Trends and socioeconomic inequalities in the dental attendance of adult smokers in Scotland from 2009 to 2019, a repeated cross-sectional study

BACKGROUND

Smoking continues to be the single largest cause of preventable disease and death and a major contributor to health inequalities. Dental professionals are well placed to offer behavioural support in combination with pharmacotherapy to increase smoking cessation rates across the population. We aimed to assess the trends and socioeconomic inequalities in the dental attendance of adult smokers in Scotland from 2009 to 2019 and examine the potential population reach of dental settings for smoking cessation interventions.

METHODS

A secondary analysis was conducted of combined Scottish Health Surveys (SHeS) from 2009/11, 2013/15 and 2017/19. 'Recent' dental attendance (within the past two years) was the focus and descriptive analysis examined attendance of self-reported smokers compared to non-smokers and stratified by the area-based Scottish Index of Multiple Deprivation (SIMD) and individual socioeconomic measures (income, education, and occupation). Generalised linear models were used to model recent attendance in non-smokers relative to smokers adjusted by the socioeconomic measures, for each of the survey cohorts separately. Absolute differences and risk ratios were calculated with 95% Confidence Intervals (CI).

RESULTS

Recent dental attendance was generally high and increased in both smokers (70-76%) and non-smokers (84-87%) from 2009/11 to 2017/19 and increased across all SIMD groups. After adjustment for sociodemographic variables, the adjusted Risk Difference (aRD) for recent attendance between non-smokers and smokers was 8.9% (95% CI 4.6%, 13.2%) by 2017/19. Within smokers, recent attendance was 7-9% lower in those living in the most deprived areas compared to those living in the least deprived areas over the three surveys.

CONCLUSIONS

SHeS data from 2009 to 2019 demonstrated that a high and increasing proportion of smokers in the population attend the dentist, albeit slightly less frequently than non-smokers. There were large inequalities in the dental attendance of smokers, to a lesser extent in non-smokers, and these persisted over time. Dental settings provide a good potential opportunity to deliver population-level smoking cessation interventions, but smokers in the most deprived groups and older age groups may be harder to reach. Consideration should be given to ensure that these groups are given appropriate proportionate support to take up preventive interventions.

Population Percentage and Population Size of Men Who Have Sex With Men in the United States, 2017-2021: Meta-Analysis of 5 Population-Based Surveys

BACKGROUND

Male-to-male sexual transmission continues to account for the greatest proportion of new HIV diagnoses in the United States. However, calculating population-specific surveillance metrics for HIV and other sexually transmitted infections requires regularly updated estimates of the number and proportion of men who have sex with men (MSM) in the United States, which are not collected by census surveys.

OBJECTIVE

The purpose of this analysis was to estimate the number and percentage of MSM in the United States from population-based surveys.

METHODS

We used data from 5 population-based surveys to calculate weighted estimates of the proportion of MSM in the United States and pooled these estimates using meta-analytic procedures. We estimated the proportion of MSM using sexual behavior-based questions (encompassing anal or oral sex) for 3 recall periods-past 12 months, past 5 years, and lifetime. In addition, we estimated the proportion of MSM using self-reported identity and attraction survey responses. The total number of MSM and non-MSM in the United States were calculated from estimates of the percentage of MSM who reported sex with another man in the past 12 months.

RESULTS

The percentage of MSM varied by recall period: 3.3% (95% CI 1.7%-4.9%) indicated sex with another male in the past 12 months, 4.7% (95% CI 0.0%-33.8%) in the past 5 years, and 6.2% (95% CI 2.9%-9.5%) in their lifetime. There were comparable percentages of men who identified as gay or bisexual (3.4%, 95% CI 2.2%-4.6%) or who indicated that they are attracted to other men (4.9%, 95% CI 3.1%-6.7%) based on pooled estimates. Our estimate of the total number of MSM in the United States is 4,230,000 (95% CI 2,179,000-6,281,000) based on the history of recent sexual behavior (sex with another man in the past 12 months).

CONCLUSIONS

We calculated the pooled percentage and number of MSM in the United States from a meta-analysis of population-based surveys collected from 2017 to 2021. These estimates update and expand upon those derived from the Centers for Disease Control and Prevention in 2012 by including estimates of the percentage of MSM based on sexual identity and sexual attraction. The percentage and number of MSM in the United States is an important indicator for calculating population-specific disease rates and eligibility for preventive interventions such as pre-exposure prophylaxis.

Source of perceived social support and cognitive change: an 8-year prospective cohort study

OBJECTIVES

This study explored the longitudinal association between overall and individual sources of social support and global cognitive function in older adults.Methods:Data were drawn from three waves (2006, 2010 and 2014) of the Health and Retirement Study (HRS). The analytic sample included participants aged 65 and above who were married and had at least one child (N = 1319). Global cognitive function was measured through a 35-point Telephone Interview Cognitive Screen (TICS). Perceived social support was measured via questions across four sources of support (spouse, child, other family members, and friends). Analyses were conducted using Hierarchical Linear Modeling (HLM) with the addition of a cross-lagged panel model (CLPM).

RESULTS

There was no significant association between perceived overall social support and global cognitive function over time (β=-0.02, p=0.19). However, changes in perceived support from children were positively associated with changes in global cognition (β=0.05,p<0.01). In contrast, changes in perceived support from other family members were negatively associated with changes in cognitive function (β=-0.07,p<0.01).

CONCLUSIONS

Our findings highlight the importance of examining perceived social support from specific elements of one's social networks, rather than in an aggregated variable. Although the positive effect of perceived support from children may be small to moderate, the findings could provide a target for interventions to protect the cognitive function of older adults.

Using adverse childhood experiences to explore the usefulness of community health needs assessments to monitor complex determinants of health at the local level

BACKGROUND

Hospital led community health needs assessments (CHNAs) are intended to help medical care organizations assess determinants of health within the communities they serve. This study demonstrates the utility of data from non-profit hospital CHNAs to monitor complex health issues such as adverse childhood events (ACEs) at the local-level.

METHODS

CHNA data were collected from August to November 2019 and analyzed July 2021. A series of logistic regressions were used to analyze associations between ACEs, mental health conditions, and self-rated health from a convenience sample of 2831 adults from two regional hospitals that service five counties located in central Appalachia.

RESULTS

ACEs were associated with increased odds of experiencing all metal health conditions after adjusting for other exposures and demographics, including: bipolar disorder (AOR: 2.42, CL: 1.78, 3.30), chronic pain (AOR: 1.61, CL: 1.438, 1.87), depression (AOR: 2.05, CL: 1.76, 2.36), PTSD (AOR: 3.83, CL: 2.95, 4.98), and poor self-rated health (AOR: 1.88, CL: 1.65, 2.15).

CONCLUSION

Findings suggest hospital CHNAs are a useful way to assess local data and should include factors known to antecede disease including associated risks and outcomes. CHNAs may provide an opportunity to fill important gaps in community surveillance and inform local prevention and treatment strategies.

Comparing group means with the total mean in random samples, surveys, and large-scale assessments: A tutorial and software illustration

In many disciplines of the social sciences, comparisons between a group mean and the total mean is a common but also challenging task. As one solution to this statistical testing problem, we propose using linear regression with weighted effect coding. For random samples, this procedure is straightforward and easy to implement by means of standard statistical software. However, for complex or clustered samples with imputed or weighted data, which are common in survey analyses, there is a lack of easy-to-use software solutions. In this paper, we discuss scenarios that are commonly encountered in the social sciences such as heterogeneous variances, weighted samples, and clustered samples, and we describe how group means can be compared to the total mean in these situations. We introduce the R package eatRep, which is a front end that makes the presented methods easily accessible for researchers. Two empirical examples, one using survey data (MIDUS 1) and the other using large-scale assessment data (PISA 2015), are given for illustration. Annotated R code to run group to total mean comparisons is provided.

Improving multilevel regression and poststratification with structured priors

A central theme in the field of survey statistics is estimating population-level quantities through data coming from potentially non-representative samples of the population. Multilevel regression and poststratification (MRP), a model-based approach, is gaining traction against the traditional weighted approach for survey estimates. MRP estimates are susceptible to bias if there is an underlying structure that the methodology does not capture. This work aims to provide a new framework for specifying structured prior distributions that lead to bias reduction in MRP estimates. We use simulation studies to explore the benefit of these prior distributions and demonstrate their efficacy on non-representative US survey data. We show that structured prior distributions offer absolute bias reduction and variance reduction for posterior MRP estimates in a large variety of data regimes.

Design-Based Approach for Analysing Survey Data in Veterinary Research

Sample surveys are an essential approach used in veterinary research and investigation. A sample obtained from a well-designed sampling process along with robust data analysis can provide valuable insight into the attributes of the target population. Two approaches, design-based or model-based, can be used as inferential frameworks for analysing survey data. Compared to the model-based approach, the design-based approach is usually more straightforward and directly makes inferences about the finite target population (such as the dairy cows in a herd or dogs in a region) rather than an infinite superpopulation. In this paper, the concept of probability sampling and the design-based approach is briefly reviewed, followed by a discussion of the estimations and their justifications in the context of several different elementary sampling methods, including simple random sampling, stratified random sampling, and one-stage cluster sampling. Finally, a concrete example of a complex survey design (involving multistage sampling and stratification) is demonstrated, illustrating how finding unbiased estimators and their corresponding variance formulas for a complex survey builds on the techniques used in elementary sampling methods.

Estimating a Multilevel Model with Complex Survey Data: Demonstration using TIMSS

Analysis of complex survey data is demonstrated for the multilevel model. Description of specific aspects of analysis, including plausible values, sampling weights, and replicate weights is provided. Following this, example TIMSS data and models are described and results are presented.

Small Area Estimation for Disease Prevalence Mapping

Small area estimation (SAE) entails estimating characteristics of interest for domains, often geographical areas, in which there may be few or no samples available. SAE has a long history and a wide variety of methods have been suggested, from a bewildering range of philosophical standpoints. We describe design-based and model-based approaches and models that are specified at the area-level and at the unit-level, focusing on health applications and fully Bayesian spatial models. The use of auxiliary information is a key ingredient for successful inference when response data are sparse and we discuss a number of approaches that allow the inclusion of covariate data. SAE for HIV prevalence, using data collected from a Demographic Health Survey in Malawi in 2015-2016, is used to illustrate a number of techniques. The potential use of SAE techniques for outcomes related to COVID-19 is discussed.

Epidemiology of fall and its socioeconomic risk factors in community-dwelling Korean elderly

Although falls in older people are a major public health problem globally, to date there are scarce reports on socioeconomic risk factors for falls. The aim of the present study was to investigate the epidemiology of fall, its associated socioeconomic risk factors and relative importance among community-dwelling Korean elderly. Secondary analysis of national survey data with 31,684 community-dwelling Korean elderly was performed. Eleven socioeconomic factors (age, gender, household type, marital status, education level, current occupation, past occupation, income, wealth, number of children, and relationship satisfaction) were selected for analysing their associations with the epidemiology of fall through complex sample analysis and logistic regressions. Results showed that 15.9%~25.1% of community-dwelling Korean elderly experienced fall yearly. The groups with significantly higher fall risks were identified as older aged, being female, not married or widowed, less educated, unemployed, and having lower relationship satisfaction. Gender (adjusted odds ratio-AOR = 1.548) and relationship satisfaction (AOR = 1.276) were the utmost important fall risk factors, indicating being older female with lower relationship satisfaction were the foremost socioeconomic characteristics for risk of falling. These findings could contribute to better understanding of the socioeconomic fall risk profiles among Korean elderly and effective strategies for fall prevention.

Estimating under-five mortality in space and time in a developing world context

Accurate estimates of the under-five mortality rate in a developing world context are a key barometer of the health of a nation. This paper describes a new model to analyze survey data on mortality in this context. We are interested in both spatial and temporal description, that is wishing to estimate under-five mortality rate across regions and years and to investigate the association between the under-five mortality rate and spatially varying covariate surfaces. We illustrate the methodology by producing yearly estimates for subnational areas in Kenya over the period 1980-2014 using data from the Demographic and Health Surveys, which use stratified cluster sampling. We use a binomial likelihood with fixed effects for the urban/rural strata and random effects for the clustering to account for the complex survey design. Smoothing is carried out using Bayesian hierarchical models with continuous spatial and temporally discrete components. A key component of the model is an offset to adjust for bias due to the effects of HIV epidemics. Substantively, there has been a sharp decline in Kenya in the under-five mortality rate in the period 1980-2014, but large variability in estimated subnational rates remains. A priority for future research is understanding this variability. In exploratory work, we examine whether a variety of spatial covariate surfaces can explain the variability in under-five mortality rate. Temperature, precipitation, a measure of malaria infection prevalence, and a measure of nearness to cities were candidates for inclusion in the covariate model, but the interplay between space, time, and covariates is complex.

Relative efficiencies of two-stage sampling schemes for mean estimation in multilevel populations when cluster size is informative

In multilevel populations, there are two types of population means of an outcome variable ie, the average of all individual outcomes ignoring cluster membership and the average of cluster-specific means. To estimate the first mean, individuals can be sampled directly with simple random sampling or with two-stage sampling (TSS), that is, sampling clusters first, and then individuals within the sampled clusters. When cluster size varies in the population, three TSS schemes can be considered, ie, sampling clusters with probability proportional to cluster size and then sampling the same number of individuals per cluster; sampling clusters with equal probability and then sampling the same percentage of individuals per cluster; and sampling clusters with equal probability and then sampling the same number of individuals per cluster. Unbiased estimation of the average of all individual outcomes is discussed under each sampling scheme assuming cluster size to be informative. Furthermore, the three TSS schemes are compared in terms of efficiency with each other and with simple random sampling under the constraint of a fixed total sample size. The relative efficiency of the sampling schemes is shown to vary across different cluster size distributions. However, sampling clusters with probability proportional to size is the most efficient TSS scheme for many cluster size distributions. Model-based and design-based inference are compared and are shown to give similar results. The results are applied to the distribution of high school size in Italy and the distribution of patient list size for general practices in England.

Geostatistical Methods for Disease Mapping and Visualisation Using Data from Spatio-temporally Referenced Prevalence Surveys

In this paper, we set out general principles and develop geostatistical methods for the analysis of data from spatio-temporally referenced prevalence surveys. Our objective is to provide a tutorial guide that can be used in order to identify parsimonious geostatistical models for prevalence mapping. A general variogram-based Monte Carlo procedure is proposed to check the validity of the modelling assumptions. We describe and contrast likelihood-based and Bayesian methods of inference, showing how to account for parameter uncertainty under each of the two paradigms. We also describe extensions of the standard model for disease prevalence that can be used when stationarity of the spatio-temporal covariance function is not supported by the data. We discuss how to define predictive targets and argue that exceedance probabilities provide one of the most effective ways to convey uncertainty in prevalence estimates. We describe statistical software for the visualisation of spatio-temporal predictive summaries of prevalence through interactive animations. Finally, we illustrate an application to historical malaria prevalence data from 1 334 surveys conducted in Senegal between 1905 and 2014.