For living well, behaviors and circumstances matter just as much as psychological traits.

In 2004 through 2016, three studies in the national Midlife in the United States (MIDUS) project asked participants the open-ended question "What do you do to make life go well?". We use verbatim responses to this question to evaluate the relative importance of psychological traits and circumstances for predicting self-reported, subjective well-being. The use of an open-ended question allows us to test the hypothesis that psychological traits are more strongly associated with self-reported well-being than objective circumstances because psychological traits and well-being are similarly self-rated-meaning that they both ask respondents to decide how to place themselves on provided and unfamiliar survey scales. For this, we use automated zero-shot classification to score statements about well-being without training on existing survey measures, and we evaluate this scoring through subsequent hand-labeling. We then assess associations of this measure and closed-ended measures for health behaviors, socioeconomic circumstances, biomarkers for inflammation and glycemic control, and mortality risk over follow-up. Although the closed-ended measures were far more strongly associated with other multiple-choice self-ratings, including Big 5 personality traits, the closed- and open-ended measures were similarly associated with relatively objective indicators of health, wealth, and social connectedness. The findings suggest that psychological traits, when collected through self-ratings, predict subjective reports of well-being so strongly because of a measurement advantage-and that circumstance matters just as much when assessed using a fairer comparison.

The scientific value of numerical measures of human feelings.

Human feelings measured in integers (my happiness is an 8 out of 10, my pain 2 out of 6) have no objective scientific basis. They are "made-up" numbers on a scale that does not exist. Yet such data are extensively collected-despite criticism from, especially, economists-by governments and international organizations. We examine this paradox. We draw upon longitudinal information on the feelings and decisions of tens of thousands of randomly sampled citizens followed through time over four decades in three countries (n = 700,000 approximately). First, we show that a single feelings integer has greater predictive power than does a combined set of economic and social variables. Second, there is a clear inverse relationship between feelings integers and subsequent get-me-out-of-here actions (in the domain of neighborhoods, partners, jobs, and hospital visits). Third, this feelings-to-actions relationship takes a generic form, is consistently replicable, and is fairly close to linear in structure. Therefore, it seems that human beings can successfully operationalize an integer scale for feelings even though there is no true scale. How individuals are able to achieve this is not currently known. The implied scientific puzzle-an inherently cross-disciplinary one-demands attention.

Derivation of outcome-dependent dietary patterns for low-income women obtained from survey data using a supervised weighted overfitted latent class analysis.

Poor diet quality is a key modifiable risk factor for hypertension and disproportionately impacts low-income women. Analyzing diet-driven hypertensive outcomes in this demographic is challenging due to the complexity of dietary data and selection bias when the data come from surveys, a main data source for understanding diet-disease relationships in understudied populations. Supervised Bayesian model-based clustering methods summarize dietary data into latent patterns that holistically capture relationships among foods and a known health outcome but do not sufficiently account for complex survey design. This leads to biased estimation and inference and lack of generalizability of the patterns. To address this, we propose a supervised weighted overfitted latent class analysis (SWOLCA) based on a Bayesian pseudo-likelihood approach that integrates sampling weights into an exposure-outcome model for discrete data. Our model adjusts for stratification, clustering, and informative sampling, and handles modifying effects via interaction terms within a Markov chain Monte Carlo Gibbs sampling algorithm. Simulation studies confirm that the SWOLCA model exhibits good performance in terms of bias, precision, and coverage. Using data from the National Health and Nutrition Examination Survey (2015-2018), we demonstrate the utility of our model by characterizing dietary patterns associated with hypertensive outcomes among low-income women in the United States.

Scalar-on-function regression: Estimation and inference under complex survey designs.

Increasingly, large, nationally representative health and behavioral surveys conducted under a multistage stratified sampling scheme collect high dimensional data with correlation structured along some domain (eg, wearable sensor data measured continuously and correlated over time, imaging data with spatiotemporal correlation) with the goal of associating these data with health outcomes. Analysis of this sort requires novel methodologic work at the intersection of survey statistics and functional data analysis. Here, we address this crucial gap in the literature by proposing an estimation and inferential framework for generalizable scalar-on-function regression models for data collected under a complex survey design. We propose to: (1) estimate functional regression coefficients using weighted score equations; and (2) perform inference using novel functional balanced repeated replication and survey-weighted bootstrap for multistage survey designs. This is the first frequentist study to discuss the estimation of scalar-on-function regression models in the context of complex survey studies and to assess the validity of various inferential techniques based on re-sampling methods via a comprehensive simulation study. We implement our methods to predict mortality using diurnal activity profiles measured via wearable accelerometers using the National Health and Nutrition Examination Survey 2003-2006 data. The proposed computationally efficient methods are implemented in R software package surveySoFR.

Incorporating co-design principles and social media strategies to enhance cross-sectional online survey participation: The Birth Experience Study.

AIM: To use co-design principles to design a nationwide maternity experiences survey and to distribute the survey through social media. DESIGN: A co-designed, cross sectional, and national online survey. METHODS: Using co-design principles from study design and throughout the research process a cross-sectional, online, national survey of Australian women's experiences of maternity care was designed. Four validated survey instruments were included in the survey design. RESULTS: An extensive social media strategy was utilized, which included paid advertising, resulting in 8804 surveys for analysis and 54,896 comments responding to open text questions. DISCUSSION: The inclusion of co-design principles contributed to a well-designed survey and consumer involvement in distribution and support of the online survey. The social media distribution strategy contributed to high participation rates with overall low marketing costs. CLINICAL RELEVANCE: Maternity services should be designed to provide woman-centered, individualized care. This survey was co-designed with maternity users and maternity organizations to explore women's recent experiences of maternity care in Australia. The outcomes of this study will highlight the factors that contribute to positive and negative experiences in maternity services. PATIENT OR PUBLIC CONTRIBUTION: As a co-designed study, there was consumer engagement from the design of the study, throughout the research process.

Creating a Bot-tleneck for malicious AI: Psychological methods for bot detection.

The standard approach for detecting and preventing bots from doing harm online involves CAPTCHAs. However, recent AI research, including our own in this manuscript, suggests that bots can complete many common CAPTCHAs with ease. The most effective methodology for identifying potential bots involves completing image-processing, causal-reasoning based, free-response questions that are hand coded by human analysts. However, this approach is labor intensive, slow, and inefficient. Moreover, with the advent of Generative AI such as GPT and Bard, it may soon be obsolete. Here, we develop and test various automated, bot-screening questions, grounded in psychological research, to serve as a proactive screen against bots. Utilizing hand coded free-response questions in the naturalistic domain of MTurkers recruited for a Qualtrics survey, we identify 18.9% of our sample to be potential bots, whereas Google's reCAPTCHA V3 identified only 1.7% to be potential bots. We then look at the performance of these potential bots on our novel bot-screeners, each of which has different strengths and weaknesses but all of which outperform CAPTCHAs.

Associations between postoperative cognitive dysfunction, serum interleukin-6 and postoperative delirium among patients after coronary artery bypass grafting: A mediation analysis.

BACKGROUND: POCD is a common complication among patients who underwent coronary artery bypass graft (CABG), it is linked to loss of independence and reduced quality of life. AIM: To examine the association between postoperative cognitive dysfunction (POCD), postoperative delirium (POD) and interleukin-6 (IL-6). DESIGN: A prospective cohort study. METHODS: Patients who underwent elective isolated CABG were enrolled. POCD was assessed by a set of cognitive function tools. Delirium was assessed using the CAM-ICU. The logistic regression analyses were used to identify the predictive value of POD or IL-6 on POCD. The path analysis was used to analyse the relationship among POD, IL-6 and POCD. RESULTS: A total of 212 patients were enrolled, with 25.0% of patients developing POD and 32.5% developing POCD. The multiple logistic regression analysis revealed that patients with POD had a four-fold increased hazard of POCD (OR = 3.655), and patients with IL-6 ≥ 830.50 pg/mL at the 6th hours after surgery had a 5-fold increased risk of experiencing POCD (OR = 5.042). However, the mediation effect of POD between IL-6 and POCD was not statistically significant (ß = 0.059, p = .392). CONCLUSIONS: POD and IL-6 at the 6th hour after surgery (≥830.50 pg/mL) are two potent predictors for POCD, while POD did not play a mediation effect between IL-6 and POCD. RELEVANCE TO CLINICAL PRACTICE: Early identification of risk factors (e.g., delirium assessment and testing for serum IL-6 levels) by clinical nurses for POCD may contribute to the clinical practice for the targeted prevention nursing strategies.

Robust Designs for Prospective Randomized Trials Surveying Sensitive Topics.

We consider the problem of designing a prospective randomized trial in which the outcome data will be self-reported and will involve sensitive topics. Our interest is in how a researcher can adequately power her study when some respondents misreport the binary outcome of interest. To correct the power calculations, we first obtain expressions for the bias and variance induced by misreporting. We model the problem by assuming each individual in our study is a member of one "reporting class": a true-reporter, false-reporter, never-reporter, or always-reporter. We show that the joint distribution of reporting classes and "response classes" (characterizing individuals' response to the treatment) will exactly define the error terms for our causal estimate. We propose a novel procedure for determining adequate sample sizes under the worst-case power corresponding to a given level of misreporting. Our problem is motivated by prior experience implementing a randomized controlled trial of a sexual-violence prevention program among adolescent girls in Kenya.

Impact of ignoring sampling design in the prediction of binary health outcomes through logistic regression: evidence from Malawi demographic and health survey under-five mortality data; 2000-2016.

The birth and death rates of a population are among the crucial vital statistics for socio-economic policy planning in any country. Since the under-five mortality rate is one of the indicators for monitoring the health of a population, it requires regular and accurate estimation. The national demographic and health survey data, that are readily available to the puplic, have become a means for answering most health-related questions among African populations, using relevant statistical methods. However, many of such applications tend to ignore survey design effect in the estimations, despite the availability of statistical tools that support the analyses. Little is known about the amount of inaccurate information that is generated when predicting under-five mortality rates. This study estimates and compares the bias encountered when applying unweighted and weighted logistic regression methods to predict under-five mortality rate in Malawi using nationwide survey data. The Malawi demographic and health survey data of 2004, 2010, and 2015-16 were used to determine the bias. The analyses were carried out in R software version 3.6.3 and Stata version 12.0. A logistic regression model that included various bio- and socio-demographic factors concerning the child, mother and households was used to estimate the under-five mortality rate. The results showed that accuracy of predicting the national under-five mortality rate hinges on cluster-weighting of the overall predicted probability of child-deaths, regardless of whether the model was weighted or not. Weighting the model caused small positive and negative changes in various fixed-effect estimates, which diffused the result of weighting in the fitted probabilities of deaths. In turn, there was no difference between the overall predicted mortality rate obtained using the weighted model and that obtained in the unweighted model. We recommend considering survey cluster-weights during the computation of overall predicted probability of events for a binary health outcome. This can be done without worrying about the weights during model fitting, whose aim is prediction of the population parameter.

Applying Responsive Survey Design to Small-Scale Surveys: Campus Surveys of Sexual Misconduct.

Responsive survey design is a technique aimed at improving the efficiency or quality of surveys by using incoming data from the field to make design changes. The technique was pioneered on large national surveys, but the tools can also be applied on the smaller-scale surveys most commonly used by sociologists. We demonstrate responsive survey design in a small-scale, list-based sample survey of students on the topic of sexual misconduct. We investigate the impact of individual incentive levels and a two-phase responsive design with changes to mode of contact as approaches for limiting the potential of nonresponse bias in data from such surveys. Our analyses demonstrate that a two-phase design introducing telephone and face-to-face reminders to complete the survey can produce stronger change in response rates and characteristics of those who respond than higher incentive levels. These findings offer tools for sociologists designing smaller-scale surveys of special populations or sensitive topics.

Global Perceptions on Social Accountability and Outcomes: A Survey of Medical Schools.

Phenomenon: Social accountability has become a universal component in medical education. However, medical schools have little guidance for operationalizing and applying this concept in practice. This study explored institutional practices and administrative perceptions of social accountability in medical education. Approach: An online survey was distributed to a purposeful sample of English-speaking undergraduate medical school deans and program directors/leads from 245 institutions in 14 countries. The survey comprised of 38-items related to program mission statements, admission processes, curricular content, and educational outcomes. Survey items were developed using previous literature and categorized using a context-input-process-products (CIPP) evaluation model. Exploratory Factor Analysis (EFA) was used to assess the inter-relationship among survey items. Reliability and internal consistency of items were evaluated using McDonald's Omega. Findings: Results from 81 medical schools in 14 countries collected between February and June 2020 are presented. Institutional commonalities of social accountability were observed. However, our findings suggest programs focus predominately on educational inputs and processes, and not necessarily on outcomes. Findings from our EFA demonstrated excellent internal consistency and reliability. Four-factors were extracted: (1) selection and recruitment; (2) institutional mandates; (3) institutional activities; and (4) community awareness, accounting for 71% of the variance. McDonald's Omega reliability estimates for subscales ranged from 0.80-0.87. Insights: This study identified common practices of social accountability. While many medical schools expressed an institutional commitment to social accountability, their effects on the community remain unknown and not evaluated. Overall, this paper offers programs and educators a psychometrically supported tool to aid in the operationalization and reliability of evaluating social accountability.

Achieving Robust Compressive Sensing Seismic Acquisition with a Two-Step Sampling Approach.

The compressive sensing (CS) framework offers a cost-effective alternative to dense alias-free sampling. Designing seismic layouts based on the CS technique imposes the use of specific sampling patterns in addition to the logistical and geophysical requirements. We propose a two-step design process for generating CS-based schemes suitable for seismic applications. During the first step, uniform random sampling is used to generate a random scheme, which is supported theoretically by the restricted isometry property. Following that, designated samples are added to the random scheme to control the maximum distance between adjacent sources (or receivers). The null space property theoretically justifies the additional samples of the second step. Our sampling method generates sampling patterns with a CS theoretical background, controlled distance between adjacent samples, and a flexible number of active and omitted samples. The robustness of two-step sampling schemes for reallocated samples is investigated and CS reconstruction tests are performed. In addition, using this approach, a CS-based 3D seismic survey is designed, and the distributions of traces in fold maps and rose diagrams are analyzed. It is shown that the two-step scheme is suitable for CS-based seismic surveys and field applications.

Using machine learning and qualitative interviews to design a five-question survey module for women's agency.

Open-ended interview questions elicit rich information about people's lives, but in large-scale surveys, social scientists often need to measure complex concepts using only a few close-ended questions. We propose a new method to design a short survey measure for such cases by combining mixed-methods data collection and machine learning. We identify the best survey questions based on how well they predict a benchmark measure of the concept derived from qualitative interviews. We apply the method to create a survey module and index for women's agency. We measure agency for 209 married women in Haryana, India, first, through a semi-structured interview and, second, through a large set of close-ended questions. We use qualitative coding methods to score each woman's agency based on the interview, which we use as a benchmark measure of agency. To determine the close-ended questions most predictive of the benchmark, we apply statistical algorithms that build on LASSO and random forest but constrain how many variables are selected for the model (five in our case). The resulting five-question index is as strongly correlated with the coded qualitative interview as is an index that uses all of the candidate questions. This approach of selecting survey questions based on their statistical correspondence to coded qualitative interviews could be used to design short survey modules for many other latent constructs.

Using poverty maps to improve the design of household surveys: the evidence from Tunisia.

In this paper we aim to propose a new method for improving the design effect of household surveys based on a two-stage design in which the first stage clusters, or Primary Selection Units (PSUs), are stratified along administrative boundaries. Improvement of the design effect can result in more precise survey estimates (smaller standard errors and confidence intervals) or in the reduction of the necessary sample size, i.e. a reduction in the budget needed for a survey. The proposed method is based on the availability of a previously conducted poverty maps, i.e. spatial descriptions of the distribution of per capita consumption expenditures, that are finely disaggregated in small geographic units, such as cities, municipalities, districts or other administrative partitions of a country that are directly linked to PSUs. Such information is then used to select PSUs with systematic sampling by introducing further implicit stratification in the survey design, so as to maximise the improvement of the design effect. Since per capita consumption expenditures estimated at PSU level from the poverty mapping are affected by (small) standard errors, in the paper we also perform a simulation study in order to take into account this addition variability.

Coronavirus conspiracy beliefs, mistrust, and compliance: taking measurement seriously.

BACKGROUND: Freeman et al. (, Psychological Medicine, 21, 1-13) argue that there is widespread support for coronavirus conspiracy theories in England. We hypothesise that their estimates of prevalence are inflated due to a flawed research design. When asking respondents to their survey to agree or disagree with pro-conspiracy statements, they used a biased set of response options: four agree options and only one disagree option (and no 'don't know' option). We also hypothesise that due to these flawed measures, the Freeman et al. approach under-estimates the strength of the correlation between conspiracy beliefs and compliance. Finally, we hypothesise that, due to reliance on bivariate correlations, Freeman et al. over-estimate the causal connection between conspiracy beliefs and compliance. METHODS: In a pre-registered study, we conduct an experiment embedded in a survey of a representative sample of 2057 adults in England (fieldwork: 16-19 July 2020). RESULTS: Measured using our advocated 'best practice' approach (balanced response options, with a don't know option), prevalence of support for coronavirus conspiracies is only around five-eighths (62.3%) of that indicated by the Freeman et al. approach. We report mixed results on our correlation and causation hypotheses. CONCLUSIONS: To avoid over-estimating prevalence of support for coronavirus conspiracies, we advocate using a balanced rather than imbalanced set of response options, and including a don't know option.

Estimating animal abundance at multiple scales by spatially explicit capture-recapture.

Information about how animal abundance varies across landscapes is needed to inform management action but is costly and time-consuming to obtain; surveys of a single population distributed over a large area can take years to complete. Surveys employing small, spatially replicated sampling units improve efficiency, but statistical estimators rely on assumptions that constrain survey design or become less reasonable as larger areas are sampled. Efficient methods that avoid assumptions about similarity of detectability or density among replicates are therefore appealing. Using simulations and data from >3500 black bears sampled on 73 independent study areas in Ontario, Canada, we (1) quantified bias induced by unmodeled spatial heterogeneity in detectability and density; (2) evaluated novel, design-based estimators of average density across replicate study areas; and (3) evaluated two estimators of the variance of average density across study areas: an analytic estimator that assumed an underlying homogeneous spatial Poisson point process for the distribution of animals' activity centers, and an empirical estimator of variance across study areas. In simulations where detectability varied in space, assuming spatially constant detectability yielded density estimates that were negatively biased by 20% to 30%; estimating local detectability and density from local data and treating study areas as independent, equal replicates when estimating average density across study areas using the design-based estimator yielded unbiased estimates at local and landscape scales. Similarly, detectability of black bears varied among study areas and estimates of bear density at landscape scales were higher when no information was shared across study areas when estimating detectability. This approach also maximized precision (relative SEs of estimates of average black bear density ranged from 7% to 18%) and computational efficiency. In simulations, the analytic variance estimator was robust to threefold variation in local densities but the empirical estimator performed poorly. Conducting multiple, similar SECR surveys and treating them as independent replicates during analyses allowed us to efficiently estimate density at multiple scales and extents while avoiding biases caused by pooling spatially heterogeneous data. This approach enables researchers to address a wide range of ecological or management-related questions and is applicable with most types of SECR data.

Development of a factorial survey for use in an international study examining clinicians' likelihood to support the decision to initiate invasive long-term ventilation for a child (the TechChild study).

BACKGROUND: The decision to initiate invasive long-term ventilation for a child with complex medical needs can be extremely challenging. TechChild is a research programme that aims to explore the liminal space between initial consideration of such technology dependence and the final decision. This paper presents a best practice example of the development of a unique use of the factorial survey method to identify the main influencing factors in this critical juncture in a child's care. METHODS: We developed a within-subjects design factorial survey. In phase 1 (design) we defined the survey goal (dependent variable, mode and sample). We defined and constructed the factors and factor levels (independent variables) using previous qualitative research and existing scientific literature. We further refined these factors based on expert feedback from expert clinicians and a statistician. In phase two (pretesting), we subjected the survey tool to several iterations (cognitive interviewing, face validity testing, statistical review, usability testing). In phase three (piloting) testing focused on feasibility testing with members of the target population (n = 18). Ethical approval was obtained from the then host institution's Health Sciences Ethics Committee. RESULTS: Initial refinement of factors was guided by literature and interviews with clinicians and grouped into four broad categories: Clinical, Child and Family, Organisational, and Professional characteristics. Extensive iterative consultations with clinical and statistical experts, including analysis of cognitive interviews, identified best practice in terms of appropriate: inclusion and order of clinical content; cognitive load and number of factors; as well as language used to suit an international audience. The pilot study confirmed feasibility of the survey. The final survey comprised a 43-item online tool including two age-based sets of clinical vignettes, eight of which were randomly presented to each participant from a total vignette population of 480. CONCLUSIONS: This paper clearly explains the processes involved in the development of a factorial survey for the online environment that is internationally appropriate, relevant, and useful to research an increasingly important subject in modern healthcare. This paper provides a framework for researchers to apply a factorial survey approach in wider health research, making this underutilised approach more accessible to a wider audience.

A field experiment characterizing variable detection rates during plant surveys.

Surveys aimed at finding threatened and invasive species can be challenging due to individual rarity and low and variable individual detection rates. Detection rate in plant surveys typically varies due to differences among observers, among the individual plants being surveyed (targets), and across background environments. Interactions among these 3 components may occur but are rarely estimated due to limited replication and control during data collection. We conducted an experiment to investigate sources of variation in detection of 2 Pilosella species that are invasive and sparsely distributed in the Alpine National Park, Australia. These species are superficially similar in appearance to other yellow-flowered plants occurring in this landscape. We controlled the presence and color of flowers on target Pilosella plants and controlled their placement in plots, which were selected for their variation in cover of non-target yellow flowers and dominant vegetation type. Observers mimicked Pilosella surveys in the plots and reported 1 categorical and 4 quantitative indicators of their survey experience level. We applied survival analysis to detection data to model the influence of both controlled and uncontrolled variables on detection rate. Orange- and yellow-flowering Pilosella in grass- and heath-dominated vegetation were detected at a higher rate than nonflowering Pilosella. However, this detection gain diminished as the cover of other co-occurring yellow-flowering species increased. Recent experience with Pilosella surveys improved detection rate. Detection experiments are a direct and accessible means of understanding detection processes and interpreting survey data for threatened and invasive species. Our detection findings have been used for survey planning and can inform progress toward eradication. Interaction of target and background characteristics determined detection rate, which enhanced predictions in the Pilosella eradication program and demonstrated the difficulty of transferring detection findings into untested environments.

Un Experimento de Campo que Caracteriza las Tasas Variables de Detección en los Censos de Plantas Resumen Los censos enfocados en encontrar especies amenazadas e invasoras pueden ser un reto debido a la rareza individual y las tasas bajas y variables de detección individual. Las tasas de detección en los censos botánicos varían comúnmente por las diferencias entre los observadores, entre las plantas individuales que se están censando (objetivo de búsqueda) y en el entorno ambiental. La interacción entre estos tres componentes puede ocurrir, pero rara vez se calcula debido a la replicación y control limitados durante la recolección de datos. Realizamos un experimento para investigar el origen de las variaciones en la detección de dos especies de Pilosella que son invasoras y están distribuidas escasamente en el Parque Nacional Alpino en Australia. Estas especies son superficialmente similares en apariencia a otras plantas de flores amarillas que habitan este paisaje. Controlamos la presencia y el color de las flores en las plantas de Pilosella, así como su colocación en lotes, los cuales fueron seleccionados por su variación en la cobertura de flores amarillas y tipos de vegetación circundantes. Los observadores imitaron los censos de Pilosella en los lotes y reportaron un indicador categórico y cuatro cuantitativos de su nivel de experiencia en censos. Aplicamos el análisis de supervivencia a los datos de detección para modelar la influencia de las variables controladas y no controladas sobre la tasa de detección. Las plantas de Pilosella con flores amarillas y anaranjadas en la vegetación dominada por pastos y brezales fueron detectadas con una tasa mayor que las plantas de Pilosella sin flores. Sin embargo, esta ganancia en la detección disminuyó conforme incrementó la cobertura de otras plantas con flores amarillas. La experiencia reciente de los observadores con censos de Pilosella aumentó la tasa de detección. Los experimentos de detección son un medio directo y accesible para entender los procesos de detección e interpretar los datos de los censos de especies amenazadas e invasoras. Nuestros resultados en la detección han sido utilizados para la planeación de censos y pueden guiar el progreso hacia la erradicación. La interacción de las características diana y del entorno determinaron la tasa de detección, la cual mejoró las predicciones en el programa de erradicación de Pilosella y demostró la dificultad de transferir los resultados de detección hacia ambientes sin ensayos.

Methods for conducting trends analysis: roadmap for comparing outcomes from three national HIV Population-based household surveys in Kenya (2007, 2012, and 2018).

BACKGROUND: For assessing the HIV epidemic in Kenya, a series of independent HIV indicator household-based surveys of similar design can be used to investigate the trends in key indicators relevant to HIV prevention and control and to describe geographic and sociodemographic disparities, assess the impact of interventions, and develop strategies. We developed methods and tools to facilitate a robust analysis of trends across three national household-based surveys conducted in Kenya in 2007, 2012, and 2018. METHODS: We used data from the 2007 and 2012 Kenya AIDS Indicator surveys (KAIS 2007 and KAIS 2012) and the 2018 Kenya Population-based HIV Impact Assessment (KENPHIA 2018). To assess the design and other variables of interest from each study, variables were recoded to ensure that they had equivalent meanings across the three surveys. After assessing weighting procedures for comparability, we used the KAIS 2012 nonresponse weighting procedure to revise normalized KENPHIA weights. Analyses were restricted to geographic areas covered by all three surveys. The revised analysis files were then merged into a single file for pooled analysis. We assessed distributions of age, sex, household wealth, and urban/rural status to identify unexpected changes between surveys. To demonstrate how a trend analysis can be carried out, we used continuous, binary, and time-to-event variables as examples. Specifically, temporal trends in age at first sex and having received an HIV test in the last 12 months were used to demonstrate the proposed analytical approach. These were assessed with respondent-specific variables (age, sex, level of education, and marital status) and household variables (place of residence and wealth index). All analyses were conducted in SAS 9.4, but analysis files were created in Stata and R format to support additional analyses. RESULTS: This study demonstrates trends in selected indicators to illustrate the approach that can be used in similar settings. The incidence of early sexual debut decreased from 11.63 (95% CI: 10.95-12.34) per 1,000 person-years at risk in 2007 to 10.45 (95% CI: 9.75-11.2) per 1,000 person-years at risk in 2012 and to 9.58 (95% CI: 9.08-10.1) per 1,000 person-years at risk in 2018. HIV-testing rates increased from 12.6% (95% CI: 11.6%-13.6%) in 2007 to 56.1% (95% CI: 54.6%-57.6%) in 2012 but decreased slightly to 55.6% [95% CI: 54.6%-56.6%) in 2018. The decrease in incidence of early sexual debut could be convincingly demonstrated between 2007 and 2012 but not between 2012 and 2018. Similarly, there was virtually no difference between HIV Testing rates in 2012 and 2018. CONCLUSIONS: Our approach can be used to support trend comparisons for variables in HIV surveys in low-income settings. Independent national household surveys can be assessed for comparability, adjusted as appropriate, and used to estimate trends in key indicators. Analyzing trends over time can not only provide insights into Kenya's progress toward HIV epidemic control but also identify gaps.

Response to "The Time has Come for All States to Measure Racial Discrimination: A Call to Action for the Pregnancy Risk Assessment Monitoring System (PRAMS)".

We respond to a recent call to action for the Pregnancy Risk Assessment Monitoring System (PRAMS) to include a "core" question or validated measure on discrimination to allow for systematic assessment of the impact of racial discrimination on adverse birth outcomes among a large population-based sample in the United States. We outline activities of the CDC PRAMS project that relate to this call to action.