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The adverse effects of multiple health risk factors have been well-documented; however, still understudied are the effects of early smoking in the context of multiple health risk factors. This study aimed to examine the role of early smoking initiation in relation to several health risk factors, including heavy drinking, physical inactivity, and obesity in later life among ever smokers in the USA. The National Health Interview Survey (NHIS) data from 2006 through 2018 were analyzed. The primary dependent variables were presence of three other risk factors: heavy drinking, physical inactivity, and obesity. The independent variable was the age of smoking initiation. Logit regression models were constructed to evaluate the association between smoking initiation and multiple health risk factors. All analyses were done in 2022. Among US adult smokers, 18.2% started smoking before age 15 (early initiators), 55.9% at ages 15–18 (middle initiators), and 25.9% at age 20 or later (late initiators). Compared to late smoking initiators, the odds of engaging in additional health risk factors increased by 37.3% among early initiators (OR = 1.373, 95% CI = 1.316, 1.432) and 7.7% among middle initiators (OR = 1.077, 95% CI= 1.041, 1.116). Additionally, current smoking was associated with higher odds (OR = 1.369, 95% CI = 1.322, 1.417) of having additional health risk factors compared to former smoking, with one exception: current smokers had lower odds of obesity (OR = 0.566, 95% CI = 0.537, 0.597). Tobacco control programs to prevent adolescents from initiating smoking may have the potential to prevent other health risk factors in adulthood. [ABSTRACT FROM AUTHOR]

A study mapped the trajectory of depression and its components such as depressive mood and somatic expression of depression for one-year after an initial cancer diagnosis, revealing the complex nature of the psychological response to the cancer experience. The results of the analysis showed the importance of psychological assessment and symptom management during cancer treatment.

In this study, Black and White family caregivers are compared in terms of the division of labor among primary caregivers and secondary helpers. Three patterns of the division of labor were distinguished: (1) the primary caregiver as exclusive provider of care, (2) supplemental care (at least some care tasks are shared among at least two providers), and (3) specialization (different tasks are carried out by different providers). Employing both matching (by care recipients' diagnosis and number of functional impairments) and statistical controls of relevant confounders (income, employment, gender, type of residency, etc.), African American caregivers were found to be significantly less likely to provide all care alone without the help of secondary helpers., Recently, several research reports have dealt with the type of assistance received by African American and White elderly, with a focus on possible differences in reliance on formal versus informal [...]

Four common health risk behaviors have the greatest impact on all-cause mortality risk, but studies are needed with larger samples and the appropriate age range for cigarette smokers. We examined the impact of smoking in the context of multiple health behaviors on all-cause mortality using a nationally representative sample of adults aged 30 and older in the United States. National Health Interview Survey data from 1997 to 2005 were linked to the National Death Index with a follow-up to December 2015. The primary dependent variable was all-cause mortality, and the primary predictors were smoking, heavy drinking, physical inactivity, and unhealthy weight (underweight or obesity). The sample contained 189,087 individuals (≥ age 30; population estimate = 140.7 million). Our primary statistical analysis tool involved fitting Cox proportional hazards models. Our findings demonstrated that smoking led to the highest mortality risk among the four risk behaviors examined, but more than half of smokers engaged in at least one additional health risk behavior. Smokers who engaged in multiple health behaviors experienced higher increased mortality risks: smoking combined with one other health risk behavior increased mortality risk by 32% and by 82% when combined with two behaviors. Engaging in all four risk behaviors more than doubled the mortality risk of smokers. Smoking cessation interventions that address multiple risk behaviors—physical inactivity, heavy drinking, and unhealthy weight—will likely prevent premature death better than interventions that address only smoking. [ABSTRACT FROM AUTHOR]

Studies on the cost of dementia care reveal that care costs increase by $1,158 for every additional dependency in an activity of daily living. Every additional $10,000 increase in household income increases dependence on paid services by 5%. Caregiving for the patient in the caregiver's household lowers care cost by 25%.

Abstract Background The search for a reliable, valid and cost-effective comorbidity risk adjustment method for outcomes research continues to be a challenge. The most widely used tool, the Charlson Comorbidity Index (CCI) is limited due to frequent missing data in medical records and administrative data. Patient self-report data has the potential to be more complete but has not been widely used. The purpose of this study was to evaluate the performance of the Self-Administered Comorbidity Questionnaire (SCQ) to predict functional capacity, quality of life (QOL) health outcomes compared to CCI medical records data. Method An SCQ-score was generated from patient interview, and the CCI score was generated by medical record review for 525 patients hospitalized for Acute Coronary Syndrome (ACS) at baseline, three months and eight months post-discharge. Linear regression models assessed the extent to which there were differences in the ability of comorbidity measures to predict functional capacity (Activity Status Index [ASI] scores) and quality of life (EuroQOL 5D [EQ5D] scores). Results The CCI (R2 = 0.245; p = 0.132) did not predict quality of life scores while the SCQ self-report method (R2 = 0.265; p < 0.0005) predicted the EQ5D scores. However, the CCI was almost as good as the SCQ for predicting the ASI scores at three and six months and performed slightly better in predicting ASI at eight-month follow up (R2 = 0.370; p < 0.0005 vs. R2 = 0.358; p < 0.0005) respectively. Only age, gender, family income and Center for Epidemiologic Studies-Depression (CESD) scores showed significant association with both measures in predicting QOL and functional capacity. Conclusions Although our model R-squares were fairly low, these results show that the self-report SCQ index is a good alternative method to predict QOL health outcomes when compared to a CCI medical record score. Both measures predicted physical functioning similarly. This suggests that patient self-reported comorbidity data can be used for predicting physical functional capacity and QOL and can serve as a reliable risk adjustment measure. Self-report comorbidity data may provide a cost-effective alternative method for risk adjustment in clinical research, health policy and organizational improvement analyses. Trial registration Clinical Trials.gov NCT00416026

Abstract Background The Body Mass Index (BMI) based on self-reported height and weight ("self-reported BMI") in epidemiologic studies is subject to measurement error. However, because of the ease and efficiency in gathering height and weight information through interviews, it remains important to assess the extent of error present in self-reported BMI measures and to explore possible adjustment factors as well as valid uses of such self-reported measures. Methods Using the combined 2001-2006 data from the continuous National Health and Nutrition Examination Survey, discrepancies between BMI measures based on self-reported and physical height and weight measures are estimated and socio-demographic predictors of such discrepancies are identified. Employing adjustments derived from the socio-demographic predictors, the self-reported measures of height and weight in the 2001-2006 National Health Interview Survey are used for population estimates of overweight & obesity as well as the prediction of health risks associated with large BMI values. The analysis relies on two-way frequency tables as well as linear and logistic regression models. All point and variance estimates take into account the complex survey design of the studies involved. Results Self-reported BMI values tend to overestimate measured BMI values at the low end of the BMI scale (< 22) and underestimate BMI values at the high end, particularly at values > 28. The discrepancies also vary systematically with age (younger and older respondents underestimate their BMI more than respondents aged 42-55), gender and the ethnic/racial background of the respondents. BMI scores, adjusted for socio-demographic characteristics of the respondents, tend to narrow, but do not eliminate misclassification of obese people as merely overweight, but health risk estimates associated with variations in BMI values are virtually the same, whether based on self-report or measured BMI values. Conclusion BMI values based on self-reported height and weight, if corrected for biases associated with socio-demographic characteristics of the survey respondents, can be used to estimate health risks associated with variations in BMI, particularly when using parametric prediction models.

Abstract Background Comorbidity is an important adjustment measure in research focusing on outcomes such as health status and mortality. One recurrent methodological issue concerns the concordance of comorbidity data obtained from different reporting sources. The purpose of these prospectively planned analyses was to examine the concordance of comorbidity data obtained from patient self-report survey interviews and hospital medical record documentation. Methods Comorbidity data were obtained using survey interviews and medical record entries from 525 hospitalized Acute Coronary Syndrome patients. Frequencies and descriptive statistics of individual and composite comorbidity data from both sources were completed. Individual item agreement was evaluated with simple and weighted kappas, Spearman Rho coefficients for composite scores. Results On average, patients reported more comorbidities during their patient survey interviews (mean = 1.78, SD = 1.99) than providers had documented in medical records (mean = 1.27, SD = 1.43). Higher proportions of positive responses were obtained from self-reports compared to medical records for all conditions except congestive heart failure and renal disease. Older age and higher depressive symptom levels were significantly associated with poorer levels of data concordance. Conclusion These results demonstrate that survey comorbidity data from ACS patients may not be entirely concordat with medical record documentation. In the absence of a gold standard, it is possible that hospital records did not include all pre-admission comorbidities and these patient survey interview methods may need to be refined. Self-report methods to facilitate some patients' complete recall of comorbid conditions may need to be refined by health services researchers. Trial Registration ClinicalTrials.gov NCT00416026.