0057 Supporting Statement Part B One on One Final V2 clean

0057 Supporting Statement Part B One on One Final V2 clean.docx

SHIP-SMP Survey of One-on-One Assistance

OMB: 0985-0057

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SHIP–SMP Survey of One-on-One Assistance OMB 0985-0057 Supporting Statement


B. Collections of Information Employing Statistical Methods

  1. Describe the potential respondent universe and any sampling or other respondent selection methods to be used



The respondent universe for this survey project will be the list of all individuals who obtained one-on-one assistance during the Medicare non-enrollment period (spring/summer) and during the Medicare Open Enrollment Period (October 15 – December 7). Based on initial samples, there are over 50,000 non-redundant individuals who receive Medicare one-on-one assistance services in an average week across all states/territories. ACL will focus on a non-redundant respondent universe so as not to bias this study towards individual opinion or place an undue burden on individual respondents.

To generate a sample with a 95% confidence level at the national level (with all 54 states/territories represented), 384 responses will be required for the SHIP survey and 384 for the SMP program. ACL anticipates collecting 400 completed surveys for each program each year, for a total collection of 2,400 completed surveys, or 800 surveys per year. The annual collection is slightly larger than the required number due to rounding differences that emerge after applying state/territory percentages to the required collection number. For example, smaller states like Maine or New Hampshire may only represent 0.4% of the total Medicare beneficiary population. Multiplying 0.4% by 384 results in a response total of 1.5, which needs to be rounded to the closest whole number. Doing so results in an estimate of two (2) responses for the relevant state(s), thereby increasing the overall response total.

ACL will randomly select a group of individuals who received Medicare assistance from each program at two points each year. Table B-1-1 indicates how many completed surveys ACL expects each year.





Table B-1-1: Expected Number of Completed SHIP/SMP Surveys by Year

Period

Responses Needed for a Representative Survey*

Contacts Needed to Achieve a 50% Response Rate

Expected Responses

Year 1

768

1536

800

Year 2

768

1536

800

Year 3

768

1536

800

GRAND TOTAL

2,304

4,608

2,400



*These response totals are based on 2,069,442 one-on-one assistance sessions across all 54 states/territories in 2022 This data was pulled from ACL’s STARS system.

  1. Describe the procedures for the collection of information

Program data will be stratified at the state/territory level, to determine how many responses will be collected from a given state/territory each year. The states/territories will be stratified by the number of Medicare-eligible residents in the state/territory. This stratification will inform the sampling methodology, as a state that requires 10 responses will produce an initial sample of 20 entries (assuming a 50% response rate), while a state that requires one response will produce a sample of two entries. The sample selection and stratification will apply to both SHIP and SMP programs at two points in time:

    1. Outside of Medicare Open Enrollment Period (spring/summer)

    2. During Medicare Open Enrollment Period (October 15 – December 7).

During the weeks identified for sampling, program representatives will notify individuals who request one-on-one Medicare assistance that a third party might contact them to assess their satisfaction with their experience. On each Monday during this period ACL will download the complete collection of one-on-one assistance sessions that occurred during the previous week. ACL will then randomly select potential respondents to call, with the expectation that 200 responses will be collected outside the Medicare Open Enrollment Period for each program and 200 responses will be collected during the Medicare Open Enrollment Period for each program. All completed responses will be entered electronically into a password-protected Microsoft Excel database, where ACL can track state/territory progress in survey completion and maintain data for analysis.

    1. * Estimation procedure,

ACL will derive its total sample based on three factors:

    1. The number of unique (i.e., non-redundant) individuals who have received one-on-one assistance at two points each year (those that received service during the Medicare Open Enrollment Period and those that received service outside of the Medicare Open Enrollment Period),

    2. The number of respondents required to achieve a 95% confidence level and,

    3. A margin of error of +/- 5%.


    1. * Degree of accuracy needed for the purpose described in the justification,

In order to generate a national sample, 384 responses are required per year for each program. ACL will pursue a slightly larger collection (400 responses per year per program) to account for rounding differences after stratifying the 54 participating states/territories.

    1. * Unusual problems requiring specialized sampling procedures, and

There are no unusual problems requiring specialized sampling in this collection.

    1. * Any use of periodic (less frequent than annual) data collection cycles to reduce burden.

This is an annual survey.

  1. Describe methods to maximize response rates and to deal with issues of non-response



Based on ACL's review of the existing survey and similar surveys, a 50% response rate is expected. To achieve this rate of response, several best practices will be used, including:

  1. Using a notable agency for data collection:

All letters, surveys, and email announcements sent to SHIP/SMP representatives will contain ACL's seal and logo, so as to improve the confidence respondents have with the survey.

  1. Advance Warning of the Survey Request:

Each respondent will be notified during his/her assistance session that he/she may be contacted in the following week to assess his/her satisfaction with Medicare assistance services.

  1. Computer-Assisted Telephone Interviewing:

This method of collecting data is less burdensome and time consuming than traditional paper surveys.

These methods will minimize non-response. ACL has conducted a preliminary analysis of non-response bias for the existing version of this survey and has found no evidence of non-response bias. While there are meaningful differences in response rates by age groups, where those under the age of 75 are more likely to respond than those 75 or older, there were no meaningful difference in the actual responses generated by individuals of different age ranges.

  1. Describe any tests of procedures or methods to be undertaken



As this is a renewal of an existing collection, ACL has performed extensive testing of the survey instrument and its administration. To date, over 2500 completed surveys have been collected, with an expectation of 3400 completed surveys by May 2023. Based on this data collection, ACL has adjusted the expected time to complete the survey from eight minutes to six minutes. Informal discussions with State Directors about this survey have been universally positive, indicating that there have been no complaints about undue burden or a lack of utility in the data collection.



  1. Provide the name and telephone number of individuals consulted on statistical aspects of the design and the name of the agency unit, contractor(s), grantee(s), or other person(s) who will actually collect and/or analyze the information for the agency

Two members of the ACL staff will play a primary role in this project:

    1. Shefy Simon – Project Officer, [email protected].

    2. Rebecca Kinney – Director, Office of Healthcare Information and Counseling, [email protected]

Keybridge Research, in partnership with CG Strategy ([email protected])

202-864-6423.

With respect to specific tasks, the following individuals were responsible for:

    1. Designing the data collection: Ms. Kinney, Ms. Simon, and Ms. Yeh.

    2. Collecting the data: Ms. Simon and Ms. Yeh.

    3. Analyzing the data: Ms. Yeh.



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