Standardized Incidence Ratio and Standardized Mortality Ratio of Malignant Mesothelioma in a Worker Cohort Using Employment Insurance Database in Korea

Malignant mesothelioma is one of the appropriate indicators for assessing the carcinogenic effects of asbestos. This study compared the risk ratio of mesothelioma according to the industry in the worker cohort. A cohort was constructed using the Korean employment insurance system during 1995–2017, enrolling 13,285,895 men and 10,452,705 women. The standardized mortality ratio (SMR) and standardized incidence ratio (SIR) were calculated using the indirect standardization method. There were 641 malignant mesotheliomas that occurred; the SIR was significantly higher than the general population (men 1.36, 95% confidence interval(CI) 1.24–1.48, women 1.44, 95% CI: 1.23–1.7). More than half (52.8%) of malignant mesothelioma cases occurred in the manufacturing (n = 240, 38.6%, SIR: men, 1.72, 95% CI: 1.37–2.15, women, 3.31, 95% CI: 1.71–5.79) and construction industries (n = 88, 14.2%, SIR: men, 1.54 95% CI: 1.33–1.78, women, 1.62 95% CI: 1.25–2.11). The accommodation and food service (men, 2.56 95% CI: 1.28–4.58, women 1.35, 95% CI: 0.65–2.48) and real estate (men 1.34, 95% CI: 0.98–1.83, women 1.95, 95% CI: 0.78–4.02) also showed a high SIR, indicating the risk of asbestos-containing materials in old buildings. The incidence of malignant mesothelioma is likely to increase in the future, given the long latency of this disease. Moreover, long-term follow-up studies will be needed.

COVID-19 mortality across selected states in India: the role of age structure.

Background Mortality rates provide an opportunity to identify and act on the health system intervention for preventing deaths. Hence, it is essential to appreciate the influence of age structure while reporting mortality for a better summary of the magnitude of the epidemic. Objectives We described and compared the pattern of COVID-19 mortality standardized by age between selected states and India from January to November 2020. Methods We initially estimated the Indian population for 2020 using the decadal growth rate from the previous census (2011). This was followed by estimations of crude and age-adjusted mortality rate per million for India and the selected states. We used this information to perform indirect standardization and derive the age-standardized mortality rates for the states for comparison. In addition, we derived a ratio for age-standardized mortality to compare across age groups within the state. We extracted information regarding COVID-19 deaths from the Integrated Disease Surveillance Programme special surveillance portal up to November 16, 2020. Results The crude mortality rate of India stands at 88.9 per million population(118,883/1,337,328,910). Age-adjusted mortality rate (per million) was highest for Delhi (300.5) and lowest for Kerala (35.9).The age-standardized mortality rate (per million) for India is (<15 years=1.6, 15-29 years=6.3, 30-44 years=35.9, 45-59 years=198.8, 60-74 years=571.2, & ≥75 years=931.6). The ratios for age-standardized mortality increase proportionately from 45-59 years age group across all the states. Conclusion There is high COVID-19 mortality not only among the elderly ages, but we also identified heavy impact of COVID-19 on the working population. Therefore, we recommend further evaluation of age-adjusted mortality for all States and inclusion of variables like gender, socio-economic status for standardization while identifying at-risk populations and implementing priority public health actions. Keywords COVID-19, Mortality, Age Standardized Mortality Rate, Indirect Standardization.

Process variations between Swiss units treating neonates with hypoxic-ischemic encephalopathy and their effect on short-term outcome

Objective To compare therapeutic hypothermia (TH) treatment of term and near-term neonates with hypoxic-ischemic encephalopathy (HIE) between neonatal units. Study design Population-based, retrospective analysis of TH initiation and maintenance, and of diagnostic imaging. The comparison between units was based on crude data analysis, indirect standardization, and adjusted logistic regression. Results TH was provided to 570 neonates with HIE between 2011 and 2018 in 10 Swiss units. We excluded 121 off-protocol cooled neonates to avoid selection bias. Of the remaining 449 neonates, the outcome was favorable to international benchmarks, but there were large unit-to-unit variations in baseline perinatal data and TH management. A total of 5% neonates did not reach target temperature within 7 h (3–10% between units), and 29% experienced over- or undercooling (0–38%). Conclusion Although the neonates had favorable short-term outcomes, areas for improvement remain for Swiss units in both process and outcome measures.

A Comparison of statistical methods for hospital performance assessment

During hospital quality improvement activities, statistical approaches are critical to help assess hospital performance for benchmarking. Current statistical approaches are used primarily for research and reimbursement purposes. In this multiinstitutional study, these established statistical methods were evaluated for quality improvement applications. Leveraging a dataset of 42,199 patients who underwent coronary artery bypass grafting surgery from 2014 to 2016 across 90 hospitals, six statistical approaches were applied. The non-shrinkage methods were: (1) indirect standardization without hospital effect; (2) indirect standardization with hospital fixed effect; (3) direct standardization with hospital fixed effect. The shrinkage methods were: (4) indirect standardization with hospital random effect; (5) direct standardization with hospital random effect; (6) Bayesian method. Hospital performance related to operative mortality and major morbidity or mortality was compared across methods based on variation in adjusted rates, rankings, and performance outliers. Method performance was evaluated across procedure volume terciles: small (< 96 cases/year), medium (96-171), and large (> 171). Shrinkage methods reduced inter-hospital variation (min-max) for mortality (observed: 0%-10%; adjusted: 1.5%-2.4%) and major morbidity or mortality (observed: 2.6%-35%; adjusted: 6.9%-17.5%). Shrinkage methods shrunk hospital rates toward the group mean. Direct standardization with hospital random effect, compared to fixed effect, resulted in 16.7%-38.9% of hospitals changing quintile mortality ranking. Indirect standardization with hospital random effect resulted in no performance outliers among small and medium hospitals for mortality, while logistic and fixed effect methods identified one small and three medium outlier hospitals. The choice of statistical method greatly impacts hospital ranking and performance outlier’ status. These findings should be considered when benchmarking hospital performance for hospital quality improvement activities.

Indirect Standardization as a Case Mix Adjustment Method to Improve Comparison of Children’s Hospitals’ Antimicrobial Use

Antimicrobial use (AU) in days of therapy per 1000 patient-days (DOT/1000pd) varies widely among children’s hospitals. We evaluated indirect standardization to adjust AU for case mix, a source of variation inadequately addressed by current measurements. Hospitalizations from the Pediatric Health Information System were grouped into 85 clinical strata. Observed to expected (O:E) ratios were calculated by indirect standardization and compared to DOT/1000pd. Outliers were defined by O:E z-scores. Antibacterial DOT/1000pd ranged from 345 to 776 (2.2-fold variation; interquartile range [IQR] 552-679), whereas O:E ratios ranged from 0.8 to 1.14 (1.4-fold variation; IQR 0.93-1.05). O:E ratios were moderately correlated with DOT/1000pd (correlation estimate 0.44; 95% CI 0.19-0.64; p=0.0009). Using indirect standardization to adjust for case mix reduces apparent AU variation and may enhance stewardship efforts by providing adjusted comparisons to inform interventions.

1348. Indirect Standardization to Improve Comparison of Children’s Hospitals’ Antimicrobial Use

Background Antimicrobial use (AU) measured by days of therapy per 1000 patient-days (DOT/1000pd), the most established metric, varies widely between children’s hospitals despite robust adoption of antimicrobial stewardship. Differences in diagnoses and procedures (case mix) between hospitals are a source of AU variation not included in adjustment methods such as the Standardized Antimicrobial Administration Ratio. In this study, we evaluated an indirect standardization method to adjust children’s hospital AU for case mix. Methods This multicenter retrospective cohort study included 51 children’s hospitals participating in the Pediatric Health Information System database from 2016-2018. All inpatient, observation, and neonatal admissions were included, with a total of 2,558,948 discharges. Hospitalizations were grouped into 83 strata defined based on All Patients Refined Diagnosis Related Groups (APR-DRGs). Observed to expected (O:E) ratios were calculated by indirect standardization of mean antibiotic DOT per case, with expected values from 2016-2018 and observed values from 2018, and compared to DOT/1000pd. Outlier hospitals were defined by O:E z-scores corresponding to below 10th percentile (low outlier) and above 90th percentile (high outlier). Results Antibacterial DOT/1000pd ranged from 345 to 776 (2.2-fold variation from lowest to highest), whereas O:E ratios ranged from 0.8 to 1.14 (1.4-fold variation from lowest to highest) (Figure 1). O:E ratios were moderately correlated with DOT/1000pd (correlation estimate 0.45; 95% CI 0.19-0.64; p=0.0008). Three high outlier hospitals and 6 low outlier hospitals were identified. Examining hospitals with comparably high DOT/1000pd but discordant O:E ratios, differences could be explained by variation in both case mix and condition-specific AU within strata defined by APR-DRGs. Figure 1. Individual hospitals labeled on the X-axis, ordered by level of antibacterial DOT/1000pd (left axis), represented by bars. Diamonds represent O:E ratios derived by indirect standardization (right axis). Outlier hospitals (low and high) are highlighted in yellow. Dashed horizontal lines represent 10th percentile (lower) and 90th percentile (upper) limits of the O:E ratio distribution. Conclusion The observed variation in DOT/1000pd between hospitals is reduced when indirect standardization is applied to account for case mix differences. This approach can be adapted for more specific uses including clinical conditions, patient populations, or antimicrobial agents. Indirect standardization may enhance stewardship efforts by providing adjusted comparisons that incorporate case mix differences between hospitals. Disclosures All Authors: No reported disclosures

Beyond Deaths per Capita: Comparative CoViD-19 Mortality Indicators

The number of CoViD-19 deaths more reliably tracks the progression of the disease across populations than the number of confirmed cases. Substantial age and sex differences in CoViD-19 death rates imply that the number of deaths should be adjusted not just for the total size of the population, but also for its age-and-sex distribution. Following well-established practices in demography, this article discusses several measures based on the number of CoViD-19 deaths over time and across populations. The first measure is an unstandardized occurrence/exposure rate comparable to the Crude Death Rate. To date, the highest value has been in New York, where at its peak it exceeded the state 2017 Crude Death Rate. The second measure is an indirectly standardized rate that can be derived even when the breakdown of CoViD-19 deaths by age and sex required for direct standardization is unavailable. For populations with such breakdowns, we show that direct and indirect standardization yield similar results.Standardization modifies comparison across populations: while New Jersey now has the highest unstandardized rate, Baja California (Mexico) has the highest standardized rate. Finally, extant life tables allow to estimate reductions in life expectancy at birth. In the US, life expectancy is projected to decline this year by more (-.68) than the worst year of the HIV epidemic, or the worst three years of the opioid crisis, and to reach its lowest level since 2008. Substantially larger reductions, exceeding two years, are projected for Ecuador, Chile, New York, New Jersey and Peru.

Standardization and Age-Distribution of COVID-19: Implications for Variability in Case Fatality and Outbreak Identification

BackgroundEpidemiological data from the COVID-19 pandemic has demonstrated variability in attack rates by age, and country-to-country variability in case fatality ratio (CFR).ObjectiveTo use direct and indirect standardization for insights into the impact of age-specific under-reporting on between-country variability in CFR, and apparent size of COVID-19 epidemics.DesignPost-hoc secondary data analysis (“case studies”), and mathematical modeling.SettingChina, global.InterventionsNone.MeasurementsData were extracted from a sentinel epidemiological study by the Chinese Center for Disease Control (CCDC) that describes attack rates and CFR for COVID-19 in China prior to February 12, 2020. Standardized morbidity ratios (SMR) were used to impute missing cases and adjust CFR. Age-specific attack rates and CFR were applied to different countries with differing age structures (Italy, Japan, Indonesia, and Egypt), in order to generate estimates for CFR, apparent epidemic size, and time to outbreak recognition for identical age-specific attack rates.ResultsSMR demonstrated that 50-70% of cases were likely missed during the Chinese epidemic. Adjustment for under-recognition of younger cases decreased CFR from 2.4% to 0.8% (assuming 50% case ascertainment in older individuals). Standardizing the Chinese epidemic to countries with older populations (Italy, and Japan) resulted in larger apparent epidemic sizes, higher CFR and earlier outbreak recognition. The opposite effect was demonstrated for countries with younger populations (Indonesia, and Egypt).LimitationsSecondary data analysis based on a single country at an early stage of the COVID-19 pandemic, with no attempt to incorporate second order effects (ICU saturation) on CFR.ConclusionDirect and indirect standardization are simple tools that provide key insights into between-country variation in the apparent size and severity of COVID-19 epidemics.FundingThe research was supported by a grant to DNF from the Canadian Institutes for Health Research (2019 COVID-19 rapid researching funding OV4-170360).