Dealing with missing data in a multi-question depression scale: a comparison of imputation methods

BACKGROUND: Missing data present a challenge to many research projects. The problem is often pronounced in studies utilizing self-report scales, and literature addressing different strategies for dealing with missing data in such circumstances is scarce. The objective of this study was to compare six different imputation techniques for dealing with missing data in the Zung Self-reported Depression scale (SDS). METHODS: 1580 participants from a surgical outcomes study completed the SDS. The SDS is a 20 question scale that respondents complete by circling a value of 1 to 4 for each question. The sum of the responses is calculated and respondents are classified as exhibiting depressive symptoms when their total score is over 40. Missing values were simulated by randomly selecting questions whose values were then deleted (a missing completely at random simulation). Additionally, a missing at random and missing not at random simulation were completed. Six imputation methods were then considered; 1) multiple imputation, 2) single regression, 3) individual mean, 4) overall mean, 5) participant's preceding response, and 6) random selection of a value from 1 to 4. For each method, the imputed mean SDS score and standard deviation were compared to the population statistics. The Spearman correlation coefficient, percent misclassified and the Kappa statistic were also calculated. RESULTS: When 10% of values are missing, all the imputation methods except random selection produce Kappa statistics greater than 0.80 indicating 'near perfect' agreement. MI produces the most valid imputed values with a high Kappa statistic (0.89), although both single regression and individual mean imputation also produced favorable results. As the percent of missing information increased to 30%, or when unbalanced missing data were introduced, MI maintained a high Kappa statistic. The individual mean and single regression method produced Kappas in the 'substantial agreement' range (0.76 and 0.74 respectively). CONCLUSION: Multiple imputation is the most accurate method for dealing with missing data in most of the missind data scenarios we assessed for the SDS. Imputing the individual's mean is also an appropriate and simple method for dealing with missing data that may be more interpretable to the majority of medical readers. Researchers should consider conducting methodological assessments such as this one when confronted with missing data. The optimal method should balance validity, ease of interpretability for readers, and analysis expertise of the research team

Comparison of distance measures in spatial analytical modeling for health service planning

<p>Abstract</p> <p>Background</p> <p>Several methodological approaches have been used to estimate distance in health service research. In this study, focusing on cardiac catheterization services, Euclidean, Manhattan, and the less widely known Minkowski distance metrics are used to estimate distances from patient residence to hospital. Distance metrics typically produce less accurate estimates than actual measurements, but each metric provides a single model of travel over a given network. Therefore, distance metrics, unlike actual measurements, can be directly used in spatial analytical modeling. Euclidean distance is most often used, but unlikely the most appropriate metric. Minkowski distance is a more promising method. Distances estimated with each metric are contrasted with road distance and travel time measurements, and an optimized Minkowski distance is implemented in spatial analytical modeling.</p> <p>Methods</p> <p>Road distance and travel time are calculated from the postal code of residence of each patient undergoing cardiac catheterization to the pertinent hospital. The Minkowski metric is optimized, to approximate travel time and road distance, respectively. Distance estimates and distance measurements are then compared using descriptive statistics and visual mapping methods. The optimized Minkowski metric is implemented, via the spatial weight matrix, in a spatial regression model identifying socio-economic factors significantly associated with cardiac catheterization.</p> <p>Results</p> <p>The Minkowski coefficient that best approximates road distance is 1.54; 1.31 best approximates travel time. The latter is also a good predictor of road distance, thus providing the best single model of travel from patient's residence to hospital. The Euclidean metric and the optimal Minkowski metric are alternatively implemented in the regression model, and the results compared. The Minkowski method produces more reliable results than the traditional Euclidean metric.</p> <p>Conclusion</p> <p>Road distance and travel time measurements are the most accurate estimates, but cannot be directly implemented in spatial analytical modeling. Euclidean distance tends to underestimate road distance and travel time; Manhattan distance tends to overestimate both. The optimized Minkowski distance partially overcomes their shortcomings; it provides a single model of travel over the network. The method is flexible, suitable for analytical modeling, and more accurate than the traditional metrics; its use ultimately increases the reliability of spatial analytical models.</p

Living Alone, Patient Sex and Mortality After Acute Myocardial Infarction

BACKGROUND: Psychosocial factors, including social support, affect outcomes of cardiovascular disease, but can be difficult to measure. Whether these factors have different effects on mortality post-acute myocardial infarction (AMI) in men and women is not clear. OBJECTIVE: To examine the association between living alone, a proxy for social support, and mortality postdischarge AMI and to explore whether this association is modified by patient sex. DESIGN: Historical cohort study. PARTICIPANTS/SETTING: All patients discharged with a primary diagnosis of AMI in a major urban center during the 1998–1999 fiscal year. MEASUREMENTS: Patients’ sociodemographic and clinical characteristics were obtained by standardized chart review and linked to vital statistics data through December 2001. RESULTS: Of 880 patients, 164 (18.6%) were living alone at admission and they were significantly more likely to be older and female than those living with others. Living alone was independently associated with mortality [adjusted hazard ratio (HR) 1.6, 95% confidence interval (CI) 1.0–2.5], but interacted with patient sex. Men living alone had the highest mortality risk (adjusted HR 2.0, 95% CI 1.1–3.7), followed by women living alone (adjusted HR 1.2, 95% CI 0.7–2.2), men living with others (reference, HR 1.0), and women living with others (adjusted HR 0.9, 95% CI 0.5–1.5). CONCLUSIONS: Living alone, an easily measured psychosocial factor, is associated with significantly increased longer-term mortality for men following AMI. Further prospective studies are needed to confirm the usefulness of living alone as a prognostic factor and to identify the potentially modifiable mechanisms underlying this increased risk

Identifying priorities in methodological research using ICD-9-CM and ICD-10 administrative data: report from an international consortium

BACKGROUND: Health administrative data are frequently used for health services and population health research. Comparative research using these data has been facilitated by the use of a standard system for coding diagnoses, the International Classification of Diseases (ICD). Research using the data must deal with data quality and validity limitations which arise because the data are not created for research purposes. This paper presents a list of high-priority methodological areas for researchers using health administrative data. METHODS: A group of researchers and users of health administrative data from Canada, the United States, Switzerland, Australia, China and the United Kingdom came together in June 2005 in Banff, Canada to discuss and identify high-priority methodological research areas. The generation of ideas for research focussed not only on matters relating to the use of administrative data in health services and population health research, but also on the challenges created in transitioning from ICD-9 to ICD-10. After the brain-storming session, voting took place to rank-order the suggested projects. Participants were asked to rate the importance of each project from 1 (low priority) to 10 (high priority). Average ranks were computed to prioritise the projects. RESULTS: Thirteen potential areas of research were identified, some of which represented preparatory work rather than research per se. The three most highly ranked priorities were the documentation of data fields in each country's hospital administrative data (average score 8.4), the translation of patient safety indicators from ICD-9 to ICD-10 (average score 8.0), and the development and validation of algorithms to verify the logic and internal consistency of coding in hospital abstract data (average score 7.0). CONCLUSION: The group discussions resulted in a list of expert views on critical international priorities for future methodological research relating to health administrative data. The consortium's members welcome contacts from investigators involved in research using health administrative data, especially in cross-jurisdictional collaborative studies or in studies that illustrate the application of ICD-10

Temporal Artery versus Bladder Thermometry during Adult Medical-Surgical Intensive Care Monitoring: An Observational Study

Abstract Background We sought to evaluate agreement between a new and widely implemented method of temperature measurement in critical care, temporal artery thermometry and an established method of core temperature measurement, bladder thermometry as performed in clinical practice. Methods Temperatures were simultaneously recorded hourly (n = 736 observations) using both devices as part of routine clinical monitoring in 14 critically ill adult patients with temperatures ranging ≥1°C prior to consent. Results The mean difference between temporal artery and bladder temperatures measured was -0.44°C (95% confidence interval, -0.47°C to -0.41°C), with temporal artery readings lower than bladder temperatures. Agreement between the two devices was greatest for normothermia (36.0°C to < 38.3°C) (mean difference -0.35°C [95% confidence interval, -0.37°C to -0.33°C]). The temporal artery thermometer recorded higher temperatures during hypothermia (< 36°C) (mean difference 0.66°C [95% confidence interval, 0.53°C to 0.79°C]) and lower temperatures during hyperthermia (≥38.3°C) (mean difference -0.90°C [95% confidence interval, -0.99°C to -0.81°C]). The sensitivity for detecting fever (core temperature ≥38.3°C) using the temporal artery thermometer was 0.26 (95% confidence interval, 0.20 to 0.33), and the specificity was 0.99 (95% confidence interval, 0.98 to 0.99). The positive likelihood ratio for fever was 24.6 (95% confidence interval, 10.7 to 56.8); the negative likelihood ratio was 0.75 (95% confidence interval, 0.68 to 0.82). Conclusions Temporal artery thermometry produces somewhat surprising disagreement with an established method of core temperature measurement and should not to be used in situations where body temperature needs to be measured with accuracy

Improved accuracy of co-morbidity coding over time after the introduction of ICD-10 administrative data

BACKGROUND: Co-morbidity information derived from administrative data needs to be validated to allow its regular use. We assessed evolution in the accuracy of coding for Charlson and Elixhauser co-morbidities at three time points over a 5-year period, following the introduction of the International Classification of Diseases, 10th Revision (ICD-10), coding of hospital discharges.METHODS: Cross-sectional time trend evaluation study of coding accuracy using hospital chart data of 3'499 randomly selected patients who were discharged in 1999, 2001 and 2003, from two teaching and one non-teaching hospital in Switzerland. We measured sensitivity, positive predictive and Kappa values for agreement between administrative data coded with ICD-10 and chart data as the 'reference standard' for recording 36 co-morbidities.RESULTS: For the 17 the Charlson co-morbidities, the sensitivity - median (min-max) - was 36.5% (17.4-64.1) in 1999, 42.5% (22.2-64.6) in 2001 and 42.8% (8.4-75.6) in 2003. For the 29 Elixhauser co-morbidities, the sensitivity was 34.2% (1.9-64.1) in 1999, 38.6% (10.5-66.5) in 2001 and 41.6% (5.1-76.5) in 2003. Between 1999 and 2003, sensitivity estimates increased for 30 co-morbidities and decreased for 6 co-morbidities. The increase in sensitivities was statistically significant for six conditions and the decrease significant for one. Kappa values were increased for 29 co-morbidities and decreased for seven.CONCLUSIONS: Accuracy of administrative data in recording clinical conditions improved slightly between 1999 and 2003. These findings are of relevance to all jurisdictions introducing new coding systems, because they demonstrate a phenomenon of improved administrative data accuracy that may relate to a coding 'learning curve' with the new coding system

Age- and gender-specific risk of death after first hospitalization for heart failure

<p>Abstract</p> <p>Background</p> <p>Hospitalization for heart failure (HF) is associated with high-in-hospital and short- and long-term post discharge mortality. Age and gender are important predictors of mortality in hospitalized HF patients. However, studies assessing short- and long-term risk of death stratified by age and gender are scarce.</p> <p>Methods</p> <p>A nationwide cohort was identified (ICD-9 codes 402, 428) and followed through linkage of national registries. The crude 28-day, 1-year and 5-year mortality was computed by age and gender. Cox regression models were used for each period to study sex differences adjusting for potential confounders (age and comorbidities).</p> <p>Results</p> <p>14,529 men, mean age 74 ± 11 years and 14,524 women, mean age 78 ± 11 years were identified. Mortality risk after admission for HF increased with age and the risk of death was higher among men than women. Hazard ratio's (men versus women and adjusted for age and co-morbidity) were 1.21 (95%CI 1.14 to 1.28), 1.26 (95% CI 1.21 to 1.31), and 1.28 (95%CI 1.24 to 1.31) for 28 days, 1 year and 5 years mortality, respectively.</p> <p>Conclusions</p> <p>This study clearly shows age- and gender differences in short- and long-term risk of death after first hospitalization for HF with men having higher short- and long-term risk of death than women. As our study population includes both men and women from all ages, the estimates we provide maybe a good reflection of 'daily practice' risk of death and therefore be valuable for clinicians and policymakers.</p

Management of congestive heart failure: a gender gap may still exist. Observations from a contemporary cohort

BACKGROUND: Unlike other cardiovascular diseases the incidence and prevalence of congestive heart failure (CHF) continues to increase. While gender differences in coronary artery disease have been well described, to date, there has been a relative paucity of similar data in patients with CHF. We conducted a pilot study to evaluate the profile and management of patients with CHF at a tertiary care centre to determine if a gender difference exists. METHODS: A chart review was performed at a tertiary care centre on consecutive patients admitted with a primary diagnosis of CHF between June 1997 and 1998. Co-morbidity, diagnostic investigations, and management of CHF were recorded. Comparisons between male and female patients were conducted. RESULTS: One hundred and forty five patients were reviewed. There were 80 male (M) and 65 female (F) patients of similar age [71.6 vs. 71.3 (M vs. F), p = NS]. Male patients were more likely to have had a previous myocardial infarction (66% vs. 35%, p < 0.01) and revascularization (41% vs. 20%, p < 0.05), and had worse left ventricular ejection fraction (LVEF) than women, [median LVEF 3 vs. 2 (M vs. F), p < 0.01]. Male patients were more likely to have a non-invasive assessment of left ventricular (LV) function [85% vs. 69%, (M vs. F), p < 0.05]. A logistic regression analysis suggests that amongst those without coronary disease, males were more likely to receive non-invasive testing. There were no differences in the use of prescribed medications, in this cohort. CONCLUSIONS: This pilot study demonstrated that there seem to be important gender differences in the profile and management of patients with CHF. Importantly women were less likely to have an evaluation of LV function. As assessment of LV function has significant implications on patient management, this data justifies the need for larger studies to assess gender differences in CHF profile and treatment

Leisure Time Physical Activity of Moderate to Vigorous Intensity and Mortality: A Large Pooled Cohort Analysis

Background: Leisure time physical activity reduces the risk of premature mortality, but the years of life expectancy gained at different levels remains unclear. Our objective was to determine the years of life gained after age 40 associated with various levels of physical activity, both overall and according to body mass index (BMI) groups, in a large pooled analysis. Methods and Findings: We examined the association of leisure time physical activity with mortality during follow-up in pooled data from six prospective cohort studies in the National Cancer Institute Cohort Consortium, comprising 654,827 individuals, 21–90 y of age. Physical activity was categorized by metabolic equivalent hours per week (MET-h/wk). Life expectancies and years of life gained/lost were calculated using direct adjusted survival curves (for participants 40+ years of age), with 95% confidence intervals (CIs) derived by bootstrap. The study includes a median 10 y of follow-up and 82,465 deaths. A physical activity level of 0.1–3.74 MET-h/wk, equivalent to brisk walking for up to 75 min/wk, was associated with a gain of 1.8 (95% CI: 1.6–2.0) y in life expectancy relative to no leisure time activity (0 MET-h/wk). Higher levels of physical activity were associated with greater gains in life expectancy, with a gain of 4.5 (95% CI: 4.3–4.7) y at the highest level (22.5+ MET-h/wk, equivalent to brisk walking for 450+ min/wk). Substantial gains were also observed in each BMI group. In joint analyses, being active (7.5+ MET-h/wk) and normal weight (BMI 18.5–24.9) was associated with a gain of 7.2 (95% CI: 6.5–7.9) y of life compared to being inactive (0 MET-h/wk) and obese (BMI 35.0+). A limitation was that physical activity and BMI were ascertained by self report. Conclusions: More leisure time physical activity was associated with longer life expectancy across a range of activity levels and BMI groups