On modelling response propensity for dwelling unit (DU) level non-response adjustment in the Medical Expenditure Panel Survey (MEPS)

Non-response is a common problem in household sample surveys. The Medical Expenditure Panel Survey (MEPS), sponsored by the Agency for Healthcare Research and Quality (AHRQ), is a complex national probability sample survey. The survey is designed to produce annual national and regional estimates of health-care use, expenditures, sources of payment, and insurance coverage for the U.S. civilian non-institutionalized population. The MEPS sample is a sub-sample of respondents to the prior year's National Health Interview Survey (NHIS) conducted by the National Center for Health Statistics (NCHS). The MEPS, like most sample surveys, experiences unit, or total, non-response despite intensive efforts to maximize response rates. This paper summarizes research on comparing alternative approaches for modelling response propensity to compensate for dwelling unit (DU), i.e. household level non-response in the MEPS.Non-response in sample surveys is usually compensated for by some form of weighting adjustment to reduce the bias in survey estimates. To compensate for potential bias in survey estimates in the MEPS, two separate non-response adjustments are carried out. The first is an adjustment for DU level non-response at the round one interview to account for non-response among those households subsampled from NHIS for the MEPS. The second non-response adjustment is a person level adjustment to compensate for attrition across the five rounds of data collection. This paper deals only with the DU level non-response adjustment. Currently, the categorical search tree algorithm method, the chi-squared automatic interaction detector (CHAID), is used to model the response probability at the DU level and to create the non-response adjustment cells. In this study, we investigate an alternative approach, i.e. logistic regression to model the response probability. Main effects models and models with interaction terms are both evaluated. We further examine inclusion of the base weights as a covariate in the logistic models. We compare variability of weights of the two alternative response propensity approaches as well as direct use of propensity scores.The logistic regression approaches produce results similar to CHAID; however, using propensity scores from logistic models with interaction terms to form five classification groups for weight adjustment appears to perform best in terms of limiting variability and bias. Published in 2007 by John Wiley & Sons, Ltd.     

Bayesian non-response models for categorical data from small areas: an application to BMD and age

We provide a Bayesian analysis of data categorized into two levels of age (younger than 50 years, at least 50 years) and three levels of bone mineral density (normal, osteopenia, osteoporosis) for white females at least 20 years old in the third National Health and Nutrition Examination Survey. For the sample, the age of each individual is known, but some individuals did not have their BMD measured. We use two types of models: In the ignorable non-response models the propensity to respond does not depend on BMD and age of an individual, while in the non-ignorable non-response models it does. These are the baseline models which are used to derive all models for testing. Our non-ignorable non-response models are 'close' to the ignorable non-response models, thereby reducing the effects of the assumptions about non-respondents that cannot be tested in non-response models. We have data from 35 counties, small areas, and therefore our models are hierarchical, a feature that allows a 'borrowing of strength' across the counties, and they provide a substantial reduction in variation. The non-ignorable non-response models are generalizations of the ignorable non-response models, and therefore, the non-ignorable non-response models allow broader inference. The joint posterior density of the parameters for each model is complex, and therefore, we fit each model using Markov chain Monte Carlo methods to obtain samples which are used to make inference about BMD and age. For each county we can estimate the proportion of individuals in each BMD and age cell of the categorical table, and we can assess the relation between BMD and age using the Bayes factor. A sensitivity analysis shows that there are differences (typically small) in inference that permits different levels of association between BMD and age. A simulation study shows that there is not much difference between the baseline ignorable and non-ignorable non-response models.     

Non-response bias in a sample survey on alcohol consumption

In a non-response follow-up study, non-respondents of the original mailed questionnaire were approached again by house visits in order to compare their alcohol consumption with that of the respondents of the same mailed questionnaire. Differences in alcohol consumption between respondents and non-respondents were found. There is strong evidence for overrepresentation of non-response among abstainers, but weak evidence among frequent excessive drinkers.     

Effect on Trend Estimates of the Difference between Survey Respondents and Non-respondents: Results from 27 Populations in the WHO MONICA Project

Introduction: In the World Health Organization (WHO) MONICA (multinational MONItoring of trends and determinants in CArdiovascular disease) Project considerable effort was made to obtain basic data on non-respondents to community based surveys of cardiovascular risk factors. The first purpose of this paper is to examine differences in socio-economic and health profiles among respondents and non-respondents. The second purpose is to investigate the effect of non-response on estimates of trends. Methods:Socio-economic and health profile between respondents and non-respondents in the WHO MONICA Project final survey were compared. The potential effect of non-response on the trend estimates between the initial survey and final survey approximately ten years later was investigated using both MONICA data and hypothetical data. Results: In most of the populations, non-respondents were more likely to be single, less well educated, and had poorer lifestyles and health profiles than respondents. As an example of the consequences, temporal trends in prevalence of daily smokers are shown to be overestimated in most populations if they were based only on data from respondents. Conclusions: The socio-economic and health profiles of respondents and non-respondents differed fairly consistently across 27 populations. Hence, the estimators of population trends based on respondent data are likely to be biased. Declining response rates therefore pose a threat to the accuracy of estimates of risk factor trends in many countries.     

Predicting risk among non-respondents in prospective studies

Potential non-response bias was investigated in a follow-up study of 2,011 chronically disabled patients. 82.5% and 73.3% of the study subjects responded to self-administered mail questionnaires respectively at 6-month and 1-year follow-up. Information on employment status, the outcome of interest, of approximately 90% of the non-respondents was obtained from indirect sources. Employment rate was lower among the non-respondents than the respondents. Non-response was associated with age, social class, previous employment record, and the type of disability; but none of these characteristics were associated with the outcome. Out of the five known independent risk factors for unemployment, only one (incompletion of rehabilitation course) was associated with non-response. The employment rate among the respondents was also assessed according to the delay in response, that is the number of reminders sent to achieve response. The outcome among- the late respondents was similar to that among the nonrespondents. These data suggest that (a) risk estimates may be biased even when the response rate is greater than 80%, (b) the prevalence of risk factors among non-respondents may not indicate the presence or the degree of non-response bias, but (c) reliable estimates can be obtained from extrapolations of the rates among the respondents according to the delay in response.     

Does household enrolment reduce adverse selection in a voluntary health insurance system? Evidence from the Ghanaian National Health Insurance System

In August 2003, the Ghanaian Government made history by implementing the first National Health Insurance System (NHIS) in sub-Saharan Africa. Within 2 years, over one-third of the country had voluntarily enrolled in the NHIS. To discourage households from selectively enrolling their sickest (high-risk) members, the NHIS in the Nkoranza district offered premium waivers for all children under 18 in exchange for full household enrolment. This study aimed to test whether, despite this incentive, there is evidence suggestive of adverse selection. To accomplish this, we examined how the observed pay-off from insurance (odds and intensity of medical consumption) responds to changes in the family enrolment cost. If adverse selection were present, we would expect the odds and intensity of medical consumption to increase with family enrolment cost. A number of econometric tests were conducted using the claims database of the NHIS in Nkoranza. Households with full enrolment were analysed, for a total of 58?516 individuals from 12?515 households. Our results show that household enrolment cost is not correlated with (1) odds or intensity of inpatient use or (2) odds of adult outpatient use, and is weakly correlated with the intensity of outpatient use. We also find that household enrolment costs are positively correlated with the number of children in the household and the odds and intensity of outpatient use by children. Thus, we conclude that the child-premium waiver is an important incentive for household enrolment. This evidence suggests that adverse selection has effectively been contained, but not eliminated. We argue that since one of the main objectives of the NHIS was to increase use of necessary care, especially by children, our findings indicate a largely favourable policy outcome, but one that may carry negative financial consequences. Policy makers must balance the fiscal need to contain costs with the societal objective to cover vulnerable populations.     

Assessing response bias in random-digit dialling surveys: the telephone-prefix method.

Knowledge of the characteristics of survey non-respondents is important to determine generalizability to the population of interest. In a recent random-digit dialling survey of health behaviours only 73 per cent of the households contacted provided any information about household composition, and only 74 per cent of those actually completed the extended interview, for an overall response rate of 54 per cent. To identify possible biases we grouped all attempted phone numbers by their prefix, and looked for the association between the response rate for that prefix and other summary variables known about the prefix. A simulation study showed that the method can identify non-response biases if certain assumptions are correct. The analysis suggested that our survey data under-represent older people and those with a college education. We found no significant biases in health behaviours, possibly because the basic assumptions did not hold. This method may assist in identification of non-response bias in other studies.     

Medical consumption of respondents and non-respondents to a mailed health survey

Non-response bias can distort the results of health surveys.The occurrence of selective non-response can be assessed whendata are available for both respondents and non-respondents.The objective of this study was to compare the medical consumptionof respondents and non-respondents to a mailed health survey.A mailed health survey was conducted among approximately 13,500adults and among parents of approximately 1,500 children aged5–15 years. The net response rate was 70.4%. A panel dataset that could be matched with the health survey data was availablefor all eligible persons. This data set comprises administrativeinformation on hospitalizations, annual health care expendituresand demographic variables. The results of this study show thatresponse was associated with age, sex, degree of urbanizationand type of insurance. After correcting for differences in demographicvariables, respondents and non-respondents differ in the utilizationof several types of care. Relatively more users than non-usersresponded. Response was not associated with the utilizationof care related to severe conditions such as in-patient hospitalcare. The conclusion from this study is that when a mailed healthsurvey is used to measure medical consumption, the non-responsebias will result in a small overestimation of utilization.     

Non-response and related factors in a nation-wide health survey

Objective: To analyse selective factors associated with an unexpectedly low response rate. Subjects and methods: The baseline questionnaire survey of a large prospective follow-up study on the psychosocial health of the Finnish working-aged randomly chosen population resulted in 21,101 responses (40.0%) in 1998. The non-respondent analysis used demographic and health-related population characteristics from the official statistics and behavioural, physical and mental health-related outcome differences between early and late respondents to predict possible non-response bias. Reasons for non-response, indicated by missing responses of late respondents, and factors affecting the giving of consent were also analysed. Results: The probability of not responding was greater for men, older age groups, those with less education, divorced and widowed respondents, and respondents on disability pension. The physical health-related differences between the respondents and the general population were small and could be explained by differences in definitions. The late respondents smoked and used more psychopharmaceutical drugs than the early ones, suggesting similar features in non-respondents. The sensitive issues had a small effect on the response rate. The consent to use a medical register-based follow-up was obtained from 94.5% of the early and 90.9% of the late respondents (odds ratio: 1.70; 95% confidence interval: 1.49–1.93). Consent was more likely among respondents reporting current smoking, heavy alcohol use, panic disorder or use of tranquillisers. Conclusions: The main reasons for non-response may be the predisposing sociodemographic and behavioural factors, the length and sensitive nature of the questionnaire to some extent, and a suspicion of written consent and a connection being made between the individual and the registers mentioned on the consent form.     

On weighting the rates in non-response weights

A basic estimation strategy in sample surveys is to weight units inversely proportional to the probability of selection and response. Response weights in this method are usually estimated by the inverse of the sample-weighted response rate in an adjustment cell, that is, the ratio of the sum of the sampling weights of respondents in a cell to the sum of the sampling weights for respondents and non-respondents in that cell. We show by simulations that weighting the response rates by the sampling weights to adjust for design variables is either incorrect or unnecessary. It is incorrect, in the sense of yielding biased estimates of population quantities, if the design variables are related to survey non-response; it is unnecessary if the design variables are unrelated to survey non-response. The correct approach is to model non-response as a function of the adjustment cell and design variables, and to estimate the response weight as the inverse of the estimated response probability from this model. This approach can be implemented by creating adjustment cells that include design variables in the cross-classification, if the number of cells created in this way is not too large. Otherwise, response propensity weighting can be applied.     

Cohort reconstruction: which infants can be restudied at school age?

Longer-term follow-up of infants with specific health concerns, such as low birthweight, is critical to assessing the effect of medical interventions. This report examines the approach of reconstructing previously studied cohorts in terms of the factors discriminating between respondents and non-respondents. Follow-up was attempted during 1987-1988 for 1875 children born during a 6-month period in 1978 in three geographically defined regions in the United States, for whom 1-year assessments of health and developmental status were obtained at 1 year of age as part of a previous study. For a 25% sample, participation involved a clinic visit for developmental assessments; for the remainder an interview by telephone or home visit. Follow-up was obtained for 72.5% of the cohort. Refusal rates were low (7%); most non-response was due to an inability to locate the families. Predictors of non-response reflected primarily low socio-economic status; completion rates were not influenced by mode of assessment. The role of a tracing agency is discussed. We conclude that cohort reconstruction is feasible with response rates comparable to some prospective studies with ongoing cohort maintenance.     

Marital status,educational level and household income explain part of the excess mortality of survey non-respondents

Survey respondents and non-respondents differ in their demographic and socio-economic position. Many of the health behaviours are also known to be associated with socio-economic differences. We aimed to investigate how much of the excess mortality of survey non-respondents can be explained by the socio-economic differences between respondents and non-respondents. Questionnaire-based adult health behaviour surveys have been conducted in Finland annually since 1978. Data from the 1978 to 2002 surveys, including non-respondents, were linked with mortality data from the Finnish National Cause of Death statistics and with demographic and socio-economic register data (marital status, education and household income) obtained from Statistics Finland. The mortality follow-up lasted until 2006, in which period there were 12,762 deaths (7,994 in men and 4,768 in women) during the follow-up. Total and cause-specific mortality were higher among non-respondents in both men and women. Adjusting results for marital status, educational level and average household income decreased the excess total and cause-specific mortality of non-respondents in both men and women. Of the total excess mortality of non-respondents, 41% in men and 20% in women can be accounted for demographic and socio-economic factors. A part of the excess mortality among non-respondents can be accounted for their demographic and socio-economic characteristics. Based on these results we can assume that non-respondents tend to have more severe health problems, acute illnesses and unhealthy behaviours, such as smoking and excess alcohol use. These can be reasons for persons not taking part in population surveys.     

Does survey non-response bias the association between occupational social class and health?

AIMS: A non-response rate of 20-40%is typical in questionnaire studies. The authors evaluate non-response bias and its impact on analyses of social class inequalities in health. METHODS: Set in the context of a health survey carried out among the employees of the City of Helsinki (non-response 33%) in 2000-02. Survey response and non-response records were linked with a personnel register to provide information on occupational social class and long sickness absence spells as an indicator of health status. RESULTS: Women and employees in higher occupational social classes were more likely to respond. Non-respondents had about 20-30% higher sickness absence rates. Relative social class differences in sickness absence in the total population were similar to those among either respondents or non-respondents. CONCLUSIONS: In working populations survey non-response does not seriously bias analyses of social class inequalities in sickness absence and possibly health inequalities more generally.     

Cohort reconstruction: which infants can be restudied at school age?

Summary. Longer-term follow-up of infants with specific health concerns, such as low birthweight, is critical to assessing the effect of medical interventions. This report examines the approach of reconstructing previously studied cohorts in terms of the factors discriminating between respondents and non-respondents. Follow-up was attempted during 1987–1988 for 1875 children born during a 6-month period in 1978 in three geographically defined regions in the United States, for whom 1-year assessments of health and developmental status were obtained at 1 year of age as part of a previous study. For a 25% sample, participation involved a clinic visit for developmental assessments; for the remainder an interview by telephone or home visit. Follow-up was obtained for 72.5% of the cohort. Refusal rates were low (7%); most non-response was due to an inability to locate the families. Predictors of non-response reflected primarily low socio-economic status; completion rates were not influenced by mode of assessment. The role of a tracing agency is discussed. We conclude that cohort reconstruction is feasible with response rates comparable to some prospective studies with ongoing cohort maintenance.     

Generalizability of valuations on health states collected with the EuroQolc-questionnaire

Problem: Non-response and non-usable response were found in population surveys on valuation of health states. If non-response is selective regarding valuations, then generalization of the resulting values to the whole survey population is not permitted. This could limit the use of empirical utility values in resource allocation in health care. Methods: Response behaviour of a sample of 1400 from the Dutch general population to the mailed EuroQol^c-questionnaire was analyzed by four methods. I. Phoning resolute non-respondents; II. comparison of zip code characteristics of respondents and non-respondents (because individual data on background characteristics were not available for the non-respondents); III. analysis of response over time (wave-analysis); IV: comparison of background variables of successful (less than two valuations missing) and unsuccessful respondents, combined with analysis of the effect of these background variables on valuations. Results: No indications for selective non-response were found, although the phenomenon appeared hard to investigate. The successful response came from a slightly younger and better educated subsample. However, a general influence of age and educational level on valuations could not be shown. This finding is consistent with the literature. Conclusion: Although the existence of selective non-response cannot be excluded, its relevance can be considered to be small. This finding is encouraging for the use of empirical utility values in allocative decisions.     

Selection bias due to non-response in a health survey among patients with rheumatoid arthritis

BACKGROUND: Non-response may lead to bias in health(care) outcomes. METHODS: We compared respondents (n = 334) to a questionnaire survey among patients with rheumatoid arthritis with non-respondents (n = 68) and determined predictors of (non-)response. The bias in prevalence estimates of health characteristics and health care use was quantified. RESULTS: Self-reported pain and health care utilization were the most important predictors of (non-)response with respondents experiencing pain more often and more often using specific health care services. Bias concerned especially an underestimation of 'never having pain' (60%) and 'no contact with health care services' (51%). CONCLUSION: More insight into the phenomenon of non-response is important to assess disease burden and health care burden more precisely.     

Results and their implications from comparing respondents and proxy responses for non-respondents with cognitive difficulties on a telephone survey

Background

Limited study has been done on proxy responses for non-respondents with subjective cognitive decline (SCD) on the Behavioral Risk Factor Surveillance System (BRFSS).

Postal survey on airborne occupational exposure and respiratory disorders in Norway: causes and consequences of non-response.

STUDY OBJECTIVE--The aim was to examine causes for non-response in a community survey, and how non-response influences prevalence estimates of some exposure and disease variables, and associations between the variables. DESIGN--This was a cross sectional questionnaire study with two reminder letters. The questionnaire asked for information on smoking habits, occupational airborne exposure and respiratory disorders. SETTING--A random sample of 4992 subjects from the general population aged 15-70 years of Hordaland County, Norway. MAIN RESULTS--The overall response rate was 90%, with a 63% response to the initial letter. The response rates to the first and second reminder letters were 56% and 36% respectively. In 20% of the non-respondents an uncompleted questionnaire was returned with cause for non-response; in two thirds of these the cause for non-response was that the subject was not resident at the mailing address. A home visit to a random sample of 50 urban non-respondents provided further information on 29 subjects. A wrong address at the Central Population Registry and the subject's feeling of lack of personal benefit from a postal survey were the major reasons for non-response. Smokers were late respondents and subjects with respiratory disorders tended to be early respondents. CONCLUSION--The main reasons for non-response were a wrong mailing address and a feeling of lack of personal benefit from responding. Using only the initial letter would have changed the estimated prevalence of smokers from 39% to 35%. Otherwise, the estimated prevalence of the exposure and disease variables as well as the associations between them were only slightly changed after including the respondents to the first and second reminder letters.     

Is Ghana’s pro-poor health insurance scheme really for the poor? Evidence from Northern Ghana

Background

Protecting the poor and vulnerable against the cost of unforeseen ill health has become a global concern culminating in the 2005 World Health Assembly resolution urging member states to ensure financial protection to all citizens, especially children and women of reproductive age. Ghana provides financial protection to its citizens through the National Health Insurance Scheme (NHIS). Launched in 2004, its proponents claim that the NHIS is a pro-poor financial commitment that implements the World Health Assembly resolution.

Methods

Using 2011 survey data collected in seven districts in northern Ghana from 5469 women aged 15 to 49 the paper explores the extent to which poor child-bearing age mothers are covered by the NHIS in Ghana’s poorest and most remote region. Factors associated with enrolment into the NHIS are estimated with logistic regression models employing covariates for household relative socio-economic status (SES), location of residence and maternal educational attainment, marital status, age, religion and financial autonomy.

Results

Results from the analysis showed that 33.9 percent of women in the lowest SES quintile compared to 58.3 percent for those in the highest quintile were insured. About 60 percent of respondents were registered. However, only 40 percent had valid insurance cards indicating that over 20 percent of the registered respondents did not have insurance cards. Thus, a fifth of the respondents were women who were registered but unprotected from the burden of health care payments. Results show that the relatively well educated, prosperous, married and Christian respondents were more likely to be insured than other women. Conversely, women living in remote households that were relatively poor or where traditional religion was practised had lower odds of insurance coverage.

Conclusion

The results suggest that the NHIS is yet to achieve its goal of addressing the need of the poor for insurance against health related financial risks. To ultimately attain adequate equitable financial protection for its citizens, achieve universal health coverage in health care financing, and fully implement the World Health Assembly resolution, Ghana must reform enrolment policies in ways that guarantee pre-payment for the most poor and vulnerable households.

     

Characteristics of non-response in the Danish Health Interview Surveys, 1987-1994

BACKGROUND: The types and quantity of non-response in surveys influence the extent to which the results may be generalized. This study analysed trends in non-response in the Danish Health Interview Surveys from 1987 to 1994 and used the National Patient Registry to assess whether non-response biased the estimated population prevalence of morbidity when solely based on responders. METHODS: The data were for the 23,096 adults sampled for the Danish Health Interview Surveys in 1987, 1991 and 1994. All were followed using the National Patient Registry to obtain such information as hospital admissions. RESULTS: Non-response increased from 20.0% in 1987 to 22.6% in 1994. Four combinations of background variables characterized the non-response: gender and age; gender and civil status; county of residence and age; survey year and age. Non-respondents and respondents had identical gender- and age-standardized hospital admission rates for approximately 5 years before and 2 years after data collection, but non-respondents had a significantly higher rate immediately before and during data collection. Admissions rates were analysed according to reasons for non-response. Refusers had a lower admission rate than respondents before data collection but similar during and after data collection. The rate was higher during the whole period among ill or disabled non-respondents. Among people who could not be contacted during the data collection period a higher admission rate was only found immediately before and during data collection. CONCLUSIONS: Although admission rates differed between respondents and non-respondents these differences were too small to bias the estimated population prevalence of morbidity when solely based on respondents.