Hypertension control and related variables among urban and rural adults in Taiwan

Literature on hypertension treatment has demonstrated that a healthy life style is one of the best strategies for hypertension control. In exploring the mechanisms of behavioral change for hypertension control, a comprehensive study based on the Transtheoretical Model was carried out in Taiwan during the summer of 2000 with a sample of 350 hypertensive adults living in Taipei urban and rural areas. ^ The relationships among stages of change, processes of change and demographic factors were analyzed for six health behaviors—low fat food consumption, alcohol use, smoking, physical activity, weight control, and routine blood pressure checkups. In addition, differences were assessed between urban and rural populations in changing their behavior for hypertension control. ^ The results showed that rural populations had more difficulties than urban populations in avoiding smoking and engaging in physical activity, and the processes of change being used by urban populations were significantly greater than rural populations. The study findings support a strong association between processes and stages of change. ^ Individuals who use more processes of change will be more inclined to move from precontemplation stage to maintenance stage. Counterconditioning, which is the substitution of alternatives for the problem behaviors, in this study, significantly helped people to change diet, engage in physical activity, and check blood pressure regularly. For example, counterconditioning is eating more vegetables instead of meat, or engaging in physical activity as a time to relax rather than another task to accomplish. ^ In addition, self-reevaluation was the most important process for helping people to engage in physical activity; and social liberation was the most important process for changing diet behavior. The findings in this study may be applied to improve health behaviors among rural populations with low income and low education; however, at the same time, the obesity problems among urban populations should be prevented to control hypertension in Taiwan. ^

An empirical model to estimate the demand for primary care in urban settings

Objective. To measure the demand for primary care and its associated factors by building and estimating a demand model of primary care in urban settings.^ Data source. Secondary data from 2005 California Health Interview Survey (CHIS 2005), a population-based random-digit dial telephone survey, conducted by the UCLA Center for Health Policy Research in collaboration with the California Department of Health Services, and the Public Health Institute between July 2005 and April 2006.^ Study design. A literature review was done to specify the demand model by identifying relevant predictors and indicators. CHIS 2005 data was utilized for demand estimation.^ Analytical methods. The probit regression was used to estimate the use/non-use equation and the negative binomial regression was applied to the utilization equation with the non-negative integer dependent variable.^ Results. The model included two equations in which the use/non-use equation explained the probability of making a doctor visit in the past twelve months, and the utilization equation estimated the demand for primary conditional on at least one visit. Among independent variables, wage rate and income did not affect the primary care demand whereas age had a negative effect on demand. People with college and graduate educational level were associated with 1.03 (p < 0.05) and 1.58 (p < 0.01) more visits, respectively, compared to those with no formal education. Insurance was significantly and positively related to the demand for primary care (p < 0.01). Need for care variables exhibited positive effects on demand (p < 0.01). Existence of chronic disease was associated with 0.63 more visits, disability status was associated with 1.05 more visits, and people with poor health status had 4.24 more visits than those with excellent health status. ^ Conclusions. The average probability of visiting doctors in the past twelve months was 85% and the average number of visits was 3.45. The study emphasized the importance of need variables in explaining healthcare utilization, as well as the impact of insurance, employment and education on demand. The two-equation model of decision-making, and the probit and negative binomial regression methods, was a useful approach to demand estimation for primary care in urban settings.^

Pediatric asthma incidence in Texas and associations with ambient ozone levels in an urban area an analysis using Medicaid claims data

Few recent estimates of childhood asthma incidence exist in the literature, although the importance of incidence surveillance for understanding asthma risk factors has been recognized. Asthma prevalence, morbidity and mortality reports have repeatedly shown that low-income children are disproportionately impacted by the disease. The aim of this study was to demonstrate the utility of Medicaid claims data for providing statewide estimates of asthma incidence. Medicaid Analytic Extract (MAX) data for Texas children ages 0-17 enrolled in Medicaid between 2004 and 2007 were used to estimate incidence overall and by age group, gender, race and county of residence. A 13+ month period of continuous enrollment was required in order to distinguish incident from prevalent cases identified in the claims data. Age-adjusted incidence of asthma was 4.26/100 person-years during 2005-2007, higher than reported in other populations. Incidence rates decreased with age, were higher for males than females, differed by race, and tended to be higher in rural than urban areas. With this study, we were able to demonstrate the utility of MAX data for estimating asthma incidence, and create a dataset of incident cases to use in further analysis. ^ In subsequent analyses, we investigated a possible association between ambient air pollutants and incident asthma among Medicaid-enrolled children in Harris County Texas between 2005 and 2007. This population is at high risk for asthma, and living in an area with historically poor air quality. We used a time-stratified case-crossover design and conditional logistic regression to calculate odds ratios, adjusted for weather variables and aeroallergens, to assess the effect of increases in ozone, NO2 and PM2.5 concentrations on risk of developing asthma. Our results show that a 10 ppb increase in ozone was significantly associated with asthma during the warm season (May-October), with the strongest effect seen when a 6-day cumulative lag period was used to compute the exposure metric (OR=1.05, 95% CI, 1.02–1.08). Similar results were seen for NO2 and PM 2.5 (OR=1.07, 95% CI, 1.03–1.11 and OR=1.12, 95% CI, 1.03–1.22, respectively). PM2.5 also had significant effects in the cold season (November-April), 5-day cumulative lag: OR=1.11, 95% CI, 1.00–1.22. When compared with children in the lowest quartile of O3 exposure, the risk for children in the highest quartile was 20% higher. This study indicates that these pollutants are associated with newly-diagnosed childhood asthma in this low-income urban population, particularly during the summer months. ^