Physicians are a key to encouraging cessation of smoking among people living with HIV/AIDS: a cross-sectional study in the Kathmandu Valley, Nepal

BackgroundHIV care providers may be optimally positioned to promote smoking behaviour change in their patients, among whom smoking is both highly prevalent and uniquely harmful. Yet research on this front is scant, particularly in the developing country context. Hence, this study describes smoking behaviour among people living with HIV/AIDS (PLWHA) in the Kathmandu Valley of Nepal, and assesses the association between experience of physician-delivered smoking status assessment and readiness to quit among HIV-positive smokers.MethodsWe conducted a cross-sectional survey of PLWHA residing in the Kathmandu Valley, Nepal. Data from 321 adult PLWHA were analyzed using multiple logistic regression for correlates of current smoking and, among current smokers, of motivational readiness to quit based on the transtheoretical model (TTM) of behaviour change.ResultsOverall, 47% of participants were current smokers, with significantly higher rates among men (72%), ever- injecting drug users (IDUs), recent (30-day) alcohol consumers, those without any formal education, and those with higher HIV symptom burdens. Of 151 current smokers, 34% were thinking seriously of quitting within the next 6 months (contemplation or preparation stage of behaviour change). Adjusting for potential confounders, experience of physician-delivered smoking status assessment during any visit to a hospital or clinic in the past 12 months was associated with greater readiness to quit smoking (AOR = 3.34; 95% CI = 1.05,10.61).ConclusionsRoughly one-third of HIV-positive smokers residing in the Kathmandu Valley, Nepal, are at the contemplation or preparation stage of smoking behaviour change, with rates significantly higher among those whose physicians have asked about their smoking status during any clinical interaction over the past year. Systematic screening for smoking by physicians during routine HIV care may help to reduce the heavy burden of smoking and smoking-related morbidity and mortality within HIV-positive populations in Nepal and similar settings

Multiattribute judgements under uncertainty : a conjoint measurement approach

Includes bibliographical references (p. 19-20)

A study of a class of simple salesforce compensation plans

Includes bibliographical references (p. 33-34)

External reputation and productivity in organizations

Includes bibliographical references (p. 41)

A note on the relative performance of linear versus nonlinear compensation plans

Includes bibliographical references (p. 8-9)

Adsplit : an advertising budget allocation model

Includes bibliographical references (p. 15)

The Metric Quality of Ordered Categorical Data

We quantify the information loss incurred by categorizing an unobserved continuous variable () into an ordered categorical scale (). The continuous variable is conceptualized as true score (Ï„) (which varies across individuals) plus random error ((epsilon)), with both components assumed to be normally distributed. The index of metric quality is operationalized as 2 (, Ï„)/ 2(, Ï„), where 2 , the squared correlation coefficient, is a descriptive measure of the power of or to predict Ï„. The index is useful in defining limits on explanatory power (population 2 ) in multiple regression models in which an ordered categorical variable is regressed against a set of predictors. The index can also be used to correct correlations for the effects of ordered categorical measurement. The index of metric quality is extended to the case when several ordered categorical scales are averaged as in the multi-item measurement of a construct. We prove theoretically that as long as the error variance is “large,” the index of metric quality for the average of ordered categorical scales goes to 1 as the number of scales becomes “large.” The index for averaged data is useful in answering questions such as whether the measurement of a construct by averaging three 5-point scales is better or worse than the measurement obtained by averaging five 3-point scales. The results indicate that the loss of information by marketing researchers' ad hoc use of as opposed to the more refined is small (marketing research, rating scales, ordinal scales, polychoric correlation coefficient