The covariation of multiple risk factors in primary care: a latent class analysis

Factors Influencing Patient Receptivity to Brief Alcohol Interventions in Primary Care: An Application of Conjoint Analysis

Background: Brief alcohol interventions (BAIs) are an evidence-based practice for addressing hazardous alcohol use in primary care settings. However, numerous barriers to implementation of BAIs in routine practice have been identified, including concerns about patient receptivity to BAIs. Despite this being a commonly identified barrier to BAI implementation, little BAI implementation research has focused on patient receptivity. Objectives: This study aimed to identify the treatment preferences of primary care patients who screened positive for hazardous alcohol use and to evaluate factors that may influence patients' receptivity to BAIs delivered in primary care. We conducted a mailed survey of primary care patients (N = 245) who screened positive for hazardous alcohol use on annual screening measures based on electronic medical record data. Patients completed measures assessing treatment preferences and a conjoint analysis questionnaire designed to evaluate the relative importance of three factors (focus of the BAI, tailoring of the BAI, and familiarity with the provider delivering the BAI) for patient receptivity. Results: Conjoint analysis results revealed that familiarity with provider (with patients preferring BAIs delivered by providers they have previously met) was the most important factor in predicting patients' receptivity to BAIs. Additionally, patients preferred to discuss alcohol use in the context of another concern (focus of the BAI) and preferred personalized information tailored based on their specific health concerns (tailoring of the BAI), although these factors were not statistically significant when accounting for familiarity with provider. Conclusions: Findings of the present study have potential to inform future research on implementation of BAIs.

Screening high-risk population of persistent postpartum hypertension in women with preeclampsia using latent class cluster analysis

BACKGROUND

A significant proportion of women with preeclampsia (PE) exhibit persistent postpartum hypertension (PHTN) at 3 months postpartum associated with cardiovascular morbidity. This study aimed to screen patients with PE to identify the high-risk population with persistent PHTN.

METHODS

This retrospective cohort study enrolled 1,000 PE patients with complete parturient and postpartum blood pressure (BP) profiles at 3 months postpartum. The enrolled patients exhibited new-onset hypertension after 20 weeks of pregnancy, while those with PE superimposed upon chronic hypertension were excluded. Latent class cluster analysis (LCCA), a method of unsupervised learning in machine learning, was performed to ascertain maternal exposure clusters from eight variables and 35 subordinate risk factors. Logistic regression was applied to calculate odds ratios (OR) indicating the association between clusters and PHTN.

RESULTS

The 1,000 participants were classified into three exposure clusters (subpopulations with similar characteristics) according to persistent PHTN development: high-risk cluster (31.2%), medium-risk cluster (36.8%), and low-risk cluster (32.0%). Among the 1,000 PE patients, a total of 134 (13.4%) were diagnosed with persistent PHTN, while the percentages of persistent PHTN were24.68%, 10.05%, and 6.25% in the high-, medium-, and low-risk clusters, respectively. Persistent PHTN in the high-risk cluster was nearly five times higher (OR, 4.915; 95% confidence interval (CI), 2.92-8.27) and three times (OR, 2.931; 95% CI, 1.91-4.49) than in the low- and medium-risk clusters, respectively. Persistent PHTN did not differ between the medium- and low-risk clusters. Subjects in the high-risk cluster were older and showed higher BP, poorer prenatal organ function, more adverse pregnancy events, and greater medication requirement than the other two groups.

CONCLUSION

Patients with PE can be classified into high-, medium-, and low-risk clusters according to persistent PHTN severity; each cluster has cognizable clinical features. This study's findings stress the importance of controlling persistent PHTN to prevent future cardiovascular disease.

Associations of clustered health risk behaviors with diabetes and hypertension in White, Black, Hispanic, and Asian American adults

Background

The clustering of Big Four contributors to morbidity and mortality—alcohol misuse, smoking, poor diet, and physical inactivity—may further elevate chronic health risk, but there is limited information about their specific combinations and associated health risks for racial/ethnic minority groups. We aimed to examine patterns of clustering in risk behaviors for White, Black, Hispanic, and Asian American adults and their associations with diabetes and hypertension. As these behaviors may be socioeconomically-patterned, we also examined associations between clustering and socioeconomic status (SES).

Methods

Latent class analyses and multinomial and logistic regressions were conducted using a nationally-representative sample of United States (US) adults ages 40–70 (N = 35,322) from Waves 2 (2004–2005) and 3 (2012–2013) of the National Epidemiologic Survey on Alcohol and Related Conditions. Obesity was used as a proxy for unhealthy diet. The outcomes were diabetes and hypertension.

Results

A relatively-healthy-lifestyle class was found only among White adults. Common patterns of unhealthy clustering were found across groups with some variations: the obese-inactive class among White, Black, and Hispanic adults (and the inactive class among Asian adults); the obese-inactive-smoking class among White, Black, and Hispanic adults; the smoking-risky-drinking class among White and Hispanic adults; and the smoking-risky-drinking-inactive class among Black and Asian adults. Positive associations of unhealthier clustering (having a greater number of risk behaviors) with lower SES (i.e., family income and education) and with health conditions were more consistent for Whites than for other groups. For racial minority groups, lower education than income was more consistently associated with unhealthy clusters. The associations between unhealthier clustering and diabetes and hypertension were less clear for Blacks and Asians than for Whites, with no significant association observed for Hispanics.

Conclusion

Concerted efforts to address clustered risk behaviors in most US adults, particularly in racial/ethnic minority groups given the high prevalence of unhealthy clustering, are warranted.

Clusters of the Risk Markers and the Pattern of Premature Coronary Heart Disease: An Application of the Latent Class Analysis

Background: Coronary heart disease (CHD) is the major cause of mortality in the world with a significant impact on the younger population. The aim of this study was to identify prematurity among patients with coronary artery bypass graft surgery (CABG) based on the clustering of CHD risk factors.

Methods: Patients were recruited from an existing cohort of candidates for CABG surgery named Tehran Heart Center Coronary Outcome Measurement (THC-COM). A latent class analysis (LCA) model was formed using 11 potential risk factors as binary variables: cigarette smoking, obesity, diabetes, family history of CHD, alcohol use, opium addiction, hypertension, history of stroke, history of myocardial infarction (MI), peripheral vascular disease (PVD), and hyperlipidemia (HLP). We analyzed our data to figure out how the patients are going to be clustered based on their risk factors.

Results: For 566 patients who were studied, the mean age (SD) and BMI of patients were 59.1 (8.9) and 27.3 (4.1), respectively. The LCA model fit with two latent classes was statistically significant (G2 = 824.87, df = 21, p < 0.0001). The mean (SD) age of patients for Class I and Class II was 55.66 (8.55) and 60.87 (8.66), respectively. Class I (premature) was characterized by a high probability of smoking, alcohol consumption, opium addiction, and a history of MI (P < 0.05), and class II by a high probability of obesity, diabetes, and hypertension.

Conclusion: Latent class analysis calculated two groups of severe CHD with distinct risk markers. The younger group, which is characterized by smoking, addiction, and the history of MI, can be regarded as representative of premature CHD.

Latent Class Analysis of Lifestyle Risk Factors and Association with Overweight and/or Obesity in Children and Adolescents: Systematic Review

Background: Multiple modifiable lifestyle factors are well-known contributors to many health problems. Objectives: This study aims to determine the association between latent class analysis (LCA) of modifiable lifestyle risk factors with being overweight and/or obese for children and/or adolescents. Methods: Articles were selected from six databases, without limitation regarding language or date. The review included studies that identify latent classes of modifiable lifestyle risk factors [e.g., physical activity (PA), diet, sedentary behavior (SB), and/or unhealthy behavior] by LCA to determine the association between latent classes with being overweight and/or obese. The methodology of the selected studies was evaluated using the JBI Critical Appraisal Checklist for Analytical Cross-Sectional Studies. Owing to the heterogeneity between latent classes of modifiable lifestyle risk factors with obesity and/or being overweight, the results are described narratively. Results: Using a selection process in two phases, nine articles were included. All of the included studies were of high methodological quality. The studies were conducted in six different countries: the USA, Brazil, Canada, Portugal, Italy, and Australia. Sample sizes ranged from 166 to 18.587 children and adolescents, and in terms of age (range 5-19 years). Across study clusters characterized by low consumption of fruit and vegetables, and high consumption of fatty foods, sugar snack foods, sweets, chips and fries, low PA (<1 hour each day), and high SB (screen time and TV >2 hours/day), sleep time (<10 hours/day) were positively associated with being overweight and/or obese. Conclusion: Overall there is good evidence to support that the modifiable lifestyle risk factors clustered together by LCA should be novel targets for the treatment of obesity and its associated comorbidities.

Combinations of modifiable lifestyle behaviours in relation to colorectal cancer risk in Alberta's Tomorrow Project

The objective of this study was to identify distinct clusters of individuals that exhibit unique patterns of modifiable lifestyle-related behaviours and to determine how these patterns are associated with the risk of developing colorectal cancer (CRC). The study consisted of 26,460 participants and 267 CRC cases from Alberta's Tomorrow Project. Exploratory latent class analysis of risk behaviours (obesity, physical inactivity, meat consumption, smoking, alcohol consumption, and fruit and vegetable consumption) and Cox proportional hazard models were utilized. Seven unique behavioural groups were identified, where the risk of CRC was 2.34 to 2.87 times greater for high risk groups compared to the low risk group. Sex-specific models identified higher risk groups among men (Hazard Ratios [HRs]: 3.15 to 3.89) than among women (HRs: 1.99 to 2.19). Targeting groups defined by clustering of behaviours could potentially lead to more effective prevention of CRC on a population level.

Health Behaviors and Health Status among Middle-Aged and Older Adults with Chronic Diseases in Taiwan

Changes in lifestyle behaviors may effectively maintain or improve the health status of individuals with chronic diseases. However, such health behaviors adopted by individuals are unlikely to demonstrate similar patterns. This study analyzed the relationship between the heterogeneous latent classes of health behavior and health statuses among middle-aged and older adults with hypertension, diabetes, or hyperlipidemia in Taiwan. After selecting 2103 individuals from the 2005 and 2009 Taiwan National Health Interview Survey (NHIS), we first identified heterogeneous groups of health behaviors through latent class analysis (LCA). We further explored the relationship between each latent class of health behavior and health status through ordered logit regression. We identified the following five distinct health behavior classes: the all-controlled, exercise and relaxation, healthy diet and reduced smoking or drinking, healthy diet, and least-controlled classes. Regression results indicated that individuals in classes other than the all-controlled class all reported poor health statuses. We also found great magnitude of the coefficient estimates for individuals who reported their health status to be poor or very poor for the least-controlled class. Therefore, health authorities and medical providers may develop targeted policies and interventions that address multiple modifiable health behaviors in each distinct latent class of health behavior.

Racial/Ethnic Variations in Clustered Risk Behaviors in the U.S

INTRODUCTION

Alcohol misuse, cigarette smoking, poor diet, and physical inactivity, known as the "big four" contributors to chronic conditions and mortality, typically co-occur or cluster together, with their synergistic effect more detrimental to health than their cumulative individual effects. Little research has been reported on race/ethnicity-specific analyses of the clustering of these behaviors in the U.S. This study identified clustered risk behaviors among whites, blacks, and Hispanics and examined whether unhealthy clusters were associated with lower SES (assessed by education level and family income) and poor health status.

METHODS

A nationally representative sample of U.S. adults aged 30-69 years (n=9,761) from the 2010 and 2015 National Alcohol Surveys was used to perform latent class analysis and multinomial and logistic regression modeling in 2018-2019. Obesity was used as a proxy for unhealthy diet.

RESULTS

Three lifestyle classes were identified in each group. The relatively healthy lifestyle class was identified among whites and Hispanics. The nonsmoking and low risky drinking class among blacks, though showing a healthier lifestyle than the other 2 classes, still had relatively high prevalence of inactivity and obesity. The inactive and obese class was found in all 3 groups. Also identified were the smoking and risky drinking class among whites; the smoking and inactive class among blacks; and the smoking, inactive, and risky drinking class among Hispanics. For all 3 groups, unhealthy lifestyle classes mostly were associated with lower SES. Unhealthy lifestyle classes were also associated with poorer health status.

CONCLUSIONS

Multi-behavior interventions are warranted to address inactivity and obesity in all 3 groups and unhealthy clusters involving smoking in each group.

Behavioral health risk factor profiles in general hospital patients: identifying the need for screening and brief intervention

BACKGROUND

Little is known about the clustering of behavioral health risk factors (HRFs), namely the occurrence of 16 specific combinations of tobacco smoking, at-risk alcohol use, overweight and physical inactivity in general hospital patients. Furthermore, social inequalities in HRFs, health and life expectancy are a major concern in public health. In order to establish the need for screening and intervention in general hospital care, the study aimed to determine the co-occurrence of HRFs in patients in four medical departments, and to investigate differences by gender, age and socio-economic characteristics.

METHODS

Over 17 months, a systematic multiple HRF screening was conducted at one general hospital in northeastern Germany. In total, 6251 18-64 year old patients (92% of eligibles) participated. Proportions and confidence intervals were calculated for all 16 HRF profiles stratified by department, gender, age group, school education, and employment status.

RESULTS

In total, 92.2% of the participants (58.6% male) reported ≥1 HRF, and 65.7% ≥2 HRFs. Men (71.2%), patients aged 35-49 (67.9%) and 50-64 years (69.5%), lower educated (79.0%), and unemployed (77.8%) patients had larger proportions of ≥2 HRFs than their counterparts. In all departments, the most common HRF profiles included overweight. HRF profiles that included alcohol and/ or smoking were more common in ear-nose-throat and trauma surgery than in internal medicine and general surgery patients. Men had higher rates concerning almost all HRF profiles including ≥2 HRFs and alcohol; women concerning profiles that included ≤2 HRFs and inactivity. In older patients, profiles with ≥2 HRFs including overweight; and in younger patients, profiles with smoking and/or alcohol were more common. In lower educated patients, profiles with ≥2 HRFs including inactivity; and in higher educated patients profiles with ≤2 HRFs including alcohol were more common. Compared to others, unemployed patients had higher rates of profiles with ≥3 HRFs including smoking.

CONCLUSIONS

Two in three patients require interventions targeting two or more HRFs. The findings help to develop screening and brief intervention for patients with specific health risk profiles, that can reach most patients, including those most in need and those most hard to reach, with socio-economically disadvantaged people in particular. REGISTRY: clinicaltrials.gov: NCT01291693.

Subgroups of lifestyle patterns among hypertension patients: a latent-class analysis

BACKGROUND

Hypertension remains one of the most important preventable risk factors for diseases and death. Identifying clustered patterns of modifiable lifestyle risk factors for hypertension and demographics factors related to these clustered patterns allows for targeting health prevention interventions. Therefore, this study aims to identify latent classes of hypertensive patients' lifestyle risk factors based on the clustering of four modifiable lifestyle risk factors: eating, physical activity patterns, smoking habits, and blood pressure control.

METHODS

A total of 750 patients (Mage = 65.38 years, SDage = 9.2 years) with diagnosed hypertension in urban and rural primary health care centers in Takab (Iran) were recruited randomly from August 2016 to February 2017. Latent class analysis was performed by using proc. LCA in SAS 9.2.

RESULTS

Three classes of lifestyle patterns were identified. About 14.4% of hypertensive patients were categorized in a low-risk class (I), 54.6% in an intermediate-risk class (II), and 31% in a high-risk class (III) of lifestyle. A one-year increase in age significantly increases the risk of membership in classes II and III. Similarly, being widowed or divorced increases the risk of membership in classes II and III. Also, having a higher education level decreases the risk of membership in classes II and III.

CONCLUSIONS

This study contributes to the literature on lifestyle behaviors among older adults and provides evidence that there are considerable differences in lifestyle behaviors between subgroups of older adult patients. The three profiles of hypertensive patients' conditions suggest that because behaviors often occur simultaneously within an individual level, a latent-class approach helps cluster co-occurrence risk behaviors and focuses on interventions targeted to several healthy behaviors among high-risk patients.

Empirical-Based Typology of Health Care Utilization by Medicare Eligible Veterans

OBJECTIVE

Up to 70 percent of patients who receive care through Veterans Health Administration (VHA) facilities also receive care from non-VA providers. Using applied classification techniques, this study sought to improve understanding of how elderly VA patients use VA services and complementary use of non-VA care.

METHODS

The study included 1,721,900 veterans age 65 and older who were enrolled in VA and Medicare during 2013 with at least one VA encounter during 2013. Outpatient and inpatient encounters and medications received in VA were classified, and mutually exclusive patient subsets distinguished by patterns of VA service use were derived empirically using latent class analysis (LCA). Patient characteristics and complementary use of non-VA care were compared by patient subset.

RESULTS

Five patterns of VA service use were identified that were distinguished by quantity of VA medical and specialty services, medication complexity, and mental health services. Low VA Medical users tend to be healthier and rely on non-VA services, while High VA users have multiple high cost illnesses and concentrate their care in the VA.

CONCLUSIONS

VA patients distinguished by patterns of VA service use differ in illness burden and the use of non-VA services. This information may be useful for framing efforts to optimize access to care and care coordination for elderly VA patients.

Behavioral and mental health risk factor profiles among diverse primary care patients

Behavioral and mental health risk factors are prevalent among primary care patients and contribute substantially to premature morbidity and mortality and increased health care utilization and costs. Although prior studies have found most adults screen positive for multiple risk factors, limited research has attempted to identify factors that most commonly co-occur, which may guide future interventions. The purpose of this study was to identify subgroups of primary care patients with co-occurring risk factors and to examine sociodemographic characteristics associated with these subgroups. We assessed 12 behavioral health risk factors in a sample of adults (n=1628) receiving care from nine primary care practices across six U.S. states in 2013. Using latent class analysis, we identified four distinct patient subgroups: a 'Mental Health Risk' class (prevalence=14%; low physical activity, high stress, depressive symptoms, anxiety, and sleepiness), a 'Substance Use Risk' class (29%; highest tobacco, drug, alcohol use), a 'Dietary Risk' class (29%; high BMI, poor diet), and a 'Lower Risk' class (27%). Compared to the Lower Risk class, patients in the Mental Health Risk class were younger and less likely to be Latino/Hispanic, married, college educated, or employed. Patients in the Substance Use class tended to be younger, male, African American, unmarried, and less educated. African Americans were over 7 times more likely to be in the Dietary Risk versus Lower Risk class (OR 7.7, 95% CI 4.0-14.8). Given the heavy burden of behavioral health issues in primary care, efficiently addressing co-occurring risk factors in this setting is critical.

A Health Profile of Senior-Aged Women Veterans: A Latent Class Analysis of Condition Clusters

Background and Objectives

This study characterizes the multiple morbidities experienced by senior-aged women Veterans so that the Veterans Health Administration (VHA) and other health care systems may be better prepared to meet the health care needs of this growing cohort.

Research Design and Methods

Using the VHA’s Corporate Data Warehouse, we conducted a retrospective observational study of the 38,597 female veteran patients who were at least 65 years old and received care in the VHA during 2013 and 2014. We use a latent class analysis model to cluster diagnoses associated with inpatient and outpatient events over the years.

Results

The senior-aged women Veterans are characterized by six major classes of disease clusters. We defined these classes as: Healthy (16.24% of the cohort); Ophthalmological Disorders (13.84%); Musculoskeletal Disorders (14.22%); At Risk for Cardiovascular Disease (37.53%); Diabetic with Comorbidities (9.05%); and Multimorbid (9.12%). The patterns and prevalence of these condition classes vary by race, age, and marital status.

Discussion and Implications

Each of the six clusters can be used to develop clinical practice guidelines that are appropriate for senior-aged women Veterans. Consistent with past literature, the most common conditions in this cohort are hypertension and hyperlipidemia; together they form the most common class, “At Risk of Cardiovascular Disease (CVD)”. Results also show evidence of race-related disparities, with Blacks being more likely to be in the highest risk classes. Also, members of the cohort who are currently married having improved chances of being in the healthy class. And finally, we see a “healthy survivor” effect with the oldest women in our cohort having low overall rates of disease.

Socioeconomic inequality in clusters of health-related behaviours in Europe: latent class analysis of a cross-sectional European survey

Background

Modifiable health-related behaviours tend to cluster among most vulnerable sectors of the population, particularly those at the bottom of the social hierarchy. This study aimed to identify the clusters of health-related behaviours in 27 European countries and to examine the socioeconomic inequalities in these clusters.

Methods

Data were from Eurobarometer 72.3–2009, a cross-sectional survey of 27 European countries. The analyses were conducted in 2016. The main sections of the survey included questions pertaining to sociodemographic factors, health-related behaviours, and use of services. In this study, those aged 18 years and older were included. We selected five health-related behaviours, namely smoking, excessive alcohol consumption, frequent fresh fruit consumption, physical activity and dental check-ups. Socioeconomic position was indicated by education, subjective social status and difficulty in paying bills. Latent class analysis was conducted to explore the clusters of these five behaviours. Multinomial logistic regression model was used to examine the relationships between the clusters and socioeconomic positions adjusting for age, gender, marital status and urbanisation.

Results

The eligible total population was 23,842. Latent class analysis identified three clusters; healthy, moderate and risky clusters in this European population. Individuals with the lowest socioeconomic position were more likely to have risky and moderate clusters than healthy cluster compared to those with the highest socioeconomic position.

Conclusions

There were clear socioeconomic gradients in clusters of health-related behaviours. The findings highlight the importance of adopting interventions that address multiple health risk behaviours and policies that tackle the social determinants of health-related behaviours.

Combined treatment for at-risk drinking and smoking cessation among Puerto Ricans: A randomized clinical trial

Tobacco and alcohol use are linked behaviors that individually and synergistically increase the risk for negative health consequences. This study was a two-group, randomized clinical trial evaluating the efficacy of a behavioral intervention, "Motivation And Problem Solving Plus" (MAPS+), designed to concurrently address smoking cessation and the reduction of at-risk drinking. Targeted interventions may promote coaction, the likelihood that changing one behavior (smoking) increases the probability of changing another behavior (alcohol use). Puerto Ricans (N=202) who were smokers and at-risk drinkers were randomized to standard MAPS treatment focused exclusively on smoking cessation (S-MAPS), or MAPS+, focused on cessation and at-risk drinking reduction. Drinking outcomes included: number of at-risk drinking behaviors, heavy drinking, binge drinking, and drinking and driving. MAPS+ did not have a significant main effect on reducing at-risk drinking relative to S-MAPS. Among individuals who quit smoking, MAPS+ reduced the number of drinking behaviors, the likelihood of meeting criteria for heavy drinking relative to S-MAPS, and appeared promising for reducing binge drinking. MAPS+ did not improve drinking outcomes among individuals who were unsuccessful at quitting smoking. MAPS+ showed promise in reducing at-risk drinking among Puerto Rican smokers who successfully quit smoking, consistent with treatment enhanced coaction. Integrating an alcohol intervention into cessation treatment did not reduce engagement in treatment, or hinder cessation outcomes, and positively impacted at-risk drinking among individuals who quit smoking. Findings of coaction between smoking and drinking speak to the promise of multiple health behavior change interventions for substance use treatment and chronic disease prevention.

The Prevalence of Posttraumatic Stress Disorder in Primary Care: A Systematic Review

LEARNING OBJECTIVES

After participating in this activity, learners should be better able to:• Determine the prevalence of clinician-diagnosed posttraumatic stress disorder (PTSD) in primary care patients• Identify the prevalence of questionnaire-ascertained PTSD symptoms in primary care patients OBJECTIVE: Determine the prevalence of clinician-diagnosed PTSD and questionnaire-ascertained PTSD symptoms in primary care patients.

METHODS

A systematic review of the literature using the PRISMA method, searching MEDLINE, CINAHL, Cochrane Database, PsycINFO, EMBASE, Google Scholar, and relevant book chapter bibliographies. Studies that reported on the prevalence, including point or lifetime prevalence, of PTSD ascertained using diagnostic interviews or self-report questionnaires, or from administrative data, among patients seen in primary care were deemed eligible for inclusion. We abstracted data on the PTSD assessment tool, the mean questionnaire scores/cutoff scores, the time period of PTSD symptoms, and PTSD prevalence reported.

RESULTS

Of 10,614 titles screened, 41 studies were eligible for inclusion. The included studies assessed PTSD in a total of 7,256,826 primary care patients. The median point prevalence of PTSD across studies was 12.5%. The median point prevalence in the civilian population was 11.1%; in the special-risk population, 12.5%; and in veterans, 24.5%. The point prevalence of diagnostic interview-ascertained PTSD ranged from 2% to 32.5%, and the point prevalence of questionnaire-based substantial PTSD symptoms ranged from 2.9% to 39.1%. Lifetime prevalence of diagnostic interview-ascertained PTSD ranged from 14.5% to 48.8%. The prevalence of PTSD in administrative data-based studies ranged from 3.5% to 29.2%.

CONCLUSIONS

PTSD is common in primary care settings. Additional research on effective and generalizable interventions for PTSD in primary care is needed.

Screening high-risk clusters for developing birth defects in mothers in Shanxi Province, China: application of latent class cluster analysis

Background

Few studies on cluster-based synthetic effects of multiple risk factors for birth defects have been reported. The present study aimed to identify maternal exposure clusters, explore the association between clusters of risk factors and birth defects, and further screen women with high risk for birth defects among expectant mothers.

Methods

Data were drawn from a large-scale, retrospective epidemiological survey of birth defects from 2006 to 2008 in six counties of Shanxi Province, China, using a three-level stratified random cluster sampling technique. Overall risk factors were extracted using eight synthetic variables summed and examined as a total risk factor score: maternal delivery age, genetic factors, medical history, nutrition and folic acid deficiency, maternal illness in pregnancy, drug use in pregnancy, environmental risk factors in pregnancy, and unhealthy maternal lifestyle in pregnancy. Latent class cluster analysis was used to identify maternal exposure clusters based on these synthetic variables. Adjusted odds ratios (AOR) were used to explore associations between clusters and birth defects, after adjusting for confounding variables using logistic regression.

Results

Three latent maternal exposure clusters were identified: a high-risk (6.15 %), a moderate-risk (22.39 %), and a low-risk (71.46 %) cluster. The prevalence of birth defects was 14.08 %, 0.85 %, and 0.52 % for the high-, middle- and low-risk clusters respectively. After adjusting for maternal demographic variables, women in the high-risk cluster were nearly 31 times (AOR: 30.61, 95 % CI: [24.87, 37.67]) more likely to have an infant with birth defects than low-risk women.

Conclusions

A high-risk group of mothers in an area with a high risk for birth defects were screened in our study. Targeted interventions should be conducted with women of reproductive age to improve neonatal birth outcomes in areas with a high risk of birth defects.

Which modifiable health risk behaviours are related? A systematic review of the clustering of Smoking, Nutrition, Alcohol and Physical activity ('SNAP') health risk factors

OBJECTIVE

There is a growing body of literature examining the clustering of health risk behaviours, but little consensus about which risk factors can be expected to cluster for which sub groups of people. This systematic review aimed to examine the international literature on the clustering of smoking, poor nutrition, excess alcohol and physical inactivity (SNAP) health behaviours among adults, including associated socio-demographic variables.

METHOD

A literature search was conducted in May 2014. Studies examining at least two SNAP risk factors, and using a cluster or factor analysis technique, or comparing observed to expected prevalence of risk factor combinations, were included.

RESULTS

Fifty-six relevant studies were identified. A majority of studies (81%) reported a 'healthy' cluster characterised by the absence of any SNAP risk factors. More than half of the studies reported a clustering of alcohol with smoking, and half reported clustering of all four SNAP risk factors. The methodological quality of included studies was generally weak to moderate. Males and those with greater social disadvantage showed riskier patterns of behaviours; younger age was less clearly associated with riskier behaviours.

CONCLUSION

Clustering patterns reported here reinforce the need for health promotion interventions to target multiple behaviours, and for such efforts to be specifically designed and accessible for males and those who are socially disadvantaged.

Examining the need assessment process by identifying the need profiles of elderly care recipients in the Ten-year Long-Term Care Project (TLTCP) of Taiwan

OBJECTIVE

To deal with the increasing long-term care (LTC) needs of elderly people in Taiwan, the government launched the Ten-year Long-term Care Project (TLTCP) in 2007, and through the care management system, care plans for those in need were distributed and implemented by care managers according to the single assessment process. Based on the emphasis of linking the right need assessment to the care plan, this study aimed to explore the need profiles of LTC recipients with regard to their health indicators to serve as a validity check on the identified dependency levels and care plans in the current care management system.

DESIGN

A model based on latent class analysis (LCA) was used for dealing with the issue of health heterogeneity. LCA provides an empirical method that examines the interrelationships among health indicators and characterizes the underlying set of mutually exclusive latent classes that account for the observed indicators. The analysis included a total of 2901 elderly care recipients in the LTC dataset from a southern city, 1 of the 5 major metropolitan areas in Taiwan. The identified dependency levels of the samples and their care plans in need assessment were compared and discussed.

RESULTS

Four need profiles were explored in the LTC dataset. Apart from the low (LD) (32.95%) and moderate dependent groups (MD) (17.48%), there were 2 groups identified among the high-dependency levels, including the severe physical and psychological dependency (SPP) (26.37%) and the comorbidities and severe dependency (CSD) groups (23.20%), which in sum were approximately identified as high dependency (HD) by care managers in the LTC dataset. In addition, the CSD group currently costs more for their care plans on average in LTC services (NT. 277,081.15, approximately 9200 USD) than the SPP group (NT. 244,084.21) and the other groups.

CONCLUSION

Need assessment is a key to success in care management in LTC. The results of this study showed the importance of focusing on multifacet indicators, especially the mental and social health indicators in need assessments by improving the unified assessment process to sensitively detect those with various needs and then link them to the right care plan.

Body mass index and depressive symptoms in primary care settings: examining the moderating roles of smoking status, alcohol consumption and vigorous exercise

WHAT IS ALREADY KNOWN ABOUT THIS SUBJECT

Depressive symptoms and obesity are highly prevalent in primary care settings. Depressive symptoms and obesity are positively related; as body weight increases, individuals are more likely to display depressive symptoms.

WHAT THIS STUDY ADDS

This study examines the moderating roles of health behaviours (alcohol use, smoking status and vigorous exercise) on the relationship between body mass index and depressive symptoms. Exercise attenuates the relationship between depressive symptoms and obesity. Primary care patients often report multiple health risk behaviours and symptoms, including obesity and depressive symptomatology. This study examined the relationship between body mass index (BMI) and depressive symptomatology among primary care patients and tested its moderation by health behaviours. Primary care patients (n = 497) completed self-report questionnaires. Using three multilevel models, we tested the moderation of health behaviours on the BMI-depressive symptoms relationship. After controlling for relevant covariates, BMI was positively related to depressive symptoms. Smokers reported more depressive symptoms (P < 0.01), whereas vigorous exercisers reported fewer (P < 0.001). Alcohol consumption was not related to depressive symptoms (P > 0.05). Only vigorous exercise significantly moderated the BMI-depression relationship (P < 0.05). BMI is positively related to depressive symptoms among patients who do not participate in vigorous activity, suggesting that vigorous activity reduces the risk for depressive symptoms among patients with higher BMI.

The health heterogeneity of and health care utilization by the elderly in Taiwan

A good understanding of the health heterogeneity of elderly people, their characteristics, patterns of health care utilization and subsequent expenditures is necessary to adequately evaluate the policy options and interventions aimed at improving quality and efficiency of care for older people. This article reviews studies that used Latent Class Analysis to identify four health profiles among elderly people in Taiwan: High Comorbidity (HC), Functional Impairment (FI), Frail (FR), and Relatively Healthy (RH). Variables associated with increased likelihood of being in the FR group were older age, female gender, and living with one’s family, and these also correlated with ethnicity and level of education. The HC group tended to use more ambulatory care services compared with those in the RH group. The HC group tended to be younger, better educated, and was more likely to live in urban areas than were people in the FI group. The FI group, apart from age and gender, was less likely be of Hakka ethnicity and more likely to live with others than were individuals in the RH group. The FI group had relatively high probabilities of needing assistance, and the FR group had higher healthcare expenditures. A person-centered approach would better satisfy current healthcare needs of elderly people and help forecast future expenditures.

Health-related outcomes associated with patterns of risk factors in primary care patients

It is important to find ways to identify prevalent co-occurring health risk factors to help facilitate treatment programming. One method is to use electronic medical record (EMR) data. Funderburk et al. (J Behav Med 31:525-535, 2008) used such data and latent class analysis to identify three classes of individuals based on standard health screens administered in Veterans Affairs primary care clinics. The present study extended these results by examining the health-related outcomes for each of these identified classes. Follow-up data were collected from a subgroup of the original sample (N = 4,132). Analyses showed that class assignment predicted number of diagnoses associated with the diseases that the health screens target and number of primary care behavioral health, and emergency room encounters. The findings illustrate one way an EMR can be used to identify clusters of individuals presenting with multiple health risk factors and where the healthcare system comes in contact with them.

Clustering of modifiable biobehavioral risk factors for chronic disease in US adults: a latent class analysis

AIMS

Examining the co-occurrence patterns of modifiable biobehavioral risk factors for deadly chronic diseases (e.g. cancer, cardiovascular disease, diabetes) can elucidate the etiology of risk factors and guide disease-prevention programming. The aims of this study were to (1) identify latent classes based on the clustering of five key biobehavioral risk factors among US adults who reported at least one risk factor and (2) explore the demographic correlates of the identified latent classes.

METHODS

Participants were respondents of the National Epidemiologic Survey of Alcohol and Related Conditions (2004-2005) with at least one of the following disease risk factors in the past year (N = 22,789), which were also the latent class indicators: (1) alcohol abuse/dependence, (2) drug abuse/dependence, (3) nicotine dependence, (4) obesity, and (5) physical inactivity. Housing sample units were selected to match the US National Census in location and demographic characteristics, with young adults oversampled. Participants were administered surveys by trained interviewers.

RESULTS

Five latent classes were yielded: 'obese, active non-substance abusers' (23%); 'nicotine-dependent, active, and non-obese' (19%); 'active, non-obese alcohol abusers' (6%); 'inactive, non-substance abusers' (50%); and 'active, polysubstance abusers' (3.7%). Four classes were characterized by a 100% likelihood of having one risk factor coupled with a low or moderate likelihood of having the other four risk factors. The five classes exhibited unique demographic profiles.

CONCLUSIONS

Risk factors may cluster together in a non-monotonic fashion, with the majority of the at-risk population of US adults expected to have a high likelihood of endorsing only one of these five risk factors.

A scoping review of statistical approaches to the analysis of multiple health-related behaviours

BACKGROUND

Smoking, diet, exercise, and alcohol are leading causes of chronic disease and premature death, many engage in two or more of these behaviours concurrently. The paper identified statistical approaches used to investigate multiple behavioural risk factors.

METHOD

A scoping review of papers published in English from 2000 to 2011 was conducted; papers are related to concurrent participation in at least two of the behaviours. Statistical approaches were recorded and categorised.

RESULTS

Across 50 papers, two distinct approaches were identified. Co-occurrence analyses focused on concurrent but independent behaviours, represented by prevalence of behavioural combinations and/or by the summing behaviours into risk indexes. Clustering analyses investigated underlying associations between the concurrent behaviours, with clustering identified by divergences in observed and expected prevalence of combinations or through identification of latent or unobservable clusters. Co-occurrence was more frequently reported, but the use of clustering techniques and, in particular, cluster analytic and latent variable techniques increased across the study period.

DISCUSSION

The two approaches investigate concurrent participation in multiple health behaviours but differ in conceptualisation and analysis. Despite differences, inconsistency in the terminology describing the study of multiple health behaviours was apparent, with potential to influence understandings of concurrent health behaviours in policy and practice.

PREDICTING LATENT CLASS SCORES FOR SUBSEQUENT ANALYSIS

Latent class regression models relate covariates and latent constructs such as psychiatric disorders. Though full maximum likelihood estimation is available, estimation is often in three steps: (i) a latent class model is fitted without covariates; (ii) latent class scores are predicted; and (iii) the scores are regressed on covariates. We propose a new method for predicting class scores that, in contrast to posterior probability-based methods, yields consistent estimators of the parameters in the third step. Additionally, in simulation studies the new methodology exhibited only a minor loss of efficiency. Finally, the new and the posterior probability-based methods are compared in an analysis of mobility/exercise.

The Expanding Spectrum of Health Risks: Public Health or Harm?

Drawing on hydration as an illustrative case in point, this review discusses evidence-based perspectives on the continually expanding spectrum of promulgated health risks, including potential benefits and harms to public health. Future directions for inquiry and practice also are addressed.

Multiple Health Behavior Change: Game On or Time Out?

This review discusses evidence-based perspectives on multiple health behavior change and related interventions. Future directions for inquiry and practice also are addressed.

Spousal concordance for overall health risk status and preventive service compliance

PURPOSE

In this study we examined spousal concordance for two aggregate measures of health risk status and compliance with preventive service recommendations among 9620 pairs of cohabitating, opposite-sex married couples.

METHODS

Health risk appraisals were the primary data source to measure two outcome variables. Health risk status was compiled from 12 health risks and categorized into three levels (low-, medium-, and high-risk status). Overall preventive service compliance status was estimated by seven age-sex specific preventive service recommendations and dichotomized into lower and higher compliance status. For each of the husband and wife populations, we conducted proportional odds models and logistic regression models to assess spousal concordance for the two aggregate measures respectively. All models were adjusted for household income, one's characteristics (age, race, education, disease burden), and the same set of characteristics and the corresponding outcome variable from the spouse.

RESULTS

A positive correlation within spousal pairs was statistically significant for both health risk status and compliance status (p < .001) based on multivariate modeling. The odds ratios were similar in magnitude for the two spouse populations.

CONCLUSIONS

The analyses showed spousal concordance for aggregate measures of health behaviors. This study also provides some evidence for dominance of husband's education.