Diagnostic Strategies for Ruminant Populations

Veterinarians may be asked to assess the presence, absence, or prevalence of a disease in an animal population or to compare the effects of management factors on disease status or production performance. The scope of diagnostic investigations in ruminant populations is often limited by the availability of time, money, and animal handling infrastructure. Selecting the correct number and type of animals to sample maximizes the benefits of the investigation, while minimizing costs. To meet the objectives of the study, the veterinarian must understand the statistical elements that need to be considered to calculate the appropriate sample size.

ADAPTIVE TIME LOCATION SAMPLING FOR COMPASS, A SARS-COV-2 PREVALENCE STUDY IN FIFTEEN DIVERSE COMMUNITIES IN THE UNITED STATES

The COVPN 5002 (COMPASS) study aimed to estimate the prevalence of SARS-CoV-2 (active SARS-CoV-2 or prior SARS-CoV-2 infection) in children and adults attending public venues in 15 socio-demographically diverse communities in the United States. To protect against potential challenges in implementing traditional sampling strategies, time-location sampling (TLS) using complex sampling involving stratification, clustering of units, and unequal probabilities of selection was used to recruit individuals from neighborhoods in selected communities. The innovative design adapted to constraints such as closure of venues; changing infection hotspots; and uncertain policies. Recruitment of children and the elderly raised additional challenges in sample selection and implementation. To address these challenges, the TLS design adaptively updated both the sampling frame and the selection probabilities over time using information acquired from prior weeks. Although the study itself was specific to COVID-19, the strategies presented in this paper could serve as a case study that can be adapted for performing rigorous population-level inferences in similar settings and could help inform rapid and effective responses to future global public health challenges.

Sample Design and Estimation in the National Social Life, Health, and Aging Project: Round 3 (2015-2016)

OBJECTIVES

This article, and corresponding articles for the earlier rounds of the National Social Life, Health, and Aging Project (NSHAP), provide the scientific underpinning for the statistical analysis of NSHAP data. The 2015-2016 round of data collection for NSHAP comprised the third wave of data collection for the original cohort born 1920-1947 (C1) and the first wave of data collection for a second cohort born 1948-1965 (C2). Here we describe (a) our protocol for reinterviewing C1; (b) our approach to the sample design for C2, including the frame construction, stratification, clustering, and within-household selection; and (c) the construction of cross-sectional weights for the entire 2015-2016 sample when analyzed at the individual level or when analyzed as a sample of cohabiting couples. We also provide guidance on computing design-based standard errors.

METHODS

The sample for C2 was drawn independently of the C1 sample using the NORC U.S. National Sampling Frame. A probability sample of households containing at least one individual born 1948-1965 was drawn, and from these, each age-eligible individual was included together with their cohabiting spouse or partner (even if not age-eligible). This C2 sample was combined with the C1 sample to yield a sample representative of the U.S. population of adults born 1920-1965.

RESULTS

Among C1, we conducted 2,409 interviews corresponding to a 91% conditional response rate (i.e., among previous respondents); the unconditional three-wave response rate for the original C1 sample was 71%. Among C2, we conducted 2,368 interviews corresponding to a response rate of 76%.

DISCUSSION

Together C1 and C2 permit inference about the U.S. population of home-dwelling adults born from 1920 to 1965. In addition, three waves of data from C1 are now available, permitting longitudinal analyses of health outcomes and their determinants among older adults.

Conducting household surveys on reproductive health in urban settings: lessons from Karachi, Pakistan

Background

Data collection is the most critical stage in any population health study and correctly implementing fieldwork enhances the quality of collected information. However, even the most carefully planned large-scale household surveys can encounter many context-specific issues. This paper reflected on our research team’s recent experience conducting surveys for a quasi-experimental evaluation of a reproductive health program in urban areas of Karachi, Pakistan. We aim to describe the issues encountered and lessons learned from this process, and present some potential solutions for conducting future household surveys in similar urban environments.

Methods

The study followed a three-stage random sampling design. Initially, a Geographical Information System (GIS) was used to construct the sampling frame with union council (UC) area mapping and cluster demarcation followed by random selection of clusters in the selected UCs within the intervention and control sites. The second stage involved a complete household listing in selected clusters and the final stage was a random sampling of households with eligible women.

Result

This paper describes the issues that were encountered including technical problems related to GIS demarcation of cluster boundaries and hand-held devices for computer assisted personal interviews (CAPI), household listing, interviewing respondents on sensitive topics and their expectations, and ensuring privacy during the survey.

Conclusion

This study identifies a number of unique barriers to conducting household surveys in Karachi and highlights some key lessons for survey research in urban settlements. GIS mapping technology is a cost-effective method for developing sampling frames in resource-constrained settings. Secondly, the strategy of interviewing women immediately after the cluster is listed may be applied to make it easier to re-locate selected respondents and to reduce loss-to-follow up. Understanding local norms and developing culturally appropriate strategies to build trust with communities may significantly improve survey participation. Researchers should hire experienced female enumerators and provide continuous training on best practices for interviewing women on sensitive reproductive health topics in urban communities.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12874-021-01216-x.

Feasibility of satellite image and GIS sampling for population representative surveys: a case study from rural Guatemala

BACKGROUND

Population-representative household survey methods require up-to-date sampling frames and sample designs that minimize time and cost of fieldwork especially in low- and middle-income countries. Traditional methods such as multi-stage cluster sampling, random-walk, or spatial sampling can be cumbersome, costly or inaccurate, leading to well-known biases. However, a new tool, Epicentre's Geo-Sampler program, allows simple random sampling of structures, which can eliminate some of these biases. We describe the study design process, experiences and lessons learned using Geo-Sampler for selection of a population representative sample for a kidney disease survey in two sites in Guatemala.

RESULTS

We successfully used Epicentre's Geo-sampler tool to sample 650 structures in two semi-urban Guatemalan communities. Overall, 82% of sampled structures were residential and could be approached for recruitment. Sample selection could be conducted by one person after 30 min of training. The process from sample selection to creating field maps took approximately 40 h.

CONCLUSION

In combination with our design protocols, the Epicentre Geo-Sampler tool provided a feasible, rapid and lower-cost alternative to select a representative population sample for a prevalence survey in our semi-urban Guatemalan setting. The tool may work less well in settings with heavy arboreal cover or densely populated urban settings with multiple living units per structure. Similarly, while the method is an efficient step forward for including non-traditional living arrangements (people residing permanently or temporarily in businesses, religious institutions or other structures), it does not account for some of the most marginalized and vulnerable people in a population-the unhoused, street dwellers or people living in vehicles.

Surveys: Merging qualitative and quantitative research methods

Survey methodologies enable researchers to obtain information not routinely captured in electronic medical records, including how delivery of surgical care is perceived through the lens of a child, a parent or caregiver, or a surgeon. Here we describe the approach to using surveys to aid pediatric surgery practice. This article has two aims, (1) to review key concepts in survey creation, administration, and data management, and (2) to describe previously validated survey instruments in pediatric surgery and how to optimize selection of such instruments. The overarching goal of this primer is to give the pediatric surgeon an understanding of the approach to creating a practical survey to address the unique needs of children who require surgery.

Using satellite imagery and GPS technology to create random sampling frames in high risk environments

INTRODUCTION

Health surveys are important tools for assessing needs and informing policy decisions. However, obtaining representative samples is challenging in environments without traditional infrastructure or census data. We describe a method using satellite imagery, geographic information systems and GPS technology to obtain an accurate sample of such a population.

METHODS

The Kerenik Camp in Darfur is a conflict-heavy environment with 25,000 internally displaced persons (IDPs). We requisitioned high-resolution satellite imagery of the camp prior to arrival. Structures identified as potential domiciles were geocoded with a unique ID and coordinate. A random selection of ID numbers formed the representative sample. Researchers visited these coordinates using handheld GPS devices and administered surveys to the inhabitants.

RESULTS

2219 geocoded points were visited. Of these, 1655 (74.6%) proved to be unique domiciles. Our survey participation rate was 87.1%. The overall effective rate of completed survey per geocoded point visited was 39.1%.

DISCUSSION

Our sampling technique offers several advantages when surveying vulnerable populations. It permits the establishment of a sampling frame without need for traditional infrastructure, such as addresses or telephones. Sampling frames can be constructed remotely and prior to survey initiation, important considerations for insecure environments where time on the ground may be limited.

CONCLUSION

This technique can be used for any setting requiring a random sample, but is especially useful in insecure environments and survey areas without accessible census data, postal addresses, or telephone numbers. Sampling frames can be constructed remotely and prior to survey initiation, important considerations for environments where time on the ground may be limited.

Bias of health estimates obtained from chronic disease and risk factor surveillance systems using telephone population surveys in Australia: results from a representative face-to-face survey in Australia from 2010 to 2013

BACKGROUND

Emerging communication technologies have had an impact on population-based telephone surveys worldwide. Our objective was to examine the potential biases of health estimates in South Australia, a state of Australia, obtained via current landline telephone survey methodologies and to report on the impact of mobile-only household on household surveys.

METHODS

Data from an annual multi-stage, systematic, clustered area, face-to-face population survey, Health Omnibus Survey (approximately 3000 interviews annually), included questions about telephone ownership to assess the population that were non-contactable by current telephone sampling methods (2006 to 2013). Univariable analyses (2010 to 2013) and trend analyses were conducted for sociodemographic and health indicator variables in relation to telephone status. Relative coverage biases (RCB) of two hypothetical telephone samples was undertaken by examining the prevalence estimates of health status and health risk behaviours (2010 to 2013): directory-listed numbers, consisting mainly of landline telephone numbers and a small proportion of mobile telephone numbers; and a random digit dialling (RDD) sample of landline telephone numbers which excludes mobile-only households.

RESULTS

Telephone (landline and mobile) coverage in South Australia is very high (97%). Mobile telephone ownership increased slightly (7.4%), rising from 89.7% in 2006 to 96.3% in 2013; mobile-only households increased by 431% over the eight year period from 5.2% in 2006 to 27.6% in 2013. Only half of the households have either a mobile or landline number listed in the telephone directory. There were small differences in the prevalence estimates for current asthma, arthritis, diabetes and obesity between the hypothetical telephone samples and the overall sample. However, prevalence estimate for diabetes was slightly underestimated (RCB value of -0.077) in 2013. Mixed RCB results were found for having a mental health condition for both telephone samples. Current smoking prevalence was lower for both hypothetical telephone samples in absolute differences and RCB values: -0.136 to -0.191 for RDD landline samples and -0.129 to -0.313 for directory-listed samples.

CONCLUSION

These findings suggest landline-based sampling frames used in Australia, when appropriately weighted, produce reliable representative estimates for some health indicators but not for all. Researchers need to be aware of their limitations and potential biased estimates.

Definition of sampling units begets conclusions in ecology: the case of habitats for plant communities

In ecology, expert knowledge on habitat characteristics is often used to define sampling units such as study sites. Ecologists are especially prone to such approaches when prior sampling frames are not accessible. Here we ask to what extent can different approaches to the definition of sampling units influence the conclusions that are drawn from an ecological study? We do this by comparing a formal versus a subjective definition of sampling units within a study design which is based on well-articulated objectives and proper methodology. Both approaches are applied to tundra plant communities in mesic and snowbed habitats. For the formal approach, sampling units were first defined for each habitat in concave terrain of suitable slope using GIS. In the field, these units were only accepted as the targeted habitats if additional criteria for vegetation cover were fulfilled. For the subjective approach, sampling units were defined visually in the field, based on typical plant communities of mesic and snowbed habitats. For each approach, we collected information about plant community characteristics within a total of 11 mesic and seven snowbed units distributed between two herding districts of contrasting reindeer density. Results from the two approaches differed significantly in several plant community characteristics in both mesic and snowbed habitats. Furthermore, differences between the two approaches were not consistent because their magnitude and direction differed both between the two habitats and the two reindeer herding districts. Consequently, we could draw different conclusions on how plant diversity and relative abundance of functional groups are differentiated between the two habitats depending on the approach used. We therefore challenge ecologists to formalize the expert knowledge applied to define sampling units through a set of well-articulated rules, rather than applying it subjectively. We see this as instrumental for progress in ecology as only rules based on expert knowledge are transparent and lead to results reproducible by other ecologists.