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DATA AND METHODS The Access to Prenatal Care Survey (APCS) was a statewide frequency-matched design in which infants whose mother had no prenatal care, identified from their 1985 birth certificates, were matched to women with less than adequate prenatal care based upon the Kessner Index of prenatal care. The Kessner Index categorizes infants on the basis of the timing and frequency of prenatal care and gestational age of the infant at birth. Frequency ofvisits is classified by the American College of Obstetrics and Gynecology standard schedule of visits. These categories are Inadequate care, Intermediate care, and Adequate care. Women with adequate prenatal care must have initiated care in their first trimester and received a sufficient number of visits for the gestational age of their infants. Women receiving less-than-adequate care would have begun their care after the first trimester or received less than the standard number of visits for gestational age of their infants. The choice of less-than-adequate care as the standard ofcomparison was based upon both practical and empirical criteria. While the ideal of public health is to ensure access to adequate prenatal care for all women, an interim step is to reduce the number of women who receive no prenatal care. In the short run a more realistic goal is to push this group of women into the some-care category. Empirical data also support the importance of some care on outcome variables such as birthweight and prematurity. Ryan et al. (1980) showed Aat the greatest improvements in newborn health are between those infants with no care and those with some care. No-care infants were frequency matched to some-care infants on the basis of four match variables: mother's age, in 5-year age groups starting with 10- 14 and ending with 30 and over; mother's education, in categories less than high school, high school, and greater than high school; race, with white and nonwhite groups; and region of the state, using the four Department of Human Resources regions. Frequency matching sets sample size criteria within each category of the match variables. The goal of frequency matching is to set the percentage distribu-tion of the some-care women to that of the no-care women within each cell ofthe 5X3X2X4 matrix of match variables. In other words, the sample ofsome-care women chosen resembled, with respect to sociodemographic and geographic criteria, the no-care women. A sample of 1,200 1985 birth certificates was chosen; 600 some-care births and 600 no-care births from the automated birth registry file maintained by the State Center for Health Statistics. Lists composed of mother's name, child's name, father's name, and address were compiled by county of residence of the mother. Interviewers assigned by each county health department participating in the APCS were trained in data collection techniques and provided with lists of interviewees. Data collection began in April, 1986 and was completed in early July. A total of 709 completed interviews were collected from the 1 ,200 respondents initially selected for the study. Based upon compari-sons of 354 no-care respondents and 355 care respondents interviewed, no discernible response biases were found, with the age-race-education characteristics of the entire sample not significandy different from that of the actual respondents. Com-parisons did, however, reveal discrepancies between birth certificate entries of no prenatal care and self-report of respondents of their prenatal care. The original file of 709 was reduced by 146 respondents with discrepancies in care status. This report is limited to these 563 observations. The primary methodological tool used here to distinguish between no-care women and some-care women after controlling for sociodemographic and geographic confounders was logistic regression for the analysis of categorical dependent variables. Each logistic regression model must include match variables as predictors as well as other variables of interest. Logistic regression provides a determination of the effect of predictor variables such as marital status, employment, income, and attitudes related to primary care on the odds of receiving some prenatal care. Effiect parameters take the form of integers between and infinity with numbers less than 1 indicating a reduction in the odds of receiving care and numbers greater than 1 indicating an increase in the chances of having some pretuital care. THE PROCESS OF SEEKING PRENATAL CARE The decision to use prenatal care services is a complex interplay of attitudinal factors prevalent at the time of pregnancy and biological conditions that change with gestation, worked out within the context of the family, the broader social group, and what Levine et al. (1969) describe as the culture of medicine, the health care deUvery system. Prenatal
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Full Text | DATA AND METHODS The Access to Prenatal Care Survey (APCS) was a statewide frequency-matched design in which infants whose mother had no prenatal care, identified from their 1985 birth certificates, were matched to women with less than adequate prenatal care based upon the Kessner Index of prenatal care. The Kessner Index categorizes infants on the basis of the timing and frequency of prenatal care and gestational age of the infant at birth. Frequency ofvisits is classified by the American College of Obstetrics and Gynecology standard schedule of visits. These categories are Inadequate care, Intermediate care, and Adequate care. Women with adequate prenatal care must have initiated care in their first trimester and received a sufficient number of visits for the gestational age of their infants. Women receiving less-than-adequate care would have begun their care after the first trimester or received less than the standard number of visits for gestational age of their infants. The choice of less-than-adequate care as the standard ofcomparison was based upon both practical and empirical criteria. While the ideal of public health is to ensure access to adequate prenatal care for all women, an interim step is to reduce the number of women who receive no prenatal care. In the short run a more realistic goal is to push this group of women into the some-care category. Empirical data also support the importance of some care on outcome variables such as birthweight and prematurity. Ryan et al. (1980) showed Aat the greatest improvements in newborn health are between those infants with no care and those with some care. No-care infants were frequency matched to some-care infants on the basis of four match variables: mother's age, in 5-year age groups starting with 10- 14 and ending with 30 and over; mother's education, in categories less than high school, high school, and greater than high school; race, with white and nonwhite groups; and region of the state, using the four Department of Human Resources regions. Frequency matching sets sample size criteria within each category of the match variables. The goal of frequency matching is to set the percentage distribu-tion of the some-care women to that of the no-care women within each cell ofthe 5X3X2X4 matrix of match variables. In other words, the sample ofsome-care women chosen resembled, with respect to sociodemographic and geographic criteria, the no-care women. A sample of 1,200 1985 birth certificates was chosen; 600 some-care births and 600 no-care births from the automated birth registry file maintained by the State Center for Health Statistics. Lists composed of mother's name, child's name, father's name, and address were compiled by county of residence of the mother. Interviewers assigned by each county health department participating in the APCS were trained in data collection techniques and provided with lists of interviewees. Data collection began in April, 1986 and was completed in early July. A total of 709 completed interviews were collected from the 1 ,200 respondents initially selected for the study. Based upon compari-sons of 354 no-care respondents and 355 care respondents interviewed, no discernible response biases were found, with the age-race-education characteristics of the entire sample not significandy different from that of the actual respondents. Com-parisons did, however, reveal discrepancies between birth certificate entries of no prenatal care and self-report of respondents of their prenatal care. The original file of 709 was reduced by 146 respondents with discrepancies in care status. This report is limited to these 563 observations. The primary methodological tool used here to distinguish between no-care women and some-care women after controlling for sociodemographic and geographic confounders was logistic regression for the analysis of categorical dependent variables. Each logistic regression model must include match variables as predictors as well as other variables of interest. Logistic regression provides a determination of the effect of predictor variables such as marital status, employment, income, and attitudes related to primary care on the odds of receiving some prenatal care. Effiect parameters take the form of integers between and infinity with numbers less than 1 indicating a reduction in the odds of receiving care and numbers greater than 1 indicating an increase in the chances of having some pretuital care. THE PROCESS OF SEEKING PRENATAL CARE The decision to use prenatal care services is a complex interplay of attitudinal factors prevalent at the time of pregnancy and biological conditions that change with gestation, worked out within the context of the family, the broader social group, and what Levine et al. (1969) describe as the culture of medicine, the health care deUvery system. Prenatal |