## I. INTRODUCTION Infertility is a severe public health problem that can affect both men and women and whose impact varies among different populations and acquires relevance for specific communities. It recognizes that infertility affects $10\%$ to $15\%$ of couples of reproductive ages worldwide. In 2009, the International Committee for Monitoring Assisted Reproductive Technology (ICMART) and the World Health Organization (WHO) classified infertility as a disease of the reproductive system, establishing an international clinical and legal standard (Zegers-Hochschildet et al., 2009) $^{(1)}$. Its clinical definition is "a disease of the reproductive system defined by the failure to achieve a clinical pregnancy after 12 months or more of regular unprotected sexual intercourse" (World Health Organization, 2011)(2). Commonly neglected in developing countries, infertility brings couples social, economic, and psychological consequences. The start of any treatment requires a diagnosis. Moreover, this is no different when it comes to infertility – the better the diagnosis, the better the treatment. The most cited causes of female infertility in the medical literature are tubal obstruction, pelvic inflammatory disease, chronic anovulation, endometriosis, sexually transmitted diseases (STD), and low ovarian reserve. For male infertility, the determinants are varicocele, exposure to specific chemical components, and the resulting qualitative and quantitative changes in semen. In general, recommendations for infertility treatment are drug therapies, correction surgeries, and medically assisted reproduction techniques. The definition of infertility can vary according to the study area: clinical, epidemiological, or demographic, and it depends, according to each scientific field, on the length of exposure to the risk of pregnancy and whether couples seek live birth rather than conception (Mascaren has et al., 2012) $^{(3)}$. This imprecision makes it challenging to compare the study's results. In Brazil, no clinical or demographic data points us to the magnitude of the problem, its social characteristics, and impact. From a social perspective, this topic's need for data and public visibility is imperious. More and more women postpone the birth of their first child to gain professional qualifications and find economic stability. The national total fertility rate has remained below the minimum required for population replacement since 2003 (Rede Interagencial de Informações para a Saúde, 2014)(4). That year, the fertility rate was 2.07 births per woman, and 2015-2020 is estimated at 1.7. The fertility of mature women has become increasingly relevant. In 2000, around $22.5\%$ of the births were from mothers aged $30+$ years old. In 2022, this percentage reached $38.5\%$. The accumulated number of births from mothers aged $50+$ from 2000 to 2022 was 6324 cases, situations in which it is almost certain that some assisted reproduction technologies were used. The frequency of births in this age group is 3.6 times higher in 2022 compared to 2000. The National Demographic and Health Survey (PNDS) 2006 is part of the 5th Phase of the MEASURE DHS (Berquó et al., 2008) $^{(5)}$. It is the most recent representative data on reproductive intentions, birth, and contraceptive use for Brazilian women of reproductive age. A new round of the PNDS took place in 2023, but the data was unavailable while this article was produced. However, the 2006 PNDS does not contain complete histories of stable union and the calendar of contraceptive use in the last five years before the survey; it is an instrument used to collect detailed information about the reproductive and contraceptive background. This is a necessary tool to calculate infertility from a demographic point of view. As we could not count on the complete calendar, we based our analysis on women's self-reports of infertility or difficulty conceiving. Anyway, the 2006 PNDS is an exceptional source because it was the first survey realized during the phase of total fertility rate below replacement level (2.1 births per woman) to consider a consistent set of valuable questions for infertility studies. As we will see in the discussion section, the National Health Survey (PNS) was carried out in 2013 and 2019. However, it raises some questions about infertility and does not address the topic as comprehensively as the 2006 PNDS. Even PNDS 2023 runs the risk of adopting a more superficial perspective on infertility. This study aims to identify and characterize women of childbearing age who wish to become pregnant and declare themselves infertile or having difficulty getting pregnant. Therefore, the percentage of infertility calculated based on the women's statements should be understood as an approximate demand for infertility treatment, characterizing the likely users of these services. It also classifies women seeking treatment for pregnancy by type of service (private, health insurance, or SUS- Unified Health System, a public service) and their outcomes. It investigates the reasons why women did not seek help and associated factors. ## II. MATERIAL AND METHODS This study is based on PNDS 2006, a household survey of national representativeness of the five Brazilian macro-regions, urban and rural; 14,617 households were selected according to a stratified model of simple random conglomerates in two stages: lottery draw and household draw. Interviews were conducted with 15,575 women aged 15 to 49 in the selected households (Cavenaghi, 2008) $^{(6)}$. The survey did not include interviews with male partners. The PNDS 2006 data comes from complex probabilistic sampling; therefore, the statistical analyses considered the weights and the complex sample planning (Berquó et al., 2008) $^{(5)}$. Based on this research, we classified women according to self-reported reproductive status: infertility/difficulty conceiving, menopausal, hysterectomized, sterilized, and whether they had an infertile partner. The survey topics dealing with fertility planning and reproductive intentions were selected, and questions about infertility and the desire to become pregnant were analyzed. Women who declared themselves pregnant, sterilized, or with vasectomized partners (28.9%) did not respond to the battery of questions about seeking infertility treatment. The socio-demographic variables included are macro-region of residence, age group, socioeconomic status, and race. It should be clarified that socioeconomic status considers the educational level of the household's head, level of consumption, and access to goods and facilities. Additionally, there are five racial groups considered by the Brazilian statistical system: white, black, brown, indigenous, and Asian. Usually, the Black movements aggregate brown and black people in the same category because of their similar social disadvantages. In this paper, we work with a dichotomic variable: black (black and brown together) and, on the other side, not black (white, Asian, and indigenous). Pearson's chi-square tests were adjusted to identify associations between infertility, treatment-seeking, and those variables. The threshold of significance was set at p values $< 0.05$. The Cramer's V was calculated to determine the magnitude of this association. The advantage of Cramer's V compared to other association measures is that it can be applied to any contingency table originating from two categorical variables, regardless of the number of rows and columns in that table. The Cramer's V value varies from 0 to 1, where 0 means no association and 1 means a perfect association. The association revealed by Cramer's V can be classified as negligible, small, medium, and large. The range of values that delimit each of these categories depends on the degrees of freedom (df) for the chi-square statistic: df = (r - 1)(c - 1), with r the number of rows and c the number of columns of the contingency table. Table 1: Cramer's V: magnitude of association for categorical tables according to degrees of freedom <table><tr><td>Degrees of freedom</td><td>Negligible</td><td>Small</td><td>Medium</td><td>Large</td></tr><tr><td>1</td><td>&lt; 0.10</td><td>&lt; 0.30</td><td>&lt; 0.50</td><td>≥ 0.50</td></tr><tr><td>2</td><td>&lt; 0.07</td><td>&lt; 0.21</td><td>&lt; 0.35</td><td>≥ 0.35</td></tr><tr><td>3</td><td>&lt; 0.06</td><td>&lt; 0.17</td><td>&lt; 0.29</td><td>≥ 0.29</td></tr><tr><td>4</td><td>&lt; 0.05</td><td>&lt; 0.15</td><td>&lt; 0.25</td><td>≥ 0.25</td></tr><tr><td>5</td><td>&lt; 0.05</td><td>&lt; 0.13</td><td>&lt; 0.22</td><td>≥ 0.22</td></tr></table> The analysis was performed using Stata v. nine and SPSS v.14 software. ## III. RESULTS The large group who answered the questions $(71.1\%)$ were classified according to their reproductive capacity, in "reproductive conditions," $64\%$, and "nonreproductive condition," $7.1\%$. Women who reported being hysterectomized or in menopause also answered the same questions. Therefore, they were included in the analysis (Table 2). Table 2: Distribution of women aged 15-49 according to reproductive condition. Brazil, 2006 <table><tr><td rowspan="2">Women&#x27;s reproductive condition</td><td rowspan="2">Estimate</td><td colspan="2">95% confidence interval</td><td rowspan="2">number of un weighted cases</td></tr><tr><td>Lower Limit</td><td>Upper Limit</td></tr><tr><td>Total</td><td>100,0%</td><td>-</td><td>-</td><td>15.575</td></tr><tr><td>Pregnant women</td><td>4,2%</td><td>3,6%</td><td>4,8%</td><td>588</td></tr><tr><td>Sterilized women</td><td>21,5%</td><td>20,4%</td><td>22,8%</td><td>4.096</td></tr><tr><td>Vasectomized partners</td><td>3,2%</td><td>2,8%</td><td>3,8%</td><td>361</td></tr><tr><td>Non-pregnant, non-sterilized, and women without vasectomized partners</td><td>71,1%</td><td>69,7%</td><td>72,4%</td><td>10.530</td></tr><tr><td>Women in reproductive conditions</td><td>64,0%</td><td>63,1%</td><td>65,8%</td><td>9.704</td></tr><tr><td>Women in non-reproductive conditions*</td><td>7,1%</td><td>5,5%</td><td>7,9%</td><td>826</td></tr></table> Table 3 shows that the reproductive condition of women did not differ concerning its distribution in terms of skin color (p = 0.6032), socioeconomic status (p = 0.5756), and region (p = 0.6431). Regarding age, 58.1% of women in non-reproductive conditions were between 40 and 49 years old, while this percentage was 13.8% among women in reproductive conditions. The proportion of married/united women in non-reproductive conditions was higher (75.1%) than the other group (54%). As expected, the percentage of women without biological children was higher in the group of women in non-reproductive conditions (51.8%). Cramer's V permits affirming that there is a significant association between the non-reproductive condition and the women's age group. On the other hand, the relation of the non-reproductive condition with the marital status or the presence of biological children is relatively small, although significant. Table 3: Distribution of women according to reproductive conditions bysociodemographic characteristics. Brazil, 2006 <table><tr><td rowspan="2">Characteristics</td><td colspan="2">Non-Pregnant, Non-Sterilized Women Without Vasectomized Partners</td><td rowspan="2">Total</td><td rowspan="2">Number of Unweighted Cases</td></tr><tr><td>In Reproductive Conditions</td><td>In Non-Reproductive Conditions*</td></tr><tr><td>Race/color</td><td>100,0%</td><td>100,0%</td><td>100,0%</td><td>10.442</td></tr><tr><td>Not black</td><td>47,3%</td><td>45,6%</td><td>47,1%</td><td>4.810</td></tr><tr><td>black</td><td>52,7%</td><td>54,4%</td><td>52,9%</td><td>5.632</td></tr><tr><td>non-respondent</td><td></td><td></td><td></td><td>88</td></tr><tr><td>x2= 0,94 (p= 0,6032)</td><td></td><td></td><td>Cramer's V</td><td>0,009</td></tr><tr><td>Socioeconomic status (SES)</td><td>100,0%</td><td>100,0%</td><td>100,0%</td><td>10.486</td></tr><tr><td>A</td><td>3,2%</td><td>1,7%</td><td>3,1%</td><td>304</td></tr><tr><td>B</td><td>21,2%</td><td>20,7%</td><td>21,1%</td><td>1.949</td></tr><tr><td>C</td><td>45,6%</td><td>47,1%</td><td>45,8%</td><td>4.896</td></tr><tr><td>D</td><td>22,7%</td><td>22,2%</td><td>22,6%</td><td>2.449</td></tr><tr><td>E</td><td>7,3%</td><td>8,4%</td><td>7,4%</td><td>888</td></tr><tr><td>non-respondent</td><td></td><td></td><td></td><td>44</td></tr><tr><td>x2= 7,99 (p=0,5756)</td><td></td><td></td><td>Cramer's V</td><td>0,028</td></tr><tr><td>Age group</td><td>100,0%</td><td>100,0%</td><td>100,0%</td><td>10.530</td></tr><tr><td>15 - 24 years</td><td>44,7%</td><td>11,3%</td><td>41,6%</td><td>4.605</td></tr><tr><td>25 - 34 years</td><td>31,4%</td><td>13,6%</td><td>29,8%</td><td>3.242</td></tr><tr><td>35 - 39 years</td><td>10,1%</td><td>17,1%</td><td>10,8%</td><td>1.052</td></tr><tr><td>40 - 49 years</td><td>13,8%</td><td>58,1%</td><td>17,9%</td><td>1.631</td></tr><tr><td colspan="3">x2= 1.341,41 (p&lt; 0,0001)</td><td>Cramer's V</td><td>0,357</td></tr><tr><td>Region</td><td>100,0%</td><td>100,0%</td><td>100,0%</td><td>10.530</td></tr><tr><td>North</td><td>6,4%</td><td>6,9%</td><td>6,4%</td><td>1.609</td></tr><tr><td>Northeast</td><td>25,0%</td><td>25,8%</td><td>25,0%</td><td>2.078</td></tr><tr><td>Southeast</td><td>45,7%</td><td>46,7%</td><td>45,8%</td><td>2.365</td></tr><tr><td>South</td><td>16,6%</td><td>14,3%</td><td>16,4%</td><td>2.535</td></tr><tr><td>Midwest</td><td>6,4%</td><td>6,3%</td><td>6,4%</td><td>1.943</td></tr><tr><td colspan="3">x2= 3,54 (p= 0,6431)</td><td>Cramer's V</td><td>0,018</td></tr><tr><td>Marital Status</td><td>100,0%</td><td>100,0%</td><td>100,0%</td><td>10.521</td></tr><tr><td>Single</td><td>36,2%</td><td>15,5%</td><td>34,3%</td><td>3.721</td></tr><tr><td>Married/United</td><td>54,0%</td><td>75,1%</td><td>56,0%</td><td>5.696</td></tr><tr><td>Widow/Separated/Divorced</td><td>9,8%</td><td>9,4%</td><td>9,7%</td><td>1.104</td></tr><tr><td>Non-respondent</td><td></td><td></td><td></td><td>9</td></tr><tr><td colspan="3">x2= 180,56 (p&lt; 0,0001)</td><td>Cramer's V</td><td>0,131</td></tr><tr><td>Presence of biological children</td><td>100,0%</td><td>100,0%</td><td>100,0%</td><td>10.530</td></tr><tr><td>No</td><td>43,0%</td><td>51,8%</td><td>43,9%</td><td>4.450</td></tr><tr><td>Yes</td><td>57,0%</td><td>48,2%</td><td>56,1%</td><td>6.080</td></tr><tr><td colspan="3">x2= 27,37 (p= 0,0016)</td><td>Cramer's V</td><td>0,051</td></tr></table> Table 4 shows that of the total number of women who declared they wanted children/more children, $5.7\%$ were in non-reproductive conditions. However, only those who marked the answers "Infertile/Difficulty" getting pregnant" or "Infertile husband" as a reason for not getting pregnant were directed by the questionnaire to answer the questions about seeking and accessing infertility treatment (Table 5). Table 4: Distribution of women who want children/more children, according to reproductive conditions <table><tr><td rowspan="2">Desire to have children/or more children</td><td rowspan="2">Estimate</td><td colspan="2">Confidence interval 95%</td><td rowspan="2">Number of unweighted cases</td></tr><tr><td>Lower limit</td><td>Upper limit</td></tr><tr><td>Non-pregnant, non-sterilized women with no vasectomized partners</td><td>100,0%</td><td>100,0%</td><td>100,0%</td><td>10.530</td></tr><tr><td>Want to have children</td><td>100,0%</td><td>100,0%</td><td>100,0%</td><td>5.428</td></tr><tr><td>Women in reproductive conditions</td><td>94,3%</td><td>93,1%</td><td>95,2%</td><td>5.136</td></tr><tr><td>Women in non-reproductive conditions1</td><td>5,7%</td><td>4,8%</td><td>6,9%</td><td>292</td></tr></table> Table 5 indicates that just under half of them did not seek help $(49.1\%)$, while about $28\%$ were waiting, not receiving care, or thinking there was no solution. Almost half of those seeking help were undergoing treatment, $68.3\%$ sought SUS, and $20.3\%$ had health insurance. About $72\%$ of the women who did not seek help indicated that the main reason was the lack of a solution to their problem or that they would not get it, while $26.8\%$ cited financial reasons and $1,2\%$ did not know where to get support. Table 5: Distribution of infertile women/with difficulty conceiving/or infertile husbands who wish to have children by seeking infertility treatment. Brazil. PNDS 2006 <table><tr><td rowspan="2">Infertility treatment questions</td><td rowspan="2">Estimate</td><td colspan="2">95% confidence interval</td><td rowspan="2">number of unweighted cases*</td></tr><tr><td>Lower limit</td><td>Upper limit</td></tr><tr><td>Did you seek help to get pregnant</td><td>100,0%</td><td>-</td><td>-</td><td>246</td></tr><tr><td>Yes, not answered</td><td>7,3%</td><td>2,6%</td><td>18,8%</td><td>14</td></tr><tr><td>Yes, waiting</td><td>7,9%</td><td>3,2%</td><td>18,2%</td><td>14</td></tr><tr><td>Yes, no solution</td><td>12,9%</td><td>8,2%</td><td>19,7%</td><td>35</td></tr><tr><td>Yes, under treatment</td><td>22,7%</td><td>15,1%</td><td>32,7%</td><td>54</td></tr><tr><td>Did not seek help</td><td>49,1%</td><td>39,3%</td><td>59,1%</td><td>129</td></tr><tr><td>Non-responders</td><td></td><td></td><td></td><td>6</td></tr><tr><td>Where did you look for help</td><td>100,0%</td><td>-</td><td>-</td><td>117</td></tr><tr><td>SUS</td><td>68,3%</td><td>55,3%</td><td>79,0%</td><td>66</td></tr><tr><td>Health insurance</td><td>20,3%</td><td>11,9%</td><td>32,5%</td><td>25</td></tr><tr><td>Private clinic/doctor</td><td>11,3%</td><td>6,6%</td><td>18,5%</td><td>25</td></tr><tr><td>Other</td><td>0,1%</td><td>0,0%</td><td>0,8%</td><td>1</td></tr><tr><td>What happened when you sought help to be able to get pregnant</td><td>100,0%</td><td>-</td><td>-</td><td>114</td></tr><tr><td>Waiting for service</td><td>4,3%</td><td>2,0%</td><td>9,2%</td><td>9</td></tr><tr><td>Answered: no solution</td><td>24,4%</td><td>14,9%</td><td>37,3%</td><td>32</td></tr><tr><td>Answered:No money for treatment</td><td>11,3%</td><td>5,9%</td><td>20,6%</td><td>15</td></tr><tr><td>Attended in treatment</td><td>48,5%</td><td>34,1%</td><td>63,2%</td><td>51</td></tr><tr><td>It has not been answered</td><td>11,5%</td><td>4,5%</td><td>26,2%</td><td>7</td></tr><tr><td>Non-responders</td><td></td><td></td><td></td><td>3</td></tr><tr><td>Reason for not seeking help (main reason)</td><td>100,0%</td><td>-</td><td>-</td><td>61</td></tr><tr><td>I think there is no solution</td><td>54,7%</td><td>37,5%</td><td>70,9%</td><td>29</td></tr><tr><td>I do not think I can get help</td><td>17,3%</td><td>7,1%</td><td>36,5%</td><td>9</td></tr><tr><td>I do not know where to get it</td><td>1,2%</td><td>0,2%</td><td>7,5%</td><td>2</td></tr><tr><td>I do not have money</td><td>26,8%</td><td>15,3%</td><td>42,8%</td><td>21</td></tr><tr><td>non-responders</td><td></td><td></td><td></td><td>68</td></tr></table> Table 6 illustrates the distribution of questions about seeking infertility treatment according to sociodemographic characteristics. The results show that black women have the least desired medical care (58.7%) compared to non-black women (37.6%). The percentages of women from classes D and E who did not seek support are also high, 69.6% and 92.2%, respectively. Women at the extremes of the age group had the highest percentages of non-demand (55.9% between 15 and 24 years and 60.9% between 40 and 49 years). Regarding residency status, the North region had the highest non-demand proportion (74.1%), followed by the Midwest (68.2%) and Northeast (65.2%); in the South and Southeast regions, this percentage reached the lowest level, 30,7%, and, 39,1%, respectively. Cramer's V indicated that the sociodemographic characteristics most associated with seeking help to get pregnant are socioeconomic status, region, and race, in this order. The relation between seeking help to get pregnant and age group was insignificant $(p > 0.05)$. Table 6: Distribution of infertile women/with difficulty conceiving/or infertile husbands who wish to have children by socio-demographic characteristics, by demand for infertility treatment. Brazil, 2006 <table><tr><td rowspan="2">Sociodemographic characteristics</td><td colspan="2">Sought help to get pregnant*</td><td rowspan="2">Total</td><td rowspan="2">Number of unweighted cases</td></tr><tr><td>Yes</td><td>No</td></tr><tr><td>Race/color</td><td>50,8%</td><td>49,2%</td><td>100%</td><td>244</td></tr><tr><td>Not black</td><td>62,4%</td><td>37,6%</td><td>100%</td><td>112</td></tr><tr><td>Black</td><td>41,3%</td><td>58,7%</td><td>100%</td><td>132</td></tr><tr><td>Non-responders</td><td></td><td></td><td></td><td>8</td></tr><tr><td>χ2= 10,75 (p= 0,0225)</td><td></td><td></td><td>Cramer's V</td><td>0,210</td></tr><tr><td>Socioeconomic status</td><td>50,8%</td><td>49,2%</td><td>100%</td><td>245</td></tr><tr><td>A</td><td>61,2%</td><td>38,8%</td><td>100%</td><td>3</td></tr><tr><td>B</td><td>83,3%</td><td>16,7%</td><td>100%</td><td>32</td></tr><tr><td>C</td><td>60,9%</td><td>39,1%</td><td>100%</td><td>118</td></tr><tr><td>D</td><td>30,4%</td><td>69,6%</td><td>100%</td><td>66</td></tr><tr><td>E</td><td>7,8%</td><td>92,2%</td><td>100%</td><td>26</td></tr><tr><td>Non-responders</td><td></td><td></td><td></td><td>7</td></tr><tr><td>χ2= 46,37 (p&lt; 0,0001)</td><td></td><td></td><td>Cramer's V</td><td>0,435</td></tr><tr><td>Age group</td><td>50,9%</td><td>49,1%</td><td>100%</td><td>246</td></tr><tr><td>15-24 years</td><td>44,1%</td><td>55,9%</td><td>100%</td><td>32</td></tr><tr><td>25-34 years</td><td>55,8%</td><td>44,2%</td><td>100%</td><td>77</td></tr><tr><td>35-39 years</td><td>68,7%</td><td>31,3%</td><td>100%</td><td>48</td></tr><tr><td>40- 49 years</td><td>39,1%</td><td>60,9%</td><td>100%</td><td>89</td></tr></table> ## IV. DISCUSSION This study identified and characterized women of reproductive age who self-reported infertility, their desire to have children, and demands for treatment from a socio-demographic point of view. The adopted definition of infertility as "self-declared" was based on women's reports about infertility or difficulty in conceiving. This approach aligns with other demographic research that considers the inclusion of self-reported perceptions of infertility as one of the possibilities for inferring the potential demand for health services (Dick et al., 2003⁽⁸⁾; Larsen, 2005⁽⁹⁾; Cabrera-León et al., 2015)⁽¹¹⁾. Nonetheless, we must consider that this declaration was made at a certain point in her personal or family life cycle. Transitions over time also lead to changes in desires regarding reproductive life. Therefore, this preliminary study can be seen as a demographic picture of the situation at a given time frame. In this sense, the percentage of self-reported infertility, $7.1\%$, and the rate of those who wish to have children or more children, $5.7\%$, can be understood as an approximation of the potential demand for infertility treatments. However, not all women who meet the medical criteria for infertility indicate that they are trying or want to have a baby, which suggests that some infertile women do not need treatment. Without a desire for children, it is impossible to know if all women who declared themselves infertile see it as a problem to be solved (Greil et al., 2016) $^{(11)}$. Our study found that almost half of the women in non-reproductive conditions who want to have children did not seek help from health services, while others sought help but were not answered. Women who seek medical services differ in respect from those who do not-higher income, residents in the Southeast and the South regions of the country, and not black. This could imply a gap in the healthcare system or a lack of awareness among individuals regarding available treatment options. The demand for care in health services is an essential indicator for women to be adequately diagnosed and referred for treatment. Our findings are consonant with studies that suggest that while there is a global demand for infertility treatment, socioeconomic factors, limited access to services, and high costs contribute to social inequality in access to infertility treatments, among various factors contributing to this situation (Passet-Wittig & Greil, 2021 $^{(12)}$; Chambers et al., 2019 $^{(13)}$; Datta et al., 2016 $^{(14)}$; Cabrera-León et al., 2015 $^{(10)}$, Boivin et al., 2007) $^{(15)}$. The patterns observed in these studies seem consistent, indicating that individuals with higher socioeconomic status or educational qualifications are more inclined to seek assistance for fertility-related issues. Data from PNDS 2006 provides valuable insights into the socioeconomic strata, race of women, and regional disparities and their association with seeking help to get pregnant. Limited access to healthcare facilities, either due to geographic factors or lack of healthcare infrastructure in specific communities, can also contribute to inequalities in reproductive health. As seen in this work, women of color and women with low socioeconomic status are worse off. Racial and ethnic disparities in healthcare access and outcomes have been documented in numerous studies. These disparities often stem from a complex interplay of social, economic, and systemic factors. This highlights the intersectionality of race and socioeconomic status, indicating that being poor may exacerbate the challenges faced by black women in accessing reproductive health services. There is a potential for racial discrimination in access to reproductive health actions. This is a critical point, as bias can manifest in various ways, from unequal treatment in healthcare facilities to disparities in the quality of care. Milanezi (2024)(16) points to the discriminatory experiences of black women in the SUS bureaucracies in Rio de Janeiro. In light of literature produced by qualitative studies on health inequality, it is a plausible hypothesis that discriminatory experiences may have a bearing on the lower demand by the poorest and black women for infertility treatment in the health system. More studies on this topic must be conducted in Brazil, and appropriate data should be collected regularly. This underscores the importance of further research to understand the dynamics of fertility help-seeking behavior in this population. Lack of awareness or information about reproductive health issues and available services can also contribute to delayed or insufficient care. It is crucial to ensure that individuals are informed about the importance of seeking help early and know where to access specialized care. Table 7 summarizes the information available in each of the population surveys carried out in Brazil that address the issue of human sterility in some way. Chart 1: Information was collected by the National Demographic Survey (PNDS) in 2006 and 2023 and the National Health Survey (PNS) in 2013 and 2019 <table><tr><td>Information</td><td>PNDS 2006</td><td>PNDS 2023</td><td>PNS 2013</td><td>PNS 2019</td></tr><tr><td>Female fertility</td><td>Yes</td><td>Yes</td><td>No</td><td>No</td></tr><tr><td>Number of biological children of the woman</td><td>Yes</td><td>Yes</td><td>Noa</td><td>Noa</td></tr><tr><td>Pregnant woman</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>Woman with difficulty getting pregnant</td><td>Yes</td><td>Yes</td><td>Nob</td><td>Nob</td></tr><tr><td>Sterilized woman</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>Woman declared infertile</td><td>Yes</td><td>Yes</td><td>No</td><td>No</td></tr><tr><td>Hysterectomized woman</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>Menopausal woman</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>Vasectomized partner</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>Partner declared infertile</td><td>Yes</td><td>No</td><td>No</td><td>No</td></tr><tr><td>Desire to have children or more children</td><td>Yes</td><td>Yes</td><td>No</td><td>No</td></tr><tr><td>Sought help to get pregnant</td><td>Yes</td><td>No</td><td>Nob</td><td>Nob</td></tr><tr><td>Where the woman looked for help</td><td>Yes</td><td>No</td><td>No</td><td>No</td></tr><tr><td>What happened when the woman sought help to be able to get pregnant</td><td>Yes</td><td>No</td><td>No</td><td>No</td></tr><tr><td>Reason for not seeking help (main reason)</td><td>Yes</td><td>No</td><td>No</td><td>No</td></tr></table> In Brazil, family planning is often automatically associated with contraception, that is, the distribution of contraceptive methods, including tubal ligation and vasectomy. The other side of family planning and conception needs more effective actions by the public Unified Health System (SUS). The statistical system tends to reproduce this silence and bias. All data collected about reproductive health after 2006 did not evaluate if the women sought help to get pregnant, where, what happened when they sought help to be able to get pregnant, and the reason indicated by those women who preferred not seeking help. More than just counting the infertile women or men, public health policymakers need to know their perceptions and experiences with the services. This situation highlights a common challenge in healthcare systems, particularly reproductive health. While the SUS in Brazil, for example, emphasizes primary care as the initial point of contact for addressing infertility, barriers to accessing specialized services can impede the system's effectiveness (Berquó, Lago & Garcia, 2023)(17). Although comprehensive assistance to women's health encompasses education actions that inform about fertility, infertility, and access to infertility treatments in the public network, its full effectiveness, as agreed at the Cairo Conference in 1994, has not been implemented by Brazilian governments since then. ## V. LIMITATIONS The limitations of the study are recognized. Firstly, as infertility/difficulty in conceiving is based on self-declaration, it is impossible to determine whether this condition was based on medical diagnosis, what type of infertility (primary or secondary), and how long it has been present. Secondly, opinions are restricted to the moment of the interview. In this sense, the statement about the desire for children may shift depending on the future of the interviewee's situation. Despite the methodological restrictions mentioned, the study has the merit of bringing essential issues related to recognizing infertility as a health problem with severe implications for the individual and society. ## VI. CONCLUSIONS Addressing these challenges may involve a multi-faceted approach, such as Expanding the healthcare infrastructure, especially in underserved areas, which can help ensure that specialized services are more widely available; implementing policies or programs that provide financial assistance or coverage for infertility treatments can make these services more accessible to a broader population. The emphasis on improving access to fertility diagnoses and treatments in public health services is crucial. Ensuring that a more significant segment of the population has access to these services is vital for upholding reproductive rights and addressing the broader societal implications of infertility. The absence of systematic data on the causes and prevalence of both female and male infertility in Brazil is a significant concern. Understanding the root causes and extent of infertility is crucial for developing effective public health policies and interventions. Investing in research and data collection is essential to understanding the current landscape and tracking changes over time. The call for including critical questions about infertility, access, and perceptions about the available services in the upcoming PNDS and PNS is crucial for gaining a deeper understanding of the affected population and the types of infertility they experience. This information can serve as a foundation for developing targeted public policies and programs to address the challenges faced by individuals dealing with infertility. In conclusion, there is a compelling call to action for the State to prioritize addressing infertility issues in Brazil, recognizing the urgency of the matter, and emphasizing the potential consequences for the population. It highlights the need for proactive measures, including data collection, policy development, and improved access to fertility services, to address the multifaceted aspects of infertility and promote reproductive health and rights.

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