capture-recapture 95% credible intervals for abundance had nominal coverage, i.e., contained the simulating value for abundance in 95% of simulations, whereas distance sampling credible intervals had below nominal coverage. Moreover, North Atlantic right whale abundance estimates from distance sampling models were more sensitive to model specification compared to spatial capture-recapture estimates. When estimating abundance from line transect data, researchers should consider using search encounter spatial capture-recapture when individuals in the population of interest are identifiable, when line transects are surveyed over multiple occasions, when there is imperfect detection of individuals located on the line transect, and when it is safe to assume the population of interest is closed demographically. When line transects are surveyed over multiple occasions, researchers should be aware that individual space use may induce spatial autocorrelation in counts across transects. This is not accounted for in common distance sampling estimators and leads to overly precise abundance estimates.

Key Population Hotspots in Nigeria for Targeted HIV Program Planning: Mapping, Validation, and Reconciliation.

BackgroundWith the fourth highest HIV burden globally, Nigeria is characterized as having a mixed HIV epidemic with high HIV prevalence among key populations, including female sex workers, men who have sex with men, and people who inject drugs. Reliable and accurate mapping of key population hotspots is necessary for strategic placement of services and allocation of limited resources for targeted interventions.ObjectiveWe aimed to map and develop a profile for the hotspots of female sex workers, men who have sex with men, and people who inject drugs in 7 states of Nigeria to inform HIV prevention and service programs and in preparation for a multiple-source capture-recapture population size estimation effort.MethodsIn August 2018, 261 trained data collectors from 36 key population–led community-based organizations mapped, validated, and profiled hotspots identified during the formative assessment in 7 priority states in Nigeria designated by the United States President’s Emergency Plan for AIDS Relief. Hotspots were defined as physical venues wherein key population members frequent to socialize, seek clients, or engage in key population–defining behaviors. Hotspots were visited by data collectors, and each hotspot’s name, local government area, address, type, geographic coordinates, peak times of activity, and estimated number of key population members was recorded. The number of key population hotspots per local government area was tabulated from the final list of hotspots.ResultsA total of 13,899 key population hotspots were identified and mapped in the 7 states, that is, 1297 in Akwa Ibom, 1714 in Benue, 2666 in Cross River, 2974 in Lagos, 1550 in Nasarawa, 2494 in Rivers, and 1204 in Federal Capital Territory. The most common hotspots were those frequented by female sex workers (9593/13,899, 69.0%), followed by people who inject drugs (2729/13,899, 19.6%) and men who have sex with men (1577/13,899, 11.3%). Although hotspots were identified in all local government areas visited, more hotspots were found in metropolitan local government areas and state capitals.ConclusionsThe number of key population hotspots identified in this study is more than that previously reported in similar studies in Nigeria. Close collaboration with key population–led community-based organizations facilitated identification of many new and previously undocumented key population hotspots in the 7 states. The smaller number of hotspots of men who have sex with men than that of female sex workers and that of people who inject drugs may reflect the social pressure and stigma faced by this population since the enforcement of the 2014 Same Sex Marriage (Prohibition) Act, which prohibits engaging in intimate same-sex relationships, organizing meetings of gays, or patronizing gay businesses.

Mixed methods to assess the use of rare illicit psychoactive substances: a case study

Abstract This article presents original mixed method research to describe the use of rare illicit psychoactive substances, with special emphasis on crack cocaine in France. We first introduce a unique monitoring system committed to the observation of hard-to-reach populations. Qualitative findings rely, among others, on perennial ethnographic studies and field professionals’ knowledge to provide guidance to estimate the number of crack cocaine users. We then rely on a set of multilevel capture-recapture estimators, a statistical procedure to indirectly estimate the size of elusive populations. Since prior field evidence suggests an increasing diversity in crack cocaine users’ profiles, we provide a measure of heterogeneity to assess which estimator better fits the data. The calculated estimates are then critically reviewed and debated in light of the previously gathered information. Our results uncover both individual and institutional heterogeneity and suggest that the spread of crack cocaine in France initiated earlier than originally thought. Our case study underlines the need for field-driven assessments to put quantitative results into perspective, a necessary step to tailor efficient health policy responses.

Epidemiologic characteristics of orthopedic surgical site infections and under-reporting estimation of registries using capture-recapture analysis

BackgroundSurgical Site Infections (SSIs) are among the leading causes of the postoperative complications. This study aimed at investigating the epidemiologic characteristics of orthopedic SSIs and estimating the under-reporting of registries using the capture-recapture method.MethodsThis study, which was a registry-based, cross-sectional one, was conducted in six educational hospitals in Tehran during a one-year period, from March, 2017 to March, 2018. The data were collected from two hospital registries (National Nosocomial Infection Surveillance System (NNIS) and Health Information Management database (HIM)). First, all orthopedic SSIs registered in these sources were used to perform capture-recapture (N = 503). Second, 202 samples were randomly selected to assess patients` characteristics.ResultsTotally, 76.24% of SSIs were detected post-discharge. Staphylococcus aureus (11.38%) was the most frequently detected bacterium in orthopedic SSIs. The median time between the detection of a SSI and the discharge was 17 days. The results of a study done on 503 SSIs showed that the coverage of NNIS and HIM was 59.95 and 65.17%, respectively. After capture-recapture estimation, it was found that about 221 of orthopedic SSIs were not detected by two sources among six hospitals and the real number of SSIs were estimated to be 623 ± 36.58 (95% CI, 552–695) and under-reporting percentage was 63.32%.ConclusionTo recognize the trends of SSIs mortality and morbidity in national level, it is significant to have access to a registry with minimum underestimated data. Therefore, according to the weak coverage of NNIS and HIM among Iranian hospitals, a plan for promoting the national Infection Prevention and Control (IPC) programs and providing updated protocols is recommended.

Abundance of East coast Australian humpback whales (Megaptera novaeangliae) in 2005 estimated using multi-point sampling and capture-recapture analysis

The humpback whales (Megaptera novaeangliae) that migrate along the east coast of Australia were hunted to near extinction during the lastcentury. This remnant population is part of Breeding Stock E. Previous abundance estimates for the east Australian portion of Breeding Stock Ehave been based mainly on land-based counts. Here we present a capture-recapture abundance estimate for this population using photo-identificationdata. These data were collected at three locations on the migration route (Byron Bay – northern migration, Hervey Bay and Ballina – southernmigration) in order to estimate the population of humpback whales that migrated along the east coast of Australia in 2005. The capture-recapturedata were analysed using a variety of closed population models with a model-averaged estimate of 7,041 (95% CI 4,075–10,008) whales.

Marked Cards in the Pack: Using Playing Cards to Teach the Importance of Sample Size & Testing Assumptions in Capture–Recapture Estimations of Population Size

Estimating population size is essential for many applications in population ecology, so capture–recapture techniques to do this are often taught in secondary school classrooms and introductory university units. However, few classroom simulations of capture–recapture consider the sensitivity of results to sampling intensity, the important concept that the population size calculated is an estimate with error attached, or the consequences of violating assumptions underpinning particular capture–recapture models. We describe a simple approach to teaching the Lincoln index method of capture–recapture using packs of playing cards. Students can trial different sampling intensities, calculate 95% confidence intervals for population estimates, and explore the consequences of violating specific assumptions.

Estimating the undetected infections in the Covid-19 outbreak by harnessing capture–recapture methods

ObjectivesA major open question, affecting the decisions of policy makers, is the estimation of the true number of Covid-19 infections. Most of them are undetected, because of a large number of asymptomatic cases. We provide an efficient, easy to compute and robust lower bound estimator for the number of undetected cases. MethodsA modified version of the Chao estimator is proposed, based on the cumulative time-series distributions of cases and deaths. Heterogeneity has been addressed by assuming a geometrical distribution underlying the data generation process. An (approximated) analytical variance of the estimator has been derived to compute reliable confidence intervals at 95% level. ResultsA motivating application to the Austrian situation is provided and compared with an independent and representative study on prevalence of Covid-19 infection. Our estimates match well with the results from the independent prevalence study, but the capture–recapture estimate has less uncertainty involved as it is based on a larger sample size. Results from other European countries are mentioned in the discussion. The estimated ratio of the total estimated cases to the observed cases is around the value of 2.3 for all the analyzed countries. ConclusionsThe proposed method answers to a fundamental open question: “How many undetected cases are going around?”. CR methods provide a straightforward solution to shed light on undetected cases, incorporating heterogeneity that may arise in the probability of being detected.

Estimating the prevalence of problem drug use from drug-related mortality data.

Indirect estimation methods are required for estimating the size of populations where only a proportion of individuals are observed directly, such as problem drug users (PDUs). Capture-recapture and multiplier methods are widely used, but have been criticized as subject to bias. We propose a new approach to estimating prevalence of PDU from numbers of fatal drug-related poisonings (fDRPs) using linked databases, addressing the key limitations of simplistic 'mortality multipliers'. Our approach requires linkage of data on a large cohort of known PDUs to mortality registers and summary information concerning additional fDRPs observed outside this cohort. We model fDRP rates among the cohort and assume that rates in unobserved PDUs are equal to rates in the cohort during periods out of treatment. Prevalence is estimated in a Bayesian statistical framework, in which we simultaneously fit regression models to fDRP rates and prevalence, allowing both to vary by demographic factors and the former also by treatment status. We report a case study analysis, estimating the prevalence of opioid dependence in England in 2008/09, by gender, age group and geographical region. Overall prevalence was estimated as 0.82% (95% credible interval=0.74-0.94%) of 15-64-year-olds, which is similar to a published estimate based on capture-recapture analysis. Our modelling approach estimates prevalence from drug-related mortality data, while addressing the main limitations of simplistic multipliers. This offers an alternative approach for the common situation where available data sources do not meet the strong assumptions required for valid capture-recapture estimation. In a case study analysis, prevalence estimates based on our approach were surprisingly similar to existing capture-recapture estimates but, we argue, are based on a much more objective and justifiable modelling approach.

A spatial open-population capture-recapture model.

A spatial open-population capture-recapture model is described that extends both the non-spatial open-population model of Schwarz and Arnason and the spatially explicit closed-population model of Borchers and Efford. The superpopulation of animals available for detection at some time during a study is conceived as a two-dimensional Poisson point process. Individual probabilities of birth and death follow the conventional open-population model. Movement between sampling times may be modeled with a dispersal kernel using a recursive Markovian algorithm. Observations arise from distance-dependent sampling at an array of detectors. As in the closed-population spatial model, the observed data likelihood relies on integration over the unknown animal locations; maximization of this likelihood yields estimates of the birth, death, movement, and detection parameters. The models were fitted to data from a live-trapping study of brushtail possums (Trichosurus vulpecula) in New Zealand. Simulations confirmed that spatial modeling can greatly reduce the bias of capture-recapture survival estimates and that there is a degree of robustness to misspecification of the dispersal kernel. An R package is available that includes various extensions.

Size of the Adult HIV-Infected Population Adjusted for the Unreported AIDS Mortality in the Santa Catarina State, Brazil, 2008-2017.

To estimate the number of 15-79-year-old individuals infected with HIV in the Santa Catarina state, Brazil, during the period 2008-2017. Three official registers of the HIV-infected individuals were compiled: SINAN for the HIV/AIDS epidemiological surveillance, SIM for mortality and SISCEL for the HIV viral load and CD4/CD8 cell count. Their records were linked by a unique personal identifier. Capture-recapture estimates were obtained by log-linear modelling with both the main effects and interaction between the registers, adjusted for age, sex and period. An adjustment for underreporting of AIDS-related deaths used published data on ill-defined causes of death and AIDS mortality. After data sorting, 67340 HIV/AIDS records were identified: 29734 (44.2%) by SINAN, 5540 (8.2%) by SIM and 32066 (47.6%) by SISCEL. After record linkage, the HIV population size was estimated at 45707, whereas the capture-recapture method added 44 individuals. The number of new HIV/AIDS notifications per year increased significantly in 2014-2017 compared to the period 2011-2013 among 15-34-year-old men and less so for older men and women. Including 1512 unreported AIDS-related deaths gave an estimated 47263 HIV-infected individuals with 95% confidence interval (CI) of 47245-47282 and corresponding incidence of 93 (95% CI 91-96) p/100000. Case ascertainment of 62.9%, 78.5% and 67.8% was estimated for SINAN, SIM and SISCEL, respectively. Three major HIV/AIDS registers in Brazil showed significant under-notification of the HIV/AIDS epidemiological surveillance amenable to significant improvement by routine record linkage.

Incidence of and mortality from epidermal necrolysis (Stevens-Johnson syndrome/toxic epidermal necrolysis) in France during 2003-16: a four-source capture-recapture estimate.

Because of its rarity, the exact incidence of and mortality from epidermal necrolysis (Stevens-Johnson syndrome/toxic epidermal necrolysis) is difficult to establish and closely depends on the size and type of the data source. To estimate the incidence of and mortality due to epidermal necrolysis in France over a 14-year period. Data from four national databases were analysed. A capture-recapture analysis was performed. A total of 2635 incident cases of epidermal necrolysis were recorded in at least one of the four databases during the study period [males: 47·9%; median age: 52 (interquartile range 25-72) years]. On capture-recapture analysis, the estimated total number of cases was 5686, for an overall estimated annual incidence of 6·5 (95% confidence interval 4·1-8·9) cases per million inhabitants. The estimated annual incidence rates were 4·1 (0·3-7·9) cases per million inhabitants < 20 years of age, 3·9 (1·5-6·3) cases per million inhabitants aged 20-64 years and 13·7 (5·4-22·0) cases per million inhabitants ≥ 65 years of age. The estimated overall annual mortality rate from epidermal necrolysis was 0·9 (0·1-1·8) case per million inhabitants. It was 0·6 (0·1-1·5) case per million inhabitants aged 20-64 years and 2·8 (0·9-6·6) cases per million inhabitants ≥ 65 years of age (deaths in people < 20 years old were too rare to provide an accurate estimate). The annual incidence of epidermal necrolysis is higher than the one to five cases per million inhabitants usually reported. Such estimations could be helpful in establishing appropriate healthcare plans for people with epidermal necrolysis, in particular the need for specialized care units. What's already known about this topic? Few data are available regarding incidence of and mortality from epidermal necrolysis in the general population. Experts in epidermal necrolysis have recently proposed an annual incidence of one to five cases per million individuals. The overall mortality rate is usually reported to be between 10% and 20%. What does this study add? Using a four-source capture-recapture method and data from a 14-year period (2003-16), the annual incidence of and mortality from epidermal necrolysis were estimated to be 6·5 (95% confidence interval 4·1-8·9) and 0·9 (0·1-1·8) cases per million French inhabitants, respectively. Such estimations could be helpful in establishing appropriate healthcare plans, in particular the need for specialized care units.

Surveillance, Epidemiology, and Estimated Burden of Neonatal Abstinence Syndrome, Tennessee, 2013-2016.

Between 2003 and 2013, the rate of neonatal abstinence syndrome (NAS)-a postnatal drug withdrawal syndrome-in Tennessee increased approximately 10-fold. NAS surveillance is relatively new, and underestimation associated with surveillance has not been described. We compared data from the Tennessee NAS public health surveillance system (TNSS) with a second source of NAS data, hospital discharge data system (HDDS), and estimated the true number of infants with NAS using capture-recapture methods. We obtained NAS data on cases of NAS among Tennessee infants from TNSS and HDDS from January 1, 2013, through December 31, 2016. We matched cases of NAS identified in TNSS to cases identified in HDDS. We estimated the true number of infants with NAS by using the Lincoln-Peterson estimator capture-recapture methodology. During the study period, 4070 infants with NAS were reported to TNSS, and 5321 infants with NAS were identified in HDDS; 2757 were in both data sets. Using capture-recapture methods, the total estimated number of infants with NAS during the study period was 7855 (annual mean = 1972; estimated range = 1531-2427), which was 93% more than in TNSS and 48% more than in HDDS. Drugs used for the medication-assisted treatment of substance use disorder were the most commonly reported substances associated with NAS (n = 2389, 59%). TNSS underestimated the total burden of NAS based on the capture-recapture estimate. Case-based public health surveillance is important for monitoring the burden of and risk factors for NAS and helping guide public health interventions.

A low cost approach to estimate demographic rates using inverse modeling

Survival is a key parameter in species' management and conservation. Most methods for estimating survival require time series data, large sample sizes and, overall, costly monitoring efforts. Inverse modeling approaches can be less data hungry, however they are underused in conservation sciences. Here we present an inverse modeling approach for estimating relative survival rates of long-lived species that is mathematically straightforward and evaluate its performance under constraints common in conservation studies related to small sample sizes. Specifically, we (i) estimated the relative survival rates in a Testudo graeca population based on one-year monitoring, (ii) assessed the impact of sample size on the accuracy, and (iii) tested alternative hypotheses on the impact of fire on the survival rates. We then compared the results of our approach with capture-recapture (CRC) estimates based on long-term monitoring. Our approach (153 individuals within a single year) yielded estimates of survival rates overlapping those of CRC estimates (11 years of data and 1009 individuals) for adults and subadults, but not for juveniles. Simulation experiments showed that our method provides robust estimates if sample size is above 100 individuals. The best models describing the impact of fire on survival identified by our approach predicts a decrease in survival especially in hatchlings and juvenile individuals, similar to CRC estimates. Our work proves that inverse modeling can decrease the cost for estimating demographic rates, especially for long-lived species and as such, its use should be encouraged in conservation and management sciences.

Novel Approaches for Estimating Female Sex Worker Population Size in Conflict-Affected South Sudan.

BackgroundLimited data exist describing the population size of female sex workers (FSW) in South Sudan. A population size estimation exercise among FSW was undertaken in Juba and Nimule during the Eagle Survey.ObjectiveThe study aimed to estimate the number of FSW in Juba and Nimule to inform resource allocation and service provision for FSW.MethodsWe utilized service and unique object multipliers, and 3-source capture-recapture methods in conjunction with a respondent-driven sampling (RDS) survey to estimate the number of FSW in Juba and Nimule. For service multiplier, the number of FSW testing for HIV in 2015 (Juba) and 2016 (Nimule) was obtained from the LINKAGES program targeting FSW. Survey participants were asked whether they had been tested for HIV by LINKAGES during the relevant period. A total of 2 separate unique object distributions were conducted in Juba and Nimule. In Nimule, these were combined to produce a 3-source capture-recapture estimate. The exercise involved distribution of key chains and bangles to FSW, documentation of the number of those who received unique objects, and questions during RDS survey to assess whether participants received unique objects.ResultsIn Juba, the service multiplier method yielded an estimate of 5800 (95% CI 4927-6673) FSW. The unique object estimate (key chain and RDS participation) yielded 5306 (95% CI 4673-5939). Another estimate using RDS participation and receipt of a bangle yielded a much lower estimate of 1863 (95% CI 1776-1951), as did a 2-source estimate of key chain and bangle (2120, 95% CI 2028-2211). A 3-source capture-recapture estimate could not be produced because aggregate rather than individual level data were collected during the third capture. The multiplier estimate using key chain and RDS participation was taken as the final population estimate for FSW in Juba, which constitutes more than 6% of the female population aged 15 to 64 years.In Nimule, the service multiplier method yielded an estimate of 9384 (95% CI 8511-10,257). The 2-source estimates for key chain and RDS yielded 6973 (95% CI 4759-9186); bangles and RDS yielded a higher estimate of 13,104 (95% CI 7101-19,106); key chains and bangles yielded a lower estimate of 1322 (95% CI 1223-1420). The 3-source capture-recapture method using Bayesian nonparametric latent-class model-based estimate yielded a population of 2694 (95% CI 1689-6945), and this was selected as the final estimate for Nimule, which constitutes nearly 40% of female population aged 15 to 64 years.ConclusionsThe service and unique object multiplier, and 3-source capture-recapture methods were successfully used to estimate the number of FSW in Nimule, whereas service and unique object multiplier methods were successfully used in Juba. These methods yielded higher than previously estimated FSW population sizes. These estimates will inform resource allocation and advocacy efforts to support services for FSW.

Low-risk population size estimates in the presence of capture heterogeneity

SUMMARY Population estimation methods are used for estimating the size of a population from samples of individuals. In many applications, the probability of being observed in the sample varies across individuals, resulting in sampling bias. We show that in this setting, estimators of the population size have high and sometimes infinite risk, leading to large uncertainty in the population size. As an alternative, we propose estimating the population of individuals with observation probability exceeding a small threshold. We show that estimators of this quantity have lower risk than estimators of the total population size. The proposed approach is shown empirically to result in large reductions in mean squared error in a common model for capture-recapture population estimation with heterogeneous capture probabilities.

WED 225 Changing epidemiology of motor neurone disease in scotland

Clinical Audit Research and Evaluation for Motor Neurone Disease (CARE-MND) (formerly the Scottish MND Register) is an electronic platform for prospective, population-based research in Scotland. We present the results of recent epidemiological analyses.Capture-recapture methods were adopted to determine incidence of MND in 2015–2016: 1) notifications via CARE-MND, 2) Information Services Division (ISD) data i) hospital admissions, ii) death data, iii) riluzole prescriptions.Crude prevalence of MND in Scotland is 7.61–7.64/100,000 of the population. Direct age standardised incidence in 2015 was 3.42/100,000 (95% CI: 2.99 to 3.91); in 2016, 2.89/100,000 (95% CI: 2.50 to 3.34). Using maximum likelihood estimates, coverage of the CARE-MND platform was 99.6% (2015) and 98.1% (2016). In view of high coverage, CARE-MND estimates alone will be presented for 2017.Previous Scottish MND Register capture-recapture annual incidence estimates were 2.32/100,000 (95% CI: 2.26 to 2.37) in 1989–1998 (direct standardised). Our data suggest a changing landscape of MND in Scotland, with a rise in incidence by 36%. This could be attributed to improved neurological services in Scotland. Incidence is also 67% higher than Northern European estimates, which perhaps reflects our robust ascertainment methods. However, further work using population CARE-MND data aims to determine if genetic or environmental variables account for these findings.

A Bayesian state-space model using age-at-harvest data for estimating the population of black bears (Ursus americanus) in Wisconsin

Population estimation is essential for the conservation and management of fish and wildlife, but accurate estimates are often difficult or expensive to obtain for cryptic species across large geographical scales. Accurate statistical models with manageable financial costs and field efforts are needed for hunted populations and using age-at-harvest data may be the most practical foundation for these models. Several rigorous statistical approaches that use age-at-harvest and other data to accurately estimate populations have recently been developed, but these are often dependent on (a) accurate prior knowledge about demographic parameters of the population, (b) auxiliary data, and (c) initial population size. We developed a two-stage state-space Bayesian model for a black bear (Ursus americanus) population with age-at-harvest data, but little demographic data and no auxiliary data available, to create a statewide population estimate and test the sensitivity of the model to bias in the prior distributions of parameters and initial population size. The posterior abundance estimate from our model was similar to an independent capture-recapture estimate from tetracycline sampling and the population trend was similar to the catch-per-unit-effort for the state. Our model was also robust to bias in the prior distributions for all parameters, including initial population size, except for reporting rate. Our state-space model created a precise estimate of the black bear population in Wisconsin based on age-at-harvest data and potentially improves on previous models by using little demographic data, no auxiliary data, and not being sensitive to initial population size.