This US Census Bureau report highlights the increasing poverty and racial inequality in America. In 2004, 45.8 million people did not have health insurance. The poverty rate increased from 12.5 percent in 2003 to 12.7 percent in 2004; 1.1 million more people - a total of 37 million - lived in poverty in 2004. This rise in poverty came despite economic growth and marks the first time on record that household incomes had failed to increase for five straight years. Black households had the lowest median income; 32.7 percent of the Hispanic population was without health insurance.

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Income, Poverty, and Health Insurance Coverage in the United States: 2004

Participation rates in epidemiologic studies.

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Income and Poverty in the United States: 2013

These guidelines have been replaced by British Guideline on the Management of Asthma. A national clinical guideline. Superseded By 2012 Revision Of 2008 Guideline: British Guideline on the Management of Asthma. Thorax 2008 May; 63(Suppl 4): 1–121.

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British guideline on the management of asthma: A national clinical guideline

BACKGROUND: Postal and electronic questionnaires are widely used for data collection in epidemiological studies but non-response reduces the effective sample size and can introduce bias. Finding ways to increase response to postal and electronic questionnaires would improve the quality of health research. OBJECTIVES: To identify effective strategies to increase response to postal and electronic questionnaires. SEARCH STRATEGY: We searched 14 electronic databases to February 2008 and manually searched the reference lists of relevant trials and reviews, and all issues of two journals. We contacted the authors of all trials or reviews to ask about unpublished trials. Where necessary, we also contacted authors to confirm methods of allocation used and to clarify results presented. We assessed the eligibility of each trial using pre-defined criteria. SELECTION CRITERIA: Randomised controlled trials of methods to increase response to postal or electronic questionnaires. DATA COLLECTION AND ANALYSIS: We extracted data on the trial participants, the intervention, the number randomised to intervention and comparison groups and allocation concealment. For each strategy, we estimated pooled odds ratios (OR) and 95% confidence intervals (CI) in a random-effects model. We assessed evidence for selection bias using Egger's weighted regression method and Begg's rank correlation test and funnel plot. We assessed heterogeneity among trial odds ratios using a Chi(2) test and the degree of inconsistency between trial results was quantified using the I(2) statistic. MAIN RESULTS: PostalWe found 481 eligible trials. The trials evaluated 110 different ways of increasing response to postal questionnaires. We found substantial heterogeneity among trial results in half of the strategies. The odds of response were at least doubled using monetary incentives (odds ratio 1.87; 95% CI 1.73 to 2.04; heterogeneity P < 0.00001, I(2) = 84%), recorded delivery (1.76; 95% CI 1.43 to 2.18; P = 0.0001, I(2) = 71%), a teaser on the envelope - e.g. a comment suggesting to participants that they may benefit if they open it (3.08; 95% CI 1.27 to 7.44) and a more interesting questionnaire topic (2.00; 95% CI 1.32 to 3.04; P = 0.06, I(2) = 80%). The odds of response were substantially higher with pre-notification (1.45; 95% CI 1.29 to 1.63; P < 0.00001, I(2) = 89%), follow-up contact (1.35; 95% CI 1.18 to 1.55; P < 0.00001, I(2) = 76%), unconditional incentives (1.61; 1.36 to 1.89; P < 0.00001, I(2) = 88%), shorter questionnaires (1.64; 95% CI 1.43 to 1.87; P < 0.00001, I(2) = 91%), providing a second copy of the questionnaire at follow up (1.46; 95% CI 1.13 to 1.90; P < 0.00001, I(2) = 82%), mentioning an obligation to respond (1.61; 95% CI 1.16 to 2.22; P = 0.98, I(2) = 0%) and university sponsorship (1.32; 95% CI 1.13 to 1.54; P < 0.00001, I(2) = 83%). The odds of response were also increased with non-monetary incentives (1.15; 95% CI 1.08 to 1.22; P < 0.00001, I(2) = 79%), personalised questionnaires (1.14; 95% CI 1.07 to 1.22; P < 0.00001, I(2) = 63%), use of hand-written addresses (1.25; 95% CI 1.08 to 1.45; P = 0.32, I(2) = 14%), use of stamped return envelopes as opposed to franked return envelopes (1.24; 95% CI 1.14 to 1.35; P < 0.00001, I(2) = 69%), an assurance of confidentiality (1.33; 95% CI 1.24 to 1.42) and first class outward mailing (1.11; 95% CI 1.02 to 1.21; P = 0.78, I(2) = 0%). The odds of response were reduced when the questionnaire included questions of a sensitive nature (0.94; 95% CI 0.88 to 1.00; P = 0.51, I(2) = 0%).ElectronicWe found 32 eligible trials. The trials evaluated 27 different ways of increasing response to electronic questionnaires. We found substantial heterogeneity among trial results in half of the strategies. The odds of response were increased by more than a half using non-monetary incentives (1.72; 95% CI 1.09 to 2.72; heterogeneity P < 0.00001, I(2) = 95%), shorter e-questionnaires (1.73; 1.40 to 2.13; P = 0.08, I(2) = 68%), including a statement that others had responded (1.52; 95% CI 1.36 to 1.70), and a more interesting topic (1.85; 95% CI 1.52 to 2.26). The odds of response increased by a third using a lottery with immediate notification of results (1.37; 95% CI 1.13 to 1.65), an offer of survey results (1.36; 95% CI 1.15 to 1.61), and using a white background (1.31; 95% CI 1.10 to 1.56). The odds of response were also increased with personalised e-questionnaires (1.24; 95% CI 1.17 to 1.32; P = 0.07, I(2) = 41%), using a simple header (1.23; 95% CI 1.03 to 1.48), using textual representation of response categories (1.19; 95% CI 1.05 to 1.36), and giving a deadline (1.18; 95% CI 1.03 to 1.34). The odds of response tripled when a picture was included in an e-mail (3.05; 95% CI 1.84 to 5.06; P = 0.27, I(2) = 19%). The odds of response were reduced when "Survey" was mentioned in the e-mail subject line (0.81; 95% CI 0.67 to 0.97; P = 0.33, I(2) = 0%), and when the e-mail included a male signature (0.55; 95% CI 0.38 to 0.80; P = 0.96, I(2) = 0%). AUTHORS' CONCLUSIONS: Health researchers using postal and electronic questionnaires can increase response using the strategies shown to be effective in this systematic review.

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Methods to increase response to postal and electronic questionnaires

Survey researchers are rightly concerned with measuring the level of potential bias in estimates generated from the surveys.2 Bias in estimates can result from measurement error, processing/editing error, coverage error, and nonresponse error (Federal Committee on Statistical Methodology [FCSM] 2001). It is nonresponse bias that is the focus of this editorial and it is also the subject of the paper by Halbesleben and Whitman (2013) that this editorial accompanies.

Nonresponse bias is a perennial concern for survey researchers as not everyone we attempt to include in our surveys responds. And to the extent that nonrespondents are different from respondents on the key variables the survey was designed to study these differences could bias the very estimates the survey was designed to make. Because we often have very little information about those who do not respond, survey researchers have long focused on the response rate as a key indicator of survey quality. The assumption is that the more nonresponse there is in a survey, the higher the potential for nonresponse bias. Unfortunately, this assumption has served as a problematic diversion for survey research from the real concern of how survey nonresponse potentially biases survey estimates.

Nonresponse rates are a problematic indicator of nonresponse bias in survey research

Objectives—This report presents state-level estimates of the percentage of adults and children living in households that did not have a landline telephone but did have at least one wireless telephone. National estimates for the 12-month time period from July 2009 through June 2010 indicate that 23.9% of adults and 27.5% of children were living in these wireless-only households. Estimates are also presented for selected U.S. counties and groups of counties, for other household telephone service use categories (e.g., those that had only landlines and those that had landlines yet received all or almost all calls on wireless telephones), and for 12-month time periods since January–December 2007. Methods—Small-area statistical modeling techniques were used to estimate the prevalence of adults and children living in households with various household telephone service types for 93 disjoint geographic areas that make up the entire United States. This modeling was based on January 2007–June 2010 data from

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Wireless substitution: state-level estimates from the National Health Interview Survey, January 2007-June 2010.

Objectives—This report updates subnational estimates of the percentage of adults and children living in households that do not have a landline telephone but have at least one wireless telephone (i.e., wireless-only households). State-level estimates for 2012 are presented, along with estimates for selected U.S. counties and groups of counties, for other household telephone service use categories (e.g., those that had only landlines and those that had landlines yet received all or almost all calls on wireless telephones), and for one earlier 12-month period (July 2011–June 2012). Methods—Small-area statistical modeling techniques were used to estimate the prevalence of adults and children living in households with various household telephone service types for 93 disjoint geographic areas that make up the United States. This modeling was based on 2007–2012 data from the National Health Interview Survey, 2006–2011 data from the American Community Survey, and auxiliary information on the number of listed telephone lines per capita in 2007–2012. Results—The prevalence of wireless-only adults and children varied substantially across states. State-level estimates for 2012 ranged from 19.4% (New Jersey) to 52.3% (Idaho) of adults and from 20.6% (New Jersey) to 63.4% (Mississippi) of children.

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Wireless substitution: state-level estimates from the National Health Interview Survey, 2010-2011.

Transition to Adulthood : Delays and Unmet Needs among Adolescents and Young Adults with Asthma

Objectives—This report presents state-level estimates of the percentage of households that do not have a landline telephone, but do have at least one wireless telephone. These wireless-only households made up 14.7% of U.S. households in 2007. The report also presents state-level estimates of the percentage of adults living in wireless-only households. These wireless-only adults made up 13.6% of U.S. adults in 2007. Methods—A two-sample modeling strategy was used to estimate the prevalence of wireless-only households and adults by state. This modeling was based on data from the 2007 National Health Interview Survey and the 2008 Current Population Survey’s Annual and Social Economic Supplement. Results—The results show that the prevalence of wireless-only households and adults in 2007 varied substantially across states. State-level estimates ranged from 5.1% (Vermont) to 26.2% (Oklahoma) of households and from 4.0% (Delaware) to 25.1% (Oklahoma) of adults. In addition, approximately one out of four adults (25.4%) living in the District of Columbia were wireless-only.

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Michael Davern

Papers

Nonresponse rates are a problematic indicator of nonresponse bias in survey research

Wireless substitution: state-level estimates from the National Health Interview Survey, January 2007-June 2010.

Wireless substitution: state-level estimates from the National Health Interview Survey, 2010-2011.

Transition to Adulthood : Delays and Unmet Needs among Adolescents and Young Adults with Asthma

Wireless substitution: state-level estimates from the National Health Interview Survey, January-December 2007.