Developing a screening tool to predict disability program participation.The ability to model public benefits eligibility processes can aid in program planning, resource allocation resource allocation Managed care The constellation of activities and decisions which form the basis for prioritizing health care needs , and budgeting. Given the complexities of most public program regulations, policies, and practices, developing a simple and accurate model of eligibility for public benefits is no simple task. The success of a demonstration project currently being developed by the federal government will depend largely on the ability to construct such a model. 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. Social Security Administration (SSA (Serial Storage Architecture) A fault tolerant peripheral interface from IBM that transfers data at 80 and 160 Mbytes/sec. SSA uses SCSI commands, allowing existing software to drive SSA peripherals, which are typically disk drives. ) is designing a demonstration project that will promote employment among applicants for Social Security Disability Insurance (SSDI SSDI Social Security Disability Insurance
SSDI Social Security Death Index
SSDI Social Security Disability Income (common, but incorrect)
SSDI Supplemental Security Disability Income
SSDI Ship System Definition & Index ). The project, Early Intervention ear·ly intervention
n. Abbr. EI
A process of assessment and therapy provided to children, especially those younger than age 6, to facilitate normal cognitive and emotional development and to prevent developmental disability or delay. (EI), is one of a series of ventures that SSA is undertaking that essentially challenge the established norms and practices of the organization toward programs for persons with disabilities (Mitra & Brucker, 2004). This paper will describe one of the methodologies that will be used to select participants for El. Prior to discussing the conceptual framework For the concept in aesthetics and art criticism, see .
A conceptual framework is used in research to outline possible courses of action or to present a preferred approach to a system analysis project. for and the details of the methodology, some background information on SSDI and the disability determination process is provided.
Social Security Disability Insurance
SSDI provides cash benefits and health care coverage to persons with work-preventing disabilities and their families. As of June 2005, average monthly payments to disabled workers were $897 (SSA, 2005c). To qualify for benefits, individuals must meet stringent eligibility conditions set by SSA, the federal agency responsible for administering SSDI. Applicants must first satisfy employment history thresholds, as SSDI was intended to only provide support to persons who have had a substantial attachment to the labor force. Certain disability eligibility criteria must be satisfied as well. SSA defines disability as an inability to participate in a certain level of employment due to a medical condition that is expected to last for at least 12 months or result in death. Individuals who are blind, over the age of 55 and unable to engage in certain levels of work activity are also eligible to receive benefits. Once awarded benefits, beneficiaries can expect to receive benefits until they either medically improve, return to work at a substantial level, transition to the retirement rolls, or die. On average, beneficiaries remain on the rolls for about eleven years (Chirikos, 1995).
SSA administered SSDI to over 6.1 million disabled workers in December 2004, paying out over $71 billion in cash benefits. These figures reflect an increase of 23% in the number of beneficiaries and an increase of 44% in benefit payments over the five-year period from 2000 to 2004 (SSA, 2005a, 2005b). Given that this rate of growth has been predicted to remain fairly steady (Daub, 2002), SSA has made a concerted effort to develop return to work programs and incentives that will increase the rate at which people exit the program. Thus far, however, the return to work rate of beneficiaries has remained low, at less than 1% (SSA, 2002).
A complementary approach to encouraging program exit is to develop a method for slowing the rate of entry to SSDI. SSA is currently designing a demonstration project that would promote employment as an alternative to entering the SSDI rolls. The Ticket to Work and Work Incentives Improvement Act (TWWIIA TWWIIA Ticket to Work And Work Incentives Improvement Act of 1999 (Medicaid buy in initiative) ) of 1999 gives SSA the authority, for the first time, to provide return to work services and supports to applicants who meet the SSA definition of disability. The El demonstration is designed to test whether the early provision of temporary cash stipends, health care coverage, and employment services can efficiently return probable SSDI beneficiaries to work. The combination of immediate services and supports, coupled with an avoidance of the long, arduous ar·du·ous
1. Demanding great effort or labor; difficult: "the arduous work of preparing a Dictionary of the English Language" Thomas Macaulay.
2. disability determination process that is normally involved in securing SSDI benefits, may result in improved return to work rates for this population (Social Security Advisory Board, 2001a). A key to this approach is to develop a quick and accurate way of determining who, among all applicants, is likely to be approved for benefits.
To understand the challenges in developing an effective screening tool, it is important to gain an appreciation of the complexity of the current disability determination process. SSA determines eligibility for both SSDI and Supplemental Security Income Supplemental Security Income
A Social Security program established to help the blind, disabled, and poor. (SSI (1) See server-side include and single-system image.
(2) (Small-Scale Integration) Less than 100 transistors on a chip. See MSI, LSI, VLSI and ULSI.
1. (electronics) SSI - small scale integration.
2. ), a means-tested income assistance program for persons with disabilities, in a series of five stages (SSA, 2002). A person interested in applying for disability benefits typically first contacts the local SSA field office, by phone or in person, and completes an application. SSA claims representatives perform the first stage of review, verifying that the applicant meets certain non-medical criteria, including the accrual accrual,
n continually recurring short-term liabilities. Examples are accrued wages, taxes, and interest. of the requisite employment history and an absence of a certain level of employment. Ira person meets these two requirements, the application is sent to the Disability Determination Services Disability Determination Services, commonly called DDS, are state agencies, funded by the United States Federal Government. Their purpose is to make disability findings for the Social Security Administration. (DDS (1) (Digital Data Storage) See DAT.
(2) (Data Dictionary System) See QuickBuild and OpenDDS.
(3) (Dataphone Digital S ) office, a state-specific entity responsible for assessing disability. If, at this first point of contact, claims representatives identify individuals with certain severe disability conditions, such as heart or liver transplant liver transplant Hepatic transplant Transplant surgery A procedure that replaces a cancer conquered, metabolically defeated, or substance subjugated liver with one no longer required by its owner, many of whom donate same after an MVA Diseases requiring transplant candidates, DDS can expedite ex·pe·dite
tr.v. ex·pe·dit·ed, ex·pe·dit·ing, ex·pe·dites
1. To speed up the progress of; accelerate.
2. the determination process.
The next four stages of the benefit eligibility process involve decisions by DDS. Claims representatives at the second stage assess the severity of applicants' medical conditions See carpal tunnel syndrome, computer vision syndrome, dry eyes and deep vein thrombosis. . Persons without a condition that interferes with basic work activities are denied benefits, while those with such conditions undergo further assessment. At the third stage, applicants are evaluated regarding the type of disabling dis·a·ble
tr.v. dis·a·bled, dis·a·bling, dis·a·bles
1. To deprive of capability or effectiveness, especially to impair the physical abilities of.
2. Law To render legally disqualified. medical condition. Applicants with a condition that either meets SSA specific listings of impairments or that is equally severe are allowed SSDI benefits. Otherwise, applications proceed to the next stage. The final two stages evaluate employment capacity. For the fourth stage, applicants considered eligible to perform their previous employment are denied. At the fifth stage, applicants who are not able to perform any type of employment in the national economy are allowed, while those deemed able to perform such work are denied SSDI benefits.
Applicants who are not awarded benefits through this initial disability determination process may pursue an appeal. Denied applicants have four appellate Relating to appeals; reviews by superior courts of decisions of inferior courts or administrative agencies and other proceedings. opportunities, starting with reconsideration re·con·sid·er
v. re·con·sid·ered, re·con·sid·er·ing, re·con·sid·ers
1. To consider again, especially with intent to alter or modify a previous decision.
2. at the DDS level and ending at the federal court. The average time involved for a DDS decision is 106 days, while for the appellate process, the time for a decision can range from 95 to 477 days, depending on the level of appeal (SSA, 2003a). In contrast, the EI demonstration needs to estimate the probability of benefit receipt the first day an applicant comes into contact with SSA.
Persons who are detached de·tached
1. Separated; disconnected.
2. Standing apart from others; separate. from the labor force for an extended period of time have been found to have a more difficult transition back to employment (Palmer, 1990). To achieve the highest level of success, El processes must support the identification of suitable participants at the earliest point of contact with SSA. While the thoroughness of the usual SSA disability determination process provides some obvious advantages in terms of accuracy, the wait that the process requires imposes a delay in return to work efforts.
Screening Tool Requirements
Certain real world constraints CONSTRAINTS - A language for solving constraints using value inference.
["CONSTRAINTS: A Language for Expressing Almost-Hierarchical Descriptions", G.J. Sussman et al, Artif Intell 14(1):1-39 (Aug 1980)]. affect the development of a screening tool for the El demonstration. In designing the screening process, care must be taken to minimize changes to the usual flow of SSA field office business in order to avoid imposing greater burdens on the claims representatives and the timeliness of application processing. The El screening tool should have the capacity to be applied quickly by SSA claims representatives, using claimant CLAIMANT. In the courts of admiralty, when the suit is in rem, the cause is entitled in the Dame of the libellant against the thing libelled, as A B v. Ten cases of calico and it preserves that title through the whole progress of the suit. reported information. Claimant reporting is necessary because no confirmation for either the condition or its severity will be gathered from medical professionals during this initial screening. The screening tool is limited to a small number of variables, preferably pref·er·a·ble
More desirable or worthy than another; preferred: Coffee is preferable to tea, I think.
pref ones that are normally collected by SSA in the first phase of the SSDI disability determination process, so that claims representatives will not have to spend excessive time on the screening task. Despite these time and resource constraints, the screening tool must be able to assess applicants reliably and validly. Previous research on screening tools and their effectiveness can help to inform the development of the El screening tool.
As Waddell. Burton, and Main (2003) point out, screening tools have inherent limitations. The ability to identify persons likely to move to a long-term disability program may be condition specific or may depend on the timing of the screening instrument. Significant external events, such as unemployment rates and labor market labor market A place where labor is exchanged for wages; an LM is defined by geography, education and technical expertise, occupation, licensure or certification requirements, and job experience conditions, may be difficult to model. Another source of error rests in the use of self-report measures to identify an individual's eligibility for a program, thereby giving an applicant the capacity to bias the screen towards acceptance.
The SSDI eligibility process is difficult to model for several reasons. As evidenced from the previous discussion, the SSA disability determination process has multiple stages and is dynamic, changing from year to year based on programmatic pro·gram·mat·ic
1. Of, relating to, or having a program.
2. Following an overall plan or schedule: a step-by-step, programmatic approach to problem solving.
3. considerations and court decisions. Despite the process being a uniform system, the application or interpretation of determination policies and procedures Policies and Procedures are a set of documents that describe an organization's policies for operation and the procedures necessary to fulfill the policies. They are often initiated because of some external requirement, such as environmental compliance or other governmental is both region and state specific. For example, in 2000, state-level DDS approval rates for SSDI ranged from 31% to 65% (Social Security Advisory Board, 2001b). State residence has been identified by others as a variable that may affect the probability of SSDI receipt (Hu, Lahiri, Vaughan, & Wixon, 1997; Kreider, 1999).
The adjudication The legal process of resolving a dispute. The formal giving or pronouncing of a judgment or decree in a court proceeding; also the judgment or decision given. The entry of a decree by a court in respect to the parties in a case. process further complicates the ability to model SSDI. Some denied individuals are more tenacious te·na·cious
1. Clinging to another object or surface; adhesive.
2. Holding together firmly; cohesive.
viscid; adhesive. in appealing initial disability decisions. For instance, during the fiscal year 2002, of every 62 persons denied at the initial stage of determination, 22 had reconsiderations, another 19 appealed to the hearings level, and 5 submitted their cases to the appeals council review (SSA, 2003b). The approximate success rate at each of those levels was 14%, 63%, and 20%, respectively. The success rates may reflect the presentation of additional data or other factors rather than a deviation DEVIATION, insurance, contracts. A voluntary departure, without necessity, or any reasonable cause, from the regular and usual course of the voyage insured.
2. from the determination model used by SSA.
In sum, a number of constraints, including the realities of administering a screening tool in a public agency, the reliance on self-report data, the limitations inherent in screening tools, and the complexities of the eligibility process, provide challenges to the development of an effective screening tool.
Previous SSDI Prediction Models This article outlines the various propagation models currently used by the wireless industry for signal transmission at both 900 MHz and 1800 MHz. We start with the foundation of free-space transmission, followed by Picquenard’s multiple knife edge diffraction model.
A review of previously developed statistical models of the SSDI process can guide variable selection for the current analysis. Modeling the SSDI application and decision processes is not a new initiative. Rather than screening applicants for selection into a specific return to work program, however, the focus has previously been on developing models of the application and decision process or on evaluating policy options for the administration of SSDI. Data from national surveys, sometimes in tandem Adv. 1. in tandem - one behind the other; "ride tandem on a bicycle built for two"; "riding horses down the path in tandem"
tandem with SSA administrative data, has generally been used to examine those individuals who apply for and receive SSDI during a specified period. The advantage of using national data sets is the presence of variables that are not included in SSA administrative data.
Kreider (1999) examined the impact of SSDI on male labor force participation and the opportunity costs Opportunity costs
The difference in the actual performance of a particular investment and some other desired investment adjusted for fixed costs and execution costs. It often refers to the most valuable alternative that is given up. of applying for and receiving SSDI. He found that receipt of benefits was related to age, disability level, regional considerations, and being married at the onset of the disability. Another study focused on older working adults and the probability of SSDI acceptance (Kreider & Riphahn, 2000). Significant variables for SSDI receipt among males at either stage, initial application or appeal, included age at application, race, education, the proportion of applicants accepted in a region, residing in the northeast, the year of application, having a greater number of children, poor sight, and back or heart conditions. Among women, factors that affected SSDI receipt included the age at application, the proportion of applicants accepted in a region, marital status marital status,
n the legal standing of a person in regard to his or her marriage state. , the year of application, illnesses related to kidney, bladder bladder /blad·der/ (blad´er)
1. a membranous sac, such as one serving as receptacle for a secretion.
2. urinary bladder. , stomach, or ulcers 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. , having a psychological condition, poor sight, and the number of children. A similar type of analysis focused on those with mental disorders mental disorders: see bipolar disorder; paranoia; psychiatry; psychosis; schizophrenia. (Bilder & Mechanic, 2003). Significant variables that influenced SSDI receipt among applicants included education, instrumental activities of daily living instrumental activities of daily living A series of life functions necessary for maintaining a person's immediate environment–eg, obtaining food, cooking, laundering, housecleaning, managing one's medications, phone use; IADL measures a , restricted activity days, being unable to work, and having another SSDI or SSI recipient in the household.
Alternative modeling approaches have emphasized multistage mul·ti·stage
1. Functioning in more than one stage: a multistage design project.
2. Relating to or composed of two or more propulsion units. modeling techniques (e.g., Dwyer, Hu, Vaughan, & Wixon, 2001; Hu et al., 1997; Lahiri, Vaughan, & Wixon, 1995). Hu et al., for example, used a four-stage sequential model The sequential model (also known as the KNF model) is a theory that describes co-operativity of proteins subunits. Overview
This model suggests that the subunits of multimeric proteins have two conformational states. The binding of the ligand causes conformational change. with different factors considered at each stage, including over 50 variables to approximate the DDS decision process. The equations accounted for 16% to 46% of the variance of the disability decision at each stage, with good predictive validity In psychometrics, predictive validity is the extent to which a scale predicts scores on some criterion measure.
For example, the validity of a cognitive test for job performance is the correlation between test scores and, for example, supervisor performance ratings. in the resulting equations. When using only one equation for the disability decision, the model accounted for only 18% of the variance; the predicted allowance rate was also lower than the rate in the multi-stage equations. The development of just one equation eliminated certain factors, such as education and functional limitations, that play an important role within the SSA disability decision process and in fact were significant in the sequential equations.
Using a similar process, Benitez-Silva, Buchinsky, Chan, Rust, and Sheidvasser (1999) examined the application, receipt, and appeals process and developed two models: one that involved factors related to the four-step decision model for the first decision and a separate model for the appeals process. For both models, the variable with the strongest influence was a dichotomous di·chot·o·mous
1. Divided or dividing into two parts or classifications.
2. Characterized by dichotomy.
di·chot measure that indicated a health limitation that prevented work. For the four-step decision model, they attempted to include variables at each step that were relevant to the SSA decision process. For instance, a model of the first step, being able to engage in substantial employment activity, included net wealth, total hours worked, average hours worked per week for a month period after application, previous SSDI or SSI applications, and earnings in the preceding year. Overall, significant variables included hours worked per week after application, previous applications to SSDI/SSI, having a stroke, the age at application, and the year of application.
An alternative approach in considering the decision process is to look at the differences between successful and unsuccessful SSDI applicants. Unsuccessful applicants have been found more likely to be female, younger, non-white, married, and educated (Kennedy, Olney, Richer, & Newsom, 2002). Health-related variables, such as functional limitations, activities of daily living, and health severity, were also found to be more severe for successful applicants than for unsuccessful applicants. However, this study compared the two groups alter the application process had occurred.
Several issues related to the above research limit the applicability of the models to the current problem of identifying SSDI recipients from the applicant pool. First, SSDI and SSI program involvement are often combined. While the disability determination process is the same for both programs and applicants may be considered or screened for both programs, practical differences may exist between those who apply for one versus the other. The El demonstration will reject those who apply for SSI, even if they are also applying for SSDI. Second, the number of variables used in the predictive equations is quite large. Such a large number is problematic when considering that an El screening procedure must be applied quickly and easily by claims representatives. The need for a brief screen comprised of only a few variables prohibits the use of a complicated multistage model. One must also assume that claims representatives will gather information differently from that obtained for public surveys. While data survey experts are trained to be fontal font 1
1. A basin for holding baptismal water in a church.
2. A receptacle for holy water; a stoup.
3. The oil reservoir in an oil-burning lamp.
4. and uniform in their collection of data, claims representatives may prompt, direct, and clarify responses from applicants to facilitate the case. Another issue with public surveys is that several variables that were identified as significant, such as the year of application, are impossible to include in a useable predictive model for SSDI applicants. Finally, in addition to those persons who were awarded benefits at the DDS level, a screening tool must also predict which applicants would successfully navigate (1) "Surfing the Web." To move from page to page on the Web.
(2) To move through the menu structure in a software application. the adjudication process.
Developing a model to predict SSDI benefit receipt requires both application information and benefit decision information. The primary source of data for this analysis was a set of 1996 disability benefits application case files collected from three SSA field offices (New Brunswick, New Jersey This article is about the city in New Jersey. For the Canadian province, see New Brunswick.
New Brunswick, also known as "the Healthcare City" or "Hub City", is a city and the county seat of the County of Middlesex, New Jersey, USA. ; Portland, Oregon; and Cheyenne, Wyoming Cheyenne is the capital of the U.S. state of Wyoming. It is the principal city of the Cheyenne, Wyoming Metropolitan Statistical Area which encompasses all of Laramie County, Wyoming. As of September 2005, it had an estimated population of 55,362. ). Applicant files were selected based primarily on administrative accessibility, not random selection criteria. The data contained no personal identifiers such as the applicant's Social Security number, name, or address. In total, information from 548 different applicants from the three offices was obtained, three cases of which were dropped because they were 65 years of age and therefore not eligible for SSDI. Data from the application files were entered into a database and linked, by SSA, with SSA administrative data containing benefit decision information. The case files that were collected from the field offices included a larger than normal percentage of cases that went on to receive approval for benefits. The data set had an applicant approval rate of 65%, far higher than the national 1996 average approval rate of 49% (SSA, 2003a), though this latter number may not include approvals through the appeals process.
The data included demographic, medical, education, and employment history information derived from SSA administrative records (SSA 831 electronic data file) and forms (SSA forms 16, 3367, 3368, and 3369). Some differences existed in the data available among regions, as some regions had adapted certain forms or collected additional information. Only information that was common to all regions was used for this analysis.
Variable selection was guided by previous research results and by the information available in the application files. The following independent variables were considered for inclusion in the model: age, education level, marital status, average annual earnings, mental and physical limitations, whether or not the onset of the disability coincided with work cessation cessation Vox populi The stopping of a thing. See Smoking cessation. , and the body systems affected by the disabling condition. All of these variables met two important criteria: they were available through self-report from the applicant and they were available as part of the normal application information collection process.
Rather than using a continuous variable, age was divided into five dichotomous variables based on SSA classifications: less than 45 years of age, between 45 and 49 years, between 50 and 54 years, between 55 and 59 years, and 60 years and over. Education level was represented as three dichotomous variables describing the highest level achieved: less than high school, high school degree or equivalent, or schooling beyond the high school level. The SSA application form was used to identify marital status. About half (48%) of the applicant sample was married.
Annual earnings information was limited to SSA generated earnings categories. SSA calculates earnings categories by excluding earnings from the year of application and the year prior to application, on the assumption that the illness or injury could negatively affect such income, and by averaging earnings from the previous five years. For example, for a person applying in 1996, income from 1995 and 1996 was excluded and income from 1990 to 1994 was included. This average was compared to the national average of earnings during the years considered. Earnings were placed into one of five dichotomous variables based on SSA classifications: marginal (equal to or less than 2080 (the number of hours of full-time employment in one year) multiplied mul·ti·ply 1
v. mul·ti·plied, mul·ti·ply·ing, mul·ti·plies
1. To increase the amount, number, or degree of.
2. Mathematics To perform multiplication on. by the minimum wage, or less than $10,712), low (more than marginal and up to 75% of the national average, or between $10,713 and $20,606), average (between 75% and 125% of the national average, or between $20,607 and $34,344), high (between 125% and 200% of the national average, or between $34,345 and $54,951), and very high (over 200% of the national average, or over $54,951). The largest proportion (46%) of applicants in this sample had a marginal level of earnings.
Limitations for activities of daily living, or functional limitations, are often used to categorize cat·e·go·rize
tr.v. cat·e·go·rized, cat·e·go·riz·ing, cat·e·go·riz·es
To put into a category or categories; classify.
cat the extent of a disability. Ten categories of functional limitations were included in the SSA application data: reading, writing, answering, hearing, sitting, understanding, using hands, breathing, seeing, and walking1. Claims representatives were asked to note whether the applicant exhibited any of the above listed limitations. Because of the quality of the data (up to 55% of the individual limitations sections had missing information), dichotomous variables were used to indicate the presence or absence of a functional limitation along two dimensions: physical and mental limitations. Physical limitations include limitations with hearing, sitting, using hands, breathing, seeing, or walking, while mental limitations involve problems with reading, writing, answering, or understanding. Only 12% of the sample (21% of valid responses) had a record of a physical limitation and 10% (16% of valid responses) had a record of a mental limitation.
The data included information on when a person stopped working after their illness or injury began. This information was coded into a dichotomous variable to indicate whether the applicant had stopped work at the same time as the onset of the disability. Such information identifies an acute condition, causing immediate work cessation, rather than a chronic condition, where there is a sufficient lag in the time from the date of onset until the impairment Impairment
1. A reduction in a company's stated capital.
2. The total capital that is less than the par value of the company's capital stock.
1. This is usually reduced because of poorly estimated losses or gains.
2. led to the discontinuation dis·con·tin·u·a·tion
A cessation; a discontinuance.
Noun 1. discontinuation - the act of discontinuing or breaking off; an interruption (temporary or permanent)
discontinuance of work. More than one-fourth (28%) of the applicants stopped work with the onset of their condition.
SSA categorizes impairments into one of fourteen different body systems. Although the data set included body systems as identified by a medical professional, SSA staff will not have access to full medical evidence or records during the EI screening process, and so the applicant's self-reported disability was used to assess the relevant body system. Applicants could list up to two types of affected body systems. Rather than use the first reported body system, primary and secondary systems were both considered. For analytical analytical, analytic
pertaining to or emanating from analysis.
control of confounding by analysis of the results of a trial or test. purposes, 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. , genito-urinary, hemic/lymphatic, multiple body systems, and skin body systems were combined into an "other" category because of the minimal reporting of these conditions. The body systems with the highest prevalence in the sample were musculoskeletal musculoskeletal /mus·cu·lo·skel·e·tal/ (-skel´e-t'l) pertaining to or comprising the skeleton and muscles.
Relating to or involving the muscles and the skeleton. (41%), mental disorders (20%), and cardiovascular (15%).
As indicated above, the administrative data was incomplete for many cases. Because common methods of missing data imputation IMPUTATION. The judgment by which we declare that an agent is the cause of his free action, or of the result of it, whether good or ill. Wolff, Sec. 3. (such as complete case analysis, mean imputation, or hot-deck imputation) result in bias, a multiple imputation Multiple imputation is a statistical technique for analyzing incomplete data sets. See also
1. Characterized by or involving repetition, recurrence, reiteration, or repetitiousness.
2. Grammar Frequentative.
Noun 1. algorithms to predict the missing value, its advantage is that it inserts a random element or uncertainty into the imputed value Imputed value
Refers to the value of an asset, service, or company that is not physically recorded in any accounts but is implicit in the product, e.g., the opportunity cost of cash remaining in a savings account and not invested. . Multiple imputed Attributed vicariously.
In the legal sense, the term imputed is used to describe an action, fact, or quality, the knowledge of which is charged to an individual based upon the actions of another for whom the individual is responsible rather than on the individual's data sets were created, 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. separately, and the results averaged into a final set of parameters using PROC (language) PROC - The job control language used in the Pick operating system.
["Exploring the Pick Operating System", J.E. Sisk et al, Hayden 1986]. MI and MIANALYZE procedures in 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. version 8.2.
Those cases that met terminal or presumptive pre·sump·tive
1. Providing a reasonable basis for belief or acceptance.
2. Founded on probability or presumption.
pre·sump disability criteria were identified based on the applicant-reported condition. Terminal cases were those in which it was assumed that the illness or injury results in death, the person had a diagnosis of AIDS, or the applicant was receiving hospice hospice, program of humane and supportive care for the terminally ill and their families; the term also applies to a professional facility that provides care to dying patients who can no longer be cared for at home. care. Presumptive disability cases were those in which an applicant had an impairment considered untreatable Un`treat´a`ble
a. 1. Incapable of being treated; not practicable. . Examples of presumptive conditions include waiting for a heart or liver transplant, chronic pulmonary pulmonary /pul·mo·nary/ (pool´mo-nar?e)
1. pertaining to the lungs.
2. pertaining to the pulmonary artery.
Of, relating to, or affecting the lungs. or heart failure, or being comatose co·ma·tose
1. Of, relating to, or affected with coma.
2. Marked by lethargy; torpid.
comatose (kō´m for 30 days or more. For these cases, rather than including them in modeling SSDI receipt, an assumption was made that they would automatically be screened as eligible. These cases were therefore excluded from the initial modeling process.
After the terminal and presumptive cases were removed, a cross-validation technique was adapted from Pedhazur (1982), dividing the data into two groups: one to create the models (a scoring sample) and one to test the models (a validation See validate.
validation - The stage in the software life-cycle at the end of the development process where software is evaluated to ensure that it complies with the requirements. sample). Eighty percent of the cases were placed in the scoring sample, with the remaining 20% used for validation. The validation sample also included a random sample of 20% of the terminal and presumptive cases, thereby simulating the actual applicant pool.
Three models for predicting SSDI receipt were developed using the modeling sample. The dependent variable for all models was a dichotomous variable of benefit allowance. Logistic regression In statistics, logistic regression is a regression model for binomially distributed response/dependent variables. It is useful for modeling the probability of an event occurring as a function of other factors. analysis was used for two of the models. Whereas linear regression Linear regression
A statistical technique for fitting a straight line to a set of data points. is more appropriate when the dependent variable is continuous, logistic regression is appropriate when the dependent variable is dichotomous. Logistic regression can be used to classify clas·si·fy
tr.v. clas·si·fied, clas·si·fy·ing, clas·si·fies
1. To arrange or organize according to class or category.
2. To designate (a document, for example) as confidential, secret, or top secret. individuals into one of two populations, in this case, those who receive benefits and those who do not. The results provide a predicted probability that an individual belongs to a specified group (Afifi & Clark, 1997; Long, 1997). The first model used logistic regression and included all of the independent variables described above in the measures section. A second logistic regression model was based on variables that were significant in the first model at the p < .10 level. For the third model, a sum of scores scale was developed where each of the categorical That which is unqualified or unconditional.
A categorical imperative is a rule, command, or moral obligation that is absolutely and universally binding.
Categorical is also used to describe programs limited to or designed for certain classes of people. variables was assigned values from 0 to 4 relative to the degree that individuals with the value obtained SSDI benefits (see, for example, Bellini, Neath Neath (nēth), Welsh Castell-nedd, town (1981 pop. 48,687), Neath Port Talbot, S Wales, on the Neath River. Neath is both a market and an industrial town. Metallurgy and a growing petrochemical industry are important. , and Bolton, 1995). The advantage of this method is that the scale is easy to use and simple to interpret.
For each of the models, the validation sample was used to assess accuracy, and classification tables were developed based on predicted and actual receipt of SSDI. Comparison of the models was based on five measures that are commonly used to assess the accuracy of a screening tool (Howell, 1997; Lemeshow, Teres teres /te·res/ (te´rez) [L.] long and round.
Being round and long. Used of certain muscles and ligaments.
[L.] long and round. , Avrunin, & Pastides, 1988): sensitivity (the correct classification of those persons who received SSDI), specificity (the correct classification of those people who were denied SSDI benefits), predictive value pre·dic·tive value
The likelihood that a positive test result indicates disease or that a negative test result excludes disease.
a measure used by clinicians to interpret diagnostic test results. of acceptance (the proportion of applicants predicted to receive SSDI who were accepted), predictive value of denial (the proportion of applicants predicted to be denied who were denied), and efficiency (the total proportion of applicants who were correctly classified). In addition, the proportion of SSDI recipients allowed by the model was calculated, as well as the phi coefficient Noun 1. phi coefficient - an index of the relation between any two sets of scores that can both be represented on ordered binary dimensions (e.g., male-female)
fourfold point correlation, phi correlation , a measure of association for dichotomous variables. All analyses used SAS version 8.2.
Table 1 shows the distribution of the sample applicants by variable, as well as the proportion of denials and allowances. The sample included a large portion of beneficiaries who were young, had a high school education, had marginal earnings, and did not have physical or mental limitations. Most of the sample did not stop work with the onset of illness. Musculoskeletal and mental disorders were the most commonly listed disorders. Applicants who received SSDI benefits were more likely to be aged 55 to 59, have more than a high school education, be married, have average or higher earnings, and have physical or mental limitations. Persons who experienced more acute conditions and were forced to stop working at the same time their disability started were less likely to receive SSDI. Applicants with a condition affecting the cardiovascular system cardiovascular system: see circulatory system.
System of vessels that convey blood to and from tissues throughout the body, bringing nutrients and oxygen and removing wastes and carbon dioxide. had high levels of SSDI receipt, while applicants with musculoskeletal and mental disorders had lower levels.
In Table 2, the coefficients and standard errors of the logistic regression equations are presented. With Model 1, all variables were included on the presumption A conclusion made as to the existence or nonexistence of a fact that must be drawn from other evidence that is admitted and proven to be true. A Rule of Law.
If certain facts are established, a judge or jury must assume another fact that the law recognizes as a logical that, though they were not significant, they had been shown by previous research to be related to the application decision. Variables that were significant at the p < 0.10 level included having a mental limitation, low to high earnings, being between 55 and 59 years of age, having a condition whose onset did not coincide with leaving employment, and having a sense related impairment. Those variables formed the basis for the second model.
The variables and their values are listed for the sum of scores model in Table 3. Values for each variable were based on the ability of each variable to discriminate dis·crim·i·nate
v. dis·crim·i·nat·ed, dis·crim·i·nat·ing, dis·crim·i·nates
a. between SSDI acceptance and denial. Categories that related to a greater denial rate were coded as zero, while items related to acceptance rates were coded from one to four, depending upon the magnitude of the effect. For instance, the two variables assigned the highest codes, mental limitations and age between 55 and 59, had an allowance rate twice their denial rate. In contrast, being married was only a marginal indicator for receipt of SSDI, and so that variable carried a weight of two. Applicants within the modeling sample received scores ranging from one to 23.
To contrast the models, the actual rates of SSDI receipt were compared with the predicted rates. For Models 1 and 2, a probability of benefit receipt was calculated based on the logistic lo·gis·tic also lo·gis·ti·cal
1. Of or relating to symbolic logic.
2. Of or relating to logistics.
[Medieval Latin logisticus, of calculation equation developed for each model. Though 0.50 is generally used as a cutoff for logistic models logistic models,
n.pl statistical models that describe the relationship between a qualitative dependent variable (that is, one that can take only certain discrete values, such as the presence or absence of a disease) and an independent variable. (e.g., Dwyer et al., 2001; Lemeshow et al., 1988), to obtain a more restrictive model, a probability of 0.60 or above was used as indicative of SSDI receipt. For Model 3, the sum of scores model, the rates of acceptance and denial for each score were examined, using as the criterion value the score above which applicants moved to the SSDI rolls in greater proportion than the general sample. Persons scoring eight or above were more likely to be allowed benefits.
Table 4 compares the three models on the ability to classify applicants on both the scoring and the validation samples. The acceptance rate, the proportion predicted to receive SSDI, ranged from 55% to 60% using the scoring sample and from 60% to 65% using the validation sample. Model 1, the logistic model which included all Variables, was not as accurate as either Model 2, the logistic model with significant variables, or Model 3, the sum of scores model. In both the scoring and validation samples, Model 2 had the highest acceptance rate and sensitivity (the ability to identify successful SSDI applicants), with a slight advantage for the predictive value of denial (i.e., the proportion of individuals who were predicted to be denied and were actually denied). Model 3 had the highest specificity (the ability to identify denied SSDI applicants) and predictive value of acceptance (i.e., the proportion of individuals who were predicted to be accepted and were actually accepted). Model 3 also had a slightly better phi coefficient, though the effect size was small in both samples. Both Models 2 and 3 were equally efficient, correctly predicting the SSDI outcome of 68% of the scoring sample and 62% of the validation sample.
All models were less accurate with the validation sample than the scoring sample. Though sensitivity was similar and predictive value of denial slightly better, other comparison measures decreased.
The ability to model a benefits determination process is critical for many federal and state programs. With any model, accuracy is compromised by using simple formulae to model a complex eligibility process. When the process modeled involves disability, a concept with inherent difficulties in its definition, the issue becomes even more problematic. Despite these difficulties, two of the models developed here can reasonably identify those persons who would qualify for or be denied SSDI benefits. The sum of scores model was slightly better in predicting denied applicants, while the logistic model was slightly better in predicting successful applicants.
The legislation that authorized au·thor·ize
tr.v. au·thor·ized, au·thor·iz·ing, au·thor·iz·es
1. To grant authority or power to.
2. To give permission for; sanction: the El demonstration states that services can only be provided to applicants with impairments that may reasonably be presumed to be disabling (TWWIIA, 1999, Section 301). Each of the models discussed was successful about two-thirds of the time in predicting which applicants were awarded benefits. Accuracy for predicting who was denied benefits was not as high. The demonstration is intended to "do no harm," and persons who are inaccurately screened out will be able to continue pursuing SSDI benefits in the usual manner.
The implementation of any of the proposed models will allow SSA to assess, with a reasonable level of accuracy and in a relatively efficient manner, the likelihood of an applicant receiving benefits. Given the constraints on available data and the realities related to implementing this tool in a real world environment, the models developed here have obvious limitations. The data used to develop the models may not be representative of the SSDI applicant pool. As mentioned earlier, the interpretation of the disability determination decision processes may vary from region to region and such fluctuations could have influenced the data. Furthermore, the data were skewed skewed
curve of a usually unimodal distribution with one tail drawn out more than the other and the median will lie above or below the mean.
skewed Epidemiology adjective Referring to an asymmetrical distribution of a population or of data towards applicants who had been approved for benefits. The amount of missing data, despite using an imputation approach, may also have influenced the models and the ability to predict awards and denials.
As a tool to identify SSDI recipients, the selection cut-off cut-off Anesthesiology The point at which elongation of the carbon chain of the 1-alkanol family of anesthetics results in a precipitous drop in the anesthetic potential of these agents–eg, at > 12 carbons in length, there is little anesthetic activity, point is itself flexible and can be adjusted to meet the policy needs and objectives of the demonstration. This point is illustrated using the restricted logistic model (Model 2) on the validation sample. Using 0.60 as the probability selection cutoff, we could anticipate that of every 100 people in this sample who apply, 66 would be identified as likely to receive SSDI. However, based on the sensitivity and an actual acceptance rate of 54% (SSA, 2003b), of those 66 persons who would be flagged, 41 would actually receive SSDI and 25 would not, though they would be offered access to the EI program. This latter group represents the false positives or deadweight, persons who incorrectly receive the intervention.
The selection screen can be tailored to include a large number of individuals (emphasizing sensitivity) or minimize the false positive rate (increasing specificity), either by lowering or raising the cut-off point, respectively, but it is difficult for a screening tool to have both high sensitivity and specificity (Waddell, Burton, and Main, 2003). Projects or programs using a screen must decide which path to take. Continuing with the illustration, we could use a higher cut point (e.g., taking the 90th 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 probability score from the model sample for those who are denied benefits, or 0.7577) to reduce the deadweight. This criterion would result in values for sensitivity, specificity, predicted value of acceptance, and predicted value of denial for the restricted logistic model of 32%, 75%, 63%, and 45%, respectively, with a screening acceptance rate of 29%. As the example shows, the ability to predict those who will receive SSDI declines while the ability to predict who is denied increases, with no change in the predictive value of acceptance. Of 100 people who apply, 29 would be identified by the screening tool as SSDI candidates, of whom 17 would actually receive SSDI benefits and 12 would be false positives. While this change would decrease the deadweight, fewer actual beneficiaries would be offered services that could help them.
SSA will have the unique opportunity, through the EI process demonstration, to evaluate the screening tools on key criteria related to accuracy, efficiency, and equity. SSA will also have the ability to design a program that will be cost effective, no matter which criteria are used to select people into the program. Program cost forecasts can include estimates of deadweight so that program expenditures can be adjusted accordingly. The successful identification of probable beneficiaries is but one factor that will influence overall program costs-benefit ratios.
During the planned EI process evaluation, data will be collected to determine if the selection model correctly identifies SSDI beneficiaries, and the same data will be used to correct and improve the model. A significant aspect of the EI program evaluation Program evaluation is a formalized approach to studying and assessing projects, policies and program and determining if they 'work'. Program evaluation is used in government and the private sector and it's taught in numerous universities. process is whether or not a model identifies SSDI beneficiaries accurately, or if it excludes a portion or particular subset A group of commands or functions that do not include all the capabilities of the original specification. Software or hardware components designed for the subset will also work with the original. of beneficiaries. Finally, SSA will observe the use of a screening process in SSA field offices, thereby assessing the effects of its administration and how such a process affects the claims representative 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. .
Todd C. Honeycutt and Debra Brucker completed initial drafts of this article while working for the Program for Disability Research at Rutgers, the State University of New Jersey.
The work presented here was performed pursuant to a grant from the U.S. Social Security Administration (SSA) funded as part of the Disability Research Institute. The opinions and conclusions expressed are solely those of the authors and should not be construed as representing the opinions or policy of SSA or any agency of the Federal Government.
The authors wish to thank Monroe Berkowitz Monroe Berkowitz is Professor of Economics Emeritus at Rutgers University, New Brunswick, New Jersey. He is a leading authority on the economics of disability and rehabilitation in public programs, private disability insurance, and public and private rehabilitation systems in the U. , John Hennessey John Hennessey may refer to:
Afifi, A. A., & Clark, V. (1997). Computer-aided multivariate analysis multivariate analysis,
n a statistical approach used to evaluate multiple variables.
n a set of techniques used when variation in several variables has to be studied simultaneously. . Boca Raton Boca Raton (bō`kə rətōn`), city (1990 pop. 61,492), Palm Beach co., SE Fla., on the Atlantic; inc. 1925. Boca Raton is a popular resort and retirement community that experienced significant industrial development in the 1970s and 80s. , FL: CRC (Cyclical Redundancy Checking) An error checking technique used to ensure the accuracy of transmitting digital data. The transmitted messages are divided into predetermined lengths which, used as dividends, are divided by a fixed divisor. Press.
Allison, P. (2001). Missing data. Thousand Oaks Thousand Oaks, residential city (1990 pop. 104,352), Ventura co., S Calif., in a farm area; inc. 1964. Avocados, citrus, vegetables, strawberries, and nursery products are grown. , CA: Sage.
Barzi, F., & Woodward, M. (2004). Imputations of missing values In statistics, missing values are a common occurrence. Several statistical methods have been developed to deal with this problem. Missing values mean that no data value is stored for the variable in the current observation. in practice: Results from imputations of serum cholesterol in 28 cohort studies A cohort study is a form of longitudinal study used in medicine and social science. It is one type of study design.
In medicine, it is usually undertaken to obtain evidence to try to refute the existence of a suspected association between cause and disease; failure to refute . American Journal of Epidemiology epidemiology, field of medicine concerned with the study of epidemics, outbreaks of disease that affect large numbers of people. Epidemiologists, using sophisticated statistical analyses, field investigations, and complex laboratory techniques, investigate the cause , 160, 34-45.
Bellini, J., Neath, J., & Bolton, B. (1995). Development of a scale of social disadvantage for vocational rehabilitation Noun 1. vocational rehabilitation - providing training in a specific trade with the aim of gaining employment
rehabilitation - the restoration of someone to a useful place in society . Journal of Rehabilitation rehabilitation: see physical therapy. Administration, 19(2), 107-118.
Benitez-Silva, H., Buchinsky, M., Chan, H. M., Rust, J., & Sheidvasser, S. (1999). An empirical analysis of the Social Security Disability application, appeal, and award process. Labour Economics, 6, 147-178.
Bilder, S., & Mechanic, D. (2003). Navigating (networking, hypertext) navigating - Finding your way around. Often used of the Internet, particularly the World-Wide Web.
A browser is a tool for navigating hypertext documents. the disability process: Persons with mental disorders applying for and receiving disability benefits. Milbank Quarterly, 81(1), 751-06.
Briggs, A., Clark, T., Wolstenholme, J., & Clarke, P. (2003). Missing ... presumed at random: cost-analysis of incomplete data. Health Economics, 12, 377-392.
Chirikos, T. N. (1995). Medicare and the Social Security Disability Insurance program. Health Affairs, Winter 1995, 244-252.
Daub, H. (2002). Reforming the Disability Insurance and Supplemental Security Income Disability programs (statement before the US House of Representatives Committee on Ways and Means WAYS AND MEANS. In legislative assemblies there is usually appointed a committee whose duties are to inquire into, and propose to the house, the ways and means to be adopted to raise funds for the use of the government. This body is called the committee of ways and means. , Subcommittee sub·com·mit·tee
A subordinate committee composed of members appointed from a main committee.
Noun on Social Security).
Dwyer, D., Hu, J., Vaughan, D. R., & Wixon, B. (2001). Counting the disabled: Using survey self-reports to estimate medical eligibility for Social Security's disability programs (ORES Working Paper Series No. 90). Washington, DC: Social Security Administration.
Ellis, B. H., Shannon, E. D., Cox, J. K., Aiken, L., & Fowler, B. M. (2004). Chronic conditions: Results of the Medicare health outcomes survey, 1998-2000. Health Care Financing Review, 25, 75-91.
Gu, D., & Yi, Z. (2004). Sociodemographic effects on the onset and recovery of ADL disability among Chinese oldest-old. Demographic Research, 11, 1-42.
Howell, D. C. (1997). Statistical methods for psychology. Belmont, CA: Duxbury Press.
Hu, J., Lahiri, K., Vaughan, D. R., & Wixon, B. (1997). A structural model of Social Security's disability determination process (ORES Working Paper Series No. 72). Washington, DC: Social Security Administration.
Kennedy, J., Olney, M., Richer, T., & Newsom, M. (2002). A profile of SSDI applicants and beneficiaries, age 18-64: Estimates from the 1994 and 1995 National Health Interview Surveys on Disability. Retrieved March 22, 2003, from http:// www.als.uiuc.edu/dri/pubs/pubs.htm
Kreider, B. (1999). Social Security Disability Insurance: Applications, awards, and lifetime income flows. Journal of Labor Economics The Journal of Labor Economics, published by the University of Chicago Press presents international research examining issues affecting the economy as well as social and private behavior. , 17(4), 784-827.
Kreider, B., & Riphahn, R. T. (2000). Explaining applications to the U.S. disability system. Journal of Human Resources The fancy word for "people." The human resources department within an organization, years ago known as the "personnel department," manages the administrative aspects of the employees. , 35(1), 82-115.
Lahiri, K., Vaughan, D., & Wixon, B. (1995). Modeling SSA's sequential disability determination process using matched SIPP See SIP.
SIPP - Single Inline Pin Package data. Social Security Bulletin, 58, 3-42.
Lemeshow, S., Teres, D., Avrunin, J. S., & Pastides, H. (1988). Predicting the outcome of intensive care unity patients. Journal of the American Statistical Association Established in 1888 and published quarterly in March, June, September, and December, the Journal of the American Statistical Association (JASA) has long been considered the premier journal of statistical science. , 83, 348-356.
Long, J. S. (1997). Regression regression, in psychology: see defense mechanism.
In statistics, a process for determining a line or curve that best represents the general trend of a data set. models for discrete and limited dependent variables. Thousand Oaks, CA: Sage.
Mitra, S., & Brucker, D. (2004). The early intervention project: An innovative initiative to return disability insurance applicants to work. Journal of Disability Policy Studies, 15, 159-167.
Palmer, E. (1990). Potential gains from early intervention in sickness SICKNESS. By sickness is understood any affection of the body which deprives it temporarily of the power to fulfill its usual functions.
2. Sickness is either such as affects the body generally, or only some parts of it. absenteeism ab·sen·tee·ism
1. Habitual failure to appear, especially for work or other regular duty.
2. The rate of occurrence of habitual absence from work or duty. at the workplace: An overview of the current state of knowledge and trends in Sweden. In M. Berkowitz (Ed.), Forging Linkages: Modifying Disability Benefit Programs to Encourage Employment, 9-13. New York New York, state, United States
New York, Middle Atlantic state of the United States. It is bordered by Vermont, Massachusetts, Connecticut, and the Atlantic Ocean (E), New Jersey and Pennsylvania (S), Lakes Erie and Ontario and the Canadian province of : Rehabilitation International Rehabilitation International (RI) is an international disability rights organization with a secretariat based in New York U.S.. Founded in 1922, RI has more than 700 member organizations in 90 countries. .
Patrician, P. A. (2002). Multiple imputation for missing data. Research in Nursing and Health, 25, 76-84.
Pedhazur, E. J. (1982). Multiple regression Multiple regression
The estimated relationship between a dependent variable and more than one explanatory variable. in behavioral research. Fort Worth, TX: Harcourt Brace Jovanovich.
Social Security Administration. (2002). Annual statistical report on the Social Security Disability Insurance program. Baltimore, MD: Author.
Social Security Administration. (2003a). Disability process May 1, 2002. Retrieved July 31, 2003, from http://www.ssa.gov/ disability/disability_process_frameset The HTML tag that divides a Web page into sections (frames). The following HTML divides the screen page into two horizontal frames with the top frame having 25% of the screen and the bottom frame having 75%. See frames.
<frameset rows=25%, 75%"> </frameset> re> .html
Social Security Administration. (2003b). Flow of cases through the disability process. Retrieved July 31, 2003, from http:// www.ssa.gov/disability/disability_process_welcome_2002.htm
Social Security Administration. (2005a). Disability insurance benefit payments. Retrieved August 30, 2005, from http:// www.ssa.gov/OACT/STATS/table4a6.html
Social Security Administration. (2005b). Number of disabled workers and their dependents receiving benefits on December 31, 1970-2004. Retrieved August 30, 2005, from http://www. ssa.gov/OACT/STATS/DIbenies.html
Social Security Administration. (2005c). Fact sheet on the Old-Age, Survivors and Disability Insurance Program. Retrieved August 30, 2005, from http://www.ssa.gov/OACT/FACTS/
Social Security Advisory Board. (2001a). Charting the future of SSA's disability programs: The need for fundamental change. Washington, DC: Author.
Social Security Advisory Board. (2001b). Disability decision making: Data and materials. Washington, DC: Author.
Ticket to Work and Work Incentive Improvement Act (TWWIIA) of 1999, PL 106-170, 42 U.S.C. [section] 1305, et seq et seq. (et seek) n. abbreviation for the Latin phrase et sequentes meaning "and the following." It is commonly used by lawyers to include numbered lists, pages or sections after the first number is stated, as in "the rules of the road are found in Vehicle Code .
Waddell, G., Burton, A. K., & Main, C. J. (2003). Screening to identify people at risk of long-term incapacity The absence of legal ability, competence, or qualifications.
An individual incapacitated by infancy, for example, does not have the legal ability to enter into certain types of agreements, such as marriage or contracts. for work. London: Royal Society of Medicine.
Todd C. Honeycutt
Rutgers University Rutgers University, main campus at New Brunswick, N.J.; land-grant and state supported; coeducational except for Douglass College; chartered 1766 as Queen's College, opened 1771. Campuses and Facilities
Rutgers maintains three campuses.
(1) The SSA application form was changed in 1998 to include the following limitations: hearing, reading, breathing, understanding, coherency co·her·en·cy
n. pl. co·her·en·cies
Noun 1. coherency - the state of cohering or sticking together
coherence, cohesion, cohesiveness , concentrating, talking, answering, sitting, standing, walking, seeing, using hand(s), and writing.
Todd C. Honeycutt, 20 Woodland Terrace, High Bridge, NJ, 08829. Email: firstname.lastname@example.org
Table 1 Sample Characteristics, Denial Rates, and Allowance Rates Total Denied (% of total) (% of denied) N 545 191 Age Group 18-44 235 (43%) 103 (53%) 45-49 80 (15%) 25 (13%) 50-54 69 (13%) 21 (11%) 55-59 92 (17%) 20 (10%) 60+ 65 (12%) 22 (12%) Missing 4 (1%) 0 (0%) Education Level Less than HS 109 (20%) 46 (24%) High School 248 (46%) 85 (45%) More than HS 146 (27%) 45 (23%) Missing 42 (8%) 15 (8%) Marital Status Married 259 (48%) 79 (41%) Not married 281 (51%) 110 (58%) Missing 6 (1%) 2 (1%) Earnings Level Marginal 250 (46%) 111 (41%) Low 112 (20%) 39 (20%) Average 91 (17%) 23 (12%) High 62 (11%) 13 (7%) Very high 26 (5%) 5 (3%) Missing 4 (1%) 0 (0%) Physical Limitations Yes 64 (12%) 17 (9%) No 238 (43%) 88 (46%) Missing 243 (45%) 86 (45%) Mental Limitations Yes 54 (10%) 8 (4%) No 251 (46%) 97 (51%) Missing 240 (44%) 86 (45%) Onset with Work Cessation Yes 151 (28%) 58 (30%) No 334 (61%) 107 (56%) Missing 60 (11%) 26 (14%) Body System Musculoskeletal 219 (40%) 90 (47%) Senses/speech 25 (5%) 3 (2%) Respiratory 38 (7%) 14 (7%) Cardiovascular 82 (15%) 21 (11%) Endocrine 37 (7%) 9 (5%) Neurological 62 (11%) 21 (11%) Mental disorders 109 (20%) 47 (24%) Neoplastic 31 (6%) 4 (2%) Immune deficiency 33 (6%) 6 (3%) Other 29 (5%) 8 (4%) Missing 30 (6%) 15 (8%) Allowed (% of allowed) N 354 Age Group 18-44 132 (37%) 45-49 55 (16%) 50-54 48 (14%) 55-59 72 (20%) 60+ 43 (12%) Missing 4 (1%) Education Level Less than HS 63 (18%) High School 163 (46%) More than HS 101 (28%) Missing 27 (8%) Marital Status Married 181 (51%) Not married 170 (48%) Missing 4 (1%) Earnings Level Marginal 139 (39%) Low 73 (21%) Average 68 (19%) High 49 (14%) Very high 21 (6%) Missing 4 (1%) Physical Limitations Yes 47 (13%) No 150 (42%) Missing 157 (44%) Mental Limitations Yes 46 (13%) No 154 (44%) Missing 154 (44%) Onset with Work Cessation Yes 93 (26%) No 227 (64%) Missing 34 (10%) Body System Musculoskeletal 129 (36%) Senses/speech 22 (6%) Respiratory 24 (7%) Cardiovascular 61 (17%) Endocrine 28 (8%) Neurological 41 (12%) Mental disorders 62 (18%) Neoplastic 27 (8%) Immune deficiency 27 (8%) Other 21 (6%) Missing 15 (4%) Note. Persons had up to two affected body systems. Table 2 Logistic Regression Results for Full and Restricted Models Model 1 (Full Logistic Model) Coefficient SE Intercept -0.445 0.323 Age Group 18-44 a 45-49 0.222 0.348 50-54 0.672 0.449 55-59 0.846 * 0.398 60+ 0.445 0.400 Education Level Less than HS -0.276 0.316 High School (a) More than HS -0.078 0.308 Marital Status Married 0.336 0.249 Earnings Level Marginal (a) Low 0.538 (+) 0.317 Average 1.083 ** 0.362 High 1.235 ** 0.467 Very high 1.047 0.667 Physical Limitations 0.178 0.426 Mental Limitations 1.135 * 0.461 Onset with Work Cessation -0.547 (+) 0.295 Body System Musculoskeletal (a) Senses/speech 1.794 (+) 1.029 Respiratory 0.494 0.538 Cardiovascular 0.438 0.361 Endocrine 0.276 0.485 Neurological 0.136 0.382 Mental disorders -0.098 0.326 Neoplastic 1.250 0.822 Immune deficiency 1.100 0.771 Other 0.192 0.542 Model 2 (Restricted Logistic Model) Coefficient SE Intercept 0.048 0.183 Age Group 18-44 a 45-49 50-54 55-59 0.853 * 0.357 60+ Education Level Less than HS High School (a) More than HS Marital Status Married Earnings Level Marginal (a) Low 0.404 0.293 Average 1.067 ** 0.336 High 1.176 ** 0.440 Very high Physical Limitations Mental Limitations 1.024 * 0.449 Onset with Work Cessation -0.336 0.264 Body System Musculoskeletal (a) Senses/speech 1.582 1.021 Respiratory Cardiovascular Endocrine Neurological Mental disorders Neoplastic Immune deficiency Other Note. For purposes of estimation and comparison, the reference categories are persons less than 45 years old, with marginal earnings, HS education, and musculoskeletal disorders. (a) Denotes reference group for Model 1. (+) p < .10. * p < .05. ** p < .01. Table 3 Sum of Scores Model Variables, Values, and Denial and Allowance Rates Variable Value Denied % Allowed Age Group 18-44 0 57% 38% 45-54 2 23% 27% 55-59 4 9% 22% 60-65 1 11% 13% Education Level Less than high school 0 26% 21% High school or more than high school 1 74% 79% Marital Status Not married 0 60% 47% Married 2 40% 53% Earnings Level Marginal 0 59% 38% Low 1 20% 20% Average, high, or very high 3 21% 42% Physical Limitations Yes 2 18% 24% No 0 82% 76% Mental Limitations Yes 4 9% 21% No 0 91% 79% Onset with Work Cessation Yes 0 35% 32% No 1 65% 68% Primary Condition Body System Musculoskeletal, multiple body systems, 0 79% 59% neurological, or mental All other conditions 3 21% 41% Secondary Condition Body System Respiratory or mental 0 20% 14% None or any other disorder 1 69% 67% Senses, cardiovascular, endocrine, or 3 11% 20% neurological Total 100% 100% Table 4 Comparison of Models in Predicting SSDI Receipt, by Scoring and Validation Samples Model 1 Model 2 Model 3 (Full (Restricted (Sum of Logistic) Logistic) Scores) Scoring Sample Acceptance Rate 58% 60% 55% Sensitivity 70% 72% 68% Specificity 63% 62% 69% Predictive Value of Acceptance 78% 78% 80% Predictive Value of Denial 53% 54% 53% Efficiency 67% 68% 68% Phi Coefficient 0.32 0.32 0.35 Validation Sample Acceptance Rate 65% 66% 60% Sensitivity 70% 74% 69% Specificity 42% 45% 53% Predictive Value of Acceptance 62% 64% 66% Predictive Value of Denial 51% 57% 56% Efficiency 58% 62% 62% Phi Coefficient 0.13 0.20 0.22