Telephone survey: post-stratification adjustments to compensate non-coverage bias in city of Rio Branco, Northern Brazil

Bernal, Regina Tomie Ivata; Malta, Deborah Carvalho; de Araujo, Thiago Santos; Silva, Nilza Nunes da

doi:10.1590/S0034-8910.2013047003798

Abstract

OBJECTIVE

To evaluate the effects of using post-stratification weight to correct the bias due to low coverage of households with telephones.

METHODS

A Comparison was made of results collected by the Household Survey with those of the VIGITEL (Telephone Survey to Monitor Risk and Protective Factors for Chronic Diseases) in Rio Branco, Northern Brazil, in 2007 whose coverage was 40% of landline phones. The potential bias in the VIGITEL survey was expressed by the difference between the rates of prevalence of the VIGITEL and Household Survey, calculated as the square root mean square error (MSE) as a measure of the accuracy of the estimate.

RESULTS

The weighting procedure of VIGITEL corrected potential bias in the prevalence of consumption of fruit and vegetables, meat with visible fat, smoking, bad self-assessment of health status and morbidity of cholesterol or triglycerides. In the prevalence of physical activity in leisure time and morbidity of asthma, bronchial asthma, chronic bronchitis or emphysema, the procedure adopted by VIGITEL did not reduce the potential bias.

CONCLUSIONS

in order to construct post-stratification weights which minimize the potential bias in estimates of the variables due to low coverage of households with landlines, it becomes necessary to use alternative methods of weighting and strategies of selecting external variables.

Health Surveys, methods ; Interviews as Topic ; Bias (Epidemiology) ; Data Collection, methods

INTRODUCTION

The Ministry of Health, Department of Health Monitoring (SVS/MS) established the Telephone Survey System for Monitoring Risk and Protection Factors for Chronic Illness (VIGITEL), in the 26 Brazilian state capitals and the Federal District in 2006. This system collects information on risk factors, such as smoking, excessive consumption of foods high in saturated fat, being overweight, sedentary lifestyle and excessive consumption of alcohol. Factors of protection for which data were collected included information on exercise, fruit and vegetable consumption and cancer prevention. ^aaMinistério da Saúde. VIGITEL Brasil 2006: vigilância de fatores de risco e proteção de doenças crônicas por inquérito telefônico: estimativas sobre frequência e distribuição sócio-demográfica de fatores de risco e proteção para doenças crônicas nas capitais dos 26 estados brasileiros e no Distrito Federal em 2006. Brasília (DF); 2007. (Série G. Estatística e Informação em Saúde).

Six years after its implementation, VIGITEL has been consolidated as a tool for collecting data. The telephone survey has various advantages, such as low operational cost and rapidity in the process of divulging results, compared to the Household Survey.^16,17,22 However, existing scientific production brings to light three main questions: (a) the validity of estimates obtained in the Telephone Survey, due to the exclusion of household without a landline,^2,4,11,18, ^bbKerckhove W, Montaquila JM, Carver PR, Brick JM. An evaluation of bias in the 2007 National Household Education Surveys Program: results from a special data collection effort. Washington (DC): US Department of Education, National Center for Education Statistics; 2008. (NCES 2009-029) [cited 2013 may 15]. Available from: http://nces.ed.gov/pubs2009/2009029_1.pdf ,c (b) the increased lack of response^9, ^ddCDC − Center for Disease Control and Prevention. Behavioral Risk Factor Surveillance System. Atlanta (GA); 2011 [cited 2013 May 14]. Available from: http://www.cdc.gov/brfss/annual_data/annual_2011.htm and (c) the methodological procedures for obtaining valid estimates.^11,12,14, ^ccFlores-Cervantes I, Brick JM, Jones ME, Westat J, DiSogra C, Yen W. Weighting for nontelephone household in the 2001 California Health Interview Survey. In: Proceedings of the Joint Statistical Meetings Section on Survey Research Methods; 2002 Aug 11-15; New York. p.1002-7 [cited 2013 may 14]. Available from: http://www.amstat.org/sections/srms/Proceedings/y2002/Files/JSM2002-000661.pdf

The experience of VIGITEL in Brazil differs from that of other countries, such as the Behavior Risk Factor Surveillance System (BRFSS) ^ddCDC − Center for Disease Control and Prevention. Behavioral Risk Factor Surveillance System. Atlanta (GA); 2011 [cited 2013 May 14]. Available from: http://www.cdc.gov/brfss/annual_data/annual_2011.htm in the United States, with regards to the levels of no response. In the more than twenty years of the use of this methodology in the United States, a decline in the response rate ^ccFlores-Cervantes I, Brick JM, Jones ME, Westat J, DiSogra C, Yen W. Weighting for nontelephone household in the 2001 California Health Interview Survey. In: Proceedings of the Joint Statistical Meetings Section on Survey Research Methods; 2002 Aug 11-15; New York. p.1002-7 [cited 2013 may 14]. Available from: http://www.amstat.org/sections/srms/Proceedings/y2002/Files/JSM2002-000661.pdf ^, ^eeBrick JM, Keeler S, Bell B, Chandler K. National Household Education Survey of 1993: adjusting for coverage bias using telephone service interruption data: technical report. Washington (DC): U.S. Department of Education, National Center for Education Statistics; 1996. (NCES 97-336) [cited 2010 Nov 24]. Available from: http://0-nces.ed.gov.opac.acc.msmc.edu/pubs/97336.pdf in the last decade was observed, whereas VIGITEL progressively reduced the refusal rate (2,5% in the latest edition). This contributes to the reduction in bias and the strengthening of this strategy in the country. ^ffMinistério da Saúde. VIGITEL Brasil 2010: vigilância de fatores de risco e proteção de doenças crônicas por inquérito telefônico: estimativas sobre frequência e distribuição sócio-demográfica de fatores de risco e proteção para doenças crônicas nas capitais dos 26 estados brasileiros e no Distrito Federal em 2010. Brasília (DF); 2011. (Série G. Estatística e Informação em Saúde).

Although the number of households in Brazil with a landline has increased since 2000, the results of the National Household Survey (PNAD) carried out by the Brazilian Institute of Geography and Statistics (IBGE), ^ggInstituto Brasileiro de Geografia e Estatística. Microdados da Pesquisa Nacional por Amostra de Domicílios. Rio de Janeiro; 2007. in 2007, shows estimates of around 33% Palmas, Northern Brazil) and 78% (São Paulo, Southeastern Brazil) of households in urban areas in the capitals served by at least one residential telephone line (RTL). In the municipality of Rio Branco, Northern Brazil, 40% of households had access to a residential telephone. The lowest figures were found in the North and Northeast, (40% and 48%, respectively). In the Midwest, this rate was 57%, whereas the highest figures were in the South and Southeast (71% and 76%, respectively).

Due to the exclusion of the population without a telephone, VIGITEL uses weighting to calibrate the statistical inferences, aiming to correct possible bias introduced by low rates of landline coverage. This study aimed to analyze the effects of the use of post-stratification to correct bias due to low landline coverage.

METHODS

A comparison was made between the rates of prevalence found in household and telephone surveys on risk and protection factors for non-communicable chronic illness. The rates of prevalence of the household survey were considered as population values ^bbKerckhove W, Montaquila JM, Carver PR, Brick JM. An evaluation of bias in the 2007 National Household Education Surveys Program: results from a special data collection effort. Washington (DC): US Department of Education, National Center for Education Statistics; 2008. (NCES 2009-029) [cited 2013 may 15]. Available from: http://nces.ed.gov/pubs2009/2009029_1.pdf as they dealt with a sample of households both with and without a landline. The assumptions of the study are that the rates of prevalence obtained in the household survey show negligible bias and that there is independence between the VIGITEL and the household samples, whose rates of response were 71% and 87% respectively.

The VIGITEL and the household survey were carried out in Rio Branco, AC, in 2007. Around 40% of private residences had at least one telephone line, according to data from the IBGE’s PNAD. ^ggInstituto Brasileiro de Geografia e Estatística. Microdados da Pesquisa Nacional por Amostra de Domicílios. Rio de Janeiro; 2007.

In the household survey, 1,515 adults aged over 18 were interviewed between March 2007 and September 2008 (64% of the interviews were carried out in 2007). VIGITEL interviewed 2,010 adults aged over 18 between July and December 2007. The two studies used the same questionnaire on the subjects of Exercise, Food Intake, Smoking, Alcohol, Perception and Reported Morbidity.

The data from the household survey were used to identify rates of prevalence associated with owning a landline and to characterize the socio-demographic profile of the population excluded from VIGITEL. The first stage of the study consisted in selecting the variables for evaluating the potential bias in VIGITEL. Due to the data collection going on into 2008, it was necessary to carry out a hypothesis test (Test t ) to test the equality of the parameters of the variables obtained in 2007 and 2008 (H₀: P₂₀₀₇ = P₂₀₀₈), with a level of significance of 5%. Of the 29 variables suggested, 18 were selected to study the potential VIGITEL bias. The variables selected were: food intake (beans five or more days/week; fruit, legumes and vegetables (FLV) regularly; recommended FLV; fatty meat; whole milk; soft drinks five or more days/week); physical activity during leisure time; sedentary lifestyle;excessive consumption of alcohol; smoker; ex-smoker; self-evaluation of health as very bad and reported comorbidities (high blood pressure; diabetes, heart attack, stroke or cerebrovascular accident; high cholesterol or triglycerides; asthma, asthmatic bronchitis or emphysema; osteoporosis). The variables (Y) of the study were qualitative and dichotomized (1 = yes; 0 = no).

The adults in the study were divided into two groups; those who had a residential telephone line (RTL) and those who did not. This allowed rates of prevalence associated with ownership of a telephone to be used to test the hypothesis for the difference in means between the populations with and without telephones (5% significance). These data allowed the socio-demographic profile of the adult population excluded from the VIGITEL system to be characterized. The multiple logistic regression model ^hhPaula GA. Modelos de regressão com apoio computacional. São Paulo: Instituto de Matemática e Estatística da USP; 2004. was used (

), in which π(χ) expresses the probability of not having access to an RTL, given the following characteristics x_p (age group, reported skin color, schooling and number of members in the household). The variables which describe the profile of those resident in households without a landline were used to construct post-stratification weightings, to even out potential bias introduced by the sample design. The independent variables are qualitative, and the last category is considered as the reference. The results of the multiple logistic regression are expressed as the odds ratio for a specific category x_p and for the reference. An odds ratio = 1 indicates that the odds are equally probable in both groups. Values > 1 indicate how many times greater the chance is for the first group.

The sample distributions for the household survey and for VIGITEL were adjusted for the same population in order to make comparisons between the two samples. The post-stratification weighting according to age, sex and schooling were used, using data from the 2007 PNAD ^ggInstituto Brasileiro de Geografia e Estatística. Microdados da Pesquisa Nacional por Amostra de Domicílios. Rio de Janeiro; 2007. as an external source of data for constructing the weighting. The VIGITEL system used the weighting per cell method^11, ^ddCDC − Center for Disease Control and Prevention. Behavioral Risk Factor Surveillance System. Atlanta (GA); 2011 [cited 2013 May 14]. Available from: http://www.cdc.gov/brfss/annual_data/annual_2011.htm to obtain post-stratification weighting according to age, sex and schooling, with the aim of smoothing out potential bias because of low landline coverage. These weightings were constructed based on data from the 2000 Census as an external data source, and made available in the database. To control the time effect, new post-stratification weightings were constructed based on PNAD ^ggInstituto Brasileiro de Geografia e Estatística. Microdados da Pesquisa Nacional por Amostra de Domicílios. Rio de Janeiro; 2007. data. These weightings were found using the ratio between relative frequency of the estimated population of the PNAD ^ggInstituto Brasileiro de Geografia e Estatística. Microdados da Pesquisa Nacional por Amostra de Domicílios. Rio de Janeiro; 2007. and the VIGITEL sample in each cell. These frequencies were obtained using their respective sample weightings and those of the variables of the complex sample design. In total, 36 cells were accrued, composed of sex (F;M), age group of 18 to 24, 25 to 34, 35 to 44, 45 to 54, 55 to 64 and 65 and over, and years of schooling of 0 to 8, 9 to 11 and 12 or more. The cell composed of women in the 65 and over age group with 12 or more years of schooling was grouped together with the cell containing women aged between 55 and 64, totaling 35 cells. The final weighting attributed to each individual interviewed by VIGITEL was composed of the weight of the sample multiplied by the post-stratification weightin

The effect of the post-stratification weighting on the variables raised by VIGITEL were expressed by the difference between the prevalence weighted by the sample weighting and the weighted prevalence of the final weighting.

The potential bias of the VIGITEL survey was expressed by the difference in rates of prevalence of the VIGITEL and household surveys (

).⁵5. Ferreira AD, César CC, Malta DC, Andrade ACS, Ramos CGC, Proietti FA, et al. Validade de estimativas obtidas por inquérito telefônico: comparação entre VIGITEL 2008 e Inquérito Saúde em Beagá. Rev Bras Epidemiol. 2011;14(Supl 1):16-30. DOI:10.1590/S1415-790X2011000500003
https://doi.org/10.1590/S1415-790X201100... , ¹³13. Malta DC, Moura EC, Silva SA, Oliveira PPV, Costa e Silva VL. Prevalência do tabagismo em adultos residentes nas capitais dos estados e no Distrito Federal, Brasil, 2008. J Bras Pneumol. 2010;36(1):73-83. DOI:10.1590/S1806-37132010000100013.
https://doi.org/10.1590/S1806-3713201000... The mean square error (MSE) (

)⁵5. Ferreira AD, César CC, Malta DC, Andrade ACS, Ramos CGC, Proietti FA, et al. Validade de estimativas obtidas por inquérito telefônico: comparação entre VIGITEL 2008 e Inquérito Saúde em Beagá. Rev Bras Epidemiol. 2011;14(Supl 1):16-30. DOI:10.1590/S1415-790X2011000500003
https://doi.org/10.1590/S1415-790X201100... , ¹³13. Malta DC, Moura EC, Silva SA, Oliveira PPV, Costa e Silva VL. Prevalência do tabagismo em adultos residentes nas capitais dos estados e no Distrito Federal, Brasil, 2008. J Bras Pneumol. 2010;36(1):73-83. DOI:10.1590/S1806-37132010000100013.
https://doi.org/10.1590/S1806-3713201000... was used to measure total error composed of sample error and systematic error. The square root of MSE (RMSE) gives the expected distance between the VIGITEL rate of prevalence and that of the population value.

RESULTS

Significant differences were found in seven of the 18 variables when comparing the rates of prevalence of the groups who did and did not possess a RTL ( Table 1 ). The group with the RTL differed in the prevalence of regular fruit, legumes and vegetable consumption (FLV), consumption of fatty meat, physical activity during leisure time, smoker, self-evaluation of health as very bad and in the prevalence of reported morbidities of high cholesterol or triglycerides and asthma, asthmatic bronchitis, chronic bronchitis or emphysema.

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Table 1.
Difference in rates of prevalence of the variables in question according to landline ownership in the household survey. Rio Branco, Northern Brazil, 2007.

The odds of an adult not having a RTL diminished as schooling increased. The same relationship was found between the number of adults in households for age group and among those who reported their skin color as not white ( Table 2 ).

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Table 2.
Estimate of the odds ratio associated with not having a RTL obtained in multiple logistic regression model. Household survey. Rio Branco, Northern Brazil, 2007.

Due to the exclusion of households without a landline, the age group pyramid of the VIGITEL sample differed from that of the population of the PNAD ^ggInstituto Brasileiro de Geografia e Estatística. Microdados da Pesquisa Nacional por Amostra de Domicílios. Rio de Janeiro; 2007. in 2007. This difference also occurred for years of schooling. Using the weighting procedure to correct statistical inference, the sample distribution of the VIGITEL survey was adjusted for the population estimated using the PNAD ^ggInstituto Brasileiro de Geografia e Estatística. Microdados da Pesquisa Nacional por Amostra de Domicílios. Rio de Janeiro; 2007. as an external source. The use of post-stratification weighting in the statistical analyses meant that the age pyramid estimated by the VIGITEL sample was the same as that of the population. The same occurred in the distribution of years of schooling ( Figure ).

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Figure.
Pyramid of age and distribution of the variable schooling according to the household survey. Rio Branco, AC, 2007.

The weighting increased the rates of prevalence of smoking, alcohol consumption and food intake, except for the regular consumption of FLV in VIGITEL. The weightings decreased in four of the six estimates of rates of prevalence which made up reported morbidity. The estimated rates of prevalence for exercise, consumption of whole milk and self-evaluation of health as very bad changed by a question of decimals ( Table 3 ).

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Table 3.
Difference between weighted rates of prevalence according to variables in question. Household survey. Rio Branco, Northern Brazil, 2007.

The rates of prevalence of soft drink consumption on five or more days/week, consumption of whole milk, ex-smoker, reported morbidity of high blood pressure, asthma, asthmatic bronchitis, chronic bronchitis or emphysema were underestimated by more than 3% in the VIGITEL survey. The rates of prevalence of fatty meat consumption, sedentarylifestyle, excessive alcohol consumption, self-evaluation of health as very bad and reported morbidities of heart attack, diabetes, high cholesterol and osteoporosis showed levels of bias between -3% and 3%. VIGITEL overestimated the prevalence of consumption of beans on five more days/week and physical activity during leisure time by over 3% ( Table 4 ).

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Table 4.
Bias and mean squared error (MSE) of rates of prevalence according to VIGITEL. Rio Branco, Northern Brazil, 2007.

The median of errors in the estimated rates of prevalence by VIGITEL was 2.18%, expressed by the square root of the square mean error. It is notable that errors in the rates of prevalence for consumption of soft drinks and whole milk, physical activity during leisure time and reported morbidity of high blood pressure were in the third quartile ( Table 4 ).

DISCUSSION

In 2007, 41% of private residences in the city Rio Branco had a landline, according to data from the household survey. The results of the survey indicate seven variables with potential bias in the estimates due to low coverage of landlines. These variables are: regular FLV consumption, fatty meat consumption, physical activity during leisure time, smoking, self-evaluation of health as very bad and reported morbidities of high cholesterol or triglycerides and asthma, asthmatic bronchitis, chronic bronchitis or emphysema.

The results of the household survey show that those without a landline are concentrated in the groups with lower levels of schooling, those whose reported skin color was not white, among individuals aged 18 to 34 and in households with one or two inhabitants. These results are consistent with those of other studies which identify the profile of individuals without a landline.^9, ⁱiThornberry OT Jr, Massey JT. Correcting for undercoverage bias in Random Digit Dialed National Health Surveys. Washington (DC): National Center for Health Statistics; 1978 [cited 2010 Oct 25]. Available from: http://www.amstat.org/sections/srms/proceedings/papers/1978_045.pdf Study based on the Doctoral Thesis of Regina Tomie Ivata Bernal, presented to the Post-Graduate Program in Public Health of the Departamento de Epidemiologia da Faculdade de Saúde Pública, Universidade de São Paulo , 2011.

VIGITEL underestimated the rates of prevalence which composed the group of risk factors. This result is consistent with those presented by Battaglia et al,²2. Belánd Y, St-Pierre M. Mode effects in the Canadian Community Health Survey: a comparison of CATI and CAPI. In: Lepkowsky JM, et al. Advances in telephone survey methodology. Hoboken (NJ): John Wiley & Sons; 2008. p297-314 (Wiley Series in Survey Methodology). which evaluate the effect of weighting to correct bias in the estimates due to the 50% lack of response rate. Béland & St-Pierre³3. Bernal R, Silva NN. Cobertura de linhas telefônicas residenciais e vícios potenciais em estudos epidemiológicos. Rev Saude Publica. 2009;43(3):421-6. DOI:10.1590/S0034-89102009005000024.
https://doi.org/10.1590/S0034-8910200900... found similar results for the rates of prevalence of smokers and alcohol consumption.

The results here show that VIGITEL tends to underestimate rates of prevalence in areas of low landline coverage. The rates of prevalence of indicators (smoking, consuming soft drinks and fatty meat, self-evaluation of health as very bad) are present at higher proportions in populations with low levels of schooling.¹1. Barros MBA, Zanchetta LM, Moura EC, Malta DC. Auto-avaliação de saúde e fatores associados, Brasil, 2006. Rev Saude Publica. 2009;43(Supl 2):27-37. DOI:10.1590/S0034-89102009000900005
https://doi.org/10.1590/S0034-8910200900... , ¹⁰10. Kalton G. Compensating for missing survey data. Ann Arbor: University of Michigan Institute for Social Research; 1983. , ¹⁵15. Nathan G. Telesurvey methodologies for household surveys: a review and some thoughts for the future. Surv Methodol. 2001;27(1):7-31. DOI:10.1590/S0034-89102005000100007.
https://doi.org/10.1590/S0034-8910200500... , ²⁰20. Waksberg, J. Sampling methods for random digit dialing. Journal of the Americam Statistical Association 1978; 73: 40-46. [citado 2013 mai 14]. Disponível em: http://libra.msra.cn/Publication/3706086/sampling-methods-for-random-digit-dialing
http://libra.msra.cn/Publication/3706086...

‘Protective’ indicators (consuming FLV and exercise) are present in greater proportions among populations with higher levels of schooling. However, they tend to be overestimated.⁷7. Francisco PMSB, Barros MBA, Segri NJ, Alves MCGP, Cesar CLG, Malta DC. Comparação de estimativas para auto-relato de condições crônicas entre inquérito domiciliar e telefônico – Campinas (SP), Brasil. Rev Bras Epidemiol. 2011;14(Supl 1):5-15. DOI:10.1590/S1415-790X2011000500002
https://doi.org/10.1590/S1415-790X201100...

Of the seven rates of prevalence indicated by the household survey with potential bias due to the association between prevalence and owning a landline, the VIGITEL post-stratification weighting partially eliminated bias in rates of prevalence of regular FLV consumption, fatty meat consumption, smoking and reported morbidity of high cholesterol or triglycerides. The weighting procedure adopted by the system did not correct the bias in the rates of prevalence of asthma, asthmatic bronchitis, chronic bronchitis or emphysema and physical activity during leisure time.

Recent Brazilian studies evaluate the magnitude of the bias found in results produced by the VIGITEL system by comparing them with telephone or household surveys. Viacava et al²¹21. Waksberg J. Sampling methods for random digit dialing. J Am Stat Assoc. 1978;73(361):40-6. evaluated the bias of mammogram coverage in women between 50 and 60 in the 26 state capitals and the Federal District, comparing the results of VIGITEL 2007 with data from the 2003 and 2006 PNAD. The bias was expressed by the absolute difference between the rates of prevalence in VIGITEL and of the estimated population based on estimated coverage using the equation 100*[(d*f)+e(1-f)], in which d is the prevalence estimated by VIGITEL; f is the percentage of the female population aged between 50 and 69; e = c*d; c = b/a; a is equal to the percentage of women aged between 50 and 69 with a land line in the PNAD; b is equal to the percentage of women aged between 50 and 69 who do not have a landline according to the PNAD. Francisco et al⁸8. Groves RM. Nonresponse rates and nonresponse bias in household survey. Public Opin Q. 2006;70(5 Spec N°):646-75. DOI:10.1093/poq/nfl033
https://doi.org/10.1093/poq/nfl033... evaluated the presence of bias in the VIGITEL estimates carried out in Campinas, Southeastern Brazil, in 2008 with the Health Survey (ISA), Campinas 2008, using the equation (p_VIGITEL) = p_VIGITEL – p_{isa_campinas}) and the Student’s t-test for two independent samples. Ferreira et al⁵5. Ferreira AD, César CC, Malta DC, Andrade ACS, Ramos CGC, Proietti FA, et al. Validade de estimativas obtidas por inquérito telefônico: comparação entre VIGITEL 2008 e Inquérito Saúde em Beagá. Rev Bras Epidemiol. 2011;14(Supl 1):16-30. DOI:10.1590/S1415-790X2011000500003
https://doi.org/10.1590/S1415-790X201100... evaluated the bias in VIGITEL estimates in Belo Horizonte, Southeastern Brazil, with a 74% landline coverage, comparing the VIGITEL results with those of a household health survey in Belo Horizonte (Beagá) using the equation ((p_VIGITEL) = p_VIGITEL – p_beaga) and the Student’s t-test for two independent samples. Segri et al¹⁹19. Viacava F, Souza-Junior PRB, Moreira RS. Estimativas da cobertura de mamografia segundo inquéritos de saúde no Brasil. Rev Saude Publica. 2009;43(Supl 2):117-25. DOI:10.1590/S0034-89102009000900015.
https://doi.org/10.1590/S0034-8910200900... evaluated the presence of bias in the VIGITEL estimates of preventative mammography examinations in the municipality of São Paulo in 2008, comparing them with the household health survey in São Paulo 2008. The difference between the surveys was expressed by the ratio of prevalence as a means of detecting bias.

In the evaluation of potential bias in the prevalence of high blood pressure, the results from Rio Branco and Campinas show the presence of bias in the results presented by the VIGITEL system in municipalities with low and high landline coverage, respectively. Ferreira et al⁶6. Florindo AA, Hallal PC, Moura EC, Malta DC. Prática de atividades físicas e fatores associados em adultos, Brasil, 2006. Rev Saude Publica. 2009;43(Supl 2):65-73. DOI:10.1590/S0034-89102009000900009
https://doi.org/10.1590/S0034-8910200900... showed that post-stratification weighting smooths out bias in the prevalence of high blood pressure. When evaluating mammogram coverage, Viacava et al²¹21. Waksberg J. Sampling methods for random digit dialing. J Am Stat Assoc. 1978;73(361):40-6. showed that bias in the VIGITEL survey diminished with the increase in landline coverage, varying from 3.4% in São Paulo, to 24.2% in João Pessoa. Segri et al¹⁹19. Viacava F, Souza-Junior PRB, Moreira RS. Estimativas da cobertura de mamografia segundo inquéritos de saúde no Brasil. Rev Saude Publica. 2009;43(Supl 2):117-25. DOI:10.1590/S0034-89102009000900015.
https://doi.org/10.1590/S0034-8910200900... detected significant difference between the surveys in obtaining estimates of the prevalence of mammogram coverage in the population group excluded from VIGITEL in São Paulo, composed of females with 0 to 8 years of schooling.

These studies show the presence of bias in the prevalence of high blood pressure in municipalities with low and high landline coverage and in mammogram coverage in municipalities with high coverage. These differences can be attributed to the databases, to the study population, to differences in methodology used to estimate bias or to the statistical techniques used in the analyses.

Those without landlines are concentrated in those of lower socioeconomic status, and in Rio Branco greater rates of prevalence in variables associated with risk factors, such as smoking, consumption of fatty meat and self-evaluation of health as very bad were found. Those without a landline had lower rates of prevalence of physical activity during leisure time and regular FLV consumption.

The VIGITEL weighting procedure corrected potential bias in five of the seven variables indicated by the household survey, due to low household coverage. In the rates of prevalence for physical activity during leisure time and reported morbidity of asthma, asthmatic bronchitis, chronic asthma or emphysema, however, the procedure adopted by VIGITEL did not reduce the bias. These results need to be validated in other state capitals with low landline coverage. Studies comparing VIGITEL and household surveys in cities with high landline coverage are also necessary in order to validate the results in the municipality of Rio Branco, AC.

It is necessary to use alternative weighting methods and strategies of selecting external variables to construct the post-stratification weightings which minimize the potential bias in the rates of prevalence for physical activity during leisure time and reported morbidity of asthma, asthmatic bronchitis, chronic bronchitis or emphysema, which will be explored in future analyses.

Recent studies evaluating the potential bias in VIGITEL highlight divergences in detecting potential bias in high blood pressure and in mammogram coverage. These differences can be attributed to the database, the population of the study, methodological differences used in estimating bias or to the statistical techniques used in the analyses.

This reinforces the importance of studies of the methodology for estimating bias due to lack of response and low coverage as an object of epidemiological research.

References

¹
Barros MBA, Zanchetta LM, Moura EC, Malta DC. Auto-avaliação de saúde e fatores associados, Brasil, 2006. Rev Saude Publica. 2009;43(Supl 2):27-37. DOI:10.1590/S0034-89102009000900005
» https://doi.org/10.1590/S0034-89102009000900005
²
Belánd Y, St-Pierre M. Mode effects in the Canadian Community Health Survey: a comparison of CATI and CAPI. In: Lepkowsky JM, et al. Advances in telephone survey methodology. Hoboken (NJ): John Wiley & Sons; 2008. p297-314 (Wiley Series in Survey Methodology).
³
Bernal R, Silva NN. Cobertura de linhas telefônicas residenciais e vícios potenciais em estudos epidemiológicos. Rev Saude Publica. 2009;43(3):421-6. DOI:10.1590/S0034-89102009005000024.
» https://doi.org/10.1590/S0034-89102009005000024
⁴
Cochran WG. Sampling techniques. 3.ed. New York : John Wiley & Sons; 1977.
⁵
Ferreira AD, César CC, Malta DC, Andrade ACS, Ramos CGC, Proietti FA, et al. Validade de estimativas obtidas por inquérito telefônico: comparação entre VIGITEL 2008 e Inquérito Saúde em Beagá. Rev Bras Epidemiol. 2011;14(Supl 1):16-30. DOI:10.1590/S1415-790X2011000500003
» https://doi.org/10.1590/S1415-790X2011000500003
⁶
Florindo AA, Hallal PC, Moura EC, Malta DC. Prática de atividades físicas e fatores associados em adultos, Brasil, 2006. Rev Saude Publica. 2009;43(Supl 2):65-73. DOI:10.1590/S0034-89102009000900009
» https://doi.org/10.1590/S0034-89102009000900009
⁷
Francisco PMSB, Barros MBA, Segri NJ, Alves MCGP, Cesar CLG, Malta DC. Comparação de estimativas para auto-relato de condições crônicas entre inquérito domiciliar e telefônico – Campinas (SP), Brasil. Rev Bras Epidemiol. 2011;14(Supl 1):5-15. DOI:10.1590/S1415-790X2011000500002
» https://doi.org/10.1590/S1415-790X2011000500002
⁸
Groves RM. Nonresponse rates and nonresponse bias in household survey. Public Opin Q. 2006;70(5 Spec N°):646-75. DOI:10.1093/poq/nfl033
» https://doi.org/10.1093/poq/nfl033
⁹
Jaime PC, Figueiredo ICR, Moura EC, Malta DC. Fatores associados ao consumo de frutas e hortaliças no Brasil, 2006. Rev Saude Publica. 2009;43(Supl 2):59-64. DOI:10.1590/S0034-89102009000900008
» https://doi.org/10.1590/S0034-89102009000900008
¹⁰
Kalton G. Compensating for missing survey data. Ann Arbor: University of Michigan Institute for Social Research; 1983.
¹¹
Kish L. Survey sampling. New York: John Wiley & Sons; 1965.
¹²
Lepkowsky JM, Tucker C, Brick JM, De Leeuw ED, Japec L, Lavrakas PJ, et al. Advances in telephone survey methodology. Hoboken (NJ): John Wiley & Sons; 2008. (Wiley Series in Survey Methodology).
¹³
Malta DC, Moura EC, Silva SA, Oliveira PPV, Costa e Silva VL. Prevalência do tabagismo em adultos residentes nas capitais dos estados e no Distrito Federal, Brasil, 2008. J Bras Pneumol. 2010;36(1):73-83. DOI:10.1590/S1806-37132010000100013.
» https://doi.org/10.1590/S1806-37132010000100013
¹⁴
Monteiro CA, Moura EC, Jaime PC, Lucca A, Florindo AA, Figueiredo ICR, et al. Monitoramento de fatores de risco para doenças crônicas por entrevistas telefônicas. Rev Saude Publica. 2005;39(1):47-57. DOI:10.1590/S0034-89102005000100007.
» https://doi.org/10.1590/S0034-89102005000100007
¹⁵
Nathan G. Telesurvey methodologies for household surveys: a review and some thoughts for the future. Surv Methodol. 2001;27(1):7-31. DOI:10.1590/S0034-89102005000100007.
» https://doi.org/10.1590/S0034-89102005000100007
¹⁶
Segri NJ, Cesar CLG, Barros MBA, Carandina L, Goldbaum M. Inquérito de saúde: comparação dos entrevistados segundo posse de linha telefônica residencial. Rev Saude Publica. 2010;44(3):503-12. DOI:10.1590/S0034-89102010005000012.
» https://doi.org/10.1590/S0034-89102010005000012
¹⁷
Segri NJ, Francisco PMSB, Alves MCGP, Barros MBA, Cesar CLG, Goldbaum M, et al. Práticas preventivas de detecção de câncer em mulheres: comparação das estimativas dos inquéritos de saúde (ISA - Capital) e vigilância de fatores de risco e proteção para doenças crônicas por inquérito telefônico (VIGITEL - São Paulo). Rev Bras Epidemiol. 2011;14(Supl 1):31-43. DOI:10.1590/S1415-790X2011000500004.
» https://doi.org/10.1590/S1415-790X2011000500004
¹⁸
Silva GA, Valente JG, Almeida LM, Moura EC, Malta DC. Tabagismo e escolaridade no Brasil, 2006. Rev Saude Publica. 2009;43(Supl 2):48-56. DOI:10.1590/S0034-89102009000900007.
» https://doi.org/10.1590/S0034-89102009000900007
¹⁹
Viacava F, Souza-Junior PRB, Moreira RS. Estimativas da cobertura de mamografia segundo inquéritos de saúde no Brasil. Rev Saude Publica. 2009;43(Supl 2):117-25. DOI:10.1590/S0034-89102009000900015.
» https://doi.org/10.1590/S0034-89102009000900015
²⁰
Waksberg, J. Sampling methods for random digit dialing. Journal of the Americam Statistical Association 1978; 73: 40-46. [citado 2013 mai 14]. Disponível em: http://libra.msra.cn/Publication/3706086/sampling-methods-for-random-digit-dialing
» http://libra.msra.cn/Publication/3706086/sampling-methods-for-random-digit-dialing
²¹
Waksberg J. Sampling methods for random digit dialing. J Am Stat Assoc. 1978;73(361):40-6.

a
Ministério da Saúde. VIGITEL Brasil 2006: vigilância de fatores de risco e proteção de doenças crônicas por inquérito telefônico: estimativas sobre frequência e distribuição sócio-demográfica de fatores de risco e proteção para doenças crônicas nas capitais dos 26 estados brasileiros e no Distrito Federal em 2006. Brasília (DF); 2007. (Série G. Estatística e Informação em Saúde).
b
Kerckhove W, Montaquila JM, Carver PR, Brick JM. An evaluation of bias in the 2007 National Household Education Surveys Program: results from a special data collection effort. Washington (DC): US Department of Education, National Center for Education Statistics; 2008. (NCES 2009-029) [cited 2013 may 15]. Available from: http://nces.ed.gov/pubs2009/2009029_1.pdf
c
Flores-Cervantes I, Brick JM, Jones ME, Westat J, DiSogra C, Yen W. Weighting for nontelephone household in the 2001 California Health Interview Survey. In: Proceedings of the Joint Statistical Meetings Section on Survey Research Methods; 2002 Aug 11-15; New York. p.1002-7 [cited 2013 may 14]. Available from: http://www.amstat.org/sections/srms/Proceedings/y2002/Files/JSM2002-000661.pdf
d
CDC − Center for Disease Control and Prevention. Behavioral Risk Factor Surveillance System. Atlanta (GA); 2011 [cited 2013 May 14]. Available from: http://www.cdc.gov/brfss/annual_data/annual_2011.htm
e
Brick JM, Keeler S, Bell B, Chandler K. National Household Education Survey of 1993: adjusting for coverage bias using telephone service interruption data: technical report. Washington (DC): U.S. Department of Education, National Center for Education Statistics; 1996. (NCES 97-336) [cited 2010 Nov 24]. Available from: http://0-nces.ed.gov.opac.acc.msmc.edu/pubs/97336.pdf
f
Ministério da Saúde. VIGITEL Brasil 2010: vigilância de fatores de risco e proteção de doenças crônicas por inquérito telefônico: estimativas sobre frequência e distribuição sócio-demográfica de fatores de risco e proteção para doenças crônicas nas capitais dos 26 estados brasileiros e no Distrito Federal em 2010. Brasília (DF); 2011. (Série G. Estatística e Informação em Saúde).
g
Instituto Brasileiro de Geografia e Estatística. Microdados da Pesquisa Nacional por Amostra de Domicílios. Rio de Janeiro; 2007.
h
Paula GA. Modelos de regressão com apoio computacional. São Paulo: Instituto de Matemática e Estatística da USP; 2004.
i
Thornberry OT Jr, Massey JT. Correcting for undercoverage bias in Random Digit Dialed National Health Surveys. Washington (DC): National Center for Health Statistics; 1978 [cited 2010 Oct 25]. Available from: http://www.amstat.org/sections/srms/proceedings/papers/1978_045.pdf Study based on the Doctoral Thesis of Regina Tomie Ivata Bernal, presented to the Post-Graduate Program in Public Health of the Departamento de Epidemiologia da Faculdade de Saúde Pública, Universidade de São Paulo , 2011.

Publication Dates

Publication in this collection
June 2013

History

Received
4 Oct 2011
Accepted
17 Aug 2012

This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License, which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

[1] ¹
Barros MBA, Zanchetta LM, Moura EC, Malta DC. Auto-avaliação de saúde e fatores associados, Brasil, 2006. Rev Saude Publica. 2009;43(Supl 2):27-37. DOI:10.1590/S0034-89102009000900005
» https://doi.org/10.1590/S0034-89102009000900005

[2] ²
Belánd Y, St-Pierre M. Mode effects in the Canadian Community Health Survey: a comparison of CATI and CAPI. In: Lepkowsky JM, et al. Advances in telephone survey methodology. Hoboken (NJ): John Wiley & Sons; 2008. p297-314 (Wiley Series in Survey Methodology).

[3] ³
Bernal R, Silva NN. Cobertura de linhas telefônicas residenciais e vícios potenciais em estudos epidemiológicos. Rev Saude Publica. 2009;43(3):421-6. DOI:10.1590/S0034-89102009005000024.
» https://doi.org/10.1590/S0034-89102009005000024

[4] ⁴
Cochran WG. Sampling techniques. 3.ed. New York : John Wiley & Sons; 1977.

[5] ⁵
Ferreira AD, César CC, Malta DC, Andrade ACS, Ramos CGC, Proietti FA, et al. Validade de estimativas obtidas por inquérito telefônico: comparação entre VIGITEL 2008 e Inquérito Saúde em Beagá. Rev Bras Epidemiol. 2011;14(Supl 1):16-30. DOI:10.1590/S1415-790X2011000500003
» https://doi.org/10.1590/S1415-790X2011000500003

[6] ⁶
Florindo AA, Hallal PC, Moura EC, Malta DC. Prática de atividades físicas e fatores associados em adultos, Brasil, 2006. Rev Saude Publica. 2009;43(Supl 2):65-73. DOI:10.1590/S0034-89102009000900009
» https://doi.org/10.1590/S0034-89102009000900009

[7] ⁷
Francisco PMSB, Barros MBA, Segri NJ, Alves MCGP, Cesar CLG, Malta DC. Comparação de estimativas para auto-relato de condições crônicas entre inquérito domiciliar e telefônico – Campinas (SP), Brasil. Rev Bras Epidemiol. 2011;14(Supl 1):5-15. DOI:10.1590/S1415-790X2011000500002
» https://doi.org/10.1590/S1415-790X2011000500002

[8] ⁸
Groves RM. Nonresponse rates and nonresponse bias in household survey. Public Opin Q. 2006;70(5 Spec N°):646-75. DOI:10.1093/poq/nfl033
» https://doi.org/10.1093/poq/nfl033

[9] ⁹
Jaime PC, Figueiredo ICR, Moura EC, Malta DC. Fatores associados ao consumo de frutas e hortaliças no Brasil, 2006. Rev Saude Publica. 2009;43(Supl 2):59-64. DOI:10.1590/S0034-89102009000900008
» https://doi.org/10.1590/S0034-89102009000900008

[10] ¹⁰
Kalton G. Compensating for missing survey data. Ann Arbor: University of Michigan Institute for Social Research; 1983.

[11] ¹¹
Kish L. Survey sampling. New York: John Wiley & Sons; 1965.

[12] ¹²
Lepkowsky JM, Tucker C, Brick JM, De Leeuw ED, Japec L, Lavrakas PJ, et al. Advances in telephone survey methodology. Hoboken (NJ): John Wiley & Sons; 2008. (Wiley Series in Survey Methodology).

[13] ¹³
Malta DC, Moura EC, Silva SA, Oliveira PPV, Costa e Silva VL. Prevalência do tabagismo em adultos residentes nas capitais dos estados e no Distrito Federal, Brasil, 2008. J Bras Pneumol. 2010;36(1):73-83. DOI:10.1590/S1806-37132010000100013.
» https://doi.org/10.1590/S1806-37132010000100013

[14] ¹⁴
Monteiro CA, Moura EC, Jaime PC, Lucca A, Florindo AA, Figueiredo ICR, et al. Monitoramento de fatores de risco para doenças crônicas por entrevistas telefônicas. Rev Saude Publica. 2005;39(1):47-57. DOI:10.1590/S0034-89102005000100007.
» https://doi.org/10.1590/S0034-89102005000100007

[15] ¹⁵
Nathan G. Telesurvey methodologies for household surveys: a review and some thoughts for the future. Surv Methodol. 2001;27(1):7-31. DOI:10.1590/S0034-89102005000100007.
» https://doi.org/10.1590/S0034-89102005000100007

[16] ¹⁶
Segri NJ, Cesar CLG, Barros MBA, Carandina L, Goldbaum M. Inquérito de saúde: comparação dos entrevistados segundo posse de linha telefônica residencial. Rev Saude Publica. 2010;44(3):503-12. DOI:10.1590/S0034-89102010005000012.
» https://doi.org/10.1590/S0034-89102010005000012

[17] ¹⁷
Segri NJ, Francisco PMSB, Alves MCGP, Barros MBA, Cesar CLG, Goldbaum M, et al. Práticas preventivas de detecção de câncer em mulheres: comparação das estimativas dos inquéritos de saúde (ISA - Capital) e vigilância de fatores de risco e proteção para doenças crônicas por inquérito telefônico (VIGITEL - São Paulo). Rev Bras Epidemiol. 2011;14(Supl 1):31-43. DOI:10.1590/S1415-790X2011000500004.
» https://doi.org/10.1590/S1415-790X2011000500004

[18] ¹⁸
Silva GA, Valente JG, Almeida LM, Moura EC, Malta DC. Tabagismo e escolaridade no Brasil, 2006. Rev Saude Publica. 2009;43(Supl 2):48-56. DOI:10.1590/S0034-89102009000900007.
» https://doi.org/10.1590/S0034-89102009000900007

[19] ¹⁹
Viacava F, Souza-Junior PRB, Moreira RS. Estimativas da cobertura de mamografia segundo inquéritos de saúde no Brasil. Rev Saude Publica. 2009;43(Supl 2):117-25. DOI:10.1590/S0034-89102009000900015.
» https://doi.org/10.1590/S0034-89102009000900015

[20] ²⁰
Waksberg, J. Sampling methods for random digit dialing. Journal of the Americam Statistical Association 1978; 73: 40-46. [citado 2013 mai 14]. Disponível em: http://libra.msra.cn/Publication/3706086/sampling-methods-for-random-digit-dialing
» http://libra.msra.cn/Publication/3706086/sampling-methods-for-random-digit-dialing

[21] ²¹
Waksberg J. Sampling methods for random digit dialing. J Am Stat Assoc. 1978;73(361):40-6.

Variable		With telephone (n = 644)	Without telephone (n = 871)	Difference

		Prevalence (p₁)	Prevalence (p₂)	p₁- p₂	p-value

Food intake
	Beans (5 or + days/week)	62.89	62.39	0.50	0.85
	FLV regularly	24.33	11.21	13.13	0.00
	FLV recommended	5.79	4.10	1.69	0.16
	Fatty meat	28.74	34.21	-5.46	0.03
	Whole milk	68.45	71.93	-3.48	0.16
	Soft drinks (5 or + days/week)	40.16	40.60	-0.45	0.87
Exercise
	Physical activity during leisure time	13.45	9.61	3.84	0.02
	Sedentary lifestyle	20.10	23.60	3.50	0.11
Alcohol consumption
	Excessive consumption	17.45	17.11	0.35	0.87
Smoking
	Smoker	13.49	22.13	-8.65	0.00
	Ex-smoker	30.69	29.41	1.28	0.64
Self-evaluation of health
	Very bad	3.39	8.30	-4.91	0.00
Reported morbidity
	High blood pressure	28.82	24.56	4.25	0.08
	Diabetes	4.78	4.85	-0.07	0.95
	Heart attack, stroke or CVA	3.28	3.17	0.10	0.92
	High cholesterol or triglycerides	19.10	14.32	4.78	0.02
	Asthma, asthmatic bronchitis. chronic bronchitis or emphysema	11.65	16.56	-4.92	0.01
	Osteoporosis	4.85	3.44	1.41	0.17

Variable		Odds ratio	Standard error	t	p > t

Schooling (year)
	[0;4]	7.16	1.58	8.93	0.00
	[5;8]	4.62	1.19	5.93	0.020
	[9;11]	2.59	0.51	4.86	0.00
	[12;20]	1.00
Number of household members
	1	3.74	0.78	6.33	0.00
	2	1.97	0.39	3.38	0.00
	[3;8]	1.00
Age group
	[18;24]	4.43	0.94	7.00	0.00
	[25;34]	3.93	0.80	6.74	0.00
	[35;44]	2.04	0.42	3.46	0.00
	[45;96]	1.00
Skin color
	Not white	1.90	0.44	2.77	0.01
	White	1.00

Variable		Sample weighting		Final weighting^a		Difference

		Prevalence (A)	Standard error	Prevalênce (B)	Standard error	(B-A)

Food intake
	Beans (5 or + days/week)	64.01	1.17	66.74	1.28	2.73
	FLV regularly	15.33	0.89	14.96	1.01	-0.38
	FLV recommended	3.77	0.51	4.01	0.66	0.24
	Fatty meat	28.44	1.12	30.46	1.33	2.02
	Whole milk	61.82	1.19	61.98	1.37	0.17
	Soft drinks (5 or + days/week)	30.07	1.13	31.62	1.32	1.55
Exercise
	Physical activity during leisure time	17.71	0.95	17.81	1.12	0.10
	Sedentary lifestyle	22.60	1.00	22.10	1.17	-0.50
Alcohol consumption
	Excessive consumption	14.58	0.87	15.13	1.04	0.55
Smoking
	Smoker	13.47	0.82	16.34	1.13	2.87
	Ex-smoker	26.13	1.07	26.66	1.23	0.53
Self-evaluation of health
	Very bad	6.01	0.56	6.02	0.64	0.01
Reported morbidity
	High blood pressure	22.16	1.01	20.33	1.06	-1.83
	Diabetes	4.78	0.52	4.04	0.47	-0.73
	Heart attack, stroke or CVA	2.05	0.36	2.36	0.45	0.31
	High cholesterol or triglycerides	17.61	0.92	15.71	0.96	-1.90
	Asthma, asthmatic bronchitis, chronic bronchitis or emphysema	11.07	0.77	11.40	0.94	0.33
	Osteoporosis	4.69	0.52	3.80	0.51	-0.90

Variables		Household survey	VIGITEL (n = 2,010)
		Prevalence	Prevalence	Variance	Bias^(a)	EQM	√ EQM
		Prevalence	Prevalence	Variance	Bias^(a)	Var + Vicio²	√ EQM

Food intake
	Beans (5 or + days/week)	62.59	66.74	1.64	4.15	18.87	4.34
	FLV regularly	16.58	14.96	1.02	-1.63	3.66	1.91
	FLV recommended	4.79	4.01	0.43	-0.78	1.05	1.02
	Fatty meat	31.97	30.46	1.76	-1.51	4.03	2.01
	Whole milk	70.51	61.98	1.87	-8.52	74.50	8.63
	Soft drinks (5 or + days/week)	40.42	31.62	1.75	-8.81	79.30	8.90
Exercise
	Physical activity during leisure time	11.18	17.81	1.26	6.63	45.26	6.73
	Sedentary lifestyle	22.13	22.10	1.32	-0.03	1.32	1.15
Alcohol consumption
	Excessive consumption	17.25	15.13	1.08	-2.12	5.56	2.36
Smoking
	Smoker	18.59	16.34	1.28	-2.25	6.34	2.52
	Ex-smoker	29.97	26.66	1.50	-3.32	12.50	3.53
Self-evaluation of health
	Very bad	6.29	6.02	0.41	-0.27	0.48	0.69
Reported morbidity
	High blood pressure	26.30	20.33	1.13	-5.97	36.81	6.07
	Diabetes	4.82	4.04	0.22	-0.78	0.82	0.91
	Heart attack, stroke or CVA	3.22	2.36	0.20	-0.85	0.93	0.97
	High cholesterol or triglycerides	16.28	15.71	0.92	-0.57	1.24	1.11
	Asthma, asthmatic bronchitis, chronic bronchitis or emphysema	14.55	11.40	0.88	-3.16	10.83	3.29
	Osteoporosis	3.79	5.03	0.37	1.24	1.90	1.38