U.S. Army Disease and Nonbattle Injury Model, Refined in Afghanistan and Iraq
Since the conclusion of Operation Desert Shield/Operation Desert Storm in 1991, U.S. military forces have been involved in a variety of missions around the globe. There have been a multitude of deployments for a variety of missions; through them all, disease and nonbattle injuries A person who becomes a casualty due to circumstances not directly attributable to hostile action or terrorist activity. Also called NBI. (DNBIs) have remained a threat to U.S. troops. Recognizing that throughout history more casualties have been caused by DNBIs than by combat action,1 the U.S. Army has developed new health policies, termed Force Health Protection. These policies place "greater emphasis on helping service members ... stay healthy and fit and on preventing injury and illness" and recognize that "greater attention has to be focused on non-battle-related health risks."2 Force Health Protection strategies for recent deployed operations have included personal physical fitness, camp sanitation sanitation: see plumbing; sanitary science. measures, improved medical screening of reservists, prophylaxis prophylaxis (prō'fĭlăk`sĭs), measures designed to prevent the occurrence of disease or its dissemination. Some examples of prophylaxis are immunization against serious diseases such as smallpox or diphtheria; quarantine to confine against malaria malaria, infectious parasitic disease that can be either acute or chronic and is frequently recurrent. Malaria is common in Africa, Central and South America, the Mediterranean countries, Asia, and many of the Pacific islands. , and environmental surveillance of the deployment destination.3 These precautionary pre·cau·tion·ar·y also pre·cau·tion·al
Of, relating to, or constituting a precaution: taking precautionary measures; gave precautionary advice.
Adj. 1. measures are challenged by hos tile tile, one of the ceramic products used in building, to which group brick and terra-cotta also belong. The term designates the finished baked clay—the material of a wide variety of units used in architecture and engineering, such as wall slabs or blocks, floor environments, infrequently in·fre·quent
1. Not occurring regularly; occasional or rare: an infrequent guest.
2. observed diseases,4-6 use of a greater proportion of female troops,7 mission requirements abating the use of proper procedures,1 and the ever-present Adj. 1. ever-present - being always present
present - being or existing in a specified place; "the murderer is present in this room"; "present at the wedding"; "present at the creation" mental stress in soldiers at war Soldiers at War is a turn based strategy game set in World War II. You take control eight-men squads through the campaign of fifteen, historically-based missions starting in north Africa and ending in Germany. .7 Taken together, these preparatory pre·par·a·to·ry
1. Serving to make ready or prepare; introductory. See Synonyms at preliminary.
2. Relating to or engaged in study or training that serves as preparation for advanced education: and situational factors underscore The underscore character (_) is often used to make file, field and variable names more readable when blank spaces are not allowed. For example, NOVEL_1A.DOC, FIRST_NAME and Start_Routine.
(character) underscore - _, ASCII 95. the fact that providing adequate medical resources to care for the inevitable DNBIs remains a major concern for Army medical planners. Toward that end, analysis of DNBI DNBI disease and nonbattle injury (US DoD)
DNBI Dynamic Narrow-Band Interference rates in past deployments and development of models to predict DNBI incidence in future operations are vital.
Wojcik et al.8 analyzed an·a·lyze
tr.v. an·a·lyzed, an·a·lyz·ing, an·a·lyz·es
1. To examine methodically by separating into parts and studying their interrelations.
2. Chemistry To make a chemical analysis of.
3. DNBI admission data from Operation Desert Shield/Operation Desert Storm, which took place in 1991, and proposed a model indicating that DNBI admission rates vary during the three phases of a war-fighting operation (build-up build·up also build-up
1. The act or process of amassing or increasing: a military buildup; a buildup of tension during the strike.
2. , combat, and postcombat/stabilization). The model also demonstrated that planners should abandon the traditional use of overall or average rates to predict requirements for medical resources, instead using the 95th percentile percentile,
n the number in a frequency distribution below which a certain percentage of fees will fall. E.g., the ninetieth percentile is the number that divides the distribution of fees into the lower 90% and the upper 10%, or that fee level daily rate to ensure adequate staffing for all but the very busiest days, when more-intense effort would be required. The major distinction between overall admission rates and 95th percentile daily rates is as follows. The overall rate takes into account the total number of hospital admissions (incidences) that occurred within the examined period of time (expressed as the total number of soldier-days at risk during the time under investigation). The 95th percentile daily rate is the daily hospital admission rate such that 95% of the days within the examined time demonstrated admission rates below the 95th percentile threshold and 5% of days experienced rates above the 95th percentile level. The current study verifies these conclusions and builds on them by analyzing the rates of hospital admissions for DNBIs during recent troop deployments to Afghanistan Afghanistan (ăfgăn`ĭstăn', ăfgän'ĭstän`), officially Islamic Republic of Afghanistan, republic (2005 est. pop. 29,929,000), 249,999 sq mi (647,497 sq km), S central Asia. and Iraq Iraq or Irak (both: ēräk`, ĭrăk`), officially Republic of Iraq, republic (2005 est. pop. 26,075,000), 167,924 sq mi (434,924 sq km), SW Asia. , refining refining, any of various processes for separating impurities from crude or semifinished materials. It includes the finer processes of metallurgy, the fractional distillation of petroleum into its commercial products, and the purifying of cane, beet, and maple sugar the model for medical deployment planning Operational planning directed toward the movement of forces and sustainment resources from their original locations to a specific operational area for conducting the joint operations contemplated in a given plan. and improving the Army's ability to predict DNBI incidence in future warfighting operations.
Operations Enduring Freedom and Iraqi Freedom
The DNBI model for war-fighting operations developed by Wojcik et al.8 was applied to data from Operation Enduring Freedom (OEF OEF Operation Enduring Freedom (US government response to September 11, 2001 terrorism attacks)
OEF Oxford Economic Forecasting
OEF Oregon Entrepreneurs Forum
OEF Optimal Extension Fields ) in Afghanistan and Operation Iraqi Freedom (OIF OIF Operation Iraqi Freedom
OIF Organisation Internationale de la Francophonie (French: International Organization of Francophonie)
OIF Office for Intellectual Freedom (American Library Association) ). Records from existing databases, including U.S. Army soldier population data from the Defense Manpower Data Center The Defense Manpower Data Center (DMDC) serves under the Office of the Secretary of Defense to collate personnel, manpower, training, financial, and other data for the Department of Defense. and health care data from the Army Patient Administration Systems and Biostatistics biostatistics /bio·sta·tis·tics/ (-stah-tis´tiks) biometry.
The science of statistics applied to the analysis of biological or medical data. Activity, were used. DNBI admission rates for U.S. Army soldiers deployed to Afghanistan and Iraq were examined in aggregate for each operation, according to according to
1. As stated or indicated by; on the authority of: according to historians.
2. In keeping with: according to instructions.
3. phase (Iraq only), and on a daily basis by using percentile analysis. Force composition variables included age, gender, officer/enlisted status, force component (regular, reserve, or National Guard), and unit type (combat, combat support, combat service support, medical, or other). The model produced disease rates and nonbattle injury rates in addition to combined DNBI rates for each operation. The model recognizes that three distinct phases (build-up, ground combat, and postcombat/stabilization) exist in some war-fighting deployments, whereas the nature of other deployments precludes such a division.
Soldier population data obtained from the Defense Manpower Data Center identified U.S. Army soldiers who were deployed to Afghanistan and Iraq according to date of arrival in theater and date of departure from theater. Demographic and unit identification data were included in these records.
Inpatient inpatient /in·pa·tient/ (in´pa-shent) a patient who comes to a hospital or other health care facility for diagnosis or treatment that requires an overnight stay.
n. health care data for these soldiers for the period of their deployments were obtained from the Patient Administration Systems and Biostatistics Activity standard inpatient data record database. The standard inpatient data record is the official electronic record of a hospitalization hospitalization /hos·pi·tal·iza·tion/ (hos?pi-t'l-i-za´shun)
1. the placing of a patient in a hospital for treatment.
2. the term of confinement in a hospital. in a Department of Defense medical facility. Nonbatde casualty health care records were selected by excluding NATO Standardization Agreement The record of an agreement among several or all the member nations to adopt like or similar military equipment, ammunition, supplies, and stores; and operational, logistic, and administrative procedures. 2050 trauma indicator codes O (direct result of war) and 1 (other battle casualties) and Standardization standardization
In industry, the development and application of standards that make it possible to manufacture a large volume of interchangeable parts. Standardization may focus on engineering standards, such as properties of materials, fits and tolerances, and drafting Agreement 2050 cause-of-injury codes 300 through 479 (injuries caused by enemy action). Dis ease diagnoses included all International Classification of Diseases, 9th Revision, Clinical Modification (ICD-9-CM ICD-9-CM International Classification of Disease, 9th edition, Clinical Modification
A standardized classification of disease, injuries, and causes of death, by etiology and anatomic localization and codified into a 6-digit number, which allows ) codes except 800 through 959, which were classified as nonbattle injuries. Principal diagnoses were analyzed according to major ICD-9-CM groups. The examined inpatient data included records of hospital admissions in the theater of operation and records of soldiers from these operations admitted to military medical facilities in Europe Europe (yr`əp), 6th largest continent, c.4,000,000 sq mi (10,360,000 sq km) including adjacent islands (1992 est. pop. 512,000,000). and the United States United States, officially United States of America, republic (2005 est. pop. 295,734,000), 3,539,227 sq mi (9,166,598 sq km), North America. The United States is the world's third largest country in population and the fourth largest country in area. during the soldiers' deployments. These records were reduced to episodes of care so that, as a patient moved from one facility to the next for additional care for an incident, only one episode was counted per incident.
Admission rates were determined by merging the health care data records with the soldier population data. The operation start dates were September September: see month. 2001, for OEF and September 1, 2002, for OIF. Records through December December: see month. 2004, were examined for both operations.
Overall, daily, and average daily admission rates for the entire force and for each gender were calculated by using the inpatient data and the deployment data. Percentile analysis of the rates was also performed. Daily rates at the 95th percentile were determined and compared with overall mean and mode rates. Overall admission rates were expressed as the number of admissions per 1,000 soldier-days, and daily rates were expressed as the number of admissions per 1,000 soldiers present in theater on that day. For OIF, three distinct phases were analyzed, that is, build-up (September 1, 2002, to March 19, 2003), ground combat (March 20, 2003, to April 30, 2003), and stabilization Stabilization
The action undertakes a country when it buys and sells its own currency to protect its exchange value.
Actions registered competitive traders undertake by on the NYSE to meet the exchange requirement that 75% of their traded be stabilizing, meaning that sell orders (May 1, 2003, to December 31, 2004). Distinct operational phases did not exist in OEF; therefore, no phase rates were analyzed.
Admission and disposition dates were used to calculate overall and daily prevalence rates. Overall prevalence rates were defined as the total number of inpatient days per 1,000 soldier-days, and daily prevalence rates were stated as the total number of inpatients per 1,000 soldiers present in theater on that day. These data give insight into the overall workload The term workload can refer to a number of different yet related entities. An amount of labor
While a precise definition of a workload is elusive, a commonly accepted definition is the hypothetical relationship between a group or individual human operator and task demands. of the medical facilities, providing information on the total DNBI inpatient population.
Adjusted risk analyses based on Poisson regression In statistics, the Poisson regression model attributes to a response variable Y a Poisson distribution whose expected value depends on a predictor variable x, typically in the following way:
baseline - released version ], combat support, or combat service support, including medical), component (active duty [baseline], National Guard, or reserve), grade group (officer [baseline] or enlisted en·list·ed
Of, relating to, or being a member of a military rank below a commissioned officer or warrant officer.
Adjective ), gender (male [baseline] or female), and age group (<20 years of age [baseline], 20-29 years of age, 30-39 years of age, 40-49 years of age, or =.50 years of age). The number of days in theater was used as the offset variable. Odds ratios and corresponding 95% confidence intervals confidence interval,
n a statistical device used to determine the range within which an acceptable datum would fall. Confidence intervals are usually expressed in percentages, typically 95% or 99%. were obtained.
Deployed Soldier Population
The study examined all U.S. Army soldiers deployed to Afghanistan and Iraq for the dates identified above. For OEF, 92,793 soldiers were deployed for a total of 17,852,063 soldier-days. For OIF, 417,412 soldiers were deployed for 92,891,635 soldierdays. Female soldiers constituted 8.3% of the total for OEF and 11.3% for OIF. The median age of deployed soldiers was 27.0 years for both campaigns. In OEF, the regular forces carried 68.3% of the load, with the reserve and National Guard forces contributing 11.4% and 20.4%, respectively. For OIF, the distribution was 61.5%, 15.6%, and 22.9% for regular, reserve, and National Guard forces, respectively. During both recent operations, a dramatic reduction in medical personnel occurred; the proportion of soldiers serving in a medical unit decreased from 13.6% of deployed soldiers during Operation Desert Shield/ Operation Desert Storm Noun 1. Operation Desert Storm - the United States and its allies defeated Iraq in a ground war that lasted 100 hours (1991)
Gulf War, Persian Gulf War - a war fought between Iraq and a coalition led by the United States that freed Kuwait from Iraqi invaders; 8 to 2.9% and 3.9% during OEF and OIF, respectively. Detailed demographic information can be found in Tables I and II.
Soldiers deployed to Afghanistan generated a total of 2,460 DNBI inpatient episodes. Of those, 79.5% were disease episodes and 20.5% were nonbattle injury episodes. The following admission rates were calculated for the overall operation: 0.110 admissions per 1,000 soldierdays for disease, 0.028 admissions per 1,000 soldier-days for nonbattle injury, and 0.138 admissions per 1,000 soldier-days for DNBI. The 95th percentile daily rates were 0.280 admissions per 1,000 soldiers for disease, 0.127 admissions per 1,000 soldiers for nonbattle injury, and 0.332 admissions per 1,000 soldiers for DNBI.
Soldiers deployed to Iraq generated a total of 13,377 DNBI inpatient episodes, with 71.8% disease episodes and 28.2% nonbattle injury episodes. The admission rates for the overall operation were 0.103 admissions per 1,000 soldier-days for disease, 0.041 admissions per 1,000 soldier-days for nonbattle injury, and 0.144 admissions per 1,000 soldier-days for DNBI. The 95th percentile daily rates were 0.235 admissions per 1,000 soldiers for disease, 0.095 admissions per 1,000 soldiers for nonbattle injury, and 0.281 admissions per 1,000 soldiers for DNBI in Off.
The overall disease rate was slightly lower for U.S. Army soldiers in Iraq than in Afghanistan, whereas the overall nonbattle injury and DNBI rates for OIF were slightly higher than the rates for OEF. Each of these rates was lower than the corresponding rate for Operation Desert Shield/Operation Desert Storm.8 The 95th percentile daily admission rates, which serve as a planning factor A multiplier used in planning to estimate the amount and type of effort involved in a contemplated operation. Planning factors are often expressed as rates, ratios, or lengths of time. for medical resources, were lower in OIF than in OEF.
Rates for U.S. Army soldiers in Iraq are also presented according to the phase of the operation. In Off, the overall DNBI rate increased from the build-up phase to the combat phase and then decreased for the rest of the operation (0.138 admissions per 1,000 soldier-days in the build-up phase, 0.176 admissions per 1,000 soldier-days in the combat phase, and 0.142 admissions per 1,000 soldier-days in the stabilization phase). However, the 95th percentile daily DNBI rates were highest during the build-up phase, decreased in the combat phase, and decreased again in the stabilization phase (0.334 admissions per 1,000 soldiers in the build-up phase, 0.251 admissions per 1,000 soldiers in the combat phase, and 0.230 admissions per 1,000 soldiers in the stabilization phase).
Off and OEF admission rate results are shown in Table III. A comparison of various percentile daily admission rates for Operation Desert Shield/Operation Desert Storm, OEF, and OIF appears in Table IV. A similar comparison between the phases of Operation Desert Shield/Operation Desert Storm and OIF is presented in Table V.
The patterns for inpatient load were generally similar to those for admissions. OEF admissions produced a total of 11,886 inpatient days, that is, 8,672 for disease and 3,214 for nonbattle injury, resulting in prevalence rates of 0.486 inpatient days per 1,000 soldier-days for disease, 0.180 inpatient days per 1,000 soldier-days for nonbattle injury, and 0.666 inpatient days per 1,000 soldier-days for total DNBI. The 95th percentile daily prevalence rates were 0.929,0.464, and 1.206 inpatients per 1,000 soldiers for disease, nonbattle injury, and DNBI, respectively.
Hospital admissions for U.S. Army soldiers in Iraq produced a total of 66,909 inpatient days, that is, 45,033 for disease and 21,876 for nonbattle injury, resulting in prevalence rates of 0.485 inpatient days per 1,000 soldier-days for disease, 0.236 inpatient days per 1,000 soldier-days for nonbattle injury, and 0.720 inpatient days per 1,000 soldier-days for DNBI. The 95th percentile daily prevalence rates were 0.888 inpatients per 1,000 soldiers for disease, 0.393 inpatients per 1,000 soldiers for nonbattle injury, and 1.117 inpatients per 1,000 soldiers for DNBI.
We noticed, when analyzing the prevalence rates for hospital admissions in Iraq according to phase of operation, that the overall prevalence rates for DNBI increased during the combat phase and then decreased in the stabilization phase (from 0.629 inpatient days per 1,000 soldier-days to 0.780 inpatient days per 1,000 soldier-days to 0.721 inpatient days per 1,000 soldier-days). The 95th percentile prevalence rates for DNBI decreased markedly during the combat phase and then increased slightly during the stabilization phase (from 1.468 inpatients per 1,000 soldiers to 0.976 inpatients per 1,000 soldiers to 1.006 inpatients per 1,000 soldiers).
A comparison of various percentile daily prevalence rates among Operation Desert Shield/Operation Desert Storm, OEF, and OIF is displayed in Table VI. A similar comparison between the phases of Operation Desert Shield/Operation Desert Storm and OIF is presented in Table VII.
From the existing taxonomy taxonomy: see classification.
In biology, the classification of organisms into a hierarchy of groupings, from the general to the particular, that reflect evolutionary and usually morphological relationships: kingdom, phylum, class, order, of 19 major ICD-9-CM diagnosis groups based on 3-digit diagnoses, we identified the top diagnostic groups that were the major reason for hospital admission. For OEF, the most commonly occurring principal diagnoses were in the major ICD-9-CM group of codes 800 to 959, injury, accounting for ~21 % of the admissions. Filling out the top three ICD-9-CM groups were codes 520 to 579, diseases of the digestive Ulcers (Digestive) Definition
In general, an ulcer is any eroded area of skin or a mucous membrane, marked by tissue disintegration. In common usage, however, ulcer usually is used to refer to disorders in the upper digestive tract. system (17.7%), and codes 780 to 799, symptoms, signs, and ill-defined conditions (11.2%). These three ICD ICD International Classification of Diseases (of the World Health Organization); intrauterine contraceptive device.
abbr. 9-CM groups also constituted the top three groups during the days with the 5% highest admission rates (the days with daily rates above the 95th percentile daily rate), as well as being the top three groups during the other days of the operation (Table VIII).
Similarly, for OIF, the most commonly occurring principal diagnoses were in the major ICD-9-CM group of codes 800 to 959, injury. For the overall operation, the combat phase, and the stabilization phase, >28% of the principal diagnoses were in this group; the build-up phase had >16% in the injury group. The next two ICD-9-CM groups, overall and for each phase, were codes 520 to 579, diseases of the digestive system (overall, 11.7%; build-up, 16.1%; combat, 10.5%; stabilization, 11.5%), and codes 780 to 799, symptoms, signs, and ill-defined conditions (overall, 11.4%; build-up, 12.7%; combat, 10.1%; stabilization, 11.4%). The top three major ICD-9-CM groups for admissions during the days with the 5% highest admission rates were the same as those for the other days of the overall operation, those at or below the 95th percentile daily rate (Table IX). In addition, in the build-up, ground combat, and stabilization phases, the three most frequently occurring diagnosis groups were the same for the highest rate days as for the remaining days.
We performed two separate analyses, one to identify risk factors associated with hospitalization because of disease and the other to identify risk factors involved in nonbattle injury hospitalization. Table X presents results of the adjusted risk analysis based on Poisson regression separately for disease and nonbattle injury during OEF, and Table XI presents results of that analysis for OIF. For both campaigns, unit type had the opposite impact on disease admissions as on nonbattle injury admissions. Female soldiers in both OIF and OEF demonstrated higher risks (60% in OIF and 83% in OEF) of a disease admission and lower risks (43% in both OIF and OEF) of a nonbattle injury admission, compared with male soldiers.
With respect to the Army component, the reserve and National Guard forces in OIF experienced significantly higher risks of both disease and nonbattle injury admissions, compared with active duty soldiers. That relationship was not visible in OEF, where reservists had a 23% lower risk of disease than did active duty soldiers. There was no significant difference in the risk of a nonbattle injury admission in OEF for reservists or National Guard members, compared with active duty soldiers, but enlisted soldiers were substantially more likely to have a disease admission (odds ratio, 2.98) and also were markedly more likely to have a nonbattle injury admission (odds ratio, 4.13), compared with officers.
In both campaigns, age played a significant factor. The risk of being admitted to a hospital because of disease increased with each age category, compared with soldiers <20 years of age. With respect to hospital admissions related to nonbattle injuries, each age group demonstrated lower risk than the baseline category of soldiers <20 years of age.
For OEF, soldiers in both combat support and combat service support units were more likely to have a disease admission (odds ratios of 1.42 and 1.79, respectively) but were less likely to have a nonbattle injury admission (odds ratios of 0.72 and 0.61, respectively), compared with soldiers in combat units. For Off, soldiers in combat service support units had 30% higher risk of a disease admission than did soldiers in combat units; however, soldiers in both combat support and combat service support units had ~20% lower risk of a nonbattle injury admission than did soldiers in combat units.
A major observation resulting from this analysis is that recent war-fighting operations experienced an ~50% reduction in overall disease, nonbattle injury, and DNBI admission rates, compared with rates recorded in the previous campaign of Operation Desert Shield/Operation Desert Storm.8 There were still, of course, days with high incidence rates, but the 95th percentile daily DNBI rates in OEF and OIF were lower by 15% and 28%, respectively, compared with those in Operation Desert Shield/Operation Desert Storm. In the OIF stabilization phase, the 95th percentile daily DNBI rate decreased by 38%, compared with the postcombat phase in Operation Desert Shield/ Operation Desert Storm. The overall improvement leads to the conclusion that the Army's recent Force Health Protection initiatives have had a positive effect on troop safety and health. Adding to this success is the fact that this reduction in DNBI incidence rates took place concurrently with a reduction in medical manpower (<4% of soldiers in both OEF and OIF served in a medical unit, compared with >13% of soldiers during Operation Desert Shield/Operation Desert Storm). Further research is needed to understand the dynamics of the decreasing trend in the DNBI incidence rates over time. Before we impose any changes in policy and doctrine, we need to evaluate whether the decreasing trend continues to hold as both campaigns unfold unfold - inline .
The prevalence rates tended to track with the admission rates, as would be expected with consistent disposition policies across the theater medical system. In fact, the 95th percentile daily DNBI prevalence rates for OEF decreased by almost 62%, compared with Operation Desert Shield/Operation Desert Storm; during the OIF campaign, the 95th percentile daily DNBI prevalence rate decreased by >64%, compared with Operation Desert Shield/Operation Desert Storm. That decrease was even more pronounced during the OIF stabilization phase (71 % lower than in Operation Desert Shield/Operation Desert Storm). Also, there were only 3 of 200 days in the OIF build-up phase that exceeded the 95th percentile DNBI prevalence rate from Operation Desert Shield/Operation Desert Storm and no days in the ground combat and postcombat/stabilization periods that would constitute any concern for Army medical planners that there would be a sufficient number of beds available. These findings indicate that the rates obtained from the Operation Desert Shield/ Operation Desert Storm experience can be used with confidence in planning for future war-fighting operations and that use of the 95th percentile rates to staff and to equip e·quip
tr.v. e·quipped, e·quip·ping, e·quips
a. To supply with necessities such as tools or provisions.
b. deployed medical units is a valid practical approach to medical planning.
Risk analysis revealed different patterns for disease and nonbattle injuries. Female soldiers consistently demonstrated a much higher likelihood of hospital admissions because of disease, compared with male soldiers (83% higher in OEF and 61% higher in OIF), but significantly lower chances of nonbattle injury hospitalizations (43%) in both campaigns. Possible reasons for higher disease risks for women are that women are not as well prepared medically as men for deployment to primitive conditions or that they have an increased rate of exposure to disease because of their relatively higher prevalence in medical units. Possible explanations for the lower injury risk for women are that women are more careful in accident-prone accident-prone adjective Referring to a person's real or percieved tendency to suffer from accidents of various types situations than are male soldiers or that they have reduced exposure to such situations because of their lesser presence in combat units, relative to men.
The findings showed that the type of unit in which a soldier served had an impact on the risk level associated with a disease or nonbattle injury admission. In OEF, there was a 42% increased risk of hospitalization attributable to disease for combat support units and an almost 80% increased risk for combat service support units. In OIF, that risk was more subtle and was significant only for combat service support units. The likelihood of hospitalization because of nonbattle injury was significantly lower for any unit type, compared with combat units, in both operations. Worth mentioning is an increased risk of being hospitalized because of nonbattle injuries for reservists in both operations (48% increased risk in OIF and 25% increased risk in OEF). Reservists also had a 12% increased chance of diseaserelated hospitalizations in OIF.
The discussion above highlighted similar trends in certain groups of risk factors for both campaigns. However, National Guard soldiers demonstrated a higher risk of disease (16%) and nonbattle injury (17%) hospital admissions in Off but not in OEF. Also, reservists showed a different level of risk in Off (significantly increased for both disease and nonbattle injury admissions) than in OEF (nonsignificantly lower for disease admissions, by 23%). These findings illustrate that the two ongoing campaigns have different dynamics and levels of risk.
Our original research8 indicated that the admission rates varied among the phases of the operation. The presented results from OIF, the only recent operation to have distinct phases, validate To prove something to be sound or logical. Also to certify conformance to a standard. Contrast with "verify," which means to prove something to be correct.
For example, data entry validity checking determines whether the data make sense (numbers fall within a range, numeric data the previous research by showing that the overall DNBI rate increased during the combat phase and then returned to precombat levels. The strength of these findings is underscored by the size of the population and the number of medical records studied, that is, >110 million soldier-days and > 15,000 inpatient episodes for OEF and Off combined. At this time, commanders' ability to implement Force Health Protection policies appears to be working to attain historically low DNBI incidence rates. More research is needed to determine whether this trend endures and whether policies continue to be effectively implemented.
ACKNOWLEDGMENTS See About this product.
We acknowledge Toby A. Dunn for assistance with data extraction Data extraction is the act or process of retrieving (binary) data out of (usually unstructured or badly structured) data sources for further data processing or data storage (data migration). and SAS (1) (SAS Institute Inc., Cary, NC, www.sas.com) A software company that specializes in data warehousing and decision support software based on the SAS System. Founded in 1976, SAS is one of the world's largest privately held software companies. See SAS System. programming support for this project.
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