Predictor variables Sample Clauses
Predictor variables. The self-report questionnaires covered anxiety, physical health, personality pathology, negative life events, post-traumatic stress, social support, self-efficacy, and coping style. The level of anxiety was measured with the subscale of the Dutch version of the Hospital Anxiety and Depression Scale (HADS-A; ▇▇▇▇▇▇▇ & ▇▇▇▇▇▇, 1983). A cut- off of 8 is recommended to distinguish between high and low anxiety levels. The α in this sample was .80. As an indication of physical health the presence of chronic medical conditions was assessed at pre-treatment and at the 14-month FU; this was done with a checklist of nine chronic medical conditions covering cardiovasculair diseases, pulmonary conditions, brain damage, diabetes, rheumatism, arthrosis, dysplasia (Central Bureau of Statistics, 1989). Furthermore, the scales for pain and physical functioning of the Medical Outcome Study Short Form General Health Survey (MOS-SF-20; ▇▇▇▇▇▇, 1992; ▇▇▇▇▇▇▇ & ▇▇▇▇, 1988) were used as indications of physical health. Personality pathology was assessed with the Questionnaire of Personality Traits - VKP (in Dutch: Vragenlijst voor Kenmerken van de Persoonlijkheid), an inventory with items based on the DSM-IV and ICD-10 definitions and criteria of personality disorders (Duijsens, Eurelings-▇▇▇▇▇▇▇▇, & ▇▇▇▇▇▇▇▇, 1996; Duijsens, Haringsma, & Eurelings-▇▇▇▇▇▇▇▇, 1999). At pretrreament the DSM-IV section which consisted of 149 items (including the passive-aggressive and the depressed personality disorders) was administered. The VKP yields a diagnosis and a dimensional score for each specific personality disorder (PD). The latter can be summed into a dimensional score for each cluster and into a total sumscore (PD-NOS). The cluster scores and the sum score were used as predictor variables. The experience of negative life events at pretrreament was measured with a checklist based on the Negative Life Events Questionnaire used by ▇▇▇▇▇▇ and ▇▇ ▇▇▇▇▇ (2001). It covers different developmental periods, such as childhood, adulthood, and events in the past year. A sumscore was calculated for the whole life span. Current posttraumatic stress was assessed with the Dutch version of the Impact of Event Scale (IES; ▇▇▇▇, ▇▇▇▇▇▇, & ▇▇▇▇▇▇▇, 1986; ▇▇▇▇▇▇▇▇, ▇▇▇▇▇▇, & ▇▇▇▇▇▇▇, 1979). It has 15 items; in this sample the α was .94. Social support was assessed with the abbreviated version of the Social Support List-Interaction (SSL112-I), which is intended for use with elderly adults (▇▇▇▇▇▇ & ▇▇▇ ▇▇▇▇,...
Predictor variables. The primary exposure for this study was DM, as classified by ICD-9-CM codes (Appendix 1). Type 1 DM was inclusive of ICD-9-CM codes 250.x1 and 250.x3. Type 2 DM was inclusive of ICD-9-CM codes 250.x0 and 250.x2. Additional independent variables examined included patient demographic characteristics (i.e. age, sex, race, median household income of zip code of residence, health insurance status, and hospital admission source), other comorbid conditions, and hospital characteristics (i.e. ownership, region, location, and teaching status). All independent variables were examined as categorical variables. Years 2000 through 2008 of the NIS included 14 potential comorbidities, and years 2009 through 2011 included 24 potential comorbidities. The AHRQ Clinical Classification Software (CCS) was utilized to determine all comorbidities except for tobacco dependency, history of tobacco use, chronic kidney disease, and drug dependency (Appendix 1). The AHRQ CCS consolidates pertinent ICD-9-CM codes into one code for given diagnoses. The CCS code for HIV is 5, which is inclusive of the following ICD-9- CM codes for HIV diagnosis: 042, 0420 – 0422, 0429 – 0433, 0439 – 0440, 0449, 07953, 27910, 27919, 79571, 7958, and V08. The CCS code for chronic obstructive pulmonary disorder (COPD) is 127, which is inclusive of the following ICD-9-CM codes: 490, 4910 – 4912, 49120 – 49122, 4918 – 4920, 4928, 494, 4940, 4941, and 496. The indicator variable for cancer included multiple CCS codes for the following types of cancers: head and neck, esophagus, stomach, colon, rectum and anus, liver and intrahepatic bile duct, pancreas, other GI organs (and peritoneum), bronchus and lungs, other respiratory and intrathoracic, bone and connective tissue, melanomas of skin, other non-epithelial cancer of skin, breast, uterus, cervix, ovary, other female genital organs, prostate, testis, other male genital organs, bladder, kidney and renal pelvis, other urinary organs, brain and nervous system, thyroid, Hodgkin`s disease, non-Hodgkin`s lymphoma, leukemias, multiple myeloma, secondary malignancies, and malignant neoplasm without specification of site. Tobacco dependency was specified by ICD-9-CM code 305.1, and history of tobacco use was specified as ICD-9-CM code V15.82. Chronic kidney disease was specified as ICD-9-CM code 585.9. Drug dependency was specified as ICD-9-CM codes 30400 – 30403, 30410 – 30413, 30420 – 30423, 30430 – 30433, 30440 – 30443, 30450 – 30453, 30460 – 30463, 30470 – 30473,...
Predictor variables. The following demographic information for the adolescent cohort were examined: age, sex, insurance status, and proximity to care. An attempt to examine race, weight, height, body mass index, and primary language was conducted, but these variables were sparsely reported, and were not included in the analysis. Age was computed as of 01/01/2010. All patients in the adolescent cohort were between the ages of 16 and 21 years old.
Predictor variables. In total, 22 environmental predictor variables were used in this study (Table 6). Table 6: Predictor variables used in boosted regression tree analyses. Type (cont = continuous; cat = categorical; bin = binary), range, mean and coefficient of variation (CV) are listed. Categorical pressures are ranked with the lowest categories representing lowest population densities resp. no shoreline bank modifications. Variable (unit) Type Range Mean CV (%) Number of benthic nets cont 1 – 101 21.0 64.2 Latitude cont 41.40 – 69.70 56.1247 10.1 Longitude cont -4.62 – 30.78 12.5314 46.9 Altitude (m) cont -1 – 1739 233.1 104 Average monthly temperature (°C) cont -3.7 – 15.7 5.9 55.4 Minimum mean monthly temperature (°C) cont -16.7 – 9.8 -3.4 137 Maximum mean monthly temperature (°C) cont 6.8 – 23 .1 15.7 14.4 Amplitude air temperature (°C) cont 9.9 – 29.5 19.0 16.2 Area (km2) cont 0.02 – 113 2.0 308 Mean depth (m) cont 0.5 – 97.2 6.0 122 Maximum depth (m) cont 1.0 – 190 16.7 107 pH cont 6.0 – 10.0 7.1 11.6 Total phosphorus (µg L-1) cont 1.0 – 516 28.6 178 Agricultural land cover in catchment (%) cont 0 – 97.9 24.9 117 Natural land cover in catchment (%) cont 2.1 – 100 71.2 43.1 Population density in catchment cat 1 – 4 Shoreline bank modification cat 1 – 5 Upstream impoundment (yes, no) bin Loss of downstream connectivity (yes, no) bin Water-level fluctuation (yes, no) bin Stocking (yes, no) bin Type (natural, artificial) bin Lake location was characterised using latitudinal and longitudinal coordinates and altitude (m a.s.l.). Monthly mean air temperature variables on the lake’s location were obtained from a climate model with a spatial resolution of 10’ latitude/longitude and taking into account elevation differences (New et al., 2002). The temperature amplitude (difference between mean temperature in July and January) was used as a proxy for seasonality. Lake morphology was characterised by area (km2), mean and maximum depth (m). Anthropogenic pressures included eutrophication variables (annual mean total phosphorus concentration in the lake (µg L-1), percentage of natural and agricultural grounds (▇▇▇▇▇▇ land cover; CLC) and human population density in the catchment (inhabitants km-2; four classes). Shoreline- bank modifications were assessed by local experts on a ranked scale from 1 (no modifications) to 5 (highly modified). Hydrological modifications were evaluated on a binary scale (presence, absence) and included the existence of upstream impoundments, loss of down...
Predictor variables. The predictor variables included in this study were age, race, sex, and CHD severity. These variables were considered because they are well established risk factors for transition into adult care, or care maintenance for individuals with CHD in the literature.
Predictor variables. A literature review was performed to identify the variables that are known to be associated with risk perception. Associations were reported for demographic and psychometric variables (personal characteristics) and for exter- nal variables relating to the risk source and its local context (Table 1). With this questionnaire, demographic and psychometric variables were studied in the local context of the Moerdijk region. External variables–such as the level of control over the risk source(s), voluntariness of exposure, history of risk incidents, and the cul- tural context (e.g., general beliefs about industry and chemicals)–were not included in the questionnaire, because we assumed that their value is largely determined by the risk context and not by interindividual variation. These factors are considered in the Discussion and Conclusions section. Downloaded by [Radboud Universiteit Nijmegen] at 05:54 07 August 2013 The questionnaire contained questions on personal characteristics that are known to be related to risk perception, that is, age, gender, children, main source of income (assuming that people with social security benefits as the main source of income have a lower socioeconomic status), educational level, residence, and resi- dential history (as an indicator for the psychometric variable place-identity, i.e., the local attachment to a specific geographical place). Furthermore, the questionnaire contained statements about satisfaction with the living environment (neighborhood satisfaction), satisfaction with risk communication, trust in risk management, famil- iarity with the pollution sources, and personal knowledge about effects of industrial activities (see Tables 1 and 5). Agreement with the statements was scored using 4-point Likert-type items; the scores were entered as predictor variables in the mul- tiple regression model. Finally, the questionnaire contained statements to elicit the positive or negative affect toward industry and traffic, which we used to construct two new predictor variables (i.e., affect toward industry and affect toward traffic).
Predictor variables. The predictor variables included in this study were age, race, sex, and CHD severity. These variables were considered because they are well established risk factors for transition into adult care or care maintenance for individuals with CHD. Patients in this study were between the age of 9 and 62 as of January 1, 2008. Age was classified into five age groups: 9-11, 12-13, 14-17, 18-24, and 25-62 for descriptive statistics. For statistical modeling, age was categorized into 3 categories: ‘1’ for those < 18 years, ‘2’ for those 18-24 years, and ‘3’ for those 25-62 years. This variable was determined by subtracting the patient’s date of birth from 01/01/2008. The youngest age grouping, <18, served as the reference group. Race was classified into the following 4 categories: ‘1’ for whites, ‘2’ for blacks, ‘3’ for other (i.e., American Indian/Alaskan Native, Asian, or Native Hawaiian/other Pacific Islander), and ’4’ for unknown. Whites served as the reference group. While race was included in the descriptive table, it was omitted from the modeling and not included in any further analyses due to the amount of missing data. Sex was coded ‘0’ for females and ‘1’ for males. Females served as the referent group for modeling. This variable was classified into five groupings based on ▇▇▇▇▇▇▇ et al.’s hierarchical classification of CHD (Appendix A): 1) Severe; 2) Shunts; 3) Shunt plus Valves; 4) Valves; 5) Other unspecified CHD anomalies. Other unspecified CHD anomalies served as the reference group in modeling. Simple descriptive statistics were performed for each predictor variable. Frequencies for race, sex, and CHD severity were computed within each age category. For bivariate analyses, chi-square was used to test the differences in characteristics (i.e., age, sex, CHD severity) of those who had a Medicaid claim during the 2008-2010 surveillance period and those who did not, and those whose Medicaid claims included a CHD diagnosis during the 2008-2010 surveillance period and those whose claims did not have a CHD diagnosis during the 2008-2010 surveillance period. Chi-square was applied to test the differences in characteristics of: 1) those residing within the five metro-Atlanta counties from 1999-2010 and who had a Medicaid claim during the 2008-2010 surveillance period, and those residing outside the five metro-Atlanta counties between 1999- 2007 but moved into the five metro-Atlanta counties during the 2008-2010 surveillance period and had a Medicaid claim durin...