Engineering topics

Mendez, C.

Publications and source records attributed to Mendez, C..

3 records

NASA NTRS2015

Assessing the Effect of Spaceflight on the Propensity for Astronauts to Develop Disc Herniation

A previous study reported that the instantaneous risk of developing a Herniated Nucleus Pulposus (HNP) was higher in astronauts who had flown at least one mission, as compared with those in the corps who had not yet flown. However, the study only analyzed time to HNP after the first mission (if any) and did not account for the possible effects of multiple missions. While many HNPs occurred well into astronauts' careers or in somecases years after retirement, the higher incidence of HNPs relatively soon after completion of space missions appears to indicate that spaceflight may lead to an increased risk of HNP. In addition, when an HNP occurs after spaceflight, is it related to previous spaceflight exposure? The purpose of this study was to investigate whether multiple missions, sex, age, vehicle landing dynamics, and flight duration affect the risk of developing an HNP usinga competing risks model. The outcome of the study will inform the Human System Risk Board assessment of back pain, inform the risk of injury due to dynamic loads, and update the previous dataset, which contained events up to December 31, 2006.

Feiveson, A.↗

NASA NTRS2014

The Relationship Between CO2 Levels and CO2 Related Symptoms Reported on the ISS

Medical Operations, Toxicology, and the Lifetime Surveillance of Astronaut Health collaborated to assess the association of CO2 levels on board the International Space Station and USOS crew reported symptoms inflight, i.e. headache and vision changes. Private Medical Conference (PMC) documents and the weekly Space Medicine Operations Team (SMOT) Notes were used to provide a robust data set of inflight medical events. All events and non‐events were documented independent of CO2 levels and other potential contributors. Average (arithmetic mean) and single point maximum ppCO2 was calculated for the 24 hours and 7 days prior to the PMC or SMOT date and time provided by LSAH. Observations falling within the first 7 days of flight (147) were removed from the datasets analyzed to avoid confounding with Space Adaptation Syndrome. The final analysis was based on 1716 observations. For headache, 46 headaches were observed. CO2 level, age at launch, time inflight, and data source were all significantly associated with headache. In particular, for each 1 mmHg increase in CO2, the odds of a crewmember reporting a headache doubled. For vision changes, 29 reports of vision changes were observed. These observations were not found to be statistically associated with CO2 levels as analyzed. While the incidence of headache has was not high (~3%), headaches may be an indicator of underlying increases in intracranial pressure, which may result likely from the synergy between CO2‐induced cerebral vasodilatation and decreased venous drainage in microgravity. Vision changes were inconsistently reported and as a result did not align appropriately with the CO2 levels. Further analysis is needed. Our results support ongoing efforts to lower the CO2 exposure limits in spacecraft.

VanBaalen, M.↗

NASA NTRS2014

Missing Data and Multiple Imputation: An Unbiased Approach

The default method of dealing with missing data in statistical analyses is to only use the complete observations (complete case analysis), which can lead to unexpected bias when data do not meet the assumption of missing completely at random (MCAR). For the assumption of MCAR to be met, missingness cannot be related to either the observed or unobserved variables. A less stringent assumption, missing at random (MAR), requires that missingness not be associated with the value of the missing variable itself, but can be associated with the other observed variables. When data are truly MAR as opposed to MCAR, the default complete case analysis method can lead to biased results. There are statistical options available to adjust for data that are MAR, including multiple imputation (MI) which is consistent and efficient at estimating effects. Multiple imputation uses informing variables to determine statistical distributions for each piece of missing data. Then multiple datasets are created by randomly drawing on the distributions for each piece of missing data. Since MI is efficient, only a limited number, usually less than 20, of imputed datasets are required to get stable estimates. Each imputed dataset is analyzed using standard statistical techniques, and then results are combined to get overall estimates of effect. A simulation study will be demonstrated to show the results of using the default complete case analysis, and MI in a linear regression of MCAR and MAR simulated data. Further, MI was successfully applied to the association study of CO2 levels and headaches when initial analysis showed there may be an underlying association between missing CO2 levels and reported headaches. Through MI, we were able to show that there is a strong association between average CO2 levels and the risk of headaches. Each unit increase in CO2 (mmHg) resulted in a doubling in the odds of reported headaches.

Foy, M.↗