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Background: Approximately one-half of cervical cancer cases in the United States occur in underscreened or never-screened women. We examined predictors to completing Papanicolaou (Pap) testing and whether a positive human papillomavirus (HPV) self-collection result affects Pap testing adherence among underscreened women. Materials and Methods: Low-income women aged 30–65 years who reported no Pap testing in ≥4 years were recruited in North Carolina. Knowledge, attitudes, and barriers regarding cervical cancer and Pap testing were assessed by telephone questionnaires. We mailed self-collection kits for HPV testing and provided information regarding where to obtain affordable Pap testing. Participants received $45 for completing all activities. We used multivariable logistic regression to assess the predictors of longer reported time since last Pap (≥10 vs. 4–9 years) and of completion of Pap testing following study enrollment (follow-up Pap). Results: Participants (n = 230) were primarily black (55%), uninsured (64%), and with ≤high school education (59%). Cost and finding an affordable clinic were the most commonly reported barriers to screening. White women and those with ≤high school education reported longer intervals since last Pap test. Half of the participants reported completing a follow-up Pap test (55%). Women with a positive HPV self-collection were five times more likely to report completing a follow-up Pap test than those with negative self-collection (odds ratio = 5.1, 95% confidence interval 1.4–25.7). Conclusions: Improving awareness of resources for affordable screening could increase cervical cancer screening in underserved women. Home-based HPV self-collection represents an opportunity to re-engage infrequently screened women into preventive screening services.

Measurement of skull ossification patterns is a standard method for aging various mammalian species and has been used to age sea otters Enhydra lutris from Russia, California, and Alaska. Cementum annuli counts have also been verified as an accurate aging method for sea otters in Alaska. In this study, we compared cementum annuli count results and skull ossification patterns as methods for aging the northern sea otter, E. l. kenyoni, in Washington State. We found significant agreement between the two methods, suggesting that either method could be used to age sea otters in Washington. We found that ossification of the squamosal–jugal suture at the ventral glenoid fossa can be used to differentiate male subadults from adults. To assist field biologists or others without access to cementum annuli or skull ossification analysis techniques, we analyzed a suite of morphologic, physiologic, and developmental characteristics to assess whether a set of these more easily accessible parameters could also predict age class. We identified tooth condition score, evidence of reproductive activity in females, and tooth eruption pattern as the most useful criteria for classifying sea otters in Washington. We created a simple decision tree based on characteristics accessible in the field or at necropsy, which can be used to reliably predict age class of Washington sea otters as determined by cementum annuli. These techniques offer field biologists and marine mammal stranding networks a replicable, cost-conscious methodology to gather useful biological information from sea otters.

Background Testing for high-risk human papillomavirus (HPV) infection using mailed, self-collected samples is a promising approach to increase screening in women who do not attend clinic screening at recommended intervals. Methods To assess this intervention among high-risk women in the United States, 429 women without a Papanicolaou (Pap) test in 4 or more years (overdue by US guidelines) were recruited from the general population. Participants aged 30 to 65 years were mailed a kit to self-collect a cervicovaginal sample at home, return the sample by mail, and receive HPV results by telephone, with referral to follow-up cytological Pap testing at a local clinic. Cervicovaginal self-samples were collected with a Viba brush, stored in Scope mouthwash, and tested by Hybrid Capture 2. Data were collected in 2010 to 2011 and analyzed in 2017. Results Two-thirds (64%) of participants returned a self-collected sample, of whom 15% tested HPV DNA positive. Human papillomavirus self-test-positive women reported higher rates of follow-up Pap tests (82%) than did those with self-test negative results (51%). No demographic differences were found in self-test return rate or HPV positivity. High acceptability was reported in participant surveys: most women (81%) had "mostly positive" overall thoughts about the self-test, and most reported being comfortable receiving the kit in the mail (99%), returning their self-collected sample by mail (82%), and receiving their test results by telephone (97%). Conclusions Conducting HPV self-testing through population-based recruitment, mailed kit delivery and return by mail, and results delivery by telephone has the potential to reach a broad segment of US underscreened women.

Leprosy (or Hansen’s disease) remains an important public health challenge globally, with an estimated 5.5 million total number of cases and 200,000–300,000 new cases reported annually. The nine-banded armadillo (Dasypus novemcinctus) is the only known natural non-human vertebrate host of Mycobacterium leprae, the causative agent of leprosy, in the Americas, yet gaps in knowledge remain regarding the dynamics of leprosy in wild populations. Here, we used data from a six-year study of a population of armadillos in Mississippi, USA to quantify the influence of leprosy on armadillo population dynamics, and to investigate leprosy dynamics within the host population. Leprosy reduced annual survival of adult armadillos by ∼15%, and growth rate of the population by ∼13%. The annual infection rate for adult armadillos (i.e., probability that a non-leprous adult armadillo seroconverts, conditional on survival) was 0.18, with no possibility of recovery. Assuming frequency-dependent transmission of leprosy, 18% to 25% of the adult armadillos will acquire leprosy infection in the long run. Finally, the basic reproductive ratio (R0) was 1.36, suggesting 36% increase in seroprevalence per leprosy generation. Assuming that leprosy generation time is 3–5 years, M. leprae will spread within the armadillo population at the rate of 7–12% per year. Our results are consistent with recent evidence that leprosy infection in armadillos in the USA is spreading rapidly with a concomitant increase in risk for zoonotic transmissions.

Leaf-cutter ants serve as keystone species that affect and advance diversity, productivity, and nutrient flow in tropical habitats. Field work was conducted in Costa Rica between May and June 2017 to investigate the role of environmental factors on biomass transport by leaf-cutter ants. Thirty leaf-cutter ant trails were identified along hiking paths throughout La Selva Biological Station, and measures corresponding to foraging efficiency (i.e. speed of biomass transport, average mass of forage transported, trail workforce composition, and biomass transport rate) were documented, along with potential environmental determinants of foraging efficiency (e.g. time of day, precipitation, canopy cover, proximity to forage source and colony). Multiple linear regression models indicated that time of day was significantly associated with workforce composition, weight of forage transported, speed of transport, and biomass transport rate. Precipitation was associated with workforce composition, weight of forage transported, and biomass transport rate. Substrate type was associated with speed of transport and biomass transport rate. Forage source proximity was associated with workforce composition, while proximity to colony was associated with weight of forage transported. Biomass transport rate was also associated with canopy cover. This study characterizes key features of the foraging ecology of leaf-cutter ants, and demonstrates how foraging efficiency is affected by environmental factors.

We use Demographic and Health Survey (DHS) data to evaluate the impact of random spatial displacements on analyses that involve assigning covariate values from ancillary areal and point feature data. We introduce a method to determine the maximum probability covariate (MPC), and compare this to the naive covariate (NC) selection method with respect to obtaining the true covariate of interest. The MPC selection method outperforms the NC selection method by increasing the probability that the correct covariate is chosen. Proposed guidelines also address how characteristics of ancillary areal and point features contribute to uncertainty in covariate assignment.

Interference occurs when the treatment of one person affects the outcome of another. For example, in infectious diseases, whether one individual is vaccinated may affect whether another individual becomes infected or develops disease. Quantifying such indirect (or spillover) effects of vaccination could have important public health or policy implications. In this article we use recently developed inverse-probability weighted (IPW) estimators of treatment effects in the presence of interference to analyze an individually-randomized, placebo-controlled trial of cholera vaccination that targeted 121,982 individuals in Matlab, Bangladesh. Because these IPW estimators have not been employed previously, a simulation study was also conducted to assess the empirical behavior of the estimators in settings similar to the cholera vaccine trial. Simulation study results demonstrate the IPW estimators can yield unbiased estimates of the direct, indirect, total, and overall effects of vaccination when there is interference provided the untestable no unmeasured confounders assumption holds and the group-level propensity score model is correctly specified. Application of the IPW estimators to the cholera vaccine trial indicates the presence of interference. For example, the IPW estimates suggest on average 5.29 fewer cases of cholera per 1000 person-years (95% confidence interval 2.61, 7.96) will occur among unvaccinated individuals within neighborhoods with 60% vaccine coverage compared to neighborhoods with 32% coverage. Our analysis also demonstrates how not accounting for interference can render misleading conclusions about the public health utility of vaccination.

We used 15 years (1992–2006) of capture–mark–recapture (CMR) data obtained from a population of nine-banded armadillos (Dasypus novemcinctus) located at the Tall Timbers Research Station near Tallahassee, Florida and multistate CMR models to estimate and model capture probabilities, annual apparent survival, and transition probabilities between reproductive and nonreproductive states (for adult females only). Using an information theoretic approach, we then examined various influences on these parameters. Across all years, capture probability, p, was higher for adults than for yearlings, and higher for males than for females. There was also substantial yearly variation. Conditional on survival, the annual transition probability, ψ, for reproductive adult females to remain reproductive was 0.853 ± 0.044; the estimate for nonreproductive adult females to become reproductive was 0.388 ± 0.060. Annual apparent survival, S, was lowest for juveniles (S = 0.541 ± 0.118) and highest for reproductive adul...

Interventions to mitigate household air pollution (HAP) from cooking with solid fuels often fail to take into account the role of access to freely available woodfuels in determining fuel choice and willingness to adopt clean cooking technologies, key factors in mitigating the burden of HAP. We use national-scale remote sensing data on land use land cover change, and population representative data from two waves of the Malawi Living Standards Measurement Survey to explore the relationship between land use change and the type of fuel households use, time spent collecting fuel, and expenditures on fuel, hypothesizing that land use dynamics influence household-level choice of primary cooking fuel. We find considerable heterogeneity with respect to regeneration and deforestation/degradation dynamics and evidence of spatial clustering. We find that regeneration of forests and woodlands increases the share of households that collect fuelwood, whereas deforestation and degradation lead households to purchase fuelwood. We also find that relatively large share of land under woody savannah or degraded forest (vs. fully stocked forest) increases fuel collection time. Areas with regeneration happening at broader scale experience increases in fuel expenditures. Our findings have implications for the spatial targeting of interventions designed to mitigate household air pollution.

We evaluate the impacts of random spatial displacements on analyses that involve distance measures from displaced Demographic and Health Survey (DHS) clusters to nearest ancillary point or line features, such as health resources or roads. We use simulation and case studies to address the effects of this introduced error, and propose use of regression calibration (RC) to reduce its impact. Results suggest that RC outperforms analyses involving naive distance-based covariate assignments by reducing the bias and MSE of the main estimator in most settings. Proposed guidelines also address the effect of the spatial density of destination features on observed bias.

The Gopher Tortoise (Gopherus polyphemus) is a keystone species whose burrowing activities are of critical importance for upland ecosystems. Perhaps no age class is as enigmatic or vulnerable as hatchlings, and understanding the survival dynamics of hatchlings is critical for the parameterization of population viability models that will help guide adaptive management of this threatened species. Meta-analysis is a particularly useful technique for integrating results of published studies that provide different estimates of a single parameter of interest. A summary value from multiple studies could therefore provide a more representative estimate of a vital rate across heterogeneous study populations. We estimated hatchling survivorship from a natural population in North Florida using hatchling cohorts from a 3-yr telemetry study. To provide continuous estimates of survival through time, we fit weekly telemetry data to Weibull survival curves. We then incorporated the least biased estimate of hatch...

This study presents a case study of how social network and spatial analytical methods can be used simultaneously for disease transmission modeling. The paper first reviews strategies employed in previous studies and then offers the example of transmission of two bacterial diarrheal diseases in rural Bangladesh. The goal is to understand how diseases vary socially above and beyond the effects of the local neighborhood context. Patterns of cholera and shigellosis incidence are analyzed in space and within kinship-based social networks in Matlab, Bangladesh. Data include a spatially referenced longitudinal demographic database which consists of approximately 200,000 people and laboratory-confirmed cholera and shigellosis cases from 1983 to 2003. Matrices are created of kinship ties between households using a complete network design and distance matrices are also created to model spatial relationships. Moran's I statistics are calculated to measure clustering within both social and spatial matrices. Combined spatial effects-spatial disturbance models are built to simultaneously analyze spatial and social effects while controlling for local environmental context. Results indicate that cholera and shigellosis always clusters in space and only sometimes within social networks. This suggests that the local environment is most important for understanding transmission of both diseases however kinship-based social networks also influence their transmission. Simultaneous spatial and social network analysis can help us better understand disease transmission and this study has offered several strategies on how.

Despite a heightened interest regarding the role of infectious diseases in wildlife conservation, few studies have explicitly addressed the impacts of chronic, persistent diseases on long-term host population dynamics. Using mycoplasmal upper respiratory tract disease (URTD) within natural gopher tortoise Gopherus polyphemus populations as a model system, we investigated the influence of chronic recurring disease epizootics on host population dynamics and persistence using matrix population models and Markov chain models for temporally autocorrelated environments. By treating epizootics as a form of environmental stochasticity, we evaluated host population dynamics across varying levels of outbreak duration (r), outbreak recurrence (f ), and disease-induced mortality (m). Baseline results indicated a declining growth rate (l) for populations under unexposed or enzootic conditions (lEnzootic  0.903, 95% CI: 0.765–1.04), and a median time to quasiextinction of 29 years (range: 28–30 years). Under recurring epizootics, stochastic growth rates overlapped with baseline growth rates, and ranged between 0.838–0.902. Median quasi-extinction times under recurring epizootics also overlapped for most scenarios with those of baseline conditions, and ranged between 18–29 years, with both metrics decreasing as a function of f and m. Overall, baseline (enzootic) conditions had a greater impact on l than epizootic conditions, and demographic vital rates were proportionately more influential on l than disease- or outbreak-associated parameters. Lower-level elasticities revealed that, among disease- and outbreak-associated parameters, increases in m, force of infection (φ), and f negatively influenced l. The impact of disease on host population dynamics depended primarily on how often a population underwent an epizootic state, rather than how long the epizootic persisted within the exposed population. The modeling framework presented in this paper could be widely applied to a range of wildlife disease systems in which hosts suffer from persistent recurring diseases.

Upper respiratory tract disease (URTD) caused by Mycoplasma agassizii has been hypothesized to contribute to the decline of some wild populations of gopher tortoises (Gopherus polyphemus). However, the force of infection (FOI) and the effect of URTD on survival in free-ranging tortoise populations remain unknown. Using four years (2003-2006) of mark-recapture and epidemiological data collected from 10 populations of gopher tortoises in central Florida, USA, we estimated the FOI (probability per year of a susceptible tortoise becoming infected) and the effect of URTD (i.e., seropositivity to M. agassizii) on apparent survival rates. Sites with high (or = 25%) seroprevalence had substantially higher FOI (0.22 +/- 0.03; mean +/- SE) than low (25%) seroprevalence sites (0.04 +/- 0.01). Our results provide the first quantitative evidence that the rate of transmission of M. agassizii is directly related to the seroprevalence of the population. Seropositive tortoises had higher apparent survival (0.99 +/- 0.0001) than seronegatives (0.88 +/- 0.03), possibly because seropositive tortoises represent individuals that survived the initial infection, developed chronic disease, and experienced lower mortality during the four-year span of our study. However, two lines of evidence suggested possible effects of mycoplasmal URTD on tortoise survival. First, one plausible model suggested that susceptible (seronegative) tortoises in high seroprevalence sites had lower apparent survival rates than did susceptible tortoises in low seroprevalence sites, indicating a possible acute effect of infection. Second, the number of dead tortoise remains detected during annual site surveys increased significantly with increasing site seroprevalence, from approximately 1 to approximately 5 shell remains per 100 individuals. If (as our results suggest) URTD in fact reduces adult survival, it could adversely influence the population dynamics and persistence of this late- maturing, long-lived species.

We introduce a method for statistically identifying the optimal locations of deep tubewells (dtws) to be installed in Matlab, Bangladesh. Dtw installations serve to mitigate exposure to naturally occurring arsenic found at groundwater depths less than 200 meters, a serious environmental health threat for the population of Bangladesh. We introduce an objective function, which incorporates both arsenic level and nearest town population size, to identify optimal locations for dtw placement. Assuming complete knowledge of the arsenic surface, we then demonstrate how minimizing the objective function over a domain favors dtws placed in areas with high arsenic values and close to largely populated regions. Given only a partial realization of the arsenic surface over a domain, we use a Bayesian spatial statistical model to predict the full arsenic surface and estimate the optimal dtw locations. The uncertainty associated with these estimated locations is correctly characterized as well. The new method is applied to a dataset from a village in Matlab and the estimated optimal locations are analyzed along with their respective 95% credible regions.

We introduce a method for statistically identifying the optimal locations of deep tubewells (dtws) to be installed in Matlab, Bangladesh. Dtw installations serve to mitigate exposure to naturally occurring arsenic found at groundwater depths less than 200 meters, a serious environmental health threat for the population of Bangladesh. We introduce an objective function, which incorporates both arsenic level and nearest town population size, to identify optimal locations for dtw placement. Assuming complete knowledge of the arsenic surface, we then demonstrate how minimizing the objective function over a domain favors dtws placed in areas with high arsenic values and close to largely populated regions. Given only a partial realization of the arsenic surface over a domain, we use a Bayesian spatial statistical model to predict the full arsenic surface and estimate the optimal dtw locations. The uncertainty associated with these estimated locations is correctly characterized as well. The new method is applied to a dataset from a village in Matlab and the estimated optimal locations are analyzed along with their respective 95% credible regions.

Understanding why certain species can successfully colonize new areas while others do not is a central question in ecology. The nine-banded armadillo (Dasypus novemcinctus) is a conspicuous example of a successful invader, having colonized much of the southern United States in the last 200 years. We used 15 years (1992-2006) of capture-mark-recapture data from a population of armadillos in northern Florida in order to estimate, and examine relationships among, various demographic parameters that may have contributed to this ongoing range expansion. Modeling across a range of values for γ, the probability of juveniles surviving in the population until first capture, we found that population growth rates varied from 0.80 for γ = 0.1, to 1.03 for γ = 1.0. Growth rates approached 1.0 only when γ ≥ 0.80, a situation that might not occur commonly because of the high rate of disappearance of juveniles. Net reproductive rate increased linearly with γ, but life expectancy (estimated at 3 years) was independent of γ. We also found that growth rates were lower during a 3-year period of hardwood removal that removed preferred habitat than in the years preceding or following. Life-table response experiment (LTRE) analysis indicated the decrease in growth rate during logging was primarily due to changes in survival rates of adults. Likewise, elasticity analyses of both deterministic and stochastic population growth rates revealed that survival parameters were more influential on population growth than were those related to reproduction. Collectively, our results are consistent with recent theories regarding biological invasions which posit that populations no longer at the leading edge of range expansion do not exhibit strong positive growth rates, and that high reproductive output is less critical in predicting the likelihood of successful invasion than are life-history strategies that emphasize allocation of resources to future, as opposed to current, reproduction.

Interference occurs when the treatment of one person affects the outcome of another. For example, in infectious diseases, whether one individual is vaccinated may affect whether another individual becomes infected or develops disease. Quantifying such indirect (or spillover) effects of vaccination could have important public health or policy implications. In this article we use recently developed inverse-probability weighted (IPW) estimators of treatment effects in the presence of interference to analyze an individually-randomized, placebo-controlled trial of cholera vaccination that targeted 121,982 individuals in Matlab, Bangladesh. Because these IPW estimators have not been employed previously, a simulation study was also conducted to assess the empirical behavior of the estimators in settings similar to the cholera vaccine trial. Simulation study results demonstrate the IPW estimators can yield unbiased estimates of the direct, indirect, total, and overall effects of vaccination when there is interference provided the untestable no unmeasured confounders assumption holds and the group-level propensity score model is correctly specified. Application of the IPW estimators to the cholera vaccine trial indicates the presence of interference. For example, the IPW estimates suggest on average 5.29 fewer cases of cholera per 1000 person-years (95% confidence interval 2.61, 7.96) will occur among unvaccinated individuals within neighborhoods with 60% vaccine coverage compared to neighborhoods with 32% coverage. Our analysis also demonstrates how not accounting for interference can render misleading conclusions about the public health utility of vaccination.

The nine-banded armadillo ( Dasypus novemcinctus ) is the only known nonhuman reservoir of Mycobacterium leprae , the causative agent of Hansen's disease or leprosy. We conducted a 6-yr study on a wild population of armadillos in western Mississippi that was exposed to M. leprae to evaluate the importance of demographic and spatial risk factors on individual antibody status. We found that spatially derived covariates were not predictive of antibody status. Furthermore, analyses revealed no evidence of clustering by antibody-positive individuals. Lactating females and adult males had higher odds of being antibody positive than did nonlactating females. No juveniles or yearlings were antibody positive. Results of these analyses support the hypothesis that M. leprae infection patterns are spatially homogeneous within this armadillo population. Further research related to movement patterns, contact among individuals, antibody status, and environmental factors could help address hypotheses related to the role of environmental transmission on M. leprae infection and the mechanisms underlying the differential infection patterns among demographic groups.

We develop novel methods for conceptualizing geographic space and social networks to evaluate their respective and combined contributions to childhood diarrheal incidence. After defining maternal networks according to direct familial linkages between females, and road networks using satellite imagery of the study area, we use a spatial econometrics model to evaluate the significance of correlation terms relating childhood diarrheal incidence to the incidence observed within respective networks. Disease was significantly clustered within road networks across time, but only inconsistently correlated within maternal networks. These methods could be widely applied to systems in which both social and spatial processes jointly influence health outcomes.