Women’s post-abortion contraceptive use: Are predictors the same for immediate and future uptake of contraception? Evidence from Ghana

This study seeks to identify the socio-demographic, reproductive, partner-related, and facility-level characteristics associated with women’s immediate and subsequent use of post-abortion contraception in Ghana. Secondary data from the 2017 Ghana Maternal Health Survey were utilized in this study. The weighted data comprised 1,880 women who had ever had an abortion within the five years preceding the survey. Binary logistic regression analyses were performed to examine the associations between the predictor and outcome variables. Health provider and women’s socio-demographic characteristics were significantly associated with women’s use of post-abortion contraception. Health provider’s counselling on family planning prior to or after abortion and place of residence were associated with both immediate and subsequent post-abortion uptake of contraception. Among subsequent post-abortion contraceptive users, older women (35–49), women in a union, and women who had used contraception prior to becoming pregnant were strong predictors. Partner-related and reproductive variables did not predict immediate and subsequent use of contraception following abortion. Individual and structural/institutional level characteristics are important in increasing women’s acceptance and use of contraception post abortion. Improving and intensifying family planning counselling services at the health facility is critical in increasing contraceptive prevalence among abortion seekers.

The auto-monitoring of geo-technical centrifuge operating state based on weighted data fusion

There are many parameters which could reflect the operating state of geotechnical centrifuge. However, only one parameter is detected generally ; this is insuficient and unsafe for the running of the geotechnical centrifuge. This paper put forward an auto-running sate monitoring method which based on the multi-parameters’ weighted data fusion. The way by multi-sensor acquirring the running state data of the geotechnical centrifuge, then processing the data with weighted data fusion could produce the comprehensive running state parameter, which feed forward to the control system to keep the equipment running in a safe manner. The method in this paper could be implemented automatically and the result for safety monitroing is sufficient, the effect is much more efficient.

A New Measure of Population Health in a Complex Sampling Survey: The 2019 Georgia Behavioral Risk Factor Surveillance Survey – Unweighted, Weighted, and Imputed Weighted Data

Background: Measuring population health requires a well-defined denominator. The Behavioral Risk Factor Surveillance Survey (BRFSS) is designed to provide one for state-level populations. In 2019, the U.S. state of Georgia tested a new module to study recent bereavement among its 8.1 million residents aged 18 years and older. This is the first population-level assessment of bereavement. In BRFSS, bereavement is defined as the fact of a death. The term grief is not used because it denotes emotions related to that death. There is evidence from cohort studies linking new bereavement to subsequent mortality and health care utilization. Methods: BRFSS data are obtained using list-assisted, random digit dialing from the non-institutionalized population aged 18 years and older within primary statistical units. Data came from both landline telephones and cellular phones. Three questions were added to the end of the latest BRFSS asking about death of family or friend in 2018 or 2019. To evaluate data from this new module, the report presents three statistical approaches - unweighted panel data, weighted data, and weighted data using multiple imputation of missing responses. The estimated prevalence of bereavement and its standard error under each data scenario is calculated. Results: The threat to the validity of data from this new module are bias due to small samples and missingness. The unweighted panel contains 5,206 persons (70.9 percent response). Among these, 2,396 persons (46.0 %) responded ‘Yes’ to ‘Have you experienced the death of a family member or close friend in the years 2018 or 2019? To estimate the size of the population, weights are applied. With weighted data and removal of missing responses yields a prevalence of 45.56 % (SE = 1.13) with a population estimate of 4,937,056 persons. Using multiple imputation to keep missing responses, the prevalence is 45.80 % (SE = 1.18) with a denominator of 8,164,018 persons. Conclusions: New bereavement can be ascertained in a surveillance survey without bias due to refusals. Multiple imputation provides a population size estimate that is comparable to U.S. Census bureau. More field testing is required to replicate these results in other states.

Prevalence of Smokeless Tobacco Use in India and Its Association With Occupations: A LASI Study

More than two-thirds of death in developing countries are due to non-communicable diseases (NCDs), and tobacco is a leading risk factor. Among different socio-demographic factors, occupation and its corelates have impact on use of smokeless tobacco (SLT) and the evidence in India is limited. The objectives of this study are to find out the overall preva-lence of SLT use and its pattern of association with various occupation and associated variables. Methods: This study used data from Longitudinal Ageing Study in India (LASI) wave 1. Current and ever users of SLT are taken into consideration as target population. For the data analysis, survey-weighted tools have been applied for descriptive statistics and multivariable logistic re-gression model. The weighted data analysis has been done using R. Results and Discussion: From the sample size of 65561, 38% have ever used either smoking or SLT, of them, 40 % use to-bacco in smoke form, 51 % use SLT and 9 % take both. At the population level, 22.8% and 20.4% are ever and current users of SLT respectively. Type, place, and workload in the occupation found to be significantly associated with SLT use. Workplace tobacco-cessation-policy for infor-mal-workers is required to manage this issue.

SARS-CoV-2 Cumulative Incidence and Period Seroprevalence: Results from a Statewide Population-Based Serosurvey in California

Background California has reported the largest number of COVID-19 cases of any U.S. state, with more than 3.5 million confirmed as of March 2021. However, the full breadth of SARS-CoV-2 transmission in California is unknown since reported cases only represent a fraction of all infections. Methods We conducted a population-based serosurvey, utilizing mailed, home-based SARS-CoV-2 antibody testing along with a demographic and behavioral survey. We weighted data from a random sample to represent the adult California population and estimated period seroprevalence overall and by participant characteristics. Seroprevalence estimates were adjusted for waning antibodies to produce statewide estimates of cumulative incidence, the infection fatality ratio (IFR), and the reported fraction. Results California’s SARS-CoV-2 weighted seroprevalence during August–December 2020 was 4.6% (95% CI: 2.8–7.4%). Estimated cumulative incidence as of November 2, 2020 was 8.7% (95% CrI: 6.4%–11.5%), indicating 2,660,441 adults (95% CrI: 1,959,218–3,532,380) had been infected. The estimated IFR was 0.8% (95% CrI: 0.6%–1.0%), and the estimated percentage of infections reported to the California Department of Public Health was 31%. Disparately high risk for infection was observed among persons of Hispanic/Latinx ethnicity and people with no health insurance and who reported working outside the home. Conclusions We present the first statewide SARS-CoV-2 cumulative incidence estimate among adults in California. As of November 2020, approximately one in three SARS-CoV-2 infections in California adults had been identified by public health surveillance. When accounting for unreported SARS-CoV-2 infections, disparities by race/ethnicity seen in case-based surveillance persist.

Determinants of adherence to COVID-19 preventive behaviours in Canada: Results from the iCARE Study

Objective: Key to slowing the spread of SARS-Cov-2 is adherence to preventive behaviours promoted through government policies, which may be influenced by policy awareness, attitudes and concerns about the virus and its impacts. This study assessed determinants of adherence to major coronavirus preventive behaviours, including demographics, attitudes and concerns, among Canadians during the first pandemic wave. Methods: As part of the iCARE study (www.iCAREstudy.com), we weighted data from two population-based, online surveys (April and June, 2020) of Canadian adults. Questions tapped into behaviour change constructs. Multivariate regression models identified determinants of adherence. Results: Data from 6,008 respondents (51% female) were weighted for age, sex, and province. Awareness of government policies was high at both time points (80-99%), and adherence to prevention behaviours was high in April (87.5%-93.5%) but decreased over time, particularly for avoiding social gatherings (68.1%). Adherence was worse among men, those aged 25 and under, and those currently working. Aligned with the Health Beliefs Model, perceptions of the importance of prevention behaviours and the nature of peoples COVID-19-related concerns were highly predictive of adherence. Interestingly, health and social/economic concerns predicted better adherence, but having greater personal financial concerns predicted worse adherence at both time points. Conclusion: Adherence to COVID-19 prevention behaviours was worse among men, younger adults, and workers, and deteriorated over time. Perceived importance of prevention behaviours measures and health and social/economic concerns predicted better adherence, but personal financial concerns predicted worse adherence. Results have implications for tailoring policy and communication strategies during subsequent pandemic waves.

Weighted recurrence network for characterizing continuous dynamical systems

Recurrence network (RN) is a powerful tool for the analysis of complex dynamical systems. It integrates complex network theory with the idea of recurrence of a trajectory, i.e. whether two state vectors are close neighbors in a phase space. However, the differences in proximity between connected state vectors are not considered in the RN construction. Here, we propose a weighted state vector recurrence network method which assigns weights to network links based on the proximity of the two connected state vectors. On the basis, we further propose a weighted data segment recurrence network that takes continuous data segments as nodes for the analysis of noisy time series. The feasibility of the proposed methods is illustrated based on the Lorenz system. Finally, an application to five types of EEG recordings is conducted to demonstrate the potentials of the proposed methods in the study of real-world data.

Abstract P160: Associations Between Metabolic Syndrome Severity And Race/ethnicity, Socioeconomic, And Lifestyle Factors

Introduction/Purpose: Metabolic syndrome (MetS) is a disorder characterized by a cluster of cardiometabolic conditions that increase the risk of cardiovascular disease and diabetes. While MetS is treatable through lifestyle changes and medication, factors of race/ethnicity, socioeconomic, and lifestyle may influence MetS severity. Methods: The National Health and Nutrition Examination Survey (NHANES) 2015-2016 dataset was used for this secondary data analysis. Weighted data from individuals aged ≥ 20, not pregnant during the time of the assessment, with self-reported Non-Hispanic (NH) white, NH black, or Hispanic race/ethnicity, and with the variables needed to calculate MetS severity were included in the analysis (N=1850). Severity of MetS was determined using the formula developed by Gurika and DeBoer (2014) and expressed as a Z-score adjusted for sex and race. The severity score includes systolic blood pressure, triglycerides, HDL cholesterol, glucose, and body mass index. Socioeconomic variables included education level and the ratio between annual household income and poverty level. Lifestyle factors included sleep duration, dietary quality, sedentary time, and meeting the physical activity (PA) recommendation of 500 MET-mins/week (yes/no). Z-scores were converted to percentiles, which were categorized into quartiles. Regression analysis was used to examine the associations between MetS severity and race/ethnicity, socioeconomic, and lifestyle factors. Results: Based on weighted data, prevalence of MetS severity >75 th percentile differed across age (20% for 20-40 y/o, 36.4% for 41-60 y/o, 33.0% for 61-80 y/o) and across racial/ethnic (29.4% for NH white, 29.1% for NH black, and 34.9% for Hispanic) groups. Mean MetS severity scores were significantly different between the age and race/ethnicity groups (p <. 001 and p = .014, respectively). While MetS severity scores were not significantly different among the four income to poverty-index ratio groups, the difference between the lowest (≤ 133%) and highest (≥ 500%) group was significantly different (p = .02). In the regression model, a higher quality diet (p <.001), meeting the PA recommendations (p = .008), and higher education level (p = .027) were associated with lower mean MetS severity score. After adjusting for socioeconomic and lifestyle factors, being in the Hispanic race/ethnicity group was associated with higher MetS severity scores (p = .020) compare to being in the NH white group. Conclusions: Diet, physical activity, and educational level were associated with MetS severity score. After adjusting for socioeconomic and lifestyle factors, being in the Hispanic group was associated with the highest MetS severity scores. Research and preventive efforts should focus on groups at increased risk for MetS and severity. Culturally appropriate education and interventions are needed to address this health inequity.

Principal Component Analysis for Weighted Data in the Procedure of Multidimensional Statistical Forecasting

Purpose of the research. Let’s assume that the dynamics of the state of some object is being investigated. Its state is described by a system of specified indicators. Among them, some may be a linear combination of other indicators. The aim of any forecasting procedure is to solve two problems: first, to estimate the expected forecast value, and second, to estimate the confidence interval for possible other forecast values. The prediction procedure is multidimensional. Since the indicators describe the same object, in addition to explicit dependencies, there may be hidden dependencies among them. The principal component analysis effectively takes into account the variation of data in the system of the studied indicators. Therefore, it is desirable to use this method in the forecasting procedure. The results of forecasting would be more adequate if it were possible to implement different forecasting strategies. But this will require a modification of the traditional principal component analysis. Therefore, this is the main aim of this study. A related aim is to investigate the possibility of solving the second forecasting problem, which is more complex than the first one. Materials and research methods. When estimating the confidence interval, it is necessary to specify the procedure for estimating the expected forecast value. At the same time, it would be useful to use the methods of multidimensional time series. Usually, different time series models use the concept of time lag. Their number and weight significance in the model may be different. In this study, we propose a time series model based on the exponential smoothing method. The prediction procedure is multidimensional. It will rely on the rule of agreed upon data change. Therefore, the algorithm for predictive evaluation of a particular indicator is presented in a form that will be convenient for building and practical use of this rule in the future. The principal component analysis should take into account the weights of the indicator values. This is necessary for the implementation of various strategies for estimating the boundaries of the forecast values interval. The proposed standardization of weighted data promotes to the implementation of the main theorem of factor analysis. This ensures the construction of an orthonormal basis in the factor area. At the same time, it was not necessary to build an iterative algorithm, which is typical for such studies. Results. For the test data set, comparative calculations were performed using the traditional and weighted principal component analysis. It shows that the main characteristics of the component analysis are preserved. One of the indicators under consideration clearly depends on the others. Therefore, both methods show that the number of factors is less than the number of indicators. All indicators have a good relationship with the factors. In the traditional method, the dependent indicator is included in the first main component. In the modified method, this indicator is better related to the second component. Conclusion. It was shown that the elements of the factor matrix corresponding to the forecast time can be expressed as weighted averages of the previous factor values. This will allow us to estimate the limits of the confidence interval for each individual indicator, as well as for the complex indicator of the entire system. This takes into account both the consistency of data changes and the forecasting strategy.