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Relevance of Health Knowledge in Reporting Maternal Health Complications and Use of Maternal Health Care in India Shraboni Patra, Perianayagam Arokiasamy & Srinivas Goli To cite this article: Shraboni Patra, Perianayagam Arokiasamy & Srinivas Goli (2014): Relevance of Health Knowledge in Reporting Maternal Health Complications and Use of Maternal Health Care in India, Health Care for Women International, DOI: 10.1080/07399332.2014.946509 To link to this article: http://dx.doi.org/10.1080/07399332.2014.946509

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Date: 06 November 2015, At: 02:42

Health Care for Women International, 0:1–19, 2014 Copyright © Taylor & Francis Group, LLC ISSN: 0739-9332 print / 1096-4665 online DOI: 10.1080/07399332.2014.946509

Relevance of Health Knowledge in Reporting Maternal Health Complications and Use of Maternal Health Care in India SHRABONI PATRA

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Senior Research Scholar, International Institute for Population Sciences, Mumbai, Maharashtra, India

PERIANAYAGAM AROKIASAMY Department of Development Studies, International Institute for Population Sciences, Mumbai, Maharashtra, India

SRINIVAS GOLI Department of Development Studies, Giri Institute of Development Studies, Lucknow, India

We measured levels of women’s health knowledge and their association with the reporting of maternal health complications and related health care use. We found that women with higher levels of health knowledge reported more pregnancy and postnatal complications, and used more maternal health care services. Education has a positive impact on health, but education alone is not enough to ensure recognizing and reporting of health complications and increasing the demand for maternal health care services. We conclude that the provision of health education for women will help them to identify maternal health complications and improve their reporting and related health care use. Self-reported measures of morbidity in developing countries have been viewed with considerable scepticism by the researchers because these measures of morbidity are misleading (Manesh, Sheldon, Pickett, & Carr-Hill, 2008; Subramanian, Subramanian, Selvaraj, & Kawachi, 2009). Based on their empirical assessment, Jain and colleagues (2012) showed that self-reported morbidity data are failing to show existing socioeconomic variations in maternal morbidity by socioeconomic standing of women. According to Sen Received 23 July 2013; accepted 16 July 2014. Address correspondence to Shraboni Patra, Senior Research Scholar, International Institute for Population Sciences, Govandi Station Road, Deonar, Mumbai, 400088 Maharashtra, India. E-mail: [email protected] 1

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(2002), an individual assessment of health is directly contingent on the social experience, and this leads to less reporting of illness among socially disadvantaged people as they fail to perceive the presence of health deficits. The majority of the poor and illiterate women do not report morbidity and do not go to health facilities because they fail to recognize that they have a morbid condition. Thus, the lack of health knowledge and different health beliefs have been considered to be the main reasons for less reporting of morbidity. Health knowledge is a relatively new concept in health promotion research. It is a much broader term than health literacy (Pfizer Inc., 1998). While the World Health Organization (WHO, 1998, p. 10) described “health literacy is the achievement of a level of knowledge, personal skills and confidence to take action to improve personal and community health by changing personal lifestyles and living conditions”; until now, however, there has been no universally accepted definition of health knowledge. Nevertheless, researchers in a global context found that health knowledge is critically important for both reporting of health problems and health care use (Nutbeam, 1998). For instance, Parker (2000) identified several patients with the most general and convoluted health care problems are at greater risk for misunderstanding their diagnosis, medications and instructions on how to take care of their health problems. Other researchers also found that by improving people’s access to health information and awareness is critical to maximizing the acquiescence to use health care facilities (Nutbeam, 2008; Singh & Patra, 2013). Health knowledge, regardless of education, can have an impact on reporting of health problems and decision making of women in seeking health care services (Jahan, 2000; Ratzan, 2001; United States Department of Health and Human Services [USDHHS], Office of Disease Prevention and Health Promotion, 2010). Moronkola and colleagues (2006) revealed that the impact of belief in personal and community health practices is very strong because belief may not always be scientific and it may make one right or wrong in access to health care. In the same study, the authors also found that reproductive health knowledge is important for women to understand a woman’s health and well-being. Researchers in public health also suggested the consequences of health knowledge that include improved self-reported health status, lower health care costs, increased understanding of health problems, shorter hospitalizations, and less frequent use of health care services (Altindag, Cannonier, & Mocan, 2010; Bhatt, Reid, Lewis, & Asnani, 2011; Kickbusch et al., 2001). The concept of health knowledge, especially in terms of maternal health among women in India, is important because 72% of women still believe that medical attention is not necessary during childbirth (International Institute for Population Sciences [IIPS] & Macro International, 2007). Maternal health care use in terms of key indicators, such as three or more antenatal care services (ANCs) use and institutional deliveries, is

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not satisfactory. On average, one out of every five women is not receiving antenatal care services for their last birth (IIPS & Macro International, 2007). With the introduction of cash incentive scheme Janani Suraksha Yojana under the National Rural Health Mission, however, the percentage of births that were delivered in health facilities increased steadily from 26% in 1993 to 58% in 2009 (IIPS & Macro Internationals, 2007; Registrar General of India [RGI], 2010). Nevertheless, it is substantially lower as compared with the developed countries (Goli & Jaleel, 2014; Lim et al., 2010). Even in the urban areas, despite the proximity of health care services, use of ANCs and institutional delivery, even among educated women, is far below the expected levels (Goli, Riddhi, & Arokiasamy, 2013). Further, such poor treatment-seeking attitude could be attributable to a low level of health knowledge among Indian women (Carroll et al., 2007; Desai & Alva, 1998). Although identification of health problems and health care use largely depends on health beliefs and knowledge, across the globe there have been few efforts to link health knowledge of women with their self-reported health status and health care use. To our knowledge, however, there is no study in India that examined health knowledge of women and its association with self-reported health status and health care use. There is a need, therefore, to measure the extent and pattern of health knowledge among Indian women. Further, an assessment of the interaction among these three aspects: level of health knowledge, reporting of maternal health problems, and use of medical care in an Indian context is important for planning and preparing strategies to improve maternal health outcomes, thereby achieving the Millennium Development Goals-5 (MDG-5). Thus, we have looked at the twofold objectives for this study. The first objective is to examine whether the reporting of maternal health complications is defined by the level of women’s health knowledge. The second one is to study the extent of disparity in the use of maternal health care services by the levels of health knowledge among women.

METHODS Data Source We have used information available in the India Human Development Survey (IDHS; Desai et al., 2007) for the assessment of the objectives of this study. The IHDS is the collaborative project of researchers from the University of Maryland and National Council of Applied Economic Research (NCAER), New Delhi. For the first time, the IHDS 2005 has provided information regarding women’s health belief and practices. In this survey, women in the age group of 15–49 years were asked to report their health beliefs, hygiene, and sanitation practices along with regular maternal health information like prenatal, natal, and postnatal complications and health care use (Desai et al., 2010a).

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Sample Design The IHDS was administered to a nationally representative sample of 41,554 households located across all the states and union territories of India with the exception of Andaman and Nicobar and Lakshadweep and covers the urban as well as the rural sample. One woman of reproductive age group (15–49 year) from each household was selected for an interview, to whom the questions regarding health knowledge and maternal health complications and health care use were asked (Desai et al., 2010b). Villages and urban blocks (comprising 150–200 households) formed the primary sampling units (PSUs) from which the households were selected. The urban and rural PSUs were selected by means of different sample design. In order to draw a random sample of urban households, all the urban areas in the states were listed in the order of their size, with a number of blocks selected from each urban area allocated based on Probability Proportional to Sizes. Once the number of blocks for each urban area was determined, the enumeration blocks were drawn randomly. From each of the Census Enumeration Blocks, complete household listing was conducted, and a sample of 15 households was selected per block. For sampling purposes, some smaller states were combined with nearby larger states. The rural sample contains about half of the households that were interviewed initially by the National Council of Applied Economic Research (NCAER, New Delhi) in 1993–94, in a survey titled the Human Development Profile of India (HDPI). The other halves of the samples were drawn from both districts surveyed in HDPI as well as from the districts situated in the states and union territories not covered in HDPI. The original HDPI was a random sample of 33,230 households, located in 16 major states, 195 districts, and 1,765 villages. In states where the 1993–94 survey was conducted and recontact details were available, 13,593 households were randomly selected and reinterviewed in 2005 (Desai et al., 2010b).

Variables Predictor variables. The main predictor variable of this study is “health knowledge.” We have constructed a health knowledge index (HKI) based on questions asked to women about their health beliefs, practices, and reproductive knowledge, and required the respondents to specify each item as “true” or “false.” The list of questions asked to women in relation to health knowledge follows: Do you know in which part of the menstrual cycle a woman is less likely to get pregnant? Is it good to drink 1–2 glasses of milk every day during pregnancy? When you were pregnant, did you squeeze out milk from your breast to feed your child? Do men become physically weak even months after sterilization? Do you think that the first thin milk that comes out after a baby is born is good for the baby? Although all these

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questions do not directly reflect knowledge of maternal health, in the absence of direct data they could be important proxies of reproductive health knowledge of a woman. We have used socioeconomic and demographic characteristics of the women as control variables, however, which are recoded for the purpose of analyses and effective comparison of the results: age group of women (15–24, 25–34, and 35–49 years); place of residence (rural, urban); literacy (illiterate and literate); education level in terms of completed years of schooling, i.e., no education, primary (1–4years), upper primary and secondary (5–10 years) and higher (above 10 years); religion (Hindu, Muslim, Christian, and Others, including Sikh, Buddhist, Jain, Tribal, Other, None); caste (“scheduled caste” and “scheduled tribe,” “other backward caste,” and “Others,” including Brahmin and others); and wealth quintile (lowest, second, middle, fourth and highest for poorest, poorer, middle, richer, and richest, respectively). Dependent variables. Dependent variables used in the study are also recoded purposively, such as antenatal check-up (no check-up, less than three check-ups and three or more check-ups), postnatal check-up within 2 months of delivery (no check-up, check-up within 2 months, and check-ups after 2 months of delivery, for the last 5 years), place of delivery (institutional and noninstitutional), pregnancy complications (include night blindness, blurred vision, and convulsions not from fever, excessive fatigue, anemia, and vaginal bleeding), and postnatal complications (include excessive vaginal bleeding and very high fever).

Statistical Analyses We have performed the statistical analysis of the study in two stages: first, differentials in health knowledge by sociodemographic background characteristics of women, such as age group of women, place of residence, educational level of women, caste, religion, and wealth quintile of women, are assessed using multinomial logistic regression analyses. Further, the Multiple Classification Analysis (MCA) conversion model is used to convert beta coefficients into adjusted percentages. In the second stage, the influence of health knowledge on pregnancy complications; postdelivery complications; and antenatal care, delivery care, and postnatal care are examined. Bivariate and trivariate cross-tabulation and binary logistic regression models are used to estimate the effects of health knowledge on reporting of pregnancy and postnatal complications and ante-natal care, delivery care, and postnatal care use. Below, we have presented mathematical forms of the models used in this study. Factor analysis with the Principal Component Analysis Method. In general, the factors analysis is a method that reduces data dimensionality by performing a covariance analysis between factors. We have performed the

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factor analyses using the Principal Component Analysis (PCA) method. Each question used to compute health knowledge index has been assigned a weight (factor score) generated through the PCA method, and the resulting scores are standardized in relation to the normal distribution with a mean of zero and standard deviation of one (Gwatkin, Rutstein, Johnson, Pande, & Wagstaff, 2000). Women are ranked according to score, with a higher score representing a higher level of health knowledge. Based on HKI scores, three groups of women have been determined: no or low knowledge, medium knowledge, and high knowledge. An alpha test with p < .05 significance level is applied to examine the internal consistency among the variables chosen for computing HKI. Multinomial logistic regression and MCA conversion model. The multinomial logistic regression is commonly used when the independent variables include both numerical and nominal measures and the outcome variable (dependent variable) encompasses three or more than three categories. For instance, health knowledge index, in this study, includes three categories: no or low knowledge, medium knowledge, and high knowledge. With this dependent variable, we have written a mathematical form of multinomial logistic regression and MCA as below:   b P1 = a1 + ∗X j , Z 1 = L og 1j P3   b P2 = a2 + ∗X j, Z 2 = L og 2j P3 and P1 + P2 + P3 = 1, where ai i = 1,2 : constants, • bij i = 1,2; j = 1,2 . . . .n : multinomial regression coefficient. • P1 = Estimated probability of reporting no or low health knowledge by women aged 15 to 49 years. • P2 = Estimated probability of reporting medium health knowledge by women aged 15 to 49 years. • P3 = Estimated probability of reporting high health knowledge by women aged 15 to 49 years is the reference category. For the sake of simplicity in the interpretation of results, we have converted the multinomial logistic regression coefficients into adjusted percentages. The procedure consists of following steps:

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Step 1: • By using the regression coefficient and mean values of independent variables, the probability is computed as: exp i ) • Pi = {1+exp(Z , i = 1, 2, 3 and P3 = 1 – P1 + P2 where Z was the estimated (Zi )} value of response for all categories of each variable. Step 2:

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• To obtain the percentage values, the probability P was multiplied by 100. Binary logistic regression analysis. The rationale behind the use of binary logistic regression is almost similar to that of multinomial logistic regression, but it is typically applied when the dependent variable forms a binary variable. The advantage of logistic regression analysis is that it requires no assumption about the distribution of the independent variables, and the regression coefficient can be interpreted in terms of odds ratios. The binary logistic regression model is commonly estimated by maximum likelihood function. For the dependent variables, the logistic model takes the following general form: Logit p = b0 + b1 x1 + b2 x2 + b3 x3 + . . . . . . . . . . . . . . . . . . bk xk + ek Log p/1 − p = b0 + b1 x1 + b2 x2 + b3 x3 + . . . . . . . . . . . . . . . . . . bk xk + ek where b0 are intercepts and b1 , b2 , b3 , . . . . . . . . . . . . . . . bk represents the coefficients of each of the predictor variables in the model while ek is an error term. The natural logarithms of the odds of the outcomes are represented by ek .

RESULTS Women’s Health Knowledge by Background Characteristics The adjusted percentages, estimated from multinomial logistic regression analysis, indicate socioeconomic differences in the level of a woman’s health knowledge. The percentage of women with higher health knowledge is found to be increased with an increase in the age of women, which indicates that knowledge is an accumulation of experiences that grow with age. The percentage of women with higher health knowledge (34%) is significantly higher in the urban area as compared with women in the rural area (33%). By education level, health knowledge is found greater among women with higher education (35%) than women with less education or no education (32%). Thus, the level of education shows a clear positive association with the level of health knowledge. Similarly, women belonging to the richest wealth quintile have a higher level of health knowledge (37%) than women

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TABLE 1 Adjusted Percentage of Women Aged 15–49 Years by Health Knowledge1 and Background Characteristics, India, 2005 Adjusted percentage of women by health knowledge

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Background characteristics Age group (years) R 15–24 25–34 35–49 Place of residence R Rural Urban Education level R No education Primary Upper primary and secondary Higher education Wealth quintile R Poorest Poor Middle Rich Richest Caste Scheduled castes and scheduled R tribe  Other backward classes Others Religion R Hindu Muslim Christian Others Total

No or low

Medium

High

Sample of women

39.1 38.6 40.1

27.7 27.5 26.4

33.2 33.9 33.4

21,194 16,480 18,575

38.8 40.6

27.9 25.3∗∗∗

33.3 34.0∗

36,064 20,185

40.7 40.3 38.4

27.0 25.7 27.3∗∗

32.4 34.0 34.3∗∗∗

20,501 3,881 22,985

36.6

28.8∗∗∗

34.6∗∗∗

8,564

44.9 45.4 40.9 37.1 31.1

25.0 23.1 26.1 28.7∗∗∗ 31.6∗∗∗

30.1 31.6 33.1∗∗∗ 34.1∗∗∗ 37.3∗∗∗

7,256 7,139 7,802 8,715 9,803

44.6

23.4

32.0

11,772

40.0

26.7∗∗∗

33.3∗∗∗

16,286

33.6

31.4∗∗∗

35.0∗∗∗

13,496

39.7 41.6 24.8 37.1 39.3

26.6 28.0∗∗ 41.1∗∗∗ 26.2 27.2

33.7 30.5∗∗∗ 34.1∗∗∗ 36.7∗∗ 33.5

33,527 4,788 1,376 16,558 56,249

Note: 1Health Knowledge Index is predicting women’s reproductive health status (pregnancy complications and postnatal complications); Total includes women with missing information on wealth and caste who are not shown separately; R and no or low knowledge; significance level: ∗∗∗ p < .001; ∗∗ p < .01; ∗ p < .05. reference categories are 

of the poorest wealth quintile (30%). Compared with women belonging to other religions, the percentage of women with the higher level of health knowledge is lower among the Muslim women (Table 1).

Women’s Health Knowledge Versus Reporting of Pregnancy Complications Table 2 presents the percentage of women who reported pregnancy complications and postnatal complications by their health knowledge level and

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Age group (years) 15–24 25–34 35–49 Place of residence Rural Urban Women’s education No education Primary education Upper primary and secondary Higher education Wealth quintile Poorest Poor Middle Rich Richest

Background characteristics 45.5 42.2 48.2 45.5 45.3 46.5 42.8 45.9 42.8 47.8 44.7 46.8 49.9 39.3

45.4 47.7

47.2 43.9

42.6

53.9

44.7 50.5 45.0 47.7 42.4

Medium knowledge

44.9 44.7 48.2

No or low knowledge

54.9 52.4 55.9 48.9 43.7

46.6

53.2

48.2 58.7

51.1 49.7

50.3 49.4 52.2

High knowledge

Pregnancy complications (any)

1

10.61∗∗

0.43

0.33

3.03

Chisquare

10.9 8.6 10.1 16.1 16.7

9.8

10.2

13.3 10.5

12.1 11.3

14.7 9.4 10.6

No or low knowledge

13.7 16.9 11.7 21.4 5.5

13.5

12.9

16.5 6.6

14.3 12.8

15.7 10.9 14.6

Medium knowledge

7.42∗

0.03

0.86

Chi-square

4.15 18.1 14.2 10.1 14.8 11.9 (Continued on next page)

7.2

15.5

15.1 11.1

14.1 14.4

11.9 15.4 15.7

High knowledge

Postnatal complications (any)

2

TABLE 2 Percentage of Women Aged 15 to 49 Years Who Experienced Any Pregnancy Complications or Postnatal Complications According to Health Knowledge of Women by Their Background Characteristics, India, 2005

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10 40.3

42.7 52.2 42.3 69.5 45.3 31.7 45.4

47.5

50.2

45.4 52.4 31.3 40.2 46.5

Medium knowledge

41.0

No or low knowledge

51.6 50.8 31.6 43.2 50.4

48.2

53.1

49.9

High knowledge

18.99∗∗∗

1.92

Chisquare

12.1 11.2 2.0 11.5 11.7

13.0

12.7

10.0

No or low knowledge

14.4 15.3 0.5 12.9 13.55

21.5

10.1

11.7

Medium knowledge

13.5 21.1 0.0 15.8 14.25

15.7

13.2

14.4

High knowledge

Postnatal complications (any)

2

9.02∗∗

3.48

Chi-square

Note: 1Pregnancy complications include night blindness, blurred vision, convulsion not from fever, excessive fatigue, anemia and vaginal bleeding. Reference period: Last 5 years preceding the survey. Total includes women with missing information on wealth and caste who are not shown separately. 2Postnatal complications include vaginal bleeding and very high fever; reference period: last 5 years preceding the survey; total includes women with missing information on wealth and caste who are not shown separately; Significance level: ∗∗∗ p < .001; ∗∗ p < .01; ∗ p < .05.

Caste Scheduled castes and scheduled tribes Other backward classes Others Religion Hindu Muslim Christian Others Total

Background characteristics

Pregnancy complications (any)

1

TABLE 2 Percentage of Women Aged 15 to 49 Years Who Experienced Any Pregnancy Complications or Postnatal Complications According to Health Knowledge of Women by Their Background Characteristics, India, 2005 (Continued)

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background characteristics. By level of health knowledge, the percentages of women who reported pregnancy complications are 46% and 50%, respectively, among no or low and high health knowledge women. The highest level of health knowledge is associated with the highest reported pregnancy complications. Our key finding from these results is that within the same socioeconomic status, such as a rural place of residence, poor economic status, and no education, the reported pregnancy complications by women is increased significantly with an increase in the level of health knowledge. This pattern was even sustained among women with higher education. The odds ratios of the logistic regression analysis of self-reported pregnancy complications by women are presented in Table 3. In comparison with the women with no health knowledge (OR = 1.000, p < .05), women with high health knowledge are significantly more likely to report pregnancy complications (OR = 1.139, p < .01). Table 2 shows the percentage of women reporting postnatal complications by their health knowledge level and background characteristics. The reported postnatal complication is increased with an increase in the level of health knowledge, and such a pattern is consistent for all the socioeconomic groups. The result of logistic regression analysis, after controlling for other socioeconomic background characteristics, has also shown that women with higher health knowledge are significantly more likely to report greater postnatal complications (OR = 1.378, p < .01) than women with no or low health knowledge (OR = 1.000, p < .05; Table 3).

Women’s Health Knowledge Versus Maternal Health Care Use The bivariate cross-tabulation analyses (Table 4) show that the percentage of women who reported receiving at least three antenatal check-ups increased with an increase in health knowledge level. Whereas ANCs use is only 52% among women with no or low health knowledge, it increased to 72% for women with high health knowledge. Further, the trivariate cross-tabulation analyses indicate that with the same socioeconomic and demographic background, the percentage of women who attended at least three antenatal check-ups varied greatly by their health knowledge levels. An important finding from the trivariate analyses is that, compared with lower socioeconomic groups, women in higher socioeconomic groups have shown greater variation in antenatal care services use by their health knowledge level. We have found that the women who received the highest level (90%) of three and more antenatal check-ups belong to the richest wealth quintile with higher health knowledge. In contrast, the women who received the lowest (42%) of three and more antenatal check-ups belonged to the poorest wealth quintile with no or low health knowledge (Table 4). The odds ratios, estimated from the logistic regression analysis (Table 5), also support the fact that women with medium (OR = 1.581, p < .001) and high (OR = 1.434, p < .001) health knowledge are significantly more likely to receive at least

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TABLE 3 Results of Logistic Regression Analysis of Pregnancy Complications and Postnatal Complications Experienced by Women Aged 15–49 Years by Their Health Knowledge and Background Characteristics, India, 2005 Maternal health problems

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Background characteristics Health knowledge R No or low Medium High Age group (years) R 15–24 age  25–34 age 35–49 age Place of residence R Rural  Urban Level of education R No education  Primary education Upper primary and secondary education Higher education Wealth quintile R Poorest  Poor Middle Rich Richest Caste Scheduled caste and scheduled R tribe  Other backward class Others Religion R Hindu  Muslim Christian Other

Pregnancy complications Expβ (CIs of Exp β)

Postnatal complications Expβ (CIs of Exp β)

1 1.057 (0.930,1.202) 1.139∗∗ (1.013,1.280)

1 1.049 (0.749,1.468) 1.378∗∗ (1.032,1.841)

1 0.912 (0.805,1.034) 1.007 (0.890,1.140)

1 0.805 (0.581,1.115) 0.937 (0.691,1.270)

1 1.052 (0.942,1.176)

1 1.144 (0.865,1.512)

1 0.978 (0.799,1.198) 0.957 (0.850,1.076)

1 0.567∗∗ (0.322,1.001) 0.756∗ (0.563,1.016)

0.956 (0.807,1.133)

0.678∗ (0.435,1.055)

1 1.005 (0.851,1.187) 0.947 (0.803,1.118) 0.865∗ (0.732,1.021) 0.781∗∗∗ (0.662,0.920)

1 0.830 (0.564,1.221) 0.808 (0.548,1.191) 0.910 (0.617,1.341) 0.622∗∗ (0.404,0.956)

1

1

1.096 (0.966,1.243)

1.101 (0.801,1.512)

1.129∗ (0.980,1.300)

1.449∗∗ (1.007, 2.086)

1 1.303∗∗∗ (1.110,1.529) 0.875 (0.634,1.206) 1.318∗∗ (1.009,1.723)

1 1.252 (0.881,1.780) 0.000 (0.0) 1.276 (0.649,2.506)

R Reference category of different characteristics; Significance level: Note:  .05.

∗∗∗ p

< .001;

∗∗ p

< .01; ∗ p

Relevance of Health Knowledge in Reporting Maternal Health Complications and Use of Maternal Health Care in India.

We measured levels of women's health knowledge and their association with the reporting of maternal health complications and related health care use. ...
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