Statistical process control charts for monitoring military injuries

Schuh, Anna; Canham-Chervak, Michelle; Jones, Bruce H.

doi:10.1136/injuryprev-2016-042089

Cited by 10 publications

(8 citation statements)

References 31 publications

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“…Demonstrate the application of statistical control charts to monitoring trends in injury rates over time 46 How can the military evaluate scientific evidence to determine which training-related injury prevention strategies and equipment are effective?…”

Section: Example Methodological Questions Solutionmentioning

confidence: 99%

“…45 Part of this effort is the adoption of system engineering methodology to enable long-term monitoring of installation injury trends and identification of statistically significant shifts indicating when injury rates are unacceptably high or when significant reductions have been achieved. 46 The final step addresses the question: How effective is what was done? The APHC has been requested to evaluate the benefits of unit-specific training programs.…”

Section: Public Health Questionmentioning

confidence: 99%

See 1 more Smart Citation

Musculoskeletal training injury prevention in the U.S. Army: Evolution of the science and the public health approach

Jones¹,

Hauschild²,

Canham-Chervak³

2018

Journal of Science and Medicine in Sport

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Injuries cause more morbidity among soldiers in the U.S. Army than any other health condition. Over two-thirds of U.S. soldiers' injuries occur gradually from cumulative micro-traumatic damage to the musculoskeletal system as a result of physical training activities. Paradoxically, the very physical training activities required to improve soldier performance also result in injury. Determining the amounts and types of physical training that maximize performance while minimizing injuries requires scientific evidence. This evidence must be incorporated into a framework that ensures scientific gaps are addressed and prevention efforts are evaluated. The five-step public health approach has proven to be an effective construct for Army public health to organize and build an injury prevention program. Steps include: 1) surveillance to define the magnitude of the problem, 2) research and field investigations to identify causes and risk factors, 3) intervention trials and systematic reviews to determine what works to address leading risk factors, 4) program and policy implementation to execute prevention, and 5) program evaluation to assess effectiveness. Dissemination is also needed to ensure availability of scientific lessons learned. Although the steps may not be conducted in order, the capability to perform each step is necessary to sustain a successful program and make progress toward injury control and prevention. As with many U.S. public health successes (e.g., seatbelts, smoking cessation), the full process can take decades. As described in this paper, the U.S. Army uses the public health approach to assure that, as the science evolves, it is translated into effective prevention.

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Section: Example Methodological Questions Solutionmentioning

confidence: 99%

Section: Public Health Questionmentioning

confidence: 99%

Musculoskeletal training injury prevention in the U.S. Army: Evolution of the science and the public health approach

Jones¹,

Hauschild²,

Canham-Chervak³

2018

Journal of Science and Medicine in Sport

Self Cite

View full text Add to dashboard Cite

show abstract

“…As a robust graphical statistical tool, control charts are used in a variety of industries to identify modifiable variations in a process. Within health care, control charts have been implemented in quality improvement in a variety of fields, 29-35 ranging from military injuries to transplant lists to neonatal care. Control charts determine whether a process is in control—minimum nonrandom or “assignable” variation with predictable variation.…”

Section: Introductionmentioning

confidence: 99%

Preparatory Time–Related Hand Surgery Operating Room Inefficiency: A Systems Analysis

Milone

Hacquebord

Catalano

et al. 2019

Hand (New York, N,Y.)

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Background: No study exists on preparatory time—from patient’s entrance into the operating room to skin incision—and its role in hand surgery operating room inefficiency. The purpose of this study was to investigate the length and variability of preparatory time and assess the relationship between several variables and preparatory time. Methods: Consecutive upper extremity cases performed for a period of 1 month by hand surgeons were reviewed at 3 surgical sites. Preparatory time was compared across locations. Cases at one location were further analyzed to assess the relationship between preparatory time and several variables. Both traditional statistical methods and Shewhart control charts, a quality control tool, were used for data analysis. Results: A total of 288 cases were performed. The mean preparatory times at the 3 sites were 25.1, 25.7, and 20.7 minutes, respectivley. Aggregated preparatory time averaged 24.4 (range 7-61) minutes, was 75% the length of the surgical time, and accounted for 34% of total operating room time. Control charts confirmed substantial variability at all locations, signifying a poorly defined process. At a single site, where 189 cases were performed by 14 different surgeons, there was no difference in preparatory time by case type, American Society of Anesthesiologists status, or case start time. Preparatory time varied by surgeon and anesthesia type. Conclusions: Preparatory time was found to be a source of inefficiency, independent of the surgical site. Control charts reinforced large variations, signifying a poorly designed process. Surgeon seemingly plays an important, albeit likely indirect, role. Efforts to improve operating room workflow should include preparatory time.

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“…Applications include monitoring: mortality rates using observed-expected [10], CUMSU [11], and p charts [10]; hospital length-of-stay using exponentially weighted moving average [12], CUMSU [12], and Shewhart charts [13]; surgical infection rates using Q [14] and p charts [15]; and delivery outcomes for maternity wards using observed-expected charts [16]. In health surveillance settings, U′ charts have been used to monitor injury rates of military personnel [17] and Shewhart and CUMSU charts have been used to detect changes in child blood lead levels [18]. In addition, open source software has already been developed to apply control charts to infectious disease surveillance using REDCap, R, and the R Shiny package [19].…”

Section: Introductionmentioning

confidence: 99%

Control charts for chronic disease surveillance: testing algorithm sensitivity to changes in data coding

et al. 2022

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Background Algorithms used to identify disease cases in administrative health data may be sensitive to changes in the data over time. Control charts can be used to assess how variations in administrative health data impact the stability of estimated trends in incidence and prevalence for administrative data algorithms. We compared the stability of incidence and prevalence trends for multiple juvenile diabetes algorithms using observed-expected control charts. Methods Eighteen validated algorithms for juvenile diabetes were applied to administrative health data from Manitoba, Canada between 1975 and 2018. Trends in disease incidence and prevalence for each algorithm were modelled using negative binomial regression and generalized estimating equations; model-predicted case counts were plotted against observed counts. Control limits were set as predicted case count ±0.8*standard deviation. Differences in the frequency of out-of-control observations for each algorithm were assessed using McNemar’s test with Holm-Bonferroni adjustment. Results The proportion of out-of-control observations for incidence and prevalence ranged from 0.57 to 0.76 and 0.45 to 0.83, respectively. McNemar’s test revealed no difference in the frequency of out-of-control observations across algorithms. A sensitivity analysis with relaxed control limits (2*standard deviation) detected fewer out-of-control years (incidence 0.19 to 0.33; prevalence 0.07 to 0.52), but differences in stability across some algorithms for prevalence. Conclusions Our study using control charts to compare stability of trends in incidence and prevalence for juvenile diabetes algorithms found no differences for disease incidence. Differences were observed between select algorithms for disease prevalence when using wider control limits.

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Statistical process control charts for monitoring military injuries

Abstract: Control charts can be used to drive progress towards injury reduction goals by indicating statistically significant increases and decreases in injury rates. Future applications to military subpopulations, other health outcome metrics and chart enhancements are suggested.

Cited by 10 publications

References 31 publications

Musculoskeletal training injury prevention in the U.S. Army: Evolution of the science and the public health approach

Musculoskeletal training injury prevention in the U.S. Army: Evolution of the science and the public health approach

Preparatory Time–Related Hand Surgery Operating Room Inefficiency: A Systems Analysis

Control charts for chronic disease surveillance: testing algorithm sensitivity to changes in data coding

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