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The lack of a reliable national sampling frame poses a major challenge for conducting representative population and household surveys, particularly in developing countries affected by displacement and rapid territorial change. This study addresses this gap by developing Armenia’s first digitized national sampling frame, where accessible survey frames are severely limited. We introduce an innovative pre-EA tool to semi-automatically construct the digital sampling frame using publicly available datasets. Compared with traditional approaches, this method outperforms in several ways: it enables rapid, semi-automated frame construction, minimizes resource requirements, eliminates geometric errors associated with manual digitization, and produces pre-census EAs (pre-EAs) that both nest within administrative boundaries and align with visible ground features. The approach also integrates gridded population data to reflect recent urbanization and migration, generating pre-census EAs and urban–rural classifications suitable for national surveys. The sampling frame was successfully applied in the World Bank’s “Listening to Armenia” survey. Overall, the study demonstrates that automated, data-driven approaches can efficiently produce accurate, scalable, and adaptable national sampling frames, offering potential utility in other countries facing similar constraints.

Partial occlusion and background clutter in camera video surveillance affect the accuracy of video-based person re-identification (re-ID). To address these problems, we propose a person re-ID method based on random erasure of frame sampling and temporal weight aggregation of mutual information of partial and global features. First, for the case in which the target person is interfered or partially occluded, the frame sampling–random erasure (FSE) method is used for data enhancement to effectively alleviate the occlusion problem, improve the generalization ability of the model, and match persons more accurately. Second, to further improve the re-ID accuracy of video-based persons and learn more discriminative feature representations, we use a ResNet-50 network to extract global and partial features and fuse these features to obtain frame-level features. In the time dimension, based on a mutual information–temporal weight aggregation (MI–TWA) module, the partial features are added according to different weights and the global features are added according to equal weights and connected to output sequence features. The proposed method is extensively experimented on three public video datasets, MARS, DukeMTMC-VideoReID, and PRID-2011; the mean average precision (mAP) values are 82.4%, 94.1%, and 95.3% and Rank-1 values are 86.4%, 94.8%, and 95.2%, respectively.

A complete sampling frame (CSF) is needed for the development of probability sampling structures; utilisation of a spatial sampling frame (SSF) was the objective of the present study. We used two sampling methods, simple random sampling (SRS) and stratified random sampling (STRS), to compare the prevalence estimates delivered by a CSF to that by a SSF when applied to self-reported hypertension and diabetes mellitus in a semi-urban setting and in a rural one. A CSF based on Geodatabase of all households and all individuals was available for our study that focused on adults aged 18-69 years in the two settings. A single digitized shapefile of solely household regions/structures as SSF was developed using Google Earth and employed for the study. The results from the two sampling frames were similar and not significantly different. All 95%CI calculations contained the prevalence rates of the two medical conditions except for one occasion based on STRS and CSF. The SRS based on CSF showed a minimum 95% CI width for diabetes mellitus, whereas SSF showed a minimum 95% CI width for hypertension. The coefficient of variation exceeded 10.0% on six occasions for CSF but only once for SSF, which was found to be as efficient as CSF.

IntroductionThe objective of the Health Population Africa (HPAfrica) study is to determine health behaviour and population-based factors, including socioeconomic, ethnographic, hygiene and sanitation factors, at sites of the Severe Typhoid Fever in Africa (SETA) programme. SETA aims to investigate healthcare facility-based fever surveillance in Burkina Faso, the Democratic Republic of the Congo, Ethiopia, Ghana, Madagascar and Nigeria. Meaningful disease burden estimates require adjustment for health behaviour patterns, which are assumed to vary among a study population.Methods and analysisFor the minimum sample size of household interviews required, the assumptions of an infinite population, a design effect and age-stratification and sex-stratification are considered. In the absence of a population sampling frame or household list, a spatial approach will be used to generate geographic random points with an Aeronautical Reconnaissance Coverage Geographic Information System tool. Printouts of Google Earth Pro satellite imagery visualise these points. Data of interest will be assessed in different seasons by applying population-weighted stratified sampling. An Android-based application and a web service will be developed for electronic data capturing and synchronisation with the database server in real time. Sampling weights will be computed to adjust for possible differences in selection probabilities. Descriptive data analyses will be performed in order to assess baseline information of each study population and age-stratified and sex-stratified health behaviour. This will allow adjusting disease burden estimates. In addition, multivariate analyses will be applied to look into associations between health behaviour, population-based factors and the disease burden as determined in the SETA study.Ethics and disseminationEthic approvals for this protocol were obtained by the Institutional Review Board of the International Vaccine Institute (No. 2016–0003) and by all collaborating institutions of participating countries. It is anticipated to disseminate findings from this study through publication on a peer-reviewed journal.

This article discusses methodological challenges of using big data that rely on specific sites and services as their sampling frames, focusing on social network sites in particular. It draws on survey data to show that people do not select into the use of such sites randomly. Instead, use is biased in certain ways yielding samples that limit the generalizability of findings. Results show that age, gender, race/ethnicity, socioeconomic status, online experiences, and Internet skills all influence the social network sites people use and thus where traces of their behavior show up. This has implications for the types of conclusions one can draw from data derived from users of specific sites. The article ends by noting how big data studies can address the shortcomings that result from biased sampling frames.

The spatial distribution of a natural resource is an important consideration in designing an efficient survey or monitoring program for the resource. Generally, sample sites that are spatially balanced, that is, more or less evenly dispersed over the extent of the resource, are more efficient than simple random sampling. We review a unified strategy for selecting spatially balanced probability samples of natural resources. The technique is based on creating a function that maps two-dimensional space into one-dimensional space, thereby defining an ordered spatial address. We use a restricted randomization to randomly order the addresses, so that systematic sampling along the randomly ordered linear structure results in a spatially well-balanced random sample. Variable inclusion probability, proportional to an arbitrary positive ancillary variable, is easily accommodated. The basic technique selects points in a two-dimensional continuum, but is also applicable to sampling finite populations or one-dimensional continua embedded in two-dimensional space. An extension of the basic technique gives a way to order the sample points so that any set of consecutively numbered points is in itself a spatially well-balanced sample. This latter property is extremely useful in adjusting the sample for the frame imperfections common in environmental sampling.

Background Stakeholder-related methodologies for low- and middle-income countries (LMICs) have primarily focused on stakeholder engagement or identification of specific, well-defined populations. Current stakeholder mapping research methods do not provide sufficient sampling processes for defining and implementing a sampling frame for poorly defined populations. In this paper we develop a unique stakeholder mapping methodology and apply it to the Transforming health: The role and impact of women's leadership in the health sector (THRIVE) study, aimed at generating evidence to support investment in women’s leadership in global health decision-making in reproductive, maternal, newborn, child, and adolescent health, and nutrition (RMNCAH-N) and immunization across sub-Saharan Africa (SSA). Though current literature has examined challenges women have faced to reach leadership roles, there are no methods for systematically identifying women leaders, and leaders in RMNCAH-N and immunization have not been uniformly well-defined or systematically documented. Consequently, understanding the impact of women’s leadership on health and healthcare policies is lacking. Results We developed a stakeholder mapping methodology to ensure accurate identification and representation of leaders in RMNCAH-N and immunization in Sub-Saharan Africa who could serve as a “sampling universe” for further investigation into the impact of women leaders. We began by defining what constituted a “leader” and “leader-adjacent” individual. Using a matrix, we refined the target sample of stakeholders and created uniform inclusion criteria. Stakeholder mapping was guided by the following strategic steps for each SSA country: screen government webpages; contact UN/multilateral agencies; conduct a systematic Google and social media search; identify relevant academic and grey literature; contact professional and personal connections in SSA; cross-check leads against a pre-defined matrix of stakeholder levels; and in-country validation. Inputs were collated into a shared Excel sheet. At the end of the stakeholder mapping exercise, we had systematically identified 3,901 leads. On average, 81 stakeholders were identified for each country. Approximately 38% ( n  = 1353) of the identified individual stakeholders were women. Conclusions This paper’s focus on creating a sampling universe of women leaders in RMNCAH-N and immunization in SSA fills a gap in current operational and implementation research. The insights derived from the adaptation and application of this methodology highlight the value of a structured approach to capturing the complexities of stakeholder and leadership dynamics in global health, particularly when applied to systematically map health topics or disciplines that lack databases or public records.

Background Health examination surveys (HESs), carried out in Europe since the 1950’s, provide valuable information about the general population’s health for health monitoring, policy making, and research. Survey participation rates, important for representativeness, have been falling. International comparisons are hampered by differing exclusion criteria and definitions for non-response. Method Information was collected about seven national HESs in Europe conducted in 2007–2012. These surveys can be classified into household and individual-based surveys, depending on the sampling frames used. Participation rates of randomly selected adult samples were calculated for four survey modules using standardised definitions and compared by sex, age-group, geographical areas within countries, and over time, where possible. Results All surveys covered residents not just citizens; three countries excluded those in institutions. In two surveys, physical examinations and blood sample collection were conducted at the participants’ home; the others occurred at examination clinics. Recruitment processes varied considerably between surveys. Monetary incentives were used in four surveys. Initial participation rates aged 35–64 were 45 % in the Netherlands (phase II), 54 % in Germany (new and previous participants combined), 55 % in Italy, and 65 % in Finland. In Ireland, England and Scotland, household participation rates were 66 %, 66 % and 63 % respectively. Participation rates were generally higher in women and increased with age. Almost all participants attending an examination centre agreed to all modules but surveys conducted in the participants’ home had falling responses to each stage. Participation rates in most primate cities were substantially lower than the national average. Age-standardized response rates to blood pressure measurement among those aged 35–64 in Finland, Germany and England fell by 0.7-1.5 percentage points p.a. between 1998–2002 and 2010–2012. Longer trends in some countries show a more marked fall. Conclusions The coverage of the general population in these seven national HESs was good, based on the sampling frames used and the sample sizes. Pre-notification and reminders were used effectively in those with highest participation rates. Participation rates varied by age, sex, geographical area, and survey design. They have fallen in most countries; the Netherlands data shows that they can be maintained at higher levels but at much higher cost.

This paper assesses trends in three survey outcome rates within four prominent crossnationalcomparative surveys conducted in European countries in the 21st century: theEuropean Quality of Life Survey, the European Social Survey, the European ValuesStudy, and the International Social Survey Programme. These projects are recognisedfor their high-quality sampling and fieldwork procedures, extensive track records, andcommitment to rigorous methodological standards. The analysis is based on 753national surveys conducted on probability samples of the general population in 36European countries from 1999 to 2018. We investigated whether two essential surveycharacteristics, namely sampling frames and data collection modes, moderated thedecrease of survey outcome rates over time. To analyse these relationships, thesurvey year was included as the explanatory variable, and we applied multi-level linearregressions with surveys nested within countries. Additionally, the project name wasincorporated as a fixed factor, and the sampling frame and mode of data collectionwere control variables for the effect of time. Our study provides valuable insights intothe challenges of conducting high-quality Pan-European cross-national comparativesurveys over nearly two decades. We observed a consistent decline in survey outcomerates, irrespective of country or project. Neither the sampling frame nor the datacollection mode moderated this decline. Hence, even though personal register samplesand Face-to-Face interviews are often regarded as enhancements to overall surveyquality, their application does not effectively counter the factors causing a decline insurvey outcome rates.

Calculation of the cutoff probability in the Rand-ESU method. Identification of weak points of the method through the random sampling of spanning trees, also the Rand-ESU method with has implemented in the igraph library. Implemented in library igraph. Comparison of methods for counting subgraphs at four vertices. Prospects for the development of the spanning tree sampling method.