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Project Period: Project Publication: Construction industry experiences with equipment interventions for health and safetyexternal icon Barriers and Aids to Intervention Effectiveness This purpose of this study is to identify barriers and aids to implementing interventions for musculoskeletal disorder prevention in the residential construction industry.
Interventions included safety improvements relevant to residential construction such as specific engineering interventions, as well as safety program development and implementation. D, Steve Wurzelbacher, Ph. Project Period: present. Evaluation of Slip, Trip, and Fall Prevention Practices in Food Services The purpose of this research project was to evaluate the efficacy of slip, trip, and fall STF prevention practices in the food services industry.
A randomized controlled trial was conducted to evaluate the effectiveness of slip-resistant shoes in reducing slips and falls injuries. The findings of this study involved data from approximately 9, employees over a four and a half year period. By demonstrating the effectiveness of STF prevention measures, it is anticipated that food service companies initially hesitant to expend time and money on prevention programs with unknown effectiveness could use these research findings to help justify implementing prevention strategies.
Bell, Ph. Analyses will control for industry type, company size, prior loss history, and risk control consultation frequency. E-mail: lls0 cdc. E-mail: von3 cdc. IH air and noise survey forms were collected from ten organizations. Data fields on the forms were evaluated for importance and a study list of core fields was developed. The core study list was presented to an IH review panel for review before finalization.
Both state-based and private insurers were surveyed about the collection and use of industrial hygiene data for risk reduction and safety and health research. The SAW or RTW process helps integrate employees, who have been absent due to injury or illness and have been prescribed an opioid, back into the workforce. Email: yoz7 cdc. Email: ype0 cdc.
Healthcare factors under study include access and quality of care, type of treatments, prescription drug usage, and care coordination. E-mail: olj9 cdc. E-mail: srw3 cdc. We are now pursuing publication of a succinct version of the white paper in a peer-reviewed journal and publication of the white paper as a document co-branded by NIOSH and IWH.
Data on health-related costs for family members of these employees also are included. In , additional years and modules of data were purchased and a series of articles were published or continued to be drafted and are at different stages of review and clearance. Incidence and Cost of Depression after Occupational Injury This study examined if injured workers were more likely than non-injured workers to be treated for depression after an occupational injury and estimated the cost paid by group medical insurance.
Nearly , injured and non-injured workers were drawn from the Thomson Reuters MarketScan data and we used descriptive, logistic, and two-part model regression analyses. Conclusions: Injured workers were more likely than non-injured workers to suffer from depression during the study period. This project will also investigate if work-related injury or illness has consequences on employment status, such as, early retirement, from full time status to part time status, and others.
D; Office of the Director; E-mail: fwa4 cdc. Estimating the Direct and Indirect Costs of Injuries and Illnesses in Wholesale and Retail Trade Sectors This study will estimate the direct and indirect costs of all occupational fatal and nonfatal injuries in the various sectors and sub-sectors of wholesale and retail trade.
Costs and Benefits of Workplace Health Improvements This project encompasses several efforts to improve methods of evaluating the costs and benefits of health and safety improvements. Various methods used to estimate these costs and benefits are being compared and assessed in order to develop a comprehensive inventory of potentially relevant costs and benefits, and a summary of methods for estimating them. The results will be published as a journal article which can be used as a basis for guidance that can be used by both employers and researchers.
Because blocking strategies can influence linkage success, Christen and Goiser recommend that researchers report the specific steps of their blocking strategy. The m-probability can be estimated based on values reported in published linkage literature or by taking a random sample of pairs from the comparison space, assigning match status via manual review, and calculating the probability that two records agree on a particular identifier when they are true matches.
Calculating value-specific u-probabilities for an identifier based on the frequency of each value and the likelihood that two records would agree on a given value simply by chance yields additional information. For instance, a match on a rare surname such as Lebowski is less likely to occur by chance, and is thereby assigned greater weight than a match on a common surname such as Smith.
This principle can be applied to any linkage identifier for which values are differentially distributed. When two records agree on an identifier, an agreement weight is calculated by dividing the m-probability by the u-probability and taking the log2 of the quotient.
For example, if the probability that true matches agree on month of birth is 97 percent and the probability that false matches randomly agree on month of birth is 8. When two records disagree on an identifier, a disagreement weight is calculated by dividing 1 minus the m-probability by 1 minus the u-probability.
For example, the disagreement weight for month of birth would be calculated as log2 [ While the method above accounts for the discriminatory power of the identifier, it does not yet take into account the degree to which records agree on a given identifier. This is important for fields where typographical errors are likely to occur e. Assigning partial agreement weights in situations where two strings do not match character for character can account for minor typographical errors, including spelling errors in names or transposed digits in dates or SSNs.
If all of the characters in a string are matched character by character across two files, then the agreement weight is maximized set at the full agreement weight. For example, lower weights would be assigned for short names or when the first characters of the string are not matched between the two files. The full agreement weight for the identifier can then be multiplied by the string comparator value to generate a partial agreement weight. For example, if the full agreement weight for first name is 12 and the string comparator value is 0.
Once the weights, full and partial, for each identifier have been calculated, the linkage score for each matched pair is equal to the sum of the weights across all linkage identifiers. Use of string comparator methods may significantly improve match rates if a large number of typographical errors are expected. An initial assessment of linkage quality can be gained by plotting the match scores in a histogram.
If the linkage algorithm is working properly, then the plot should show a bimodal distribution of scores, with one large peak among the lower scores for the large proportion of likely nonmatches and a second smaller peak among the higher scores for the smaller set of likely matches. Depending on the research question and the nature of the study, the initial threshold can be adjusted to be more conservative higher score or more liberal lower score.
A more conservative threshold will maximize the specificity of the linkage decision, as only those record pairs with a high score will be counted as matches. Conversely, a more liberal threshold will maximize the sensitivity of the linkage decision to possible matches. Cook and colleagues 44 define the cutoff threshold as the difference between the desired weight and the starting weight. If P is the desired probability that two records were not matched together by chance i. If the desired value of P is 0.
If the computed linkage score is greater than or equal to the cutoff threshold, then the record pair is classified as a match. If the computed linkage score is less than the cutoff threshold, then the record pair is classified as a nonmatch. Researchers wishing to maximize the sensitivity of the algorithm to potential matches can relax this threshold somewhat and manually review all record pairs with scores near the calculated cutoff.
Once this process is complete, a sample of the match decisions made by the linkage algorithm should be reviewed to ensure that the algorithm performed as intended. By reviewing match decisions, you can often identify conditions in which the algorithm could use some tweaking to account for difficult cases.
Twins are another difficult case, as they have the same birthdate, frequently have similar names, and often have SSNs that differ on only one to two digits. By reviewing a sample of match decisions, you can tweak your algorithm to account for each of these cases, thereby improving the performance of the algorithm. This will be particularly important for researchers who hope to reuse the algorithm for future linkages e. A summary of steps for performing probabilistic record linkage is provided below.
Summary of Steps for Probabilistic Record Linkage Estimate the m and u probabilities for each linking variable using the observed frequency of agreement and disagreement patterns among all pairs, commonly generated using the EM expectation-maximization algorithm described by Fellegi-Sunter.
Calculate agreement and disagreement weights using the m and u probabilities. Calculate a total linking weight for each pair by summing the individual linking weights for each linkage variable. Compare the total linkage weight to a threshold above which pairs are considered a link. The threshold is set using information generated in step 1. Alternative Linkage Methods The methods presented above are those most commonly used in registry-to-claims linkages.
Other methods are available for researchers who have more challenging linkage scenarios. The EM algorithm 61 , 62 is an iterative approach to estimating m- and u-probabilities. According to Winkler, the EM algorithm provides very accurate estimates of m- and u-probabilities in situations where the amount of typographical error in the identifiers is minimal, but performs poorly when the identifiers contain numerous typographical errors.
In the sorted-neighborhood approach, 88 the data sources are stacked and sorted by various combinations of the available identifiers. For each sort, all records within a window of n-records are compared. The Bayesian approach 89 is an alternative to the frequentist approach presented above.
The computer science literature also includes distance-based techniques 90 as well as unsupervised and supervised machine-learning approaches. In information-rich scenarios where direct identifiers are available and of good quality, deterministic methods have been recommended.
Beyond these broad guidelines, the decision is left to the researchers and their goals for the project. Ultimately, every researcher must weigh the pros and cons of the available methods in the context of the project and choose the method that best fits the budget, timeline, allotted resources, and research question. Evaluating Linkage Algorithms In record linkage, there are two types of accuracy errors.
A Type I error occurs when a true nonmatch is classified as a match. A Type II error occurs when a true match is classified as a nonmatch. Minimizing these errors is critical, particularly when the product will be used in a cohort study, 65 where linkage error can introduce bias into analyses. In the health sciences literature, the four metrics most often used to evaluate the accuracy of a linkage algorithm are: 1 sensitivity, 2 specificity, 3 positive predictive value, and 4 negative predictive value.
Sensitivity measures the ability of an algorithm to correctly classify true matches as matches. Specificity measures the ability of an algorithm to correctly classify true nonmatches as nonmatches. PPV represents the proportion of matched pairs classified by the algorithm as matches that are true matches.
NPV represents the proportion of matched pairs classified by the algorithm as nonmatches that are true nonmatches. Due to the large number of potential matches identified during the blocking phase, the bulk of the comparison space will be made up of true nonmatches. For this reason, Christen and Goiser 87 argue that linkage metrics that include true nonmatches e. Instead, they recommend metrics such as the f-measure, 92 which represents the harmonic mean of the sensitivity, and PPV, which is not influenced by the large number of true nonmatches.
These metrics represent tradeoffs between sensitivity and specificity. Investigators hoping to maximize sensitivity may increase their false-positive rate, subsequently resulting in a smaller PPV. Those wishing to maximize specificity may increase their false-negative rate. If the goals of the investigation are to estimate all or most true cases, then investigators may want to focus on sensitivity. Otherwise, if the goals are to reduce the likelihood of including false positives, the researcher may focus on specificity.
While there is no hard rule, a good linkage algorithm will typically have values of sensitivity, PPV, and the f-measure in excess of 95 percent. However, what is acceptable depends on the context of the study. On the other hand, if a study is exploratory, a lower percent match may be acceptable. What is acceptable may also vary depending on the frequency of the outcome. Researchers studying a rare disease may seek to emphasize sensitivity to maximize the sample size, while a researcher studying a more frequently occurring disease may want to emphasize PPV to ensure that matches identified by the algorithm are true matches.
Validating Linkage Results The final step of the linkage process is the validation of the match results. Initial steps for determining linkage validity are to look for ties in which multiple record pairs identified as matches by the algorithm have exactly the same set of values. Where possible, ties should be adjudicated by reference to additional information. If no additional information is available, then the record pairs should be classified as nonmatches.
If an algorithm is successful, there will be few to no ties. The next step is to assess the extent to which your matched sample reflects the target population. If estimates indicate that 60 percent of cancer patients in the State are 65 and older, then it is reasonable to expect that 60 percent of the patients in the cancer registry will be matched with Medicare.
If, instead, the researcher finds that only 30 percent of patients in the cancer registry are successfully matched, this may serve as a signal that there is a problem with the matching algorithm. While not well documented in the literature, some form of manual review is typically employed to check the results. Before starting the manual review process, a set of decision rules is developed to standardize the decision process across reviewers.
Next, a random sample is drawn from the set of all potential matches identified during the blocking phase. Following the decision rules, one or more reviewers then determine whether each potential match is a match or nonmatch.
Finally, the decisions documented during the manual review process are used as a gold standard against which the decisions made by the algorithm are compared, allowing for the calculation of the sensitivity, PPV, and f-measure of the algorithm. A good algorithm should have scores of 95 percent or better across the three metrics. Final Remarks In this chapter, we have provided an overview of data linkage methodology from the point of data delivery to the reporting of the linkage results.
We noted that if data quality is good e. In this scenario, deterministic linkage methods are accurate, straightforward, and easy to implement. If data quality is poor e. In these scenarios, iterative deterministic techniques or more sophisticated probabilistic techniques are recommended. Combining deterministic and probabilistic methods can improve efficiency and save computational resources.
When combining methods, a deterministic match on all identifiers can be executed first to identify certain matches. The remaining record pairs that disagree on at least one of the identifiers can be submitted for probabilistic matching.
Using deterministic and probabilistic methods in this stepwise fashion reduces the number of record pairs that will be processed in the more resource-intensive probabilistic matching phase. In order to limit the computational resources required to compare the Cartesian product of all possible matches, blocking should be implemented to reduce the comparison space to record pairs that agree on some basic criteria e.
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