ArticleSexually transmitted infections2020
Effect of weighting for sampling and non-response on estimates of STI prevalence in the third British National Survey of Sexual Attitudes and Lifestyles (Natsal-3).
Article in Sexually transmitted infections, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
3 citing papers in PubMed, 3 citations in OpenAlex.
- Impact of sampling and data collection methods on maternity survey response: a randomised controlled trial of paper and push-to-web surveys and a concurrent social media survey.BMC medical research methodology · 2023Trial
- Sexual Contact Patterns in High-Income Countries-A Comparative Analysis Using Data From Germany, the United Kingdom, and the United States.Frontiers in epidemiology · 2022Article
- External validity of prevalence estimates from the national maternity surveys in England: The impact of response rate.PloS one · 2020Article
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Authors and funding
7 authors at 3 institutions in 3 countries.
Funding
Abstract
objectivesIn addition to researcher-designed sampling biases, population-representative surveys for biomarker measurement of STIs often have substantial missingness due to non-contact, non-consent and other study-implementation issues. STI prevalence estimates may be biased if this missingness is related to STI risk. We investigated how accounting for sampling, interview non-response and non-provision of biological samples affects prevalence estimates in the third National Survey of Sexual Attitudes and Lifestyles (Natsal-3).
methodsNatsal-3 was a multistage, clustered and stratified probability sample of 16-74 year-olds conducted between 2010 and 2012. Individuals were sampled from all private residential addresses in Britain; respondents aged 16-44 were further sampled to provide a urine specimen based on characteristics including self-reported sexual behaviours. We generated prevalence estimates and confidence intervals for six STIs in five stages: first without accounting for sampling or non-response, then applying inverse-probability weights cumulatively accounting for interview sampling, interview non-response, urine sampling and urine non-response.
resultsInterview non-completion occurred for 42.3% of interview-sampled individuals; urine non-completion occurred for 43.5% of urine-sampled individuals. Interview-sampled individuals, interview respondents, those selected for urine samples and those providing urine samples were each in turn slightly more at-risk for most STIs, leading to lower prevalence estimates after incorporating each set of weights. Researcher-controlled sampling had more impact than respondent-controlled response.
conclusionsAccounting for both sampling structures and willingness to interview or provide urine specimens can affect national STI prevalence estimates. Using both types of weights, as was done in Natsal-3, is important in reporting on population-based biomarker surveys.
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