Limited Survey Data Research Articles

Loneliness, adolescence, and global mental health: Soledad and structural violence in Mexico.

In this article, we call into question recent public health claims that loneliness is a problem of epidemic proportions. Current research on this topic is hindered by an overreliance on limited survey data and by paradigmatic imbalance that delineates the study of loneliness to psychological, cognitive, neuroendocrinological and immunological effects, social functioning, physical health, mortality, and gene effects. The article emphasizes that scientific approaches to the phenomena of loneliness are more appropriately conceived and investigated as inherently matters for social, relational, cultural, and contextual analysis of subjective experience. Studies of loneliness and possible relationships to mental health status require investigations of social, environmental, and institutional structures as well as families, peers, friends, counselors, and health providers. This article takes a step in this direction through examining the lived experience of 35 high school students and their families living under conditions of social adversity in Tijuana, B.C., Mexico, with attention to anxiety and depression. Utilizing ethnographic interviews, observations, and psychological screening tools, we provide an overview for the group and illustrate the interrelations of subjective experience and social environment through a case study. These data reveal the vital role of understandings of loneliness, depression, and anxiety from the perspectives of adolescents themselves. We conclude that future studies of loneliness are best informed by in-depth data on subjective experience in relation to social features to advance understandings within the field of global mental health and allied fields.

Twin Cities Mobile Market Food Delivery Model: A Preliminary Study Describing Results of A Customer Intercept Survey and Point of Sale Data for 2016

ABSTRACT Mobile food markets are a potential solution to combat well-documented healthy food access disparities. This descriptive study analyzes sales and limited survey data of a mobile market aiming to increase access to affordable, healthy foods from all food groups. Of 2016 sales, 50% were fruits/vegetables, 20% were proteins, and 18% were dairy. Of food items stocked, 42% were fruits/vegetables, 18% were proteins, and 18% were dry-goods. Frequent mobile market shopping was associated with self-reported positive purchasing- and dietary-behaviors. These exploratory findings suggest mobile markets selling affordable, healthy foods may be a compelling option for households facing constraints of low-food access.

Estimating the size of urban populations using Landsat images: a case study of Bo, Sierra Leone, West Africa

BackgroundThis is the third paper in a 3-paper series evaluating alternative models for rapidly estimating neighborhood populations using limited survey data, augmented with aerial imagery.MethodsBayesian methods were used to sample the large solution space of candidate regression models for estimating population density.ResultsWe accurately estimated the population densities and counts of 20 neighborhoods in the city of Bo, Sierra Leone, using statistical measures derived from Landsat multi-band satellite imagery. The best regression model proposed estimated the latter with an absolute median proportional error of 8.0%, while the total population of the 20 neighborhoods was estimated with an error of less than 1.0%. We also compare our results with those obtained using an empirical Bayes approach.ConclusionsOur approach provides a rapid and effective method for constructing predictive models for population densities and counts utilizing remote sensing imagery. Our results, including cross-validation analysis, suggest that masking non-urban areas in the Landsat section images prior to computing the candidate covariate regressors should further improve model generality.

Medication Adherence Prediction Through Online Social Forums: A Case Study of Fibromyalgia.

BackgroundMedication nonadherence can compound into severe medical problems for patients. Identifying patients who are likely to become nonadherent may help reduce these problems. Data-driven machine learning models can predict medication adherence by using selected indicators from patients’ past health records. Sources of data for these models traditionally fall under two main categories: (1) proprietary data from insurance claims, pharmacy prescriptions, or electronic medical records and (2) survey data collected from representative groups of patients. Models developed using these data sources often are limited because they are proprietary, subject to high cost, have limited scalability, or lack timely accessibility. These limitations suggest that social health forums might be an alternate source of data for adherence prediction. Indeed, these data are accessible, affordable, timely, and available at scale. However, they can be inaccurate.ObjectiveThis paper proposes a medication adherence machine learning model for fibromyalgia therapies that can mitigate the inaccuracy of social health forum data.MethodsTransfer learning is a machine learning technique that allows knowledge acquired from one dataset to be transferred to another dataset. In this study, predictive adherence models for the target disease were first developed by using accurate but limited survey data. These models were then used to predict medication adherence from health social forum data. Random forest, an ensemble machine learning technique, was used to develop the predictive models. This transfer learning methodology is demonstrated in this study by examining data from the Medical Expenditure Panel Survey and the PatientsLikeMe social health forum.ResultsWhen the models are carefully designed, less than a 5% difference in accuracy is observed between the Medical Expenditure Panel Survey and the PatientsLikeMe medication adherence predictions for fibromyalgia treatments. This design must take into consideration the mapping between the predictors and the outcomes in the two datasets.ConclusionsThis study exemplifies the potential and limitations of transfer learning in medication adherence–predictive models based on survey data and social health forum data. The proposed approach can make timely medication adherence monitoring cost-effective and widely accessible. Additional investigation is needed to improve the robustness of the approach and extend its applicability to other therapies and other sources of data.

Bayesian networks for spatial learning: a workflow on using limited survey data for intelligent learning in spatial agent-based models

Machine learning (ML) algorithms steer agent decisions in agent-based models (ABMs), serving as a vehicle for implementing behaviour changes during simulation runs. However, when training an ML algorithm, obtaining large sets of micro-level human behaviour data is often problematic. Information on human behaviour is often collected via surveys of relatively small sample sizes. This paper presents a methodology for training a learning algorithm to guide agent behaviour in a spatial ABM using a limited survey data sample. We apply different implementation strategies using survey data and Bayesian networks (BNs). By being grounded in probabilistic directed graphical models, BNs stand out among other learning algorithms in that they can be based on expert knowledge and/or known datasets. This paper presents four alternative implementations of data-driven BNs to support agent decisions in a spatial ABM. We differentiate between training BNs prior to, or during the simulation runs, using only survey data or a combination of survey data and expert knowledge. The four different implementations are then illustrated using a spatial ABM of cholera diffusion for Kumasi, Ghana. The results indicate that a balance between expert knowledge and survey data provides the best control over the learning process of the agents and produces the most realistic agent behaviour.

Testing the Proposed Municipality Resilience Index to Climate Change Shocks and Stresses in Mbale Municipality in Eastern Uganda

Since climate change shocks and stresses cannot be fully prevented, building resilient urban areas is gaining more attention in the global community. By building resilience, the negative impacts of climate change shocks and stresses can be alleviated. Several indices have been developed to measure urban resilience. Yet, most of these indices focus more on objective methods which require robust bio-physical and socio-economic data sets which are generally lacking in many developing countries. To reduce this challenge, the use of subjective methods has recently been suggested. This study proposed and tested a Municipality Resilience Index (MRI) which employed a subjective method to assess the resilience of Mbale municipality in Eastern Uganda against climate change shocks and stresses. The proposed MRI includes 46 variables describing the physical, social, economic and institutional dimensions. The MRI can be applied in any municipality in developing countries facing climate related shocks and stresses and with limited survey data. The application of this index to Mbale municipality shows that the municipality has a low resilience index of 0.2. Similarly, most variables in the four dimensions of resilience reflected very low resilience scores with other divisions being more resilient than the others. Furthermore, the social dimension has the lowest score as compared to the physical, economic and institutional dimensions. The findings indicate a spatial variability in the contribution of the resilience dimensions within this small geographic confine. Moreover, the findings show the strengths and weaknesses in the different dimensions of the proposed MRI. This can act as a guide for policy and practitioners on which sectors to target in order to enhance the resilience of Mbale municipality.

Satellite data indicates multidimensional variation of agricultural production in land consolidation area

Land consolidation (LC) is an innovative way to improve agricultural production. Spatiotemporal pattern of agricultural production in land consolidation area (LCA) is difficult to quantify with limited field observations and survey data. Satellite data has advantages on recording vegetation status changes frequently, which is very supportive of estimating variation of agricultural production. In this paper, we used Net Primary Productivity (NPP), Normal Difference Vegetation Index (NDVI), and Multiple Band Drought Index (MBDI) from satellite data, to examine five attributes (irrigation capacity, multiple cropping index, crop phenology, farmland productivity, and production stability) of agricultural production after land consolidation (LC) at a site in China. Results show that there were no significant spatial differences in irrigation capacity for farmland in few years after LC due to consistent climatic conditions and uniform irrigation and drainage system. Multiple cropping index shows a pattern of “first reducing, then growing, last reducing”, which may result from the disturbed “water-soil” environment and weak farmers' intention. Interannual variation of spatial distribution of phenology for the second-season crop is larger than that for the first-season crop since LC implementation adjusts short-term land use and management. With the improvement of production conditions and balanced distribution of production elements, farmland productivity has been improved and its differences among various farmland patches imply a reducing trend. Production in LCA is slightly less stable than that in the control area (TCA) where LC is not carried out because of limited and short-term effect from LC. We concluded that satellite data presents variation of agricultural production in LCA from different dimensions of time, space and attributes. Multidimensional variation of agricultural production is decided by several factors, including climate condition, LC activity, and farmers' intention.

Understanding activity engagement across weekdays and weekend days: A multivariate multiple discrete-continuous modeling approach

This paper is motivated by the increasing recognition that modeling activity-travel demand for a single day of the week, as is done in virtually all travel forecasting models, may be inadequate in capturing underlying processes that govern activity-travel scheduling behavior. The considerable variability in daily travel suggests that there are important complementary relationships and competing tradeoffs involved in scheduling and allocating time to various activities across days of the week. Both limited survey data availability and methodological challenges in modeling week-long activity-travel schedules have precluded the development of multi-day activity-travel demand models. With passive and technology-based data collection methods increasingly in vogue, the collection of multi-day travel data may become increasingly commonplace in the years ahead. This paper addresses the methodological challenge associated with modeling multi-day activity-travel demand by formulating a multivariate multiple discrete-continuous probit (MDCP) model system. The comprehensive framework ties together two MDCP model components, one corresponding to weekday time allocation and the other to weekend activity-time allocation. By tying the two MDCP components together, the model system also captures relationships in activity-time allocation between weekdays on the one hand and weekend days on the other. Model estimation on a week-long travel diary data set from the United Kingdom shows that there are significant inter-relationships between weekdays and weekend days in activity-travel scheduling behavior. The model system presented in this paper may serve as a higher-level multi-day activity scheduler in conjunction with existing daily activity-based travel models.

"My mentor didn't speak to me for the first four weeks": Perceived Unfairness experienced by nursing students in clinical practice settings.

To explore the perceived unfairness experienced by student nurses during their undergraduate clinical placements. It is important that student nurses feel supported by practice staff during their clinical placement education experiences. However, it has been reported that learners can feel ignored, unsupported and bullied by others in the clinical environment and this has a detrimental effect on their learning. It is important to understand the student nurse perspective and explore ways in which their feelings of belongingness might be enhanced in the clinical area. A descriptive narrative approach was used to explore the qualitative data generated by the survey and interviews. Limited closed-question survey data were acquired to explore a selection of quantified survey responses about placements and mentorship. A survey was conducted with 1,425 student nurses from adult and mental health degree nursing pathways, across nine institutions in the North West of England, UK. Unstructured interviews were undertaken with 22 student nurses from across these nine institutions. The data generated from both methods (free-text survey responses and interview) were thematically analysed. There were times when student nurses felt that they had been treated unfairly by various members of the healthcare team during their clinical placements. Unfairness was related to being ignored and unsupported or being used as an "extra pair of hands" and having their supernumerary status ignored. Student nurses want to have feelings of belongingness in the clinical area and value enthusiasm for teaching from mentors. Certain positive mentor qualities were identified through the data in this study. These have been used to inform a tiered model of mentorship, to inform future thinking about student nurse education. Student nurses can feel like they are being treated unfairly in the clinical area in numerous ways. Identifying ways in which mentorship practice can be developed to adequately support education is important. This can lead to satisfaction and development on both sides of the student/educator relationship. Exploring student nurse perceptions of their learning is important when attempting to enhance educational practice in the clinical setting.

Application of smart card data in validating a large-scale multi-modal transit assignment model

The accuracy of transit assignment plays an important role in the successful design and operation of a transit system. The majority of previous studies on validating transit assignment models has used limited survey data or has lacked a large-scale multimodal and high quality dataset. Considering the advantages of smart card [automatic fare collection (AFC)] systems, the aims of this study are to put forward a methodological framework to validate existing transit assignment models and to quantify the performance of these models. Our study combines data from three sources: the General Transit Feed Specification, an AFC system, and a strategic transport model from a large-scale multimodal public transport network, namely the South-East Queensland (SEQ) network in Australia. The AFC system in SEQ has provided a very large and highly accurate dataset on passenger boardings and alightings for the three transit modes of bus, rail and ferry. Following a data analysis, an origin–destination trip matrix is estimated for the AM peak period using AFC data as an input to the transit assignment model. Then, the results of the transit assignment model are compared with the actual passengers’ route choices over the same period, at different levels of aggregation. The model performance is quantified by each route (and direction), by each segment of each route (and direction), and by each stop. The results indicate that relatively tighter thresholds are required to validate the transit assignment at the segment level than at the stop level. Furthermore, the validation results indicate that the greatest error is realized for the bus mode, while the level of accuracy in the rail mode is the best. The results suggest a segment-level analysis should be used as the most useful level of aggregation for future calibration and validation of transit assignment models.

Optimizing coastal and marine spatial planning through the use of high-resolution benthic sensitivity models

Effective management of coastal and marine resources requires knowledge of how community sensitivity varies spatially. With this in mind, we developed a benthic sensitivity index (SI), based on the distribution and abundance of five ecological groups that can be used to assess community tolerance to organic enrichment and other disturbances. The index, projected as a high-resolution map, ranks communities from those dominated by sensitive and ecologically important species (i.e. low SI values) to those composed mainly of tolerant and/or opportunistic species (i.e., high SI values). Applying our model to a multiple-use case study in southeast Brazil, we were able to show considerable variability in the sensitivity of communities across the study area that was relatively stable over time. This allowed us to evaluate the possible direct (i.e., spatially overlapping) and indirect effects (i.e., cumulative changes to the physical environment) of a range of activities on sensitive and ecologically diverse benthic communities. Our approach and the resulting high-resolution maps hold promise for a range of spatial planning applications, including the development of coastal infrastructure, assessments of the representativeness of marine protected areas and other activities such as the selection of appropriate locations for dredge spoil dumping. Overall, we present a novel and transparent way of extrapolating limited survey data to provide spatial and temporal information on the sensitivity of benthic communities in multiple-use coastal and marine areas.

Towards a flexible Decision Support Tool for MSY-based Marine Protected Area design for skates and rays

It is recommended that demersal elasmobranchs be managed using spatial proxies for Maximum Sustainable Yield. Here we combine escapement biomass—the percentage of the stock which must be retained each year to conserve it—with maps of predicted Catch Per Unit Effort (CPUE) of four ray species [cuckoo (Leucoraja naevus), thornback (Raja clavata), blonde (Raja brachyura), and spotted (Raja montagui)], created using Boosted Regression Tree modelling. We then use a Decision Support Tool to generate location and size options for Marine Protected Areas to protect these stocks, based on the priorities of the various stakeholders, notably the minimisation of fishing effort displacement. Variations of conservation/fishing priorities are simulated, as well as differential priorities for individual species, with a focus on protecting nursery grounds and spawning areas. Prioritizing high CPUE cells results in a smaller closed area that displaces the most fishing effort, whereas prioritizing low fishing effort results in a larger closed area that displaces the least fishing effort. The final result is a complete software package that produces maps of predicted species CPUE from limited survey data, and allows disparate stakeholders and policymakers to discuss management options within a mapping interface.

A Bayesian state-space model to estimate population biomass with catch and limited survey data: application to the thornback ray (Raja clavata) in the Bay of Biscay

The thornback ray (Raja clavata ) in the Bay of Biscay is presumed to have declined during the 20th Century. To evaluate this decline and estimate biomass trajectories, a hypothetical catch time series was created for the period 1903–2013. A Bayesian state-space biomass production model with a Schaefer production function was fitted to the hypothetical catch time series and to a shorter research vessel Catch Per Unit Eeffort (CPUE) time series (1973–2013, with missing years). A censored likelihood made it possible to obtain biomass estimates without a CPUE time series or only with an estimate of biomass depletion. A simulation-estimation approach showed a high sensitivity of results to the prior for the intrinsic growth rate. The model provided biomass trajectories which corroborated and quantified the decline of the Bay of Biscay population. The estimated biomass corresponding to the maximum sustainable yield, B MSY , was 32 000 tonnes, which is 17 times higher than the estimated biomass in 2014. The biomass estimates obtained without a CPUE time series were highly uncertain. Adding a current biomass depletion observation improved precision, though the biomass time trend was sensitive to this value. Results should be interpreted carefully as several assumptions were necessary to create the long catch time series and to define informative priors, notably for the intrinsic growth rate. Despite this, the results confirm the depleted state of the thornback ray in the Bay of Biscay with the estimated biomass in 2014 being around 3% of carrying capacity.

Comparing Strategies for Estimating Constituency Opinion from National Survey Samples

Political scientists interested in estimating how public opinion varies by constituency have developed several strategies for supplementing limited constituency survey data with additional sources of information. We present two evaluation studies in the previously unexamined context of British constituency-level opinion: an external validation study of party vote share in the 2010 general election and a cross-validation of opinion toward the European Union. We find that most of the gains over direct estimation come from the inclusion of constituency-level predictors, which are also the easiest source of additional information to incorporate. Individual-level predictors combined with post-stratification particularly improve estimates from unrepresentative samples, and geographic local smoothing can compensate for weak constituency-level predictors. We argue that these findings are likely to be representative of applications of these methods where the number of constituencies is large.

A morphometric comparison of the Namib and southwest Kalahari dunefields using ASTER GDEM data

The increased availability of digital elevation models and satellite image data enable testing of morphometric relationships between sand dune variables (dune height, spacing and equivalent sand thickness), which were originally established using limited field survey data. These long-established geomorphological hypotheses can now be tested against very much larger samples than were possible when available data were limited to what could be collected by field surveys alone. This project uses ASTER global digital elevation model (GDEM) data to compare morphometric relationships between sand dune variables in the southwest Kalahari dunefield to those of the Namib sand sea, to test whether the relationships found in an active sand sea (Namib) also hold for the fixed dune system of the nearby southwest Kalahari. The data show significant morphometric differences between the simple linear dunes of the Namib sand sea and the southwest Kalahari; the latter do not show the expected positive relationship between dune height and spacing. The southwest Kalahari dunes show a similar range of dune spacings, but they are less tall, on average, than the Namib sand sea dunes. There is a clear spatial pattern to these morphometric data; the tallest and most closely spaced dunes are towards the southeast of the Kalahari dunefield; and this is where the highest values of equivalent sand thickness result. We consider the possible reasons for the observed differences and highlight the need for more studies comparing sand seas and dunefields from different environmental settings.

An agent-based model to simulate tsetse fly distribution and control techniques: A case study in Nguruman, Kenya

BackgroundAfrican trypanosomiasis, also known as “sleeping sickness” in humans and “nagana” in livestock is an important vector-borne disease in Sub-Saharan Africa. Control of trypanosomiasis has focused on eliminating the vector, the tsetse fly (Glossina, spp.). Effective tsetse fly control planning requires models to predict tsetse population and distribution changes over time and space. Traditional planning models have used statistical tools to predict tsetse distributions and have been hindered by limited field survey data. Methodology/resultsWe developed an Agent-Based Model (ABM) to provide timing and location information for tsetse fly control without presence/absence training data. The model is driven by daily remotely-sensed environment data. The model provides a flexible tool linking environmental changes with individual biology to analyze tsetse control methods such as aerial insecticide spraying, wild animal control, releasing irradiated sterile tsetse males, and land use and cover modification. SignificanceThis is a bottom-up process-based model with freely available data as inputs that can be easily transferred to a new area. The tsetse population simulation more closely approximates real conditions than those using traditional statistical models making it a useful tool in tsetse fly control planning.

Methods for determining the uncertainty of population estimates derived from satellite imagery and limited survey data: a case study of Bo city, Sierra Leone.

This study demonstrates the use of bootstrap methods to estimate the total population of urban and periurban areas using satellite imagery and limited survey data. We conducted complete household surveys in 20 neighborhoods in the city of Bo, Sierra Leone, which collectively were home to 25,954 persons living in 1,979 residential structures. For five of those twenty sections, we quantized the rooftop areas of structures extracted from satellite images. We used bootstrap statistical methods to estimate the total population of the pooled sections, including the associated uncertainty intervals, as a function of sample size. Evaluations based either on rooftop area per person or on the mean number of occupants per residence both converged on the true population size. We demonstrate with this simulation that demographic surveys of a relatively small proportion of residences can provide a foundation for accurately estimating the total population in conjunction with aerial photographs.

Bayesian networks for habitat suitability modeling: a potential tool for conservation planning with scarce resources.

Bayesian networks (BN) have been increasingly used for habitat suitability modeling of threatened species due to their potential to construct robust models with limited survey data. However, previous applications of this approach have only occurred in countries where human and budget resources are highly available, but the highest concentrations of threatened vertebrates globally are located in the tropics where resources are much more limited. We assessed the effectiveness of Bayesian networks in generating habitat suitability models in Thailand, a biodiversity-rich country where the knowledge base is typically sparse for a wide range of threatened species. The Bayesian network approach was used to generate habitat suitability maps for 52 threatened vertebrate species in Thailand, using a range of evidence types, from relatively well-documented species with good local knowledge to poorly documented species, with few local experts. Published information and expert knowledge were used to define habitat requirements. Focal species were categorized into 22 groups based on known habitat preferences, and then habitat suitability models were constructed with outcomes represented spatially. Models had a consistent structure with three major components: potential habitat, known range, and threat level. Model classification sensitivity was tested using presence-only field data for 21 species. Habitat models for 12 species were relatively sensitive (>70% congruency between observed and predicted locations), three were moderately congruent, and six were poor. Classification sensitivity tended to be high for bird models and moderate for mammals, whereas sensitivity for reptiles was low, presumably reflecting the relatively poor knowledge base for reptiles in the region. Bayesian network models show significant potential for biodiversity-rich regions with scarce resources, although they require further refinement and testing. It is possible that one detailed ecological study is sufficient to develop a model with reasonable sensitivity, but BN models for species groups with no quantitative data continue to be problematic.

Uncertainty assessment for mapping changes in soil organic matter using sparse legacy soil data and dense new-measured data in a typical black soil region of China

A total of 89 legacy soil organic matter (SOM) data (0–20 cm) sourced from the soil survey in 1980 in Hailun County of Northeastern China and 268 SOM data (0–20 cm) measured in the area in 2008 were collected in this study. The most straightforward mapping methods such as ordinary kriging (OK), inverse distance weighting interpolation (IDW), and the soil type-based method (ST) were used to derive the spatial patterns of changes in SOM; and the uncertainties associated with the mapping methods were quantitatively assessed through the uncertainty intervals of SOM realizations that were generated by sequential Gaussian simulation and/or Monte Carlo simulation. Results showed that the overall trend of changes in SOM over the last 28 years in the area decreased. The spatial patterns of changes in SOM derived by OK2008–OK1980 (abbreviations before and after the dash represent the mapping method for SOM data in 2008 and 1980, respectively) and IDW2008–OK1980, OK2008–IDW1980 and IDW2008–IDW1980, OK2008–ST1980 and IDW2008–ST1980 are similar. The change map obtained by the ST2008–ST1980 method is also similar to those derived by OK2008–ST1980 and IDW2008–ST1980 methods, but local details of changes in SOM presented by the ST2008–ST1980 method are coarser than those obtained by the OK2008–ST1980 and IDW2008–ST1980 methods. The OK2008–ST1980 method, with an uncertainty tradeoff compared to the OK2008–OK1980 and ST2008–ST1980 methods, can be considered as the most suitable method for mapping changes in SOM in the area due to the limited legacy soil survey data and inaccurate sample location information records in the area.

Preference for flavoured cigar brands among youth, young adults and adults in the USA

BackgroundWhile cigarette consumption in the USA continues to decline, cigar consumption has increased. Tobacco-trade publications suggest that flavoured cigars are driving the recent growth in cigar consumption. Limited survey data...