Appendix G: Data Usage
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1 Appendix G: Data Usage G.1 Instructions for Using Weights For the purposes of design-based (variance) estimation, the data file includes the following design variables: WT_A, WT_C, adjusted survey weights for adult-level and child-level estimates and analyses STRATA, a stratum indicator for generating design-based variance estimators Sampling variances for the weighted estimates that account for the complex sample design can be computed with statistical software such as SUDAAN, STATA, or SAS. An example SUDAAN statement would necessitate a Nest statement where STRATUM is specified, and a Design statement with a WR specification for a with-replacement sampling design (approximation). An example follows for a health insurance variable (INSRD_A) that is tabulated by region. Proc Descript Data= OMAS.ssd Filetype=sas Design=WR; Weight WT_A; Nest STRATA; Var INSRD_A_IMP; Tables REGION; Class REGION; Title OMAS, Percent of adults insured by region ; Print Percent SEPercent; The example SAS code below shows how to compute the weighted percentage of adults insured statewide. Proc Surveymeans Data= OMAS mean; STRATA; Weight WT_A; Var INSRD_A_IMP: Class INSRD_A_IMP; Domain REGION; run; The example STATA code below shows how to compute the weighted percentage of adults uninsured statewide. svyset _n [pweight=wt_a], strata(strata) vce(linearized) singleunit(certainty) xi, noomit: svy: tabulate INSRD_A_IMP, level(95) ci deff Methodology Report G-1
2 Appendix G: Data Usage 2015 Ohio Medicaid Assessment Survey G.2 Limitations and Cautions When Using the Data The 2015 OMAS carries with it the following limitations and cautions regarding use of the data: The data were collected via telephone only. A telephone-only approach precluded the ability to: Collect information from consumers of the sampled population without valid telephone numbers. Maximize the number of attempts to reach nonrespondents; a mail and telephone survey method would increase the number of attempts. Reach respondents in a manner that is most suitable for themselves; for example, respondents with limited speaking abilities may be more likely to conduct the survey via mail because they would not be required to talk to an interviewer. Minimize bias that may result from only one mode of data collection; a study conducted in 1998 with the SF-36 questionnaire found that younger adults were more likely to refuse to participate when the study was administered via mail, while older adults were more likely to refuse telephone interviews (Perkins & Sanson-Fisher, 1998). Interviews were only conducted with households that could speak English or Spanish well enough to be interviewed. Thus, non English- and non Spanish-speaking households were excluded from the survey. As identified by the final dispositions, less than one-tenth of 1% of households contacted were unable to complete the survey because of a language barrier situation. The literature indicates that the use of proxies can introduce bias to the survey results. A number of studies have shown consistent differences between self- and proxy reporting (Bassett et al., 1990; Ellis et al., 2003; Epstein et al., 1989; Kovar & Wright, 1973; Mathiowetz & Groves, 1985; Todorov, 2003). The research has shown that proxies have difficulty measuring another person s behaviors or disabilities because they have a different perception of the behavior or disability when it is not their own. The availability of the information also can be an issue when using proxies because they may not have the direct knowledge to accurately respond about another person s behavior or opinions. Proxies were limited to cases where the selected household member had a long-term or permanent physical or mental impairment. Of the more than 42,000 cases in the final data file, fewer than 1% were completed by proxy. Unrelated to the adult section, the child section was always by proxy. The inability to verify the information collected and the reliance on self-reported insurance status and health behaviors is another limitation of the study. Although both live and recorded interviewer monitoring verified the information as recorded by the interviewers, this survey s protocols did not allow for the verification of respondent s insurance status by obtaining a copy of their insurance card. Research has shown that differences occur when comparing claims data and medical records to self-reported information provided in a telephone survey (Fowles et al., 1999). The above limitations as they relate to the ability to use the 2015 OMAS data are standard to any RDD telephone survey in that: G-2 Methodology Report
3 The data can only be generalized to the population surveyed (i.e., the information cannot be generalized to households without telephones). Comparisons made to other data sources for Ohio must be done with the understanding that differences in the data could result from differences in the how the survey was designed and conducted not necessarily because of actual differences in the population of interest. To maximize coverage when conducting a telephone study, using a dual frame of landline and cell phone numbers is necessary. The 2015 OMAS used an overlapping dual frame design. This design included respondents who could have been captured from either frame. This poses several methodological challenges related to a person with both a landline and cell phone having multiple chances of being selected. As discussed in the section on weighting, the 2015 OMAS used a single-frame estimation technique to account for this overlap and to ensure proper weights for inference to the target population. When considering subpopulations sizes with OMAS data analysis, the OMAS OMAS EC recommends using the National Center for Health Statistics guidelines for health surveillance suppression of cell sizes of 10 or less to protect against the likelihood of a breach of potential identification ( G.3 Survey Dispositions The following presents the final dispositions for the entire study overall, and by region stratum, and county. For details, see Tables G-1 through G Interview 1.2 Partial Interview 1.3 Refusals 2.2 Noncontact 3.1 Unknown, No Answer 3.2 Unknown Household 3.9 Unknown Other 4.2 Fax/Data Line 4.3 Nonworking, Disconnected Number 4.4 Tech Circumstance (incl. Changed Number, Cellular s, Pagers) 4.5 Nonresidence (incl. Businesses, Dorms) 4.7 No Eligible Respondent (incl. No Adults, Not Qualified for Oversample) Methodology Report G-3
4 G-4 Methodology Report Table G-1. Final Dispositions Overall Landline 15,279 1,372 24, ,471 76,809 7,346 14, ,250 74,441 Cell 24,453 2,264 32,536 9,047 3,745 46,260 92, , ,990 96,705 Overall 39,732 3,636 56,699 9,643 3,782 67, ,361 7,516 39, , ,146 Table G-2. Medicaid Region No. Final Dispositions by Medicaid Region Sampling Medicaid Region North Central 2, , ,643 11, , ,343 15,463 2 Northeast 10,845 1,045 16,232 3,221 1,199 21,830 53,923 2,576 13, ,118 44,649 3 Northeast Central 3, , ,610 15, , ,192 11,733 4 Northwest 2, , ,899 9, , ,018 8,466 5 South Central 7, ,881 1, ,208 28,716 1,323 6, ,996 28,969 6 Southeast 3, , ,559 12, , ,707 12,169 7 Southwest 9, ,404 2, ,982 37,960 1,770 7, ,866 49,697 Table G-3. Final Dispositions by County Type Region No. Sampling County 1 Rural Appalachian 6, ,554 1, ,354 23, , ,110 24,217 2 Metro 20,877 1,962 32,999 5,803 2,179 40, ,785 4,982 24, , ,330 3 Rural Non-Appalachian 5, ,028 1, ,830 21, , ,024 22,690 4 Suburban 6, ,118 1, ,277 21,556 1,046 5, ,658 21,909 Appendix G: Data Usage 2015 Ohio Medicaid Assessment Survey
5 Methodology Report G-5 Final Disposition by Sampling 1 Adams County LL Allen County LL Ashland County LL Ashtabula County LL Athens County LL Auglaize County LL Belmont County LL Brown County LL Butler County Hispanic Surname LL Butler County Asian Surname LL Butler County LL , ,072 2, Carroll County LL Champaign County LL Clark County LL Clermont County Asian Surname LL Clermont County LL , Clinton County LL Columbiana County LL Coshocton County LL Crawford County LL
6 Methodology Report G-6 Final Disposition by Sampling 21 Cuyahoga County Hispanic Surname 22 Cuyahoga County Asian Surname 23 Cuyahoga County AA Low 24 Cuyahoga County AA Medium 25 Cuyahoga County AA High LL , ,225 LL , LL ,187 4, ,663 3,048 LL , ,263 1,226 LL , Darke County LL Defiance County LL Delaware County Asian Surname LL Delaware County LL , Erie County LL Fairfield County LL , Fayette County LL Franklin County Hispanic Surname LL Franklin County Asian Surname LL , Franklin County AA Low LL , ,917 1,773
7 Methodology Report G-7 Final Disposition by Sampling 36 Franklin County AA Medium 37 Franklin County AA High LL , ,040 LL , , Fulton County LL Gallia County LL Geauga County LL Greene County Asian Surname LL Greene County LL Guernsey County LL Hamilton County Hispanic Surname LL Hamilton County Asian Surname LL Hamilton County AA Low 47 Hamilton County AA Medium 48 Hamilton County AA High LL , ,548 LL , ,908 LL , ,710 7, Hancock County LL Hardin County LL Harrison County LL Henry County LL
8 Methodology Report G-8 Final Disposition by Sampling 53 Highland County LL Hocking County LL Holmes County LL Huron County LL Jackson County LL Jefferson County LL Knox County LL Lake County Hispanic Surname LL Lake County Asian Surname LL Lake County LL , Lawrence County LL Licking County LL , , Logan County LL Lorain County Hispanic Surname LL Lorain County Asian Surname LL Lorain County LL , Lucas County Hispanic Surname LL Lucas County Asian Surname LL
9 Methodology Report G-9 Final Disposition by Sampling 71 Lucas County AA Low LL , ,035 1, Lucas County AA Medium LL Lucas County AA High LL , Madison County LL Mahoning County Hispanic Surname LL Mahoning County LL , Marion County LL Medina County LL Meigs County LL Mercer County LL Miami County LL Monroe County LL Montgomery County Hispanic Surname LL Montgomery County LL Montgomery County AA Low 86 Montgomery County AA Medium 87 Montgomery County AA High LL , ,052 LL LL , ,101
10 Methodology Report G-10 Final Disposition by Sampling 88 Morgan County LL Morrow County LL Muskingum County LL Noble County LL Ottawa County LL Paulding County LL Perry County LL Pickaway County LL Pike County LL Portage County LL , Preble County LL Putnam County LL Richland County LL Ross County LL Sandusky County LL Scioto County LL Seneca County LL Shelby County LL Stark County Hispanic Surname LL Stark County Asian Surname LL Stark County LL , , ,
11 Methodology Report G-11 Final Disposition by Sampling 109 Summit County Hispanic Surname LL Summit County Asian Surname LL Summit County LL , ,322 5, , ,926 2, Trumbull County LL , Tuscarawas County LL Union County LL Van Wert County LL Vinton County LL Warren County Asian Surname LL Warren County LL , , Washington County LL Wayne County LL Williams County LL Wood County LL Wyandot County LL Adams County CELL Allen County CELL , , Ashland County CELL Ashtabula County CELL , , Athens County CELL , ,067
12 Methodology Report G-12 Final Disposition by Sampling 129 Auglaize County CELL Belmont County CELL Brown County CELL Butler County CELL Champaign County CELL Clark County CELL Clermont County CELL Clinton County CELL Columbiana County CELL Coshocton County CELL Crawford County CELL Cuyahoga County AA Low 142 Cuyahoga County AA High CELL ,367 CELL 1, , ,084 5, , , Darke County CELL Defiance County CELL Delaware County CELL Erie County CELL Fairfield County CELL Fayette County CELL Franklin County AA Low CELL
13 Methodology Report G-13 Final Disposition by Sampling 150 Franklin County AA High CELL 2, , ,021 6, , ,019 7, Fulton County CELL Gallia County CELL Geauga County CELL Greene County CELL Guernsey County CELL Hamilton County CELL 2, , ,625 8, , ,254 9, Hancock County CELL , , Hardin County CELL Harrison County CELL Henry County CELL Highland County CELL Hocking County CELL Holmes County CELL Huron County CELL Jackson County CELL Jefferson County CELL Knox County CELL , , Lake County CELL , ,991 3, , Lawrence County CELL Licking County CELL , Logan County CELL
14 Methodology Report G-14 Final Disposition by Sampling 172 Lorain County CELL ,202 2, , Lucas County CELL 1, , ,451 5, , , Madison County CELL Mahoning County CELL ,239 2, , Marion County CELL Medina County CELL , , Meigs County CELL Mercer County CELL , , Miami County CELL Monroe County CELL Montgomery County CELL 1, , ,437 6, , , Morgan County CELL Morrow County CELL Muskingum County CELL , Noble County CELL Ottawa County CELL Paulding County CELL Perry County CELL Pickaway County CELL Pike County CELL Portage County CELL Preble County CELL
15 Methodology Report G-15 Final Disposition by Sampling 194 Putnam County CELL Richland County CELL , , Ross County CELL , , Sandusky County CELL Scioto County CELL , , Seneca County CELL Shelby County CELL Stark County CELL , ,629 3, , , Summit County CELL 1, , ,937 5, , , Trumbull County CELL Tuscarawas County CELL , Union County CELL Van Wert County CELL Warren County CELL Washington County CELL Wayne County CELL , , Williams County CELL Wood County CELL Wyandot County CELL
16 Methodology Report G-16
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