Hospital, health, and community burden after oil refinery fires, Richmond, California 2007 and 2012.

INTRODUCTION: Emergency Departments experience a significant census burst after disasters. The aim of this study is to describe patient presentations at Emergency Departments in Contra Costa County, California following chemical release incidents at an oil refinery in 2007 and 2012. Specific areas of focus include hospital and community burden with an emphasis on disease classes. METHODS: Searching 4 weeks before through 4 weeks after each event, Emergency Department abstracts identified patients living in Contra Costa County and seeking care there or in neighboring Alameda County. City and ZIP-code of residence established proximity to the refinery. This provided the following contrast groups: Event (2007, 2012), time (before, after), location (bayside, rest of county), and within bayside, warned or not warned to shelter in place. Using the Multi-Level Clinical Classification Software, we classified primary health groups recorded 4 weeks before and after the events, then summarized the data, calculated rates, and made tables, graphs, and maps to highlight findings. RESULTS: Number of visits meeting selection criteria totalled 105020 records. Visits increased modestly but statistically significantly after the 2007 incident. After the 2012 incident, two Emergency Departments took the brunt of the surge. Censuses increased from less than 600 a week each to respectively 5719 and 3072 the first week, with the greatest number 2 days post-event. It took 4 weeks for censuses to return to normal. The most common diagnosis groups that spiked were nervous/sensory, respiratory, circulatory, and injury. Bayside communities had statistically significant increases in residents seeking care. Specifically, visits of residents in warned communities nearest the refinery increased by a factor of 3.7 while visits of residents in other nearby un-warned communities increased by a factor of 1.5. CONCLUSIONS: The 2012 Emergency Department census peaked in the first week and did not return to normal for 4 weeks. Diagnoses changed to reflect conditions associated with reactions to chemical exposures. Surrounding communities and nearby hospitals experienced significant emergent burdens. In addition to changes from such events in patient diagnoses and community burden, the discussion highlights the long-term implications of failures to require adequate monitoring and warning systems and failures of health planning.

Reproductive outcomes after non-occupational exposure to hexavalent chromium, Willits California, 1983-2014.

BACKGROUND: From 1963-1995, a factory in Willits, Mendocino County, CA used toxic hexavalent chromium (Cr(VI)) without adequate measures to protect the population. We use longitudinal hospital data to compare reproductive outcomes for two generations in Willits and two generations in the Rest of County (ROC). This is the first study to quantify the reproductive impact of Cr(VI) in a non-occupational population. METHODS: We searched California hospital discharge data (1983-2014) to find Mendocino County residents born 1950 or later. ZIP-code 95490 identifies Willits residents, with all others living in ROC. We used the Multi-Level Clinical Classification Software (CCS) to classify health outcomes. First, we calculated the crude birth rate using an external census denominator. The next two models used self-contained denominators to assess health of infants and two generations of pregnant women. Finally, we focused on non-pregnant females and, for comparison, males. Here we added admissions for people who moved, linked and summarized admissions to the person level, and calculated rates per census population. RESULTS: We found 29311 newborn records in ROC and 5036 from Willits. At start of period, Willits birth rate was low and did not recover until 12 years after Plant closure. While the Plant was open, respiratory conditions, perinatal jaundice, and birth defect rates were higher for Willits infants compared to ROC, but improved post-closure. Risk for abnormal birthweight and term was high and remained high over the study period. During the period under study, we identified 31444 admissions of pregnant ROC women and 5558 from Willits. Willits women had significantly higher risk of pregnancy loss compared to ROC, whether stratified by generation, age group, or pre- and post-closure. Regardless of when exposed, Willits women continued to have significantly higher rates of in-hospital terminations, as animal studies of Cr(VI) exposure predict. In life course models, non-pregnant Willits women have significantly higher risk of reproductive organ conditions and neoplasms compared to ROC. CONCLUSIONS: Adverse reproductive outcomes are elevated and consistent with animal studies. General health outcomes reflect the same broad effect reported for occupationally exposed workers. For the first time, the detrimental reproductive effects of non-occupational Cr(VI) exposure in human females and their infants is reported.

Longitudinal analysis of health outcomes after exposure to toxics, Willits California, 1991-2012: application of the cohort-period (cross-sequential) design.

BACKGROUND: About 1963, a factory in Willits, Mendocino County (County), California added chrome plating to the manufacture of steel products. After years of residents reporting high illness rates, the State undertook a series of investigations. They found exposures had been high and warranted further research into possible health effects. Applying the seldom-used cross-sequential design, we tested if Willits had an excess rate of adverse health conditions, compared to people of the same sex and cohort living in the rest of county (ROC). This is the first report on long-term health outcomes for Willits. METHODS: Hospital discharge data for 1991-2012 were searched to find admissions for people born between 1940 and 1989 who ever gave the County as their residence. Diagnoses and procedures were classified to Level 1 (body systems) of the Multi-level Clinical Classification Software (CCS). We analyzed 796,917 diagnoses and 289,980 procedures found on 117,799 admissions of 43,234 patients who lived in the County at some time between 1991 and 2012. Of these, 7,564 lived in Willits. We summarized data to the person-level then the group level over cohort-period (cross-sequential) to control the age by time relationship, then calculated incidence rates, relative risk, and excess case statistics, each with confidence limits. A secondary analysis focused on whether Willits differed markedly from the rest of County (ROC). Specifically, other than the presence of the Plant, did Willits differ so much that those differences could plausibly explain outcome differences? RESULTS: Illness was excessive in the exposed group (Willits) compared to the unexposed (ROC). Overall number of excess cases attributable to living in Willits was estimated: Men, 301 (95% confidence limit (CL) 200-398), women: 696 (CL 569-820). CONCLUSIONS: This study demonstrates the strength of the cross-sequential design. Willits and ROC had comparable disadvantages relative to the State. Yet, when stratified by cohort, Willits had more illness per population. Little is known about the health effect of chemicals used at Willits on a non-occupationally exposed population. We recommend a follow-up study to evaluate the long-term health of people who lived in Willits during childhood and the reproductive age.

Relationship between discharge practices and intensive care unit in-hospital mortality performance: evidence of a discharge bias.

CONTEXT: Current intensive care unit performance measures include in-hospital mortality after intensive care unit admission. This measure does not account for deaths occurring after transfer to another hospital or soon after discharge and therefore, may be biased. OBJECTIVE: Determine how transfer rates to other acute care hospitals and early post-discharge mortality rates impact hospital performance assessments using an in-hospital mortality model. DESIGN, SETTING, AND PARTICIPANTS: Data were retrospectively collected on 10,502 eligible intensive care unit patients across 35 California hospitals between 2001 and 2004. MEASURES: We calculated the rates of acute care hospital transfers and early post-discharge mortality (30-day overall mortality-30-day in-hospital mortality) for each hospital. We assessed hospital performance with standardized mortality ratios (SMRs) using the Mortality Probability Model III. Using regression models, we explored the relationship between in-hospital SMRs and the rates of hospital transfers or early post-discharge mortality. We explored the same relationship using a 30-day SMR. RESULTS: In multivariable models, for each 1% increase in patients transferred to another acute care hospital, there was an in-hospital SMR reduction of -0.021 (-0.040-0.001). Additionally, a 1% increase in early post-discharge mortality was associated with an in-hospital SMR reduction of -0.049 (-0.142-0.045). Assessing hospital performance based upon 30-day mortality end point resulted in SMRs closer to 1.0 for hospitals at high and low ends of in-hospital mortality performance. CONCLUSIONS: Variations in transfer rates and potentially discharge timing appear to bias in-hospital SMR calculations. A 30-day mortality model is a potential alternative that may limit this bias.

Variation in ICU risk-adjusted mortality: impact of methods of assessment and potential confounders.

BACKGROUND: Federal and state agencies are considering ICU performance assessment and public reporting; however, an accurate method for measuring performance must be selected. In this study, we determine whether a substantial variation in ICU mortality performance still exists in modern ICUs, and compare the predictive accuracy, reliability, and data burden of existing ICU risk-adjustment models. METHODS: A retrospective chart review of 11,300 ICU patients from 35 California hospitals from 2001 to 2004 was performed. We calculated standardized mortality ratios (SMRs) for each hospital using the mortality probability model III (MPM(0) III), the simplified acute physiology score (SAPS) II, and the acute physiology and chronic health evaluation (APACHE) IV risk-adjustment models. We compared discrimination, calibration, data reliability, and abstraction time for the models. RESULTS: Regardless of the model used, there was a large variation in SMRs among the ICUs studied. The discrimination and calibration were adequate for all risk-adjustment models. APACHE IV had the best discrimination (area under the receiver operating characteristic curve [AUC], 0.892) compared to MPM(0) III (AUC, 0.809), and SAPS II (AUC, 0.873; p < 0.001). The models differed substantially in data abstraction times, as follows: MPM(0)III, 11.1 min (95% confidence interval [CI], 8.7 to 13.4); SAPS II, 19.6 min (95% CI, 17.0 to 22.2); and APACHE IV, 37.3 min (95% CI, 28.0 to 46.6). CONCLUSIONS: We found substantial variation in the ICU risk-adjusted mortality rates that persisted regardless of the risk-adjustment model. With unlimited resources, the APACHE IV model offers the best predictive accuracy. If constrained by cost and manual data collection, the MPM(0) III model offers a viable alternative without a substantial loss in accuracy.

The effects of duration of caregiving on institutionalization.

PURPOSE: Our objective in this analysis was to determine how the duration of caregiving interacts with key care demands (i.e., severity of problem behaviors) to influence the institutionalization of individuals suffering from dementia. METHODS: We utilized multiregional data from 4,761 caregivers of individuals with dementia over a 3-year period. We conducted multinomial logistic and Cox proportional hazards analyses to determine the moderating effects of duration on behavior problems when institutionalization was predicted. Baseline covariates included the context of care, primary objective stressors, primary subjective stressors, resources, and global outcomes. RESULTS: The Duration of care x Behavior problems interaction term was not significant in the multinomial regression or Cox hazards models. However, main effects models demonstrated that more recent caregivers were more likely to institutionalize individuals with dementia than respondents in different stages of the caregiving career. IMPLICATIONS: The results emphasize the need to (a) broaden scientific conceptualizations to consider duration of care as integral; (b) refine targeting when interventions are administered early in the dementia caregiving process; and (c) understand patterns of attrition when caregiver adaptation is modeled over time.

Predictors of nursing home placement in African Americans with dementia.

The objective of the present study was to identify predictors of institutionalization in African Americans who suffer from dementia. Data were derived from the Medicare Alzheimer's Disease Demonstration Evaluation (MADDE), which collected information on Alzheimer's patients and their family caregivers over a 3-year period. The baseline MADDE sample included 667 older African Americans suffering from dementia recruited from eight catchment areas in the United States. A Cox proportional hazards model was used to create a predictive model of institutionalization. Subsequent analyses found that care recipient age, sex, Medicaid eligibility, and cognitive impairment; site; and caregiving burden were significant predictors of time to placement. The results, among the first to examine predictors of nursing home placement of cognitively impaired African Americans, emphasize the clinical implications and complex interplay of race, dementia, and caregiving context in the institutionalization process.

Caregiving and institutionalization of cognitively impaired older people: utilizing dynamic predictors of change.

PURPOSE: The purpose of this study was to identify reliable predictors of nursing home entry over a 3-year period in a sample of 3,944 persons with dementia who resided in a home setting at baseline. Strengths of the analysis include a multiregional recruitment strategy, incorporation of salient caregiver characteristics, and a 3-year prospective design that allows for the modeling of change in important variables (e.g., care recipient functional status or caregiving indicators) when time to institutionalization is predicted. DESIGN AND METHODS: Data were derived from the control sample of the Medicare Alzheimer's Disease Demonstration Evaluation (MADDE). A Cox proportional hazards model was used to predict time to institutionalization among individuals with dementia (baseline was enrollment into MADDE). Predictors included care recipient demographics, caregiver demographics, and time-varying measurements of care recipient functional status, caregiving indicators, and service utilization. Indicators of change were also incorporated to capitalize on the prospective data available. RESULTS: Although several results were consistent with prior findings, caregiving indicators (i.e., burden and self-rated health) and community-based service use were significant predictors of earlier placement. Change in caregiver instrumental activities of daily living and care recipient activities of daily living were also related to expedited institutionalization. IMPLICATIONS: The findings emphasize the importance of incorporating both care recipient and caregiver function and service use patterns when targeting programs designed to prevent or delay institutionalization for people with dementia.

Predicting 12-month mortality for persons with dementia.

OBJECTIVES: We develop and test a model of 12-month mortality among persons (N=3858) with organic dementia. METHODS: Data are from caregiver interviews and claims records for persons enrolled in the Medicare Alzheimer's Disease Demonstration Evaluation. Information covers the year prior to enrollment through 36 months. We used Proportional hazards models to predict time to death. We estimated two starting points: first, the date of randomization, and second, the date of maximum difficulty in performing two or more activities of daily living (ADLs). RESULTS: The final model includes age, gender, ADL difficulty, medical conditions, prior year hospitalizations, and whether a daughter was the primary caregiver. We combined hazard ratios to produce a cumulative mortality risk score. Model discrimination is reasonable for both models (c statistics of.72 and.69, respectively), and calibration tests were nonsignificant. DISCUSSION: The model's efficiency, as measured by the ratio of false positives (those predicted to die, but who lived) to true positives (those predicted to die and who did die) ranged from fewer than 1:1 to more than 4:1 as the model's sensitivity increased. This ratio was lower in the two or more ADL difficulty model. A validation test of the prediction model found comparable sensitivity and specificity (c statistic of.69) to the reference model.

The impact of statistical choices on neonatal intensive care unit quality ratings based on nosocomial infection rates.

OBJECTIVE: To examine the extent to which performance assessment methods affect the percentage of neonatal intensive care units (NICUs) and very low-birth-weight (VLBW) infants included in performance assessments, the distribution of NICU performance ratings, and the level of agreement in those ratings. DESIGN: Cross-sectional study based on risk-adjusted nosocomial infection rates. SETTING: NICUs belonging to the California Perinatal Quality Care Collaborative 2007-2008. PARTICIPANTS: One hundred twenty-six California NICUs and 10 487 VLBW infants. MAIN EXPOSURES: Three performance assessment choices: (1) excluding "low-volume" NICUs (those caring for <30 VLBW infants per year) vs a criterion based on confidence intervals, (2) using Bayesian vs frequentist hierarchical models, and (3) pooling data across 1 vs 2 years. MAIN OUTCOME MEASURES: Proportion of NICUs and patients included in quality assessment, distribution of ratings for NICUs, and agreement between methods using the κ statistic. RESULTS: Depending on the methods applied, 51% to 85% of NICUs and 72% to 96% of VLBW infants were included in performance assessments, 76% to 87% of NICUs were considered "average," and the level of agreement between NICU ratings ranged from 0.23 to 0.89. CONCLUSIONS: The percentage of NICUs included in performance assessments and their ratings can shift dramatically depending on performance measurement method. Physicians, payers, and policymakers should continue to closely examine which existing performance assessment methods are most appropriate for evaluating pediatric care quality.

Predictors of early postdischarge mortality in critically ill patients: a retrospective cohort study from the California Intensive Care Outcomes project.

PURPOSE: Existing intensive care unit (ICU) mortality measurement systems address in-hospital mortality only. However, early postdischarge mortality contributes significantly to overall 30-day mortality. Factors associated with early postdischarge mortality are unknown. METHODS: We performed a retrospective study of 8484 ICU patients. Our primary outcome was early postdischarge mortality: death after hospital discharge and 30 days or less from ICU admission. Cox regression models assessed the association between patient, hospital, and utilization factors and the primary outcome. RESULTS: In multivariate analyses, the hazard for early postdischarge mortality increased with rising severity of illness and decreased with full-code status (hazard ratio [HR], 0.33; 95% confidence interval [CI], 0.21-0.49). Compared with discharges home, early postdischarge mortality was highest for acute care transfers (HR, 3.18; 95% CI, 2.45-4.12). Finally, patients with very short ICU length of stay (<1 day) had greater early postdischarge mortality (HR, 1.86; 95% CI; 1.32-2.61) than those with longest stays (≥7 days). CONCLUSIONS: Early postdischarge mortality is associated with patient preferences (full-code status) and decisions regarding timing and location of discharge. These findings have important implications for anyone attempting to measure or improve ICU performance and who rely on in-hospital mortality measures to do so.

Mortality probability model III and simplified acute physiology score II: assessing their value in predicting length of stay and comparison to APACHE IV.

BACKGROUND: To develop and compare ICU length-of-stay (LOS) risk-adjustment models using three commonly used mortality or LOS prediction models. METHODS: Between 2001 and 2004, we performed a retrospective, observational study of 11,295 ICU patients from 35 hospitals in the California Intensive Care Outcomes Project. We compared the accuracy of the following three LOS models: a recalibrated acute physiology and chronic health evaluation (APACHE) IV-LOS model; and models developed using risk factors in the mortality probability model III at zero hours (MPM(0)) and the simplified acute physiology score (SAPS) II mortality prediction model. We evaluated models by calculating the following: (1) grouped coefficients of determination; (2) differences between observed and predicted LOS across subgroups; and (3) intraclass correlations of observed/expected LOS ratios between models. RESULTS: The grouped coefficients of determination were APACHE IV with coefficients recalibrated to the LOS values of the study cohort (APACHE IVrecal) [R(2) = 0.422], mortality probability model III at zero hours (MPM(0) III) [R(2) = 0.279], and simplified acute physiology score (SAPS II) [R(2) = 0.008]. For each decile of predicted ICU LOS, the mean predicted LOS vs the observed LOS was significantly different (p